diff --git a/.gitmodules b/.gitmodules index 100d42ff0fcc9c935dea6fd5108ab9171b9018c5..3079d76d1001923555a86d730fc05c6dc453fc6d 100644 --- a/.gitmodules +++ b/.gitmodules @@ -13,3 +13,9 @@ [submodule "PaddleNLP/knowledge-driven-dialogue"] path = PaddleNLP/knowledge-driven-dialogue url = https://github.com/baidu/knowledge-driven-dialogue +[submodule "PaddleNLP/language_representations_kit"] + path = PaddleNLP/language_representations_kit + url = https://github.com/PaddlePaddle/LARK +[submodule "PaddleNLP/knowledge_driven_dialogue"] + path = PaddleNLP/knowledge_driven_dialogue + url = https://github.com/baidu/knowledge-driven-dialogue/ diff --git a/PaddleNLP/LAC b/PaddleNLP/LAC deleted file mode 160000 index a4eb73b2fb64d8aab8499a1184edf4fc386f8268..0000000000000000000000000000000000000000 --- a/PaddleNLP/LAC +++ /dev/null @@ -1 +0,0 @@ -Subproject commit a4eb73b2fb64d8aab8499a1184edf4fc386f8268 diff --git a/PaddleNLP/LARK b/PaddleNLP/LARK deleted file mode 160000 index 77ab80a7061024c4b28f0b41fdd6ba42d5e6d9e1..0000000000000000000000000000000000000000 --- a/PaddleNLP/LARK +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 77ab80a7061024c4b28f0b41fdd6ba42d5e6d9e1 diff --git a/PaddleNLP/README.md b/PaddleNLP/README.md index fa81f6a27df879e68e704491225c1fee10930c93..7132e1490afd937ffe32a888e506ad13c5060154 100644 --- a/PaddleNLP/README.md +++ b/PaddleNLP/README.md @@ -9,7 +9,7 @@ Machine Translation, NMT)等阶段。在 NMT 成熟后，机器翻译才真正 本实例所实现的 Transformer 就是一个基于自注意力机制的机器翻译模型，其中不再有RNN或CNN结构，而是完全利用 Attention 学习语言中的上下文依赖。相较于RNN/CNN, 这种结构在单层内计算复杂度更低、易于并行化、对长程依赖更易建模，最终在多种语言之间取得了最好的翻译效果。 -- [Transformer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/neural_machine_translation/transformer/README_cn.md) +- [Transformer](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/neural_machine_translation/transformer/README.md) 中文词法分析 @@ -35,7 +35,7 @@ Machine Translation, NMT)等阶段。在 NMT 成熟后，机器翻译才真正 本例所开放的DAM (Deep Attention Matching Network)为百度自然语言处理部发表于ACL-2018的工作，用于检索式聊天机器人多轮对话中应答的选择。DAM受Transformer的启发，其网络结构完全基于注意力(attention)机制，利用栈式的self-attention结构分别学习不同粒度下应答和语境的语义表示，然后利用cross-attention获取应答与语境之间的相关性，在两个大规模多轮对话数据集上的表现均好于其它模型。 -- [Deep Attention Matching Network](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/deep_attention_matching_net) +- [Deep Attention Matching Network](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/dialogue_model_toolkit/deep_attention_matching) AnyQ ---- @@ -53,4 +53,4 @@ SimNet是百度自然语言处理部于2013年自主研发的语义匹配框架 百度阅读理解数据集是由百度自然语言处理部开源的一个真实世界数据集，所有的问题、原文都来源于实际数据(百度搜索引擎数据和百度知道问答社区)，答案是由人类回答的。每个问题都对应多个答案，数据集包含200k问题、1000k原文和420k答案，是目前最大的中文MRC数据集。百度同时开源了对应的阅读理解模型，称为DuReader，采用当前通用的网络分层结构，通过双向attention机制捕捉问题和原文之间的交互关系，生成query-aware的原文表示，最终基于query-aware的原文表示通过point network预测答案范围。 -- [DuReader in PaddlePaddle Fluid](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/machine_reading_comprehension/README.md) +- [DuReader in PaddlePaddle Fluid](https://github.com/PaddlePaddle/models/blob/develop/PaddleNLP/reading_comprehension) \ No newline at end of file diff --git a/PaddleNLP/Senta b/PaddleNLP/Senta deleted file mode 160000 index dc1af6a83dd1372055158ac6d17f6d14b3a0f0f8..0000000000000000000000000000000000000000 --- a/PaddleNLP/Senta +++ /dev/null @@ -1 +0,0 @@ -Subproject commit dc1af6a83dd1372055158ac6d17f6d14b3a0f0f8 diff --git a/PaddleNLP/SimNet b/PaddleNLP/SimNet deleted file mode 160000 index b3e096b92f26720f6e3b020b374e11aa0748c032..0000000000000000000000000000000000000000 --- a/PaddleNLP/SimNet +++ /dev/null @@ -1 +0,0 @@ -Subproject commit b3e096b92f26720f6e3b020b374e11aa0748c032 diff --git a/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/README.md b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8888a05d96461a341c6b6b30aab3e6bf6d39bcfa --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/README.md @@ -0,0 +1,153 @@ +# Auto Dialogue Evaluation +## 简介 +### 任务说明 +对话自动评估（Auto Dialogue Evaluation）评估开放领域对话系统的回复质量，能够帮助企业或个人快速评估对话系统的回复质量，减少人工评估成本。 +1. 在无标注数据的情况下，利用负采样训练匹配模型作为评估工具，实现对多个对话系统回复质量排序； +2. 利用少量标注数据（特定对话系统或场景的人工打分），在匹配模型基础上进行微调，可以显著该对话系统或场景的评估效果。 + +### 效果说明 +我们以四个不同的对话系统（seq2seq\_naive／seq2seq\_att／keywords／human）为例，使用对话自动评估工具进行自动评估。 +1. 无标注数据情况下，直接使用预训练好的评估工具进行评估； + 在四个对话系统上，自动评估打分和人工评估打分spearman相关系数，如下： + + /|seq2seq\_naive|seq2seq\_att|keywords|human + --|:--:|--:|:--:|--: + cor|0.361|0.343|0.324|0.288 + + 对四个系统平均得分排序： + + 人工评估|k(0.591) test.txt.utf8.seg +``` + +### 代码结构说明 +main.py：该项目的主函数，封装包括训练、预测、评估的部分 + +config.py：定义了该项目模型的相关配置，包括具体模型类别、以及模型的超参数 + +reader.py：定义了读入数据，加载词典的功能 + +evaluation.py：定义评估函数 + +init.py:定义模型load函数 + +run.sh：训练、预测、评估运行脚本 + +## 其他 +如何贡献代码 + +如果你可以修复某个issue或者增加一个新功能，欢迎给我们提交PR。如果对应的PR被接受了，我们将根据贡献的质量和难度进行打分（0-5分，越高越好）。如果你累计获得了10分，可以联系我们获得面试机会或者为你写推荐信。 diff --git a/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/config.py b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/config.py new file mode 100755 index 0000000000000000000000000000000000000000..4d1ae4dc4636aab5a4e0497fde94fabef72f2fdf --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/config.py @@ -0,0 +1,77 @@ +""" +Auto Dialogue Evaluation. +""" + +import argparse +import six + +def parse_args(): + """ + Auto Dialogue Evaluation Config + """ + parser = argparse.ArgumentParser('Automatic Dialogue Evaluation.') + parser.add_argument( + '--do_train', type=bool, default=False, help='Whether to perform training.') + parser.add_argument( + '--do_val', type=bool, default=False, help='Whether to perform evaluation.') + parser.add_argument( + '--do_infer', type=bool, default=False, help='Whether to perform inference.') + parser.add_argument( + '--loss_type', type=str, default='CLS', help='Loss type, CLS or L2.') + + #data path + parser.add_argument( + '--train_path', type=str, default=None, help='Path of training data') + parser.add_argument( + '--val_path', type=str, default=None, help='Path of validation data') + parser.add_argument( + '--test_path', type=str, default=None, help='Path of validation data') + parser.add_argument( + '--save_path', type=str, default='tmp', help='Save path') + + #step fit for data size + parser.add_argument( + '--print_step', type=int, default=50, help='Print step') + parser.add_argument( + '--save_step', type=int, default=400, help='Save step') + parser.add_argument( + '--num_scan_data', type=int, default=20, help='Save step') + + parser.add_argument( + '--word_emb_init', type=str, default=None, help='Path to the initial word embedding') + parser.add_argument( + '--init_model', type=str, default=None, help='Path to the init model') + + parser.add_argument( + '--use_cuda', + action='store_true', + help='If set, use cuda for training.') + parser.add_argument( + '--batch_size', type=int, default=256, help='Batch size') + parser.add_argument( + '--hidden_size', type=int, default=256, help='Hidden size') + parser.add_argument( + '--emb_size', type=int, default=256, help='Embedding size') + parser.add_argument( + '--vocab_size', type=int, default=484016, help='Vocabulary size') + parser.add_argument( + '--learning_rate', type=float, default=0.001, help='Learning rate') + parser.add_argument( + '--sample_pro', type=float, default=0.1, help='Sample probability for training data') + parser.add_argument( + '--max_len', type=int, default=50, help='Max length for sentences') + + args = parser.parse_args() + return args + + +def print_arguments(args): + """ + Print Config + """ + print('----------- Configuration Arguments -----------') + for arg, value in sorted(six.iteritems(vars(args))): + print('%s: %s' % (arg, value)) + print('------------------------------------------------') + + diff --git a/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/download_data.sh b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/download_data.sh new file mode 100755 index 0000000000000000000000000000000000000000..7f97c75ec2880d147aca89a4e3e91f8e64cdcd4b --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/download_data.sh @@ -0,0 +1,2 @@ +wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_dataset-1.0.0.tar.gz +tar -xzf auto_dialogue_evaluation_dataset-1.0.0.tar.gz diff --git a/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/evaluation.py b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/evaluation.py new file mode 100755 index 0000000000000000000000000000000000000000..8c8a7041c332d1bde8fbc2e6ebaafbc975482462 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/evaluation.py @@ -0,0 +1,57 @@ +""" +Evaluation for auto dialogue evaluation +""" + +import sys +import numpy as np +import pandas as pd + +def get_p_at_n_in_m(data, n, m, ind): + """ + Get n in m + """ + pos_score = data[ind][0] + curr = data[ind : ind+m] + curr = sorted(curr, key = lambda x: x[0], reverse=True) + + if curr[n-1][0] <= pos_score: + return 1 + return 0 + + +def evaluate_Recall(data): + """ + Evaluate Recall + """ + p_at_1_in_2 = 0.0 + p_at_1_in_10 = 0.0 + p_at_2_in_10 = 0.0 + p_at_5_in_10 = 0.0 + + length = len(data) / 10 + + for i in xrange(0, length): + ind = i * 10 + assert data[ind][1] == 1 + + p_at_1_in_2 += get_p_at_n_in_m(data, 1, 2, ind) + p_at_1_in_10 += get_p_at_n_in_m(data, 1, 10, ind) + p_at_2_in_10 += get_p_at_n_in_m(data, 2, 10, ind) + p_at_5_in_10 += get_p_at_n_in_m(data, 5, 10, ind) + + recall_dict = { + '1_in_2': p_at_1_in_2 / length, + '1_in_10': p_at_1_in_10 / length, + '2_in_10': p_at_2_in_10 / length, + '5_in_10': p_at_5_in_10 / length} + + return recall_dict + + +def evaluate_cor(pred, true): + """ + Evaluate cor + """ + df = pd.DataFrame({'pred': pred, 'true': true}) + cor_matrix = df.corr('spearman') + return cor_matrix['pred']['true'] diff --git a/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/init.py b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/init.py new file mode 100755 index 0000000000000000000000000000000000000000..d56fb34b47261570ee8b48a801909fb8181800a3 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/init.py @@ -0,0 +1,53 @@ +""" +Init for pretrained para +""" + +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function + +import os +import six +import ast +import copy + +import numpy as np +import paddle.fluid as fluid + +def init_pretraining_params(exe, + pretraining_params_path, + main_program): + """ + Init pretraining params + """ + assert os.path.exists(pretraining_params_path + ), "[%s] cann't be found." % pretraining_params_path + + def existed_params(var): + """ + Test existed + """ + if not isinstance(var, fluid.framework.Parameter): + return False + return os.path.exists(os.path.join(pretraining_params_path, var.name)) + + fluid.io.load_vars( + exe, + pretraining_params_path, + main_program=main_program, + predicate=existed_params) + print("Load pretraining parameters from {}.".format( + pretraining_params_path)) + diff --git a/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/main.py b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/main.py new file mode 100755 index 0000000000000000000000000000000000000000..ad04cd8b1f81a739dc1fe2fa0a24adc36eb41017 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/main.py @@ -0,0 +1,461 @@ +""" +Auto dialogue evaluation task +""" + +import os +import sys +import six +import numpy as np +import time +import multiprocessing +import paddle +import paddle.fluid as fluid +import reader as reader +import evaluation as eva +import init as init + +try: + import cPickle as pickle #python 2 +except ImportError as e: + import pickle #python 3 + +sys.path.append('../../models/dialogue_model_toolkit/auto_dialogue_evaluation/') +from net import Network +import config + +def train(args): + """Train + """ + if not os.path.exists(args.save_path): + os.makedirs(args.save_path) + + net = Network(args.vocab_size, args.emb_size, args.hidden_size) + + train_program = fluid.Program() + train_startup = fluid.Program() + if "CE_MODE_X" in os.environ: + train_program.random_seed = 110 + train_startup.random_seed = 110 + with fluid.program_guard(train_program, train_startup): + with fluid.unique_name.guard(): + logits, loss = net.network(args.loss_type) + loss.persistable = True + logits.persistable = True + # gradient clipping + fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByValue( + max=1.0, min=-1.0)) + + optimizer = fluid.optimizer.Adam( + learning_rate=args.learning_rate) + optimizer.minimize(loss) + print("begin memory optimization ...") + fluid.memory_optimize(train_program) + print("end memory optimization ...") + + test_program = fluid.Program() + test_startup = fluid.Program() + if "CE_MODE_X" in os.environ: + test_program.random_seed = 110 + test_startup.random_seed = 110 + with fluid.program_guard(test_program, test_startup): + with fluid.unique_name.guard(): + logits, loss = net.network(args.loss_type) + loss.persistable = True + logits.persistable = True + + test_program = test_program.clone(for_test=True) + if args.use_cuda: + place = fluid.CUDAPlace(0) + dev_count = fluid.core.get_cuda_device_count() + else: + place = fluid.CPUPlace() + dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) + + print("device count %d" % dev_count) + print("theoretical memory usage: ") + print( + fluid.contrib.memory_usage( + program=train_program, batch_size=args.batch_size)) + + exe = fluid.Executor(place) + exe.run(train_startup) + exe.run(test_startup) + + train_exe = fluid.ParallelExecutor( + use_cuda=args.use_cuda, loss_name=loss.name, main_program=train_program) + + test_exe = fluid.ParallelExecutor( + use_cuda=args.use_cuda, + main_program=test_program, + share_vars_from=train_exe) + + if args.word_emb_init is not None: + print("start loading word embedding init ...") + if six.PY2: + word_emb = np.array(pickle.load(open(args.word_emb_init, + 'rb'))).astype('float32') + else: + word_emb = np.array( + pickle.load( + open(args.word_emb_init, 'rb'), encoding="bytes")).astype( + 'float32') + net.set_word_embedding(word_emb, place) + print("finish init word embedding ...") + + print("start loading data ...") + + def train_with_feed(batch_data): + """ + Train on one batch + """ + #to do get_feed_names + feed_dict = dict(zip(net.get_feed_names(), batch_data)) + + cost = train_exe.run(feed=feed_dict, fetch_list=[loss.name]) + return cost[0] + + def test_with_feed(batch_data): + """ + Test on one batch + """ + feed_dict = dict(zip(net.get_feed_names(), batch_data)) + + score = test_exe.run(feed=feed_dict, fetch_list=[logits.name]) + return score[0] + + def evaluate(): + """ + Evaluate to choose model + """ + val_batches = reader.batch_reader( + args.val_path, args.batch_size, place, args.max_len, 1) + scores = [] + labels = [] + for batch in val_batches: + scores.extend(test_with_feed(batch)) + labels.extend([x[0] for x in batch[2]]) + + return eva.evaluate_Recall(zip(scores, labels)) + + def save_exe(step, best_recall): + """ + Save exe conditional + """ + recall_dict = evaluate() + print('evaluation recall result:') + print('1_in_2: %s\t1_in_10: %s\t2_in_10: %s\t5_in_10: %s' % ( + recall_dict['1_in_2'], recall_dict['1_in_10'], + recall_dict['2_in_10'], recall_dict['5_in_10'])) + + if recall_dict['1_in_10'] > best_recall and step != 0: + fluid.io.save_inference_model(args.save_path, + net.get_feed_inference_names(), + logits, exe, main_program=train_program) + + print("Save model at step %d ... " % step) + print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))) + best_recall = recall_dict['1_in_10'] + return best_recall + + # train over different epoches + global_step, train_time = 0, 0.0 + best_recall = 0 + for epoch in six.moves.xrange(args.num_scan_data): + train_batches = reader.batch_reader( + args.train_path, args.batch_size, place, + args.max_len, args.sample_pro) + + begin_time = time.time() + sum_cost = 0 + for batch in train_batches: + if (args.save_path is not None) and (global_step % args.save_step == 0): + best_recall = save_exe(global_step, best_recall) + + cost = train_with_feed(batch) + global_step += 1 + sum_cost += cost.mean() + + if global_step % args.print_step == 0: + print('training step %s avg loss %s' % (global_step, sum_cost / args.print_step)) + sum_cost = 0 + + pass_time_cost = time.time() - begin_time + train_time += pass_time_cost + print("Pass {0}, pass_time_cost {1}" + .format(epoch, "%2.2f sec" % pass_time_cost)) + + +def finetune(args): + """ + Finetune + """ + if not os.path.exists(args.save_path): + os.makedirs(args.save_path) + + net = Network(args.vocab_size, args.emb_size, args.hidden_size) + + train_program = fluid.Program() + train_startup = fluid.Program() + if "CE_MODE_X" in os.environ: + train_program.random_seed = 110 + train_startup.random_seed = 110 + with fluid.program_guard(train_program, train_startup): + with fluid.unique_name.guard(): + logits, loss = net.network(args.loss_type) + loss.persistable = True + logits.persistable = True + # gradient clipping + fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByValue( + max=1.0, min=-1.0)) + + optimizer = fluid.optimizer.Adam( + learning_rate=fluid.layers.exponential_decay( + learning_rate=args.learning_rate, + decay_steps=400, + decay_rate=0.9, + staircase=True)) + optimizer.minimize(loss) + print("begin memory optimization ...") + fluid.memory_optimize(train_program) + print("end memory optimization ...") + + test_program = fluid.Program() + test_startup = fluid.Program() + if "CE_MODE_X" in os.environ: + test_program.random_seed = 110 + test_startup.random_seed = 110 + with fluid.program_guard(test_program, test_startup): + with fluid.unique_name.guard(): + logits, loss = net.network(args.loss_type) + loss.persistable = True + logits.persistable = True + + test_program = test_program.clone(for_test=True) + if args.use_cuda: + place = fluid.CUDAPlace(0) + dev_count = fluid.core.get_cuda_device_count() + else: + place = fluid.CPUPlace() + dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) + + print("device count %d" % dev_count) + print("theoretical memory usage: ") + print( + fluid.contrib.memory_usage( + program=train_program, batch_size=args.batch_size)) + + exe = fluid.Executor(place) + exe.run(train_startup) + exe.run(test_startup) + + train_exe = fluid.ParallelExecutor( + use_cuda=args.use_cuda, loss_name=loss.name, main_program=train_program) + + test_exe = fluid.ParallelExecutor( + use_cuda=args.use_cuda, + main_program=test_program, + share_vars_from=train_exe) + + if args.init_model: + init.init_pretraining_params( + exe, + args.init_model, + main_program=train_startup) + print('sccuess init %s' % args.init_model) + + print("start loading data ...") + + def train_with_feed(batch_data): + """ + Train on one batch + """ + #to do get_feed_names + feed_dict = dict(zip(net.get_feed_names(), batch_data)) + + cost = train_exe.run(feed=feed_dict, fetch_list=[loss.name]) + return cost[0] + + def test_with_feed(batch_data): + """ + Test on one batch + """ + feed_dict = dict(zip(net.get_feed_names(), batch_data)) + + score = test_exe.run(feed=feed_dict, fetch_list=[logits.name]) + return score[0] + + def evaluate(): + """ + Evaluate to choose model + """ + val_batches = reader.batch_reader( + args.val_path, args.batch_size, place, args.max_len, 1) + scores = [] + labels = [] + for batch in val_batches: + scores.extend(test_with_feed(batch)) + labels.extend([x[0] for x in batch[2]]) + scores = [x[0] for x in scores] + return eva.evaluate_cor(scores, labels) + + def save_exe(step, best_cor): + """ + Save exe conditional + """ + cor = evaluate() + print('evaluation cor relevance %s' % cor) + if cor > best_cor and step != 0: + fluid.io.save_inference_model(args.save_path, + net.get_feed_inference_names(), logits, + exe, main_program=train_program) + print("Save model at step %d ... " % step) + print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))) + best_cor = cor + return best_cor + + # train over different epoches + global_step, train_time = 0, 0.0 + best_cor = 0.0 + pre_index = -1 + for epoch in six.moves.xrange(args.num_scan_data): + train_batches = reader.batch_reader( + args.train_path, + args.batch_size, place, + args.max_len, args.sample_pro) + + begin_time = time.time() + sum_cost = 0 + for batch in train_batches: + if (args.save_path is not None) and (global_step % args.save_step == 0): + best_cor = save_exe(global_step, best_cor) + + cost = train_with_feed(batch) + global_step += 1 + sum_cost += cost.mean() + + if global_step % args.print_step == 0: + print('training step %s avg loss %s' % (global_step, sum_cost / args.print_step)) + sum_cost = 0 + + pass_time_cost = time.time() - begin_time + train_time += pass_time_cost + print("Pass {0}, pass_time_cost {1}" + .format(epoch, "%2.2f sec" % pass_time_cost)) + + +def evaluate(args): + """ + Evaluate model for both pretrained and finetuned + """ + place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace() + exe = fluid.Executor(place) + + t0 = time.time() + + with fluid.scope_guard(fluid.core.Scope()): + infer_program, feed_target_names, fetch_vars = fluid.io.load_inference_model( + args.init_model, exe) + + global_step, infer_time = 0, 0.0 + test_batches = reader.batch_reader( + args.test_path, args.batch_size, place, + args.max_len, 1) + scores = [] + labels = [] + for batch in test_batches: + logits = exe.run( + infer_program, + feed = { + 'context_wordseq': batch[0], + 'response_wordseq': batch[1]}, + fetch_list = fetch_vars) + logits = [x[0] for x in logits[0]] + + scores.extend(logits) + labels.extend([x[0] for x in batch[2]]) + + mean_score = sum(scores)/len(scores) + if args.loss_type == 'CLS': + recall_dict = eva.evaluate_Recall(zip(scores, labels)) + print('mean score: %s' % mean_score) + print('evaluation recall result:') + print('1_in_2: %s\t1_in_10: %s\t2_in_10: %s\t5_in_10: %s' % ( + recall_dict['1_in_2'], recall_dict['1_in_10'], + recall_dict['2_in_10'], recall_dict['5_in_10'])) + elif args.loss_type == 'L2': + cor = eva.evaluate_cor(scores, labels) + print('mean score: %s\nevaluation cor resuls:%s' % (mean_score, cor)) + else: + raise ValueError + + t1 = time.time() + print("finish evaluate model:%s on data:%s time_cost(s):%.2f" % + (args.init_model, args.test_path, t1 - t0)) + + +def infer(args): + """ + Inference function + """ + place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace() + exe = fluid.Executor(place) + + t0 = time.time() + + with fluid.scope_guard(fluid.core.Scope()): + infer_program, feed_target_names, fetch_vars = fluid.io.load_inference_model( + args.init_model, exe) + + global_step, infer_time = 0, 0.0 + test_batches = reader.batch_reader( + args.test_path, args.batch_size, place, + args.max_len, 1) + scores = [] + for batch in test_batches: + logits = exe.run( + infer_program, + feed = { + 'context_wordseq': batch[0], + 'response_wordseq': batch[1]}, + fetch_list = fetch_vars) + logits = [x[0] for x in logits[0]] + + scores.extend(logits) + + in_file = open(args.test_path, 'r') + out_path = args.test_path + '.infer' + out_file = open(out_path, 'w') + for line, s in zip(in_file, scores): + out_file.write('%s\t%s\n' % (line.strip(), s)) + + in_file.close() + out_file.close() + + t1 = time.time() + print("finish infer model:%s out file: %s time_cost(s):%.2f" % + (args.init_model, out_path, t1 - t0)) + + +def main(): + """ + main + """ + args = config.parse_args() + config.print_arguments(args) + + if args.do_train == True: + if args.loss_type == 'CLS': + train(args) + elif args.loss_type == 'L2': + finetune(args) + else: + raise ValueError + elif args.do_val == True: + evaluate(args) + elif args.do_infer == True: + infer(args) + else: + raise ValueError + +if __name__ == '__main__': + main() diff --git a/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/model_files/download_model.sh b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/model_files/download_model.sh new file mode 100644 index 0000000000000000000000000000000000000000..3005708361597da0951fb7003c06f7aa2e239dd7 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/model_files/download_model.sh @@ -0,0 +1,11 @@ +#matching pretrained +wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_matching_pretrained-1.0.0.tar.gz +tar -xzf auto_dialogue_evaluation_matching_pretrained-1.0.0.tar.gz + +#finetuned +for task in seq2seq_naive seq2seq_att keywords human +do + wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_${task}_finetuned-1.0.0.tar.gz + tar -xzf auto_dialogue_evaluation_${task}_finetuned-1.0.0.tar.gz +done + diff --git a/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/reader.py b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/reader.py new file mode 100755 index 0000000000000000000000000000000000000000..bcf15523ca66c1278ca37d302fd9adc6654772c5 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/reader.py @@ -0,0 +1,76 @@ +""" +Reader for auto dialogue evaluation +""" + +import sys +import time +import numpy as np +import random + +import paddle.fluid as fluid +import paddle + +def to_lodtensor(data, place): + """ + Convert to LODtensor + """ + seq_lens = [len(seq) for seq in data] + cur_len = 0 + lod = [cur_len] + for l in seq_lens: + cur_len += l + lod.append(cur_len) + flattened_data = np.concatenate(data, axis=0).astype("int64") + flattened_data = flattened_data.reshape([len(flattened_data), 1]) + res = fluid.LoDTensor() + res.set(flattened_data, place) + res.set_lod([lod]) + return res + + +def reshape_batch(batch, place): + """ + Reshape batch + """ + context_reshape = to_lodtensor([dat[0] for dat in batch], place) + response_reshape = to_lodtensor([dat[1] for dat in batch], place) + label_reshape = [dat[2] for dat in batch] + return (context_reshape, response_reshape, label_reshape) + + +def batch_reader(data_path, + batch_size, + place, + max_len=50, + sample_pro=1): + """ + Yield batch + """ + batch = [] + with open(data_path, 'r') as f: + Print = True + for line in f: + #sample for training data + if sample_pro < 1: + if random.random() > sample_pro: + continue + + tokens = line.strip().split('\t') + assert len(tokens) == 3 + context = [int(x) for x in tokens[0].split()[:max_len]] + response = [int(x) for x in tokens[1].split()[:max_len]] + + label = [int(tokens[2])] + #label = int(tokens[2]) + instance = (context, response, label) + + if len(batch) < batch_size: + batch.append(instance) + else: + if len(batch) == batch_size: + yield reshape_batch(batch, place) + batch = [instance] + + if len(batch) == batch_size: + yield reshape_batch(batch, place) + diff --git a/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/run.sh b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/run.sh new file mode 100755 index 0000000000000000000000000000000000000000..d1bc8957e53e7c67b5e029daf43a1f729595405d --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/run.sh @@ -0,0 +1,64 @@ +export CUDA_VISIBLE_DEVICES=4 +export FLAGS_eager_delete_tensor_gb=0.0 + +#pretrain +python -u main.py \ + --do_train True \ + --use_cuda \ + --save_path model_files_tmp/matching_pretrained \ + --train_path data/unlabel_data/train.ids \ + --val_path data/unlabel_data/val.ids + +#finetune based on one task +TASK=human +python -u main.py \ + --do_train True \ + --loss_type L2 \ + --use_cuda \ + --save_path model_files_tmp/${TASK}_finetuned \ + --init_model model_files/matching_pretrained \ + --train_path data/label_data/$TASK/train.ids \ + --val_path data/label_data/$TASK/val.ids \ + --print_step 1 \ + --save_step 1 \ + --num_scan_data 50 + +#evaluate pretrained model by Recall +python -u main.py \ + --do_val True \ + --use_cuda \ + --test_path data/unlabel_data/test.ids \ + --init_model model_files/matching_pretrained \ + --loss_type CLS + +#evaluate pretrained model by Cor +for task in seq2seq_naive seq2seq_att keywords human +do + echo $task + python -u main.py \ + --do_val True \ + --use_cuda \ + --test_path data/label_data/$task/test.ids \ + --init_model model_files/matching_pretrained \ + --loss_type L2 +done + +#evaluate finetuned model by Cor +for task in seq2seq_naive seq2seq_att keywords human +do + echo $task + python -u main.py \ + --do_val True \ + --use_cuda \ + --test_path data/label_data/$task/test.ids \ + --init_model model_files/${task}_finetuned \ + --loss_type L2 +done + +#infer +TASK=human +python -u main.py \ + --do_infer True \ + --use_cuda \ + --test_path data/label_data/$TASK/test.ids \ + --init_model model_files/${TASK}_finetuned diff --git a/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/run_CPU.sh b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/run_CPU.sh new file mode 100755 index 0000000000000000000000000000000000000000..31c44803a560fe05ea10fb222a3d49ccd9147a9b --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/run_CPU.sh @@ -0,0 +1,59 @@ +export FLAGS_eager_delete_tensor_gb=0.0 + +#pretrain +python -u main.py \ + --do_train True \ + --sample_pro 0.9 \ + --batch_size 64 \ + --save_path model_files_tmp/matching_pretrained \ + --train_path data/unlabel_data/train.ids \ + --val_path data/unlabel_data/val.ids + +#finetune based on one task +TASK=human +python -u main.py \ + --do_train True \ + --loss_type L2 \ + --save_path model_files_tmp/${TASK}_finetuned \ + --init_model model_files/matching_pretrained \ + --train_path data/label_data/$TASK/train.ids \ + --val_path data/label_data/$TASK/val.ids \ + --print_step 1 \ + --save_step 1 \ + --num_scan_data 50 + +#evaluate pretrained model by Recall +python -u main.py \ + --do_val True \ + --test_path data/unlabel_data/test.ids \ + --init_model model_files/matching_pretrained \ + --loss_type CLS + +#evaluate pretrained model by Cor +for task in seq2seq_naive seq2seq_att keywords human +do + echo $task + python -u main.py \ + --do_val True \ + --test_path data/label_data/$task/test.ids \ + --init_model model_files/matching_pretrained \ + --loss_type L2 +done + +#evaluate finetuned model by Cor +for task in seq2seq_naive seq2seq_att keywords human +do + echo $task + python -u main.py \ + --do_val True \ + --test_path data/label_data/$task/test.ids \ + --init_model model_files/${task}_finetuned \ + --loss_type L2 +done + +#infer +TASK=human +python -u main.py \ + --do_infer True \ + --test_path data/label_data/$TASK/test.ids \ + --init_model model_files/${TASK}_finetuned diff --git a/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/README.md b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/README.md new file mode 100755 index 0000000000000000000000000000000000000000..477a0c3f9c045dec13443e6a0d3e76c915772648 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/README.md @@ -0,0 +1,90 @@ +# __Deep Attention Matching Network__ +## 简介 +### 任务说明 +深度注意力机制模型（Deep Attention Matching Network）是开放领域多轮对话匹配模型。根据多轮对话历史和候选回复内容，排序出最合适的回复。 +网络结构如下，更多内容可以参考论文:[http://aclweb.org/anthology/P18-1103](http://aclweb.org/anthology/P18-1103). + +

+
+Overview of Deep Attention Matching Network +

+ +### 效果说明 +该模型在两个公开数据集上效果如下： + +

+
+

+ +## 快速开始 +### 安装说明 +1. paddle安装 + + 本项目依赖于 Paddlepaddle Fluid 1.3.1，请参考安装指南进行安装。 + +2. 安装代码 +3. 环境依赖 +### 开始第一次模型调用 +1. 数据准备 + + 下载经过预处理的数据，运行该脚本之后，data目录下会存在unlabel和douban两个文件夹。 + ``` + cd data + sh download_data.sh + ``` +2. 模型训练 + ``` + python -u main.py \ + --do_train True \ + --use_cuda \ + --data_path ./data/ubuntu/data_small.pkl \ + --save_path ./model_files/ubuntu \ + --use_pyreader \ + --vocab_size 434512 \ + --_EOS_ 28270 \ + --batch_size 32 + ``` +3. 模型评估 + ``` + python -u main.py \ + --do_test True \ + --use_cuda \ + --data_path ./data/ubuntu/data_small.pkl \ + --save_path ./model_files/ubuntu/step_372 \ + --model_path ./model_files/ubuntu/step_372 \ + --vocab_size 434512 \ + --_EOS_ 28270 \ + --batch_size 100 + ``` +## 进阶使用 +### 任务定义与建模 +多轮对话匹配任务输入是多轮对话历史和候选回复，输出是回复匹配得分，根据匹配得分排序。 +### 模型原理介绍 +可以参考论文:[http://aclweb.org/anthology/P18-1103](http://aclweb.org/anthology/P18-1103). +### 数据格式说明 +训练、预测、评估使用的数据示例如下，数据由三列组成，以制表符（'\t'）分隔，第一列是以空 +格分开的上文id，第二列是以空格分开的回复id，第三列是标签 + +注：本项目额外提供了分词预处理脚本（在preprocess目录下），可供用户使用，具体使用方法如 +下： +``` +python tokenizer.py \ + --test_data_dir ./test.txt.utf8 \ + --batch_size 1 > test.txt.utf8.seg +``` +### 代码结构说明 +main.py：该项目的主函数，封装包括训练、预测的部分 + +config.py：定义了该项目模型的相关配置，包括具体模型类别、以及模型的超参数 + +reader.py：定义了读入数据，加载词典的功能 + +evaluation.py：定义评估函数 + +run.sh：训练、预测运行脚本 + +## 其他 +如何贡献代码 + +如果你可以修复某个issue或者增加一个新功能，欢迎给我们提交PR。如果对应的PR被接受了，我们将根据贡献的质量和难度进行打分（0-5分，越高越好）。如果你累计获得了10分，可以联系我们获得面试机会或者为你写推荐信。 + diff --git a/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/config.py b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/config.py new file mode 100644 index 0000000000000000000000000000000000000000..6b9c664942ff2ea23e0f284662dbeae172caca3c --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/config.py @@ -0,0 +1,123 @@ +""" +Deep Attention Matching Network +""" + +import argparse +import six + +def parse_args(): + """ + Deep Attention Matching Network Config + """ + parser = argparse.ArgumentParser("DAM Config") + + parser.add_argument( + '--do_train', + type=bool, + default=False, + help='Whether to perform training.') + parser.add_argument( + '--do_test', + type=bool, + default=False, + help='Whether to perform training.') + + parser.add_argument( + '--batch_size', + type=int, + default=256, + help='Batch size for training. (default: %(default)d)') + parser.add_argument( + '--num_scan_data', + type=int, + default=2, + help='Number of pass for training. (default: %(default)d)') + parser.add_argument( + '--learning_rate', + type=float, + default=1e-3, + help='Learning rate used to train. (default: %(default)f)') + parser.add_argument( + '--data_path', + type=str, + default="data/data_small.pkl", + help='Path to training data. (default: %(default)s)') + parser.add_argument( + '--save_path', + type=str, + default="saved_models", + help='Path to save trained models. (default: %(default)s)') + parser.add_argument( + '--model_path', + type=str, + default=None, + help='Path to load well-trained models. (default: %(default)s)') + parser.add_argument( + '--use_cuda', + action='store_true', + help='If set, use cuda for training.') + parser.add_argument( + '--use_pyreader', + action='store_true', + help='If set, use pyreader for reading data.') + parser.add_argument( + '--ext_eval', + action='store_true', + help='If set, use MAP, MRR ect for evaluation.') + parser.add_argument( + '--max_turn_num', + type=int, + default=9, + help='Maximum number of utterances in context.') + parser.add_argument( + '--max_turn_len', + type=int, + default=50, + help='Maximum length of setences in turns.') + parser.add_argument( + '--word_emb_init', + type=str, + default=None, + help='Path to the initial word embedding.') + parser.add_argument( + '--vocab_size', + type=int, + default=434512, + help='The size of vocabulary.') + parser.add_argument( + '--emb_size', + type=int, + default=200, + help='The dimension of word embedding.') + parser.add_argument( + '--_EOS_', + type=int, + default=28270, + help='The id for the end of sentence in vocabulary.') + parser.add_argument( + '--stack_num', + type=int, + default=5, + help='The number of stacked attentive modules in network.') + parser.add_argument( + '--channel1_num', + type=int, + default=32, + help="The channels' number of the 1st conv3d layer's output.") + parser.add_argument( + '--channel2_num', + type=int, + default=16, + help="The channels' number of the 2nd conv3d layer's output.") + args = parser.parse_args() + return args + + +def print_arguments(args): + """ + Print Config + """ + print('----------- Configuration Arguments -----------') + for arg, value in sorted(six.iteritems(vars(args))): + print('%s: %s' % (arg, value)) + print('------------------------------------------------') diff --git a/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/data/download_data.sh b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/data/download_data.sh new file mode 100755 index 0000000000000000000000000000000000000000..cf7a182c2802c91eccbf32a70106f594505efd09 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/data/download_data.sh @@ -0,0 +1,40 @@ +ubuntu_url=http://dam-data.cdn.bcebos.com/ubuntu.tar.gz +ubuntu_md5=9d7db116a040530a16f68dc0ab44e4b6 + +if [ ! -e ubuntu.tar.gz ]; then + wget -c $ubuntu_url +fi + +echo "Checking md5 sum ..." +md5sum_tmp=`md5sum ubuntu.tar.gz | cut -d ' ' -f1` + +if [ $md5sum_tmp != $ubuntu_md5 ]; then + echo "Md5sum check failed, please remove and redownload ubuntu.tar.gz" + exit 1 +fi + +echo "Untar ubuntu.tar.gz ..." + +tar -xzvf ubuntu.tar.gz +mv data ubuntu + +douban_url=http://dam-data.cdn.bcebos.com/douban.tar.gz +douban_md5=e07ca68f21c20e09efb3e8b247194405 + +if [ ! -e douban.tar.gz ]; then + wget -c $douban_url +fi + +echo "Checking md5 sum ..." +md5sum_tmp=`md5sum douban.tar.gz | cut -d ' ' -f1` + +if [ $md5sum_tmp != $douban_md5 ]; then + echo "Md5sum check failed, please remove and redownload douban.tar.gz" + exit 1 +fi + +echo "Untar douban.tar.gz ..." + +tar -xzvf douban.tar.gz +mv data douban + diff --git a/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/evaluation.py b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/evaluation.py new file mode 100755 index 0000000000000000000000000000000000000000..8edc8000691df61bbe4e122f60568a6d33a966b8 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/evaluation.py @@ -0,0 +1,153 @@ +""" +Evaluation +""" + +import sys +import six +import numpy as np +from sklearn.metrics import average_precision_score + +def evaluate_ubuntu(file_path): + """ + Evaluate on ubuntu data + """ + def get_p_at_n_in_m(data, n, m, ind): + """ + Recall n at m + """ + pos_score = data[ind][0] + curr = data[ind:ind + m] + curr = sorted(curr, key=lambda x: x[0], reverse=True) + + if curr[n - 1][0] <= pos_score: + return 1 + return 0 + + data = [] + with open(file_path, 'r') as file: + for line in file: + line = line.strip() + tokens = line.split("\t") + + if len(tokens) != 2: + continue + + data.append((float(tokens[0]), int(tokens[1]))) + + #assert len(data) % 10 == 0 + + p_at_1_in_2 = 0.0 + p_at_1_in_10 = 0.0 + p_at_2_in_10 = 0.0 + p_at_5_in_10 = 0.0 + + length = len(data) // 10 + + for i in six.moves.xrange(0, length): + ind = i * 10 + assert data[ind][1] == 1 + + p_at_1_in_2 += get_p_at_n_in_m(data, 1, 2, ind) + p_at_1_in_10 += get_p_at_n_in_m(data, 1, 10, ind) + p_at_2_in_10 += get_p_at_n_in_m(data, 2, 10, ind) + p_at_5_in_10 += get_p_at_n_in_m(data, 5, 10, ind) + + result_dict = { + "1_in_2": p_at_1_in_2 / length, + "1_in_10": p_at_1_in_10 / length, + "2_in_10": p_at_2_in_10 / length, + "5_in_10": p_at_5_in_10 / length} + + return result_dict + + +def evaluate_douban(file_path): + """ + Evaluate douban data + """ + def mean_average_precision(sort_data): + """ + Evaluate mean average precision + """ + count_1 = 0 + sum_precision = 0 + for index in six.moves.xrange(len(sort_data)): + if sort_data[index][1] == 1: + count_1 += 1 + sum_precision += 1.0 * count_1 / (index + 1) + return sum_precision / count_1 + + def mean_reciprocal_rank(sort_data): + """ + Evaluate MRR + """ + sort_lable = [s_d[1] for s_d in sort_data] + assert 1 in sort_lable + return 1.0 / (1 + sort_lable.index(1)) + + def precision_at_position_1(sort_data): + """ + Evaluate precision + """ + if sort_data[0][1] == 1: + return 1 + else: + return 0 + + def recall_at_position_k_in_10(sort_data, k): + """" + Evaluate recall + """ + sort_lable = [s_d[1] for s_d in sort_data] + select_lable = sort_lable[:k] + return 1.0 * select_lable.count(1) / sort_lable.count(1) + + def evaluation_one_session(data): + """ + Evaluate one session + """ + sort_data = sorted(data, key=lambda x: x[0], reverse=True) + m_a_p = mean_average_precision(sort_data) + m_r_r = mean_reciprocal_rank(sort_data) + p_1 = precision_at_position_1(sort_data) + r_1 = recall_at_position_k_in_10(sort_data, 1) + r_2 = recall_at_position_k_in_10(sort_data, 2) + r_5 = recall_at_position_k_in_10(sort_data, 5) + return m_a_p, m_r_r, p_1, r_1, r_2, r_5 + + sum_m_a_p = 0 + sum_m_r_r = 0 + sum_p_1 = 0 + sum_r_1 = 0 + sum_r_2 = 0 + sum_r_5 = 0 + i = 0 + total_num = 0 + with open(file_path, 'r') as infile: + for line in infile: + if i % 10 == 0: + data = [] + + tokens = line.strip().split('\t') + data.append((float(tokens[0]), int(tokens[1]))) + if i % 10 == 9: + total_num += 1 + m_a_p, m_r_r, p_1, r_1, r_2, r_5 = evaluation_one_session(data) + sum_m_a_p += m_a_p + sum_m_r_r += m_r_r + sum_p_1 += p_1 + sum_r_1 += r_1 + sum_r_2 += r_2 + sum_r_5 += r_5 + i += 1 + + result_dict = { + "MAP": 1.0 * sum_m_a_p / total_num, + "MRR": 1.0 * sum_m_r_r / total_num, + "P_1": 1.0 * sum_p_1 / total_num, + "1_in_10": 1.0 * sum_r_1 / total_num, + "2_in_10": 1.0 * sum_r_2 / total_num, + "5_in_10": 1.0 * sum_r_5 / total_num} + return result_dict + + diff --git a/PaddleNLP/deep_attention_matching_net/images/Figure1.png b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/images/Figure1.png old mode 100644 new mode 100755 similarity index 100% rename from PaddleNLP/deep_attention_matching_net/images/Figure1.png rename to PaddleNLP/dialogue_model_toolkit/deep_attention_matching/images/Figure1.png diff --git a/PaddleNLP/deep_attention_matching_net/images/Figure2.png b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/images/Figure2.png old mode 100644 new mode 100755 similarity index 100% rename from PaddleNLP/deep_attention_matching_net/images/Figure2.png rename to PaddleNLP/dialogue_model_toolkit/deep_attention_matching/images/Figure2.png diff --git a/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/main.py b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/main.py new file mode 100755 index 0000000000000000000000000000000000000000..b7f05e546668c41dd91481f5b2d62702d466d076 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/main.py @@ -0,0 +1,465 @@ +""" +Deep Attention Matching Network +""" +import sys +import os +import six +import numpy as np +import time +import multiprocessing +import paddle +import paddle.fluid as fluid +import reader as reader +from util import mkdir +import evaluation as eva +import config + +try: + import cPickle as pickle #python 2 +except ImportError as e: + import pickle #python 3 + +sys.path.append('../../models/dialogue_model_toolkit/deep_attention_matching/') + +from net import Net + +def evaluate(score_path, result_file_path): + """ + Evaluate both douban and ubuntu dataset + """ + if args.ext_eval: + result = eva.evaluate_douban(score_path) + else: + result = eva.evaluate_ubuntu(score_path) + #write evaluation result + with open(result_file_path, 'w') as out_file: + for p_at in result: + out_file.write(p_at + '\t' + str(result[p_at]) + '\n') + print('finish evaluation') + print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))) + + +def test_with_feed(exe, program, feed_names, fetch_list, score_path, batches, + batch_num, dev_count): + """ + Test with feed + """ + score_file = open(score_path, 'w') + for it in six.moves.xrange(batch_num // dev_count): + feed_list = [] + for dev in six.moves.xrange(dev_count): + val_index = it * dev_count + dev + batch_data = reader.make_one_batch_input(batches, val_index) + feed_dict = dict(zip(feed_names, batch_data)) + feed_list.append(feed_dict) + + predicts = exe.run(feed=feed_list, fetch_list=fetch_list) + + scores = np.array(predicts[0]) + for dev in six.moves.xrange(dev_count): + val_index = it * dev_count + dev + for i in six.moves.xrange(args.batch_size): + score_file.write( + str(scores[args.batch_size * dev + i][0]) + '\t' + str( + batches["label"][val_index][i]) + '\n') + score_file.close() + + +def test_with_pyreader(exe, program, pyreader, fetch_list, score_path, batches, + batch_num, dev_count): + """ + Test with pyreader + """ + def data_provider(): + """ + Data reader + """ + for index in six.moves.xrange(batch_num): + yield reader.make_one_batch_input(batches, index) + + score_file = open(score_path, 'w') + pyreader.decorate_tensor_provider(data_provider) + it = 0 + pyreader.start() + while True: + try: + predicts = exe.run(fetch_list=fetch_list) + + scores = np.array(predicts[0]) + for dev in six.moves.xrange(dev_count): + val_index = it * dev_count + dev + for i in six.moves.xrange(args.batch_size): + score_file.write( + str(scores[args.batch_size * dev + i][0]) + '\t' + str( + batches["label"][val_index][i]) + '\n') + it += 1 + except fluid.core.EOFException: + pyreader.reset() + break + score_file.close() + + +def train(args): + """ + Train Program + """ + if not os.path.exists(args.save_path): + os.makedirs(args.save_path) + + # data data_config + data_conf = { + "batch_size": args.batch_size, + "max_turn_num": args.max_turn_num, + "max_turn_len": args.max_turn_len, + "_EOS_": args._EOS_, + } + + dam = Net(args.max_turn_num, args.max_turn_len, args.vocab_size, + args.emb_size, args.stack_num, args.channel1_num, + args.channel2_num) + + train_program = fluid.Program() + train_startup = fluid.Program() + if "CE_MODE_X" in os.environ: + train_program.random_seed = 110 + train_startup.random_seed = 110 + with fluid.program_guard(train_program, train_startup): + with fluid.unique_name.guard(): + if args.use_pyreader: + train_pyreader = dam.create_py_reader( + capacity=10, name='train_reader') + else: + dam.create_data_layers() + loss, logits = dam.create_network() + loss.persistable = True + logits.persistable = True + # gradient clipping + fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByValue( + max=1.0, min=-1.0)) + + optimizer = fluid.optimizer.Adam( + learning_rate=fluid.layers.exponential_decay( + learning_rate=args.learning_rate, + decay_steps=400, + decay_rate=0.9, + staircase=True)) + optimizer.minimize(loss) + print("begin memory optimization ...") + print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))) + fluid.memory_optimize(train_program) + print("end memory optimization ...") + print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))) + + test_program = fluid.Program() + test_startup = fluid.Program() + if "CE_MODE_X" in os.environ: + test_program.random_seed = 110 + test_startup.random_seed = 110 + with fluid.program_guard(test_program, test_startup): + with fluid.unique_name.guard(): + if args.use_pyreader: + test_pyreader = dam.create_py_reader( + capacity=10, name='test_reader') + else: + dam.create_data_layers() + + loss, logits = dam.create_network() + loss.persistable = True + logits.persistable = True + + test_program = test_program.clone(for_test=True) + + if args.use_cuda: + place = fluid.CUDAPlace(0) + dev_count = fluid.core.get_cuda_device_count() + else: + place = fluid.CPUPlace() + dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) + + print("device count %d" % dev_count) + print("theoretical memory usage: ") + print( + fluid.contrib.memory_usage( + program=train_program, batch_size=args.batch_size)) + + exe = fluid.Executor(place) + exe.run(train_startup) + exe.run(test_startup) + + train_exe = fluid.ParallelExecutor( + use_cuda=args.use_cuda, loss_name=loss.name, main_program=train_program) + + test_exe = fluid.ParallelExecutor( + use_cuda=args.use_cuda, + main_program=test_program, + share_vars_from=train_exe) + + if args.word_emb_init is not None: + print("start loading word embedding init ...") + if six.PY2: + word_emb = np.array(pickle.load(open(args.word_emb_init, + 'rb'))).astype('float32') + else: + word_emb = np.array( + pickle.load( + open(args.word_emb_init, 'rb'), encoding="bytes")).astype( + 'float32') + dam.set_word_embedding(word_emb, place) + print("finish init word embedding ...") + + print("start loading data ...") + with open(args.data_path, 'rb') as f: + if six.PY2: + train_data, val_data, test_data = pickle.load(f) + else: + train_data, val_data, test_data = pickle.load(f, encoding="bytes") + print("finish loading data ...") + + val_batches = reader.build_batches(val_data, data_conf) + + batch_num = len(train_data[six.b('y')]) // args.batch_size + val_batch_num = len(val_batches["response"]) + + print_step = max(1, batch_num // (dev_count * 100)) + save_step = max(1, batch_num // (dev_count * 10)) + + print("begin model training ...") + print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))) + + def train_with_feed(step): + """ + Train on one epoch data by feeding + """ + ave_cost = 0.0 + for it in six.moves.xrange(batch_num // dev_count): + feed_list = [] + for dev in six.moves.xrange(dev_count): + index = it * dev_count + dev + batch_data = reader.make_one_batch_input(train_batches, index) + feed_dict = dict(zip(dam.get_feed_names(), batch_data)) + feed_list.append(feed_dict) + + cost = train_exe.run(feed=feed_list, fetch_list=[loss.name]) + + ave_cost += np.array(cost[0]).mean() + step = step + 1 + if step % print_step == 0: + print("processed: [" + str(step * dev_count * 1.0 / batch_num) + + "] ave loss: [" + str(ave_cost / print_step) + "]") + ave_cost = 0.0 + + if (args.save_path is not None) and (step % save_step == 0): + save_path = os.path.join(args.save_path, "step_" + str(step)) + print("Save model at step %d ... " % step) + print( + time.strftime('%Y-%m-%d %H:%M:%S', + time.localtime(time.time()))) + fluid.io.save_persistables(exe, save_path, train_program) + + score_path = os.path.join(args.save_path, 'score.' + str(step)) + test_with_feed(test_exe, test_program, + dam.get_feed_names(), [logits.name], score_path, + val_batches, val_batch_num, dev_count) + + result_file_path = os.path.join(args.save_path, + 'result.' + str(step)) + evaluate(score_path, result_file_path) + return step, np.array(cost[0]).mean() + + def train_with_pyreader(step): + """ + Train on one epoch with pyreader + """ + def data_provider(): + """ + Data reader + """ + for index in six.moves.xrange(batch_num): + yield reader.make_one_batch_input(train_batches, index) + + train_pyreader.decorate_tensor_provider(data_provider) + + ave_cost = 0.0 + train_pyreader.start() + while True: + try: + cost = train_exe.run(fetch_list=[loss.name]) + + ave_cost += np.array(cost[0]).mean() + step = step + 1 + if step % print_step == 0: + print("processed: [" + str(step * dev_count * 1.0 / + batch_num) + "] ave loss: [" + + str(ave_cost / print_step) + "]") + ave_cost = 0.0 + + if (args.save_path is not None) and (step % save_step == 0): + save_path = os.path.join(args.save_path, + "step_" + str(step)) + print("Save model at step %d ... " % step) + print( + time.strftime('%Y-%m-%d %H:%M:%S', + time.localtime(time.time()))) + fluid.io.save_persistables(exe, save_path, train_program) + + score_path = os.path.join(args.save_path, + 'score.' + str(step)) + test_with_pyreader(test_exe, test_program, test_pyreader, + [logits.name], score_path, val_batches, + val_batch_num, dev_count) + + result_file_path = os.path.join(args.save_path, + 'result.' + str(step)) + evaluate(score_path, result_file_path) + + except fluid.core.EOFException: + train_pyreader.reset() + break + return step, np.array(cost[0]).mean() + + # train over different epoches + global_step, train_time = 0, 0.0 + for epoch in six.moves.xrange(args.num_scan_data): + shuffle_train = reader.unison_shuffle( + train_data, seed=110 if ("CE_MODE_X" in os.environ) else None) + train_batches = reader.build_batches(shuffle_train, data_conf) + + begin_time = time.time() + if args.use_pyreader: + global_step, last_cost = train_with_pyreader(global_step) + else: + global_step, last_cost = train_with_feed(global_step) + + pass_time_cost = time.time() - begin_time + train_time += pass_time_cost + print("Pass {0}, pass_time_cost {1}" + .format(epoch, "%2.2f sec" % pass_time_cost)) + # For internal continuous evaluation + if "CE_MODE_X" in os.environ: + print("kpis train_cost %f" % last_cost) + print("kpis train_duration %f" % train_time) + + +def test(args): + """ + Test + """ + if not os.path.exists(args.save_path): + mkdir(args.save_path) + if not os.path.exists(args.model_path): + raise ValueError("Invalid model init path %s" % args.model_path) + # data data_config + data_conf = { + "batch_size": args.batch_size, + "max_turn_num": args.max_turn_num, + "max_turn_len": args.max_turn_len, + "_EOS_": args._EOS_, + } + + dam = Net(args.max_turn_num, args.max_turn_len, args.vocab_size, + args.emb_size, args.stack_num, args.channel1_num, + args.channel2_num) + dam.create_data_layers() + loss, logits = dam.create_network() + + loss.persistable = True + logits.persistable = True + + # gradient clipping + fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByValue( + max=1.0, min=-1.0)) + + test_program = fluid.default_main_program().clone(for_test=True) + optimizer = fluid.optimizer.Adam( + learning_rate=fluid.layers.exponential_decay( + learning_rate=args.learning_rate, + decay_steps=400, + decay_rate=0.9, + staircase=True)) + optimizer.minimize(loss) + + print("begin memory optimization ...") + print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))) + fluid.memory_optimize(fluid.default_main_program()) + print("end memory optimization ...") + print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))) + + if args.use_cuda: + place = fluid.CUDAPlace(0) + dev_count = fluid.core.get_cuda_device_count() + else: + place = fluid.CPUPlace() + #dev_count = multiprocessing.cpu_count() + dev_count = 1 + + exe = fluid.Executor(place) + exe.run(fluid.default_startup_program()) + + fluid.io.load_persistables(exe, args.model_path) + + test_exe = fluid.ParallelExecutor( + use_cuda=args.use_cuda, main_program=test_program) + + print("start loading data ...") + with open(args.data_path, 'rb') as f: + if six.PY2: + train_data, val_data, test_data = pickle.load(f) + else: + train_data, val_data, test_data = pickle.load(f, encoding="bytes") + print("finish loading data ...") + + test_batches = reader.build_batches(test_data, data_conf) + + test_batch_num = len(test_batches["response"]) + + print("test batch num: %d" % test_batch_num) + + print("begin inference ...") + print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))) + + score_path = os.path.join(args.save_path, 'score.txt') + score_file = open(score_path, 'w') + + for it in six.moves.xrange(test_batch_num // dev_count): + feed_list = [] + for dev in six.moves.xrange(dev_count): + index = it * dev_count + dev + batch_data = reader.make_one_batch_input(test_batches, index) + feed_dict = dict(zip(dam.get_feed_names(), batch_data)) + feed_list.append(feed_dict) + + predicts = test_exe.run(feed=feed_list, fetch_list=[logits.name]) + + scores = np.array(predicts[0]) + print("step = %d" % it) + + for dev in six.moves.xrange(dev_count): + index = it * dev_count + dev + for i in six.moves.xrange(args.batch_size): + score_file.write( + str(scores[args.batch_size * dev + i][0]) + '\t' + str( + test_batches["label"][index][i]) + '\n') + + score_file.close() + + #write evaluation result + if args.ext_eval: + result = eva.evaluate_douban(score_path) + else: + result = eva.evaluate_ubuntu(score_path) + result_file_path = os.path.join(args.save_path, 'result.txt') + with open(result_file_path, 'w') as out_file: + for metric in result: + out_file.write(metric + '\t' + str(result[metric]) + '\n') + print('finish test') + print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))) + + +if __name__ == '__main__': + args = config.parse_args() + config.print_arguments(args) + if args.do_train: + train(args) + + if args.do_test: + test(args) diff --git a/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/reader.py b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/reader.py new file mode 100755 index 0000000000000000000000000000000000000000..fd46fc32fb59d2e93d73624a763792b5f041c541 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/reader.py @@ -0,0 +1,267 @@ +""" +Reader for deep attention matching network +""" + +import six +import numpy as np + +try: + import cPickle as pickle #python 2 +except ImportError as e: + import pickle #python 3 + + +def unison_shuffle(data, seed=None): + """ + Shuffle data + """ + if seed is not None: + np.random.seed(seed) + + y = np.array(data[six.b('y')]) + c = np.array(data[six.b('c')]) + r = np.array(data[six.b('r')]) + + assert len(y) == len(c) == len(r) + p = np.random.permutation(len(y)) + print(p) + shuffle_data = {six.b('y'): y[p], six.b('c'): c[p], six.b('r'): r[p]} + return shuffle_data + + +def split_c(c, split_id): + """ + Split + c is a list, example context + split_id is a integer, conf[_EOS_] + return nested list + """ + turns = [[]] + for _id in c: + if _id != split_id: + turns[-1].append(_id) + else: + turns.append([]) + if turns[-1] == [] and len(turns) > 1: + turns.pop() + return turns + + +def normalize_length(_list, length, cut_type='tail'): + """_list is a list or nested list, example turns/r/single turn c + cut_type is head or tail, if _list len > length is used + return a list len=length and min(read_length, length) + """ + real_length = len(_list) + if real_length == 0: + return [0] * length, 0 + + if real_length <= length: + if not isinstance(_list[0], list): + _list.extend([0] * (length - real_length)) + else: + _list.extend([[]] * (length - real_length)) + return _list, real_length + + if cut_type == 'head': + return _list[:length], length + if cut_type == 'tail': + return _list[-length:], length + + +def produce_one_sample(data, + index, + split_id, + max_turn_num, + max_turn_len, + turn_cut_type='tail', + term_cut_type='tail'): + """max_turn_num=10 + max_turn_len=50 + return y, nor_turns_nor_c, nor_r, turn_len, term_len, r_len + """ + c = data[six.b('c')][index] + r = data[six.b('r')][index][:] + y = data[six.b('y')][index] + + turns = split_c(c, split_id) + #normalize turns_c length, nor_turns length is max_turn_num + nor_turns, turn_len = normalize_length(turns, max_turn_num, turn_cut_type) + + nor_turns_nor_c = [] + term_len = [] + #nor_turn_nor_c length is max_turn_num, element is a list length is max_turn_len + for c in nor_turns: + #nor_c length is max_turn_len + nor_c, nor_c_len = normalize_length(c, max_turn_len, term_cut_type) + nor_turns_nor_c.append(nor_c) + term_len.append(nor_c_len) + + nor_r, r_len = normalize_length(r, max_turn_len, term_cut_type) + + return y, nor_turns_nor_c, nor_r, turn_len, term_len, r_len + + +def build_one_batch(data, + batch_index, + conf, + turn_cut_type='tail', + term_cut_type='tail'): + """ + Build one batch + """ + _turns = [] + _tt_turns_len = [] + _every_turn_len = [] + + _response = [] + _response_len = [] + + _label = [] + + for i in six.moves.xrange(conf['batch_size']): + index = batch_index * conf['batch_size'] + i + y, nor_turns_nor_c, nor_r, turn_len, term_len, r_len = produce_one_sample( + data, index, conf['_EOS_'], conf['max_turn_num'], + conf['max_turn_len'], turn_cut_type, term_cut_type) + + _label.append(y) + _turns.append(nor_turns_nor_c) + _response.append(nor_r) + _every_turn_len.append(term_len) + _tt_turns_len.append(turn_len) + _response_len.append(r_len) + + return _turns, _tt_turns_len, _every_turn_len, _response, _response_len, _label + + +def build_one_batch_dict(data, + batch_index, + conf, + turn_cut_type='tail', + term_cut_type='tail'): + """ + Build one batch dict + """ + _turns, _tt_turns_len, _every_turn_len, _response, _response_len, _label = build_one_batch( + data, batch_index, conf, turn_cut_type, term_cut_type) + ans = { + 'turns': _turns, + 'tt_turns_len': _tt_turns_len, + 'every_turn_len': _every_turn_len, + 'response': _response, + 'response_len': _response_len, + 'label': _label + } + return ans + + +def build_batches(data, conf, turn_cut_type='tail', term_cut_type='tail'): + """ + Build batches + """ + _turns_batches = [] + _tt_turns_len_batches = [] + _every_turn_len_batches = [] + + _response_batches = [] + _response_len_batches = [] + + _label_batches = [] + + batch_len = len(data[six.b('y')]) // conf['batch_size'] + for batch_index in six.moves.range(batch_len): + _turns, _tt_turns_len, _every_turn_len, _response, _response_len, _label = build_one_batch( + data, batch_index, conf, turn_cut_type='tail', term_cut_type='tail') + + _turns_batches.append(_turns) + _tt_turns_len_batches.append(_tt_turns_len) + _every_turn_len_batches.append(_every_turn_len) + + _response_batches.append(_response) + _response_len_batches.append(_response_len) + + _label_batches.append(_label) + + ans = { + "turns": _turns_batches, + "tt_turns_len": _tt_turns_len_batches, + "every_turn_len": _every_turn_len_batches, + "response": _response_batches, + "response_len": _response_len_batches, + "label": _label_batches + } + + return ans + + +def make_one_batch_input(data_batches, index): + """Split turns and return feeding data. + + Args: + data_batches: All data batches + index: The index for current batch + + Return: + feeding dictionary + """ + + turns = np.array(data_batches["turns"][index]) + tt_turns_len = np.array(data_batches["tt_turns_len"][index]) + every_turn_len = np.array(data_batches["every_turn_len"][index]) + response = np.array(data_batches["response"][index]) + response_len = np.array(data_batches["response_len"][index]) + + batch_size = turns.shape[0] + max_turn_num = turns.shape[1] + max_turn_len = turns.shape[2] + + turns_list = [turns[:, i, :] for i in six.moves.xrange(max_turn_num)] + every_turn_len_list = [ + every_turn_len[:, i] for i in six.moves.xrange(max_turn_num) + ] + + feed_list = [] + for i, turn in enumerate(turns_list): + turn = np.expand_dims(turn, axis=-1) + feed_list.append(turn) + + for i, turn_len in enumerate(every_turn_len_list): + turn_mask = np.ones((batch_size, max_turn_len, 1)).astype("float32") + for row in six.moves.xrange(batch_size): + turn_mask[row, turn_len[row]:, 0] = 0 + feed_list.append(turn_mask) + + response = np.expand_dims(response, axis=-1) + feed_list.append(response) + + response_mask = np.ones((batch_size, max_turn_len, 1)).astype("float32") + for row in six.moves.xrange(batch_size): + response_mask[row, response_len[row]:, 0] = 0 + feed_list.append(response_mask) + + label = np.array([data_batches["label"][index]]).reshape( + [-1, 1]).astype("float32") + feed_list.append(label) + + return feed_list + + +if __name__ == '__main__': + conf = { + "batch_size": 256, + "max_turn_num": 10, + "max_turn_len": 50, + "_EOS_": 28270, + } + with open('../ubuntu/data/data_small.pkl', 'rb') as f: + if six.PY2: + train, val, test = pickle.load(f) + else: + train, val, test = pickle.load(f, encoding="bytes") + print('load data success') + + train_batches = build_batches(train, conf) + val_batches = build_batches(val, conf) + test_batches = build_batches(test, conf) + print('build batches success') diff --git a/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/run.sh b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/run.sh new file mode 100755 index 0000000000000000000000000000000000000000..47395cb7dd55743ccac55bc173550f0762d478f6 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/run.sh @@ -0,0 +1,49 @@ +export CUDA_VISIBLE_DEVICES=3 +export FLAGS_eager_delete_tensor_gb=0.0 + +#train on ubuntu +python -u main.py \ + --do_train True \ + --use_cuda \ + --data_path ./data/ubuntu/data_small.pkl \ + --save_path ./model_files/ubuntu \ + --use_pyreader \ + --vocab_size 434512 \ + --_EOS_ 28270 \ + --batch_size 32 + +#test on ubuntu +python -u main.py \ + --do_test True \ + --use_cuda \ + --data_path ./data/ubuntu/data_small.pkl \ + --save_path ./model_files/ubuntu/step_31 \ + --model_path ./model_files/ubuntu/step_31 \ + --vocab_size 434512 \ + --_EOS_ 28270 \ + --batch_size 100 + +#train on douban +python -u main.py \ + --do_train True \ + --use_cuda \ + --data_path ./data/douban/data_small.pkl \ + --save_path ./model_files/douban \ + --use_pyreader \ + --vocab_size 172130 \ + --_EOS_ 1 \ + --channel1_num 16 \ + --batch_size 32 + +#test on douban +python -u main.py \ + --do_test True \ + --use_cuda \ + --ext_eval \ + --data_path ./data/douban/data_small.pkl \ + --save_path ./model_files/douban/step_31 \ + --model_path ./model_files/douban/step_31 \ + --vocab_size 172130 \ + --_EOS_ 1 \ + --channel1_num 16 \ + --batch_size 32 diff --git a/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/run_CPU.sh b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/run_CPU.sh new file mode 100755 index 0000000000000000000000000000000000000000..091eda1f2b0e41c2d767ba5fae0930baeab6ee78 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/run_CPU.sh @@ -0,0 +1,49 @@ +export CPU_NUM=1 +export FLAGS_eager_delete_tensor_gb=0.0 + +#train on ubuntu +python -u main.py \ + --do_train True \ + --data_path ./data/ubuntu/data_small.pkl \ + --save_path ./model_files_cpu/ubuntu \ + --use_pyreader \ + --stack_num 2 \ + --vocab_size 434512 \ + --_EOS_ 28270 \ + --batch_size 32 + +#test on ubuntu +python -u main.py \ + --do_test True \ + --data_path ./data/ubuntu/data_small.pkl \ + --save_path ./model_files_cpu/ubuntu/step_31 \ + --model_path ./model_files_cpu/ubuntu/step_31 \ + --stack_num 2 \ + --vocab_size 434512 \ + --_EOS_ 28270 \ + --batch_size 40 + +#train on douban +python -u main.py \ + --do_train True \ + --data_path ./data/douban/data_small.pkl \ + --save_path ./model_files_cpu/douban \ + --use_pyreader \ + --stack_num 2 \ + --vocab_size 172130 \ + --_EOS_ 1 \ + --channel1_num 16 \ + --batch_size 32 + +#test on douban +python -u main.py \ + --do_test True \ + --ext_eval \ + --data_path ./data/douban/data_small.pkl \ + --save_path ./model_files_cpu/douban/step_31 \ + --model_path ./model_files_cpu/douban/step_31 \ + --stack_num 2 \ + --vocab_size 172130 \ + --_EOS_ 1 \ + --channel1_num 16 \ + --batch_size 40 diff --git a/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/util.py b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/util.py new file mode 100755 index 0000000000000000000000000000000000000000..709d58996a6e3c59c3a8d0106f78fda2b791dfab --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/deep_attention_matching/util.py @@ -0,0 +1,41 @@ +"""" +Utils +""" + +import six +import os + + +def print_arguments(args): + """ + Print arguments + """ + print('----------- Configuration Arguments -----------') + for arg, value in sorted(six.iteritems(vars(args))): + print('%s: %s' % (arg, value)) + print('------------------------------------------------') + + +def mkdir(path): + """ + Mkdir + """ + if not os.path.isdir(path): + mkdir(os.path.split(path)[0]) + else: + return + os.mkdir(path) + + +def pos_encoding_init(): + """ + Pos encoding init + """ + pass + + +def scaled_dot_product_attention(): + """ + Scaleed dot product attention + """ + pass diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/README.md b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/README.md new file mode 100644 index 0000000000000000000000000000000000000000..96d44f9296bded21a6c4a53fc35d8b5f99cc9462 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/README.md @@ -0,0 +1,276 @@ +# 对话通用理解模块DGU + - [一、简介](#一、简介) + - [二、快速开始](#二、快速开始) + - [三、进阶使用](#三、进阶使用) + - [四、其他](#四、其他) + +## 一、简介 + +### 任务说明 + +    对话相关的任务中，Dialogue System常常需要根据场景的变化去解决多种多样的任务。任务的多样性（意图识别、槽位解析、DA识别、DST等等），以及领域训练数据的稀少，给Dialogue System的研究和应用带来了巨大的困难和挑战，要使得dialogue system得到更好的发展，需要开发一个通用的对话理解模型。为此，我们给出了基于BERT的对话通用理解模块(DGU: DialogueGeneralUnderstanding)，通过实验表明，使用base-model(BERT)并结合常见的学习范式，就可以在几乎全部对话理解任务上取得比肩甚至超越各个领域业内最好的模型的效果，展现了学习一个通用对话理解模型的巨大潜力。 + +### 效果说明 + +    a、效果上，我们基于对话相关的业内公开数据集进行评测，效果如下表所示： + +| task_name | udc | udc | udc | atis_slot | dstc2 | atis_intent | swda | mrda | +| :------ | :------ | :------ | :------ | :------| :------ | :------ | :------ | :------ | +| 对话任务 | 匹配 | 匹配 | 匹配 | 槽位解析 | DST | 意图识别 | DA | DA | +| 任务类型 | 分类 | 分类 | 分类 | 序列标注 | 多标签分类 | 分类 | 分类 | 分类 | +| 任务名称 | udc | udc | udc| atis_slot | dstc2 | atis_intent | swda | mrda | +| 评估指标 | R1@10 | R2@10 | R5@10 | F1 | JOINT ACC | ACC | ACC | ACC | +| SOTA | 76.70% | 87.40% | 96.90% | 96.89% | 74.50% | 98.32% | 81.30% | 91.70% | +| DGU | 82.02% | 90.43% | 97.75% | 97.10% | 89.57% | 97.65% | 80.19% | 91.43% | + +    b、数据集说明： + +``` +UDC: Ubuntu Corpus V1; +ATIS: 微软提供的公开数据集DSTC2，Airline Travel Information System; +DSTC2: 对话状态跟踪挑战（Dialog State Tracking Challenge）2; +MRDA: Meeting Recorder Dialogue Act; +SWDA：Switchboard Dialogue Act Corpus; +``` + +## 二、快速开始 + +### 1、安装说明 + +####   a、paddle安装 + +    本项目依赖于Paddle Fluid 1.3.1，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装 + +####   b、安装代码 + +    克隆数据集代码库到本地 + +``` +git clone https://github.com/PaddlePaddle/models.git +cd models/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding +``` + +####   c、环境依赖 + +    python版本依赖python 2.7 + +### 2、开始第一次模型调用 + +####   a、数据准备（数据、模型下载，预处理） + +    i、数据下载 + +``` +sh download_data.sh +``` + +    ii、(非必需)下载的数据集中已提供了训练集，测试集和验证集，用户如果需要重新生成某数据集的训练数据，可执行： + +``` +cd dialogue_general_understanding/scripts && sh run_build_data.sh task_name +parameters： +task_name: udc, swda, mrda, atis, dstc2 +``` + +####   b、模型下载 + +    该项目中，我们基于BERT开发了相关的对话模型，对话模型训练时需要依赖BERT的模型做fine-tuning, 且提供了目前公开数据集上训练好的多个对话模型。 + +    i、BERT pretrain模型下载： + +``` +sh download_pretrain_model.sh +``` + +    ii、dialogue_general_understanding模块内对话相关模型下载： + +      方式一：基于PaddleHub命令行工具（PaddleHub安装方式 https://github.com/PaddlePaddle/PaddleHub) + +``` +hub download dmtk_models --output_path ./ +tar -xvf dmtk_models_1.0.0.tar.gz +``` + +      方式二：直接下载 + +``` +sh download_models.sh +``` + +####   c、CPU、GPU训练设置 + +    CPU训练和预测: + +``` +请将run_train.sh和run_predict.sh内如下两行参数设置为: +1、export CUDA_VISIBLE_DEVICES= +2、--use_cuda false +``` + +    GPU训练和预测: + +``` +请修改run_train.sh和run_predict.sh内如下两行参数设置为: +1、export CUDA_VISIBLE_DEVICES=4 (用户可自行指定空闲的卡) +2、--use_cuda true +``` + +####   d、训练 + +    方式一(推荐)： + +``` +sh run_train.sh task_name +parameters： +task_name: udc, swda, mrda, atis_intent, atis_slot, dstc2 +``` + +    方式二： + +``` +python -u train.py --task_name mrda \ # name model to use. [udc|swda|mrda|atis_intent|atis_slot|dstc2] + + --use_cuda true \ # If set, use GPU for training. + --do_train true \ # Whether to perform training. + --do_val true \ # Whether to perform evaluation on dev data set. + --do_test true \ # Whether to perform evaluation on test data set. + --epoch 10 \ # Number of epoches for fine-tuning. + --batch_size 4096 \ # Total examples' number in batch for training. see also --in_tokens. + --data_dir ./data/mrda \ # Path to training data. + --bert_config_path ./uncased_L-12_H-768_A-12/bert_config.json \ # Path to the json file for bert model config. + --vocab_path ./uncased_L-12_H-768_A-12/vocab.txt \ # Vocabulary path. + --init_pretraining_params ./uncased_L-12_H-768_A-12/params \ # Init pre-training params which preforms fine-tuning from + --checkpoints ./output/mrda \ # Path to save checkpoints. + --save_steps 200 \ # The steps interval to save checkpoints. + --learning_rate 2e-5 \ # Learning rate used to train with warmup. + --weight_decay 0.01 \ # Weight decay rate for L2 regularizer. + --max_seq_len 128 \ # Number of words of the longest seqence. + --skip_steps 100 \ # The steps interval to print loss. + --validation_steps 500 \ # The steps interval to evaluate model performance. + --num_iteration_per_drop_scope 10 \ # The iteration intervals to clean up temporary variables. + --use_fp16 false # If set, use fp16 for training. +``` + +####   e、预测 （推荐f的方式来进行预测评估） + +    方式一(推荐)： + +``` +sh run_predict.sh task_name +parameters： +task_name: udc, swda, mrda, atis_intent, atis_slot, dstc2 +``` + +    方式二： + +``` +python -u predict.py --task_name mrda \ # name model to use. [udc|swda|mrda|atis_intent|atis_slot|dstc2] +--use_cuda true \ # If set, use GPU for training. +--batch_size 4096 \ # Total examples' number in batch for training. see also --in_tokens. +--init_checkpoint ./output/mrda/step_6500 \ # Init model +--data_dir ./data/mrda \ # Path to training data. +--vocab_path ./uncased_L-12_H-768_A-12/vocab.txt \ # Vocabulary path. +--max_seq_len 128 \ # Number of words of the longest seqence. +--bert_config_path ./uncased_L-12_H-768_A-12/bert_config.json # Path to the json file for bert model config. +``` + +####   f、预测+评估（推荐） + +    dialogue_general_understanding模块内提供已训练好的对话模型，可通过sh download_models.sh下载，用户如果不训练模型的时候，可使用提供模型进行预测评估： + +``` +sh run_eval_metrics.sh task_name +parameters： +task_name: udc, swda, mrda, atis_intent, atis_slot, dstc2 +``` + +## 三、进阶使用 + +### 1、任务定义与建模 + +    dialogue_general_understanding模块，针对数据集开发了相关的模型训练过程，支持分类，多标签分类，序列标注等任务，用户可针对自己的数据集，进行相关的模型定制； + +### 2、模型原理介绍 + +    本项目针对对话理解相关的问题，底层基于BERT，上层定义范式(分类，多标签分类，序列标注), 开源了一系列公开数据集相关的模型，供用户可配置地使用： + +### 3、数据格式说明 + +    训练、预测、评估使用的数据可以由用户根据实际的对话应用场景，自己组织数据。输入网络的数据格式统一为，示例如下： + +``` +[CLS] token11 token12 token13 [INNER_SEP] token11 token12 token13 [SEP] token21 token22 token23 [SEP] token31 token32 token33 [SEP] +``` + +    输入数据以[CLS]开始，[SEP]分割内容为对话内容相关三部分，如上文，当前句，下文等，如[SEP]分割的每部分内部由多轮组成的话，使用[INNER_SEP]进行分割；第二部分和第三部分部分皆可缺省； + +    目前dialogue_general_understanding模块内已将数据准备部分集成到代码内，用户可根据上面输入数据格式，组装自己的数据； +### 4、代码结构说明 + +``` +. +├── run_train.sh # 训练执行脚本 +├── run_predict.sh # 预测执行脚本 +├── run_eval_metrics.sh # 评估执行脚本 +├── download_data.sh # 下载数据脚本 +├── download_models.sh # 下载对话模型脚本 +├── download_pretrain_model.sh # 下载bert pretrain模型脚本 +├── train.py # train流程 +├── predict.py # predict流程 +├── eval_metrics.py # 指标评估 +├── define_predict_pack.py # 封装预测结果 +├── finetune_args.py # 模型训练相关的配置参数 +├── batching.py # 封装yield batch数据 +├── optimization.py # 模型优化器 +├── tokenization.py # tokenizer工具 +├── reader/data_reader.py： # 数据的处理和组装过程，每个数据集都定义一个类进行处理 +├── README.md # 文档 +├── utils/* # 定义了其他常用的功能函数 +└── scripts # 数据处理脚本集合 + ├── run_build_data.sh # 数据处理运行脚本 + ├── build_atis_dataset.py # 构建atis_intent和atis_slot训练数据 + ├── build_dstc2_dataset.py # 构建dstc2训练数据 + ├── build_mrda_dataset.py # 构建mrda训练数据 + ├── build_swda_dataset.py # 构建swda训练数据 + ├── commonlib.py # 数据处理通用方法 + └── conf # 公开数据集中训练集、验证集、测试集划分 + +../../models/dialogue_model_toolkit/dialogue_general_understanding +├── bert.py # 底层bert模型 +├── define_paradigm.py # 上层网络范式 +└── create_model.py # 创建底层bert模型+上层网络范式网络结构 +``` + +### 5、如何组建自己的模型 + +    用户可以根据自己的需求，组建自定义的模型，具体方法如下所示： + +    i、自定义数据 + +      如用户目前有数据集为**task_name**, 则在**data**下定义**task_name**文件夹，将数据集存放进去；在**reader/data_reader.py**中，新增自定义的数据处理的类，如**udc**数据集对应**UDCProcessor**; 在**train.py**内设置**task_name**和**processor**的对应关系(如**processors = {'udc': reader.UDCProcessor}**)，以及当前的数据集训练时是否是否使用**in_tokens**的方式计算batch大小(如：**in_tokens = {'udc': True}**) + +    ii、 自定义上层网络范式 + +      如果用户自定义模型属于分类、多分类和序列标注这3种类型其中一个，则只需要在**paddle-nlp/models/dialogue_model_toolkit/dialogue_general_understanding/define_paradigm.py** 内指明**task_name**和相应上层范式函数的对应关系即可，如用户自定义模型属于其他模型，则需要自定义上层范式函数并指明其与**task_name**之间的关系； + +    iii、自定义预测封装接口 + +      用户可在define_predict_pack.py内定义task_name和自定义封装预测接口的对应关系； + +### 6、如何训练 + +    i、按照上文所述的数据组织形式，组织自己的训练、评估、预测数据 + +    ii、运行训练脚本 + +``` +sh run_train.sh task_name +parameters： +task_name: 用户自定义名称 +``` + +## 四、其他 + +### 如何贡献代码 + +    如果你可以修复某个issue或者增加一个新功能，欢迎给我们提交PR。如果对应的PR被接受了，我们将根据贡献的质量和难度进行打分（0-5分，越高越好）。如果你累计获得了10分，可以联系我们获得面试机会或者为你写推荐信。 diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/batching.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/batching.py new file mode 100644 index 0000000000000000000000000000000000000000..2e63f6e003397b8b60aae841f243eae1f787975e --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/batching.py @@ -0,0 +1,204 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Mask, padding and batching.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + + +def mask(batch_tokens, total_token_num, vocab_size, CLS=1, SEP=2, MASK=3): + """ + Add mask for batch_tokens, return out, mask_label, mask_pos; + Note: mask_pos responding the batch_tokens after padded; + """ + max_len = max([len(sent) for sent in batch_tokens]) + mask_label = [] + mask_pos = [] + prob_mask = np.random.rand(total_token_num) + # Note: the first token is [CLS], so [low=1] + replace_ids = np.random.randint(1, high=vocab_size, size=total_token_num) + pre_sent_len = 0 + prob_index = 0 + for sent_index, sent in enumerate(batch_tokens): + mask_flag = False + prob_index += pre_sent_len + for token_index, token in enumerate(sent): + prob = prob_mask[prob_index + token_index] + if prob > 0.15: + continue + elif 0.03 < prob <= 0.15: + # mask + if token != SEP and token != CLS: + mask_label.append(sent[token_index]) + sent[token_index] = MASK + mask_flag = True + mask_pos.append(sent_index * max_len + token_index) + elif 0.015 < prob <= 0.03: + # random replace + if token != SEP and token != CLS: + mask_label.append(sent[token_index]) + sent[token_index] = replace_ids[prob_index + token_index] + mask_flag = True + mask_pos.append(sent_index * max_len + token_index) + else: + # keep the original token + if token != SEP and token != CLS: + mask_label.append(sent[token_index]) + mask_pos.append(sent_index * max_len + token_index) + pre_sent_len = len(sent) + + # ensure at least mask one word in a sentence + while not mask_flag: + token_index = int(np.random.randint(1, high=len(sent) - 1, size=1)) + if sent[token_index] != SEP and sent[token_index] != CLS: + mask_label.append(sent[token_index]) + sent[token_index] = MASK + mask_flag = True + mask_pos.append(sent_index * max_len + token_index) + mask_label = np.array(mask_label).astype("int64").reshape([-1, 1]) + mask_pos = np.array(mask_pos).astype("int64").reshape([-1, 1]) + return batch_tokens, mask_label, mask_pos + + +def prepare_batch_data(insts, + max_len, + total_token_num, + voc_size=0, + pad_id=None, + cls_id=None, + sep_id=None, + mask_id=None, + return_input_mask=True, + return_max_len=True, + return_num_token=False): + """ + 1. generate Tensor of data + 2. generate Tensor of position + 3. generate self attention mask, [shape: batch_size * max_len * max_len] + """ + + batch_src_ids = [inst[0] for inst in insts] + batch_sent_ids = [inst[1] for inst in insts] + batch_pos_ids = [inst[2] for inst in insts] + labels_list = [] + # compatible with squad, whose example includes start/end positions, + # or unique id + + if isinstance(insts[0][3], list): + if max_len != -1: + labels_list = [inst[3] + [0] * (max_len - len(inst[3])) for inst in insts] + labels_list = [np.array(labels_list).astype("int64").reshape([-1, max_len])] + else: + labels_list = [inst[3] for inst in insts] + labels_list = [np.array(labels_list).astype("int64")] + else: + for i in range(3, len(insts[0]), 1): + labels = [inst[i] for inst in insts] + labels = np.array(labels).astype("int64").reshape([-1, 1]) + labels_list.append(labels) + + # First step: do mask without padding + if mask_id >= 0: + out, mask_label, mask_pos = mask( + batch_src_ids, + total_token_num, + vocab_size=voc_size, + CLS=cls_id, + SEP=sep_id, + MASK=mask_id) + else: + out = batch_src_ids + # Second step: padding + src_id, self_input_mask = pad_batch_data( + out, + max_len, + pad_idx=pad_id, + return_input_mask=True) + pos_id = pad_batch_data( + batch_pos_ids, + max_len, + pad_idx=pad_id, + return_pos=False, + return_input_mask=False) + sent_id = pad_batch_data( + batch_sent_ids, + max_len, + pad_idx=pad_id, + return_pos=False, + return_input_mask=False) + + if mask_id >= 0: + return_list = [ + src_id, pos_id, sent_id, self_input_mask, mask_label, mask_pos + ] + labels_list + else: + return_list = [src_id, pos_id, sent_id, self_input_mask] + labels_list + + return return_list if len(return_list) > 1 else return_list[0] + + +def pad_batch_data(insts, + max_len_in, + pad_idx=0, + return_pos=False, + return_input_mask=False, + return_max_len=False, + return_num_token=False): + """ + Pad the instances to the max sequence length in batch, and generate the + corresponding position data and attention bias. + """ + return_list = [] + max_len = max_len_in if max_len_in != -1 else max(len(inst) for inst in insts) + # Any token included in dict can be used to pad, since the paddings' loss + # will be masked out by weights and make no effect on parameter gradients. + + inst_data = np.array( + [inst + list([pad_idx] * (max_len - len(inst))) for inst in insts + ]) + return_list += [inst_data.astype("int64").reshape([-1, max_len, 1])] + + # position data + if return_pos: + inst_pos = np.array([ + list(range(0, len(inst))) + [pad_idx] * (max_len - len(inst)) + for inst in insts + ]) + + return_list += [inst_pos.astype("int64").reshape([-1, max_len, 1])] + + if return_input_mask: + # This is used to avoid attention on paddings. + input_mask_data = np.array([[1] * len(inst) + [0] * + (max_len - len(inst)) for inst in insts]) + input_mask_data = np.expand_dims(input_mask_data, axis=-1) + return_list += [input_mask_data.astype("float32")] + + if return_max_len: + return_list += [max_len] + + if return_num_token: + num_token = 0 + for inst in insts: + num_token += len(inst) + return_list += [num_token] + + return return_list if len(return_list) > 1 else return_list[0] + + +if __name__ == "__main__": + pass diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/define_predict_pack.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/define_predict_pack.py new file mode 100644 index 0000000000000000000000000000000000000000..ada844498d4f7ce85f85142b7e46ad8fc79d9f89 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/define_predict_pack.py @@ -0,0 +1,85 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""define prediction results""" + +import re +import sys +import numpy as np + +import paddle +import paddle.fluid as fluid + + +class DefinePredict(object): + """ + Packaging Prediction Results + """ + def __init__(self): + """ + init + """ + self.task_map = {'udc': 'get_matching_res', + 'swda': 'get_cls_res', + 'mrda': 'get_cls_res', + 'atis_intent': 'get_cls_res', + 'atis_slot': 'get_sequence_tagging', + 'dstc2': 'get_multi_cls_res', + 'dstc2_asr': 'get_multi_cls_res', + 'multi-woz': 'get_multi_cls_res'} + + def get_matching_res(self, probs, params=None): + """ + get matching score + """ + probs = list(probs) + return probs[1] + + def get_cls_res(self, probs, params=None): + """ + get da classify tag + """ + probs = list(probs) + max_prob = max(probs) + tag = probs.index(max_prob) + return tag + + def get_sequence_tagging(self, probs, params=None): + """ + get sequence tagging tag + """ + labels = [] + batch_labels = np.array(probs).reshape(-1, params) + labels = [" ".join([str(l) for l in list(l_l)]) for l_l in batch_labels] + return labels + + def get_multi_cls_res(self, probs, params=None): + """ + get dst classify tag + """ + labels = [] + probs = list(probs) + for i in range(len(probs)): + if probs[i] >= 0.5: + labels.append(i) + if not labels: + max_prob = max(probs) + label_str = str(probs.index(max_prob)) + else: + label_str = " ".join([str(l) for l in sorted(labels)]) + + return label_str + + + + diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/download_data.sh b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/download_data.sh new file mode 100644 index 0000000000000000000000000000000000000000..c7fe4a9944cac109bf66fa108baf1616c7a789d6 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/download_data.sh @@ -0,0 +1,3 @@ +wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/dmtk_data_1.0.0.tar.gz +tar -xvf dmtk_data_1.0.0.tar.gz +rm dmtk_data_1.0.0.tar.gz diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/download_models.sh b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/download_models.sh new file mode 100644 index 0000000000000000000000000000000000000000..fdb146d46cd48c2c1d2a7fd923074f46ab8d0436 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/download_models.sh @@ -0,0 +1,3 @@ +wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/dmtk_models_1.0.0.tar.gz +tar -xvf dmtk_models_1.0.0.tar.gz +rm dmtk_models_1.0.0.tar.gz diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/download_pretrain_model.sh b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/download_pretrain_model.sh new file mode 100644 index 0000000000000000000000000000000000000000..5591926c327a42fe442e28f77817bad4275dc360 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/download_pretrain_model.sh @@ -0,0 +1,3 @@ +wget --no-check-certificate https://bert-models.bj.bcebos.com/uncased_L-12_H-768_A-12.tar.gz +tar -xvf uncased_L-12_H-768_A-12.tar.gz +rm uncased_L-12_H-768_A-12.tar.gz diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/eval_metrics.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/eval_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..d8967b7c7fa5889e016c594ba8472ff1fa892de2 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/eval_metrics.py @@ -0,0 +1,369 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +evaluate task metrics +""" + +import sys + + +class EvalDA(object): + """ + evaluate da testset, swda|mrda + """ + def __init__(self, task_name, pred): + """ + predict file + """ + self.pred_file = pred + if task_name == 'swda': + self.refer_file = "./data/swda/test.txt" + elif task_name == "mrda": + self.refer_file = "./data/mrda/test.txt" + + def load_data(self): + """ + load reference label and predict label + """ + pred_label = [] + refer_label = [] + with open(self.refer_file, 'r') as fr: + for line in fr: + label = line.rstrip('\n').split('\t')[1] + refer_label.append(int(label)) + idx = 0 + with open(self.pred_file, 'r') as fr: + for line in fr: + elems = line.rstrip('\n').split('\t') + if len(elems) != 2 or not elems[0].isdigit(): + continue + tag_id = int(elems[1]) + pred_label.append(tag_id) + return pred_label, refer_label + + def evaluate(self): + """ + calculate acc metrics + """ + pred_label, refer_label = self.load_data() + common_num = 0 + total_num = len(pred_label) + for i in range(total_num): + if pred_label[i] == refer_label[i]: + common_num += 1 + acc = float(common_num) / total_num + return acc + + +class EvalATISIntent(object): + """ + evaluate da testset, swda|mrda + """ + def __init__(self, pred): + """ + predict file + """ + self.pred_file = pred + self.refer_file = "./data/atis/atis_intent/test.txt" + + def load_data(self): + """ + load reference label and predict label + """ + pred_label = [] + refer_label = [] + with open(self.refer_file, 'r') as fr: + for line in fr: + label = line.rstrip('\n').split('\t')[0] + refer_label.append(int(label)) + idx = 0 + with open(self.pred_file, 'r') as fr: + for line in fr: + elems = line.rstrip('\n').split('\t') + if len(elems) != 2 or not elems[0].isdigit(): + continue + tag_id = int(elems[1]) + pred_label.append(tag_id) + return pred_label, refer_label + + def evaluate(self): + """ + calculate acc metrics + """ + pred_label, refer_label = self.load_data() + common_num = 0 + total_num = len(pred_label) + for i in range(total_num): + if pred_label[i] == refer_label[i]: + common_num += 1 + acc = float(common_num) / total_num + return acc + + +class EvalATISSlot(object): + """ + evaluate atis slot + """ + def __init__(self, pred): + """ + pred file + """ + self.pred_file = pred + self.refer_file = "./data/atis/atis_slot/test.txt" + + def load_data(self): + """ + load reference label and predict label + """ + pred_label = [] + refer_label = [] + with open(self.refer_file, 'r') as fr: + for line in fr: + labels = line.rstrip('\n').split('\t')[1].split() + labels = [int(l) for l in labels] + refer_label.append(labels) + with open(self.pred_file, 'r') as fr: + for line in fr: + if len(line.split('\t')) != 2 or not line[0].isdigit(): + continue + labels = line.rstrip('\n').split('\t')[1].split()[1:] + labels = [int(l) for l in labels] + pred_label.append(labels) + pred_label_equal = [] + refer_label_equal = [] + assert len(refer_label) == len(pred_label) + for i in range(len(refer_label)): + num = len(refer_label[i]) + refer_label_equal.extend(refer_label[i]) + pred_label[i] = pred_label[i][: num] + pred_label_equal.extend(pred_label[i]) + + return pred_label_equal, refer_label_equal + + def evaluate(self): + """ + evaluate f1_micro score + """ + pred_label, refer_label = self.load_data() + tp = dict() + fn = dict() + fp = dict() + for i in range(len(refer_label)): + if refer_label[i] == pred_label[i]: + if refer_label[i] not in tp: + tp[refer_label[i]] = 0 + tp[refer_label[i]] += 1 + else: + if pred_label[i] not in fp: + fp[pred_label[i]] = 0 + fp[pred_label[i]] += 1 + if refer_label[i] not in fn: + fn[refer_label[i]] = 0 + fn[refer_label[i]] += 1 + + results = ["label precision recall"] + for i in range(0, 130): + if i not in tp: + results.append(" %s: 0.0 0.0" % i) + continue + if i in fp: + precision = float(tp[i]) / (tp[i] + fp[i]) + else: + precision = 1.0 + if i in fn: + recall = float(tp[i]) / (tp[i] + fn[i]) + else: + recall = 1.0 + results.append(" %s: %.4f %.4f" % (i, precision, recall)) + tp_total = sum(tp.values()) + fn_total = sum(fn.values()) + fp_total = sum(fp.values()) + p_total = float(tp_total) / (tp_total + fp_total) + r_total = float(tp_total) / (tp_total + fn_total) + f_micro = 2 * p_total * r_total / (p_total + r_total) + results.append("f1_micro: %.4f" % (f_micro)) + return "\n".join(results) + + +class EvalUDC(object): + """ + evaluate udc + """ + def __init__(self, pred): + """ + predict file + """ + self.pred_file = pred + self.refer_file = "./data/udc/test.txt" + + def load_data(self): + """ + load reference label and predict label + """ + data = [] + refer_label = [] + with open(self.refer_file, 'r') as fr: + for line in fr: + label = line.rstrip('\n').split('\t')[0] + refer_label.append(label) + idx = 0 + with open(self.pred_file, 'r') as fr: + for line in fr: + elems = line.rstrip('\n').split('\t') + if len(elems) != 2 or not elems[0].isdigit(): + continue + match_prob = elems[1] + data.append((float(match_prob), int(refer_label[idx]))) + idx += 1 + return data + + def get_p_at_n_in_m(self, data, n, m, ind): + """ + calculate precision in recall n + """ + pos_score = data[ind][0] + curr = data[ind: ind + m] + curr = sorted(curr, key = lambda x: x[0], reverse = True) + + if curr[n - 1][0] <= pos_score: + return 1 + return 0 + + def evaluate(self): + """ + calculate udc data + """ + data = self.load_data() + assert len(data) % 10 == 0 + + p_at_1_in_2 = 0.0 + p_at_1_in_10 = 0.0 + p_at_2_in_10 = 0.0 + p_at_5_in_10 = 0.0 + + length = len(data)/10 + + for i in range(0, length): + ind = i * 10 + assert data[ind][1] == 1 + + p_at_1_in_2 += self.get_p_at_n_in_m(data, 1, 2, ind) + p_at_1_in_10 += self.get_p_at_n_in_m(data, 1, 10, ind) + p_at_2_in_10 += self.get_p_at_n_in_m(data, 2, 10, ind) + p_at_5_in_10 += self.get_p_at_n_in_m(data, 5, 10, ind) + + metrics_out = [p_at_1_in_2 / length, p_at_1_in_10 / length, \ + p_at_2_in_10 / length, p_at_5_in_10 / length] + return metrics_out + + +class EvalDSTC2(object): + """ + evaluate dst testset, dstc2 + """ + def __init__(self, task_name, pred): + """ + predict file + """ + self.task_name = task_name + self.pred_file = pred + self.refer_file = "./data/dstc2/%s/test.txt" % self.task_name + + def load_data(self): + """ + load reference label and predict label + """ + pred_label = [] + refer_label = [] + with open(self.refer_file, 'r') as fr: + for line in fr: + line = line.strip('\n') + labels = [int(l) for l in line.split('\t')[-1].split()] + labels = sorted(list(set(labels))) + refer_label.append(" ".join([str(l) for l in labels])) + all_pred = [] + with open(self.pred_file, 'r') as fr: + for line in fr: + line = line.strip('\n') + all_pred.append(line) + all_pred = all_pred[len(all_pred) - len(refer_label):] + for line in all_pred: + labels = [int(l) for l in line.split('\t')[-1].split()] + labels = sorted(list(set(labels))) + pred_label.append(" ".join([str(l) for l in labels])) + return pred_label, refer_label + + def evaluate(self): + """ + calculate joint acc && overall acc + """ + overall_all = 0.0 + correct_joint = 0 + pred_label, refer_label = self.load_data() + for i in range(len(refer_label)): + if refer_label[i] != pred_label[i]: + continue + correct_joint += 1 + joint_all = float(correct_joint) / len(refer_label) + metrics_out = [joint_all, overall_all] + return metrics_out + + +if __name__ == "__main__": + if len(sys.argv[1:]) < 2: + print("please input task_name predict_file") + + task_name = sys.argv[1] + pred_file = sys.argv[2] + + if task_name.lower() == 'udc': + eval_inst = EvalUDC(pred_file) + eval_metrics = eval_inst.evaluate() + print("MATCHING TASK: %s metrics in testset: " % task_name) + print("R1@2: %s" % eval_metrics[0]) + print("R1@10: %s" % eval_metrics[1]) + print("R2@10: %s" % eval_metrics[2]) + print("R5@10: %s" % eval_metrics[3]) + + elif task_name.lower() in ['swda', 'mrda']: + eval_inst = EvalDA(task_name.lower(), pred_file) + eval_metrics = eval_inst.evaluate() + print("DA TASK: %s metrics in testset: " % task_name) + print("ACC: %s" % eval_metrics) + + elif task_name.lower() == 'atis_intent': + eval_inst = EvalATISIntent(pred_file) + eval_metrics = eval_inst.evaluate() + print("INTENTION TASK: %s metrics in testset: " % task_name) + print("ACC: %s" % eval_metrics) + + elif task_name.lower() == 'atis_slot': + eval_inst = EvalATISSlot(pred_file) + eval_metrics = eval_inst.evaluate() + print("SLOT FILLING TASK: %s metrics in testset: " % task_name) + print(eval_metrics) + elif task_name.lower() in ['dstc2', 'dstc2_asr']: + eval_inst = EvalDSTC2(task_name.lower(), pred_file) + eval_metrics = eval_inst.evaluate() + print("DST TASK: %s metrics in testset: " % task_name) + print("JOINT ACC: %s" % eval_metrics[0]) + elif task_name.lower() == "multi-woz": + eval_inst = EvalMultiWoz(pred_file) + eval_metrics = eval_inst.evaluate() + print("DST TASK: %s metrics in testset: " % task_name) + print("JOINT ACC: %s" % eval_metrics[0]) + print("OVERALL ACC: %s" % eval_metrics[1]) + else: + print("task name not in [udc|swda|mrda|atis_intent|atis_slot|dstc2|dstc2_asr|multi-woz]") + diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/finetune_args.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/finetune_args.py new file mode 100644 index 0000000000000000000000000000000000000000..b88307ee07c87af115ad40a9c47134ee380ca268 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/finetune_args.py @@ -0,0 +1,74 @@ +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Finetuning on classification tasks.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import time +import argparse + +from utils.args import ArgumentGroup + +parser = argparse.ArgumentParser(__doc__) +model_g = ArgumentGroup(parser, "model", "model configuration and paths.") +model_g.add_arg("bert_config_path", str, None, "Path to the json file for bert model config.") +model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.") +model_g.add_arg("init_pretraining_params", str, None, + "Init pre-training params which preforms fine-tuning from. If the " + "arg 'init_checkpoint' has been set, this argument wouldn't be valid.") +model_g.add_arg("checkpoints", str, "checkpoints", "Path to save checkpoints.") +model_g.add_arg("save_inference_model_path", str, None, "Path to save model.") + +train_g = ArgumentGroup(parser, "training", "training options.") +train_g.add_arg("epoch", int, 3, "Number of epoches for fine-tuning.") +train_g.add_arg("learning_rate", float, 5e-5, "Learning rate used to train with warmup.") +train_g.add_arg("lr_scheduler", str, "linear_warmup_decay", + "scheduler of learning rate.", choices=['linear_warmup_decay', 'noam_decay']) +train_g.add_arg("weight_decay", float, 0.01, "Weight decay rate for L2 regularizer.") +train_g.add_arg("warmup_proportion", float, 0.1, + "Proportion of training steps to perform linear learning rate warmup for.") +train_g.add_arg("save_steps", int, 10000, "The steps interval to save checkpoints.") +train_g.add_arg("validation_steps", int, 1000, "The steps interval to evaluate model performance.") +train_g.add_arg("use_fp16", bool, False, "Whether to use fp16 mixed precision training.") +train_g.add_arg("loss_scaling", float, 1.0, + "Loss scaling factor for mixed precision training, only valid when use_fp16 is enabled.") + +log_g = ArgumentGroup(parser, "logging", "logging related.") +log_g.add_arg("skip_steps", int, 10, "The steps interval to print loss.") +log_g.add_arg("verbose", bool, False, "Whether to output verbose log.") + +data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options") +data_g.add_arg("data_dir", str, None, "Path to training data.") +data_g.add_arg("vocab_path", str, None, "Vocabulary path.") +data_g.add_arg("max_seq_len", int, 512, "Number of words of the longest seqence.") +data_g.add_arg("batch_size", int, 32, "Total examples' number in batch for training. see also --in_tokens.") +data_g.add_arg("do_lower_case", bool, True, + "Whether to lower case the input text. Should be True for uncased models and False for cased models.") +data_g.add_arg("random_seed", int, 0, "Random seed.") + +run_type_g = ArgumentGroup(parser, "run_type", "running type options.") +run_type_g.add_arg("use_cuda", bool, True, "If set, use GPU for training.") +run_type_g.add_arg("use_fast_executor", bool, False, "If set, use fast parallel executor (in experiment).") +run_type_g.add_arg("num_iteration_per_drop_scope", int, 1, "Ihe iteration intervals to clean up temporary variables.") +run_type_g.add_arg("task_name", str, None, + "The name of task to perform fine-tuning, " + "should be in {'udc', 'swda', 'mrda', 'atis_slot', 'atis_intent', 'dstc2'}.") +run_type_g.add_arg("do_train", bool, True, "Whether to perform training.") +run_type_g.add_arg("do_val", bool, True, "Whether to perform evaluation on dev data set.") +run_type_g.add_arg("do_test", bool, True, "Whether to perform evaluation on test data set.") + + diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/optimization.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/optimization.py new file mode 100644 index 0000000000000000000000000000000000000000..e010bca9cdd57dd90e34961f2b1102888627d27a --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/optimization.py @@ -0,0 +1,139 @@ +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Optimization and learning rate scheduling.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import paddle.fluid as fluid +from utils.fp16 import create_master_params_grads, master_param_to_train_param + + +def linear_warmup_decay(learning_rate, warmup_steps, num_train_steps): + """ Applies linear warmup of learning rate from 0 and decay to 0.""" + with fluid.default_main_program()._lr_schedule_guard(): + lr = fluid.layers.tensor.create_global_var( + shape=[1], + value=0.0, + dtype='float32', + persistable=True, + name="scheduled_learning_rate") + + global_step = fluid.layers.learning_rate_scheduler._decay_step_counter() + + with fluid.layers.control_flow.Switch() as switch: + with switch.case(global_step < warmup_steps): + warmup_lr = learning_rate * (global_step / warmup_steps) + fluid.layers.tensor.assign(warmup_lr, lr) + with switch.default(): + decayed_lr = fluid.layers.learning_rate_scheduler.polynomial_decay( + learning_rate=learning_rate, + decay_steps=num_train_steps, + end_learning_rate=0.0, + power=1.0, + cycle=False) + fluid.layers.tensor.assign(decayed_lr, lr) + + return lr + + +def optimization(loss, + warmup_steps, + num_train_steps, + learning_rate, + train_program, + startup_prog, + weight_decay, + scheduler='linear_warmup_decay', + use_fp16=False, + loss_scaling=1.0): + if warmup_steps > 0: + if scheduler == 'noam_decay': + scheduled_lr = fluid.layers.learning_rate_scheduler\ + .noam_decay(1/(warmup_steps *(learning_rate ** 2)), + warmup_steps) + elif scheduler == 'linear_warmup_decay': + scheduled_lr = linear_warmup_decay(learning_rate, warmup_steps, + num_train_steps) + else: + raise ValueError("Unkown learning rate scheduler, should be " + "'noam_decay' or 'linear_warmup_decay'") + optimizer = fluid.optimizer.Adam(learning_rate=scheduled_lr) + else: + optimizer = fluid.optimizer.Adam(learning_rate=learning_rate) + scheduled_lr = learning_rate + + clip_norm_thres = 1.0 + # When using mixed precision training, scale the gradient clip threshold + # by loss_scaling + if use_fp16 and loss_scaling > 1.0: + clip_norm_thres *= loss_scaling + fluid.clip.set_gradient_clip( + clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=clip_norm_thres)) + + def exclude_from_weight_decay(name): + if name.find("layer_norm") > -1: + return True + bias_suffix = ["_bias", "_b", ".b_0"] + for suffix in bias_suffix: + if name.endswith(suffix): + return True + return False + + param_list = dict() + + if use_fp16: + param_grads = optimizer.backward(loss) + master_param_grads = create_master_params_grads( + param_grads, train_program, startup_prog, loss_scaling) + + for param, _ in master_param_grads: + param_list[param.name] = param * 1.0 + param_list[param.name].stop_gradient = True + + optimizer.apply_gradients(master_param_grads) + + if weight_decay > 0: + for param, grad in master_param_grads: + if exclude_from_weight_decay(param.name.rstrip(".master")): + continue + with param.block.program._optimized_guard( + [param, grad]), fluid.framework.name_scope("weight_decay"): + updated_param = param - param_list[ + param.name] * weight_decay * scheduled_lr + fluid.layers.assign(output=param, input=updated_param) + + master_param_to_train_param(master_param_grads, param_grads, + train_program) + + else: + for param in train_program.global_block().all_parameters(): + param_list[param.name] = param * 1.0 + param_list[param.name].stop_gradient = True + + _, param_grads = optimizer.minimize(loss) + + if weight_decay > 0: + for param, grad in param_grads: + if exclude_from_weight_decay(param.name): + continue + with param.block.program._optimized_guard( + [param, grad]), fluid.framework.name_scope("weight_decay"): + updated_param = param - param_list[ + param.name] * weight_decay * scheduled_lr + fluid.layers.assign(output=param, input=updated_param) + + return scheduled_lr diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/predict.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..5faf6e4b21018ee0bc460ebc1585b9bf02aad1cf --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/predict.py @@ -0,0 +1,164 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Load checkpoint of running classifier to do prediction and save inference model.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys +import time +import numpy as np +import multiprocessing + +import paddle +import paddle.fluid as fluid + +from finetune_args import parser +from utils.args import print_arguments +from utils.init import init_pretraining_params, init_checkpoint + +import define_predict_pack +import reader.data_reader as reader + +_WORK_DIR = os.path.split(os.path.realpath(__file__))[0] +sys.path.append('../../models/dialogue_model_toolkit/dialogue_general_understanding') + +from bert import BertConfig, BertModel +from create_model import create_model +import define_paradigm + + +def main(args): + """main function""" + bert_config = BertConfig(args.bert_config_path) + bert_config.print_config() + + task_name = args.task_name.lower() + paradigm_inst = define_paradigm.Paradigm(task_name) + pred_inst = define_predict_pack.DefinePredict() + pred_func = getattr(pred_inst, pred_inst.task_map[task_name]) + + processors = { + 'udc': reader.UDCProcessor, + 'swda': reader.SWDAProcessor, + 'mrda': reader.MRDAProcessor, + 'atis_slot': reader.ATISSlotProcessor, + 'atis_intent': reader.ATISIntentProcessor, + 'dstc2': reader.DSTC2Processor, + 'dstc2_asr': reader.DSTC2Processor, + } + + in_tokens = { + 'udc': True, + 'swda': True, + 'mrda': True, + 'atis_slot': False, + 'atis_intent': True, + 'dstc2': True, + 'dstc2_asr': True + } + + processor = processors[task_name](data_dir=args.data_dir, + vocab_path=args.vocab_path, + max_seq_len=args.max_seq_len, + do_lower_case=args.do_lower_case, + in_tokens=in_tokens[task_name], + task_name=task_name, + random_seed=args.random_seed) + num_labels = len(processor.get_labels()) + + predict_prog = fluid.Program() + predict_startup = fluid.Program() + with fluid.program_guard(predict_prog, predict_startup): + with fluid.unique_name.guard(): + pred_results = create_model( + args, + pyreader_name='predict_reader', + bert_config=bert_config, + num_labels=num_labels, + paradigm_inst=paradigm_inst, + is_prediction=True) + predict_pyreader = pred_results.get('pyreader', None) + probs = pred_results.get('probs', None) + feed_target_names = pred_results.get('feed_target_names', None) + + predict_prog = predict_prog.clone(for_test=True) + + if args.use_cuda: + place = fluid.CUDAPlace(0) + dev_count = fluid.core.get_cuda_device_count() + else: + place = fluid.CPUPlace() + dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) + + place = fluid.CUDAPlace(0) if args.use_cuda == True else fluid.CPUPlace() + exe = fluid.Executor(place) + exe.run(predict_startup) + + if args.init_checkpoint: + init_pretraining_params(exe, args.init_checkpoint, predict_prog) + else: + raise ValueError("args 'init_checkpoint' should be set for prediction!") + + predict_exe = fluid.ParallelExecutor( + use_cuda=args.use_cuda, main_program=predict_prog) + + test_data_generator = processor.data_generator( + batch_size=args.batch_size, + phase='test', + epoch=1, + shuffle=False) + predict_pyreader.decorate_tensor_provider(test_data_generator) + + predict_pyreader.start() + all_results = [] + time_begin = time.time() + while True: + try: + results = predict_exe.run(fetch_list=[probs.name]) + all_results.extend(results[0]) + except fluid.core.EOFException: + predict_pyreader.reset() + break + time_end = time.time() + + np.set_printoptions(precision=4, suppress=True) + print("-------------- prediction results --------------") + print("example_id\t" + ' '.join(processor.get_labels())) + if in_tokens[task_name]: + for index, result in enumerate(all_results): + tags = pred_func(result) + print("%s\t%s" % (index, tags)) + else: + tags = pred_func(all_results, args.max_seq_len) + for index, tag in enumerate(tags): + print("%s\t%s" % (index, tag)) + + if args.save_inference_model_path: + _, ckpt_dir = os.path.split(args.init_checkpoint) + dir_name = ckpt_dir + '_inference_model' + model_path = os.path.join(args.save_inference_model_path, dir_name) + fluid.io.save_inference_model( + model_path, + feed_target_names, [probs], + exe, + main_program=predict_prog) + + +if __name__ == '__main__': + args = parser.parse_args() + print_arguments(args) + main(args) diff --git a/PaddleNLP/chinese_ner/__init__.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/reader/__init__.py similarity index 100% rename from PaddleNLP/chinese_ner/__init__.py rename to PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/reader/__init__.py diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/reader/data_reader.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/reader/data_reader.py new file mode 100644 index 0000000000000000000000000000000000000000..29e2d17144f8f38eaa11dc5305dac96a124bf09d --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/reader/data_reader.py @@ -0,0 +1,794 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""data reader""" +import os +import types +import csv +import numpy as np +import tokenization +from batching import prepare_batch_data + + +class DataProcessor(object): + """Base class for data converters for sequence classification data sets.""" + + def __init__(self, + data_dir, + vocab_path, + max_seq_len, + do_lower_case, + in_tokens, + task_name, + random_seed=None): + self.data_dir = data_dir + self.max_seq_len = max_seq_len + self.tokenizer = tokenization.FullTokenizer( + vocab_file=vocab_path, do_lower_case=do_lower_case) + self.vocab = self.tokenizer.vocab + self.in_tokens = in_tokens + + np.random.seed(random_seed) + + self.current_train_example = -1 + self.num_examples = {'train': -1, 'dev': -1, 'test': -1} + self.current_train_epoch = -1 + self.task_name = task_name + + def get_train_examples(self, data_dir): + """Gets a collection of `InputExample`s for the train set.""" + raise NotImplementedError() + + def get_dev_examples(self, data_dir): + """Gets a collection of `InputExample`s for the dev set.""" + raise NotImplementedError() + + def get_test_examples(self, data_dir): + """Gets a collection of `InputExample`s for prediction.""" + raise NotImplementedError() + + def get_labels(self): + """Gets the list of labels for this data set.""" + raise NotImplementedError() + + def convert_example(self, index, example, labels, max_seq_len, tokenizer): + """Converts a single `InputExample` into a single `InputFeatures`.""" + feature = convert_single_example(index, example, labels, max_seq_len, + tokenizer, self.task_name) + return feature + + def generate_instance(self, feature): + """ + generate instance with given feature + + Args: + feature: InputFeatures(object). A single set of features of data. + """ + input_pos = list(range(len(feature.input_ids))) + return [ + feature.input_ids, feature.segment_ids, input_pos, feature.label_id + ] + + def generate_batch_data(self, + batch_data, + max_len, + total_token_num, + voc_size=-1, + mask_id=-1, + return_input_mask=True, + return_max_len=False, + return_num_token=False): + """generate batch data""" + return prepare_batch_data( + batch_data, + max_len, + total_token_num, + voc_size=-1, + pad_id=self.vocab["[PAD]"], + cls_id=self.vocab["[CLS]"], + sep_id=self.vocab["[SEP]"], + mask_id=-1, + return_input_mask=True, + return_max_len=False, + return_num_token=False) + + @classmethod + def _read_tsv(cls, input_file, quotechar=None): + """Reads a tab separated value file.""" + with open(input_file, "r") as f: + reader = csv.reader(f, delimiter="\t", quotechar=quotechar) + lines = [] + for line in reader: + lines.append(line) + return lines + + def get_num_examples(self, phase): + """Get number of examples for train, dev or test.""" + if phase not in ['train', 'dev', 'test']: + raise ValueError( + "Unknown phase, which should be in ['train', 'dev', 'test'].") + return self.num_examples[phase] + + def get_train_progress(self): + """Gets progress for training phase.""" + return self.current_train_example, self.current_train_epoch + + def data_generator(self, batch_size, phase='train', epoch=1, shuffle=False): + """ + Generate data for train, dev or test. + + Args: + batch_size: int. The batch size of generated data. + phase: string. The phase for which to generate data. + epoch: int. Total epoches to generate data. + shuffle: bool. Whether to shuffle examples. + """ + if phase == 'train': + examples = self.get_train_examples(self.data_dir) + self.num_examples['train'] = len(examples) + elif phase == 'dev': + examples = self.get_dev_examples(self.data_dir) + self.num_examples['dev'] = len(examples) + elif phase == 'test': + examples = self.get_test_examples(self.data_dir) + self.num_examples['test'] = len(examples) + else: + raise ValueError( + "Unknown phase, which should be in ['train', 'dev', 'test'].") + + def instance_reader(): + """generate instance data""" + for epoch_index in range(epoch): + if shuffle: + np.random.shuffle(examples) + if phase == 'train': + self.current_train_epoch = epoch_index + for (index, example) in enumerate(examples): + if phase == 'train': + self.current_train_example = index + 1 + feature = self.convert_example( + index, example, + self.get_labels(), self.max_seq_len, self.tokenizer) + + instance = self.generate_instance(feature) + yield instance + + def batch_reader(reader, batch_size, in_tokens): + """read batch data""" + batch, total_token_num, max_len = [], 0, 0 + for instance in reader(): + token_ids, sent_ids, pos_ids, label = instance[:4] + max_len = max(max_len, len(token_ids)) + if in_tokens: + to_append = (len(batch) + 1) * max_len <= batch_size + else: + to_append = len(batch) < batch_size + if to_append: + batch.append(instance) + total_token_num += len(token_ids) + else: + yield batch, total_token_num + batch, total_token_num, max_len = [instance], len( + token_ids), len(token_ids) + + if len(batch) > 0: + yield batch, total_token_num + + def wrapper(): + """yield batch data to network""" + for batch_data, total_token_num in batch_reader( + instance_reader, batch_size, self.in_tokens): + if self.in_tokens: + max_seq = -1 + else: + max_seq = self.max_seq_len + batch_data = self.generate_batch_data( + batch_data, + max_seq, + total_token_num, + voc_size=-1, + mask_id=-1, + return_input_mask=True, + return_max_len=False, + return_num_token=False) + yield batch_data + + return wrapper + + +class InputExample(object): + """A single training/test example for simple sequence classification.""" + + def __init__(self, guid, text_a, text_b=None, text_c=None, label=None): + """Constructs a InputExample. + + Args: + guid: Unique id for the example. + text_a: string. The untokenized text of the first sequence. For single + sequence tasks, only this sequence must be specified. + text_b: (Optional) string. The untokenized text of the second sequence. + Only must be specified for sequence pair tasks. + label: (Optional) string. The label of the example. This should be + specified for train and dev examples, but not for test examples. + """ + self.guid = guid + self.text_a = text_a + self.text_b = text_b + self.text_c = text_c + self.label = label + + +def _truncate_seq_pair(tokens_a, tokens_b, max_length): + """Truncates a sequence pair in place to the maximum length.""" + + # This is a simple heuristic which will always truncate the longer sequence + # one token at a time. This makes more sense than truncating an equal percent + # of tokens from each, since if one sequence is very short then each token + # that's truncated likely contains more information than a longer sequence. + while True: + total_length = len(tokens_a) + len(tokens_b) + if total_length <= max_length: + break + if len(tokens_a) > len(tokens_b): + tokens_a.pop() + else: + tokens_b.pop() + + +class InputFeatures(object): + """A single set of features of data.""" + + def __init__(self, input_ids, input_mask, segment_ids, label_id): + self.input_ids = input_ids + self.input_mask = input_mask + self.segment_ids = segment_ids + self.label_id = label_id + + +class UDCProcessor(DataProcessor): + """Processor for the UDC data set.""" + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = [] + for (i, line) in enumerate(lines): + guid = "%s-%d" % (set_type, i) + text_a = "\t".join(line[1: -1]) + text_a = tokenization.convert_to_unicode(text_a) + text_a = text_a.split('\t') + text_b = line[-1] + text_b = tokenization.convert_to_unicode(text_b) + label = tokenization.convert_to_unicode(line[0]) + examples.append( + InputExample( + guid=guid, text_a=text_a, text_b=text_b, label=label)) + return examples + + def get_train_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "train.txt")) + examples = self._create_examples(lines, "train") + return examples + + def get_dev_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "dev.txt")) + examples = self._create_examples(lines, "dev") + return examples + + def get_test_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "test.txt")) + examples = self._create_examples(lines, "test") + return examples + + def get_labels(self): + """See base class.""" + return ["0", "1"] + + +class SWDAProcessor(DataProcessor): + """Processor for the SWDA data set.""" + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = create_multi_turn_examples(lines, set_type) + return examples + + def get_train_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "train.txt")) + examples = self._create_examples(lines, "train") + return examples + + def get_dev_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "dev.txt")) + examples = self._create_examples(lines, "dev") + return examples + + def get_test_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "test.txt")) + examples = self._create_examples(lines, "test") + return examples + + def get_labels(self): + """See base class.""" + labels = range(42) + labels = [str(label) for label in labels] + return labels + + +class MRDAProcessor(DataProcessor): + """Processor for the MRDA data set.""" + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = create_multi_turn_examples(lines, set_type) + return examples + + def get_train_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "train.txt")) + examples = self._create_examples(lines, "train") + return examples + + def get_dev_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "dev.txt")) + examples = self._create_examples(lines, "dev") + return examples + + def get_test_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "test.txt")) + examples = self._create_examples(lines, "test") + return examples + + def get_labels(self): + """See base class.""" + labels = range(42) + labels = [str(label) for label in labels] + return labels + + +class ATISSlotProcessor(DataProcessor): + """Processor for the ATIS Slot data set.""" + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = [] + for (i, line) in enumerate(lines): + guid = "%s-%d" % (set_type, i) + text_a = line[0] + label = line[1] + text_a = tokenization.convert_to_unicode(text_a) + label_list = label.split() + examples.append( + InputExample( + guid=guid, text_a=text_a, label=label_list)) + return examples + + def get_train_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "train.txt")) + examples = self._create_examples(lines, "train") + return examples + + def get_dev_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "dev.txt")) + examples = self._create_examples(lines, "dev") + return examples + + def get_test_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "test.txt")) + examples = self._create_examples(lines, "test") + return examples + + def get_labels(self): + """See base class.""" + labels = range(130) + labels = [str(label) for label in labels] + return labels + + +class ATISIntentProcessor(DataProcessor): + """Processor for the ATIS intent data set.""" + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = [] + for (i, line) in enumerate(lines): + guid = "%s-%d" % (set_type, i) + text_a = line[1] + text_a = tokenization.convert_to_unicode(text_a) + label = tokenization.convert_to_unicode(line[0]) + examples.append( + InputExample( + guid=guid, text_a=text_a, label=label)) + return examples + + def get_train_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "train.txt")) + examples = self._create_examples(lines, "train") + return examples + + def get_dev_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "dev.txt")) + examples = self._create_examples(lines, "dev") + return examples + + def get_test_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "test.txt")) + examples = self._create_examples(lines, "test") + return examples + + def get_labels(self): + """See base class.""" + labels = range(26) + labels = [str(label) for label in labels] + return labels + + +class DSTC2Processor(DataProcessor): + """Processor for the DSTC2 data set.""" + def _create_turns(self, conv_example): + """create multi turn dataset""" + samples = [] + max_turns = 20 + for i in range(len(conv_example)): + conv_turns = conv_example[max(i - max_turns, 0): i + 1] + conv_info = "\1".join([sample[0] for sample in conv_turns]) + samples.append((conv_info.split('\1'), conv_example[i][1])) + return samples + + def _create_examples(self, lines, set_type): + """Creates examples for multi-turn dialogue sets.""" + examples = [] + conv_id = -1 + index = 0 + conv_example = [] + for (i, line) in enumerate(lines): + conv_no = line[0] + text_a = line[1] + label_list = line[2].split() + if conv_no != conv_id and i != 0: + samples = self._create_turns(conv_example) + for sample in samples: + guid = "%s-%s" % (set_type, index) + index += 1 + history = sample[0] + dst_label = sample[1] + examples.append(InputExample(guid=guid, text_a=history, label=dst_label)) + conv_example = [] + conv_id = conv_no + if i == 0: + conv_id = conv_no + conv_example.append((text_a, label_list)) + if conv_example: + samples = self._create_turns(conv_example) + for sample in samples: + guid = "%s-%s" % (set_type, index) + index += 1 + history = sample[0] + dst_label = sample[1] + examples.append(InputExample(guid=guid, text_a=history, label=dst_label)) + return examples + + def get_train_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "train.txt")) + examples = self._create_examples(lines, "train") + return examples + + def get_dev_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "dev.txt")) + examples = self._create_examples(lines, "dev") + return examples + + def get_test_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "test.txt")) + examples = self._create_examples(lines, "test") + return examples + + def get_labels(self): + """See base class.""" + labels = range(217) + labels = [str(label) for label in labels] + return labels + + +class MULTIWOZProcessor(DataProcessor): + """Processor for the MULTIWOZ data set.""" + def _create_turns(self, conv_example): + """create multi turn dataset""" + samples = [] + max_turns = 2 + for i in range(len(conv_example)): + prefix_turns = conv_example[max(i - max_turns, 0): i] + conv_info = "\1".join([turn[0] for turn in prefix_turns]) + current_turns = conv_example[i][0] + samples.append((conv_info.split('\1'), current_turns.split('\1'), conv_example[i][1])) + return samples + + def _create_examples(self, lines, set_type): + """Creates examples for multi-turn dialogue sets.""" + examples = [] + conv_id = -1 + index = 0 + conv_example = [] + for (i, line) in enumerate(lines): + conv_no = line[0] + text_a = line[2] + label_list = line[1].split() + if conv_no != conv_id and i != 0: + samples = self._create_turns(conv_example) + for sample in samples: + guid = "%s-%s" % (set_type, index) + index += 1 + history = sample[0] + current = sample[1] + dst_label = sample[2] + examples.append(InputExample(guid=guid, text_a=history, text_b=current, label=dst_label)) + conv_example = [] + conv_id = conv_no + if i == 0: + conv_id = conv_no + conv_example.append((text_a, label_list)) + if conv_example: + samples = self._create_turns(conv_example) + for sample in samples: + guid = "%s-%s" % (set_type, index) + index += 1 + history = sample[0] + current = sample[1] + dst_label = sample[2] + examples.append(InputExample(guid=guid, text_a=history, text_b=current, label=dst_label)) + return examples + + def get_train_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "train.txt")) + examples = self._create_examples(lines, "train") + return examples + + def get_dev_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "dev.txt")) + examples = self._create_examples(lines, "dev") + return examples + + def get_test_examples(self, data_dir): + """See base class.""" + examples = [] + lines = self._read_tsv(os.path.join(data_dir, "test.txt")) + examples = self._create_examples(lines, "test") + return examples + + def get_labels(self): + """See base class.""" + labels = range(722) + labels = [str(label) for label in labels] + return labels + + +def create_dialogue_examples(conv): + """Creates dialogue sample""" + samples = [] + for i in range(len(conv)): + cur_txt = "%s : %s" % (conv[i][2], conv[i][3]) + pre_txt = ["%s : %s" % (c[2], c[3]) for c in conv[max(0, i - 5): i]] + suf_txt = ["%s : %s" % (c[2], c[3]) for c in conv[i + 1: min(len(conv), i + 3)]] + sample = [conv[i][1], pre_txt, cur_txt, suf_txt] + samples.append(sample) + return samples + + +def create_multi_turn_examples(lines, set_type): + """Creates examples for multi-turn dialogue sets.""" + conv_id = -1 + examples = [] + conv_example = [] + index = 0 + for (i, line) in enumerate(lines): + tokens = line + conv_no = tokens[0] + if conv_no != conv_id and i != 0: + samples = create_dialogue_examples(conv_example) + for sample in samples: + guid = "%s-%s" % (set_type, index) + index += 1 + label = sample[0] + text_a = sample[1] + text_b = sample[2] + text_c = sample[3] + examples.append( + InputExample(guid=guid, text_a=text_a, text_b=text_b, text_c=text_c, label=label)) + conv_example = [] + conv_id = conv_no + if i == 0: + conv_id = conv_no + conv_example.append(tokens) + if conv_example: + samples = create_dialogue_examples(conv_example) + for sample in samples: + guid = "%s-%s" % (set_type, index) + index += 1 + label = sample[0] + text_a = sample[1] + text_b = sample[2] + text_c = sample[3] + examples.append( + InputExample(guid=guid, text_a=text_a, text_b=text_b, text_c=text_c, label=label)) + return examples + + +def convert_tokens(tokens, sep_id, tokenizer): + """Converts tokens to ids""" + tokens_ids = [] + if not tokens: + return tokens_ids + if isinstance(tokens, list): + for text in tokens: + tok_text = tokenizer.tokenize(text) + ids = tokenizer.convert_tokens_to_ids(tok_text) + tokens_ids.extend(ids) + if text != tokens[-1]: + tokens_ids.append(sep_id) + else: + tok_text = tokenizer.tokenize(tokens) + tokens_ids = tokenizer.convert_tokens_to_ids(tok_text) + return tokens_ids + + +def convert_single_example(ex_index, example, label_list, max_seq_length, + tokenizer, task_name): + """Converts a single DA `InputExample` into a single `InputFeatures`.""" + label_map = {} + SEP = 102 + CLS = 101 + + if task_name == 'udc': + INNER_SEP = 1 + limit_length = 60 + elif task_name == 'swda': + INNER_SEP = 1 + limit_length = 50 + elif task_name == 'mrda': + INNER_SEP = 1 + limit_length = 50 + elif task_name == 'atis_intent': + INNER_SEP = -1 + limit_length = -1 + elif task_name == 'atis_slot': + INNER_SEP = -1 + limit_length = -1 + elif task_name == 'dstc2': + INNER_SEP = 1 + limit_length = -1 + elif task_name == 'dstc2_asr': + INNER_SEP = 1 + limit_length = -1 + elif task_name == 'multi-woz': + INNER_SEP = 1 + limit_length = 200 + for (i, label) in enumerate(label_list): + label_map[label] = i + + tokens_a = example.text_a + tokens_b = example.text_b + tokens_c = example.text_c + + tokens_a_ids = convert_tokens(tokens_a, INNER_SEP, tokenizer) + tokens_b_ids = convert_tokens(tokens_b, INNER_SEP, tokenizer) + tokens_c_ids = convert_tokens(tokens_c, INNER_SEP, tokenizer) + + if tokens_b_ids: + tokens_b_ids = tokens_b_ids[:min(limit_length, len(tokens_b_ids))] + else: + tokens_a_ids = tokens_a_ids[len(tokens_a_ids) - max_seq_length + 2:] + if not tokens_c_ids: + if len(tokens_a_ids) > max_seq_length - len(tokens_b_ids) - 3: + tokens_a_ids = tokens_a_ids[len(tokens_a_ids) - max_seq_length + len(tokens_b_ids) + 3:] + else: + if len(tokens_a_ids) + len(tokens_b_ids) + len(tokens_c_ids) > max_seq_length - 4: + left_num = max_seq_length - len(tokens_b_ids) - 4 + if len(tokens_a_ids) > len(tokens_c_ids): + if not tokens_c_ids: + tokens_a_ids = tokens_a_ids[max(0, len(tokens_a_ids) - left_num):] + else: + suffix_num = int(left_num / 2) + tokens_c_ids = tokens_c_ids[: min(len(tokens_c_ids), suffix_num)] + prefix_num = left_num - len(tokens_c_ids) + tokens_a_ids = tokens_a_ids[max(0, len(tokens_a_ids) - prefix_num):] + else: + if not tokens_a_ids: + tokens_c_ids = tokens_c_ids[max(0, len(tokens_c_ids) - left_num):] + else: + prefix_num = int(left_num / 2) + tokens_a_ids = tokens_a_ids[max(0, len(tokens_a_ids) - prefix_num):] + suffix_num = left_num - len(tokens_a_ids) + tokens_c_ids = tokens_c_ids[: min(len(tokens_c_ids), suffix_num)] + + input_ids = [] + segment_ids = [] + input_ids.append(CLS) + segment_ids.append(0) + input_ids.extend(tokens_a_ids) + segment_ids.extend([0] * len(tokens_a_ids)) + input_ids.append(SEP) + segment_ids.append(0) + if tokens_b_ids: + input_ids.extend(tokens_b_ids) + segment_ids.extend([1] * len(tokens_b_ids)) + input_ids.append(SEP) + segment_ids.append(1) + if tokens_c_ids: + input_ids.extend(tokens_c_ids) + segment_ids.extend([0] * len(tokens_c_ids)) + input_ids.append(SEP) + segment_ids.append(0) + + input_mask = [1] * len(input_ids) + if task_name == 'atis_slot': + label_id = [0] + [label_map[l] for l in example.label] + [0] + elif task_name in ['dstc2', 'dstc2_asr', 'multi-woz']: + label_id_enty = [label_map[l] for l in example.label] + label_id = [] + for i in range(len(label_map)): + if i in label_id_enty: + label_id.append(1) + else: + label_id.append(0) + else: + label_id = label_map[example.label] + + if ex_index < 5: + print("*** Example ***") + print("guid: %s" % (example.guid)) + print("input_ids: %s" % " ".join([str(x) for x in input_ids])) + print("input_mask: %s" % " ".join([str(x) for x in input_mask])) + print("segment_ids: %s" % " ".join([str(x) for x in segment_ids])) + print("label: %s (id = %s)" % (example.label, label_id)) + feature = InputFeatures( + input_ids=input_ids, + input_mask=input_mask, + segment_ids=segment_ids, + label_id=label_id) + + return feature + + diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/run_eval_metrics.sh b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/run_eval_metrics.sh new file mode 100644 index 0000000000000000000000000000000000000000..52ab3d18c63f95692b38134e7b80bc01953c1139 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/run_eval_metrics.sh @@ -0,0 +1,12 @@ +#!/bin/bash + +TASK_NAME=$1 +PRED_FILE="./pred_"${TASK_NAME} +PYTHON_PATH="python" + +echo "run predict............................" +sh run_predict.sh ${TASK_NAME} > ${PRED_FILE} + +echo "eval_metrics..........................." +${PYTHON_PATH} eval_metrics.py ${TASK_NAME} ${PRED_FILE} + diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/run_predict.sh b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/run_predict.sh new file mode 100644 index 0000000000000000000000000000000000000000..977e9ad5bf5501692f5a9b8675f615319c00bc61 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/run_predict.sh @@ -0,0 +1,57 @@ +#!/bin/bash +export CUDA_VISIBLE_DEVICES=4 +export CPU_NUM=1 + +TASK_NAME=$1 +BERT_BASE_PATH="./uncased_L-12_H-768_A-12" +INPUT_PATH="./data/${TASK_NAME}" +OUTPUT_PATH="./output/${TASK_NAME}" +PYTHON_PATH="python" + +if [ "$TASK_NAME" = "udc" ] +then + best_model="step_62500" + max_seq_len=210 + batch_size=6720 +elif [ "$TASK_NAME" = "swda" ] +then + best_model="step_12500" + max_seq_len=128 + batch_size=6720 +elif [ "$TASK_NAME" = "mrda" ] +then + best_model="step_6500" + max_seq_len=128 + batch_size=6720 +elif [ "$TASK_NAME" = "atis_intent" ] +then + best_model="step_600" + max_seq_len=128 + batch_size=4096 + INPUT_PATH="./data/atis/${TASK_NAME}" +elif [ "$TASK_NAME" = "atis_slot" ] +then + best_model="step_7500" + max_seq_len=128 + batch_size=32 + INPUT_PATH="./data/atis/${TASK_NAME}" +elif [ "$TASK_NAME" = "dstc2" ] +then + best_model="step_12000" + max_seq_len=700 + batch_size=6000 + INPUT_PATH="./data/dstc2/${TASK_NAME}" +else + echo "not support ${TASK_NAME} dataset.." + exit 255 +fi + +$PYTHON_PATH -u predict.py --task_name ${TASK_NAME} \ + --use_cuda true\ + --batch_size ${batch_size} \ + --init_checkpoint ${OUTPUT_PATH}/${best_model} \ + --data_dir ${INPUT_PATH} \ + --vocab_path ${BERT_BASE_PATH}/vocab.txt \ + --max_seq_len ${max_seq_len} \ + --bert_config_path ${BERT_BASE_PATH}/bert_config.json + diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/run_train.sh b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/run_train.sh new file mode 100644 index 0000000000000000000000000000000000000000..781c20009218c6b0033aaf8e3e31da3f7bafd029 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/run_train.sh @@ -0,0 +1,100 @@ +#!/bin/bash +export CUDA_VISIBLE_DEVICES=3 +export CPU_NUM=1 + +TASK_NAME=$1 +typeset -l TASK_NAME + +BERT_BASE_PATH="./uncased_L-12_H-768_A-12" +INPUT_PATH="./data/${TASK_NAME}" +OUTPUT_PATH="./output/${TASK_NAME}" +PYTHON_PATH="python" + +DO_TRAIN=true +DO_VAL=true +DO_TEST=true + +#parameter configuration +if [ "${TASK_NAME}" = "udc" ] +then + save_steps=1000 + max_seq_len=210 + skip_steps=1000 + batch_size=6720 + epoch=2 + learning_rate=2e-5 + DO_VAL=false + DO_TEST=false +elif [ "${TASK_NAME}" = "swda" ] +then + save_steps=500 + max_seq_len=128 + skip_steps=200 + batch_size=6720 + epoch=10 + learning_rate=2e-5 +elif [ "${TASK_NAME}" = "mrda" ] +then + save_steps=500 + max_seq_len=128 + skip_steps=200 + batch_size=4096 + epoch=4 + learning_rate=2e-5 +elif [ "${TASK_NAME}" = "atis_intent" ] +then + save_steps=100 + max_seq_len=128 + skip_steps=10 + batch_size=4096 + epoch=20 + learning_rate=2e-5 + INPUT_PATH="./data/atis/${TASK_NAME}" +elif [ "${TASK_NAME}" = "atis_slot" ] +then + save_steps=100 + max_seq_len=128 + skip_steps=10 + batch_size=32 + epoch=50 + learning_rate=2e-5 + INPUT_PATH="./data/atis/${TASK_NAME}" +elif [ "${TASK_NAME}" = "dstc2" ] +then + save_steps=400 + max_seq_len=256 + skip_steps=20 + batch_size=8192 + epoch=40 + learning_rate=5e-5 + INPUT_PATH="./data/dstc2/${TASK_NAME}" +else + echo "not support ${TASK_NAME} dataset.." + exit 255 +fi + +# build train, dev, test dataset +cd scripts && sh run_build_data.sh ${TASK_NAME} && cd .. + +#training +$PYTHON_PATH -u train.py --task_name ${TASK_NAME} \ + --use_cuda true\ + --do_train ${DO_TRAIN} \ + --do_val ${DO_VAL} \ + --do_test ${DO_TEST} \ + --epoch ${epoch} \ + --batch_size ${batch_size} \ + --data_dir ${INPUT_PATH} \ + --bert_config_path ${BERT_BASE_PATH}/bert_config.json \ + --vocab_path ${BERT_BASE_PATH}/vocab.txt \ + --init_pretraining_params ${BERT_BASE_PATH}/params \ + --checkpoints ${OUTPUT_PATH} \ + --save_steps ${save_steps} \ + --learning_rate ${learning_rate} \ + --weight_decay 0.01 \ + --max_seq_len ${max_seq_len} \ + --skip_steps ${skip_steps} \ + --validation_steps 1000000 \ + --num_iteration_per_drop_scope 10 \ + --use_fp16 false + diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/README.md b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/README.md new file mode 100644 index 0000000000000000000000000000000000000000..dd99e0a0c4c799f37117892e42dee799cf542704 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/README.md @@ -0,0 +1,20 @@ +scripts：运行数据处理脚本目录 +运行命令： + sh run_build_data.sh [udc|swda|mrda|atis] + +生成DA任务所需要的训练集、开发集、测试集时: + sh run_build_data.sh swda + sh run_build_data.sh mrda + 生成数据分别在open-dialog/data/swda和open-dialog/data/mrda + +生成DST任务所需的训练集、开发集、测试集时: + sh run_build_data.sh dstc2 + 生成数据分别在open-dialog/data/dstc2 + +生成意图解析, 槽位识别任务所需训练集、开发集、测试集时: + sh run_build_data.sh atis + 生成槽位识别数据在open-dialog/data/atis/atis_slot + 生成意图识别数据在open-dialog/data/atis/atis_intent + + + diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/build_atis_dataset.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/build_atis_dataset.py new file mode 100755 index 0000000000000000000000000000000000000000..2dafb1bc7feb0ea5ae332dd4151d558f12afa2a9 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/build_atis_dataset.py @@ -0,0 +1,155 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""build swda train dev test dataset""" + +import json +import sys +import csv +import os +import re + + +class ATIS(object): + """ + nlu dataset atis data process + """ + def __init__(self): + """ + init instance + """ + self.slot_id = 2 + self.slot_dict = {"PAD": 0, "O": 1} + self.intent_id = 0 + self.intent_dict = dict() + self.src_dir = "../data/atis/source_data" + self.out_slot_dir = "../data/atis/atis_slot" + self.out_intent_dir = "../data/atis/atis_intent" + self.map_tag_slot = "../data/atis/atis_slot/map_tag_slot_id.txt" + self.map_tag_intent = "../data/atis/atis_intent/map_tag_intent_id.txt" + + def _load_file(self, data_type): + """ + load dataset filename + """ + slot_stat = os.path.exists(self.out_slot_dir) + if not slot_stat: + os.makedirs(self.out_slot_dir) + intent_stat = os.path.exists(self.out_intent_dir) + if not intent_stat: + os.makedirs(self.out_intent_dir) + src_examples = [] + json_file = os.path.join(self.src_dir, "%s.json" % data_type) + with open(json_file, 'r') as load_f: + json_dict = json.load(load_f) + examples = json_dict['rasa_nlu_data']['common_examples'] + for example in examples: + text = example.get('text') + intent = example.get('intent') + entities = example.get('entities') + src_examples.append((text, intent, entities)) + return src_examples + + def _parser_intent_data(self, examples, data_type): + """ + parser intent dataset + """ + out_filename = "%s/%s.txt" % (self.out_intent_dir, data_type) + with open(out_filename, 'w') as fw: + for example in examples: + if example[1] not in self.intent_dict: + self.intent_dict[example[1]] = self.intent_id + self.intent_id += 1 + fw.write("%s\t%s\n" % (self.intent_dict[example[1]], example[0].lower())) + + with open(self.map_tag_intent, 'w') as fw: + for tag in self.intent_dict: + fw.write("%s\t%s\n" % (tag, self.intent_dict[tag])) + + def _parser_slot_data(self, examples, data_type): + """ + parser slot dataset + """ + out_filename = "%s/%s.txt" % (self.out_slot_dir, data_type) + with open(out_filename, 'w') as fw: + for example in examples: + tags = [] + text = example[0] + entities = example[2] + if not entities: + tags = [str(self.slot_dict['O'])] * len(text.strip().split()) + continue + for i in range(len(entities)): + enty = entities[i] + start = enty['start'] + value_num = len(enty['value'].split()) + tags_slot = [] + for j in range(value_num): + if j == 0: + bround_tag = "B" + else: + bround_tag = "I" + tag = "%s-%s" % (bround_tag, enty['entity']) + if tag not in self.slot_dict: + self.slot_dict[tag] = self.slot_id + self.slot_id += 1 + tags_slot.append(str(self.slot_dict[tag])) + if i == 0: + if start not in [0, 1]: + prefix_num = len(text[: start].strip().split()) + tags.extend([str(self.slot_dict['O'])] * prefix_num) + tags.extend(tags_slot) + else: + prefix_num = len(text[entities[i - 1]['end']: start].strip().split()) + tags.extend([str(self.slot_dict['O'])] * prefix_num) + tags.extend(tags_slot) + if entities[-1]['end'] < len(text): + suffix_num = len(text[entities[-1]['end']:].strip().split()) + tags.extend([str(self.slot_dict['O'])] * suffix_num) + fw.write("%s\t%s\n" % (text.encode('utf8'), " ".join(tags).encode('utf8'))) + + with open(self.map_tag_slot, 'w') as fw: + for slot in self.slot_dict: + fw.write("%s\t%s\n" % (slot, self.slot_dict[slot])) + + def get_train_dataset(self): + """ + parser train dataset and print train.txt + """ + train_examples = self._load_file("train") + self._parser_intent_data(train_examples, "train") + self._parser_slot_data(train_examples, "train") + + def get_test_dataset(self): + """ + parser test dataset and print test.txt + """ + test_examples = self._load_file("test") + self._parser_intent_data(test_examples, "test") + self._parser_slot_data(test_examples, "test") + + def main(self): + """ + run data process + """ + self.get_train_dataset() + self.get_test_dataset() + + +if __name__ == "__main__": + atis_inst = ATIS() + atis_inst.main() + + + + diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/build_dstc2_dataset.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/build_dstc2_dataset.py new file mode 100755 index 0000000000000000000000000000000000000000..b38de16b97165d55957e47aa3020834f43b8e71d --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/build_dstc2_dataset.py @@ -0,0 +1,149 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""build mrda train dev test dataset""" + +import json +import sys +import csv +import os +import re + +import commonlib + + +class DSTC2(object): + """ + dialogue state tracking dstc2 data process + """ + def __init__(self): + """ + init instance + """ + self.map_tag_dict = {} + self.out_dir = "../data/dstc2/dstc2" + self.out_asr_dir = "../data/dstc2/dstc2_asr" + self.data_list = "./conf/dstc2.conf" + self.map_tag = "../data/dstc2/dstc2/map_tag_id.txt" + self.src_dir = "../data/dstc2/source_data" + self.onto_json = "../data/dstc2/source_data/ontology_dstc2.json" + self._load_file() + self._load_ontology() + + def _load_file(self): + """ + load dataset filename + """ + self.data_dict = commonlib.load_dict(self.data_list) + for data_type in self.data_dict: + for i in range(len(self.data_dict[data_type])): + self.data_dict[data_type][i] = os.path.join(self.src_dir, self.data_dict[data_type][i]) + + def _load_ontology(self): + """ + load ontology tag + """ + tag_id = 1 + self.map_tag_dict['none'] = 0 + with open(self.onto_json, 'r') as fr: + ontology = json.load(fr) + slots_values = ontology['informable'] + for slot in slots_values: + for value in slots_values[slot]: + key = "%s_%s" % (slot, value) + self.map_tag_dict[key] = tag_id + tag_id += 1 + key = "%s_none" % (slot) + self.map_tag_dict[key] = tag_id + tag_id += 1 + + def _parser_dataset(self, data_type): + """ + parser train dev test dataset + """ + stat = os.path.exists(self.out_dir) + if not stat: + os.makedirs(self.out_dir) + asr_stat = os.path.exists(self.out_asr_dir) + if not asr_stat: + os.makedirs(self.out_asr_dir) + out_file = os.path.join(self.out_dir, "%s.txt" % data_type) + out_asr_file = os.path.join(self.out_asr_dir, "%s.txt" % data_type) + with open(out_file, 'w') as fw, open(out_asr_file, 'w') as fw_asr: + data_list = self.data_dict.get(data_type) + for fn in data_list: + log_file = os.path.join(fn, "log.json") + label_file = os.path.join(fn, "label.json") + with open(log_file, 'r') as f_log, open(label_file, 'r') as f_label: + log_json = json.load(f_log) + label_json = json.load(f_label) + session_id = log_json['session-id'] + assert len(label_json["turns"]) == len(log_json["turns"]) + for i in range(len(label_json["turns"])): + log_turn = log_json["turns"][i] + label_turn = label_json["turns"][i] + assert log_turn["turn-index"] == label_turn["turn-index"] + labels = ["%s_%s" % (slot, label_turn["goal-labels"][slot]) for slot in label_turn["goal-labels"]] + labels_ids = " ".join([str(self.map_tag_dict.get(label, self.map_tag_dict["%s_none" % label.split('_')[0]])) for label in labels]) + mach = log_turn['output']['transcript'] + user = label_turn['transcription'] + if not labels_ids.strip(): + labels_ids = self.map_tag_dict['none'] + out = "%s\t%s\1%s\t%s" % (session_id, mach, user, labels_ids) + user_asr = log_turn['input']['live']['asr-hyps'][0]['asr-hyp'].strip() + out_asr = "%s\t%s\1%s\t%s" % (session_id, mach, user_asr, labels_ids) + fw.write("%s\n" % out.encode('utf8')) + fw_asr.write("%s\n" % out_asr.encode('utf8')) + + def get_train_dataset(self): + """ + parser train dataset and print train.txt + """ + self._parser_dataset("train") + + def get_dev_dataset(self): + """ + parser dev dataset and print dev.txt + """ + self._parser_dataset("dev") + + def get_test_dataset(self): + """ + parser test dataset and print test.txt + """ + self._parser_dataset("test") + + def get_labels(self): + """ + get tag and map ids file + """ + with open(self.map_tag, 'w') as fw: + for elem in self.map_tag_dict: + fw.write("%s\t%s\n" % (elem, self.map_tag_dict[elem])) + + def main(self): + """ + run data process + """ + self.get_train_dataset() + self.get_dev_dataset() + self.get_test_dataset() + self.get_labels() + +if __name__ == "__main__": + dstc_inst = DSTC2() + dstc_inst.main() + + + + diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/build_mrda_dataset.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/build_mrda_dataset.py new file mode 100755 index 0000000000000000000000000000000000000000..ee03d937608937c233161a3aa3bc3302ea5dfe8c --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/build_mrda_dataset.py @@ -0,0 +1,165 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""build mrda train dev test dataset""" + +import sys +import csv +import os +import re + +import commonlib + + +class MRDA(object): + """ + dialogue act dataset mrda data process + """ + def __init__(self): + """ + init instance + """ + self.tag_id = 0 + self.map_tag_dict = dict() + self.out_dir = "../data/mrda" + self.data_list = "./conf/mrda.conf" + self.map_tag = "../data/mrda/map_tag_id.txt" + self.voc_map_tag = "../data/mrda/source_data/icsi_mrda+hs_corpus_050512/classmaps/map_01b_expanded_w_split" + self.src_dir = "../data/mrda/source_data/icsi_mrda+hs_corpus_050512/data" + self._load_file() + self.tag_dict = commonlib.load_voc(self.voc_map_tag) + + def _load_file(self): + """ + load dataset filename + """ + self.dadb_dict = {} + self.trans_dict = {} + self.data_dict = commonlib.load_dict(self.data_list) + file_list, file_path = commonlib.get_file_list(self.src_dir) + for i in range(len(file_list)): + name = file_list[i] + keyword = name.split('.')[0] + if 'dadb' in name: + self.dadb_dict[keyword] = file_path[i] + if 'trans' in name: + self.trans_dict[keyword] = file_path[i] + + def load_dadb(self, data_type): + """ + load dadb dataset + """ + dadb_dict = {} + conv_id_list = [] + dadb_list = self.data_dict[data_type] + for dadb_key in dadb_list: + dadb_file = self.dadb_dict[dadb_key] + with open(dadb_file, 'r') as fr: + row = csv.reader(fr, delimiter = ',') + for line in row: + elems = line + conv_id = elems[2] + conv_id_list.append(conv_id) + if len(elems) != 14: + continue + error_code = elems[3] + da_tag = elems[-9] + da_ori_tag = elems[-6] + dadb_dict[conv_id] = (error_code, da_ori_tag, da_tag) + return dadb_dict, conv_id_list + + def load_trans(self, data_type): + """load trans data""" + trans_dict = {} + trans_list = self.data_dict[data_type] + for trans_key in trans_list: + trans_file = self.trans_dict[trans_key] + with open(trans_file, 'r') as fr: + row = csv.reader(fr, delimiter = ',') + for line in row: + elems = line + if len(elems) != 3: + continue + conv_id = elems[0] + text = elems[1] + text_process = elems[2] + trans_dict[conv_id] = (text, text_process) + return trans_dict + + def _parser_dataset(self, data_type): + """ + parser train dev test dataset + """ + out_filename = "%s/%s.txt" % (self.out_dir, data_type) + dadb_dict, conv_id_list = self.load_dadb(data_type) + trans_dict = self.load_trans(data_type) + with open(out_filename, 'w') as fw: + for elem in conv_id_list: + v_dadb = dadb_dict[elem] + v_trans = trans_dict[elem] + da_tag = v_dadb[2] + if da_tag not in self.tag_dict: + continue + tag = self.tag_dict[da_tag] + if tag == "Z": + continue + if tag not in self.map_tag_dict: + self.map_tag_dict[tag] = self.tag_id + self.tag_id += 1 + caller = elem.split('_')[0].split('-')[-1] + conv_no = elem.split('_')[0].split('-')[0] + out = "%s\t%s\t%s\t%s" % (conv_no, self.map_tag_dict[tag], caller, v_trans[0]) + fw.write("%s\n" % out) + + def get_train_dataset(self): + """ + parser train dataset and print train.txt + """ + self._parser_dataset("train") + + def get_dev_dataset(self): + """ + parser dev dataset and print dev.txt + """ + self._parser_dataset("dev") + + def get_test_dataset(self): + """ + parser test dataset and print test.txt + """ + self._parser_dataset("test") + + def get_labels(self): + """ + get tag and map ids file + """ + with open(self.map_tag, 'w') as fw: + for elem in self.map_tag_dict: + fw.write("%s\t%s\n" % (elem, self.map_tag_dict[elem])) + + def main(self): + """ + run data process + """ + self.get_train_dataset() + self.get_dev_dataset() + self.get_test_dataset() + self.get_labels() + +if __name__ == "__main__": + mrda_inst = MRDA() + mrda_inst.main() + + + + diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/build_swda_dataset.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/build_swda_dataset.py new file mode 100755 index 0000000000000000000000000000000000000000..ba25e3701517b94a07a4bac20282aca344e5bcc5 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/build_swda_dataset.py @@ -0,0 +1,231 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""build swda train dev test dataset""" + +import sys +import csv +import os +import re + +import commonlib + + +class SWDA(object): + """ + dialogue act dataset swda data process + """ + def __init__(self): + """ + init instance + """ + self.tag_id = 0 + self.map_tag_dict = dict() + self.out_dir = "../data/swda" + self.data_list = "./conf/swda.conf" + self.map_tag = "../data/swda/map_tag_id.txt" + self.src_dir = "../data/swda/source_data/swda" + self._load_file() + + def _load_file(self): + """ + load dataset filename + """ + self.data_dict = commonlib.load_dict(self.data_list) + self.file_dict = {} + child_dir = commonlib.get_dir_list(self.src_dir) + for chd in child_dir: + file_list, file_path = commonlib.get_file_list(chd) + for i in range(len(file_list)): + name = file_list[i] + keyword = "sw%s" % name.split('.')[0].split('_')[-1] + self.file_dict[keyword] = file_path[i] + + def _parser_dataset(self, data_type): + """ + parser train dev test dataset + """ + out_filename = "%s/%s.txt" % (self.out_dir, data_type) + with open(out_filename, 'w') as fw: + for name in self.data_dict[data_type]: + file_path = self.file_dict[name] + with open(file_path, 'r') as fr: + idx = 0 + row = csv.reader(fr, delimiter = ',') + for r in row: + if idx == 0: + idx += 1 + continue + out = self._parser_utterence(r) + fw.write("%s\n" % out) + + def _clean_text(self, text): + """ + text cleaning for dialogue act dataset + """ + if text.startswith('<') and text.endswith('>.'): + return text + if "[" in text or "]" in text: + stat = True + else: + stat = False + group = re.findall("\[.*?\+.*?\]", text) + while group and stat: + for elem in group: + elem_src = elem + elem = re.sub('\+', '', elem.lstrip('[').rstrip(']')) + text = text.replace(elem_src, elem) + if "[" in text or "]" in text: + stat = True + else: + stat = False + group = re.findall("\[.*?\+.*?\]", text) + if "{" in text or "}" in text: + stat = True + else: + stat = False + group = re.findall("{[A-Z].*?}", text) + while group and stat: + child_group = re.findall("{[A-Z]*(.*?)}", text) + for i in range(len(group)): + text = text.replace(group[i], child_group[i]) + if "{" in text or "}" in text: + stat = True + else: + stat = False + group = re.findall("{[A-Z].*?}", text) + if "(" in text or ")" in text: + stat = True + else: + stat = False + group = re.findall("\(\(.*?\)\)", text) + while group and stat: + for elem in group: + if elem: + elem_clean = re.sub("\(|\)", "", elem) + text = text.replace(elem, elem_clean) + else: + text = text.replace(elem, "mumblex") + if "(" in text or ")" in text: + stat = True + else: + stat = False + group = re.findall("\(\((.*?)\)\)", text) + + group = re.findall("\<.*?\>", text) + if group: + for elem in group: + text = text.replace(elem, "") + + text = re.sub(r" \'s", "\'s", text) + text = re.sub(r" n\'t", "n\'t", text) + text = re.sub(r" \'t", "\'t", text) + text = re.sub(" +", " ", text) + text = text.rstrip('\/').strip().strip('-') + text = re.sub("\[|\]|\+|\>|\<|\{|\}", "", text) + return text.strip().lower() + + def _map_tag(self, da_tag): + """ + map tag to 42 classes + """ + curr_da_tags = [] + curr_das = re.split(r"\s*[,;]\s*", da_tag) + for curr_da in curr_das: + if curr_da == "qy_d" or curr_da == "qw^d" or curr_da == "b^m": + pass + elif curr_da == "nn^e": + curr_da = "ng" + elif curr_da == "ny^e": + curr_da = "na" + else: + curr_da = re.sub(r'(.)\^.*', r'\1', curr_da) + curr_da = re.sub(r'[\(\)@*]', '', curr_da) + tag = curr_da + if tag in ('qr', 'qy'): + tag = 'qy' + elif tag in ('fe', 'ba'): + tag = 'ba' + elif tag in ('oo', 'co', 'cc'): + tag = 'oo_co_cc' + elif tag in ('fx', 'sv'): + tag = 'sv' + elif tag in ('aap', 'am'): + tag = 'aap_am' + elif tag in ('arp', 'nd'): + tag = 'arp_nd' + elif tag in ('fo', 'o', 'fw', '"', 'by', 'bc'): + tag = 'fo_o_fw_"_by_bc' + curr_da = tag + curr_da_tags.append(curr_da) + if curr_da_tags[0] not in self.map_tag_dict: + self.map_tag_dict[curr_da_tags[0]] = self.tag_id + self.tag_id += 1 + return self.map_tag_dict[curr_da_tags[0]] + + def _parser_utterence(self, line): + """ + parser one turn dialogue + """ + conversation_no = line[2] + act_tag = line[4] + caller = line[5] + text = line[8] + text = self._clean_text(text) + act_tag = self._map_tag(act_tag) + + out = "%s\t%s\t%s\t%s" % (conversation_no, act_tag, caller, text) + return out + + def get_train_dataset(self): + """ + parser train dataset and print train.txt + """ + self._parser_dataset("train") + + def get_dev_dataset(self): + """ + parser dev dataset and print dev.txt + """ + self._parser_dataset("dev") + + def get_test_dataset(self): + """ + parser test dataset and print test.txt + """ + self._parser_dataset("test") + + def get_labels(self): + """ + get tag and map ids file + """ + with open(self.map_tag, 'w') as fw: + for elem in self.map_tag_dict: + fw.write("%s\t%s\n" % (elem, self.map_tag_dict[elem])) + + def main(self): + """ + run data process + """ + self.get_train_dataset() + self.get_dev_dataset() + self.get_test_dataset() + self.get_labels() + +if __name__ == "__main__": + swda_inst = SWDA() + swda_inst.main() + + + + diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/commonlib.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/commonlib.py new file mode 100755 index 0000000000000000000000000000000000000000..9706af30349d1fe300b1b747721b79fb31ae3b11 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/commonlib.py @@ -0,0 +1,74 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""common function""" +import sys +import os + + +def get_file_list(dir_name): + """ + get file list in directory + """ + file_list = list() + file_path = list() + for root, dirs, files in os.walk(dir_name): + for file in files: + file_list.append(file) + file_path.append(os.path.join(root, file)) + return file_list, file_path + + +def get_dir_list(dir_name): + """ + get directory names + """ + child_dir = [] + dir_list = os.listdir(dir_name) + for cur_file in dir_list: + path = os.path.join(dir_name, cur_file) + if not os.path.isdir(path): + continue + child_dir.append(path) + return child_dir + + +def load_dict(conf): + """ + load swda dataset config + """ + conf_dict = dict() + with open(conf, 'r') as fr: + for line in fr: + line = line.strip() + elems = line.split('\t') + if elems[0] not in conf_dict: + conf_dict[elems[0]] = [] + conf_dict[elems[0]].append(elems[1]) + return conf_dict + + +def load_voc(conf): + """ + load map dict + """ + map_dict = {} + with open(conf, 'r') as fr: + for line in fr: + line = line.strip() + elems = line.split('\t') + map_dict[elems[0]] = elems[1] + return map_dict + + + diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/conf/dstc2.conf b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/conf/dstc2.conf new file mode 100755 index 0000000000000000000000000000000000000000..c40b94eed2c3b74f2364ec8e75b0f7d0df60791b 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Mar13_S1A1/voip-da4a08ad84-20130328_155250 +train Mar13_S0A1/voip-da4a08ad84-20130328_155023 +train Mar13_S1A1/voip-da4a08ad84-20130328_155120 +train Mar13_S0A0/voip-da4a08ad84-20130328_160315 +train Mar13_S1A0/voip-14f776f781-20130328_140514 +train Mar13_S0A1/voip-14f776f781-20130328_141638 +train Mar13_S0A1/voip-14f776f781-20130328_115724 +train Mar13_S1A1/voip-14f776f781-20130329_033249 +train Mar13_S0A1/voip-14f776f781-20130328_123358 +train Mar13_S0A0/voip-14f776f781-20130328_151904 +train Mar13_S0A0/voip-14f776f781-20130328_121622 +train Mar13_S1A1/voip-14f776f781-20130328_142356 +train Mar13_S0A0/voip-14f776f781-20130328_123813 +train Mar13_S1A1/voip-1b51204ef5-20130401_173145 +train Mar13_S0A1/voip-1b51204ef5-20130401_172934 +train Mar13_S1A0/voip-1b51204ef5-20130401_145241 +train Mar13_S1A0/voip-1b51204ef5-20130401_150524 +train Mar13_S1A1/voip-1b51204ef5-20130401_145742 +train Mar13_S0A1/voip-1b51204ef5-20130401_145115 +train Mar13_S1A0/voip-fd0c0fb514-20130328_114840 +train Mar13_S1A1/voip-fd0c0fb514-20130328_114152 +train Mar13_S0A1/voip-fd0c0fb514-20130328_114024 +train Mar13_S0A1/voip-e3b4879e0d-20130327_030628 +train Mar13_S0A0/voip-e3b4879e0d-20130327_181026 +train Mar13_S1A1/voip-e3b4879e0d-20130326_215705 +train Mar13_S1A0/voip-e3b4879e0d-20130327_031211 +train Mar13_S1A1/voip-e3b4879e0d-20130327_182147 +train Mar13_S1A1/voip-e3b4879e0d-20130326_024455 +train Mar13_S0A1/voip-e3b4879e0d-20130327_033913 +train Mar13_S1A1/voip-e3b4879e0d-20130326_024044 +train Mar13_S1A1/voip-e3b4879e0d-20130326_021022 +train Mar13_S0A1/voip-e3b4879e0d-20130326_024336 +train Mar13_S0A0/voip-e3b4879e0d-20130326_023841 +train Mar13_S0A0/voip-e3b4879e0d-20130326_215254 +train Mar13_S0A0/voip-e3b4879e0d-20130326_020631 +train Mar13_S1A1/voip-e3b4879e0d-20130327_030955 +train Mar13_S1A0/voip-e3b4879e0d-20130327_181509 +train Mar13_S1A0/voip-e3b4879e0d-20130326_215515 +train Mar13_S1A0/voip-e3b4879e0d-20130326_022732 +train Mar13_S0A0/voip-e3b4879e0d-20130327_030803 +train 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Mar13_S0A1/voip-5cf59cc660-20130328_151124 +train Mar13_S0A1/voip-5cf59cc660-20130327_143126 +train Mar13_S0A0/voip-5cf59cc660-20130328_171914 +train Mar13_S0A0/voip-5cf59cc660-20130327_145740 +train Mar13_S1A1/voip-5cf59cc660-20130328_145134 +train Mar13_S1A1/voip-5cf59cc660-20130328_160837 +train Mar13_S1A1/voip-5cf59cc660-20130327_144338 +train Mar13_S0A1/voip-5cf59cc660-20130328_160946 +train Mar13_S1A1/voip-5cf59cc660-20130327_143308 +train Mar13_S1A1/voip-5cf59cc660-20130328_172556 +train Mar13_S0A0/voip-5749b16764-20130328_145900 +train Mar13_S1A0/voip-5749b16764-20130328_150143 +train Mar13_S0A0/voip-5749b16764-20130328_151730 +train Mar13_S1A1/voip-5749b16764-20130328_150400 +train Mar13_S0A1/voip-5749b16764-20130328_145608 +train Mar13_S1A1/voip-5749b16764-20130328_151234 +train Mar13_S0A1/voip-3b59a0391b-20130401_135225 +train Mar13_S1A1/voip-3b59a0391b-20130401_133524 +train Mar13_S1A1/voip-3b59a0391b-20130401_133038 +train Mar13_S1A0/voip-3b59a0391b-20130401_133238 +train Mar13_S1A1/voip-3b59a0391b-20130401_135704 +train Mar13_S0A1/voip-3b59a0391b-20130401_132324 +train Mar13_S1A1/voip-3b59a0391b-20130401_134901 +train Mar13_S0A1/voip-3b59a0391b-20130401_134053 +train Mar13_S1A0/voip-b20b6e847a-20130326_222313 +train Mar13_S1A1/voip-b20b6e847a-20130326_222030 +train Mar13_S0A1/voip-b20b6e847a-20130326_222936 +train Mar13_S0A0/voip-b20b6e847a-20130326_224116 +train Mar13_S1A0/voip-b20b6e847a-20130326_222829 +train Mar13_S0A1/voip-64150aca03-20130401_194320 +train Mar13_S1A1/voip-199d62165b-20130402_123401 +train Mar13_S1A0/voip-199d62165b-20130402_122050 +train Mar13_S1A1/voip-199d62165b-20130402_122711 +train Mar13_S0A1/voip-199d62165b-20130402_123507 +train Mar13_S1A0/voip-199d62165b-20130402_124137 +train Mar13_S1A1/voip-199d62165b-20130402_115733 +train Mar13_S1A0/voip-199d62165b-20130402_120922 +train Mar13_S0A1/voip-199d62165b-20130402_121352 +train Mar13_S1A0/voip-199d62165b-20130402_120456 +train Mar13_S0A1/voip-199d62165b-20130402_122354 +train Mar13_S1A0/voip-199d62165b-20130402_122214 +train Mar13_S1A1/voip-199d62165b-20130402_121601 +train Mar13_S0A1/voip-199d62165b-20130402_115850 +train Mar13_S0A0/voip-3860c915c2-20130328_164046 +train Mar13_S1A0/voip-3860c915c2-20130328_164431 +train Mar13_S0A1/voip-22c938c8ba-20130325_134402 +train Mar13_S1A0/voip-22c938c8ba-20130325_130740 +train Mar13_S0A0/voip-22c938c8ba-20130325_123407 +train Mar13_S0A0/voip-22c938c8ba-20130325_130445 +train Mar13_S1A0/voip-22c938c8ba-20130325_134206 +train Mar13_S0A0/voip-22c938c8ba-20130325_142552 +train Mar13_S1A0/voip-22c938c8ba-20130325_143621 +train Mar13_S0A1/voip-22c938c8ba-20130325_125039 +train Mar13_S1A1/voip-22c938c8ba-20130325_124542 +train Mar13_S0A1/voip-22c938c8ba-20130325_130208 +train Mar13_S1A0/voip-52d599db9c-20130326_213541 +train Mar13_S1A1/voip-52d599db9c-20130327_175215 +train Mar13_S1A1/voip-52d599db9c-20130402_001953 +train Mar13_S0A0/voip-52d599db9c-20130323_054632 +train Mar13_S0A1/voip-52d599db9c-20130326_214515 +train Mar13_S0A1/voip-52d599db9c-20130326_213117 +train Mar13_S1A0/voip-52d599db9c-20130326_214326 +train Mar13_S1A1/voip-52d599db9c-20130326_212814 +train Mar13_S1A1/voip-52d599db9c-20130326_214615 +train Mar13_S0A1/voip-52d599db9c-20130402_001838 +train Mar13_S0A1/voip-52d599db9c-20130323_075058 +train Mar13_S1A0/voip-52d599db9c-20130402_002245 +train Mar13_S0A1/voip-52d599db9c-20130325_135450 +train Mar13_S0A1/voip-52d599db9c-20130328_154633 +train Mar13_S1A1/voip-52d599db9c-20130326_010952 +train Mar13_S1A0/voip-52d599db9c-20130323_054736 +train Mar13_S1A0/voip-52d599db9c-20130402_002522 +train Mar13_S0A0/voip-52d599db9c-20130323_074800 +train Mar13_S0A0/voip-52d599db9c-20130326_212959 +train Mar13_S1A1/voip-52d599db9c-20130402_000814 +train Mar13_S0A0/voip-52d599db9c-20130326_011106 +train Mar13_S0A1/voip-0fa32b1e78-20130402_141848 +train Mar13_S0A1/voip-0fa32b1e78-20130328_234145 +train Mar13_S1A1/voip-0fa32b1e78-20130328_151950 +train Mar13_S0A1/voip-0fa32b1e78-20130402_140213 +train Mar13_S0A1/voip-0fa32b1e78-20130401_151332 +train Mar13_S1A0/voip-0fa32b1e78-20130328_234804 +train Mar13_S0A0/voip-0fa32b1e78-20130328_151140 +train Mar13_S1A1/voip-0fa32b1e78-20130328_233916 +train Mar13_S1A1/voip-0fa32b1e78-20130402_132031 +train Mar13_S1A0/voip-0fa32b1e78-20130328_233625 +train Mar13_S1A0/voip-0fa32b1e78-20130402_142351 +train Mar13_S1A1/voip-0fa32b1e78-20130328_233747 +train Mar13_S0A1/voip-0fa32b1e78-20130402_142234 +train Mar13_S1A1/voip-0fa32b1e78-20130402_142022 +train Mar13_S1A1/voip-0fa32b1e78-20130328_151336 +train Mar13_S0A1/voip-0fa32b1e78-20130328_152422 +train Mar13_S1A0/voip-0fa32b1e78-20130328_235008 +train Mar13_S1A1/voip-0fa32b1e78-20130328_153725 +train Mar13_S1A0/voip-0fa32b1e78-20130402_140846 +train Mar13_S1A0/voip-0fa32b1e78-20130328_152808 +train Mar13_S0A1/voip-0fa32b1e78-20130402_140739 +train Mar13_S1A0/voip-0fa32b1e78-20130402_140356 +train Mar13_S1A1/voip-0fa32b1e78-20130402_141117 +train Mar13_S0A1/voip-09733d9e4c-20130327_123942 +train 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Mar13_S1A1/voip-9735278861-20130401_161036 +train Mar13_S0A1/voip-9735278861-20130401_161717 +train Mar13_S1A0/voip-9735278861-20130401_151648 +train Mar13_S1A0/voip-9735278861-20130401_161215 +train Mar13_S0A1/voip-9735278861-20130401_154605 +train Mar13_S1A1/voip-9735278861-20130402_140608 +train Mar13_S1A0/voip-9735278861-20130401_155528 +train Mar13_S1A0/voip-9735278861-20130401_154251 +train Mar13_S1A1/voip-9735278861-20130401_152826 +train Mar13_S1A1/voip-9735278861-20130401_160600 +train Mar13_S1A0/voip-7e22911804-20130327_200922 +train Mar13_S0A1/voip-7e22911804-20130324_184015 +train Mar13_S1A1/voip-7e22911804-20130324_193050 +train Mar13_S1A1/voip-7e22911804-20130326_142538 +train Mar13_S0A0/voip-7e22911804-20130324_173542 +train Mar13_S1A1/voip-7e22911804-20130328_164203 +train Mar13_S1A0/voip-7e22911804-20130325_202948 +train Mar13_S0A0/voip-7e22911804-20130324_184843 +train Mar13_S0A0/voip-7e22911804-20130325_210616 +train Mar13_S0A0/voip-7e22911804-20130328_162243 +train Mar13_S1A0/voip-7e22911804-20130328_162140 +train Mar13_S0A0/voip-7e22911804-20130327_202516 +train Mar13_S0A0/voip-7e22911804-20130328_204851 +train Mar13_S0A1/voip-7e22911804-20130328_161325 +train Mar13_S0A0/voip-7e22911804-20130325_202635 +train Mar13_S1A0/voip-7e22911804-20130328_204150 +train Mar13_S0A1/voip-7e22911804-20130328_162643 +train Mar13_S1A1/voip-7e22911804-20130324_190421 +train Mar13_S0A1/voip-7e22911804-20130325_200320 +train Mar13_S0A1/voip-7e22911804-20130328_203706 +train Mar13_S0A1/voip-7e22911804-20130328_204351 +train Mar13_S0A1/voip-876ef67873-20130402_032159 +train Mar13_S0A1/voip-876ef67873-20130327_035756 +train Mar13_S0A1/voip-876ef67873-20130402_030352 +train Mar13_S0A0/voip-876ef67873-20130327_040247 +train Mar13_S1A0/voip-876ef67873-20130327_035558 +train Mar13_S1A1/voip-876ef67873-20130327_035101 +train Mar13_S1A1/voip-876ef67873-20130402_031718 +train Mar13_S0A1/voip-876ef67873-20130402_033525 +train Mar13_S1A0/voip-876ef67873-20130402_033954 +train 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Mar13_S0A1/voip-e8997b10da-20130401_151850 +train Mar13_S1A1/voip-e8997b10da-20130327_194306 +train Mar13_S1A1/voip-e8997b10da-20130329_010926 +train Mar13_S1A1/voip-e8997b10da-20130329_001948 +train Mar13_S1A0/voip-e8997b10da-20130329_000534 +train Mar13_S1A1/voip-e8997b10da-20130327_200112 +train Mar13_S0A1/voip-e8997b10da-20130329_012511 +train Mar13_S0A1/voip-e8997b10da-20130329_010205 +train Mar13_S0A1/voip-e8997b10da-20130329_000658 +train Mar13_S1A0/voip-e8997b10da-20130327_194901 +train Mar13_S0A0/voip-e8997b10da-20130327_195713 +train Mar13_S0A1/voip-e8997b10da-20130327_193641 +train Mar13_S0A1/voip-e8997b10da-20130329_012334 +train Mar13_S1A0/voip-e8997b10da-20130401_152228 +train Mar13_S1A0/voip-e8997b10da-20130329_012706 +train Mar13_S0A1/voip-e8997b10da-20130329_000819 +train Mar13_S1A1/voip-e8997b10da-20130401_152019 +train Mar13_S0A1/voip-31de0daa7b-20130402_133530 +train Mar13_S1A0/voip-31de0daa7b-20130402_133216 +train Mar13_S1A0/voip-31de0daa7b-20130401_205355 +train Mar13_S1A0/voip-31de0daa7b-20130401_204415 +train Mar13_S0A1/voip-31de0daa7b-20130401_220541 +train Mar13_S1A1/voip-31de0daa7b-20130402_132633 +train Mar13_S1A0/voip-31de0daa7b-20130401_220728 +train Mar13_S1A1/voip-31de0daa7b-20130401_205633 +train Mar13_S1A0/voip-31de0daa7b-20130401_221101 +train Mar13_S1A0/voip-31de0daa7b-20130401_220217 +train Mar13_S0A1/voip-31de0daa7b-20130401_204621 +train Mar13_S1A1/voip-31de0daa7b-20130402_133047 +train Mar13_S1A1/voip-31de0daa7b-20130401_203534 +train Mar13_S0A0/voip-52eb280e7b-20130325_130820 +train Mar13_S0A1/voip-52eb280e7b-20130326_212826 +train Mar13_S1A0/voip-52eb280e7b-20130326_214113 +train Mar13_S1A0/voip-52eb280e7b-20130326_212546 +train Mar13_S0A0/voip-52eb280e7b-20130325_124240 +train Mar13_S0A1/voip-52eb280e7b-20130326_215158 +train Mar13_S1A1/voip-52eb280e7b-20130326_211923 +train Mar13_S0A0/voip-52eb280e7b-20130326_212733 +train Mar13_S0A1/voip-52eb280e7b-20130325_131334 +train Mar13_S1A1/voip-52eb280e7b-20130325_125522 +train Mar13_S0A1/voip-52eb280e7b-20130326_213528 +train Mar13_S0A0/voip-52eb280e7b-20130326_213227 +train Mar13_S0A1/voip-52eb280e7b-20130325_130645 +train Mar13_S1A0/voip-52eb280e7b-20130325_125355 +train Mar13_S1A1/voip-52eb280e7b-20130325_132224 +train Mar13_S0A1/voip-52eb280e7b-20130325_123856 +train Mar13_S0A0/voip-52eb280e7b-20130326_214342 +train Mar13_S0A0/voip-52eb280e7b-20130325_130950 +train Mar13_S1A1/voip-52eb280e7b-20130326_215044 +train Mar13_S1A0/voip-52eb280e7b-20130325_125157 +train Mar13_S0A1/voip-2c217000af-20130328_224808 +train Mar13_S1A1/voip-2c217000af-20130328_223711 +train Mar13_S0A1/voip-2c217000af-20130328_224934 +train Mar13_S0A0/voip-2c217000af-20130325_221252 +train Mar13_S0A1/voip-2c217000af-20130325_222647 +train Mar13_S1A0/voip-2c217000af-20130328_224449 +train Mar13_S0A1/voip-2c217000af-20130325_221701 +train Mar13_S1A1/voip-2c217000af-20130325_222430 +train Mar13_S1A0/voip-2c217000af-20130325_221525 +train Mar13_S1A0/voip-2c217000af-20130328_223847 +train 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Mar13_S0A1/voip-6dbc3573bc-20130328_191519 +dev Mar13_S1A1/voip-6dbc3573bc-20130328_193508 +dev Mar13_S0A0/voip-6dbc3573bc-20130328_193216 +dev Mar13_S0A1/voip-6dbc3573bc-20130329_035304 +dev Mar13_S1A0/voip-6dbc3573bc-20130328_191808 +dev Mar13_S0A0/voip-6dbc3573bc-20130328_191939 +dev Mar13_S0A0/voip-fe4b6ef58f-20130325_224823 +dev Mar13_S0A1/voip-fe4b6ef58f-20130325_233144 +dev Mar13_S1A1/voip-fe4b6ef58f-20130325_233447 +dev Mar13_S1A0/voip-fe4b6ef58f-20130325_234625 +dev Mar13_S1A0/voip-fe4b6ef58f-20130325_234527 +dev Mar13_S0A0/voip-fe4b6ef58f-20130325_234737 +dev Mar13_S0A1/voip-fe4b6ef58f-20130325_233655 +dev Mar13_S1A0/voip-fe4b6ef58f-20130325_224609 +dev Mar13_S0A0/voip-fe4b6ef58f-20130325_233912 +dev Mar13_S1A1/voip-fe4b6ef58f-20130325_223854 +dev Mar13_S1A1/voip-fe4b6ef58f-20130325_220934 +dev Mar13_S0A1/voip-fe4b6ef58f-20130328_231737 +dev Mar13_S0A0/voip-9f989824fd-20130324_080929 +dev Mar13_S1A0/voip-9f989824fd-20130324_074759 +dev Mar13_S1A1/voip-9f989824fd-20130324_074224 +dev Mar13_S1A0/voip-9f989824fd-20130325_202120 +dev Mar13_S1A1/voip-9f989824fd-20130324_075152 +dev Mar13_S0A0/voip-9f989824fd-20130324_072907 +dev Mar13_S0A1/voip-9f989824fd-20130325_200940 +dev Mar13_S0A1/voip-9f989824fd-20130325_201803 +dev Mar13_S1A0/voip-9f989824fd-20130325_204229 +dev Mar13_S1A1/voip-9f989824fd-20130325_203925 +dev Mar13_S1A0/voip-9f989824fd-20130324_075833 +dev Mar13_S0A0/voip-9f989824fd-20130325_201640 +dev Mar13_S0A0/voip-9f989824fd-20130325_204124 +dev Mar13_S1A0/voip-9f989824fd-20130325_204551 +dev Mar13_S1A1/voip-9f989824fd-20130325_201956 +dev Mar13_S0A1/voip-9f989824fd-20130325_203533 +dev Mar13_S0A1/voip-9f989824fd-20130325_204439 +dev Mar13_S0A1/voip-9f989824fd-20130324_074401 +dev Mar13_S0A1/voip-9f989824fd-20130324_073857 +dev Mar13_S1A0/voip-9f989824fd-20130324_080805 +dev Mar13_S1A1/voip-9f989824fd-20130325_201440 +dev Mar13_S1A0/voip-9f989824fd-20130324_073316 +dev Mar13_S0A1/voip-9f989824fd-20130324_080528 +test Mar13_S2A0/voip-36440f7305-20130327_195525 +test Mar13_S2A0/voip-a8649977cf-20130323_155952 +test Mar13_S2A0/voip-48c12815b3-20130402_052503 +test Mar13_S2A0/voip-4660dd9eab-20130329_075421 +test Mar13_S2A0/voip-59bc8a2167-20130328_131523 +test Mar13_S2A0/voip-59bc8a2167-20130325_140609 +test Mar13_S2A0/voip-f22c2bf9c7-20130328_113108 +test Mar13_S2A0/voip-fce37b0ccb-20130328_144530 +test Mar13_S2A0/voip-e2a895cfe5-20130325_230413 +test Mar13_S2A0/voip-90732b027d-20130401_220107 +test Mar13_S2A0/voip-03d6592b76-20130326_014519 +test Mar13_S2A0/voip-10beae627f-20130401_165339 +test Mar13_S2A0/voip-7c3a08072d-20130326_010255 +test Mar13_S2A0/voip-ccf48b9a6a-20130329_052911 +test Mar13_S2A0/voip-0f41c16f2f-20130325_205211 +test Mar13_S2A0/voip-b08f15a787-20130326_021707 +test Mar13_S2A0/voip-f32f2cfdae-20130328_194050 +test Mar13_S2A0/voip-3be3bda933-20130326_133018 +test Mar13_S2A0/voip-597cfafdee-20130402_005455 +test Mar13_S2A0/voip-f1e8236264-20130323_005412 +test Mar13_S2A0/voip-5a464ca603-20130401_192446 +test Mar13_S2A0/voip-22c938c8ba-20130325_125501 +test Mar13_S2A0/voip-eaef6f434c-20130323_024813 +test Mar13_S2A0/voip-8d5173f3a6-20130323_013442 +test Mar13_S2A0/voip-583e7cede5-20130323_045812 +test Mar13_S2A0/voip-88b68a9a41-20130324_002532 +test Mar13_S2A0/voip-187c1708f2-20130325_130023 +test Mar13_S2A0/voip-e8997b10da-20130329_010030 +test Mar13_S2A0/voip-318851c80b-20130328_224811 +test Mar13_S2A0/voip-22756d9e8f-20130329_045224 +test Mar13_S2A0/voip-fbd422ad18-20130328_184355 +test Mar13_S2A0/voip-da10d74c3e-20130328_153635 +test Mar13_S2A0/voip-4660dd9eab-20130329_085402 +test Mar13_S2A0/voip-8991b7bff6-20130326_225512 +test Mar13_S2A0/voip-aaa44b4121-20130327_172407 +test Mar13_S2A0/voip-869dd52548-20130401_175350 +test Mar13_S2A0/voip-9819537952-20130328_231437 +test Mar13_S2A0/voip-30772678da-20130328_193737 +test Mar13_S2A0/voip-50af5438f1-20130327_032648 +test Mar13_S2A0/voip-90732b027d-20130327_184844 +test Mar13_S2A0/voip-52eb280e7b-20130326_211017 +test Mar13_S2A0/voip-88b68a9a41-20130322_223747 +test Mar13_S2A0/voip-52eb280e7b-20130325_131201 +test Mar13_S2A0/voip-31de0daa7b-20130401_203807 +test Mar13_S2A0/voip-aaa44b4121-20130327_172805 +test Mar13_S2A0/voip-2f209793f4-20130326_004945 +test Mar13_S2A0/voip-4a6ecc1f1c-20130329_154322 +test Mar13_S2A0/voip-8d5173f3a6-20130324_184114 +test Mar13_S2A0/voip-14cb91bc48-20130327_212630 +test Mar13_S2A0/voip-72e50baa85-20130326_053416 +test Mar13_S2A0/voip-be5b7bf9d9-20130401_151827 +test Mar13_S2A0/voip-78f497f314-20130324_201800 +test Mar13_S2A0/voip-a352cb5ca5-20130401_235652 +test Mar13_S2A0/voip-560cbd32a5-20130401_143541 +test Mar13_S2A0/voip-0fa32b1e78-20130402_131752 +test Mar13_S2A0/voip-db80a9e6df-20130328_233111 +test Mar13_S2A0/voip-03d6592b76-20130327_030134 +test Mar13_S2A0/voip-14cb91bc48-20130328_155141 +test Mar13_S2A0/voip-199d62165b-20130402_120014 +test Mar13_S2A0/voip-597cfafdee-20130402_010017 +test Mar13_S2A0/voip-263ab0e49f-20130326_105848 +test Mar13_S2A0/voip-da4a08ad84-20130328_160514 +test Mar13_S2A0/voip-58047f5227-20130327_031936 +test Mar13_S2A0/voip-10beae627f-20130329_154949 +test Mar13_S2A0/voip-2f209793f4-20130326_011737 +test Mar13_S2A0/voip-03c59ba692-20130324_180148 +test Mar13_S2A0/voip-187c1708f2-20130326_155526 +test Mar13_S2A0/voip-fe2783c40a-20130401_144813 +test Mar13_S2A0/voip-bde2721237-20130326_200732 +test Mar13_S2A0/voip-e9b53d6ace-20130324_223454 +test Mar13_S2A0/voip-2d3d74d091-20130325_232837 +test Mar13_S2A0/voip-869dd52548-20130326_000526 +test Mar13_S2A0/voip-4a6ecc1f1c-20130329_153339 +test Mar13_S2A0/voip-00d76b791d-20130327_012544 +test Mar13_S2A0/voip-d0341706f2-20130329_053139 +test Mar13_S2A0/voip-fbd422ad18-20130328_182536 +test Mar13_S2A0/voip-6dbc3573bc-20130328_190140 +test Mar13_S2A0/voip-ccf48b9a6a-20130329_052221 +test Mar13_S2A0/voip-381a50592b-20130326_042813 +test Mar13_S2A0/voip-0a45bc863d-20130325_203340 +test Mar13_S2A0/voip-48c12815b3-20130402_045710 +test Mar13_S2A0/voip-03d6592b76-20130326_013732 +test Mar13_S2A0/voip-3b81cbb287-20130326_031000 +test Mar13_S2A0/voip-e54437a6f0-20130326_211240 +test Mar13_S2A0/voip-ef9aa63b85-20130329_132055 +test Mar13_S2A0/voip-e54437a6f0-20130325_132456 +test Mar13_S2A0/voip-88f198881b-20130326_014324 +test Mar13_S2A0/voip-c8821c664b-20130322_215042 +test Mar13_S2A0/voip-2c217000af-20130328_224325 +test Mar13_S2A0/voip-36440f7305-20130327_201412 +test Mar13_S2A0/voip-bde2721237-20130325_155003 +test Mar13_S2A0/voip-8efef4eae9-20130325_233021 +test Mar13_S2A0/voip-d645d56d23-20130401_202916 +test Mar13_S2A0/voip-b08f15a787-20130402_070129 +test Mar13_S2A0/voip-50af5438f1-20130402_081003 +test Mar13_S2A0/voip-7e22911804-20130327_200501 +test Mar13_S2A0/voip-d645d56d23-20130402_221701 +test Mar13_S2A0/voip-0abf414c0c-20130327_000042 +test Mar13_S2A0/voip-4a6ecc1f1c-20130329_152119 +test Mar13_S2A0/voip-10beae627f-20130328_165624 +test Mar13_S2A0/voip-ccf48b9a6a-20130329_053900 +test Mar13_S2A0/voip-52d599db9c-20130326_214853 +test Mar13_S2A0/voip-ad40cf5489-20130325_180830 +test Mar13_S2A0/voip-9735278861-20130401_155012 +test Mar13_S2A0/voip-da10d74c3e-20130328_141156 +test Mar13_S2A0/voip-52eb280e7b-20130325_125005 +test Mar13_S2A0/voip-be5b7bf9d9-20130402_202238 +test Mar13_S2A0/voip-2d2d103292-20130329_040304 +test Mar13_S2A0/voip-9735278861-20130401_155901 +test Mar13_S2A0/voip-2d2d103292-20130329_035951 +test Mar13_S2A0/voip-0f41c16f2f-20130325_211046 +test Mar13_S2A0/voip-2b66f60368-20130326_201641 +test Mar13_S2A0/voip-583e7cede5-20130324_064258 +test Mar13_S2A0/voip-4a6ecc1f1c-20130328_124050 +test Mar13_S2A0/voip-48c12815b3-20130327_191202 +test Mar13_S2A0/voip-381a50592b-20130323_235040 +test Mar13_S2A0/voip-5a464ca603-20130401_132040 +test Mar13_S2A0/voip-9f989824fd-20130325_202557 +test Mar13_S2A0/voip-e99e4f4538-20130328_202515 +test Mar13_S2A0/voip-21ec2b7850-20130326_022445 +test Mar13_S2A0/voip-6dbc3573bc-20130328_192806 +test Mar13_S2A0/voip-52eb280e7b-20130326_212429 +test Mar13_S2A0/voip-30772678da-20130328_193147 +test Mar13_S2A0/voip-5a464ca603-20130401_132347 +test Mar13_S2A0/voip-bb1fd497eb-20130326_225924 +test Mar13_S2A0/voip-dd9f7810fd-20130322_231436 +test Mar13_S2A0/voip-36440f7305-20130326_140039 +test Mar13_S2A0/voip-ec3c3aaf98-20130323_141214 +test Mar13_S2A0/voip-7f9c1c8411-20130401_170150 +test Mar13_S2A0/voip-fe4b6ef58f-20130325_234219 +test Mar13_S2A0/voip-14f776f781-20130328_145126 +test Mar13_S2A0/voip-e8997b10da-20130401_151321 +test Mar13_S2A0/voip-9f447ce48e-20130328_115314 +test Mar13_S2A0/voip-a8649977cf-20130323_162034 +test Mar13_S2A0/voip-3cf7bd870d-20130327_173831 +test Mar13_S2A0/voip-922209b777-20130327_005933 +test Mar13_S2A0/voip-7e22911804-20130324_184635 +test Mar13_S2A0/voip-4f069a4136-20130402_040121 +test Mar13_S2A0/voip-263ab0e49f-20130326_104952 +test Mar13_S2A0/voip-f091d0e461-20130327_205430 +test Mar13_S2A0/voip-ad40cf5489-20130327_194606 +test Mar13_S2A0/voip-f1e8236264-20130323_004235 +test Mar13_S2A0/voip-2d2d103292-20130326_043537 +test Mar13_S2A0/voip-9735278861-20130401_161944 +test Mar13_S2A0/voip-381a50592b-20130324_001030 +test Mar13_S2A0/voip-b6618de447-20130328_155926 +test Mar13_S2A0/voip-3b59a0391b-20130401_131609 +test Mar13_S2A0/voip-e3b4879e0d-20130327_030338 +test Mar13_S2A0/voip-187c1708f2-20130325_132335 +test Mar13_S2A0/voip-da10d74c3e-20130326_000153 +test Mar13_S2A0/voip-e72dba1759-20130325_210200 +test Mar13_S2A0/voip-15d8a89cec-20130327_020407 +test Mar13_S2A0/voip-0fa32b1e78-20130402_140529 +test Mar13_S2A0/voip-3b3edac94d-20130324_202039 +test Mar13_S2A0/voip-a352cb5ca5-20130401_232957 +test Mar13_S2A0/voip-52eb280e7b-20130325_125637 +test Mar13_S2A0/voip-78f497f314-20130323_184811 +test Mar13_S2A0/voip-22c938c8ba-20130325_131944 +test Mar13_S2A0/voip-e61fa89add-20130326_005156 +test Mar13_S2A0/voip-13ff413553-20130328_175848 +test Mar13_S2A0/voip-7e22911804-20130325_202451 +test Mar13_S2A0/voip-5f71221015-20130328_114629 +test Mar13_S2A0/voip-52d599db9c-20130402_000539 +test Mar13_S2A0/voip-ab4f1dbb59-20130325_213532 +test Mar13_S2A0/voip-f22c2bf9c7-20130326_193447 +test Mar13_S2A0/voip-7e07d8f0f5-20130328_191029 +test Mar13_S2A0/voip-5cf59cc660-20130328_161332 +test Mar13_S2A0/voip-eaef6f434c-20130323_031124 +test Mar13_S2A0/voip-dcaeb62b29-20130326_034706 +test Mar13_S2A0/voip-36440f7305-20130326_200502 +test Mar13_S2A0/voip-59bc8a2167-20130325_134038 +test Mar13_S2A0/voip-e9b53d6ace-20130401_185044 +test Mar13_S2A0/voip-48c12815b3-20130326_021101 +test Mar13_S2A0/voip-ad40cf5489-20130327_191744 +test Mar13_S2A0/voip-f32f2cfdae-20130327_014232 +test Mar13_S2A0/voip-ddcaad92a1-20130325_234854 +test Mar13_S2A0/voip-50af5438f1-20130327_041743 +test Mar13_S2A0/voip-d645d56d23-20130324_001329 +test Mar13_S2A0/voip-db80a9e6df-20130328_225812 +test Mar13_S2A0/voip-fdf8b50918-20130327_024335 +test Mar13_S2A0/voip-edb8609855-20130327_183610 +test Mar13_S2A0/voip-ef9aa63b85-20130328_223650 +test Mar13_S2A0/voip-cc1b0c8a0f-20130329_003412 +test Mar13_S2A0/voip-88b68a9a41-20130322_222223 +test Mar13_S2A0/voip-e99e4f4538-20130328_200914 +test Mar13_S2A0/voip-b6618de447-20130326_211712 +test Mar13_S2A0/voip-f32f2cfdae-20130327_012930 +test Mar13_S2A0/voip-ef9aa63b85-20130328_210422 +test Mar13_S2A0/voip-922209b777-20130327_013219 +test Mar13_S2A0/voip-13ff413553-20130328_173542 +test Mar13_S2A0/voip-0999e9bb30-20130325_125249 +test Mar13_S2A0/voip-8991b7bff6-20130326_232812 +test Mar13_S2A0/voip-340dbb333e-20130325_232138 +test Mar13_S2A0/voip-3cf7bd870d-20130327_175320 +test Mar13_S2A0/voip-318851c80b-20130328_224123 +test Mar13_S2A0/voip-2f4c700ae3-20130401_191340 +test Mar13_S2A0/voip-3cf7bd870d-20130327_183216 +test Mar13_S2A0/voip-cfd5fa34d9-20130402_073708 +test Mar13_S2A0/voip-869dd52548-20130401_175909 +test Mar13_S2A0/voip-e8997b10da-20130327_192446 +test Mar13_S2A0/voip-f17e3b578c-20130328_173916 +test Mar13_S2A0/voip-ef9aa63b85-20130328_190707 +test Mar13_S2A0/voip-b08f15a787-20130402_072830 +test Mar13_S2A0/voip-fe2783c40a-20130401_150049 +test Mar13_S2A0/voip-7e07d8f0f5-20130328_190349 +test Mar13_S2A0/voip-4b7e22cc07-20130401_185340 +test Mar13_S2A0/voip-e30cb521fb-20130328_121247 +test Mar13_S2A0/voip-fdf8b50918-20130326_052130 +test Mar13_S2A0/voip-f113dbb0e1-20130327_173300 +test Mar13_S2A0/voip-bde2721237-20130325_163504 +test Mar13_S2A0/voip-0241bbae39-20130327_202609 +test Mar13_S2A0/voip-ccc459b689-20130327_003319 +test Mar13_S2A0/voip-50af5438f1-20130402_082329 +test Mar13_S2A0/voip-3b81cbb287-20130324_014524 +test Mar13_S2A0/voip-b20b6e847a-20130326_223553 +test Mar13_S2A0/voip-afd3aa91f0-20130327_190613 +test Mar13_S2A0/voip-187c1708f2-20130325_135350 +test Mar13_S2A0/voip-e99e4f4538-20130328_195519 +test Mar13_S2A0/voip-3be3bda933-20130325_195900 +test Mar13_S2A0/voip-f32f2cfdae-20130327_015459 +test Mar13_S2A0/voip-dda7c88c6e-20130323_055620 +test Mar13_S2A0/voip-b57f8ee22b-20130325_174323 +test Mar13_S2A0/voip-187c1708f2-20130327_132431 +test Mar13_S2A0/voip-78f497f314-20130323_143934 +test Mar13_S2A0/voip-d7853a398f-20130401_153507 +test Mar13_S2A0/voip-5749b16764-20130328_151006 +test Mar13_S2A0/voip-fbd422ad18-20130328_180701 +test Mar13_S2A0/voip-aaa44b4121-20130327_165607 +test Mar13_S2A0/voip-22a181cad5-20130326_022730 +test Mar13_S2A0/voip-03c59ba692-20130402_061438 +test Mar13_S2A0/voip-f32f2cfdae-20130328_195632 +test Mar13_S2A0/voip-21ec2b7850-20130325_144235 +test Mar13_S2A0/voip-90732b027d-20130401_221257 +test Mar13_S2A0/voip-2f4c700ae3-20130401_192808 +test Mar13_S2A0/voip-d7853a398f-20130401_162351 +test Mar13_S2A0/voip-4a6ecc1f1c-20130329_152622 +test Mar13_S2A0/voip-e8997b10da-20130329_000115 +test Mar13_S2A0/voip-43479eb5c2-20130324_004459 +test Mar13_S2A0/voip-2d2d103292-20130326_042359 +test Mar13_S2A0/voip-15d8a89cec-20130327_022310 +test Mar13_S2A0/voip-158a881c88-20130328_152013 +test Mar13_S2A0/voip-f113dbb0e1-20130327_173654 +test Mar13_S2A0/voip-fe4b6ef58f-20130328_232107 +test Mar13_S2A0/voip-ad40cf5489-20130327_193526 +test Mar13_S2A0/voip-edb8609855-20130327_184126 +test Mar13_S2A0/voip-b20968d1ea-20130323_110947 +test Mar13_S2A0/voip-e61fa89add-20130327_074844 +test Mar13_S2A0/voip-62cc0cc55d-20130323_155532 +test Mar13_S2A0/voip-15d8a89cec-20130327_013956 +test Mar13_S2A0/voip-e8997b10da-20130401_151533 +test Mar13_S2A0/voip-ddcaad92a1-20130326_013530 +test Mar13_S2A0/voip-be5b7bf9d9-20130401_155324 +test Mar13_S2A0/voip-b6618de447-20130328_155757 +test Mar13_S2A0/voip-9735278861-20130401_154026 +test Mar13_S2A0/voip-4c25da9a27-20130327_141325 +test Mar13_S2A0/voip-72e50baa85-20130326_050547 +test Mar13_S2A0/voip-4f069a4136-20130327_205328 +test Mar13_S2A0/voip-317a1436fe-20130325_171536 +test Mar13_S2A0/voip-aaa44b4121-20130326_060713 +test Mar13_S2A0/voip-fe2783c40a-20130401_143319 +test Mar13_S2A0/voip-2d3d74d091-20130328_140236 +test Mar13_S2A0/voip-a352cb5ca5-20130401_231920 +test Mar13_S2A0/voip-36440f7305-20130326_195310 +test Mar13_S2A0/voip-59bc8a2167-20130325_142909 +test Mar13_S2A0/voip-b08f15a787-20130402_075018 +test Mar13_S2A0/voip-e0035cc31b-20130323_211610 +test Mar13_S2A0/voip-e72dba1759-20130326_092440 +test Mar13_S2A0/voip-10beae627f-20130401_162926 +test Mar13_S2A0/voip-4f069a4136-20130402_031057 +test Mar13_S2A0/voip-d66e12b45c-20130327_173005 +test Mar13_S2A0/voip-b772dbf437-20130402_142332 +test Mar13_S2A0/voip-935947e17b-20130402_201122 +test Mar13_S2A0/voip-1b51204ef5-20130401_145436 +test Mar13_S2A0/voip-31d9d1a567-20130402_035040 +test Mar13_S2A0/voip-922209b777-20130327_003615 +test Mar13_S2A0/voip-2c217000af-20130328_224634 +test Mar13_S2A0/voip-14f776f781-20130329_033522 +test Mar13_S2A0/voip-2e134ee190-20130401_225512 +test Mar13_S2A0/voip-6d6587c57d-20130328_235437 +test Mar13_S2A0/voip-0f41c16f2f-20130401_235202 +test Mar13_S2A0/voip-e2a895cfe5-20130325_234006 +test Mar13_S2A0/voip-d0341706f2-20130329_051849 +test Mar13_S2A0/voip-876ef67873-20130402_034440 +test Mar13_S2A0/voip-22756d9e8f-20130329_044407 +test Mar13_S2A0/voip-5749b16764-20130328_150805 +test Mar13_S2A0/voip-72e50baa85-20130327_063755 +test Mar13_S2A0/voip-597cfafdee-20130328_234836 +test Mar13_S2A0/voip-b6618de447-20130328_152659 +test Mar13_S2A0/voip-b20b6e847a-20130326_222651 +test Mar13_S2A0/voip-5a464ca603-20130401_193807 +test Mar13_S2A0/voip-8d5173f3a6-20130324_185936 +test Mar13_S2A0/voip-f22c2bf9c7-20130328_115705 +test Mar13_S2A0/voip-e0035cc31b-20130323_213127 +test Mar13_S2A0/voip-0f41c16f2f-20130325_192056 +test Mar13_S2A0/voip-9819537952-20130328_234654 +test Mar13_S2A0/voip-affbf578cf-20130401_163239 +test Mar13_S2A0/voip-d7853a398f-20130402_144102 +test Mar13_S2A0/voip-8d5173f3a6-20130323_011906 +test Mar13_S2A0/voip-935947e17b-20130402_200947 +test Mar13_S2A0/voip-31d9d1a567-20130402_043117 +test Mar13_S2A0/voip-381a50592b-20130323_233842 +test Mar13_S2A0/voip-14cb91bc48-20130327_182258 +test Mar13_S2A0/voip-3860c915c2-20130328_165141 +test Mar13_S2A0/voip-381a50592b-20130326_042630 +test Mar13_S2A0/voip-f17e3b578c-20130328_173734 +test Mar13_S2A0/voip-72e50baa85-20130327_060952 +test Mar13_S2A0/voip-bde2721237-20130325_163654 +test Mar13_S2A0/voip-e9b53d6ace-20130401_212559 +test Mar13_S2A0/voip-d76f6e4f82-20130327_190818 +test Mar13_S2A0/voip-583e7cede5-20130323_061534 +test Mar13_S2A0/voip-0f41c16f2f-20130402_004710 +test Mar13_S2A0/voip-dda7c88c6e-20130323_053330 +test Mar13_S2A0/voip-59bc8a2167-20130328_125604 +test Mar13_S2A0/voip-5cf59cc660-20130328_163939 +test Mar13_S2A0/voip-4a6ecc1f1c-20130327_192159 +test Mar13_S2A0/voip-48c12815b3-20130402_050359 +test Mar13_S2A0/voip-4c0d36762a-20130328_210105 +test Mar13_S2A0/voip-3b81cbb287-20130326_024351 +test Mar13_S2A0/voip-597cfafdee-20130402_011248 +test Mar13_S2A0/voip-14cb91bc48-20130327_182707 +test Mar13_S2A0/voip-d0341706f2-20130329_052508 +test Mar13_S2A0/voip-2c217000af-20130325_222245 +test Mar13_S2A0/voip-afd3aa91f0-20130326_052804 +test Mar13_S2A0/voip-e2a895cfe5-20130326_233932 +test Mar13_S2A0/voip-318851c80b-20130328_215256 +test Mar13_S2A0/voip-b27a230d2e-20130329_043241 +test Mar13_S2A0/voip-73928a3e49-20130402_200624 +test Mar13_S2A0/voip-03c59ba692-20130324_180743 +test Mar13_S2A0/voip-ccf48b9a6a-20130329_041458 +test Mar13_S2A0/voip-b27a230d2e-20130323_051403 +test Mar13_S2A0/voip-52d599db9c-20130326_213343 +test Mar13_S2A0/voip-d7853a398f-20130401_152451 +test Mar13_S2A0/voip-869dd52548-20130401_184414 +test Mar13_S2A0/voip-340dbb333e-20130326_193257 +test Mar13_S2A0/voip-fce37b0ccb-20130328_145549 +test Mar13_S2A0/voip-03c2655d43-20130327_200228 +test Mar13_S2A0/voip-22756d9e8f-20130329_045922 +test Mar13_S2A0/voip-922209b777-20130325_160320 +test Mar13_S2A0/voip-e9b53d6ace-20130401_200646 +test Mar13_S2A0/voip-e2a895cfe5-20130326_233054 +test Mar13_S2A0/voip-0fa32b1e78-20130328_234553 +test Mar13_S2A0/voip-52d599db9c-20130402_002106 +test Mar13_S2A0/voip-7e4cdce06a-20130323_130631 +test Mar13_S2A0/voip-96f43326a4-20130324_095517 +test Mar13_S2A0/voip-8991b7bff6-20130401_174622 +test Mar13_S2A0/voip-05e7a5440b-20130328_211141 +test Mar13_S2A0/voip-e0035cc31b-20130326_205145 +test Mar13_S2A0/voip-62cc0cc55d-20130328_143853 +test Mar13_S2A0/voip-db80a9e6df-20130328_230640 +test Mar13_S2A0/voip-05e7a5440b-20130328_221829 +test Mar13_S2A0/voip-fe60dae302-20130328_170925 +test Mar13_S2A0/voip-0a45bc863d-20130326_210127 +test Mar13_S2A0/voip-155e939ebc-20130327_203704 +test Mar13_S2A0/voip-43479eb5c2-20130323_175928 +test Mar13_S2A0/voip-199d62165b-20130402_120742 +test Mar13_S2A0/voip-ad40cf5489-20130327_191513 +test Mar13_S2A0/voip-00d76b791d-20130327_011305 +test Mar13_S2A0/voip-78f497f314-20130323_183443 +test Mar13_S2A0/voip-7e22911804-20130324_191409 +test Mar13_S2A0/voip-f22c2bf9c7-20130328_121417 +test Mar13_S2A0/voip-aaa44b4121-20130326_055751 +test Mar13_S2A0/voip-7f9c1c8411-20130401_160945 +test Mar13_S2A0/voip-db80a9e6df-20130328_232734 +test Mar13_S2A0/voip-e0035cc31b-20130323_210647 +test Mar13_S2A0/voip-4c25da9a27-20130327_142052 +test Mar13_S2A0/voip-03d6592b76-20130327_024445 +test Mar13_S2A0/voip-935947e17b-20130402_201821 +test Mar13_S2A0/voip-b08f15a787-20130402_070857 +test Mar13_S2A0/voip-4c25da9a27-20130327_134538 +test Mar13_S2A0/voip-9819537952-20130328_234410 +test Mar13_S2A0/voip-ef9aa63b85-20130329_125907 +test Mar13_S2A0/voip-e8997b10da-20130329_001441 +test Mar13_S2A0/voip-14f776f781-20130328_120030 +test Mar13_S2A0/voip-7e22911804-20130328_204634 +test Mar13_S2A0/voip-f32f2cfdae-20130328_194631 +test Mar13_S2A0/voip-936ec6902a-20130328_134653 +test Mar13_S2A0/voip-583e7cede5-20130324_061814 +test Mar13_S2A0/voip-7e07d8f0f5-20130327_181332 +test Mar13_S2A0/voip-90732b027d-20130327_173404 +test Mar13_S2A0/voip-583e7cede5-20130324_055616 +test Mar13_S2A0/voip-b27a230d2e-20130329_035000 +test Mar13_S2A0/voip-87de4f7a80-20130324_153839 +test Mar13_S2A0/voip-edb8609855-20130327_182012 +test Mar13_S2A0/voip-e9b53d6ace-20130324_221634 +test Mar13_S2A0/voip-bb1fd497eb-20130325_130133 +test Mar13_S2A0/voip-3b3edac94d-20130323_203402 +test Mar13_S2A0/voip-d7aef99178-20130328_184232 +test Mar13_S2A0/voip-9f989824fd-20130325_201147 +test Mar13_S2A0/voip-4c25da9a27-20130325_181923 +test Mar13_S2A0/voip-922209b777-20130325_161915 +test Mar13_S2A0/voip-eaef6f434c-20130323_024700 +test Mar13_S2A0/voip-14cb91bc48-20130327_182851 +test Mar13_S2A0/voip-a31ca3e355-20130323_234744 +test Mar13_S2A0/voip-6d6587c57d-20130328_143231 +test Mar13_S2A0/voip-da10d74c3e-20130326_001533 +test Mar13_S2A0/voip-58047f5227-20130326_031323 +test Mar13_S2A0/voip-1b51204ef5-20130401_150210 +test Mar13_S2A0/voip-be5b7bf9d9-20130401_154326 +test Mar13_S2A0/voip-be5b7bf9d9-20130402_203957 +test Mar13_S2A0/voip-0fa32b1e78-20130328_234246 +test Mar13_S2A0/voip-db80a9e6df-20130328_233914 +test Mar13_S2A0/voip-8991b7bff6-20130326_231625 +test Mar13_S2A0/voip-39a25ab2f8-20130326_121243 +test Mar13_S2A0/voip-dd9f7810fd-20130322_223611 +test Mar13_S2A0/voip-4f069a4136-20130327_204836 +test Mar13_S2A0/voip-e3b4879e0d-20130326_022151 +test Mar13_S2A0/voip-31d9d1a567-20130402_040542 +test Mar13_S2A0/voip-bc900c7903-20130328_120449 +test Mar13_S2A0/voip-31de0daa7b-20130401_205012 +test Mar13_S2A0/voip-bb1fd497eb-20130325_165136 +test Mar13_S2A0/voip-4c0d36762a-20130328_202750 +test Mar13_S2A0/voip-21ec2b7850-20130327_044054 +test Mar13_S2A0/voip-e54437a6f0-20130325_135714 +test Mar13_S2A0/voip-ec3c3aaf98-20130323_142354 +test Mar13_S2A0/voip-4c0d36762a-20130328_201801 +test Mar13_S2A0/voip-aaa44b4121-20130326_053622 +test Mar13_S2A0/voip-58047f5227-20130326_032423 +test Mar13_S2A0/voip-58047f5227-20130327_033701 +test Mar13_S2A0/voip-e99e4f4538-20130328_202311 +test Mar13_S2A0/voip-0f41c16f2f-20130402_005250 +test Mar13_S2A0/voip-bb1fd497eb-20130326_232308 +test Mar13_S2A0/voip-597cfafdee-20130328_231637 +test Mar13_S2A0/voip-0a45bc863d-20130325_201814 +test Mar13_S2A0/voip-3be3bda933-20130326_132730 +test Mar13_S2A0/voip-d76851034e-20130326_222136 +test Mar13_S2A0/voip-58047f5227-20130327_032153 +test Mar13_S2A0/voip-8f9fb7a86b-20130328_183553 +test Mar13_S2A0/voip-bde2721237-20130325_161252 +test Mar13_S2A0/voip-4660dd9eab-20130329_080538 +test Mar13_S2A0/voip-8991b7bff6-20130401_173731 +test Mar13_S2A0/voip-7e22911804-20130328_163017 +test Mar13_S2A0/voip-7f9c1c8411-20130328_175349 +test Mar13_S2A0/voip-ab4f1dbb59-20130328_180420 +test Mar13_S2A0/voip-7e07d8f0f5-20130328_185314 +test Mar13_S2A0/voip-da4a08ad84-20130328_154102 +test Mar13_S2A0/voip-0a45bc863d-20130325_200321 +test Mar13_S2A0/voip-7f9c1c8411-20130401_164614 +test Mar13_S2A0/voip-f17e3b578c-20130328_175132 +test Mar13_S2A0/voip-4a6ecc1f1c-20130328_124754 +test Mar13_S2A0/voip-10beae627f-20130328_170130 +test Mar13_S2A0/voip-50af5438f1-20130402_090102 +test Mar13_S2A0/voip-b27a230d2e-20130329_034612 +test Mar13_S2A0/voip-a31ca3e355-20130323_223438 +test Mar13_S2A0/voip-48c12815b3-20130402_051349 +test Mar13_S2A0/voip-7e07d8f0f5-20130327_180638 +test Mar13_S2A0/voip-869dd52548-20130401_184904 +test Mar13_S2A0/voip-31d9d1a567-20130402_041419 +test Mar13_S2A0/voip-be5b7bf9d9-20130401_151019 +test Mar13_S2A0/voip-88b68a9a41-20130324_003743 +test Mar13_S2A0/voip-3b3edac94d-20130324_203441 +test Mar13_S2A0/voip-96f43326a4-20130323_070742 +test Mar13_S2A0/voip-4f069a4136-20130402_032939 +test Mar13_S2A0/voip-d225fad9df-20130328_175540 +test Mar13_S2A0/voip-e54437a6f0-20130325_142134 +test Mar13_S2A0/voip-96f43326a4-20130326_003351 +test Mar13_S2A0/voip-561b472540-20130328_123644 +test Mar13_S2A0/voip-583e7cede5-20130323_054313 +test Mar13_S2A0/voip-fdf8b50918-20130329_013239 +test Mar13_S2A0/voip-d7853a398f-20130401_153715 +test Mar13_S2A0/voip-3b59a0391b-20130401_134700 +test Mar13_S2A0/voip-158a881c88-20130328_151410 +test Mar13_S2A0/voip-dcaeb62b29-20130326_044625 +test Mar13_S2A0/voip-affbf578cf-20130401_164412 +test Mar13_S2A0/voip-13ff413553-20130328_174617 +test Mar13_S2A0/voip-ccc459b689-20130327_005145 +test Mar13_S2A0/voip-2d2d103292-20130329_041843 +test Mar13_S2A0/voip-8991b7bff6-20130401_181228 +test Mar13_S2A0/voip-90732b027d-20130401_223007 +test Mar13_S2A0/voip-d76f6e4f82-20130327_184332 +test Mar13_S2A0/voip-b20968d1ea-20130323_105740 +test Mar13_S2A0/voip-e72dba1759-20130325_205011 +test Mar13_S2A0/voip-03c59ba692-20130324_034320 +test Mar13_S2A0/voip-b772dbf437-20130402_141317 +test Mar13_S2A0/voip-340dbb333e-20130325_231340 +test Mar13_S2A0/voip-14f776f781-20130328_140648 +test Mar13_S2A0/voip-aaa44b4121-20130327_171127 +test Mar13_S2A0/voip-bb1fd497eb-20130325_164243 +test Mar13_S2A0/voip-dcaeb62b29-20130326_035420 +test Mar13_S2A0/voip-2f4c700ae3-20130401_190924 +test Mar13_S2A0/voip-908884f5fd-20130326_224635 +test Mar13_S2A0/voip-0fa32b1e78-20130328_152310 +test Mar13_S2A0/voip-318851c80b-20130328_213742 +test Mar13_S2A0/voip-199d62165b-20130402_121823 +test Mar13_S2A0/voip-10beae627f-20130328_171512 +test Mar13_S2A0/voip-340dbb333e-20130327_010702 +test Mar13_S2A0/voip-597cfafdee-20130328_232228 +test Mar13_S2A0/voip-59bc8a2167-20130328_133101 +test Mar13_S2A0/voip-afd3aa91f0-20130327_211250 +test Mar13_S2A0/voip-cfd5fa34d9-20130402_073203 +test Mar13_S2A0/voip-d0341706f2-20130329_021001 +test Mar13_S2A0/voip-4f069a4136-20130327_210716 +test Mar13_S2A0/voip-d645d56d23-20130323_235755 +test Mar13_S2A0/voip-7f9c1c8411-20130328_210648 +test Mar13_S2A0/voip-e0035cc31b-20130326_204117 +test Mar13_S2A0/voip-dda7c88c6e-20130323_053456 +test Mar13_S2A0/voip-8991b7bff6-20130401_160529 +test Mar13_S2A0/voip-f22c2bf9c7-20130326_194620 +test Mar13_S2A0/voip-e3b4879e0d-20130327_025916 +test Mar13_S2A0/voip-00d76b791d-20130327_005342 +test Mar13_S2A0/voip-2c217000af-20130328_223531 +test Mar13_S2A0/voip-317a1436fe-20130325_172654 +test Mar13_S2A0/voip-dd9f7810fd-20130322_224109 +test Mar13_S2A0/voip-bb1fd497eb-20130326_232612 +test Mar13_S2A0/voip-90732b027d-20130327_185508 +test Mar13_S2A0/voip-05e7a5440b-20130328_213317 +test Mar13_S2A0/voip-3b59a0391b-20130401_134422 +test Mar13_S2A0/voip-e72dba1759-20130326_223001 +test Mar13_S2A0/voip-935947e17b-20130402_200030 +test Mar13_S2A0/voip-202b6a3cc4-20130327_185147 +test Mar13_S2A0/voip-50af5438f1-20130402_085054 +test Mar13_S2A0/voip-b57f8ee22b-20130325_183652 +test Mar13_S2A0/voip-fce37b0ccb-20130328_145947 +test Mar13_S2A0/voip-c8ec8c76dd-20130328_205803 +test Mar13_S2A0/voip-876ef67873-20130327_040445 +test Mar13_S2A0/voip-afd3aa91f0-20130325_224502 +test Mar13_S2A0/voip-e2a895cfe5-20130325_231536 +test Mar13_S2A0/voip-d7853a398f-20130402_162013 +test Mar13_S2A0/voip-9f989824fd-20130325_204726 +test Mar13_S2A0/voip-fdf8b50918-20130329_042452 +test Mar13_S2A0/voip-9f989824fd-20130324_073738 +test Mar13_S2A0/voip-62cc0cc55d-20130328_141812 +test Mar13_S2A0/voip-ccf48b9a6a-20130329_042702 +test Mar13_S2A0/voip-597cfafdee-20130328_231125 +test Mar13_S2A0/voip-50af5438f1-20130402_082945 +test Mar13_S2A0/voip-0f41c16f2f-20130402_000400 +test Mar13_S2A0/voip-2d2d103292-20130326_041139 +test Mar13_S2A0/voip-72e50baa85-20130327_062540 +test Mar13_S2A0/voip-96f43326a4-20130323_071928 +test Mar13_S2A0/voip-96f43326a4-20130324_100133 +test Mar13_S2A0/voip-0241bbae39-20130327_194449 +test Mar13_S2A0/voip-d645d56d23-20130323_221203 +test Mar13_S2A0/voip-10beae627f-20130401_163806 +test Mar13_S2A0/voip-e9b53d6ace-20130401_203151 +test Mar13_S2A0/voip-3b3edac94d-20130324_211500 +test Mar13_S2A0/voip-5cf59cc660-20130327_143457 +test Mar13_S2A0/voip-fe60dae302-20130328_191909 +test Mar13_S2A0/voip-e61fa89add-20130326_010819 +test Mar13_S2A0/voip-fbd422ad18-20130328_185608 +test Mar13_S2A0/voip-8d5173f3a6-20130323_020057 +test Mar13_S2A0/voip-b57f8ee22b-20130326_235307 +test Mar13_S2A0/voip-4c0d36762a-20130328_212635 +test Mar13_S2A0/voip-7fdb5b39e7-20130328_221029 +test Mar13_S2A1/voip-e54437a6f0-20130325_142328 +test Mar13_S2A1/voip-f091d0e461-20130327_205954 +test Mar13_S2A1/voip-fbd422ad18-20130328_181200 +test Mar13_S2A1/voip-4c0d36762a-20130327_223800 +test Mar13_S2A1/voip-90732b027d-20130401_195930 +test Mar13_S2A1/voip-2c217000af-20130325_221054 +test Mar13_S2A1/voip-fdf8b50918-20130329_013609 +test Mar13_S2A1/voip-d7853a398f-20130402_162613 +test Mar13_S2A1/voip-2d2d103292-20130329_041742 +test Mar13_S2A1/voip-d0341706f2-20130329_021729 +test Mar13_S2A1/voip-03d6592b76-20130326_014622 +test Mar13_S2A1/voip-a7ddefaeb3-20130328_172402 +test Mar13_S2A1/voip-187c1708f2-20130325_130901 +test Mar13_S2A1/voip-e61fa89add-20130327_073821 +test Mar13_S2A1/voip-7fdb5b39e7-20130328_222404 +test Mar13_S2A1/voip-3b3edac94d-20130324_202300 +test Mar13_S2A1/voip-39a25ab2f8-20130326_121108 +test Mar13_S2A1/voip-d645d56d23-20130324_000902 +test Mar13_S2A1/voip-22756d9e8f-20130329_045750 +test Mar13_S2A1/voip-48c12815b3-20130326_011334 +test Mar13_S2A1/voip-7e22911804-20130328_173432 +test Mar13_S2A1/voip-00d76b791d-20130327_010107 +test Mar13_S2A1/voip-bb1fd497eb-20130326_233535 +test Mar13_S2A1/voip-9735278861-20130401_153108 +test Mar13_S2A1/voip-30772678da-20130328_195945 +test Mar13_S2A1/voip-ddcaad92a1-20130325_233344 +test Mar13_S2A1/voip-3b59a0391b-20130401_132600 +test Mar13_S2A1/voip-e72dba1759-20130326_215902 +test Mar13_S2A1/voip-318851c80b-20130328_215451 +test Mar13_S2A1/voip-597cfafdee-20130328_231352 +test Mar13_S2A1/voip-edb8609855-20130327_183923 +test Mar13_S2A1/voip-e9b53d6ace-20130324_221317 +test Mar13_S2A1/voip-908884f5fd-20130326_220112 +test Mar13_S2A1/voip-03c2655d43-20130327_194017 +test Mar13_S2A1/voip-b08f15a787-20130402_070354 +test Mar13_S2A1/voip-4a6ecc1f1c-20130329_154535 +test Mar13_S2A1/voip-583e7cede5-20130324_062757 +test Mar13_S2A1/voip-f22c2bf9c7-20130326_020152 +test Mar13_S2A1/voip-4c25da9a27-20130327_134727 +test Mar13_S2A1/voip-202b6a3cc4-20130327_184930 +test Mar13_S2A1/voip-b27a230d2e-20130329_032018 +test Mar13_S2A1/voip-0a45bc863d-20130325_201511 +test Mar13_S2A1/voip-9735278861-20130401_155253 +test Mar13_S2A1/voip-58047f5227-20130326_031614 +test Mar13_S2A1/voip-158a881c88-20130328_153837 +test Mar13_S2A1/voip-31d9d1a567-20130402_035402 +test Mar13_S2A1/voip-b08f15a787-20130402_073115 +test Mar13_S2A1/voip-f091d0e461-20130327_210443 +test Mar13_S2A1/voip-d645d56d23-20130401_203541 +test Mar13_S2A1/voip-7e07d8f0f5-20130328_185623 +test Mar13_S2A1/voip-187c1708f2-20130327_133125 +test Mar13_S2A1/voip-e30cb521fb-20130328_121001 +test Mar13_S2A1/voip-597cfafdee-20130402_012024 +test Mar13_S2A1/voip-381a50592b-20130326_041542 +test Mar13_S2A1/voip-199d62165b-20130402_122925 +test Mar13_S2A1/voip-869dd52548-20130401_184121 +test Mar13_S2A1/voip-0fa32b1e78-20130402_140939 +test Mar13_S2A1/voip-0fa32b1e78-20130402_131901 +test Mar13_S2A1/voip-21ec2b7850-20130327_035011 +test Mar13_S2A1/voip-03c59ba692-20130324_035050 +test Mar13_S2A1/voip-edb8609855-20130327_183018 +test Mar13_S2A1/voip-e8997b10da-20130401_152728 +test Mar13_S2A1/voip-88b68a9a41-20130322_223446 +test Mar13_S2A1/voip-381a50592b-20130323_234042 +test Mar13_S2A1/voip-d0341706f2-20130329_052923 +test Mar13_S2A1/voip-39a25ab2f8-20130325_225331 +test Mar13_S2A1/voip-da10d74c3e-20130328_144024 +test Mar13_S2A1/voip-58047f5227-20130326_033102 +test Mar13_S2A1/voip-0fa32b1e78-20130328_151651 +test Mar13_S2A1/voip-ef9aa63b85-20130329_131238 +test Mar13_S2A1/voip-7f9c1c8411-20130328_211713 +test Mar13_S2A1/voip-a617b6827c-20130323_170453 +test Mar13_S2A1/voip-249d0f617b-20130326_005728 +test Mar13_S2A1/voip-48c12815b3-20130326_034254 +test Mar13_S2A1/voip-59bc8a2167-20130328_132914 +test Mar13_S2A1/voip-5749b16764-20130328_152223 +test Mar13_S2A1/voip-59bc8a2167-20130325_131603 +test Mar13_S2A1/voip-b20b6e847a-20130326_223847 +test Mar13_S2A1/voip-597cfafdee-20130402_005736 +test Mar13_S2A1/voip-bde2721237-20130325_160816 +test Mar13_S2A1/voip-a31ca3e355-20130323_222552 +test Mar13_S2A1/voip-e8997b10da-20130329_012147 +test Mar13_S2A1/voip-bb1fd497eb-20130326_231731 +test Mar13_S2A1/voip-b08f15a787-20130326_021138 +test Mar13_S2A1/voip-31de0daa7b-20130401_204118 +test Mar13_S2A1/voip-e99e4f4538-20130327_143352 +test Mar13_S2A1/voip-7f9c1c8411-20130328_135458 +test Mar13_S2A1/voip-7e4cdce06a-20130323_125758 +test Mar13_S2A1/voip-3be3bda933-20130326_124936 +test Mar13_S2A1/voip-da10d74c3e-20130328_142219 +test Mar13_S2A1/voip-561b472540-20130328_124727 +test Mar13_S2A1/voip-10beae627f-20130328_172038 +test Mar13_S2A1/voip-ccf48b9a6a-20130329_052054 +test Mar13_S2A1/voip-8991b7bff6-20130401_180619 +test Mar13_S2A1/voip-a31ca3e355-20130324_191003 +test Mar13_S2A1/voip-52eb280e7b-20130326_212944 +test Mar13_S2A1/voip-f22c2bf9c7-20130328_114132 +test Mar13_S2A1/voip-bb1fd497eb-20130325_125615 +test Mar13_S2A1/voip-a352cb5ca5-20130401_235134 +test Mar13_S2A1/voip-fdf8b50918-20130326_050951 +test Mar13_S2A1/voip-4c25da9a27-20130327_141725 +test Mar13_S2A1/voip-b27a230d2e-20130323_051110 +test Mar13_S2A1/voip-50af5438f1-20130402_080107 +test Mar13_S2A1/voip-155e939ebc-20130327_203809 +test Mar13_S2A1/voip-dd9f7810fd-20130322_231736 +test Mar13_S2A1/voip-4f069a4136-20130327_204207 +test Mar13_S2A1/voip-5749b16764-20130328_150604 +test Mar13_S2A1/voip-0241bbae39-20130327_200234 +test Mar13_S2A1/voip-2d3d74d091-20130328_143406 +test Mar13_S2A1/voip-a617b6827c-20130323_171342 +test Mar13_S2A1/voip-935947e17b-20130328_163838 +test Mar13_S2A1/voip-f32f2cfdae-20130328_194950 +test Mar13_S2A1/voip-72e50baa85-20130326_054126 +test Mar13_S2A1/voip-50af5438f1-20130402_082131 +test Mar13_S2A1/voip-3b3edac94d-20130323_203541 +test Mar13_S2A1/voip-9f989824fd-20130324_074034 +test Mar13_S2A1/voip-afd3aa91f0-20130327_210955 +test Mar13_S2A1/voip-be5b7bf9d9-20130401_152114 +test Mar13_S2A1/voip-3cf7bd870d-20130327_174946 +test Mar13_S2A1/voip-935947e17b-20130402_194303 +test Mar13_S2A1/voip-2f4c700ae3-20130401_185847 +test Mar13_S2A1/voip-13ff413553-20130328_174442 +test Mar13_S2A1/voip-72e50baa85-20130327_061212 +test Mar13_S2A1/voip-78f497f314-20130323_143314 +test Mar13_S2A1/voip-e99e4f4538-20130328_203100 +test Mar13_S2A1/voip-340dbb333e-20130327_005111 +test Mar13_S2A1/voip-5cf59cc660-20130327_145424 +test Mar13_S2A1/voip-10beae627f-20130401_163040 +test Mar13_S2A1/voip-fe4b6ef58f-20130325_225038 +test Mar13_S2A1/voip-50af5438f1-20130402_083440 +test Mar13_S2A1/voip-7e4cdce06a-20130323_130941 +test Mar13_S2A1/voip-dcaeb62b29-20130327_084323 +test Mar13_S2A1/voip-88b68a9a41-20130324_004511 +test Mar13_S2A1/voip-dcaeb62b29-20130327_083558 +test Mar13_S2A1/voip-ef9aa63b85-20130329_132823 +test Mar13_S2A1/voip-50af5438f1-20130327_031431 +test Mar13_S2A1/voip-d225fad9df-20130328_184121 +test Mar13_S2A1/voip-31de0daa7b-20130401_210038 +test Mar13_S2A1/voip-dcaeb62b29-20130326_042524 +test Mar13_S2A1/voip-be5b7bf9d9-20130401_151504 +test Mar13_S2A1/voip-0a45bc863d-20130326_205013 +test Mar13_S2A1/voip-22756d9e8f-20130329_050240 +test Mar13_S2A1/voip-2d2d103292-20130326_043228 +test Mar13_S2A1/voip-db80a9e6df-20130328_231020 +test Mar13_S2A1/voip-48c12815b3-20130326_020904 +test Mar13_S2A1/voip-199d62165b-20130402_121242 +test Mar13_S2A1/voip-f1e8236264-20130323_002724 +test Mar13_S2A1/voip-ad40cf5489-20130327_193125 +test Mar13_S2A1/voip-f1e8236264-20130323_004924 +test Mar13_S2A1/voip-8efef4eae9-20130325_234447 +test Mar13_S2A1/voip-03d6592b76-20130327_024206 +test Mar13_S2A1/voip-ad40cf5489-20130325_181141 +test Mar13_S2A1/voip-e0035cc31b-20130326_204723 +test Mar13_S2A1/voip-4a6ecc1f1c-20130329_153452 +test Mar13_S2A1/voip-21ec2b7850-20130327_043730 +test Mar13_S2A1/voip-62cc0cc55d-20130323_160957 +test Mar13_S2A1/voip-48c12815b3-20130402_051947 +test Mar13_S2A1/voip-3be3bda933-20130326_125658 +test Mar13_S2A1/voip-597cfafdee-20130328_234958 +test Mar13_S2A1/voip-b6618de447-20130328_151803 +test Mar13_S2A1/voip-88b68a9a41-20130322_222453 +test Mar13_S2A1/voip-d6f8c4271e-20130326_220634 +test Mar13_S2A1/voip-936ec6902a-20130328_133245 +test Mar13_S2A1/voip-597cfafdee-20130328_230619 +test Mar13_S2A1/voip-62cc0cc55d-20130328_143612 +test Mar13_S2A1/voip-4c0d36762a-20130328_210344 +test Mar13_S2A1/voip-9819537952-20130327_023250 +test Mar13_S2A1/voip-f113dbb0e1-20130322_233626 +test Mar13_S2A1/voip-2d3d74d091-20130401_235614 +test Mar13_S2A1/voip-f32f2cfdae-20130327_014809 +test Mar13_S2A1/voip-e2a895cfe5-20130326_211144 +test Mar13_S2A1/voip-03c59ba692-20130402_061946 +test Mar13_S2A1/voip-f22c2bf9c7-20130326_194425 +test Mar13_S2A1/voip-317a1436fe-20130325_172530 +test Mar13_S2A1/voip-560cbd32a5-20130401_143320 +test Mar13_S2A1/voip-9f989824fd-20130324_074635 +test Mar13_S2A1/voip-2d2d103292-20130329_040425 +test Mar13_S2A1/voip-8d5173f3a6-20130323_015334 +test Mar13_S2A1/voip-13ff413553-20130328_173151 +test Mar13_S2A1/voip-3b81cbb287-20130324_015448 +test Mar13_S2A1/voip-7e22911804-20130328_203928 +test Mar13_S2A1/voip-0f41c16f2f-20130325_192738 +test Mar13_S2A1/voip-b57f8ee22b-20130326_234333 +test Mar13_S2A1/voip-52eb280e7b-20130326_210626 +test Mar13_S2A1/voip-fe2783c40a-20130401_150831 +test Mar13_S2A1/voip-2f4c700ae3-20130401_192127 +test Mar13_S2A1/voip-b6618de447-20130328_154929 +test Mar13_S2A1/voip-4a6ecc1f1c-20130328_122618 +test Mar13_S2A1/voip-e61fa89add-20130326_010554 +test Mar13_S2A1/voip-b08f15a787-20130402_074345 +test Mar13_S2A1/voip-e8997b10da-20130401_150651 +test Mar13_S2A1/voip-be5b7bf9d9-20130402_203154 +test Mar13_S2A1/voip-14f776f781-20130328_121813 +test Mar13_S2A1/voip-10beae627f-20130328_170245 +test Mar13_S2A1/voip-afd3aa91f0-20130325_225508 +test Mar13_S2A1/voip-f32f2cfdae-20130327_012648 +test Mar13_S2A1/voip-03d6592b76-20130327_025322 +test Mar13_S2A1/voip-7f9c1c8411-20130401_165827 +test Mar13_S2A1/voip-14cb91bc48-20130327_184646 +test Mar13_S2A1/voip-bde2721237-20130326_200914 +test Mar13_S2A1/voip-48c12815b3-20130402_045340 +test Mar13_S2A1/voip-05e7a5440b-20130328_213127 +test Mar13_S2A1/voip-187c1708f2-20130325_134758 +test Mar13_S2A1/voip-78f497f314-20130323_183923 +test Mar13_S2A1/voip-ddcaad92a1-20130325_235723 +test Mar13_S2A1/voip-4660dd9eab-20130329_075100 +test Mar13_S2A1/voip-ddcaad92a1-20130326_001940 +test Mar13_S2A1/voip-d7853a398f-20130402_160702 +test Mar13_S2A1/voip-f32f2cfdae-20130328_193113 +test Mar13_S2A1/voip-0a45bc863d-20130326_204426 +test Mar13_S2A1/voip-155e939ebc-20130327_202728 +test Mar13_S2A1/voip-59bc8a2167-20130325_143923 +test Mar13_S2A1/voip-8991b7bff6-20130326_231944 +test Mar13_S2A1/voip-d7aef99178-20130328_184914 +test Mar13_S2A1/voip-9735278861-20130401_161357 +test Mar13_S2A1/voip-922209b777-20130325_161315 +test Mar13_S2A1/voip-43479eb5c2-20130323_180350 +test Mar13_S2A1/voip-78f497f314-20130323_142232 +test Mar13_S2A1/voip-e99e4f4538-20130328_200114 +test Mar13_S2A1/voip-ccf48b9a6a-20130329_042843 +test Mar13_S2A1/voip-340dbb333e-20130327_011300 +test Mar13_S2A1/voip-ef9aa63b85-20130329_131801 +test Mar13_S2A1/voip-7e07d8f0f5-20130327_180243 +test Mar13_S2A1/voip-2f209793f4-20130326_004310 +test Mar13_S2A1/voip-22c938c8ba-20130325_131418 +test Mar13_S2A1/voip-b6618de447-20130328_153322 +test Mar13_S2A1/voip-d7853a398f-20130402_143833 +test Mar13_S2A1/voip-d645d56d23-20130402_034956 +test Mar13_S2A1/voip-36440f7305-20130326_141507 +test Mar13_S2A1/voip-a31ca3e355-20130323_223758 +test Mar13_S2A1/voip-db80a9e6df-20130328_225627 +test Mar13_S2A1/voip-5cf59cc660-20130328_155842 +test Mar13_S2A1/voip-5cf59cc660-20130327_143822 +test Mar13_S2A1/voip-e9b53d6ace-20130401_212408 +test Mar13_S2A1/voip-e72dba1759-20130326_215428 +test Mar13_S2A1/voip-59bc8a2167-20130328_125358 +test Mar13_S2A1/voip-922209b777-20130325_164119 +test Mar13_S2A1/voip-8f9fb7a86b-20130328_183210 +test Mar13_S2A1/voip-1b51204ef5-20130401_145857 +test Mar13_S2A1/voip-2f209793f4-20130326_010311 +test Mar13_S2A1/voip-869dd52548-20130401_180101 +test Mar13_S2A1/voip-21ec2b7850-20130325_135235 +test Mar13_S2A1/voip-876ef67873-20130402_031240 +test Mar13_S2A1/voip-2c217000af-20130325_222951 +test Mar13_S2A1/voip-e61fa89add-20130327_075051 +test Mar13_S2A1/voip-22c938c8ba-20130325_124120 +test Mar13_S2A1/voip-a352cb5ca5-20130401_232604 +test Mar13_S2A1/voip-318851c80b-20130328_220944 +test Mar13_S2A1/voip-d7aef99178-20130328_185047 +test Mar13_S2A1/voip-50af5438f1-20130327_034343 +test Mar13_S2A1/voip-03c59ba692-20130402_053803 +test Mar13_S2A1/voip-22c938c8ba-20130325_131010 +test Mar13_S2A1/voip-935947e17b-20130402_200210 +test Mar13_S2A1/voip-e3b4879e0d-20130326_015352 +test Mar13_S2A1/voip-22756d9e8f-20130329_050902 +test Mar13_S2A1/voip-187c1708f2-20130327_131214 +test Mar13_S2A1/voip-31d9d1a567-20130402_034321 +test Mar13_S2A1/voip-e54437a6f0-20130325_132124 +test Mar13_S2A1/voip-3b81cbb287-20130326_025313 +test Mar13_S2A1/voip-3be3bda933-20130326_133147 +test Mar13_S2A1/voip-340dbb333e-20130325_233930 +test Mar13_S2A1/voip-afd3aa91f0-20130325_224003 +test Mar13_S2A1/voip-340dbb333e-20130325_231507 +test Mar13_S2A1/voip-aaa44b4121-20130326_053838 +test Mar13_S2A1/voip-da10d74c3e-20130325_232439 +test Mar13_S2A1/voip-7e07d8f0f5-20130328_185451 +test Mar13_S2A1/voip-8991b7bff6-20130401_174154 +test Mar13_S2A1/voip-cc1b0c8a0f-20130327_030423 +test Mar13_S2A1/voip-d76f6e4f82-20130327_192835 +test Mar13_S2A1/voip-05e7a5440b-20130328_213802 +test Mar13_S2A1/voip-e9b53d6ace-20130401_201712 +test Mar13_S2A1/voip-cfd5fa34d9-20130402_073932 +test Mar13_S2A1/voip-340dbb333e-20130327_012139 +test Mar13_S2A1/voip-b27a230d2e-20130329_043717 +test Mar13_S2A1/voip-03c2655d43-20130327_194409 +test Mar13_S2A1/voip-4f069a4136-20130402_030819 +test Mar13_S2A1/voip-935947e17b-20130328_165200 +test Mar13_S2A1/voip-9735278861-20130401_160258 +test Mar13_S2A1/voip-a8649977cf-20130323_160101 +test Mar13_S2A1/voip-fe4b6ef58f-20130325_234847 +test Mar13_S2A1/voip-8991b7bff6-20130326_232108 +test Mar13_S2A1/voip-0f41c16f2f-20130402_005558 +test Mar13_S2A1/voip-e8997b10da-20130329_010345 +test Mar13_S2A1/voip-f4026333dc-20130327_175948 +test Mar13_S2A1/voip-249d0f617b-20130328_161028 +test Mar13_S2A1/voip-eaef6f434c-20130323_030101 +test Mar13_S2A1/voip-4c25da9a27-20130325_181538 +test Mar13_S2A1/voip-da10d74c3e-20130326_002144 +test Mar13_S2A1/voip-e2a895cfe5-20130325_233130 +test Mar13_S2A1/voip-5a464ca603-20130401_165618 +test Mar13_S2A1/voip-8efef4eae9-20130325_233556 +test Mar13_S2A1/voip-00d76b791d-20130327_010003 +test Mar13_S2A1/voip-4a6ecc1f1c-20130329_151443 +test Mar13_S2A1/voip-10beae627f-20130329_154707 +test Mar13_S2A1/voip-0241bbae39-20130327_193412 +test Mar13_S2A1/voip-561b472540-20130328_122717 +test Mar13_S2A1/voip-0fa32b1e78-20130328_153445 +test Mar13_S2A1/voip-d7853a398f-20130401_153359 +test Mar13_S2A1/voip-7f9c1c8411-20130401_162206 +test Mar13_S2A1/voip-4c0d36762a-20130328_202338 +test Mar13_S2A1/voip-22756d9e8f-20130329_044119 +test Mar13_S2A1/voip-922209b777-20130327_010427 +test Mar13_S2A1/voip-ad40cf5489-20130327_193630 +test Mar13_S2A1/voip-4c25da9a27-20130327_135205 +test Mar13_S2A1/voip-be5b7bf9d9-20130402_195840 +test Mar13_S2A1/voip-876ef67873-20130402_031929 +test Mar13_S2A1/voip-0a45bc863d-20130325_200625 +test Mar13_S2A1/voip-0fa32b1e78-20130402_132446 +test Mar13_S2A1/voip-58047f5227-20130327_033108 +test Mar13_S2A1/voip-62cc0cc55d-20130323_154821 +test Mar13_S2A1/voip-876ef67873-20130327_035355 +test Mar13_S2A1/voip-869dd52548-20130401_185021 +test Mar13_S2A1/voip-14f776f781-20130329_033917 +test Mar13_S2A1/voip-e8997b10da-20130329_002339 +test Mar13_S2A1/voip-583e7cede5-20130323_055709 +test Mar13_S2A1/voip-597cfafdee-20130328_234535 +test Mar13_S2A1/voip-5cf59cc660-20130328_163718 +test Mar13_S2A1/voip-7c3a08072d-20130326_005321 +test Mar13_S2A1/voip-199d62165b-20130402_123231 +test Mar13_S2A1/voip-9819537952-20130328_231843 +test Mar13_S2A1/voip-88b68a9a41-20130324_003305 +test Mar13_S2A1/voip-fe4b6ef58f-20130328_232533 +test Mar13_S2A1/voip-aaa44b4121-20130326_060540 +test Mar13_S2A1/voip-31de0daa7b-20130402_132852 +test Mar13_S2A1/voip-fdf8b50918-20130327_022752 +test Mar13_S2A1/voip-e0035cc31b-20130323_210142 +test Mar13_S2A1/voip-7e07d8f0f5-20130327_181018 +test Mar13_S2A1/voip-7f9c1c8411-20130401_161605 +test Mar13_S2A1/voip-d225fad9df-20130328_174737 +test Mar13_S2A1/voip-3cf7bd870d-20130327_182909 +test Mar13_S2A1/voip-bde2721237-20130326_192622 +test Mar13_S2A1/voip-381a50592b-20130323_235324 +test Mar13_S2A1/voip-7f9c1c8411-20130328_173656 +test Mar13_S2A1/voip-b27a230d2e-20130323_042340 +test Mar13_S2A1/voip-aaa44b4121-20130327_165952 +test Mar13_S2A1/voip-e61fa89add-20130327_071905 +test Mar13_S2A1/voip-e72dba1759-20130326_223325 +test Mar13_S2A1/voip-31d9d1a567-20130402_041615 +test Mar13_S2A1/voip-187c1708f2-20130327_125741 +test Mar13_S2A1/voip-bb1fd497eb-20130325_163404 +test Mar13_S2A1/voip-e99e4f4538-20130327_155041 +test Mar13_S2A1/voip-f22c2bf9c7-20130328_120313 +test Mar13_S2A1/voip-b20b6e847a-20130326_222516 +test Mar13_S2A1/voip-3cf7bd870d-20130328_212334 +test Mar13_S2A1/voip-908884f5fd-20130327_001907 +test Mar13_S2A1/voip-f22c2bf9c7-20130328_115321 +test Mar13_S2A1/voip-da10d74c3e-20130326_000905 +test Mar13_S2A1/voip-4c25da9a27-20130325_184849 +test Mar13_S2A1/voip-78f497f314-20130324_203218 +test Mar13_S2A1/voip-fe2783c40a-20130401_145319 +test Mar13_S2A1/voip-d645d56d23-20130323_235959 +test Mar13_S2A1/voip-50af5438f1-20130402_084822 +test Mar13_S2A1/voip-87de4f7a80-20130324_154432 +test Mar13_S2A1/voip-3860c915c2-20130328_164936 +test Mar13_S2A1/voip-90732b027d-20130401_220312 +test Mar13_S2A1/voip-4c25da9a27-20130325_182051 +test Mar13_S2A1/voip-fbd422ad18-20130328_183328 +test Mar13_S2A1/voip-7e22911804-20130324_185318 +test Mar13_S2A1/voip-318851c80b-20130328_215115 +test Mar13_S2A1/voip-da10d74c3e-20130328_144418 +test Mar13_S2A1/voip-1b51204ef5-20130401_145608 +test Mar13_S2A1/voip-6dbc3573bc-20130328_191134 +test Mar13_S2A1/voip-e61fa89add-20130326_013026 +test Mar13_S2A1/voip-13ff413553-20130328_171126 +test Mar13_S2A1/voip-dda7c88c6e-20130323_053821 +test Mar13_S2A1/voip-36440f7305-20130326_201603 +test Mar13_S2A1/voip-90732b027d-20130327_175055 +test Mar13_S2A1/voip-3b81cbb287-20130326_031145 +test Mar13_S2A1/voip-7f9c1c8411-20130401_164805 +test Mar13_S2A1/voip-0f41c16f2f-20130402_000017 +test Mar13_S2A1/voip-dda7c88c6e-20130323_052547 +test Mar13_S2A1/voip-72e50baa85-20130326_044852 +test Mar13_S2A1/voip-4a6ecc1f1c-20130328_124224 +test Mar13_S2A1/voip-4f069a4136-20130327_205100 +test Mar13_S2A1/voip-b20968d1ea-20130323_110448 +test Mar13_S2A1/voip-2b66f60368-20130326_202640 +test Mar13_S2A1/voip-4a6ecc1f1c-20130328_120737 +test Mar13_S2A1/voip-2d3d74d091-20130328_134957 +test Mar13_S2A1/voip-199d62165b-20130402_121930 +test Mar13_S2A1/voip-59bc8a2167-20130328_131751 +test Mar13_S2A1/voip-d76f6e4f82-20130327_233840 +test Mar13_S2A1/voip-ccc459b689-20130327_002947 +test Mar13_S2A1/voip-15d8a89cec-20130327_015153 +test Mar13_S2A1/voip-0fa32b1e78-20130328_234027 +test Mar13_S2A1/voip-fce37b0ccb-20130328_142727 +test Mar13_S2A1/voip-d7853a398f-20130401_161843 +test Mar13_S2A1/voip-3b59a0391b-20130401_133804 +test Mar13_S2A1/voip-3b3edac94d-20130326_003647 +test Mar13_S2A1/voip-3b81cbb287-20130324_020625 +test Mar13_S2A1/voip-4c0d36762a-20130328_211536 +test Mar13_S2A1/voip-0fa32b1e78-20130329_121012 +test Mar13_S2A1/voip-7e07d8f0f5-20130326_211014 +test Mar13_S2A1/voip-72e50baa85-20130327_063340 +test Mar13_S2A1/voip-dcaeb62b29-20130326_033543 +test Mar13_S2A1/voip-2d2d103292-20130329_040112 +test Mar13_S2A1/voip-be5b7bf9d9-20130402_202043 +test Mar13_S2A1/voip-90732b027d-20130401_222746 +test Mar13_S2A1/voip-202b6a3cc4-20130327_185647 +test Mar13_S2A1/voip-eaef6f434c-20130323_161453 +test Mar13_S2A1/voip-7e22911804-20130328_200848 +test Mar13_S2A1/voip-bde2721237-20130326_195205 +test Mar13_S2A1/voip-e3b4879e0d-20130326_024945 +test Mar13_S2A1/voip-2f4c700ae3-20130401_192527 +test Mar13_S2A1/voip-e8997b10da-20130327_195126 +test Mar13_S2A1/voip-e9b53d6ace-20130401_190756 +test Mar13_S2A1/voip-b57f8ee22b-20130325_184213 +test Mar13_S2A1/voip-59bc8a2167-20130325_140117 +test Mar13_S2A1/voip-03c2655d43-20130327_195040 +test Mar13_S2A1/voip-3cf7bd870d-20130327_174807 +test Mar13_S2A1/voip-e0035cc31b-20130323_211252 +test Mar13_S2A1/voip-b27a230d2e-20130323_045313 +test Mar13_S2A1/voip-b20968d1ea-20130323_113517 +test Mar13_S2A1/voip-583e7cede5-20130323_062041 +test Mar13_S2A1/voip-4660dd9eab-20130329_085305 +test Mar13_S2A1/voip-aaa44b4121-20130327_171002 +test Mar13_S2A1/voip-52d599db9c-20130401_230827 +test Mar13_S2A1/voip-b772dbf437-20130402_140732 +test Mar13_S2A1/voip-05e7a5440b-20130328_215302 +test Mar13_S2A1/voip-21ec2b7850-20130326_024118 +test Mar13_S2A1/voip-b58f1f9eb2-20130325_135605 +test Mar13_S2A1/voip-597cfafdee-20130402_011515 +test Mar13_S2A1/voip-2e134ee190-20130401_225343 +test Mar13_S2A1/voip-eaef6f434c-20130323_030255 +test Mar13_S2A1/voip-fe2783c40a-20130401_143641 +test Mar13_S2A1/voip-dcaeb62b29-20130326_041211 +test Mar13_S2A1/voip-14f776f781-20130328_150129 +test Mar13_S2A1/voip-10beae627f-20130401_164518 +test Mar13_S2A1/voip-be5b7bf9d9-20130401_154519 +test Mar13_S2A1/voip-10beae627f-20130328_133754 +test Mar13_S2A1/voip-fe2783c40a-20130401_144635 +test Mar13_S2A1/voip-fe4b6ef58f-20130325_234042 +test Mar13_S2A1/voip-cfd5fa34d9-20130402_074917 +test Mar13_S2A1/voip-90732b027d-20130327_171445 +test Mar13_S2A1/voip-340dbb333e-20130325_232704 +test Mar13_S2A1/voip-2c217000af-20130328_224036 +test Mar13_S2A1/voip-96f43326a4-20130323_065251 +test Mar13_S2A1/voip-14f776f781-20130328_140326 +test Mar13_S2A1/voip-52d599db9c-20130402_002405 +test Mar13_S2A1/voip-a8649977cf-20130323_160703 +test Mar13_S2A1/voip-d0341706f2-20130329_021314 +test Mar13_S2A1/voip-0f41c16f2f-20130325_210905 +test Mar13_S2A1/voip-e72dba1759-20130325_205417 +test Mar13_S2A1/voip-30772678da-20130328_192635 +test Mar13_S2A1/voip-dcaeb62b29-20130327_081431 +test Mar13_S2A1/voip-f1e8236264-20130323_002843 +test Mar13_S2A1/voip-10beae627f-20130328_124527 +test Mar13_S2A1/voip-5cf59cc660-20130327_142353 +test Mar13_S2A1/voip-22756d9e8f-20130329_044809 +test Mar13_S2A1/voip-7f9c1c8411-20130401_170713 +test Mar13_S2A1/voip-e8997b10da-20130401_151651 +test Mar13_S2A1/voip-317a1436fe-20130325_140754 +test Mar13_S2A1/voip-72e50baa85-20130326_052057 +test Mar13_S2A1/voip-e99e4f4538-20130328_202734 +test Mar13_S2A1/voip-3b3edac94d-20130324_211317 +test Mar13_S2A1/voip-7e07d8f0f5-20130328_190644 +test Mar13_S2A1/voip-8d5173f3a6-20130324_184257 +test Mar13_S2A1/voip-583e7cede5-20130324_060159 +test 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b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/conf/mrda.conf new file mode 100755 index 0000000000000000000000000000000000000000..43760568ccfd9c2d10338bb85648ac16aa12db93 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/conf/mrda.conf @@ -0,0 +1,73 @@ +train Bdb001 +train Bed002 +train Bed004 +train Bed005 +train Bed008 +train Bed009 +train Bed011 +train Bed013 +train Bed014 +train Bed015 +train Bed017 +train Bmr002 +train Bmr003 +train Bmr006 +train Bmr007 +train Bmr008 +train Bmr009 +train Bmr011 +train Bmr012 +train Bmr015 +train Bmr016 +train Bmr020 +train Bmr021 +train Bmr023 +train Bmr025 +train Bmr026 +train Bmr027 +train Bmr029 +train Bmr031 +train Bns001 +train Bns002 +train Bns003 +train Bro003 +train Bro005 +train Bro007 +train Bro010 +train Bro012 +train Bro013 +train Bro015 +train Bro016 +train Bro017 +train Bro019 +train Bro022 +train Bro023 +train Bro025 +train Bro026 +train Bro028 +train Bsr001 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b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/conf/swda.conf new file mode 100755 index 0000000000000000000000000000000000000000..99d453542502adfea6a29068f0d0aa1a185d1f69 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/conf/swda.conf @@ -0,0 +1,1134 @@ +train sw2005 +train sw2006 +train sw2008 +train sw2010 +train sw2012 +train sw2015 +train sw2018 +train sw2019 +train sw2020 +train sw2022 +train sw2024 +train sw2025 +train sw2027 +train sw2028 +train sw2032 +train sw2035 +train sw2038 +train sw2039 +train sw2040 +train sw2041 +train sw2051 +train sw2060 +train sw2061 +train sw2062 +train sw2064 +train sw2065 +train sw2073 +train sw2078 +train sw2079 +train sw2085 +train sw2086 +train sw2090 +train sw2092 +train sw2093 +train sw2094 +train sw2095 +train sw2101 +train sw2102 +train sw2104 +train sw2105 +train sw2107 +train sw2109 +train sw2110 +train sw2111 +train sw2113 +train sw2120 +train sw2122 +train sw2124 +train sw2125 +train sw2130 +train sw2137 +train sw2139 +train sw2145 +train sw2149 +train sw2154 +train sw2155 +train sw2157 +train sw2168 +train sw2171 +train sw2177 +train sw2178 +train sw2180 +train sw2181 +train sw2184 +train sw2185 +train sw2187 +train sw2190 +train sw2191 +train sw2197 +train sw2205 +train sw2220 +train sw2221 +train sw2226 +train sw2227 +train sw2228 +train sw2231 +train sw2232 +train sw2234 +train sw2235 +train sw2237 +train sw2241 +train sw2244 +train sw2247 +train sw2248 +train sw2249 +train sw2252 +train sw2259 +train sw2260 +train sw2262 +train sw2263 +train sw2264 +train sw2265 +train sw2266 +train sw2268 +train sw2275 +train sw2278 +train sw2279 +train sw2283 +train sw2285 +train sw2287 +train sw2290 +train sw2292 +train sw2293 +train sw2295 +train sw2296 +train sw2300 +train sw2301 +train sw2302 +train sw2303 +train sw2304 +train sw2305 +train sw2308 +train sw2309 +train sw2313 +train sw2314 +train sw2316 +train sw2323 +train sw2324 +train sw2325 +train sw2330 +train sw2331 +train sw2334 +train sw2336 +train sw2339 +train sw2342 +train sw2344 +train sw2349 +train sw2353 +train sw2354 +train sw2355 +train sw2362 +train sw2365 +train sw2366 +train sw2368 +train sw2370 +train sw2372 +train sw2376 +train sw2379 +train sw2380 +train sw2382 +train sw2383 +train sw2386 +train sw2387 +train sw2389 +train sw2393 +train sw2397 +train sw2405 +train sw2406 +train sw2407 +train sw2413 +train sw2418 +train sw2421 +train sw2423 +train sw2424 +train sw2426 +train sw2427 +train sw2429 +train sw2431 +train sw2432 +train sw2433 +train sw2435 +train sw2436 +train sw2437 +train sw2439 +train sw2442 +train sw2445 +train sw2446 +train sw2448 +train sw2450 +train sw2451 +train sw2452 +train sw2457 +train sw2460 +train sw2465 +train sw2466 +train sw2467 +train sw2469 +train sw2471 +train sw2472 +train sw2476 +train sw2477 +train sw2478 +train sw2479 +train sw2482 +train sw2483 +train sw2485 +train sw2486 +train sw2488 +train sw2490 +train sw2492 +train sw2495 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sw2652 +train sw2657 +train sw2658 +train sw2661 +train sw2662 +train sw2663 +train sw2667 +train sw2669 +train sw2672 +train sw2675 +train sw2676 +train sw2678 +train sw2679 +train sw2684 +train sw2689 +train sw2690 +train sw2691 +train sw2692 +train sw2693 +train sw2703 +train sw2707 +train sw2708 +train sw2709 +train sw2710 +train sw2711 +train sw2716 +train sw2717 +train sw2719 +train sw2723 +train sw2726 +train sw2729 +train sw2734 +train sw2736 +train sw2741 +train sw2743 +train sw2744 +train sw2749 +train sw2751 +train sw2754 +train sw2756 +train sw2759 +train sw2761 +train sw2766 +train sw2767 +train sw2768 +train sw2770 +train sw2773 +train sw2774 +train sw2775 +train sw2780 +train sw2782 +train sw2784 +train sw2785 +train sw2788 +train sw2789 +train sw2792 +train sw2793 +train sw2794 +train sw2797 +train sw2800 +train sw2803 +train sw2806 +train sw2812 +train sw2818 +train sw2819 +train sw2820 +train sw2821 +train sw2826 +train sw2827 +train sw2828 +train sw2830 +train sw2834 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sw3002 +train sw3003 +train sw3004 +train sw3007 +train sw3009 +train sw3011 +train sw3012 +train sw3013 +train sw3014 +train sw3016 +train sw3018 +train sw3019 +train sw3020 +train sw3021 +train sw3023 +train sw3025 +train sw3028 +train sw3029 +train sw3030 +train sw3034 +train sw3036 +train sw3038 +train sw3039 +train sw3040 +train sw3041 +train sw3042 +train sw3045 +train sw3047 +train sw3049 +train sw3050 +train sw3051 +train sw3052 +train sw3054 +train sw3055 +train sw3056 +train sw3057 +train sw3059 +train sw3061 +train sw3062 +train sw3063 +train sw3064 +train sw3065 +train sw3067 +train sw3068 +train sw3069 +train sw3070 +train sw3071 +train sw3073 +train sw3074 +train sw3075 +train sw3076 +train sw3077 +train sw3080 +train sw3081 +train sw3082 +train sw3083 +train sw3085 +train sw3086 +train sw3087 +train sw3088 +train sw3090 +train sw3092 +train sw3093 +train sw3095 +train sw3097 +train sw3099 +train sw3102 +train sw3103 +train sw3104 +train sw3105 +train sw3107 +train sw3108 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sw3236 +train sw3237 +train sw3238 +train sw3242 +train sw3244 +train sw3245 +train sw3247 +train sw3252 +train sw3253 +train sw3254 +train sw3256 +train sw3259 +train sw3260 +train sw3265 +train sw3266 +train sw3267 +train sw3268 +train sw3269 +train sw3270 +train sw3271 +train sw3272 +train sw3275 +train sw3276 +train sw3279 +train sw3280 +train sw3282 +train sw3283 +train sw3284 +train sw3286 +train sw3293 +train sw3294 +train sw3296 +train sw3300 +train sw3303 +train sw3304 +train sw3306 +train sw3309 +train sw3310 +train sw3311 +train sw3313 +train sw3315 +train sw3317 +train sw3319 +train sw3320 +train sw3324 +train sw3325 +train sw3326 +train sw3327 +train sw3328 +train sw3330 +train sw3331 +train sw3332 +train sw3333 +train sw3338 +train sw3340 +train sw3342 +train sw3343 +train sw3344 +train sw3345 +train sw3349 +train sw3351 +train sw3353 +train sw3355 +train sw3359 +train sw3360 +train sw3361 +train sw3362 +train sw3363 +train sw3364 +train sw3365 +train sw3367 +train sw3368 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sw4801 +train sw4812 +train sw4814 +train sw4821 +train sw4822 +train sw4826 +train sw4829 +train sw4830 +train sw4831 +train sw4834 +train sw4840 +train sw4856 +train sw4858 +train sw4859 +train sw4868 +train sw4876 +train sw4877 +train sw4880 +train sw4886 +train sw4902 +train sw4905 +train sw4908 +train sw4927 +train sw4928 +train sw4936 +train sw4940 +dev sw2053 +dev sw2067 +dev sw2071 +dev sw2072 +dev sw2160 +dev sw2163 +dev sw2175 +dev sw2253 +dev sw2289 +dev sw2299 +dev sw2340 +dev sw2373 +dev sw2395 +dev sw2399 +dev sw2455 +dev sw2501 +dev sw2534 +dev sw2558 +dev sw2593 +dev sw2594 +dev sw2598 +dev sw2620 +dev sw2621 +dev sw2623 +dev sw2630 +dev sw2653 +dev sw2713 +dev sw2755 +dev sw2772 +dev sw2776 +dev sw2790 +dev sw2832 +dev sw2839 +dev sw2842 +dev sw2854 +dev sw2874 +dev sw2888 +dev sw2889 +dev sw2944 +dev sw2959 +dev sw2981 +dev sw2989 +dev sw3015 +dev sw3046 +dev sw3072 +dev sw3096 +dev sw3148 +dev sw3156 +dev sw3181 +dev sw3184 +dev sw3190 +dev sw3191 +dev sw3202 +dev sw3207 +dev sw3239 +dev sw3246 +dev sw3250 +dev sw3251 +dev sw3255 +dev sw3257 +dev sw3281 +dev sw3288 +dev sw3290 +dev sw3291 +dev sw3334 +dev sw3346 +dev sw3352 +dev sw3354 +dev sw3382 +dev sw3433 +dev sw3445 +dev sw3491 +dev sw3497 +dev sw3500 +dev sw3506 +dev sw3509 +dev sw3554 +dev sw3576 +dev sw3584 +dev sw3587 +dev sw3658 +dev sw3659 +dev sw3666 +dev sw3675 +dev sw3686 +dev sw3697 +dev sw3711 +dev sw3769 +dev sw3797 +dev sw3810 +dev sw3811 +dev sw3921 +dev sw4004 +dev sw4026 +dev sw4037 +dev sw4048 +dev sw4072 +dev sw4318 +dev sw4321 +dev sw4347 +dev sw4356 +dev sw4372 +dev sw4572 +dev sw4633 +dev sw4660 +dev sw4697 +dev sw4707 +dev sw4716 +dev sw4736 +dev sw4802 +dev sw4890 +dev sw4917 +test sw2121 +test sw2131 +test sw2151 +test sw2229 +test sw2335 +test sw2434 +test sw2441 +test sw2461 +test sw2503 +test sw2632 +test sw2724 +test sw2752 +test sw2753 +test sw2836 +test sw2838 +test sw3528 +test sw3756 +test sw3942 +test sw3994 diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/run_build_data.sh b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/run_build_data.sh new file mode 100755 index 0000000000000000000000000000000000000000..7a08bf987b18ba71600a19b22d18cfac270fbaa8 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/scripts/run_build_data.sh @@ -0,0 +1,25 @@ +#!/bin/bash + +TASK_DATA=$1 +typeset -l TASK_DATA + +if [ "${TASK_DATA}" = "udc" ] +then + exit 0 +elif [ "${TASK_DATA}" = "swda" ] +then + python build_swda_dataset.py +elif [ "${TASK_DATA}" = "mrda" ] +then + python build_mrda_dataset.py +elif [[ "${TASK_DATA}" =~ "atis" ]] +then + python build_atis_dataset.py + cat ../data/atis/atis_slot/test.txt > ../data/atis/atis_slot/dev.txt + cat ../data/atis/atis_intent/test.txt > ../data/atis/atis_intent/dev.txt +elif [ "${TASK_DATA}" = "dstc2" ] +then + python build_dstc2_dataset.py +else + echo "can not support $TASK_DATA , please choose [swda|mrda|atis|dstc2|multi-woz]" +fi diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/tokenization.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/tokenization.py new file mode 100644 index 0000000000000000000000000000000000000000..f906b537177dce430888fbc1738cd4b33906d705 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/tokenization.py @@ -0,0 +1,370 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization classes.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import unicodedata +import six + + +def convert_to_unicode(text): + """Converts `text` to Unicode (if it's not already), assuming utf-8 input.""" + if six.PY3: + if isinstance(text, str): + return text + elif isinstance(text, bytes): + return text.decode("utf-8", "ignore") + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + elif six.PY2: + if isinstance(text, str): + return text.decode("utf-8", "ignore") + elif isinstance(text, unicode): + return text + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + else: + raise ValueError("Not running on Python2 or Python 3?") + + +def printable_text(text): + """Returns text encoded in a way suitable for print or `tf.logging`.""" + + # These functions want `str` for both Python2 and Python3, but in one case + # it's a Unicode string and in the other it's a byte string. + if six.PY3: + if isinstance(text, str): + return text + elif isinstance(text, bytes): + return text.decode("utf-8", "ignore") + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + elif six.PY2: + if isinstance(text, str): + return text + elif isinstance(text, unicode): + return text.encode("utf-8") + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + else: + raise ValueError("Not running on Python2 or Python 3?") + + +def load_vocab(vocab_file): + """Loads a vocabulary file into a dictionary.""" + vocab = collections.OrderedDict() + fin = open(vocab_file) + for num, line in enumerate(fin): + items = convert_to_unicode(line.strip()).split("\t") + if len(items) > 2: + break + token = items[0] + index = items[1] if len(items) == 2 else num + token = token.strip() + vocab[token] = int(index) + return vocab + + +def convert_by_vocab(vocab, items): + """Converts a sequence of [tokens|ids] using the vocab.""" + output = [] + for item in items: + output.append(vocab[item]) + return output + + +def convert_tokens_to_ids(vocab, tokens): + return convert_by_vocab(vocab, tokens) + + +def convert_ids_to_tokens(inv_vocab, ids): + return convert_by_vocab(inv_vocab, ids) + + +def whitespace_tokenize(text): + """Runs basic whitespace cleaning and splitting on a peice of text.""" + text = text.strip() + if not text: + return [] + tokens = text.split() + return tokens + + +class FullTokenizer(object): + """Runs end-to-end tokenziation.""" + + def __init__(self, vocab_file, do_lower_case=True): + self.vocab = load_vocab(vocab_file) + self.inv_vocab = {v: k for k, v in self.vocab.items()} + self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) + self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) + + def tokenize(self, text): + split_tokens = [] + for token in self.basic_tokenizer.tokenize(text): + for sub_token in self.wordpiece_tokenizer.tokenize(token): + split_tokens.append(sub_token) + + return split_tokens + + def convert_tokens_to_ids(self, tokens): + return convert_by_vocab(self.vocab, tokens) + + def convert_ids_to_tokens(self, ids): + return convert_by_vocab(self.inv_vocab, ids) + + +class CharTokenizer(object): + """Runs end-to-end tokenziation.""" + + def __init__(self, vocab_file, do_lower_case=True): + self.vocab = load_vocab(vocab_file) + self.inv_vocab = {v: k for k, v in self.vocab.items()} + self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) + + def tokenize(self, text): + split_tokens = [] + for token in text.lower().split(" "): + for sub_token in self.wordpiece_tokenizer.tokenize(token): + split_tokens.append(sub_token) + + return split_tokens + + def convert_tokens_to_ids(self, tokens): + return convert_by_vocab(self.vocab, tokens) + + def convert_ids_to_tokens(self, ids): + return convert_by_vocab(self.inv_vocab, ids) + + +class BasicTokenizer(object): + """Runs basic tokenization (punctuation splitting, lower casing, etc.).""" + + def __init__(self, do_lower_case=True): + """Constructs a BasicTokenizer. + + Args: + do_lower_case: Whether to lower case the input. + """ + self.do_lower_case = do_lower_case + + def tokenize(self, text): + """Tokenizes a piece of text.""" + text = convert_to_unicode(text) + text = self._clean_text(text) + + # This was added on November 1st, 2018 for the multilingual and Chinese + # models. This is also applied to the English models now, but it doesn't + # matter since the English models were not trained on any Chinese data + # and generally don't have any Chinese data in them (there are Chinese + # characters in the vocabulary because Wikipedia does have some Chinese + # words in the English Wikipedia.). + text = self._tokenize_chinese_chars(text) + + orig_tokens = whitespace_tokenize(text) + split_tokens = [] + for token in orig_tokens: + if self.do_lower_case: + token = token.lower() + token = self._run_strip_accents(token) + split_tokens.extend(self._run_split_on_punc(token)) + + output_tokens = whitespace_tokenize(" ".join(split_tokens)) + return output_tokens + + def _run_strip_accents(self, text): + """Strips accents from a piece of text.""" + text = unicodedata.normalize("NFD", text) + output = [] + for char in text: + cat = unicodedata.category(char) + if cat == "Mn": + continue + output.append(char) + return "".join(output) + + def _run_split_on_punc(self, text): + """Splits punctuation on a piece of text.""" + chars = list(text) + i = 0 + start_new_word = True + output = [] + while i < len(chars): + char = chars[i] + if _is_punctuation(char): + output.append([char]) + start_new_word = True + else: + if start_new_word: + output.append([]) + start_new_word = False + output[-1].append(char) + i += 1 + + return ["".join(x) for x in output] + + def _tokenize_chinese_chars(self, text): + """Adds whitespace around any CJK character.""" + output = [] + for char in text: + cp = ord(char) + if self._is_chinese_char(cp): + output.append(" ") + output.append(char) + output.append(" ") + else: + output.append(char) + return "".join(output) + + def _is_chinese_char(self, cp): + """Checks whether CP is the codepoint of a CJK character.""" + # This defines a "chinese character" as anything in the CJK Unicode block: + # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) + # + # Note that the CJK Unicode block is NOT all Japanese and Korean characters, + # despite its name. The modern Korean Hangul alphabet is a different block, + # as is Japanese Hiragana and Katakana. Those alphabets are used to write + # space-separated words, so they are not treated specially and handled + # like the all of the other languages. + if ((cp >= 0x4E00 and cp <= 0x9FFF) or # + (cp >= 0x3400 and cp <= 0x4DBF) or # + (cp >= 0x20000 and cp <= 0x2A6DF) or # + (cp >= 0x2A700 and cp <= 0x2B73F) or # + (cp >= 0x2B740 and cp <= 0x2B81F) or # + (cp >= 0x2B820 and cp <= 0x2CEAF) or + (cp >= 0xF900 and cp <= 0xFAFF) or # + (cp >= 0x2F800 and cp <= 0x2FA1F)): # + return True + + return False + + def _clean_text(self, text): + """Performs invalid character removal and whitespace cleanup on text.""" + output = [] + for char in text: + cp = ord(char) + if cp == 0 or cp == 0xfffd or _is_control(char): + continue + if _is_whitespace(char): + output.append(" ") + else: + output.append(char) + return "".join(output) + + +class WordpieceTokenizer(object): + """Runs WordPiece tokenziation.""" + + def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100): + self.vocab = vocab + self.unk_token = unk_token + self.max_input_chars_per_word = max_input_chars_per_word + + def tokenize(self, text): + """Tokenizes a piece of text into its word pieces. + + This uses a greedy longest-match-first algorithm to perform tokenization + using the given vocabulary. + + For example: + input = "unaffable" + output = ["un", "##aff", "##able"] + + Args: + text: A single token or whitespace separated tokens. This should have + already been passed through `BasicTokenizer. + + Returns: + A list of wordpiece tokens. + """ + + text = convert_to_unicode(text) + + output_tokens = [] + for token in whitespace_tokenize(text): + chars = list(token) + if len(chars) > self.max_input_chars_per_word: + output_tokens.append(self.unk_token) + continue + + is_bad = False + start = 0 + sub_tokens = [] + while start < len(chars): + end = len(chars) + cur_substr = None + while start < end: + substr = "".join(chars[start:end]) + if start > 0: + substr = "##" + substr + if substr in self.vocab: + cur_substr = substr + break + end -= 1 + if cur_substr is None: + is_bad = True + break + sub_tokens.append(cur_substr) + start = end + + if is_bad: + output_tokens.append(self.unk_token) + else: + output_tokens.extend(sub_tokens) + return output_tokens + + +def _is_whitespace(char): + """Checks whether `chars` is a whitespace character.""" + # \t, \n, and \r are technically contorl characters but we treat them + # as whitespace since they are generally considered as such. + if char == " " or char == "\t" or char == "\n" or char == "\r": + return True + cat = unicodedata.category(char) + if cat == "Zs": + return True + return False + + +def _is_control(char): + """Checks whether `chars` is a control character.""" + # These are technically control characters but we count them as whitespace + # characters. + if char == "\t" or char == "\n" or char == "\r": + return False + cat = unicodedata.category(char) + if cat.startswith("C"): + return True + return False + + +def _is_punctuation(char): + """Checks whether `chars` is a punctuation character.""" + cp = ord(char) + # We treat all non-letter/number ASCII as punctuation. + # Characters such as "^", "$", and "`" are not in the Unicode + # Punctuation class but we treat them as punctuation anyways, for + # consistency. + if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or + (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)): + return True + cat = unicodedata.category(char) + if cat.startswith("P"): + return True + return False diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/train.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/train.py new file mode 100644 index 0000000000000000000000000000000000000000..6d2e3295a24a88834ee0e9198ba2067c144a32f8 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/train.py @@ -0,0 +1,381 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Finetuning on dialogue tasks.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys +import time +import numpy as np +import multiprocessing + +import paddle +import paddle.fluid as fluid + +from finetune_args import parser +import reader.data_reader as reader +from optimization import optimization +from utils.args import print_arguments +from utils.init import init_checkpoint, init_pretraining_params + +_WORK_DIR = os.path.split(os.path.realpath(__file__))[0] +sys.path.append('../../models/dialogue_model_toolkit/dialogue_general_understanding') + +from bert import BertConfig, BertModel +from create_model import create_model +import define_paradigm + + +def evaluate(test_exe, test_program, test_pyreader, fetch_list, eval_phase): + """evaluate validation or test data""" + test_pyreader.start() + total_cost, total_acc, total_num_seqs = [], [], [] + time_begin = time.time() + while True: + try: + if len(fetch_list) > 2: + np_loss, np_acc, np_num_seqs = test_exe.run(fetch_list=fetch_list) + total_acc.extend(np_acc * np_num_seqs) + else: + np_loss, np_num_seqs = test_exe.run(fetch_list=fetch_list) + total_cost.extend(np_loss * np_num_seqs) + total_num_seqs.extend(np_num_seqs) + except fluid.core.EOFException: + test_pyreader.reset() + break + time_end = time.time() + current_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) + if len(fetch_list) > 2: + print("[%s evaluation] %s ave loss: %f, ave acc: %f, elapsed time: %f s" % + (eval_phase, current_time, np.sum(total_cost) / np.sum(total_num_seqs), + np.sum(total_acc) / np.sum(total_num_seqs), time_end - time_begin)) + else: + print("[%s evaluation] %s ave loss: %f, elapsed time: %f s" % + (eval_phase, current_time, np.sum(total_cost) / np.sum(total_num_seqs), + time_end - time_begin)) + + +def main(args): + """main function""" + bert_config = BertConfig(args.bert_config_path) + bert_config.print_config() + + if args.use_cuda: + place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0'))) + dev_count = fluid.core.get_cuda_device_count() + else: + place = fluid.CPUPlace() + dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) + exe = fluid.Executor(place) + + task_name = args.task_name.lower() + paradigm_inst = define_paradigm.Paradigm(task_name) + + processors = { + 'udc': reader.UDCProcessor, + 'swda': reader.SWDAProcessor, + 'mrda': reader.MRDAProcessor, + 'atis_slot': reader.ATISSlotProcessor, + 'atis_intent': reader.ATISIntentProcessor, + 'dstc2': reader.DSTC2Processor, + } + in_tokens = { + 'udc': True, + 'swda': True, + 'mrda': True, + 'atis_slot': False, + 'atis_intent': True, + 'dstc2': True, + } + + processor = processors[task_name](data_dir=args.data_dir, + vocab_path=args.vocab_path, + max_seq_len=args.max_seq_len, + do_lower_case=args.do_lower_case, + in_tokens=in_tokens[task_name], + task_name=task_name, + random_seed=args.random_seed) + + num_labels = len(processor.get_labels()) + + if not (args.do_train or args.do_val or args.do_test): + raise ValueError("For args `do_train`, `do_val` and `do_test`, at " + "least one of them must be True.") + + startup_prog = fluid.Program() + if args.random_seed is not None: + startup_prog.random_seed = args.random_seed + + if args.do_train: + train_data_generator = processor.data_generator( + batch_size=args.batch_size, + phase='train', + epoch=args.epoch, + shuffle=True) + num_train_examples = processor.get_num_examples(phase='train') + + if in_tokens[task_name]: + max_train_steps = args.epoch * num_train_examples // ( + args.batch_size // args.max_seq_len) // dev_count + else: + max_train_steps = args.epoch * num_train_examples // args.batch_size // dev_count + + warmup_steps = int(max_train_steps * args.warmup_proportion) + print("Device count: %d" % dev_count) + print("Num train examples: %d" % num_train_examples) + print("Max train steps: %d" % max_train_steps) + print("Num warmup steps: %d" % warmup_steps) + + train_program = fluid.Program() + with fluid.program_guard(train_program, startup_prog): + with fluid.unique_name.guard(): + results = create_model( + args, + pyreader_name='train_reader', + bert_config=bert_config, + num_labels=num_labels, + paradigm_inst=paradigm_inst) + train_pyreader = results.get("pyreader", None) + loss = results.get("loss", None) + probs = results.get("probs", None) + accuracy = results.get("accuracy", None) + num_seqs = results.get("num_seqs", None) + scheduled_lr = optimization( + loss=loss, + warmup_steps=warmup_steps, + num_train_steps=max_train_steps, + learning_rate=args.learning_rate, + train_program=train_program, + startup_prog=startup_prog, + weight_decay=args.weight_decay, + scheduler=args.lr_scheduler, + use_fp16=args.use_fp16, + loss_scaling=args.loss_scaling) + + if accuracy is not None: + skip_opt_set = [loss.name, probs.name, accuracy.name, num_seqs.name] + else: + skip_opt_set = [loss.name, probs.name, num_seqs.name] + fluid.memory_optimize( + input_program=train_program, + skip_opt_set=skip_opt_set) + + if args.verbose: + if in_tokens[task_name]: + lower_mem, upper_mem, unit = fluid.contrib.memory_usage( + program=train_program, + batch_size=args.batch_size // args.max_seq_len) + else: + lower_mem, upper_mem, unit = fluid.contrib.memory_usage( + program=train_program, batch_size=args.batch_size) + print("Theoretical memory usage in training: %.3f - %.3f %s" % + (lower_mem, upper_mem, unit)) + + if args.do_val or args.do_test: + test_prog = fluid.Program() + with fluid.program_guard(test_prog, startup_prog): + with fluid.unique_name.guard(): + test_results = create_model( + args, + pyreader_name='test_reader', + bert_config=bert_config, + num_labels=num_labels, + paradigm_inst=paradigm_inst) + test_pyreader = test_results.get("pyreader", None) + loss = test_results.get("loss", None) + probs = test_results.get("probs", None) + accuracy = test_results.get("accuracy", None) + num_seqs = test_results.get("num_seqs", None) + test_prog = test_prog.clone(for_test=True) + + exe.run(startup_prog) + + if args.do_train: + if args.init_checkpoint and args.init_pretraining_params: + print( + "WARNING: args 'init_checkpoint' and 'init_pretraining_params' " + "both are set! Only arg 'init_checkpoint' is made valid.") + if args.init_checkpoint: + init_checkpoint( + exe, + args.init_checkpoint, + main_program=startup_prog, + use_fp16=args.use_fp16) + elif args.init_pretraining_params: + init_pretraining_params( + exe, + args.init_pretraining_params, + main_program=startup_prog, + use_fp16=args.use_fp16) + elif args.do_val or args.do_test: + if not args.init_checkpoint: + raise ValueError("args 'init_checkpoint' should be set if" + "only doing validation or testing!") + init_checkpoint( + exe, + args.init_checkpoint, + main_program=startup_prog, + use_fp16=args.use_fp16) + + if args.do_train: + exec_strategy = fluid.ExecutionStrategy() + exec_strategy.use_experimental_executor = args.use_fast_executor + exec_strategy.num_threads = dev_count + exec_strategy.num_iteration_per_drop_scope = args.num_iteration_per_drop_scope + + train_exe = fluid.ParallelExecutor( + use_cuda=args.use_cuda, + loss_name=loss.name, + exec_strategy=exec_strategy, + main_program=train_program) + train_pyreader.decorate_tensor_provider(train_data_generator) + else: + train_exe = None + + if args.do_val or args.do_test: + test_exe = fluid.ParallelExecutor( + use_cuda=args.use_cuda, + main_program=test_prog, + share_vars_from=train_exe) + + if args.do_train: + train_pyreader.start() + steps = 0 + total_cost, total_acc, total_num_seqs = [], [], [] + time_begin = time.time() + while True: + try: + steps += 1 + if steps % args.skip_steps == 0: + if warmup_steps <= 0: + if accuracy is not None: + fetch_list = [loss.name, accuracy.name, num_seqs.name] + else: + fetch_list = [loss.name, num_seqs.name] + else: + if accuracy is not None: + fetch_list = [ + loss.name, accuracy.name, scheduled_lr.name, + num_seqs.name + ] + else: + fetch_list = [loss.name, scheduled_lr.name, num_seqs.name] + else: + fetch_list = [] + if accuracy is not None: + fetch_test_list = [loss.name, accuracy.name, num_seqs.name] + else: + fetch_test_list = [loss.name, num_seqs.name] + + outputs = train_exe.run(fetch_list=fetch_list) + + if steps % args.skip_steps == 0: + if warmup_steps <= 0: + if accuracy is not None: + np_loss, np_acc, np_num_seqs = outputs + else: + np_loss, np_num_seqs = outputs + else: + if accuracy is not None: + np_loss, np_acc, np_lr, np_num_seqs = outputs + else: + np_loss, np_lr, np_num_seqs = outputs + + total_cost.extend(np_loss * np_num_seqs) + total_num_seqs.extend(np_num_seqs) + if accuracy is not None: + total_acc.extend(np_acc * np_num_seqs) + + if args.verbose: + verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size() + verbose += "learning rate: %f" % ( + np_lr[0] + if warmup_steps > 0 else args.learning_rate) + print(verbose) + + current_example, current_epoch = processor.get_train_progress() + time_end = time.time() + used_time = time_end - time_begin + current_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) + if accuracy is not None: + print("%s epoch: %d, progress: %d/%d, step: %d, ave loss: %f, " + "ave acc: %f, speed: %f steps/s" % + (current_time, current_epoch, current_example, num_train_examples, + steps, np.sum(total_cost) / np.sum(total_num_seqs), + np.sum(total_acc) / np.sum(total_num_seqs), + args.skip_steps / used_time)) + else: + print("%s epoch: %d, progress: %d/%d, step: %d, ave loss: %f, " + "speed: %f steps/s" % + (current_time, current_epoch, current_example, num_train_examples, + steps, np.sum(total_cost) / np.sum(total_num_seqs), + args.skip_steps / used_time)) + total_cost, total_acc, total_num_seqs = [], [], [] + time_begin = time.time() + + if steps % args.save_steps == 0: + save_path = os.path.join(args.checkpoints, "step_" + str(steps)) + fluid.io.save_persistables(exe, save_path, train_program) + if steps % args.validation_steps == 0: + #evaluate dev set + if args.do_val: + test_pyreader.decorate_tensor_provider( + processor.data_generator( + batch_size=args.batch_size, + phase='dev', + epoch=1, + shuffle=False)) + evaluate(test_exe, test_prog, test_pyreader, fetch_test_list, "dev") + #evaluate test set + if args.do_test: + test_pyreader.decorate_tensor_provider( + processor.data_generator( + batch_size=args.batch_size, + phase='test', + epoch=1, + shuffle=False)) + evaluate(test_exe, test_prog, test_pyreader, fetch_test_list, "test") + except fluid.core.EOFException: + save_path = os.path.join(args.checkpoints, "step_" + str(steps)) + fluid.io.save_persistables(exe, save_path, train_program) + train_pyreader.reset() + break + #final eval on dev set + if args.do_val: + test_pyreader.decorate_tensor_provider( + processor.data_generator( + batch_size=args.batch_size, phase='dev', epoch=1, + shuffle=False)) + print("Final validation result:") + evaluate(test_exe, test_prog, test_pyreader, fetch_test_list, "dev") + + #final eval on test set + if args.do_test: + test_pyreader.decorate_tensor_provider( + processor.data_generator( + batch_size=args.batch_size, + phase='test', + epoch=1, + shuffle=False)) + print("Final test result:") + evaluate(test_exe, test_prog, test_pyreader, fetch_test_list, "test") + + +if __name__ == '__main__': + args = parser.parse_args() + print_arguments(args) + main(args) diff --git a/PaddleNLP/deep_attention_matching_net/utils/__init__.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/utils/__init__.py similarity index 100% rename from PaddleNLP/deep_attention_matching_net/utils/__init__.py rename to PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/utils/__init__.py diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/utils/args.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/utils/args.py new file mode 100644 index 0000000000000000000000000000000000000000..b9be634f0f383db61eb667df2345a89262179fd8 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/utils/args.py @@ -0,0 +1,48 @@ +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Arguments for configuration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import six +import argparse + + +def str2bool(v): + # because argparse does not support to parse "true, False" as python + # boolean directly + return v.lower() in ("true", "t", "1") + + +class ArgumentGroup(object): + def __init__(self, parser, title, des): + self._group = parser.add_argument_group(title=title, description=des) + + def add_arg(self, name, type, default, help, **kwargs): + type = str2bool if type == bool else type + self._group.add_argument( + "--" + name, + default=default, + type=type, + help=help + ' Default: %(default)s.', + **kwargs) + + +def print_arguments(args): + print('----------- Configuration Arguments -----------') + for arg, value in sorted(six.iteritems(vars(args))): + print('%s: %s' % (arg, value)) + print('------------------------------------------------') diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/utils/fp16.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/utils/fp16.py new file mode 100644 index 0000000000000000000000000000000000000000..e153c2b9a1029897def264278c5dbe72e1f369f5 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/utils/fp16.py @@ -0,0 +1,97 @@ +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function +import paddle +import paddle.fluid as fluid + + +def cast_fp16_to_fp32(i, o, prog): + prog.global_block().append_op( + type="cast", + inputs={"X": i}, + outputs={"Out": o}, + attrs={ + "in_dtype": fluid.core.VarDesc.VarType.FP16, + "out_dtype": fluid.core.VarDesc.VarType.FP32 + }) + + +def cast_fp32_to_fp16(i, o, prog): + prog.global_block().append_op( + type="cast", + inputs={"X": i}, + outputs={"Out": o}, + attrs={ + "in_dtype": fluid.core.VarDesc.VarType.FP32, + "out_dtype": fluid.core.VarDesc.VarType.FP16 + }) + + +def copy_to_master_param(p, block): + v = block.vars.get(p.name, None) + if v is None: + raise ValueError("no param name %s found!" % p.name) + new_p = fluid.framework.Parameter( + block=block, + shape=v.shape, + dtype=fluid.core.VarDesc.VarType.FP32, + type=v.type, + lod_level=v.lod_level, + stop_gradient=p.stop_gradient, + trainable=p.trainable, + optimize_attr=p.optimize_attr, + regularizer=p.regularizer, + gradient_clip_attr=p.gradient_clip_attr, + error_clip=p.error_clip, + name=v.name + ".master") + return new_p + + +def create_master_params_grads(params_grads, main_prog, startup_prog, + loss_scaling): + master_params_grads = [] + tmp_role = main_prog._current_role + OpRole = fluid.core.op_proto_and_checker_maker.OpRole + main_prog._current_role = OpRole.Backward + for p, g in params_grads: + # create master parameters + master_param = copy_to_master_param(p, main_prog.global_block()) + startup_master_param = startup_prog.global_block()._clone_variable( + master_param) + startup_p = startup_prog.global_block().var(p.name) + cast_fp16_to_fp32(startup_p, startup_master_param, startup_prog) + # cast fp16 gradients to fp32 before apply gradients + if g.name.find("layer_norm") > -1: + if loss_scaling > 1: + scaled_g = g / float(loss_scaling) + else: + scaled_g = g + master_params_grads.append([p, scaled_g]) + continue + master_grad = fluid.layers.cast(g, "float32") + if loss_scaling > 1: + master_grad = master_grad / float(loss_scaling) + master_params_grads.append([master_param, master_grad]) + main_prog._current_role = tmp_role + return master_params_grads + + +def master_param_to_train_param(master_params_grads, params_grads, main_prog): + for idx, m_p_g in enumerate(master_params_grads): + train_p, _ = params_grads[idx] + if train_p.name.find("layer_norm") > -1: + continue + with main_prog._optimized_guard([m_p_g[0], m_p_g[1]]): + cast_fp32_to_fp16(m_p_g[0], train_p, main_prog) diff --git a/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/utils/init.py b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/utils/init.py new file mode 100644 index 0000000000000000000000000000000000000000..3844d01298ecbb70aed37b467aebca62caadd391 --- /dev/null +++ b/PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/utils/init.py @@ -0,0 +1,81 @@ +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import print_function + +import os +import six +import ast +import copy + +import numpy as np +import paddle.fluid as fluid + + +def cast_fp32_to_fp16(exe, main_program): + print("Cast parameters to float16 data format.") + for param in main_program.global_block().all_parameters(): + if not param.name.endswith(".master"): + param_t = fluid.global_scope().find_var(param.name).get_tensor() + data = np.array(param_t) + if param.name.find("layer_norm") == -1: + param_t.set(np.float16(data).view(np.uint16), exe.place) + master_param_var = fluid.global_scope().find_var(param.name + + ".master") + if master_param_var is not None: + master_param_var.get_tensor().set(data, exe.place) + + +def init_checkpoint(exe, init_checkpoint_path, main_program, use_fp16=False): + assert os.path.exists( + init_checkpoint_path), "[%s] cann't be found." % init_checkpoint_path + + def existed_persitables(var): + if not fluid.io.is_persistable(var): + return False + return os.path.exists(os.path.join(init_checkpoint_path, var.name)) + + fluid.io.load_vars( + exe, + init_checkpoint_path, + main_program=main_program, + predicate=existed_persitables) + print("Load model from {}".format(init_checkpoint_path)) + + if use_fp16: + cast_fp32_to_fp16(exe, main_program) + + +def init_pretraining_params(exe, + pretraining_params_path, + main_program, + use_fp16=False): + assert os.path.exists(pretraining_params_path + ), "[%s] cann't be found." % pretraining_params_path + + def existed_params(var): + if not isinstance(var, fluid.framework.Parameter): + return False + return os.path.exists(os.path.join(pretraining_params_path, var.name)) + + fluid.io.load_vars( + exe, + pretraining_params_path, + main_program=main_program, + predicate=existed_params) + print("Load pretraining parameters from {}.".format( + pretraining_params_path)) + + if use_fp16: + cast_fp32_to_fp16(exe, main_program) diff --git a/PaddleNLP/emotion_detection/README.md b/PaddleNLP/emotion_detection/README.md new file mode 100644 index 0000000000000000000000000000000000000000..955843f61bd832552082c314b1e37528eac9c58e --- /dev/null +++ b/PaddleNLP/emotion_detection/README.md @@ -0,0 +1,172 @@ +## 简介 + +对话情绪识别（Emotion Detection，简称EmoTect），专注于识别智能对话场景中用户的情绪，针对智能对话场景中的用户文本，自动判断该文本的情绪类别并给出相应的置信度，情绪类型分为积极、消极、中性。 + +对话情绪识别适用于聊天、客服等多个场景，能够帮助企业更好地把握对话质量、改善产品的用户交互体验，也能分析客服服务质量、降低人工质检成本。可通过 [AI开放平台-对话情绪识别](http://ai.baidu.com/tech/nlp_apply/emotion_detection) 线上体验。 + +效果上，我们基于百度自建测试集（包含闲聊、客服）和nlpcc2014微博情绪数据集，进行评测，效果如下表所示，此外我们还开源了百度基于海量数据训练好的模型，该模型在聊天对话语料上fine-tune之后，可以得到更好的效果。 + +| 模型 | 闲聊 | 客服 | 微博 | +| :------| :------ | :------ | :------ | +| BOW | 90.2% | 87.6% | 74.2% | +| LSTM | 91.4% | 90.1% | 73.8% | +| Bi-LSTM | 91.2% | 89.9% | 73.6% | +| CNN | 90.8% | 90.7% | 76.3% | +| TextCNN | 91.1% | 91.0% | 76.8% | +| BERT | 93.6% | 92.3% | 78.6% | +| ERNIE | 94.4% | 94.0% | 80.6% | + + +## 快速开始 + +本项目依赖于 Python2.7 和 Paddlepaddle Fluid 1.3.2，请参考 [安装指南](http://www.paddlepaddle.org/#quick-start) 进行安装 + +#### 安装代码 + +克隆代码库到本地 +```shell +git clone https://github.com/PaddlePaddle/models.git +cd models/PaddleNLP/emotion_detection +``` + +#### 数据准备 + +下载经过预处理的数据，运行该脚本之后，会生成data目录，data目录下有训练集数据（train.tsv）、开发集数据（dev.tsv）、测试集数据（test.tsv）、 待预测数据（infer.tsv）以及对应词典（vocab.txt） +```shell +sh download_data.sh +``` + +#### 模型下载 + +我们开源了基于海量数据训练好的对话情绪识别模型（基于TextCNN、ERNIE等模型训练），可供用户直接使用，我们提供两种下载方式。 + +**方式一**：基于PaddleHub命令行工具（PaddleHub[安装方式](https://github.com/PaddlePaddle/PaddleHub)） +```shell +mkdir models && cd models +hub download emotion_detection_textcnn --output_path ./ +hub download emotion_detection_ernie_finetune --output_path ./ +tar xvf emotion_detection_textcnn-1.0.0.tar.gz +tar xvf emotion_detection_ernie_finetune-1.0.0.tar.gz +``` + +**方式二**：直接下载脚本 +```shell +sh download_model.sh +``` + +#### 模型评估 + +基于已有的预训练模型和数据，可以运行下面的命令进行测试，查看预训练的模型在测试集（test.tsv）上的评测结果 +```shell +# TextCNN 模型 +sh run.sh eval +# ERNIE 模型 +sh run_ernie.sh eval +``` + +#### 模型训练 + +基于示例的数据集，可以运行下面的命令，在训练集（train.tsv）上进行模型训练，并在开发集（dev.tsv）验证 +```shell +# TextCNN 模型 +sh run.sh train +# ERNIE 模型 +sh run_ernie.sh train +``` +训练完成后，可修改```run.sh```和```run_ernie.sh```中的init_checkpoint 参数，选择最优step的模型进行评估和预测 + +#### 模型预测 + +利用已有模型，可在未知label的数据集（infer.tsv）上进行预测，得到模型预测结果及各label的概率 +```shell +# TextCNN 模型 +sh run.sh infer +# ERNIE 模型 +sh run_ernie.sh infer +``` + +## 进阶使用 + +#### 任务定义 + +对话情绪识别任务输入是一段用户文本，输出是检测到的情绪类别，包括消极、积极、中性，这是一个经典的短文本三分类任务。 + +#### 模型原理介绍 + +本项目针对对话情绪识别问题，开源了一系列分类模型，供用户可配置地使用： + ++ BOW：Bag Of Words，是一个非序列模型，使用基本的全连接结构； ++ CNN：浅层CNN模型，能够处理变长的序列输入，提取一个局部区域之内的特征；； ++ TextCNN：多卷积核CNN模型，能够更好地捕捉句子局部相关性； ++ LSTM：单层LSTM模型，能够较好地解决序列文本中长距离依赖的问题； ++ BI-LSTM：双向单层LSTM模型，采用双向LSTM结构，更好地捕获句子中的语义特征； ++ ERNIE：百度自研基于海量数据和先验知识训练的通用文本语义表示模型，并基于此在对话情绪分类数据集上进行fine-tune获得。 + +#### 数据格式说明 + +训练、预测、评估使用的数据示例如下，数据由两列组成，以制表符（'\t'）分隔，第一列是情绪分类的类别（0表示消极；1表示中性；2表示积极），第二列是以空格分词的中文文本，文件为utf8编码。 + +```text +label text_a +0 谁 骂人 了 ？ 我 从来 不 骂人 ， 我 骂 的 都 不是 人 ， 你 是 人 吗 ？ +1 我 有事 等会儿 就 回来 和 你 聊 +2 我 见到 你 很高兴 谢谢 你 帮 我 +``` +注：本项目额外提供了分词预处理脚本（在preprocess目录下），可供用户使用，具体使用方法如下： +```shell +python tokenizer.py --test_data_dir ./test.txt.utf8 --batch_size 1 > test.txt.utf8.seg +``` + +#### 代码结构说明 + +```text +. +├── config.json # 模型配置文件 +├── config.py # 定义了该项目模型的相关配置，包括具体模型类别、以及模型的超参数 +├── reader.py # 定义了读入数据，加载词典的功能 +├── run_classifier.py # 该项目的主函数，封装包括训练、预测、评估的部分 +├── run_ernie_classifier.py # 基于ERNIE表示的项目的主函数 +├── run_ernie.sh # 基于ERNIE的训练、预测、评估运行脚本 +├── run.sh # 训练、预测、评估运行脚本 +├── utils.py # 定义了其他常用的功能函数 +``` + +#### 如何组建自己的模型 + +可以根据自己的需求，组建自定义的模型，具体方法如下所示： + +1. 定义自己的网络结构 +用户可以在 ```models/classification/nets.py``` 中，定义自己的模型，只需要增加新的函数即可。假设用户自定义的函数名为```user_net``` +2. 更改模型配置 +在 ```config.json``` 中需要将 ```model_type``` 改为用户自定义的 ```user_net``` +3. 模型训练，运行训练、评估、预测需要在 ```run.sh``` 、```run_ernie.sh``` 中将模型、数据、词典路径等配置进行修改 + +#### 如何基于百度开源模型进行 Finetune + +用户可基于百度开源的对话情绪识别模型在自有数据上实现 Finetune 训练，以期获得更好的效果提升，具体模型 Finetune 方法如下所示 + +如果用户基于开源的 TextCNN模型进行 Finetune，需要修改```run.sh```和```config.json```文件 + +```run.sh``` 脚本修改如下： +```shell +# 在train()函数中，增加--init_checkpoint选项；修改--vocab_path +--init_checkpoint ./models/textcnn +--vocab_path ./data/vocab.txt +``` + +```config.json``` 配置修改如下: +```shell +# vocab_size为词典大小，对应上面./data/vocab.txt +"vocab_size": 240465 +``` + +如果用户基于开源的 ERNIE模型进行Finetune，需要更新```run_ernie.sh```脚本，具体修改如下： +```shell +# 在train()函数中，修改--init_checkpoint选项 +--init_checkpoint ./models/ernie_finetune/params +``` + +## 如何贡献代码 + +如果你可以修复某个issue或者增加一个新功能，欢迎给我们提交PR。如果对应的PR被接受了，我们将根据贡献的质量和难度进行打分（0-5分，越高越好）。如果你累计获得了10分，可以联系我们获得面试机会或者为你写推荐信。 + diff --git a/PaddleNLP/emotion_detection/config.json b/PaddleNLP/emotion_detection/config.json new file mode 100644 index 0000000000000000000000000000000000000000..70657397e5c0592fb48728be994e8b2644d6cbf9 --- /dev/null +++ b/PaddleNLP/emotion_detection/config.json @@ -0,0 +1,4 @@ +{ + "model_type": "textcnn_net", + "vocab_size": 240465 +} diff --git a/PaddleNLP/emotion_detection/config.py b/PaddleNLP/emotion_detection/config.py new file mode 100644 index 0000000000000000000000000000000000000000..1d8f4156e4f118b40072fb12b2efe445f85afb0c --- /dev/null +++ b/PaddleNLP/emotion_detection/config.py @@ -0,0 +1,37 @@ +""" +EmoTect config +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import six +import json + +class EmoTectConfig(object): + """ + EmoTect Config + """ + def __init__(self, config_path): + self._config_dict = self._parse(config_path) + + def _parse(self, config_path): + try: + with open(config_path) as json_file: + config_dict = json.load(json_file) + except Exception: + raise IOError("Error in parsing emotect model config file '%s'" % config_path) + else: + return config_dict + + def __getitem__(self, key): + return self._config_dict[key] + + def print_config(self): + """ + Print Config + """ + for arg, value in sorted(six.iteritems(self._config_dict)): + print('%s: %s' % (arg, value)) + print('------------------------------------------------') diff --git a/PaddleNLP/emotion_detection/download_data.sh b/PaddleNLP/emotion_detection/download_data.sh new file mode 100644 index 0000000000000000000000000000000000000000..e699d4268938ed374df442bbfe8318fbc420cceb --- /dev/null +++ b/PaddleNLP/emotion_detection/download_data.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +# download dataset file to ./data/ +DATA_URL=https://baidu-nlp.bj.bcebos.com/emotion_detection-dataset-1.0.0.tar.gz +wget --no-check-certificate ${DATA_URL} + +tar xvf emotion_detection-dataset-1.0.0.tar.gz +/bin/rm emotion_detection-dataset-1.0.0.tar.gz diff --git a/PaddleNLP/emotion_detection/download_model.sh b/PaddleNLP/emotion_detection/download_model.sh new file mode 100644 index 0000000000000000000000000000000000000000..2451aff63c470f602ff05501b9b7f7342c431ca6 --- /dev/null +++ b/PaddleNLP/emotion_detection/download_model.sh @@ -0,0 +1,16 @@ +#!/bin/bash + +mkdir -p models +cd models + +# download pretrain model file to ./models/ +MODEL_CNN=https://baidu-nlp.bj.bcebos.com/emotion_detection_textcnn-1.0.0.tar.gz +MODEL_ERNIE=https://baidu-nlp.bj.bcebos.com/emotion_detection_ernie_finetune-1.0.0.tar.gz +wget --no-check-certificate ${MODEL_CNN} +wget --no-check-certificate ${MODEL_ERNIE} + +tar xvf emotion_detection_textcnn-1.0.0.tar.gz +tar xvf emotion_detection_ernie_finetune-1.0.0.tar.gz + +/bin/rm emotion_detection_textcnn-1.0.0.tar.gz +/bin/rm emotion_detection_ernie_finetune-1.0.0.tar.gz diff --git a/PaddleNLP/emotion_detection/reader.py b/PaddleNLP/emotion_detection/reader.py new file mode 100644 index 0000000000000000000000000000000000000000..75a1be574d4508b44dd76e7f25842f4073d72e17 --- /dev/null +++ b/PaddleNLP/emotion_detection/reader.py @@ -0,0 +1,90 @@ +""" +EmoTect Reader, data converters for classification data. +""" +import os + +import paddle +import paddle.fluid as fluid +import numpy as np + +from utils import load_vocab +from utils import data_reader + +class EmoTectProcessor(object): + """ + Processor class for data convertors for EmoTect. + """ + def __init__(self, + data_dir, + vocab_path, + random_seed=None): + self.data_dir = data_dir + self.vocab = load_vocab(vocab_path) + self.num_examples = {"train": -1, "dev": -1, "test": -1, "infer": -1} + np.random.seed(random_seed) + + def get_train_examples(self, data_dir, epoch=1): + """ + Load training examples + """ + return data_reader(os.path.join(self.data_dir, "train.tsv"), + self.vocab, self.num_examples, "train", epoch) + + def get_dev_examples(self, data_dir): + """ + Load dev examples + """ + return data_reader(os.path.join(self.data_dir, "dev.tsv"), + self.vocab, self.num_examples, "dev") + + def get_test_examples(self, data_dir): + """ + Load test examples + """ + return data_reader(os.path.join(self.data_dir, "test.tsv"), + self.vocab, self.num_examples, "test") + + def get_infer_examples(self, data_dir): + """ + Load infer querys + """ + return data_reader(os.path.join(self.data_dir, "infer.tsv"), + self.vocab, self.num_examples, "infer") + + def get_labels(self): + """ + Return Labels + """ + return ["0", "1", "2"] + + def get_num_examples(self, phase): + """ + Return num of examples in train, dev, test set + """ + if phase not in ['train', 'dev', 'test', 'infer']: + raise ValueError( + "Unknown phase, which should be in ['train', 'dev', 'test', 'infer'].") + return self.num_examples[phase] + + def get_train_progress(self): + """ + Get train progress + """ + return self.current_train_example, self.current_train_epoch + + def data_generator(self, batch_size, phase='train', epoch=1): + """ + Generate data for train, dev or test + """ + if phase == "train": + return paddle.batch(self.get_train_examples(self.data_dir, epoch), batch_size) + elif phase == "dev": + return paddle.batch(self.get_dev_examples(self.data_dir), batch_size) + elif phase == "test": + return paddle.batch(self.get_test_examples(self.data_dir), batch_size) + elif phase == "infer": + return paddle.batch(self.get_infer_examples(self.data_dir), batch_size) + else: + raise ValueError( + "Unknown phase, which should be in ['train', 'dev', 'test', 'infer'].") + diff --git a/PaddleNLP/emotion_detection/run.sh b/PaddleNLP/emotion_detection/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..3a6caae205399b8e831910b7ff8fe04c284100d9 --- /dev/null +++ b/PaddleNLP/emotion_detection/run.sh @@ -0,0 +1,78 @@ +#!/bin/bash +export FLAGS_enable_parallel_graph=1 +export FLAGS_sync_nccl_allreduce=1 +export CUDA_VISIBLE_DEVICES=3 +export FLAGS_fraction_of_gpu_memory_to_use=0.95 +TASK_NAME='emotion_detection' +DATA_PATH=./data/ +VOCAB_PATH=./data/vocab.txt +CKPT_PATH=./save_models/textcnn +MODEL_PATH=./models/textcnn + +# run_train on train.tsv and do_val on dev.tsv +train() { + python run_classifier.py \ + --task_name ${TASK_NAME} \ + --use_cuda false \ + --do_train true \ + --do_val true \ + --batch_size 64 \ + --data_dir ${DATA_PATH} \ + --vocab_path ${VOCAB_PATH} \ + --output_dir ${CKPT_PATH} \ + --save_steps 200 \ + --validation_steps 200 \ + --epoch 5 \ + --lr 0.002 \ + --config_path ./config.json \ + --skip_steps 200 +} +# run_eval on test.tsv +evaluate() { + python run_classifier.py \ + --task_name ${TASK_NAME} \ + --use_cuda false \ + --do_val true \ + --batch_size 128 \ + --data_dir ${DATA_PATH} \ + --vocab_path ${VOCAB_PATH} \ + --init_checkpoint ${MODEL_PATH} \ + --config_path ./config.json +} +# run_infer on infer.tsv +infer() { + python run_classifier.py \ + --task_name ${TASK_NAME} \ + --use_cuda false \ + --do_infer true \ + --batch_size 32 \ + --data_dir ${DATA_PATH} \ + --vocab_path ${VOCAB_PATH} \ + --init_checkpoint ${MODEL_PATH} \ + --config_path ./config.json +} + +main() { + local cmd=${1:-help} + case "${cmd}" in + train) + train "$@"; + ;; + eval) + evaluate "$@"; + ;; + infer) + infer "$@"; + ;; + help) + echo "Usage: ${BASH_SOURCE} {train|eval|infer}"; + return 0; + ;; + *) + echo "unsupport command [${cmd}]"; + echo "Usage: ${BASH_SOURCE} {train|eval|infer}"; + return 1; + ;; + esac +} +main "$@" diff --git a/PaddleNLP/emotion_detection/run_classifier.py b/PaddleNLP/emotion_detection/run_classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..2b792cf3ca1c27997c588f630d6b8f3f1d335b1a --- /dev/null +++ b/PaddleNLP/emotion_detection/run_classifier.py @@ -0,0 +1,344 @@ +""" +Emotion Detection Task +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import time +import argparse +import multiprocessing +import sys +sys.path.append("../") + +import paddle +import paddle.fluid as fluid +import numpy as np + +from models.classification import nets +import reader +import config +import utils + +parser = argparse.ArgumentParser(__doc__) +model_g = utils.ArgumentGroup(parser, "model", "model configuration and paths.") +model_g.add_arg("config_path", str, None, "Path to the json file for EmoTect model config.") +model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.") +model_g.add_arg("output_dir", str, None, "Directory path to save checkpoints") + +train_g = utils.ArgumentGroup(parser, "training", "training options.") +train_g.add_arg("epoch", int, 10, "Number of epoches for training.") +train_g.add_arg("save_steps", int, 10000, "The steps interval to save checkpoints.") +train_g.add_arg("validation_steps", int, 1000, "The steps interval to evaluate model performance.") +train_g.add_arg("lr", float, 0.002, "The Learning rate value for training.") + +log_g = utils.ArgumentGroup(parser, "logging", "logging related") +log_g.add_arg("skip_steps", int, 10, "The steps interval to print loss.") +log_g.add_arg("verbose", bool, False, "Whether to output verbose log") + +data_g = utils.ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options") +data_g.add_arg("data_dir", str, None, "Directory path to training data.") +data_g.add_arg("vocab_path", str, None, "Vocabulary path.") +data_g.add_arg("batch_size", int, 256, "Total examples' number in batch for training.") +data_g.add_arg("random_seed", int, 0, "Random seed.") + +run_type_g = utils.ArgumentGroup(parser, "run_type", "running type options.") +run_type_g.add_arg("use_cuda", bool, False, "If set, use GPU for training.") +run_type_g.add_arg("task_name", str, None, "The name of task to perform sentiment classification.") +run_type_g.add_arg("do_train", bool, False, "Whether to perform training.") +run_type_g.add_arg("do_val", bool, False, "Whether to perform evaluation.") +run_type_g.add_arg("do_infer", bool, False, "Whether to perform inference.") + +args = parser.parse_args() + +def create_model(args, + pyreader_name, + emotect_config, + num_labels, + is_infer=False): + """ + Create Model for sentiment classification + """ + if is_infer: + pyreader = fluid.layers.py_reader( + capacity=16, + shapes=[[-1, 1]], + dtypes=['int64'], + lod_levels=[1], + name=pyreader_name, + use_double_buffer=False) + else: + pyreader = fluid.layers.py_reader( + capacity=16, + shapes=([-1, 1], [-1, 1]), + dtypes=('int64', 'int64'), + lod_levels=(1, 0), + name=pyreader_name, + use_double_buffer=False) + + if emotect_config['model_type'] == "cnn_net": + network = nets.cnn_net + elif emotect_config['model_type'] == "bow_net": + network = nets.bow_net + elif emotect_config['model_type'] == "lstm_net": + network = nets.lstm_net + elif emotect_config['model_type'] == "bilstm_net": + network = nets.bilstm_net + elif emotect_config['model_type'] == "gru_net": + network = nets.gru_net + elif emotect_config['model_type'] == "textcnn_net": + network = nets.textcnn_net + else: + raise ValueError("Unknown network type!") + + if is_infer: + data = fluid.layers.read_file(pyreader) + probs = network(data, None, emotect_config["vocab_size"], class_dim=num_labels, is_infer=True) + return pyreader, probs + + data, label = fluid.layers.read_file(pyreader) + avg_loss, probs = network(data, label, emotect_config["vocab_size"], class_dim=num_labels) + num_seqs = fluid.layers.create_tensor(dtype='int64') + accuracy = fluid.layers.accuracy(input=probs, label=label, total=num_seqs) + return pyreader, avg_loss, accuracy, num_seqs + + +def evaluate(exe, test_program, test_pyreader, fetch_list, eval_phase): + """ + Evaluation Function + """ + test_pyreader.start() + total_cost, total_acc, total_num_seqs = [], [], [] + time_begin = time.time() + while True: + try: + np_loss, np_acc, np_num_seqs = exe.run(program=test_program, + fetch_list=fetch_list, + return_numpy=False) + np_loss = np.array(np_loss) + np_acc = np.array(np_acc) + np_num_seqs = np.array(np_num_seqs) + total_cost.extend(np_loss * np_num_seqs) + total_acc.extend(np_acc * np_num_seqs) + total_num_seqs.extend(np_num_seqs) + except fluid.core.EOFException: + test_pyreader.reset() + break + time_end = time.time() + print("[%s evaluation] avg loss: %f, avg acc: %f, elapsed time: %f s" % + (eval_phase, np.sum(total_cost) / np.sum(total_num_seqs), + np.sum(total_acc) / np.sum(total_num_seqs), time_end - time_begin)) + + +def infer(exe, infer_program, infer_pyreader, fetch_list, infer_phase): + infer_pyreader.start() + time_begin = time.time() + while True: + try: + batch_probs = exe.run(program=infer_program, + fetch_list=fetch_list, + return_numpy=True) + for probs in batch_probs[0]: + print("%d\t%f\t%f\t%f" % (np.argmax(probs), probs[0], probs[1], probs[2])) + except fluid.core.EOFException as e: + infer_pyreader.reset() + break + time_end = time.time() + print("[%s] elapsed time: %f s" % (infer_phase, time_end - time_begin)) + + +def main(args): + """ + Main Function + """ + emotect_config = config.EmoTectConfig(args.config_path) + + if args.use_cuda: + place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0'))) + else: + place = fluid.CPUPlace() + exe = fluid.Executor(place) + + task_name = args.task_name.lower() + processor = reader.EmoTectProcessor(data_dir=args.data_dir, + vocab_path=args.vocab_path, + random_seed=args.random_seed) + num_labels = len(processor.get_labels()) + + if not (args.do_train or args.do_val or args.do_infer): + raise ValueError("For args `do_train`, `do_val` and `do_infer`, at " + "least one of them must be True.") + + startup_prog = fluid.Program() + if args.random_seed is not None: + startup_prog.random_seed = args.random_seed + + if args.do_train: + train_data_generator = processor.data_generator( + batch_size=args.batch_size, + phase='train', + epoch=args.epoch) + + num_train_examples = processor.get_num_examples(phase="train") + max_train_steps = args.epoch * num_train_examples // args.batch_size + 1 + + print("Num train examples: %d" % num_train_examples) + print("Max train steps: %d" % max_train_steps) + + train_program = fluid.Program() + + with fluid.program_guard(train_program, startup_prog): + with fluid.unique_name.guard(): + train_pyreader, loss, accuracy, num_seqs = create_model( + args, + pyreader_name='train_reader', + emotect_config=emotect_config, + num_labels=num_labels, + is_infer=False) + + sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=args.lr) + sgd_optimizer.minimize(loss) + + if args.verbose: + lower_mem, upper_mem, unit = fluid.contrib.memory_usage( + program=train_program, batch_size=args.batch_size) + print("Theoretical memory usage in training: %.3f - %.3f %s" % + (lower_mem, upper_mem, unit)) + + if args.do_val: + test_prog = fluid.Program() + with fluid.program_guard(test_prog, startup_prog): + with fluid.unique_name.guard(): + test_pyreader, loss, accuracy, num_seqs = create_model( + args, + pyreader_name='test_reader', + emotect_config=emotect_config, + num_labels=num_labels, + is_infer=False) + test_prog = test_prog.clone(for_test=True) + + if args.do_infer: + test_prog = fluid.Program() + with fluid.program_guard(test_prog, startup_prog): + with fluid.unique_name.guard(): + infer_pyreader, probs = create_model( + args, + pyreader_name='infer_reader', + emotect_config=emotect_config, + num_labels=num_labels, + is_infer=True) + test_prog = test_prog.clone(for_test=True) + + exe.run(startup_prog) + + if args.do_train: + if args.init_checkpoint: + utils.init_checkpoint( + exe, + args.init_checkpoint, + main_program=startup_prog) + elif args.do_val or args.do_infer: + if not args.init_checkpoint: + raise ValueError("args 'init_checkpoint' should be set if" + "only doing validation or infer!") + utils.init_checkpoint( + exe, + args.init_checkpoint, + main_program=test_prog) + + if args.do_train: + train_exe = exe + train_pyreader.decorate_paddle_reader(train_data_generator) + else: + train_exe = None + if args.do_val or args.do_infer: + test_exe = exe + + if args.do_train: + train_pyreader.start() + steps = 0 + total_cost, total_acc, total_num_seqs = [], [], [] + time_begin = time.time() + while True: + try: + steps += 1 + if steps % args.skip_steps == 0: + fetch_list = [loss.name, accuracy.name, num_seqs.name] + else: + fetch_list = [] + + outputs = train_exe.run(program=train_program, + fetch_list=fetch_list, + return_numpy=False) + if steps % args.skip_steps == 0: + np_loss, np_acc, np_num_seqs = outputs + np_loss = np.array(np_loss) + np_acc = np.array(np_acc) + np_num_seqs = np.array(np_num_seqs) + total_cost.extend(np_loss * np_num_seqs) + total_acc.extend(np_acc * np_num_seqs) + total_num_seqs.extend(np_num_seqs) + + if args.verbose: + verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size() + print(verbose) + + time_end = time.time() + used_time = time_end - time_begin + print("step: %d, avg loss: %f, " + "avg acc: %f, speed: %f steps/s" % + (steps, np.sum(total_cost) / np.sum(total_num_seqs), + np.sum(total_acc) / np.sum(total_num_seqs), + args.skip_steps / used_time)) + total_cost, total_acc, total_num_seqs = [], [], [] + time_begin = time.time() + + if steps % args.save_steps == 0: + save_path = os.path.join(args.output_dir, "step_" + str(steps)) + fluid.io.save_persistables(exe, save_path, train_program) + + if steps % args.validation_steps == 0: + # evaluate on dev set + if args.do_val: + test_pyreader.decorate_paddle_reader( + processor.data_generator( + batch_size=args.batch_size, + phase='dev', + epoch=1)) + evaluate(test_exe, test_prog, test_pyreader, + [loss.name, accuracy.name, num_seqs.name], + "dev") + + except fluid.core.EOFException: + save_path = os.path.join(args.output_dir, "step_" + str(steps)) + fluid.io.save_persistables(exe, save_path, train_program) + train_pyreader.reset() + break + + # evaluate on test set + if not args.do_train and args.do_val: + test_pyreader.decorate_paddle_reader( + processor.data_generator( + batch_size=args.batch_size, + phase='test', + epoch=1)) + print("Final test result:") + evaluate(test_exe, test_prog, test_pyreader, + [loss.name, accuracy.name, num_seqs.name], + "test") + + # infer + if args.do_infer: + infer_pyreader.decorate_paddle_reader( + processor.data_generator( + batch_size=args.batch_size, + phase='infer', + epoch=1)) + infer(test_exe, test_prog, infer_pyreader, + [probs.name], "infer") + +if __name__ == "__main__": + utils.print_arguments(args) + main(args) diff --git a/PaddleNLP/emotion_detection/run_ernie.sh b/PaddleNLP/emotion_detection/run_ernie.sh new file mode 100644 index 0000000000000000000000000000000000000000..e90faf2f5ae1a781f24825d5ea4426242dc7870d --- /dev/null +++ b/PaddleNLP/emotion_detection/run_ernie.sh @@ -0,0 +1,85 @@ +#!/bin/bash +export FLAGS_sync_nccl_allreduce=1 +export CUDA_VISIBLE_DEVICES=2 +MODEL_PATH=./models/ernie_finetune +TASK_DATA_PATH=./data +CKPT_PATH=./save_models/ernie + +# run_train +train() { + python run_ernie_classifier.py \ + --use_cuda true \ + --verbose true \ + --do_train true \ + --do_val true \ + --batch_size 32 \ + --init_checkpoint ${MODEL_PATH}/params \ + --train_set ${TASK_DATA_PATH}/train.tsv \ + --dev_set ${TASK_DATA_PATH}/dev.tsv \ + --vocab_path ${MODEL_PATH}/vocab.txt \ + --output_dir ${CKPT_PATH} \ + --save_steps 500 \ + --validation_steps 50 \ + --epoch 3 \ + --max_seq_len 64 \ + --ernie_config_path ${MODEL_PATH}/ernie_config.json \ + --lr 2e-5 \ + --skip_steps 50 \ + --num_labels 3 \ + --random_seed 1 +} + +# run_test +evaluate() { + python run_ernie_classifier.py \ + --use_cuda true \ + --verbose true \ + --do_val true \ + --batch_size 32 \ + --init_checkpoint ${MODEL_PATH}/params \ + --test_set ${TASK_DATA_PATH}/test.tsv \ + --vocab_path ${MODEL_PATH}/vocab.txt \ + --max_seq_len 64 \ + --ernie_config_path ${MODEL_PATH}/ernie_config.json \ + --num_labels 3 +} + +# run_infer +infer() { + python run_ernie_classifier.py \ + --use_cuda true \ + --verbose true \ + --do_infer true \ + --batch_size 32 \ + --init_checkpoint ${MODEL_PATH}/params \ + --infer_set ${TASK_DATA_PATH}/infer.tsv \ + --vocab_path ${MODEL_PATH}/vocab.txt \ + --max_seq_len 64 \ + --ernie_config_path ${MODEL_PATH}/ernie_config.json \ + --num_labels 3 +} + +main() { + local cmd=${1:-help} + case "${cmd}" in + train) + train "$@"; + ;; + eval) + evaluate "$@"; + ;; + infer) + infer "$@"; + ;; + help) + echo "Usage: ${BASH_SOURCE} {train|eval|infer}"; + return 0; + ;; + *) + echo "unsupport command [${cmd}]"; + echo "Usage: ${BASH_SOURCE} {train|eval|infer}"; + return 1; + ;; + esac +} +main "$@" diff --git a/PaddleNLP/emotion_detection/run_ernie_classifier.py b/PaddleNLP/emotion_detection/run_ernie_classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..a87caf1cea90f775bf2718cd92359fee47c6ba44 --- /dev/null +++ b/PaddleNLP/emotion_detection/run_ernie_classifier.py @@ -0,0 +1,377 @@ +""" +Emotion Detection Task, based on ERNIE +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import time +import argparse +import multiprocessing +import sys +sys.path.append("../") + +import paddle +import paddle.fluid as fluid +import numpy as np + +from preprocess.ernie import task_reader +from models.representation import ernie +import utils + +# yapf: disable +parser = argparse.ArgumentParser(__doc__) +model_g = utils.ArgumentGroup(parser, "model", "model configuration and paths.") +model_g.add_arg("ernie_config_path", str, None, "Path to the json file for ernie model config.") +model_g.add_arg("senta_config_path", str, None, "Path to the json file for senta model config.") +model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.") +model_g.add_arg("output_dir", str, "checkpoints", "Path to save checkpoints") + +train_g = utils.ArgumentGroup(parser, "training", "training options.") +train_g.add_arg("epoch", int, 10, "Number of epoches for training.") +train_g.add_arg("save_steps", int, 10000, "The steps interval to save checkpoints.") +train_g.add_arg("validation_steps", int, 1000, "The steps interval to evaluate model performance.") +train_g.add_arg("lr", float, 0.002, "The Learning rate value for training.") + +log_g = utils.ArgumentGroup(parser, "logging", "logging related") +log_g.add_arg("skip_steps", int, 10, "The steps interval to print loss.") +log_g.add_arg("verbose", bool, False, "Whether to output verbose log") + +data_g = utils.ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options") +data_g.add_arg("data_dir", str, None, "Directory path to training data.") +data_g.add_arg("vocab_path", str, None, "Vocabulary path.") +data_g.add_arg("batch_size", int, 256, "Total examples' number in batch for training.") +data_g.add_arg("random_seed", int, 0, "Random seed.") +data_g.add_arg("num_labels", int, 2, "label number") +data_g.add_arg("max_seq_len", int, 512, "Number of words of the longest seqence.") +data_g.add_arg("train_set", str, None, "Path to training data.") +data_g.add_arg("test_set", str, None, "Path to test data.") +data_g.add_arg("dev_set", str, None, "Path to validation data.") +data_g.add_arg("infer_set", str, None, "Path to validation data.") +data_g.add_arg("label_map_config", str, None, "label_map_path.") +data_g.add_arg("do_lower_case", bool, True, + "Whether to lower case the input text. Should be True for uncased models and False for cased models.") + +run_type_g = utils.ArgumentGroup(parser, "run_type", "running type options.") +run_type_g.add_arg("use_cuda", bool, False, "If set, use GPU for training.") +run_type_g.add_arg("task_name", str, None, "The name of task to perform sentiment classification.") +run_type_g.add_arg("do_train", bool, False, "Whether to perform training.") +run_type_g.add_arg("do_val", bool, False, "Whether to perform evaluation.") +run_type_g.add_arg("do_infer", bool, False, "Whether to perform inference.") + +args = parser.parse_args() +# yapf: enable. + +def create_model(args, + embeddings, + labels, + is_prediction=False): + + """ + Create Model for sentiment classification based on ERNIE encoder + """ + sentence_embeddings = embeddings["sentence_embeddings"] + token_embeddings = embeddings["token_embeddings"] + + cls_feats = fluid.layers.dropout( + x=sentence_embeddings, + dropout_prob=0.1, + dropout_implementation="upscale_in_train") + logits = fluid.layers.fc( + input=cls_feats, + size=args.num_labels, + param_attr=fluid.ParamAttr( + name="cls_out_w", + initializer=fluid.initializer.TruncatedNormal(scale=0.02)), + bias_attr=fluid.ParamAttr( + name="cls_out_b", initializer=fluid.initializer.Constant(0.))) + + ce_loss, probs = fluid.layers.softmax_with_cross_entropy( + logits=logits, label=labels, return_softmax=True) + if is_prediction: + return probs + loss = fluid.layers.mean(x=ce_loss) + + num_seqs = fluid.layers.create_tensor(dtype='int64') + accuracy = fluid.layers.accuracy(input=probs, label=labels, total=num_seqs) + + return loss, accuracy, num_seqs + + +def evaluate(exe, test_program, test_pyreader, fetch_list, eval_phase): + """ + Evaluation Function + """ + test_pyreader.start() + total_cost, total_acc, total_num_seqs = [], [], [] + time_begin = time.time() + while True: + try: + np_loss, np_acc, np_num_seqs = exe.run(program=test_program, + fetch_list=fetch_list, + return_numpy=False) + np_loss = np.array(np_loss) + np_acc = np.array(np_acc) + np_num_seqs = np.array(np_num_seqs) + total_cost.extend(np_loss * np_num_seqs) + total_acc.extend(np_acc * np_num_seqs) + total_num_seqs.extend(np_num_seqs) + except fluid.core.EOFException: + test_pyreader.reset() + break + time_end = time.time() + print("[%s evaluation] avg loss: %f, ave acc: %f, elapsed time: %f s" % + (eval_phase, np.sum(total_cost) / np.sum(total_num_seqs), + np.sum(total_acc) / np.sum(total_num_seqs), time_end - time_begin)) + + +def infer(exe, infer_program, infer_pyreader, fetch_list, infer_phase): + """Infer""" + infer_pyreader.start() + time_begin = time.time() + while True: + try: + batch_probs = exe.run(program=infer_program, fetch_list=fetch_list, + return_numpy=True) + for probs in batch_probs[0]: + print("%d\t%f\t%f\t%f" % (np.argmax(probs), probs[0], probs[1], probs[2])) + except fluid.core.EOFException: + infer_pyreader.reset() + break + time_end = time.time() + print("[%s] elapsed time: %f s" % (infer_phase, time_end - time_begin)) + + +def main(args): + """ + Main Function + """ + args = parser.parse_args() + ernie_config = ernie.ErnieConfig(args.ernie_config_path) + ernie_config.print_config() + + if args.use_cuda: + place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0'))) + dev_count = fluid.core.get_cuda_device_count() + else: + place = fluid.CPUPlace() + dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) + exe = fluid.Executor(place) + + reader = task_reader.ClassifyReader( + vocab_path=args.vocab_path, + label_map_config=args.label_map_config, + max_seq_len=args.max_seq_len, + do_lower_case=args.do_lower_case, + random_seed=args.random_seed) + + if not (args.do_train or args.do_val or args.do_infer): + raise ValueError("For args `do_train`, `do_val` and `do_infer`, at " + "least one of them must be True.") + + startup_prog = fluid.Program() + if args.random_seed is not None: + startup_prog.random_seed = args.random_seed + + if args.do_train: + train_data_generator = reader.data_generator( + input_file=args.train_set, + batch_size=args.batch_size, + epoch=args.epoch, + shuffle=True, + phase="train") + + num_train_examples = reader.get_num_examples(args.train_set) + + max_train_steps = args.epoch * num_train_examples // args.batch_size // dev_count + 1 + + print("Device count: %d" % dev_count) + print("Num train examples: %d" % num_train_examples) + print("Max train steps: %d" % max_train_steps) + + train_program = fluid.Program() + + with fluid.program_guard(train_program, startup_prog): + with fluid.unique_name.guard(): + # create ernie_pyreader + train_pyreader, ernie_inputs, labels = ernie.ernie_pyreader( + args, + pyreader_name='train_reader') + + # get ernie_embeddings + embeddings = ernie.ernie_encoder(ernie_inputs, ernie_config=ernie_config) + + # user defined model based on ernie embeddings + loss, accuracy, num_seqs = create_model( + args, + embeddings, + labels=labels, + is_prediction=False) + + """ + sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=args.lr) + sgd_optimizer.minimize(loss) + """ + optimizer = fluid.optimizer.Adam(learning_rate=args.lr) + optimizer.minimize(loss) + + if args.verbose: + lower_mem, upper_mem, unit = fluid.contrib.memory_usage( + program=train_program, batch_size=args.batch_size) + print("Theoretical memory usage in training: %.3f - %.3f %s" % + (lower_mem, upper_mem, unit)) + + if args.do_val: + test_prog = fluid.Program() + with fluid.program_guard(test_prog, startup_prog): + with fluid.unique_name.guard(): + # create ernie_pyreader + test_pyreader, ernie_inputs, labels = ernie.ernie_pyreader( + args, + pyreader_name='eval_reader') + + # get ernie_embeddings + embeddings = ernie.ernie_encoder(ernie_inputs, ernie_config=ernie_config) + + # user defined model based on ernie embeddings + loss, accuracy, num_seqs = create_model( + args, + embeddings, + labels=labels, + is_prediction=False) + + test_prog = test_prog.clone(for_test=True) + + if args.do_infer: + test_prog = fluid.Program() + with fluid.program_guard(test_prog, startup_prog): + with fluid.unique_name.guard(): + infer_pyreader, ernie_inputs, labels = ernie.ernie_pyreader( + args, + pyreader_name='infer_reader') + + # get ernie_embeddings + embeddings = ernie.ernie_encoder(ernie_inputs, ernie_config=ernie_config) + + probs = create_model(args, + embeddings, + labels=labels, + is_prediction=True) + test_prog = test_prog.clone(for_test=True) + + exe.run(startup_prog) + + if args.do_train: + if args.init_checkpoint: + utils.init_checkpoint( + exe, + args.init_checkpoint, + main_program=startup_prog) + elif args.do_val or args.do_infer: + if not args.init_checkpoint: + raise ValueError("args 'init_checkpoint' should be set if" + "only doing validation or infer!") + utils.init_checkpoint( + exe, + args.init_checkpoint, + main_program=test_prog) + + if args.do_train: + train_exe = exe + train_pyreader.decorate_tensor_provider(train_data_generator) + else: + train_exe = None + if args.do_val or args.do_infer: + test_exe = exe + + if args.do_train: + train_pyreader.start() + steps = 0 + total_cost, total_acc, total_num_seqs = [], [], [] + time_begin = time.time() + while True: + try: + steps += 1 + if steps % args.skip_steps == 0: + fetch_list = [loss.name, accuracy.name, num_seqs.name] + else: + fetch_list = [] + + outputs = train_exe.run(program=train_program, fetch_list=fetch_list, return_numpy=False) + if steps % args.skip_steps == 0: + np_loss, np_acc, np_num_seqs = outputs + np_loss = np.array(np_loss) + np_acc = np.array(np_acc) + np_num_seqs = np.array(np_num_seqs) + total_cost.extend(np_loss * np_num_seqs) + total_acc.extend(np_acc * np_num_seqs) + total_num_seqs.extend(np_num_seqs) + + if args.verbose: + verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size() + print(verbose) + + time_end = time.time() + used_time = time_end - time_begin + print("step: %d, avg loss: %f, " + "avg acc: %f, speed: %f steps/s" % + (steps, np.sum(total_cost) / np.sum(total_num_seqs), + np.sum(total_acc) / np.sum(total_num_seqs), + args.skip_steps / used_time)) + total_cost, total_acc, total_num_seqs = [], [], [] + time_begin = time.time() + + if steps % args.save_steps == 0: + save_path = os.path.join(args.output_dir, "step_" + str(steps)) + fluid.io.save_persistables(exe, save_path, train_program) + + if steps % args.validation_steps == 0: + # evaluate dev set + if args.do_val: + test_pyreader.decorate_tensor_provider( + reader.data_generator( + input_file=args.dev_set, + batch_size=args.batch_size, + phase='dev', + epoch=1, + shuffle=False)) + + evaluate(exe, test_prog, test_pyreader, + [loss.name, accuracy.name, num_seqs.name], + "dev") + + except fluid.core.EOFException: + save_path = os.path.join(args.output_dir, "step_" + str(steps)) + fluid.io.save_persistables(exe, save_path, train_program) + train_pyreader.reset() + break + + # eval on test set + if not args.do_train and args.do_val: + test_pyreader.decorate_tensor_provider( + reader.data_generator( + input_file=args.test_set, + batch_size=args.batch_size, phase='test', epoch=1, + shuffle=False)) + print("Final validation result:") + evaluate(exe, test_prog, test_pyreader, + [loss.name, accuracy.name, num_seqs.name], "test") + + # infer on infer set + if args.do_infer: + infer_pyreader.decorate_tensor_provider( + reader.data_generator( + input_file=args.infer_set, + batch_size=args.batch_size, + phase='infer', + epoch=1, + shuffle=False)) + print("Final test result:") + infer(exe, test_prog, infer_pyreader, + [probs.name], "infer") + +if __name__ == "__main__": + utils.print_arguments(args) + main(args) diff --git a/PaddleNLP/emotion_detection/utils.py b/PaddleNLP/emotion_detection/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ac916d25fd3f275ed679302d1fd9309bf240c674 --- /dev/null +++ b/PaddleNLP/emotion_detection/utils.py @@ -0,0 +1,142 @@ +""" +EmoTect utilities. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import io +import os +import six +import sys +import random +import argparse + +import paddle +import paddle.fluid as fluid +import numpy as np + +def str2bool(value): + """ + String to Boolean + """ + # because argparse does not support to parse "true, False" as python + # boolean directly + return value.lower() in ("true", "t", "1") + + +class ArgumentGroup(object): + """ + Argument Class + """ + def __init__(self, parser, title, des): + self._group = parser.add_argument_group(title=title, description=des) + + def add_arg(self, name, type, default, help, **kwargs): + """ + Add argument + """ + type = str2bool if type == bool else type + self._group.add_argument( + "--" + name, + default=default, + type=type, + help=help + ' Default: %(default)s.', + **kwargs) + + +def print_arguments(args): + """ + Print Arguments + """ + print('----------- Configuration Arguments -----------') + for arg, value in sorted(six.iteritems(vars(args))): + print('%s: %s' % (arg, value)) + print('------------------------------------------------') + + +def init_checkpoint(exe, init_checkpoint_path, main_program): + """ + Init CheckPoint + """ + assert os.path.exists( + init_checkpoint_path), "[%s] cann't be found." % init_checkpoint_path + + def existed_persitables(var): + """ + If existed presitabels + """ + if not fluid.io.is_persistable(var): + return False + return os.path.exists(os.path.join(init_checkpoint_path, var.name)) + + fluid.io.load_vars( + exe, + init_checkpoint_path, + main_program=main_program, + predicate=existed_persitables) + print("Load model from {}".format(init_checkpoint_path)) + + +def data_reader(file_path, word_dict, num_examples, phrase, epoch=1): + """ + Convert word sequence into slot + """ + unk_id = len(word_dict) + all_data = [] + with io.open(file_path, "r", encoding='utf8') as fin: + for line in fin: + if line.startswith("label"): + continue + if phrase == "infer": + cols = line.strip().split("\t") + if len(cols) != 1: + query = cols[-1] + wids = [word_dict[x] if x in word_dict else unk_id + for x in query.strip().split(" ")] + all_data.append((wids,)) + else: + cols = line.strip().split("\t") + if len(cols) != 2: + sys.stderr.write("[NOTICE] Error Format Line!") + continue + label = int(cols[0]) + wids = [word_dict[x] if x in word_dict else unk_id + for x in cols[1].split(" ")] + all_data.append((wids, label)) + num_examples[phrase] = len(all_data) + + if phrase == "infer": + def reader(): + """ + Infer reader function + """ + for wids in all_data: + yield wids + return reader + + def reader(): + """ + Reader function + """ + for idx in range(epoch): + if phrase == "train": + random.shuffle(all_data) + for wids, label in all_data: + yield wids, label + return reader + + +def load_vocab(file_path): + """ + load the given vocabulary + """ + vocab = {} + with io.open(file_path, 'r', encoding='utf8') as fin: + wid = 0 + for line in fin: + if line.strip() not in vocab: + vocab[line.strip()] = wid + wid += 1 + vocab[""] = len(vocab) + return vocab diff --git a/PaddleNLP/knowledge-driven-dialogue b/PaddleNLP/knowledge_driven_dialogue similarity index 100% rename from PaddleNLP/knowledge-driven-dialogue rename to PaddleNLP/knowledge_driven_dialogue diff --git a/PaddleNLP/language_model/README.md b/PaddleNLP/language_model/README.md new file mode 100644 index 0000000000000000000000000000000000000000..adb2318825742daa34d8f2940dc0e6d9aeeba4ca --- /dev/null +++ b/PaddleNLP/language_model/README.md @@ -0,0 +1,125 @@ +# 语言模型 + +# 简介 + +## 1. 任务说明 +本文主要介绍基于lstm的语言的模型的实现，给定一个输入词序列（中文分词、英文tokenize），计算其ppl（语言模型困惑度，用户表示句子的流利程度），基于循环神经网络语言模型的介绍可以[参阅论文](https://arxiv.org/abs/1409.2329)。相对于传统的方法，基于循环神经网络的方法能够更好的解决稀疏词的问题。 + +## 2. 效果说明 +在small meidum large三个不同配置情况的ppl对比： + +| small config | train | valid | test | +| :------------- | :---------: | :--------: | :----------: | +| paddle | 40.962 | 118.111 | 112.617 | +| tensorflow | 40.492 | 118.329 | 113.788 | + +| medium config | train | valid | test | +| :------------- | :---------: | :--------: | :----------: | +| paddle | 45.620 | 87.398 | 83.682 | +| tensorflow | 45.594 | 87.363 | 84.015 | + +| large config | train | valid | test | +| :------------- | :---------: | :--------: | :----------: | +| paddle | 37.221 | 82.358 | 78.137 | +| tensorflow | 38.342 | 82.311 | 78.121 | + +## 3. 数据集 + +此任务的数据集合是采用ptb dataset，下载地址为: http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz + + +# 快速开始 + +## 1. 安装说明 + +### Paddle安装 +本项目依赖于 Paddle Fluid, 关于PaddlePaddle框架的安装教程，详见[PaddlePaddle官方网站](http://paddlepaddle.org/documentation/docs/zh/1.3/beginners_guide/install/index_cn.html)。 +### 安装代码 +### 环境依赖 + +## 2. 开始第一次模型调用 + +### 数据准备 +为了方便开发者进行测试，我们提供了数据下载脚本。用户也可以自行下载数据，并解压。 + +``` +cd data; sh download_data.sh +``` + +### 训练或fine-tune +任务训练启动命令如下： +``` +sh run.sh +``` +需要指定数据的目录，模型的大小(默认为small，用户可以选择medium， 或者large)。 + +# 进阶使用 +## 1. 任务定义与建模 +此任务目的是给定一个输入的词序列，预测下一个词出现的概率。 + +## 2. 模型原理介绍 +此任务采用了序列任务常用的rnn网络，实现了一个两层的lstm网络，然后lstm的结果去预测下一个词出现的概率。 + +由于数据的特殊性，每一个batch的last hidden和last cell会被作为下一个batch 的init hidden 和 init cell，数据的特殊性下节会介绍。 + + +## 3. 数据格式说明 +此任务的数据格式比较简单，每一行为一个已经分好词（英文的tokenize）的词序列。 + +目前的句子示例如下图所示: +``` +aer banknote berlitz calloway centrust cluett fromstein gitano guterman hydro-quebec ipo kia memotec mlx nahb punts rake regatta rubens sim snack-food ssangyong swapo wachter +pierre N years old will join the board as a nonexecutive director nov. N +mr. is chairman of n.v. the dutch publishing group +``` + +特殊说明：ptb的数据比较特殊，ptb的数据来源于一些文章，相邻的句子可能来源于一个段落或者相邻的段落，ptb 数据不能做shuffle + + + +## 4. 目录结构 + +```text +. +├── README.md # 文档 +├── run.sh # 启动脚本 +├── train.py # 训练代码 +├── reader.py # 数据读取 +├── args.py # 参数读取 +└── data # 数据下载 +../ +└── models + └── language_model + └── lm_model.py # 模型定义文件 +``` + +## 5. 如何组建自己的模型 ++ **自定义数据：** 关于数据，如果可以把自己的数据先进行分词（或者tokenize），然后放入到data目录下，并修改reader.py中文件的名称，如果句子之间没有关联，用户可以将`train.py`中更新的代码注释掉。 + ``` + init_hidden = np.array(fetch_outs[1]) + init_cell = np.array(fetch_outs[2]) + ``` + ++ **网络结构更改：** 网络只实现了基于lstm的语言模型，用户可以自己的需求更换为gru或者self等网络结构，这些实现都是在lm_model.py 中定义 + + +# 其他 + +## Copyright and License +Copyright 2017 Baidu.com, Inc. All Rights Reserved + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +## 如何贡献代码 + +如果你可以修复某个issue或者增加一个新功能，欢迎给我们提交PR。如果对应的PR被接受了，我们将根据贡献的质量和难度进行打分（0-5分，越高越好）。如果你累计获得了10分，可以联系我们获得面试机会或者为你写推荐信。 diff --git a/PaddleNLP/language_model/lstm/args.py b/PaddleNLP/language_model/args.py similarity index 100% rename from PaddleNLP/language_model/lstm/args.py rename to PaddleNLP/language_model/args.py diff --git a/PaddleNLP/language_model/lstm/data/download_data.sh b/PaddleNLP/language_model/data/download_data.sh similarity index 100% rename from PaddleNLP/language_model/lstm/data/download_data.sh rename to PaddleNLP/language_model/data/download_data.sh diff --git a/PaddleNLP/language_model/lstm/reader.py b/PaddleNLP/language_model/reader.py similarity index 100% rename from PaddleNLP/language_model/lstm/reader.py rename to PaddleNLP/language_model/reader.py diff --git a/PaddleNLP/language_model/run.sh b/PaddleNLP/language_model/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..394f5f8320c02f0c48a7d6928c458986fe7bfbdf --- /dev/null +++ b/PaddleNLP/language_model/run.sh @@ -0,0 +1,12 @@ +#!/bin/bash +export CUDA_VISIBLE_DEVICES=0 + +function run_train() { + echo "training" + python train.py \ + --data_path data/simple-examples/data/ \ + --model_type small \ + --use_gpu True +} + +run_train \ No newline at end of file diff --git a/PaddleNLP/language_model/lstm/train.py b/PaddleNLP/language_model/train.py similarity index 99% rename from PaddleNLP/language_model/lstm/train.py rename to PaddleNLP/language_model/train.py index 2599a92c7a49af28776bbd2e4886c2009e07fa9f..c6e56467b3d7640b5b656ec454f7e0d0169060c2 100644 --- a/PaddleNLP/language_model/lstm/train.py +++ b/PaddleNLP/language_model/train.py @@ -40,7 +40,8 @@ import os os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" from args import * -import lm_model +sys.path.append("../") +from models.language_model import lm_model import logging import pickle diff --git a/PaddleNLP/language_representations_kit b/PaddleNLP/language_representations_kit new file mode 160000 index 0000000000000000000000000000000000000000..b9dae026c25602b96adf7ee776ff9f894c912338 --- /dev/null +++ b/PaddleNLP/language_representations_kit @@ -0,0 +1 @@ +Subproject commit b9dae026c25602b96adf7ee776ff9f894c912338 diff --git a/PaddleNLP/lexical_analysis/README.md b/PaddleNLP/lexical_analysis/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8d56386ce49e340432277c90684442bfef0bd7ea --- /dev/null +++ b/PaddleNLP/lexical_analysis/README.md @@ -0,0 +1,149 @@ +# 中文词法分析 + +## 1. 简介 +Lexical Analysis of Chinese，简称 LAC，是一个联合的词法分析模型，能整体性地完成中文分词、词性标注、专名识别任务。效果方面，分词、词性标注、专名识别的整体准确率 88.0%，召回率 88.7%，F1 值 88.4%。此外，我们在百度开放的 [ERNIE](https://github.com/PaddlePaddle/LARK/tree/develop/ERNIE) 模型上 finetune，效果可以提升到准确率 92.0%，召回率 92.0%，F1 值 92.0%。可通过 [AI开放平台-词法分析](http://ai.baidu.com/tech/nlp/lexical) 线上体验。 + +## 2. 快速开始 +本项目依赖 Paddle 1.3.2，安装请参考官网 [快速安装](http://www.paddlepaddle.org/paddle#quick-start)。 + +### 基础模型 +运行下载数据和模型的脚本， +```bash +sh downloads.sh +``` +会生成下面几个文件夹， +```test +./data/ # 数据文件夹 +./model_baseline/ # lexical analysis 模型文件 +./model_finetuned/ # lexical analysis 在 ERNIE 上 finetune 的模型文件 +./pretrained/ # ERNIE 发布的 pretrained 模型 +``` + +模型文件也可以很方便地通过 [PaddleHub](https://github.com/PaddlePaddle/PaddleHub) 的方式来获取，参考下面的命令示例： +```bash +# install paddlehub +pip install paddlehub + +# download baseline model +hub download lexical_analysis + +# download ERNIE finetuned model +hub download lexical_analysis_finetuned +``` + +我们基于百度的海量数据训练了一个词法分析的模型，可以直接用这个模型对开放的测试集 ./data/test.tsv 进行验证， +```bash +sh run.sh test +``` + +也可以用该模型预测新的数据， +```bash +sh run.sh infer +``` + +用户也可以选择在自己的数据集上训练自己的词法分析模型，这里我们提供用于训练的脚本和代码， +``` +sh run.sh train +``` + +### 使用 ERNIE 进行 finetune +原 ERNIE 开放的模型参考 [ERNIE](https://github.com/PaddlePaddle/LARK/tree/develop/ERNIE)，这里我们为了方便，把下载的命令也放到 `download.sh` 中了。下载的预训练的 ERNIE 模型文件会放在 `./pretrained/` 目录中。 + +词法分析的模型在 `ERNIE` 上 finetune 之后取得了不错的提升效果，这里我们也开放此模型，模型放在了 `./model_finetuned` 中。运行下面的脚本在测试集上验证效果， +```bash +sh run_ernie.sh test +``` + +也可以用该模型预测新的数据， +```bash +sh run_ernie.sh infer +``` + +当然，您也可以用自己的数据去 finetune 在 ERNIE 上的效果， +```bash +sh run_ernie.sh train +``` + + +## 3. 进阶使用 + +### 任务定义与建模 +词法分析任务的输入是一个字符串（我们后面使用『句子』来指代它），而输出是句子中的词边界和词性、实体类别。序列标注是词法分析的经典建模方式。我们使用基于 GRU 的网络结构学习特征，将学习到的特征接入 CRF 解码层完成序列标注。CRF 解码层本质上是将传统 CRF 中的线性模型换成了非线性神经网络，基于句子级别的似然概率，因而能够更好的解决标记偏置问题。模型要点如下，具体细节请参考 `run_sequence_labeling.py` 代码。 +1. 输入采用 one-hot 方式表示，每个字以一个 id 表示 +2. one-hot 序列通过字表，转换为实向量表示的字向量序列； +3. 字向量序列作为双向 GRU 的输入，学习输入序列的特征表示，得到新的特性表示序列，我们堆叠了两层双向GRU以增加学习能力； +4. CRF 以 GRU 学习到的特征为输入，以标记序列为监督信号，实现序列标注。 + +词性和专名类别标签集合如下表，其中词性标签 24 个（小写字母），专名类别标签 4 个（大写字母）。这里需要说明的是，人名、地名、机名和时间四个类别，在上表中存在两套标签（PER / LOC / ORG / TIME 和 nr / ns / nt / t），被标注为第二套标签的词，是模型判断为低置信度的人名、地名、机构名和时间词。开发者可以基于这两套标签，在四个类别的准确、召回之间做出自己的权衡。 + +| 标签 | 含义 | 标签 | 含义 | 标签 | 含义 | 标签 | 含义 | +| ---- | -------- | ---- | -------- | ---- | -------- | ---- | -------- | +| n | 普通名词 | f | 方位名词 | s | 处所名词 | t | 时间 | +| nr | 人名 | ns | 地名 | nt | 机构名 | nw | 作品名 | +| nz | 其他专名 | v | 普通动词 | vd | 动副词 | vn | 名动词 | +| a | 形容词 | ad | 副形词 | an | 名形词 | d | 副词 | +| m | 数量词 | q | 量词 | r | 代词 | p | 介词 | +| c | 连词 | u | 助词 | xc | 其他虚词 | w | 标点符号 | +| PER | 人名 | LOC | 地名 | ORG | 机构名 | TIME | 时间 | + +### 模型原理介绍 +上面介绍的模型原理如下图所示：
+ + +![GRU-CRF-MODEL](./gru-crf-model.png) + +### 数据格式 +训练使用的数据可以由用户根据实际的应用场景，自己组织数据。除了第一行是 `text_a\tlabel` 固定的开头，后面的每行数据都是由两列组成，以制表符分隔，第一列是 utf-8 编码的中文文本，以 `\002` 分割，第二列是对应每个字的标注，以 `\002` 分隔。我们采用 IOB2 标注体系，即以 X-B 作为类型为 X 的词的开始，以 X-I 作为类型为 X 的词的持续，以 O 表示不关注的字（实际上，在词性、专名联合标注中，不存在 O ）。示例如下： + +```text +除\002了\002他\002续\002任\002十\002二\002届\002政\002协\002委\002员\002,\002马\002化\002腾\002,\002雷\002军\002,\002李\002彦\002宏\002也\002被\002推\002选\002为\002新\002一\002届\002全\002国\002人\002大\002代\002表\002或\002全\002国\002政\002协\002委\002员 p-B\002p-I\002r-B\002v-B\002v-I\002m-B\002m-I\002m-I\002ORG-B\002ORG-I\002n-B\002n-I\002w-B\002PER-B\002PER-I\002PER-I\002w-B\002PER-B\002PER-I\002w-B\002PER-B\002PER-I\002PER-I\002d-B\002p-B\002v-B\002v-I\002v-B\002a-B\002m-B\002m-I\002ORG-B\002ORG-I\002ORG-I\002ORG-I\002n-B\002n-I\002c-B\002n-B\002n-I\002ORG-B\002ORG-I\002n-B\002n-I +``` + ++ 我们随同代码一并发布了完全版的模型和相关的依赖数据。但是，由于模型的训练数据过于庞大，我们没有发布训练数据，仅在`data`目录下放置少数样本用以示例输入数据格式。 + ++ 模型依赖数据包括： + 1. 输入文本的词典，在`conf`目录下，对应`word.dic` + 2. 对输入文本中特殊字符进行转换的字典，在`conf`目录下，对应`q2b.dic` + 3. 标记标签的词典,在`conf`目录下，对应`tag.dic` + ++ 在训练和预测阶段，我们都需要进行原始数据的预处理，具体处理工作包括： + + 1. 从原始数据文件中抽取出句子和标签，构造句子序列和标签序列 + 2. 将句子序列中的特殊字符进行转换 + 3. 依据词典获取词对应的整数索引 + +### 代码结构说明 +```text +. +├── README.md # 本文档 +├── conf/ # 词典目录 +├── data/ # 存放数据集的目录 +├── downloads.sh # 用于下载数据和模型的脚本 +├── gru-crf-model.png # README 用到的模型图片 +├── reader.py # 文件读取相关函数 +├── run_ernie_sequence_labeling.py # 用于 finetune ERNIE 的代码 +├── run_ernie.sh # 启用上面代码的脚本 +├── run_sequence_labeling.py # 词法分析任务代码 +├── run.sh # 启用上面代码的脚本 +└── utils.py # 常用工具函数 +``` + +### 如何组建自己的模型 +如果您需要定制自己的词法分析模型，可以在 `../models/sequence_labeling/nets.py` 中添加自己的网络结构，具体接口要求可以参考默认的 `lex_net` 函数。 + +## 4. 其他 +### 在论文中引用 LAC + +如果您的学术工作成果中使用了 LAC，请您增加下述引用。我们非常欣慰 LAC 能够对您的学术工作带来帮助。 + +```text +@article{jiao2018LAC, + title={Chinese Lexical Analysis with Deep Bi-GRU-CRF Network}, + author={Jiao, Zhenyu and Sun, Shuqi and Sun, Ke}, + journal={arXiv preprint arXiv:1807.01882}, + year={2018}, + url={https://arxiv.org/abs/1807.01882} +} +``` +### 如何贡献代码 +如果你可以修复某个 issue 或者增加一个新功能，欢迎给我们提交PR。如果对应的PR被接受了，我们将根据贡献的质量和难度 进行打分（0-5分，越高越好）。如果你累计获得了 10 分，可以联系我们获得面试机会或为你写推荐信。 diff --git a/PaddleNLP/text_matching_on_quora/__init__.py b/PaddleNLP/lexical_analysis/conf/customization.dic similarity index 100% rename from PaddleNLP/text_matching_on_quora/__init__.py rename to PaddleNLP/lexical_analysis/conf/customization.dic diff --git a/PaddleNLP/lexical_analysis/conf/customization.dic.example b/PaddleNLP/lexical_analysis/conf/customization.dic.example new file mode 100644 index 0000000000000000000000000000000000000000..22ebf8a4b15ec51870c7b0d683963e1f9e75d673 --- /dev/null +++ b/PaddleNLP/lexical_analysis/conf/customization.dic.example @@ -0,0 +1,3 @@ +[D:MONTH] +月 +月份 diff --git a/PaddleNLP/lexical_analysis/conf/label_map.json b/PaddleNLP/lexical_analysis/conf/label_map.json new file mode 100644 index 0000000000000000000000000000000000000000..52011d81f0ac4b70188acdddbb6e4f42208586d1 --- /dev/null +++ b/PaddleNLP/lexical_analysis/conf/label_map.json @@ -0,0 +1 @@ +{"d-B": 8, "c-I": 7, "PER-I": 49, "nr-B": 16, "u-B": 36, "c-B": 6, "nr-I": 17, "an-I": 5, "ns-B": 18, "vn-I": 43, "w-B": 44, "an-B": 4, "PER-B": 48, "vn-B": 42, "ns-I": 19, "a-I": 1, "r-B": 30, "xc-B": 46, "LOC-B": 50, "ad-I": 3, "nz-B": 24, "u-I": 37, "a-B": 0, "ad-B": 2, "vd-I": 41, "nw-B": 22, "m-I": 13, "d-I": 9, "n-B": 14, "nz-I": 25, "vd-B": 40, "nw-I": 23, "n-I": 15, "nt-B": 20, "ORG-I": 53, "nt-I": 21, "ORG-B": 52, "LOC-I": 51, "t-B": 34, "TIME-I": 55, "O": 56, "s-I": 33, "f-I": 11, "TIME-B": 54, "t-I": 35, "f-B": 10, "s-B": 32, "r-I": 31, "q-B": 28, "v-I": 39, "v-B": 38, "w-I": 45, "q-I": 29, "p-B": 26, "xc-I": 47, "m-B": 12, "p-I": 27} \ No newline at end of file diff --git a/PaddleNLP/lexical_analysis/conf/q2b.dic b/PaddleNLP/lexical_analysis/conf/q2b.dic new file mode 100644 index 0000000000000000000000000000000000000000..d1f14691e228be8a5d5d1385ad968bcafab0c07a --- /dev/null +++ b/PaddleNLP/lexical_analysis/conf/q2b.dic @@ -0,0 +1,172 @@ +　 +、 , +。 . +— - +～ ~ +‖ | +… . +‘ ' +’ ' +“ " +” " +〔 ( +〕 ) +〈 < +〉 > +「 ' +」 ' +『 " +』 " +〖 [ +〗 ] +【 [ +】 ] +∶ : +＄ $ +！ ! +＂ " +＃ # +％ % +＆ & +＇ ' +（ ( +） ) +＊ * +＋ + +， , +－ - +． . +／ / +０ 0 +１ 1 +２ 2 +３ 3 +４ 4 +５ 5 +６ 6 +７ 7 +８ 8 +９ 9 +： : +； ; +＜ < +＝ = +＞ > +？ ? +＠ @ +Ａ a +Ｂ b +Ｃ c +Ｄ d +Ｅ e +Ｆ f +Ｇ g +Ｈ h +Ｉ i +Ｊ j +Ｋ k +Ｌ l +Ｍ m +Ｎ n +Ｏ o +Ｐ p +Ｑ q +Ｒ r +Ｓ s +Ｔ t +Ｕ u +Ｖ v +Ｗ w +Ｘ x +Ｙ y +Ｚ z +［ [ +＼ \ +］ ] +＾ ^ +＿ _ +｀ ` +ａ a +ｂ b +ｃ c +ｄ d +ｅ e +ｆ f +ｇ g +ｈ h +ｉ i +ｊ j +ｋ k +ｌ l +ｍ m +ｎ n +ｏ o +ｐ p +ｑ q +ｒ r +ｓ s +ｔ t +ｕ u +ｖ v +ｗ w +ｘ x +ｙ y +ｚ z +｛ { +｜ | +｝ } +￣ ~ +〝 " +〞 " +﹐ , +﹑ , +﹒ . +﹔ ; +﹕ : +﹖ ? +﹗ ! +﹙ ( +﹚ ) +﹛ { +﹜ { +﹝ [ +﹞ ] +﹟ # +﹠ & +﹡ * +﹢ + +﹣ - +﹤ < +﹥ > +﹦ = +﹨ \ +﹩ $ +﹪ % +﹫ @ + , +A a +B b +C c +D d +E e +F f +G g +H h +I i +J j +K k +L l +M m +N n +O o +P p +Q q +R r +S s +T t +U u +V v +W w +X x +Y y +Z z diff --git a/PaddleNLP/lexical_analysis/conf/strong_punc.dic b/PaddleNLP/lexical_analysis/conf/strong_punc.dic new file mode 100644 index 0000000000000000000000000000000000000000..595e2f673bf432fd86f8d2310d5a9adc5f824a17 --- /dev/null +++ b/PaddleNLP/lexical_analysis/conf/strong_punc.dic @@ -0,0 +1,5 @@ +! +。 +！ +; +； diff --git a/PaddleNLP/lexical_analysis/conf/tag.dic b/PaddleNLP/lexical_analysis/conf/tag.dic new file mode 100644 index 0000000000000000000000000000000000000000..753fa9670e92b45a9daa54ddcb1e2f06491a792e --- /dev/null +++ b/PaddleNLP/lexical_analysis/conf/tag.dic @@ -0,0 +1,57 @@ +0 a-B +1 a-I +2 ad-B +3 ad-I +4 an-B +5 an-I +6 c-B +7 c-I +8 d-B +9 d-I +10 f-B +11 f-I +12 m-B +13 m-I +14 n-B +15 n-I +16 nr-B +17 nr-I +18 ns-B +19 ns-I +20 nt-B +21 nt-I +22 nw-B +23 nw-I +24 nz-B +25 nz-I +26 p-B +27 p-I +28 q-B +29 q-I +30 r-B +31 r-I +32 s-B +33 s-I +34 t-B +35 t-I +36 u-B +37 u-I +38 v-B +39 v-I +40 vd-B +41 vd-I +42 vn-B +43 vn-I +44 w-B +45 w-I +46 xc-B +47 xc-I +48 PER-B +49 PER-I +50 LOC-B +51 LOC-I +52 ORG-B +53 ORG-I +54 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b/PaddleNLP/lexical_analysis/downloads.sh @@ -0,0 +1,43 @@ +#!/bin/bash + +# download baseline model file to ./model_baseline/ +if [ -d ./model_baseline/ ] +then + echo "./model_baseline/ directory already existed, ignore download" +else + wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/lexical_analysis-1.0.0.tar.gz + tar xvf lexical_analysis-1.0.0.tar.gz + /bin/rm lexical_analysis-1.0.0.tar.gz +fi + +# download dataset file to ./data/ +if [ -d ./data/ ] +then + echo "./data/ directory already existed, ignore download" +else + wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/lexical_analysis-dataset-1.0.0.tar.gz + tar xvf lexical_analysis-dataset-1.0.0.tar.gz + /bin/rm lexical_analysis-dataset-1.0.0.tar.gz +fi + +# download ERNIE pretrained model to ./pretrained/ +if [ -d ./pretrained/ ] +then + echo "./pretrained/ directory already existed, ignore download" +else + mkdir ./pretrained/ && cd ./pretrained/ + wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/ERNIE_stable-1.0.1.tar.gz + tar xvf ERNIE_stable-1.0.1.tar.gz + /bin/rm ERNIE_stable-1.0.1.tar.gz + cd ../ +fi + +# download finetuned model file to ./model_finetuned/ +if [ -d ./model_finetuned/ ] +then + echo "./model_finetuned/ directory already existed, ignored download" +else + wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/lexical_analysis_finetuned-1.0.0.tar.gz + tar xvf lexical_analysis_finetuned-1.0.0.tar.gz + /bin/rm lexical_analysis_finetuned-1.0.0.tar.gz +fi diff --git a/PaddleNLP/lexical_analysis/evaluate.py b/PaddleNLP/lexical_analysis/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..108dafa98ce6c9de6e8e8ca5d4e2692eff93d2f7 --- /dev/null +++ b/PaddleNLP/lexical_analysis/evaluate.py @@ -0,0 +1,266 @@ +#coding=utf-8 +""" +evaluate wordseg for LAC and other open-source wordseg tools +""" +from __future__ import print_function +from __future__ import division + +import sys +import os + + +def to_unicode(string): + """ string compatibility for python2 & python3 """ + if sys.version_info.major == 2 and isinstance(string, str): + return string.decode("utf-8") + else: + return string + + +def to_set(words): + """ cut list to set of (string, off) """ + off = 0 + s= set() + for w in words: + if w: + s.add((off, w)) + off += len(w) + return s + + +def cal_fscore(standard, result, split_delim=" "): + """ caculate fscore for wordseg + Param: standard, list of str, ground-truth labels , e.g. ["a b c", "d ef g"] + Param: result, list of str, predicted result, e.g. ["ab c", "d e fg"] + """ + assert len(standard) == len(result) + std, rst, cor = 0, 0, 0 + for s, r in zip(standard, result): + s = to_set(s.rstrip().split(split_delim)) + r = to_set(r.rstrip().split(split_delim)) + std += len(s) + rst += len(r) + cor += len(s & r) + p = 1.0 * cor / rst + r = 1.0 * cor / std + f = 2 * p * r / (p + r) + + print("std, rst, cor = %d, %d, %d" % (std, rst, cor)) + print("precision = %.5f, recall = %.5f, f1 = %.5f" % (p, r, f)) + #print("| | %.5f | %.5f | %.5f |" % (p, r, f)) + print("") + + return p, r, f + + +def load_testdata(datapath="./data/test_data/test_part"): + """none""" + sentences = [] + sent_seg_list = [] + for line in open(datapath): + sent, label = line.strip().split("\t") + sentences.append(sent) + + sent = to_unicode(sent) + label = label.split(" ") + assert len(sent) == len(label) + + # parse segment + words = [] + current_word = "" + for w, l in zip(sent, label): + if l.endswith("-B"): + if current_word != "": + words.append(current_word) + current_word = w + elif l.endswith("-I"): + current_word += w + elif l.endswith("-O"): + if current_word != "": + words.append(current_word) + words.append(w) + current_word = "" + else: + raise ValueError("wrong label: " + l) + if current_word != "": + words.append(current_word) + sent_seg = " ".join(words) + sent_seg_list.append(sent_seg) + print("got %d lines" % (len(sent_seg_list))) + return sent_seg_list, sentences + + +def get_lac_result(): + """ + get LAC predicted result by: + `sh run.sh | tail -n 100 > result.txt` + """ + sent_seg_list = [] + for line in open("./result.txt"): + line = line.strip().split(" ") + words = [pair.split("/")[0] for pair in line] + labels = [pair.split("/")[1] for pair in line] + sent_seg = " ".join(words) + sent_seg = to_unicode(sent_seg) + sent_seg_list.append(sent_seg) + return sent_seg_list + + +def get_jieba_result(sentences): + """ + Ref to: https://github.com/fxsjy/jieba + Install by `pip install jieba` + """ + import jieba + preds = [] + for sentence in sentences: + sent_seg = " ".join(jieba.lcut(sentence)) + sent_seg = to_unicode(sent_seg) + preds.append(sent_seg) + return preds + + +def get_thulac_result(sentences): + """ + Ref to: http://thulac.thunlp.org/ + Install by: `pip install thulac` + """ + import thulac + preds = [] + lac = thulac.thulac(seg_only=True) + for sentence in sentences: + sent_seg = lac.cut(sentence, text=True) + sent_seg = to_unicode(sent_seg) + preds.append(sent_seg) + return preds + + +def get_pkuseg_result(sentences): + """ + Ref to: https://github.com/lancopku/pkuseg-python + Install by: `pip3 install pkuseg` + You should noticed that pkuseg-python only support python3 + """ + import pkuseg + seg = pkuseg.pkuseg() + preds = [] + for sentence in sentences: + sent_seg = " ".join(seg.cut(sentence)) + sent_seg = to_unicode(sent_seg) + preds.append(sent_seg) + return preds + + +def get_hanlp_result(sentences): + """ + Ref to: https://github.com/hankcs/pyhanlp + Install by: pip install pyhanlp + (Before using pyhanlp, you need to download the model manully.) + """ + from pyhanlp import HanLP + preds = [] + for sentence in sentences: + arraylist = HanLP.segment(sentence) + sent_seg = " ".join([term.toString().split("/")[0] for term in arraylist]) + sent_seg = to_unicode(sent_seg) + preds.append(sent_seg) + return preds + + +def get_nlpir_result(sentences): + """ + Ref to: https://github.com/tsroten/pynlpir + Install by `pip install pynlpir` + Run `pynlpir update` to update License + """ + import pynlpir + pynlpir.open() + preds = [] + for sentence in sentences: + sent_seg = " ".join(pynlpir.segment(sentence, pos_tagging=False)) + sent_seg = to_unicode(sent_seg) + preds.append(sent_seg) + return preds + + +def get_ltp_result(sentences): + """ + Ref to: https://github.com/HIT-SCIR/pyltp + 1. Install by `pip install pyltp` + 2. Download models from http://ltp.ai/download.html + """ + from pyltp import Segmentor + segmentor = Segmentor() + model_path = "./ltp_data_v3.4.0/cws.model" + if not os.path.exists(model_path): + raise IOError("LTP Model do not exist! Download it first!") + segmentor.load(model_path) + preds = [] + for sentence in sentences: + sent_seg = " ".join(segmentor.segment(sentence)) + sent_seg = to_unicode(sent_seg) + preds.append(sent_seg) + segmentor.release() + + return preds + + +def print_array(array): + """print some case""" + for i in [1, 10, 20, 30, 40]: + print("case " + str(i) + ": \t" + array[i]) + + +def evaluate_all(): + """none""" + standard, sentences = load_testdata() + print_array(standard) + + # evaluate lac + preds = get_lac_result() + print("lac result:") + print_array(preds) + cal_fscore(standard=standard, result=preds) + + # evaluate jieba + preds = get_jieba_result(sentences) + print("jieba result") + print_array(preds) + cal_fscore(standard=standard, result=preds) + + # evaluate thulac + preds = get_thulac_result(sentences) + print("thulac result") + print_array(preds) + cal_fscore(standard=standard, result=preds) + + # evaluate pkuseg, but pyuseg only support python3 + if sys.version_info.major == 3: + preds = get_pkuseg_result(sentences) + print("pkuseg result") + print_array(preds) + cal_fscore(standard=standard, result=preds) + + # evaluate HanLP + preds = get_hanlp_result(sentences) + print("HanLP result") + print_array(preds) + cal_fscore(standard=standard, result=preds) + + # evaluate NLPIR + preds = get_nlpir_result(sentences) + print("NLPIR result") + print_array(preds) + cal_fscore(standard=standard, result=preds) + + # evaluate LTP + preds = get_ltp_result(sentences) + print("LTP result") + print_array(preds) + cal_fscore(standard=standard, result=preds) + + +if __name__ == "__main__": + import ipdb + #ipdb.set_trace() + evaluate_all() diff --git a/PaddleNLP/lexical_analysis/gru-crf-model.png b/PaddleNLP/lexical_analysis/gru-crf-model.png new file mode 100644 index 0000000000000000000000000000000000000000..1ca7d5cf0c5381294915dc13bfb4fbd2bf7e99dc Binary files /dev/null and b/PaddleNLP/lexical_analysis/gru-crf-model.png differ diff --git a/PaddleNLP/lexical_analysis/reader.py b/PaddleNLP/lexical_analysis/reader.py new file mode 100644 index 0000000000000000000000000000000000000000..fbedd41e120777661e755ce3fa9f0ab2a3ed2bed --- /dev/null +++ b/PaddleNLP/lexical_analysis/reader.py @@ -0,0 +1,121 @@ +""" +The file_reader converts raw corpus to input. +""" +import os +import argparse +import __future__ +import io +import glob + + +def load_kv_dict(dict_path, + reverse=False, delimiter="\t", key_func=None, value_func=None): + """ + Load key-value dict from file + """ + result_dict = {} + for line in io.open(dict_path, "r", encoding='utf8'): + terms = line.strip("\n").split(delimiter) + if len(terms) != 2: + continue + if reverse: + value, key = terms + else: + key, value = terms + if key in result_dict: + raise KeyError("key duplicated with [%s]" % (key)) + if key_func: + key = key_func(key) + if value_func: + value = value_func(value) + result_dict[key] = value + return result_dict + + +class Dataset(object): + """data reader""" + def __init__(self, args, mode="train"): + # read dict + self.word2id_dict = load_kv_dict(args.word_dict_path, reverse=True, value_func=int) + self.id2word_dict = load_kv_dict(args.word_dict_path) + self.label2id_dict = load_kv_dict(args.label_dict_path, reverse=True, value_func=int) + self.id2label_dict = load_kv_dict(args.label_dict_path) + self.word_replace_dict = load_kv_dict(args.word_rep_dict_path) + + @property + def vocab_size(self): + """vocabuary size""" + return max(self.word2id_dict.values()) + 1 + + @property + def num_labels(self): + """num_labels""" + return max(self.label2id_dict.values()) + 1 + + def get_num_examples(self, filename): + """num of line of file""" + return sum(1 for line in open(filename, "r")) + + def word_to_ids(self, words): + """convert word to word index""" + word_ids = [] + for word in words: + if word in self.word_replace_dict: + word = self.word_replace_dict[word] + if word not in self.word2id_dict: + word = "OOV" + word_id = self.word2id_dict[word] + word_ids.append(word_id) + return word_ids + + def label_to_ids(self, labels): + """convert label to label index""" + label_ids = [] + for label in labels: + if label not in self.label2id_dict: + label = "O" + label_id = self.label2id_dict[label] + label_ids.append(label_id) + return label_ids + + + def file_reader(self, filename, max_seq_len=64, mode="train"): + """ + yield (word_idx, target_idx) one by one from file, + or yield (word_idx, ) in `infer` mode + """ + def wrapper(): + fread = io.open(filename, "r", encoding="utf-8") + headline = next(fread) + headline = headline.strip().split("\t") + if mode == "infer": + assert len(headline) == 1 and headline[0] == "text_a" + for line in fread: + words = line.strip("\n").split("\002") + word_ids = self.word_to_ids(words) + yield word_ids[0:max_seq_len] + else: + assert len(headline) == 2 and headline[0] == "text_a" and headline[1] == "label" + for line in fread: + words, labels = line.strip("\n").split("\t") + word_ids = self.word_to_ids(words.split("\002")) + label_ids = self.label_to_ids(labels.split("\002")) + assert len(word_ids) == len(label_ids) + yield word_ids[0:max_seq_len], label_ids[0:max_seq_len] + fread.close() + + return wrapper + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(__doc__) + parser.add_argument("--word_dict_path", type=str, default="./conf/word.dic", help="word dict") + parser.add_argument("--label_dict_path", type=str, default="./conf/tag.dic", help="label dict") + parser.add_argument("--word_rep_dict_path", type=str, default="./conf/q2b.dic", help="word replace dict") + args = parser.parse_args() + dataset = Dataset(args) + data_generator = dataset.file_reader("data/train.tsv") + for word_idx, target_idx in data_generator(): + print(word_idx, target_idx) + print(len(word_idx), len(target_idx)) + break diff --git a/PaddleNLP/lexical_analysis/run.sh b/PaddleNLP/lexical_analysis/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..64fa33beaa6248295251a73dafdc36317265e000 --- /dev/null +++ b/PaddleNLP/lexical_analysis/run.sh @@ -0,0 +1,98 @@ +#!/bin/bash +export FLAGS_fraction_of_gpu_memory_to_use=0.5 +export FLAGS_eager_delete_tensor_gb=0.0 +export FLAGS_fast_eager_deletion_mode=1 +#export CUDA_VISIBLE_DEVICES=0,1,2,3 + +#alias python='./anaconda2/bin/python' + +function run_train() { + echo "training" + python run_sequence_labeling.py \ + --do_train True \ + --do_test True \ + --do_infer False \ + --train_data ./data/train.tsv \ + --test_data ./data/test.tsv \ + --model_save_dir ./models \ + --valid_model_per_batches 1000 \ + --save_model_per_batches 10000 \ + --batch_size 100 \ + --epoch 10 \ + --use_gpu 0 \ + --traindata_shuffle_buffer 200000 \ + --word_emb_dim 768 \ + --grnn_hidden_dim 768 \ + --bigru_num 2 \ + --base_learning_rate 1e-3 \ + --emb_learning_rate 5 \ + --crf_learning_rate 0.2 \ + --word_dict_path ./conf/word.dic \ + --label_dict_path ./conf/tag.dic \ + --word_rep_dict_path ./conf/q2b.dic +} + +function run_test() { + echo "testing" + echo "this may cost about 5 minutes if run on you CPU machine" + python run_sequence_labeling.py \ + --do_train False \ + --do_test True \ + --do_infer False \ + --batch_size 80 \ + --word_emb_dim 768 \ + --grnn_hidden_dim 768 \ + --bigru_num 2 \ + --use_gpu 0 \ + --init_checkpoint ./model_baseline \ + --test_data ./data/test.tsv \ + --word_dict_path ./conf/word.dic \ + --label_dict_path ./conf/tag.dic \ + --word_rep_dict_path ./conf/q2b.dic +} + + +function run_infer() { + echo "infering" + python run_sequence_labeling.py \ + --do_train False \ + --do_test False \ + --do_infer True \ + --batch_size 80 \ + --word_emb_dim 768 \ + --grnn_hidden_dim 768 \ + --bigru_num 2 \ + --use_gpu 0 \ + --init_checkpoint ./model_baseline/ \ + --infer_data ./data/test.tsv \ + --word_dict_path ./conf/word.dic \ + --label_dict_path ./conf/tag.dic \ + --word_rep_dict_path ./conf/q2b.dic +} + + +function main() { + local cmd=${1:-help} + case "${cmd}" in + train) + run_train "$@"; + ;; + test) + run_test "$@"; + ;; + infer) + run_infer "$@"; + ;; + help) + echo "Usage: ${BASH_SOURCE} {train|test|infer}"; + return 0; + ;; + *) + echo "unsupport command [${cmd}]"; + echo "Usage: ${BASH_SOURCE} {train|test|infer}"; + return 1; + ;; + esac +} + +main "$@" diff --git a/PaddleNLP/lexical_analysis/run_ernie.sh b/PaddleNLP/lexical_analysis/run_ernie.sh new file mode 100644 index 0000000000000000000000000000000000000000..daa450a98ecd70feab14ecfaef12a460ce65d808 --- /dev/null +++ b/PaddleNLP/lexical_analysis/run_ernie.sh @@ -0,0 +1,111 @@ +set -eux + +export FLAGS_eager_delete_tensor_gb=0.0 +export FLAGS_fast_eager_deletion_mode=1 +export FLAGS_sync_nccl_allreduce=1 +export FLAGS_selected_gpus=0 # which GPU to use +export CUDA_VISIBLE_DEVICES=0 + +ERNIE_PRETRAINED_MODEL_PATH=./pretrained/ +ERNIE_FINETUNED_MODEL_PATH=./model_finetuned/ +DATA_PATH=./data/ + +# train +function run_train() { + echo "training" + python run_ernie_sequence_labeling.py \ + --ernie_config_path "${ERNIE_PRETRAINED_MODEL_PATH}/ernie_config.json" \ + --checkpoints "./checkpoints" \ + --init_pretraining_params "${ERNIE_PRETRAINED_MODEL_PATH}/params/" \ + --epoch 10 \ + --save_steps 1000 \ + --validation_steps 1000 \ + --lr 2e-4 \ + --crf_learning_rate 0.2 \ + --init_bound 0.1 \ + --skip_steps 1 \ + --vocab_path "${ERNIE_PRETRAINED_MODEL_PATH}/vocab.txt" \ + --batch_size 64 \ + --random_seed 0 \ + --num_labels 57 \ + --max_seq_len 128 \ + --train_set "${DATA_PATH}/train.tsv" \ + --test_set "${DATA_PATH}/test.tsv" \ + --label_map_config "./conf/label_map.json" \ + --do_lower_case true \ + --use_cuda false \ + --do_train true \ + --do_test true \ + --do_infer false +} + + +function run_test() { + echo "testing" + python run_ernie_sequence_labeling.py \ + --ernie_config_path "${ERNIE_PRETRAINED_MODEL_PATH}/ernie_config.json" \ + --init_pretraining_params "${ERNIE_PRETRAINED_MODEL_PATH}/params/" \ + --init_checkpoint "${ERNIE_FINETUNED_MODEL_PATH}" \ + --init_bound 0.1 \ + --vocab_path "${ERNIE_PRETRAINED_MODEL_PATH}/vocab.txt" \ + --batch_size 64 \ + --random_seed 0 \ + --num_labels 57 \ + --max_seq_len 128 \ + --test_set "${DATA_PATH}/test.tsv" \ + --label_map_config "./conf/label_map.json" \ + --do_lower_case true \ + --use_cuda true \ + --do_train false \ + --do_test true \ + --do_infer false +} + + +function run_infer() { + echo "infering" + python run_ernie_sequence_labeling.py \ + --ernie_config_path "${ERNIE_PRETRAINED_MODEL_PATH}/ernie_config.json" \ + --init_pretraining_params "${ERNIE_PRETRAINED_MODEL_PATH}/params/" \ + --init_checkpoint "${ERNIE_FINETUNED_MODEL_PATH}" \ + --init_bound 0.1 \ + --vocab_path "${ERNIE_PRETRAINED_MODEL_PATH}/vocab.txt" \ + --batch_size 64 \ + --random_seed 0 \ + --num_labels 57 \ + --max_seq_len 128 \ + --infer_set "${DATA_PATH}/test.tsv" \ + --label_map_config "./conf/label_map.json" \ + --do_lower_case true \ + --use_cuda true \ + --do_train false \ + --do_test false \ + --do_infer true +} + + +function main() { + local cmd=${1:-help} + case "${cmd}" in + train) + run_train "$@"; + ;; + test) + run_test "$@"; + ;; + infer) + run_infer "$@"; + ;; + help) + echo "Usage: ${BASH_SOURCE} {train|test|infer}"; + return 0; + ;; + *) + echo "unsupport command [${cmd}]"; + echo "Usage: ${BASH_SOURCE} {train|test|infer}"; + return 1; + ;; + esac +} + +main "$@" diff --git a/PaddleNLP/lexical_analysis/run_ernie_sequence_labeling.py b/PaddleNLP/lexical_analysis/run_ernie_sequence_labeling.py new file mode 100644 index 0000000000000000000000000000000000000000..135c6b27fc058f20584572b3ae1c2276dc31e176 --- /dev/null +++ b/PaddleNLP/lexical_analysis/run_ernie_sequence_labeling.py @@ -0,0 +1,382 @@ +""" +Sentiment Classification Task +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import time +import math +import argparse +import numpy as np +import multiprocessing +import sys + +import paddle +import paddle.fluid as fluid +from collections import namedtuple + +sys.path.append("..") +print(sys.path) +from preprocess.ernie import task_reader + +from models.representation.ernie import ErnieConfig +from models.representation.ernie import ernie_encoder +#from models.representation.ernie import ernie_pyreader +from models.sequence_labeling import nets +import utils + + +# yapf: disable +parser = argparse.ArgumentParser(__doc__) +model_g = utils.ArgumentGroup(parser, "model", "model configuration and paths.") +model_g.add_arg("ernie_config_path", str, "../LARK/ERNIE/config/ernie_config.json", + "Path to the json file for ernie model config.") +model_g.add_arg("lac_config_path", str, None, "Path to the json file for LAC model config.") +model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.") +model_g.add_arg("checkpoints", str, None, "Path to save checkpoints") +model_g.add_arg("init_pretraining_params", str, "pretrained/params/", + "Init pre-training params which preforms fine-tuning from. If the " + "arg 'init_checkpoint' has been set, this argument wouldn't be valid.") + +train_g = utils.ArgumentGroup(parser, "training", "training options.") +train_g.add_arg("epoch", int, 10, "Number of epoches for training.") +train_g.add_arg("save_steps", int, 10000, "The steps interval to save checkpoints.") +train_g.add_arg("validation_steps", int, 1000, "The steps interval to evaluate model performance.") +train_g.add_arg("lr", float, 0.001, "The Learning rate value for training.") +train_g.add_arg("crf_learning_rate", float, 0.2, + "The real learning rate of the embedding layer will be (crf_learning_rate * base_learning_rate).") +train_g.add_arg("init_bound", float, 0.1, "init bound for initialization.") + +log_g = utils.ArgumentGroup(parser, "logging", "logging related") +log_g.add_arg("skip_steps", int, 1, "The steps interval to print loss.") + +data_g = utils.ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options") +data_g.add_arg("vocab_path", str, "../LARK/ERNIE/config/vocab.txt", "Vocabulary path.") +data_g.add_arg("batch_size", int, 3, "Total examples' number in batch for training.") +data_g.add_arg("random_seed", int, 0, "Random seed.") +data_g.add_arg("num_labels", int, 57, "label number") +data_g.add_arg("max_seq_len", int, 512, "Number of words of the longest seqence.") +data_g.add_arg("train_set", str, "./data/train.tsv", "Path to train data.") +data_g.add_arg("test_set", str, "./data/test.tsv", "Path to test data.") +data_g.add_arg("infer_set", str, "./data/test.tsv", "Path to infer data.") +data_g.add_arg("label_map_config", str, "./conf/label_map.json", "label_map_path.") +data_g.add_arg("do_lower_case", bool, True, + "Whether to lower case the input text. Should be True for uncased models and False for cased models.") + +run_type_g = utils.ArgumentGroup(parser, "run_type", "running type options.") +run_type_g.add_arg("use_cuda", bool, True, "If set, use GPU for training.") +run_type_g.add_arg("do_train", bool, True, "Whether to perform training.") +run_type_g.add_arg("do_test", bool, True, "Whether to perform testing.") +run_type_g.add_arg("do_infer", bool, True, "Whether to perform inference.") + +args = parser.parse_args() +# yapf: enable. + +def ernie_pyreader(args, pyreader_name): + """define standard ernie pyreader""" + pyreader = fluid.layers.py_reader( + capacity=50, + shapes=[[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1], + [-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1], + [-1, 1]], + dtypes=['int64', 'int64', 'int64', 'float32', 'int64', 'int64'], + lod_levels=[0, 0, 0, 0, 0, 0], + name=pyreader_name, + use_double_buffer=True) + + (src_ids, sent_ids, pos_ids, input_mask, padded_labels, seq_lens) = fluid.layers.read_file(pyreader) + + words = fluid.layers.sequence_unpad(src_ids, seq_lens) + labels = fluid.layers.sequence_unpad(padded_labels, seq_lens) + + ernie_inputs = { + "src_ids": src_ids, + "sent_ids": sent_ids, + "pos_ids": pos_ids, + "input_mask": input_mask, + "seq_lens": seq_lens + } + return pyreader, ernie_inputs, words, labels + + +def create_model(args, + embeddings, + labels, + is_prediction=False): + + """ + Create Model for LAC based on ERNIE encoder + """ + # sentence_embeddings = embeddings["sentence_embeddings"] + token_embeddings = embeddings["token_embeddings"] + + emission = fluid.layers.fc( + size=args.num_labels, + input=token_embeddings, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.Uniform( + low=-args.init_bound, high=args.init_bound), + regularizer=fluid.regularizer.L2DecayRegularizer( + regularization_coeff=1e-4))) + + crf_cost = fluid.layers.linear_chain_crf( + input=emission, + label=labels, + param_attr=fluid.ParamAttr( + name='crfw', + learning_rate=args.crf_learning_rate)) + loss = fluid.layers.mean(x=crf_cost) + + crf_decode = fluid.layers.crf_decoding( + input=emission, param_attr=fluid.ParamAttr(name='crfw')) + + (precision, recall, f1_score, num_infer_chunks, num_label_chunks, + num_correct_chunks) = fluid.layers.chunk_eval( + input=crf_decode, + label=labels, + chunk_scheme="IOB", + num_chunk_types=int(math.ceil((args.num_labels - 1) / 2.0))) + chunk_evaluator = fluid.metrics.ChunkEvaluator() + chunk_evaluator.reset() + + ret = { + "loss":loss, + "crf_decode":crf_decode, + "chunk_evaluator":chunk_evaluator, + "num_infer_chunks":num_infer_chunks, + "num_label_chunks":num_label_chunks, + "num_correct_chunks":num_correct_chunks + } + + return ret + + +def evaluate(exe, test_program, test_pyreader, test_ret): + """ + Evaluation Function + """ + test_pyreader.start() + test_ret["chunk_evaluator"].reset() + total_loss, precision, recall, f1 = [], [], [], [] + start_time = time.time() + while True: + try: + loss, nums_infer, nums_label, nums_correct = exe.run( + test_program, + fetch_list=[ + test_ret["loss"], + test_ret["num_infer_chunks"], + test_ret["num_label_chunks"], + test_ret["num_correct_chunks"], + ], + ) + total_loss.append(loss) + + test_ret["chunk_evaluator"].update(nums_infer, nums_label, nums_correct) + p, r, f = test_ret["chunk_evaluator"].eval() + precision.append(p) + recall.append(r) + f1.append(f) + + except fluid.core.EOFException: + test_pyreader.reset() + break + end_time = time.time() + print("\t[test] loss: %.5f, P: %.5f, R: %.5f, F1: %.5f, elapsed time: %.3f s" + % (np.mean(total_loss), np.mean(precision), np.mean(recall), np.mean(f1), end_time - start_time)) + + +def main(args): + """ + Main Function + """ + args = parser.parse_args() + ernie_config = ErnieConfig(args.ernie_config_path) + ernie_config.print_config() + + if args.use_cuda: + place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0'))) + dev_count = fluid.core.get_cuda_device_count() + else: + place = fluid.CPUPlace() + dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) + exe = fluid.Executor(place) + + reader = task_reader.SequenceLabelReader( + vocab_path=args.vocab_path, + label_map_config=args.label_map_config, + max_seq_len=args.max_seq_len, + do_lower_case=args.do_lower_case, + in_tokens=False, + random_seed=args.random_seed) + + if not (args.do_train or args.do_test or args.do_infer): + raise ValueError("For args `do_train`, `do_val` and `do_test`, at " + "least one of them must be True.") + + startup_prog = fluid.Program() + if args.random_seed is not None: + startup_prog.random_seed = args.random_seed + + if args.do_train: + num_train_examples = reader.get_num_examples(args.train_set) + max_train_steps = args.epoch * num_train_examples // args.batch_size // dev_count + print("Device count: %d" % dev_count) + print("Num train examples: %d" % num_train_examples) + print("Max train steps: %d" % max_train_steps) + + train_program = fluid.Program() + + with fluid.program_guard(train_program, startup_prog): + with fluid.unique_name.guard(): + # create ernie_pyreader + train_pyreader, ernie_inputs, words, labels = ernie_pyreader(args, pyreader_name='train_reader') + train_pyreader.decorate_tensor_provider( + reader.data_generator( + args.train_set, args.batch_size, args.epoch, shuffle=True, phase="train" + ) + ) + # get ernie_embeddings + embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config) + # user defined model based on ernie embeddings + train_ret = create_model(args, embeddings, labels=labels, is_prediction=False) + + optimizer = fluid.optimizer.Adam(learning_rate=args.lr) + fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=1.0)) + optimizer.minimize(train_ret["loss"]) + + lower_mem, upper_mem, unit = fluid.contrib.memory_usage( + program=train_program, batch_size=args.batch_size) + print("Theoretical memory usage in training: %.3f - %.3f %s" % + (lower_mem, upper_mem, unit)) + + if args.do_test: + test_program = fluid.Program() + with fluid.program_guard(test_program, startup_prog): + with fluid.unique_name.guard(): + # create ernie_pyreader + test_pyreader, ernie_inputs, words, labels = ernie_pyreader(args, pyreader_name='test_reader') + test_pyreader.decorate_tensor_provider( + reader.data_generator( + args.test_set, args.batch_size, phase='test', epoch=1, shuffle=False + ) + ) + # get ernie_embeddings + embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config) + # user defined model based on ernie embeddings + test_ret = create_model(args, embeddings, labels=labels, is_prediction=False) + + test_program = test_program.clone(for_test=True) + + if args.do_infer: + infer_program = fluid.Program() + with fluid.program_guard(infer_program, startup_prog): + with fluid.unique_name.guard(): + # create ernie_pyreader + infer_pyreader, ernie_inputs, words, labels = ernie_pyreader(args, pyreader_name='infer_reader') + infer_pyreader.decorate_tensor_provider( + reader.data_generator( + args.infer_set, args.batch_size, phase='infer', epoch=1, shuffle=False + ) + ) + # get ernie_embeddings + embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config) + # user defined model based on ernie embeddings + infer_ret = create_model(args, embeddings, labels=labels, is_prediction=True) + infer_ret["words"] = words + + infer_program = infer_program.clone(for_test=True) + + exe.run(startup_prog) + + # load checkpoints + if args.do_train: + if args.init_checkpoint and args.init_pretraining_params: + print("WARNING: args 'init_checkpoint' and 'init_pretraining_params' " + "both are set! Only arg 'init_checkpoint' is made valid.") + if args.init_checkpoint: + utils.init_checkpoint(exe, args.init_checkpoint, startup_prog) + elif args.init_pretraining_params: + utils.init_pretraining_params(exe, args.init_pretraining_params, startup_prog) + elif args.do_test or args.do_infer: + if not args.init_checkpoint: + raise ValueError("args 'init_checkpoint' should be set if only doing test or infer!") + utils.init_checkpoint(exe, args.init_checkpoint, startup_prog) + + if args.do_train: + train_pyreader.start() + steps = 0 + total_cost, total_acc, total_num_seqs = [], [], [] + while True: + try: + steps += 1 + if steps % args.skip_steps == 0: + fetch_list = [ + train_ret["loss"], + train_ret["num_infer_chunks"], + train_ret["num_label_chunks"], + train_ret["num_correct_chunks"], + ] + else: + fetch_list = [] + + start_time = time.time() + outputs = exe.run(program=train_program, fetch_list=fetch_list) + end_time = time.time() + if steps % args.skip_steps == 0: + loss, nums_infer, nums_label, nums_correct = outputs + train_ret["chunk_evaluator"].reset() + train_ret["chunk_evaluator"].update(nums_infer, nums_label, nums_correct) + precision, recall, f1_score = train_ret["chunk_evaluator"].eval() + print("[train] batch_id = %d, loss = %.5f, P: %.5f, R: %.5f, F1: %.5f, elapsed time %.5f, " + "pyreader queue_size: %d " % (steps, loss, precision, recall, f1_score, + end_time - start_time, train_pyreader.queue.size())) + + if steps % args.save_steps == 0: + save_path = os.path.join(args.checkpoints, "step_" + str(steps)) + print("\tsaving model as %s" % (save_path)) + fluid.io.save_persistables(exe, save_path, train_program) + + if steps % args.validation_steps == 0: + # evaluate test set + if args.do_test: + evaluate(exe, test_program, test_pyreader, test_ret) + + except fluid.core.EOFException: + save_path = os.path.join(args.checkpoints, "step_" + str(steps)) + fluid.io.save_persistables(exe, save_path, train_program) + train_pyreader.reset() + break + + # final eval on test set + if args.do_test: + evaluate(exe, test_program, test_pyreader, test_ret) + + if args.do_infer: + # create dict + id2word_dict = dict([(str(word_id), word) for word, word_id in reader.vocab.items()]) + id2label_dict = dict([(str(label_id), label) for label, label_id in reader.label_map.items()]) + Dataset = namedtuple("Dataset", ["id2word_dict", "id2label_dict"]) + dataset = Dataset(id2word_dict, id2label_dict) + + infer_pyreader.start() + while True: + try: + (words, crf_decode) = exe.run(infer_program, + fetch_list=[infer_ret["words"], infer_ret["crf_decode"]], + return_numpy=False) + # User should notice that words had been clipped if long than args.max_seq_len + results = utils.parse_result(words, crf_decode, dataset) + for result in results: + print(result) + except fluid.core.EOFException: + infer_pyreader.reset() + break + + +if __name__ == "__main__": + utils.print_arguments(args) + main(args) diff --git a/PaddleNLP/lexical_analysis/run_sequence_labeling.py b/PaddleNLP/lexical_analysis/run_sequence_labeling.py new file mode 100644 index 0000000000000000000000000000000000000000..c3aa816423d2110b61338e3f881bd0b5c85dbffe --- /dev/null +++ b/PaddleNLP/lexical_analysis/run_sequence_labeling.py @@ -0,0 +1,308 @@ +""" +Baidu's open-source Lexical Analysis tool for Chinese, including: + 1. Word Segmentation, + 2. Part-of-Speech Tagging + 3. Named Entity Recognition +""" + +from __future__ import print_function + +import os +import sys +import math +import time +import random +import argparse +import multiprocessing + +import numpy as np +import paddle +import paddle.fluid as fluid + +import reader +import utils + +sys.path.append("../") +from models.sequence_labeling import nets + + +# yapf: disable +parser = argparse.ArgumentParser(__doc__) + +# 1. model parameters +model_g = utils.ArgumentGroup(parser, "model", "model configuration") +model_g.add_arg("word_emb_dim", int, 128, "The dimension in which a word is embedded.") +model_g.add_arg("grnn_hidden_dim", int, 256, "The number of hidden nodes in the GRNN layer.") +model_g.add_arg("bigru_num", int, 2, "The number of bi_gru layers in the network.") + +# 2. data parameters +data_g = utils.ArgumentGroup(parser, "data", "data paths") +data_g.add_arg("word_dict_path", str, "./conf/word.dic", "The path of the word dictionary.") +data_g.add_arg("label_dict_path", str, "./conf/tag.dic", "The path of the label dictionary.") +data_g.add_arg("word_rep_dict_path", str, "./conf/q2b.dic", "The path of the word replacement Dictionary.") +data_g.add_arg("train_data", str, "./data/train_data", "The folder where the training data is located.") +data_g.add_arg("test_data", str, "./data/test_data", "The folder where the training data is located.") +data_g.add_arg("infer_data", str, "./data/test.tsv", "The folder where the training data is located.") +data_g.add_arg("model_save_dir", str, "./models", "The model will be saved in this path.") +data_g.add_arg("init_checkpoint", str, "", "Path to init model") + +data_g.add_arg("corpus_type_list", str, ["human", "feed", "query", "title", "news"], + "The pattern list of different types of corpus used in training.", nargs='+') +data_g.add_arg("corpus_proportion_list", float, [0.2, 0.2, 0.2, 0.2, 0.2], + "The proportion list of different types of corpus used in training.", nargs='+') + +# 3. train parameters +train_g = utils.ArgumentGroup(parser, "training", "training options") + +train_g.add_arg("do_train", bool, True, "whether to perform training") +train_g.add_arg("do_valid", bool, False, "whether to perform validation") +train_g.add_arg("do_test", bool, True, "whether to perform validation") +train_g.add_arg("do_infer", bool, False, "whether to perform inference") +train_g.add_arg("random_seed", int, 0, "random seed for training") +train_g.add_arg("save_model_per_batches", int, 10000, "Save the model once per xxxx batch of training") +train_g.add_arg("valid_model_per_batches", int, 1000, "Do the validation once per xxxx batch of training") +train_g.add_arg("batch_size", int, 80, "The number of sequences contained in a mini-batch, " + "or the maximum number of tokens (include paddings) contained in a mini-batch.") +train_g.add_arg("epoch", int, 10, "corpus iteration num") +train_g.add_arg("use_gpu", int, -1, "Whether or not to use GPU. -1 means CPU, else GPU id") +train_g.add_arg("traindata_shuffle_buffer", int, 200, "The buffer size used in shuffle the training data.") +train_g.add_arg("base_learning_rate", float, 1e-3, "The basic learning rate that affects the entire network.") +train_g.add_arg("emb_learning_rate", float, 5, + "The real learning rate of the embedding layer will be (emb_learning_rate * base_learning_rate).") +train_g.add_arg("crf_learning_rate", float, 0.2, + "The real learning rate of the embedding layer will be (crf_learning_rate * base_learning_rate).") + + +args = parser.parse_args() +# yapf: enable. + +if len(args.corpus_proportion_list) != len(args.corpus_type_list): + sys.stderr.write( + "The length of corpus_proportion_list should be equal to the length of corpus_type_list.\n" + ) + exit(-1) + +print(args) + + +def create_model(args, pyreader_name, vocab_size, num_labels): + """create lac model""" + pyreader = fluid.layers.py_reader( + capacity=50, + shapes=([-1, 1], [-1, 1]), + dtypes=('int64', 'int64'), + lod_levels=(1, 1), + name=pyreader_name, + use_double_buffer=False) + + words, targets = fluid.layers.read_file(pyreader) + avg_cost, crf_decode = nets.lex_net(words, targets, args, vocab_size, num_labels) + + (precision, recall, f1_score, num_infer_chunks, num_label_chunks, + num_correct_chunks) = fluid.layers.chunk_eval( + input=crf_decode, + label=targets, + chunk_scheme="IOB", + num_chunk_types=int(math.ceil((num_labels - 1) / 2.0))) + chunk_evaluator = fluid.metrics.ChunkEvaluator() + chunk_evaluator.reset() + + ret = { + "pyreader":pyreader, + "words":words, + "targets":targets, + "avg_cost":avg_cost, + "crf_decode":crf_decode, + "chunk_evaluator":chunk_evaluator, + "num_infer_chunks":num_infer_chunks, + "num_label_chunks":num_label_chunks, + "num_correct_chunks":num_correct_chunks + } + + return ret + + +def evaluate(exe, test_program, test_ret): + """evaluate for test data""" + test_ret["pyreader"].start() + test_ret["chunk_evaluator"].reset() + loss = [] + precision = [] + recall = [] + f1 = [] + start_time = time.time() + while True: + try: + avg_loss, nums_infer, nums_label, nums_correct = exe.run( + test_program, + fetch_list=[ + test_ret["avg_cost"], + test_ret["num_infer_chunks"], + test_ret["num_label_chunks"], + test_ret["num_correct_chunks"], + ], + ) + loss.append(avg_loss) + + test_ret["chunk_evaluator"].update(nums_infer, nums_label, nums_correct) + p, r, f = test_ret["chunk_evaluator"].eval() + + precision.append(p) + recall.append(r) + f1.append(f) + except fluid.core.EOFException: + test_ret["pyreader"].reset() + break + end_time = time.time() + print("[test] avg loss: %.5f, P: %.5f, R: %.5f, F1: %.5f, elapsed time: %.3f s" + % (np.mean(loss), np.mean(precision), + np.mean(recall), np.mean(f1), end_time - start_time)) + + +def main(args): + + startup_program = fluid.Program() + if args.random_seed is not None: + startup_program.random_seed = args.random_seed + + # prepare dataset + dataset = reader.Dataset(args) + + if args.do_train: + train_program = fluid.Program() + with fluid.program_guard(train_program, startup_program): + with fluid.unique_name.guard(): + train_ret = create_model( + args, "train_reader", dataset.vocab_size, dataset.num_labels) + train_ret["pyreader"].decorate_paddle_reader( + paddle.batch( + paddle.reader.shuffle( + dataset.file_reader(args.train_data), + buf_size=args.traindata_shuffle_buffer + ), + batch_size=args.batch_size + ) + ) + + optimizer = fluid.optimizer.Adam(learning_rate=args.base_learning_rate) + optimizer.minimize(train_ret["avg_cost"]) + + if args.do_test: + test_program = fluid.Program() + with fluid.program_guard(test_program, startup_program): + with fluid.unique_name.guard(): + test_ret = create_model( + args, "test_reader", dataset.vocab_size, dataset.num_labels) + test_ret["pyreader"].decorate_paddle_reader( + paddle.batch( + dataset.file_reader(args.test_data), + batch_size=args.batch_size + ) + ) + test_program = test_program.clone(for_test=True) # to share parameters with train model + + if args.do_infer: + infer_program = fluid.Program() + with fluid.program_guard(infer_program, startup_program): + with fluid.unique_name.guard(): + infer_ret = create_model( + args, "infer_reader", dataset.vocab_size, dataset.num_labels) + infer_ret["pyreader"].decorate_paddle_reader( + paddle.batch( + dataset.file_reader(args.infer_data), + batch_size=args.batch_size + ) + ) + infer_program = infer_program.clone(for_test=True) + + + # init executor + if args.use_gpu >= 0: + place = fluid.CUDAPlace(args.use_gpu) + dev_count = fluid.core.get_cuda_device_count() + else: + place = fluid.CPUPlace() + dev_count = multiprocessing.cpu_count() + exe = fluid.Executor(place) + exe.run(startup_program) + + # load checkpoints + if args.do_train: + if args.init_checkpoint: + utils.init_checkpoint(exe, args.init_checkpoint, train_program) + elif args.do_test: + if not args.init_checkpoint: + raise ValueError("args 'init_checkpoint' should be set if only doing validation or testing!") + utils.init_checkpoint(exe, args.init_checkpoint, test_program) + if args.do_infer: + utils.init_checkpoint(exe, args.init_checkpoint, infer_program) + + # do start to train + if args.do_train: + num_train_examples = dataset.get_num_examples(args.train_data) + max_train_steps = args.epoch * num_train_examples // args.batch_size + print("Num train examples: %d" % num_train_examples) + print("Max train steps: %d" % max_train_steps) + + batch_id = 0 + for epoch_id in range(args.epoch): + train_ret["pyreader"].start() + try: + while True: + start_time = time.time() + avg_cost, nums_infer, nums_label, nums_correct = exe.run( + train_program, + fetch_list=[ + train_ret["avg_cost"], + train_ret["num_infer_chunks"], + train_ret["num_label_chunks"], + train_ret["num_correct_chunks"], + ], + ) + end_time = time.time() + train_ret["chunk_evaluator"].reset() + train_ret["chunk_evaluator"].update(nums_infer, nums_label, nums_correct) + precision, recall, f1_score = train_ret["chunk_evaluator"].eval() + batch_id += 1 + print("[train] batch_id = %d, loss = %.5f, P: %.5f, R: %.5f, F1: %.5f, elapsed time %.5f " % ( + batch_id, avg_cost, precision, recall, f1_score, end_time - start_time)) + + # save checkpoints + if (batch_id % args.save_model_per_batches == 0): + save_path = os.path.join(args.model_save_dir, "step_" + str(batch_id)) + fluid.io.save_persistables(exe, save_path, train_program) + + # evaluate + if (batch_id % args.valid_model_per_batches == 0) and args.do_test: + evaluate(exe, test_program, test_ret) + + except fluid.core.EOFException: + save_path = os.path.join(args.model_save_dir, "step_" + str(batch_id)) + fluid.io.save_persistables(exe, save_path, train_program) + train_ret["pyreader"].reset() + # break? + + # only test + if args.do_test: + evaluate(exe, test_program, test_ret) + + if args.do_infer: + infer_ret["pyreader"].start() + while True: + try: + (words, crf_decode, ) = exe.run(infer_program, + fetch_list=[ + infer_ret["words"], + infer_ret["crf_decode"], + ], + return_numpy=False) + results = utils.parse_result(words, crf_decode, dataset) + for result in results: + print(result) + except fluid.core.EOFException: + infer_ret["pyreader"].reset() + break + + +if __name__ == "__main__": + main(args) diff --git a/PaddleNLP/lexical_analysis/utils.py b/PaddleNLP/lexical_analysis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1ef20b22eb17e3081f9d39f7b30ee751d3c504a0 --- /dev/null +++ b/PaddleNLP/lexical_analysis/utils.py @@ -0,0 +1,138 @@ +""" +util tools +""" +from __future__ import print_function +import os +import sys +import numpy as np +import paddle.fluid as fluid + + +def str2bool(v): + """ + argparse does not support True or False in python + """ + return v.lower() in ("true", "t", "1") + + +class ArgumentGroup(object): + """ + Put arguments to one group + """ + def __init__(self, parser, title, des): + """none""" + self._group = parser.add_argument_group(title=title, description=des) + + def add_arg(self, name, type, default, help, **kwargs): + """ Add argument """ + type = str2bool if type == bool else type + self._group.add_argument( + "--" + name, + default=default, + type=type, + help=help + ' Default: %(default)s.', + **kwargs) + + +def print_arguments(args): + """none""" + print('----------- Configuration Arguments -----------') + for arg, value in sorted(vars(args).items()): + print('%s: %s' % (arg, value)) + print('------------------------------------------------') + + +def to_str(string, encoding="utf-8"): + """convert to str for print""" + if sys.version_info.major == 3: + if isinstance(string, bytes): + return string.decode(encoding) + elif sys.version_info.major == 2: + if isinstance(string, unicode): + return string.encode(encoding) + return string + + +def to_lodtensor(data, place): + """ + Convert data in list into lodtensor. + """ + seq_lens = [len(seq) for seq in data] + cur_len = 0 + lod = [cur_len] + for l in seq_lens: + cur_len += l + lod.append(cur_len) + flattened_data = np.concatenate(data, axis=0).astype("int64") + flattened_data = flattened_data.reshape([len(flattened_data), 1]) + res = fluid.LoDTensor() + res.set(flattened_data, place) + res.set_lod([lod]) + return res + + +def parse_result(words, crf_decode, dataset): + """ parse result """ + offset_list = (crf_decode.lod())[0] + words = np.array(words) + crf_decode = np.array(crf_decode) + batch_size = len(offset_list) - 1 + batch_out_str = [] + for sent_index in range(batch_size): + sent_out_str = "" + sent_len = offset_list[sent_index + 1] - offset_list[sent_index] + for tag_index in range(sent_len): # iterate every word in sent + index = tag_index + offset_list[sent_index] + cur_word_id = str(words[index][0]) + cur_tag_id = str(crf_decode[index][0]) + cur_word = dataset.id2word_dict[cur_word_id] + cur_tag = dataset.id2label_dict[cur_tag_id] + sent_out_str += cur_word + u"/" + cur_tag + u" " + sent_out_str = to_str(sent_out_str.strip()) + batch_out_str.append(sent_out_str) + return batch_out_str + + +def init_checkpoint(exe, init_checkpoint_path, main_program): + """ + Init CheckPoint + """ + assert os.path.exists( + init_checkpoint_path), "[%s] cann't be found." % init_checkpoint_path + + def existed_persitables(var): + """ + If existed presitabels + """ + if not fluid.io.is_persistable(var): + return False + return os.path.exists(os.path.join(init_checkpoint_path, var.name)) + + fluid.io.load_vars( + exe, + init_checkpoint_path, + main_program=main_program, + predicate=existed_persitables) + print("Load model from {}".format(init_checkpoint_path)) + + +def init_pretraining_params(exe, + pretraining_params_path, + main_program, + use_fp16=False): + """load params of pretrained model, NOT including moment, learning_rate""" + assert os.path.exists(pretraining_params_path + ), "[%s] cann't be found." % pretraining_params_path + + def _existed_params(var): + if not isinstance(var, fluid.framework.Parameter): + return False + return os.path.exists(os.path.join(pretraining_params_path, var.name)) + + fluid.io.load_vars( + exe, + pretraining_params_path, + main_program=main_program, + predicate=_existed_params) + print("Load pretraining parameters from {}.".format( + pretraining_params_path)) diff --git a/PaddleNLP/text_matching_on_quora/models/test.py b/PaddleNLP/models/__init__.py similarity index 100% rename from PaddleNLP/text_matching_on_quora/models/test.py rename to PaddleNLP/models/__init__.py diff --git a/PaddleNLP/models/classification/__init__.py b/PaddleNLP/models/classification/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PaddleNLP/models/classification/nets.py b/PaddleNLP/models/classification/nets.py new file mode 100644 index 0000000000000000000000000000000000000000..279199e832dc64fec3a2970c510b413c39c18f13 --- /dev/null +++ b/PaddleNLP/models/classification/nets.py @@ -0,0 +1,283 @@ +""" +This module provide nets for text classification +""" + +import paddle.fluid as fluid + +def bow_net(data, + label, + dict_dim, + emb_dim=128, + hid_dim=128, + hid_dim2=96, + class_dim=2, + is_infer=False): + """ + Bow net + """ + # embedding layer + emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim]) + # bow layer + bow = fluid.layers.sequence_pool(input=emb, pool_type='sum') + bow_tanh = fluid.layers.tanh(bow) + # full connect layer + fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh") + fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh") + # softmax layer + prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax") + if is_infer: + return prediction + cost = fluid.layers.cross_entropy(input=prediction, label=label) + avg_cost = fluid.layers.mean(x=cost) + acc = fluid.layers.accuracy(input=prediction, label=label) + + return avg_cost, prediction + + +def cnn_net(data, + label, + dict_dim, + emb_dim=128, + hid_dim=128, + hid_dim2=96, + class_dim=2, + win_size=3, + is_infer=False): + """ + Conv net + """ + # embedding layer + emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim]) + + # convolution layer + conv_3 = fluid.nets.sequence_conv_pool( + input=emb, + num_filters=hid_dim, + filter_size=win_size, + act="tanh", + pool_type="max") + + # full connect layer + fc_1 = fluid.layers.fc(input=[conv_3], size=hid_dim2) + # softmax layer + prediction = fluid.layers.fc(input=[fc_1], size=class_dim, act="softmax") + if is_infer: + return prediction + cost = fluid.layers.cross_entropy(input=prediction, label=label) + avg_cost = fluid.layers.mean(x=cost) + acc = fluid.layers.accuracy(input=prediction, label=label) + + return avg_cost, prediction + + +def lstm_net(data, + label, + dict_dim, + emb_dim=128, + hid_dim=128, + hid_dim2=96, + class_dim=2, + emb_lr=30.0, + is_infer=False): + """ + Lstm net + """ + # embedding layer + emb = fluid.layers.embedding( + input=data, + size=[dict_dim, emb_dim], + param_attr=fluid.ParamAttr(learning_rate=emb_lr)) + + # Lstm layer + fc0 = fluid.layers.fc(input=emb, size=hid_dim * 4) + + lstm_h, c = fluid.layers.dynamic_lstm( + input=fc0, size=hid_dim * 4, is_reverse=False) + + # max pooling layer + lstm_max = fluid.layers.sequence_pool(input=lstm_h, pool_type='max') + lstm_max_tanh = fluid.layers.tanh(lstm_max) + + # full connect layer + fc1 = fluid.layers.fc(input=lstm_max_tanh, size=hid_dim2, act='tanh') + # softmax layer + prediction = fluid.layers.fc(input=fc1, size=class_dim, act='softmax') + if is_infer: + return prediction + cost = fluid.layers.cross_entropy(input=prediction, label=label) + avg_cost = fluid.layers.mean(x=cost) + acc = fluid.layers.accuracy(input=prediction, label=label) + + return avg_cost, prediction + + +def bilstm_net(data, + label, + dict_dim, + emb_dim=128, + hid_dim=128, + hid_dim2=96, + class_dim=2, + emb_lr=30.0, + is_infer=False): + """ + Bi-Lstm net + """ + # embedding layer + emb = fluid.layers.embedding( + input=data, + size=[dict_dim, emb_dim], + param_attr=fluid.ParamAttr(learning_rate=emb_lr)) + + fc0 = fluid.layers.fc(input=emb, size=hid_dim * 4) + rfc0 = fluid.layers.fc(input=emb, size=hid_dim * 4) + lstm_h, c = fluid.layers.dynamic_lstm( + input=fc0, size=hid_dim * 4, is_reverse=False) + rlstm_h, c = fluid.layers.dynamic_lstm( + input=rfc0, size=hid_dim * 4, is_reverse=True) + # extract last layer + lstm_last = fluid.layers.sequence_last_step(input=lstm_h) + rlstm_last = fluid.layers.sequence_last_step(input=rlstm_h) + lstm_last_tanh = fluid.layers.tanh(lstm_last) + rlstm_last_tanh = fluid.layers.tanh(rlstm_last) + + # concat layer + lstm_concat = fluid.layers.concat(input=[lstm_last, rlstm_last], axis=1) + # full connect layer + fc1 = fluid.layers.fc(input=lstm_concat, size=hid_dim2, act='tanh') + # softmax layer + prediction = fluid.layers.fc(input=fc1, size=class_dim, act='softmax') + if is_infer: + return prediction + cost = fluid.layers.cross_entropy(input=prediction, label=label) + avg_cost = fluid.layers.mean(x=cost) + acc = fluid.layers.accuracy(input=prediction, label=label) + return avg_cost, prediction + + +def gru_net(data, + label, + dict_dim, + emb_dim=128, + hid_dim=128, + hid_dim2=96, + class_dim=2, + emb_lr=30.0, + is_infer=False): + """ + gru net + """ + emb = fluid.layers.embedding( + input=data, + size=[dict_dim, emb_dim], + param_attr=fluid.ParamAttr(learning_rate=emb_lr)) + + fc0 = fluid.layers.fc(input=emb, size=hid_dim * 3) + + gru_h = fluid.layers.dynamic_gru(input=fc0, size=hid_dim, is_reverse=False) + + gru_max = fluid.layers.sequence_pool(input=gru_h, pool_type='max') + gru_max_tanh = fluid.layers.tanh(gru_max) + + fc1 = fluid.layers.fc(input=gru_max_tanh, size=hid_dim2, act='tanh') + + prediction = fluid.layers.fc(input=fc1, size=class_dim, act='softmax') + if is_infer: + return prediction + cost = fluid.layers.cross_entropy(input=prediction, label=label) + avg_cost = fluid.layers.mean(x=cost) + acc = fluid.layers.accuracy(input=prediction, label=label) + + return avg_cost, prediction + + +def textcnn_net(data, + label, + dict_dim, + emb_dim=128, + hid_dim=128, + hid_dim2=96, + class_dim=2, + win_sizes=None, + is_infer=False): + """ + Textcnn_net + """ + if win_sizes is None: + win_sizes = [1, 2, 3] + + # embedding layer + emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim]) + + # convolution layer + convs = [] + for win_size in win_sizes: + conv_h = fluid.nets.sequence_conv_pool( + input=emb, + num_filters=hid_dim, + filter_size=win_size, + act="tanh", + pool_type="max") + convs.append(conv_h) + convs_out = fluid.layers.concat(input=convs, axis=1) + + # full connect layer + fc_1 = fluid.layers.fc(input=[convs_out], size=hid_dim2, act="tanh") + # softmax layer + prediction = fluid.layers.fc(input=[fc_1], size=class_dim, act="softmax") + if is_infer: + return prediction + + cost = fluid.layers.cross_entropy(input=prediction, label=label) + avg_cost = fluid.layers.mean(x=cost) + acc = fluid.layers.accuracy(input=prediction, label=label) + return avg_cost, prediction + + +def ernie_base_net(sentence_embeddings, labels, num_labels): + """ + Ernie base net + """ + cls_feats = fluid.layers.dropout( + x=sentence_embeddings, + dropout_prob=0.1, + dropout_implementation="upscale_in_train") + logits = fluid.layers.fc( + input=cls_feats, + size=num_labels, + param_attr=fluid.ParamAttr( + name="cls_out_w", + initializer=fluid.initializer.TruncatedNormal(scale=0.02)), + bias_attr=fluid.ParamAttr( + name="cls_out_b", initializer=fluid.initializer.Constant(0.))) + ce_loss, probs = fluid.layers.softmax_with_cross_entropy( + logits=logits, label=labels, return_softmax=True) + + return ce_loss, probs + + +def ernie_bilstm_net(token_embeddings, labels, num_labels, hid_dim=128): + """ + Ernie bilstm net + """ + fc0 = fluid.layers.fc(input=token_embeddings, size=hid_dim * 4) + rfc0 = fluid.layers.fc(input=token_embeddings, size=hid_dim * 4) + lstm_h, c = fluid.layers.dynamic_lstm( + input=fc0, size=hid_dim * 4, is_reverse=False) + rlstm_h, c = fluid.layers.dynamic_lstm( + input=rfc0, size=hid_dim * 4, is_reverse=True) + # extract last layer + lstm_last = fluid.layers.sequence_last_step(input=lstm_h) + rlstm_last = fluid.layers.sequence_last_step(input=rlstm_h) + lstm_last_tanh = fluid.layers.tanh(lstm_last) + rlstm_last_tanh = fluid.layers.tanh(rlstm_last) + + # concat layer + lstm_concat = fluid.layers.concat(input=[lstm_last, rlstm_last], axis=1) + # full connect layer + logits = fluid.layers.fc(input=lstm_concat, size=num_labels, act='relu') + + ce_loss, probs = fluid.layers.softmax_with_cross_entropy( + logits=logits, label=labels, return_softmax=True) + + return ce_loss, probs diff --git a/PaddleNLP/models/dialogue_model_toolkit/auto_dialogue_evaluation/__init__.py b/PaddleNLP/models/dialogue_model_toolkit/auto_dialogue_evaluation/__init__.py new file mode 100755 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PaddleNLP/models/dialogue_model_toolkit/auto_dialogue_evaluation/net.py b/PaddleNLP/models/dialogue_model_toolkit/auto_dialogue_evaluation/net.py new file mode 100755 index 0000000000000000000000000000000000000000..22175ea9f23f05b3d275cf4a9b59c2b54ecfed66 --- /dev/null +++ b/PaddleNLP/models/dialogue_model_toolkit/auto_dialogue_evaluation/net.py @@ -0,0 +1,138 @@ +""" +Network for auto dialogue evaluation +""" +import os +import sys +import time +import six + +import numpy as np +import math +import paddle.fluid as fluid +import paddle + +class Network(object): + """ + Network + """ + def __init__(self, + vocab_size, + emb_size, + hidden_size, + clip_value=10.0, + word_emb_name="shared_word_emb", + lstm_W_name="shared_lstm_W", + lstm_bias_name="shared_lstm_bias"): + """ + Init function + """ + + self.vocab_size = vocab_size + self.emb_size = emb_size + self.hidden_size = hidden_size + self.clip_value = clip_value + self.word_emb_name = word_emb_name + self.lstm_W_name = lstm_W_name + self.lstm_bias_name = lstm_bias_name + + def network(self, loss_type='CLS'): + """ + Network definition + """ + #Input data + context_wordseq = fluid.layers.data( + name="context_wordseq", shape=[1], dtype="int64", lod_level=1) + response_wordseq = fluid.layers.data( + name="response_wordseq", shape=[1], dtype="int64", lod_level=1) + label = fluid.layers.data( + name="label", shape=[1], dtype="float32") + + self._feed_name = ["context_wordseq", "response_wordseq", "label"] + self._feed_infer_name = ["context_wordseq", "response_wordseq"] + + #emb + context_emb = fluid.layers.embedding( + input=context_wordseq, + size=[self.vocab_size, self.emb_size], + is_sparse=True, + param_attr=fluid.ParamAttr( + name=self.word_emb_name, + initializer=fluid.initializer.Normal(scale=0.1))) + + response_emb = fluid.layers.embedding( + input=response_wordseq, + size=[self.vocab_size, self.emb_size], + is_sparse=True, + param_attr=fluid.ParamAttr( + name=self.word_emb_name, + initializer=fluid.initializer.Normal(scale=0.1))) + + #fc to fit dynamic LSTM + context_fc = fluid.layers.fc( + input=context_emb, + size=self.hidden_size * 4, + param_attr=fluid.ParamAttr(name='fc_weight'), + bias_attr=fluid.ParamAttr(name='fc_bias')) + + response_fc = fluid.layers.fc( + input=response_emb, + size=self.hidden_size * 4, + param_attr=fluid.ParamAttr(name='fc_weight'), + bias_attr=fluid.ParamAttr(name='fc_bias')) + + #LSTM + context_rep, _ = fluid.layers.dynamic_lstm( + input=context_fc, + size=self.hidden_size * 4, + param_attr=fluid.ParamAttr(name=self.lstm_W_name), + bias_attr=fluid.ParamAttr(name=self.lstm_bias_name)) + context_rep = fluid.layers.sequence_last_step(context_rep) + print('context_rep shape: %s' % str(context_rep.shape)) + + response_rep, _ = fluid.layers.dynamic_lstm( + input=response_fc, + size=self.hidden_size * 4, + param_attr=fluid.ParamAttr(name=self.lstm_W_name), + bias_attr=fluid.ParamAttr(name=self.lstm_bias_name)) + response_rep = fluid.layers.sequence_last_step(input=response_rep) + print('response_rep shape: %s' % str(response_rep.shape)) + + logits = fluid.layers.bilinear_tensor_product( + context_rep, response_rep, size=1) + print('logits shape: %s' % str(logits.shape)) #[batch,1] + + if loss_type == 'CLS': + loss = fluid.layers.sigmoid_cross_entropy_with_logits(logits, label) + print('before reduce mean loss shape: %s' % str(loss.shape)) + loss = fluid.layers.reduce_mean( + fluid.layers.clip( + loss, min=-self.clip_value, max=self.clip_value)) + print('after reduce mean loss shape: %s' % str(loss.shape)) + elif loss_type == 'L2': + norm_score = 2 * fluid.layers.sigmoid(logits) + loss = fluid.layers.square_error_cost(norm_score, label) / 4 + else: + raise ValueError + + return logits, loss + + def set_word_embedding(self, word_emb, place): + """ + Set word embedding + """ + word_emb_param = fluid.global_scope().find_var( + self.word_emb_name).get_tensor() + word_emb_param.set(word_emb, place) + + def get_feed_names(self): + """ + Return feed names + """ + return self._feed_name + + def get_feed_inference_names(self): + """ + Return inference names + """ + return self._feed_infer_name + diff --git a/PaddleNLP/models/dialogue_model_toolkit/deep_attention_matching/__init__.py b/PaddleNLP/models/dialogue_model_toolkit/deep_attention_matching/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PaddleNLP/models/dialogue_model_toolkit/deep_attention_matching/layers.py b/PaddleNLP/models/dialogue_model_toolkit/deep_attention_matching/layers.py new file mode 100755 index 0000000000000000000000000000000000000000..3d4ab5dcd90ab8075ee4bc8c820875fdbb5d7f5c --- /dev/null +++ b/PaddleNLP/models/dialogue_model_toolkit/deep_attention_matching/layers.py @@ -0,0 +1,207 @@ +""" +Layers +""" + +import paddle.fluid as fluid + + +def loss(x, y, clip_value=10.0): + """Calculate the sigmoid cross entropy with logits for input(x). + + Args: + x: Variable with shape with shape [batch, dim] + y: Input label + + Returns: + loss: cross entropy + logits: prediction + """ + + logits = fluid.layers.fc( + input=x, + size=1, + bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(0.))) + loss = fluid.layers.sigmoid_cross_entropy_with_logits(x=logits, label=y) + loss = fluid.layers.reduce_mean( + fluid.layers.clip( + loss, min=-clip_value, max=clip_value)) + return loss, logits + + +def ffn(input, d_inner_hid, d_hid, name=None): + """Position-wise Feed-Forward Network + """ + + hidden = fluid.layers.fc(input=input, + size=d_inner_hid, + num_flatten_dims=2, + param_attr=fluid.ParamAttr(name=name + '_fc.w_0'), + bias_attr=fluid.ParamAttr( + name=name + '_fc.b_0', + initializer=fluid.initializer.Constant(0.)), + act="relu") + out = fluid.layers.fc(input=hidden, + size=d_hid, + num_flatten_dims=2, + param_attr=fluid.ParamAttr(name=name + '_fc.w_1'), + bias_attr=fluid.ParamAttr( + name=name + '_fc.b_1', + initializer=fluid.initializer.Constant(0.))) + return out + + +def dot_product_attention(query, + key, + value, + d_key, + q_mask=None, + k_mask=None, + dropout_rate=None, + mask_cache=None): + """Dot product layer. + + Args: + query: a tensor with shape [batch, Q_time, Q_dimension] + key: a tensor with shape [batch, time, K_dimension] + value: a tensor with shape [batch, time, V_dimension] + + q_lengths: a tensor with shape [batch] + k_lengths: a tensor with shape [batch] + + Returns: + a tensor with shape [batch, query_time, value_dimension] + + Raises: + AssertionError: if Q_dimension not equal to K_dimension when attention + type is dot. + """ + + logits = fluid.layers.matmul( + x=query, y=key, transpose_y=True, alpha=d_key ** (-0.5)) + + if (q_mask is not None) and (k_mask is not None): + if mask_cache is not None and q_mask.name in mask_cache and k_mask.name in mask_cache[ + q_mask.name]: + mask, another_mask = mask_cache[q_mask.name][k_mask.name] + else: + mask = fluid.layers.matmul(x=q_mask, y=k_mask, transpose_y=True) + another_mask = fluid.layers.scale( + mask, + scale=float(2 ** 32 - 1), + bias=float(-1), + bias_after_scale=False) + if mask_cache is not None: + if q_mask.name not in mask_cache: + mask_cache[q_mask.name] = dict() + + mask_cache[q_mask.name][k_mask.name] = [mask, another_mask] + + logits = mask * logits + another_mask + + attention = fluid.layers.softmax(logits) + if dropout_rate: + attention = fluid.layers.dropout( + input=attention, dropout_prob=dropout_rate, is_test=False, seed=2) + + atten_out = fluid.layers.matmul(x=attention, y=value) + + return atten_out + + +def block(name, + query, + key, + value, + d_key, + q_mask=None, + k_mask=None, + is_layer_norm=True, + dropout_rate=None, + mask_cache=None): + """ + Block + """ + att_out = dot_product_attention( + query, + key, + value, + d_key, + q_mask, + k_mask, + dropout_rate, + mask_cache=mask_cache) + + y = query + att_out + if is_layer_norm: + y = fluid.layers.layer_norm( + input=y, + begin_norm_axis=len(y.shape) - 1, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.Constant(1.), + name=name + '_layer_norm.w_0'), + bias_attr=fluid.ParamAttr( + initializer=fluid.initializer.Constant(0.), + name=name + '_layer_norm.b_0')) + + z = ffn(y, d_key, d_key, name) + w = y + z + if is_layer_norm: + w = fluid.layers.layer_norm( + input=w, + begin_norm_axis=len(w.shape) - 1, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.Constant(1.), + name=name + '_layer_norm.w_1'), + bias_attr=fluid.ParamAttr( + initializer=fluid.initializer.Constant(0.), + name=name + '_layer_norm.b_1')) + + return w + + +def cnn_3d(input, out_channels_0, out_channels_1, add_relu=True): + """ + CNN-3d + """ + # same padding + conv_0 = fluid.layers.conv3d( + name="conv3d_0", + input=input, + num_filters=out_channels_0, + filter_size=[3, 3, 3], + padding=[1, 1, 1], + act="elu" if add_relu else None, + param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform( + low=-0.01, high=0.01)), + bias_attr=fluid.ParamAttr( + initializer=fluid.initializer.Constant(value=0.0))) + + # same padding + pooling_0 = fluid.layers.pool3d( + input=conv_0, + pool_type="max", + pool_size=3, + pool_padding=1, + pool_stride=3) + + conv_1 = fluid.layers.conv3d( + name="conv3d_1", + input=pooling_0, + num_filters=out_channels_1, + filter_size=[3, 3, 3], + padding=[1, 1, 1], + act="elu" if add_relu else None, + param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform( + low=-0.01, high=0.01)), + bias_attr=fluid.ParamAttr( + initializer=fluid.initializer.Constant(value=0.0))) + + # same padding + pooling_1 = fluid.layers.pool3d( + input=conv_1, + pool_type="max", + pool_size=3, + pool_padding=1, + pool_stride=3) + + return pooling_1 diff --git a/PaddleNLP/models/dialogue_model_toolkit/deep_attention_matching/net.py b/PaddleNLP/models/dialogue_model_toolkit/deep_attention_matching/net.py new file mode 100755 index 0000000000000000000000000000000000000000..240b1493251aec431f0a76c73f3af9586d9e55c1 --- /dev/null +++ b/PaddleNLP/models/dialogue_model_toolkit/deep_attention_matching/net.py @@ -0,0 +1,226 @@ +""" +Deep Attention Matching Network +""" + +import six +import numpy as np +import paddle.fluid as fluid +import layers + + +class Net(object): + """ + Deep attention matching network + """ + def __init__(self, max_turn_num, max_turn_len, vocab_size, emb_size, + stack_num, channel1_num, channel2_num): + """ + Init + """ + self._max_turn_num = max_turn_num + self._max_turn_len = max_turn_len + self._vocab_size = vocab_size + self._emb_size = emb_size + self._stack_num = stack_num + self._channel1_num = channel1_num + self._channel2_num = channel2_num + self._feed_names = [] + self.word_emb_name = "shared_word_emb" + self.use_stack_op = True + self.use_mask_cache = True + self.use_sparse_embedding = True + + def create_py_reader(self, capacity, name): + """ + Create py reader + """ + # turns ids + shapes = [[-1, self._max_turn_len, 1] + for i in six.moves.xrange(self._max_turn_num)] + dtypes = ["int64" for i in six.moves.xrange(self._max_turn_num)] + # turns mask + shapes += [[-1, self._max_turn_len, 1] + for i in six.moves.xrange(self._max_turn_num)] + dtypes += ["float32" for i in six.moves.xrange(self._max_turn_num)] + + # response ids, response mask, label + shapes += [[-1, self._max_turn_len, 1], [-1, self._max_turn_len, 1], + [-1, 1]] + dtypes += ["int64", "float32", "float32"] + + py_reader = fluid.layers.py_reader( + capacity=capacity, + shapes=shapes, + lod_levels=[0] * (2 * self._max_turn_num + 3), + dtypes=dtypes, + name=name, + use_double_buffer=True) + + data_vars = fluid.layers.read_file(py_reader) + + self.turns_data = data_vars[0:self._max_turn_num] + self.turns_mask = data_vars[self._max_turn_num:2 * self._max_turn_num] + self.response = data_vars[-3] + self.response_mask = data_vars[-2] + self.label = data_vars[-1] + return py_reader + + def create_data_layers(self): + """ + Create data layer + """ + self._feed_names = [] + + self.turns_data = [] + for i in six.moves.xrange(self._max_turn_num): + name = "turn_%d" % i + turn = fluid.layers.data( + name=name, shape=[self._max_turn_len, 1], dtype="int64") + self.turns_data.append(turn) + self._feed_names.append(name) + + self.turns_mask = [] + for i in six.moves.xrange(self._max_turn_num): + name = "turn_mask_%d" % i + turn_mask = fluid.layers.data( + name=name, shape=[self._max_turn_len, 1], dtype="float32") + self.turns_mask.append(turn_mask) + self._feed_names.append(name) + + self.response = fluid.layers.data( + name="response", shape=[self._max_turn_len, 1], dtype="int64") + self.response_mask = fluid.layers.data( + name="response_mask", + shape=[self._max_turn_len, 1], + dtype="float32") + self.label = fluid.layers.data(name="label", shape=[1], dtype="float32") + self._feed_names += ["response", "response_mask", "label"] + + def get_feed_names(self): + """ + Return feed names + """ + return self._feed_names + + def set_word_embedding(self, word_emb, place): + """ + Set word embedding + """ + word_emb_param = fluid.global_scope().find_var( + self.word_emb_name).get_tensor() + word_emb_param.set(word_emb, place) + + def create_network(self): + """ + Create network + """ + mask_cache = dict() if self.use_mask_cache else None + + response_emb = fluid.layers.embedding( + input=self.response, + size=[self._vocab_size + 1, self._emb_size], + is_sparse=self.use_sparse_embedding, + param_attr=fluid.ParamAttr( + name=self.word_emb_name, + initializer=fluid.initializer.Normal(scale=0.1))) + + # response part + Hr = response_emb + Hr_stack = [Hr] + + for index in six.moves.xrange(self._stack_num): + Hr = layers.block( + name="response_self_stack" + str(index), + query=Hr, + key=Hr, + value=Hr, + d_key=self._emb_size, + q_mask=self.response_mask, + k_mask=self.response_mask, + mask_cache=mask_cache) + Hr_stack.append(Hr) + + # context part + sim_turns = [] + for t in six.moves.xrange(self._max_turn_num): + Hu = fluid.layers.embedding( + input=self.turns_data[t], + size=[self._vocab_size + 1, self._emb_size], + is_sparse=self.use_sparse_embedding, + param_attr=fluid.ParamAttr( + name=self.word_emb_name, + initializer=fluid.initializer.Normal(scale=0.1))) + Hu_stack = [Hu] + + for index in six.moves.xrange(self._stack_num): + # share parameters + Hu = layers.block( + name="turn_self_stack" + str(index), + query=Hu, + key=Hu, + value=Hu, + d_key=self._emb_size, + q_mask=self.turns_mask[t], + k_mask=self.turns_mask[t], + mask_cache=mask_cache) + Hu_stack.append(Hu) + + # cross attention + r_a_t_stack = [] + t_a_r_stack = [] + for index in six.moves.xrange(self._stack_num + 1): + t_a_r = layers.block( + name="t_attend_r_" + str(index), + query=Hu_stack[index], + key=Hr_stack[index], + value=Hr_stack[index], + d_key=self._emb_size, + q_mask=self.turns_mask[t], + k_mask=self.response_mask, + mask_cache=mask_cache) + r_a_t = layers.block( + name="r_attend_t_" + str(index), + query=Hr_stack[index], + key=Hu_stack[index], + value=Hu_stack[index], + d_key=self._emb_size, + q_mask=self.response_mask, + k_mask=self.turns_mask[t], + mask_cache=mask_cache) + + t_a_r_stack.append(t_a_r) + r_a_t_stack.append(r_a_t) + + t_a_r_stack.extend(Hu_stack) + r_a_t_stack.extend(Hr_stack) + + if self.use_stack_op: + t_a_r = fluid.layers.stack(t_a_r_stack, axis=1) + r_a_t = fluid.layers.stack(r_a_t_stack, axis=1) + else: + for index in six.moves.xrange(len(t_a_r_stack)): + t_a_r_stack[index] = fluid.layers.unsqueeze( + input=t_a_r_stack[index], axes=[1]) + r_a_t_stack[index] = fluid.layers.unsqueeze( + input=r_a_t_stack[index], axes=[1]) + + t_a_r = fluid.layers.concat(input=t_a_r_stack, axis=1) + r_a_t = fluid.layers.concat(input=r_a_t_stack, axis=1) + + # sim shape: [batch_size, 2*(stack_num+1), max_turn_len, max_turn_len] + sim = fluid.layers.matmul( + x=t_a_r, y=r_a_t, transpose_y=True, alpha=1 / np.sqrt(200.0)) + sim_turns.append(sim) + + if self.use_stack_op: + sim = fluid.layers.stack(sim_turns, axis=2) + else: + for index in six.moves.xrange(len(sim_turns)): + sim_turns[index] = fluid.layers.unsqueeze( + input=sim_turns[index], axes=[2]) + # sim shape: [batch_size, 2*(stack_num+1), max_turn_num, max_turn_len, max_turn_len] + sim = fluid.layers.concat(input=sim_turns, axis=2) + + final_info = layers.cnn_3d(sim, self._channel1_num, self._channel2_num) + loss, logits = layers.loss(final_info, self.label) + return loss, logits diff --git a/PaddleNLP/models/dialogue_model_toolkit/dialogue_general_understanding/__init__.py b/PaddleNLP/models/dialogue_model_toolkit/dialogue_general_understanding/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PaddleNLP/models/dialogue_model_toolkit/dialogue_general_understanding/bert.py b/PaddleNLP/models/dialogue_model_toolkit/dialogue_general_understanding/bert.py new file mode 100644 index 0000000000000000000000000000000000000000..445d086893141125c09b518114ae0ff4fa32fa51 --- /dev/null +++ b/PaddleNLP/models/dialogue_model_toolkit/dialogue_general_understanding/bert.py @@ -0,0 +1,231 @@ +# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""BERT model.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys +import six +import json +import numpy as np +import paddle.fluid as fluid + +_WORK_DIR = os.path.split(os.path.realpath(__file__))[0] +sys.path.append(os.path.join(_WORK_DIR, "../../")) +from transformer_encoder import encoder, pre_process_layer + + +class BertConfig(object): + def __init__(self, config_path): + self._config_dict = self._parse(config_path) + + def _parse(self, config_path): + try: + with open(config_path) as json_file: + config_dict = json.load(json_file) + except Exception: + raise IOError("Error in parsing bert model config file '%s'" % + config_path) + else: + return config_dict + + def __getitem__(self, key): + return self._config_dict[key] + + def print_config(self): + for arg, value in sorted(six.iteritems(self._config_dict)): + print('%s: %s' % (arg, value)) + print('------------------------------------------------') + + +class BertModel(object): + def __init__(self, + src_ids, + position_ids, + sentence_ids, + input_mask, + config, + weight_sharing=True, + use_fp16=False): + + self._emb_size = config['hidden_size'] + self._n_layer = config['num_hidden_layers'] + self._n_head = config['num_attention_heads'] + self._voc_size = config['vocab_size'] + self._max_position_seq_len = config['max_position_embeddings'] + self._sent_types = config['type_vocab_size'] + self._hidden_act = config['hidden_act'] + self._prepostprocess_dropout = config['hidden_dropout_prob'] + self._attention_dropout = config['attention_probs_dropout_prob'] + self._weight_sharing = weight_sharing + + self._word_emb_name = "word_embedding" + self._pos_emb_name = "pos_embedding" + self._sent_emb_name = "sent_embedding" + self._dtype = "float16" if use_fp16 else "float32" + + # Initialize all weigths by truncated normal initializer, and all biases + # will be initialized by constant zero by default. + self._param_initializer = fluid.initializer.TruncatedNormal( + scale=config['initializer_range']) + + self._build_model(src_ids, position_ids, sentence_ids, input_mask) + + def _build_model(self, src_ids, position_ids, sentence_ids, input_mask): + # padding id in vocabulary must be set to 0 + emb_out = fluid.layers.embedding( + input=src_ids, + size=[self._voc_size, self._emb_size], + dtype=self._dtype, + param_attr=fluid.ParamAttr( + name=self._word_emb_name, initializer=self._param_initializer), + is_sparse=False) + position_emb_out = fluid.layers.embedding( + input=position_ids, + size=[self._max_position_seq_len, self._emb_size], + dtype=self._dtype, + param_attr=fluid.ParamAttr( + name=self._pos_emb_name, initializer=self._param_initializer)) + + sent_emb_out = fluid.layers.embedding( + sentence_ids, + size=[self._sent_types, self._emb_size], + dtype=self._dtype, + param_attr=fluid.ParamAttr( + name=self._sent_emb_name, initializer=self._param_initializer)) + + emb_out = emb_out + position_emb_out + emb_out = emb_out + sent_emb_out + + emb_out = pre_process_layer( + emb_out, 'nd', self._prepostprocess_dropout, name='pre_encoder') + + if self._dtype == "float16": + input_mask = fluid.layers.cast(x=input_mask, dtype=self._dtype) + + self_attn_mask = fluid.layers.matmul( + x=input_mask, y=input_mask, transpose_y=True) + self_attn_mask = fluid.layers.scale( + x=self_attn_mask, scale=10000.0, bias=-1.0, bias_after_scale=False) + n_head_self_attn_mask = fluid.layers.stack( + x=[self_attn_mask] * self._n_head, axis=1) + n_head_self_attn_mask.stop_gradient = True + + self._enc_out = encoder( + enc_input=emb_out, + attn_bias=n_head_self_attn_mask, + n_layer=self._n_layer, + n_head=self._n_head, + d_key=self._emb_size // self._n_head, + d_value=self._emb_size // self._n_head, + d_model=self._emb_size, + d_inner_hid=self._emb_size * 4, + prepostprocess_dropout=self._prepostprocess_dropout, + attention_dropout=self._attention_dropout, + relu_dropout=0, + hidden_act=self._hidden_act, + preprocess_cmd="", + postprocess_cmd="dan", + param_initializer=self._param_initializer, + name='encoder') + + def get_sequence_output(self): + return self._enc_out + + def get_pooled_output(self): + """Get the first feature of each sequence for classification""" + + next_sent_feat = fluid.layers.slice( + input=self._enc_out, axes=[1], starts=[0], ends=[1]) + next_sent_feat = fluid.layers.fc( + input=next_sent_feat, + size=self._emb_size, + act="tanh", + param_attr=fluid.ParamAttr( + name="pooled_fc.w_0", initializer=self._param_initializer), + bias_attr="pooled_fc.b_0") + return next_sent_feat + + def get_pretraining_output(self, mask_label, mask_pos, labels): + """Get the loss & accuracy for pretraining""" + + mask_pos = fluid.layers.cast(x=mask_pos, dtype='int32') + + # extract the first token feature in each sentence + next_sent_feat = self.get_pooled_output() + reshaped_emb_out = fluid.layers.reshape( + x=self._enc_out, shape=[-1, self._emb_size]) + # extract masked tokens' feature + mask_feat = fluid.layers.gather(input=reshaped_emb_out, index=mask_pos) + + # transform: fc + mask_trans_feat = fluid.layers.fc( + input=mask_feat, + size=self._emb_size, + act=self._hidden_act, + param_attr=fluid.ParamAttr( + name='mask_lm_trans_fc.w_0', + initializer=self._param_initializer), + bias_attr=fluid.ParamAttr(name='mask_lm_trans_fc.b_0')) + # transform: layer norm + mask_trans_feat = pre_process_layer( + mask_trans_feat, 'n', name='mask_lm_trans') + + mask_lm_out_bias_attr = fluid.ParamAttr( + name="mask_lm_out_fc.b_0", + initializer=fluid.initializer.Constant(value=0.0)) + if self._weight_sharing: + fc_out = fluid.layers.matmul( + x=mask_trans_feat, + y=fluid.default_main_program().global_block().var( + self._word_emb_name), + transpose_y=True) + fc_out += fluid.layers.create_parameter( + shape=[self._voc_size], + dtype=self._dtype, + attr=mask_lm_out_bias_attr, + is_bias=True) + + else: + fc_out = fluid.layers.fc(input=mask_trans_feat, + size=self._voc_size, + param_attr=fluid.ParamAttr( + name="mask_lm_out_fc.w_0", + initializer=self._param_initializer), + bias_attr=mask_lm_out_bias_attr) + + mask_lm_loss = fluid.layers.softmax_with_cross_entropy( + logits=fc_out, label=mask_label) + mean_mask_lm_loss = fluid.layers.mean(mask_lm_loss) + + next_sent_fc_out = fluid.layers.fc( + input=next_sent_feat, + size=2, + param_attr=fluid.ParamAttr( + name="next_sent_fc.w_0", initializer=self._param_initializer), + bias_attr="next_sent_fc.b_0") + + next_sent_loss, next_sent_softmax = fluid.layers.softmax_with_cross_entropy( + logits=next_sent_fc_out, label=labels, return_softmax=True) + + next_sent_acc = fluid.layers.accuracy( + input=next_sent_softmax, label=labels) + + mean_next_sent_loss = fluid.layers.mean(next_sent_loss) + + loss = mean_next_sent_loss + mean_mask_lm_loss + return next_sent_acc, mean_mask_lm_loss, loss diff --git a/PaddleNLP/models/dialogue_model_toolkit/dialogue_general_understanding/create_model.py b/PaddleNLP/models/dialogue_model_toolkit/dialogue_general_understanding/create_model.py new file mode 100644 index 0000000000000000000000000000000000000000..5655877ead413c28728d3fa0425aaacf4161a405 --- /dev/null +++ b/PaddleNLP/models/dialogue_model_toolkit/dialogue_general_understanding/create_model.py @@ -0,0 +1,78 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Create model for dialogue task.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle.fluid as fluid + +from bert import BertModel + + +def create_model(args, + pyreader_name, + bert_config, + num_labels, + paradigm_inst, + is_prediction=False): + """create dialogue task model""" + if args.task_name == 'atis_slot': + label_dim = [-1, args.max_seq_len] + lod_level = 1 + elif args.task_name in ['dstc2', 'dstc2_asr', 'multi-woz']: + label_dim = [-1, num_labels] + lod_level = 0 + else: + label_dim = [-1, 1] + lod_level = 0 + pyreader = fluid.layers.py_reader( + capacity=50, + shapes=[[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1], + [-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1], label_dim], + dtypes=['int64', 'int64', 'int64', 'float32', 'int64'], + lod_levels=[0, 0, 0, 0, lod_level], + name=pyreader_name, + use_double_buffer=True) + + (src_ids, pos_ids, sent_ids, input_mask, + labels) = fluid.layers.read_file(pyreader) + + bert = BertModel( + src_ids=src_ids, + position_ids=pos_ids, + sentence_ids=sent_ids, + input_mask=input_mask, + config=bert_config, + use_fp16=args.use_fp16) + + params = {'num_labels': num_labels, + 'src_ids': src_ids, + 'pos_ids': pos_ids, + 'sent_ids': sent_ids, + 'input_mask': input_mask, + 'labels': labels, + 'is_prediction': is_prediction} + + if is_prediction: + results = paradigm_inst.paradigm(bert, params) + results['pyreader'] = pyreader + return results + + results = paradigm_inst.paradigm(bert, params) + results['pyreader'] = pyreader + return results + + diff --git a/PaddleNLP/models/dialogue_model_toolkit/dialogue_general_understanding/define_paradigm.py b/PaddleNLP/models/dialogue_model_toolkit/dialogue_general_understanding/define_paradigm.py new file mode 100644 index 0000000000000000000000000000000000000000..9b8732050c74c7315814211e5bce55b2d8af8fe9 --- /dev/null +++ b/PaddleNLP/models/dialogue_model_toolkit/dialogue_general_understanding/define_paradigm.py @@ -0,0 +1,178 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""define network paradigm""" + +import sys +import re + +import paddle +import paddle.fluid as fluid + + +class Paradigm(object): + """ + define network paradigm + """ + def __init__(self, task_name): + """ + init + """ + self.task_name = task_name + + def create_cls(self, transformer_inst, params): + """ + create classify paradigm network + """ + cls_feats = transformer_inst.get_pooled_output() + cls_feats = fluid.layers.dropout( + x=cls_feats, + dropout_prob=0.1, + dropout_implementation="upscale_in_train") + logits = fluid.layers.fc( + input=cls_feats, + size=params['num_labels'], + param_attr=fluid.ParamAttr( + name="cls_out_w", + initializer=fluid.initializer.TruncatedNormal(scale=0.02)), + bias_attr=fluid.ParamAttr( + name="cls_out_b", initializer=fluid.initializer.Constant(0.))) + + if params['is_prediction']: + probs = fluid.layers.softmax(logits) + feed_targets_name = [ + params['src_ids'].name, params['pos_ids'].name, + params['sent_ids'].name, params['input_mask'].name, + ] + results = {"probs": probs, + "feed_targets_name": feed_targets_name} + return results + + ce_loss, probs = fluid.layers.softmax_with_cross_entropy( + logits=logits, label=params['labels'], return_softmax=True) + loss = fluid.layers.reduce_mean(input=ce_loss) + num_seqs = fluid.layers.create_tensor(dtype='int64') + accuracy = fluid.layers.accuracy(input=probs, label=params['labels'], total=num_seqs) + + loss.persistable = True + probs.persistable = True + accuracy.persistable = True + num_seqs.persistable = True + + results = {"loss": loss, + "probs": probs, + "accuracy": accuracy, + "num_seqs": num_seqs} + return results + + def create_multi_cls(self, transformer_inst, params): + """ + create multi classify paradigm network + """ + cls_feats = transformer_inst.get_pooled_output() + cls_feats = fluid.layers.dropout( + x=cls_feats, + dropout_prob=0.1, + dropout_implementation="upscale_in_train") + logits = fluid.layers.fc( + input=cls_feats, + size=params['num_labels'], + param_attr=fluid.ParamAttr( + name="cls_out_w", + initializer=fluid.initializer.TruncatedNormal(scale=0.02)), + bias_attr=fluid.ParamAttr( + name="cls_out_b", initializer=fluid.initializer.Constant(0.))) + + labels_onehot = fluid.layers.cast(params["labels"], dtype='float32') + ce_loss = fluid.layers.reduce_sum(fluid.layers.sigmoid_cross_entropy_with_logits(x=logits, label=labels_onehot)) + loss = fluid.layers.reduce_mean(input=ce_loss) + probs = fluid.layers.sigmoid(logits) + + if params['is_prediction']: + feed_targets_name = [ + params['src_ids'].name, params['pos_ids'].name, + params['sent_ids'].name, params['input_mask'].name, + ] + results = {"probs": probs, + "feed_targets_name": feed_targets_name} + return results + + num_seqs = fluid.layers.tensor.fill_constant(shape=[1], dtype='int64', value=1) + + loss.persistable = True + probs.persistable = True + num_seqs.persistable = True + results = {"loss": loss, + "probs": probs, + "num_seqs": num_seqs} + return results + + def create_sequence_tagging(self, transformer_inst, params): + """ + create sequence tagging paradigm + """ + output_layer = transformer_inst.get_sequence_output() + hidden_size = output_layer.shape[-1] + output_layer = fluid.layers.stack(output_layer, axis=1) + output_layer = fluid.layers.reshape(output_layer, [-1, hidden_size]) + + logits = fluid.layers.fc(input=output_layer, size=params['num_labels']) + probs = fluid.layers.cast(fluid.layers.argmax(logits, axis=1), dtype='int32') + + if params['is_prediction']: + feed_targets_name = [ + params['src_ids'].name, params['pos_ids'].name, + params['sent_ids'].name, params['input_mask'].name, + ] + results = {"probs": probs, + "feed_targets_name": feed_targets_name} + return results + + num_seqs = fluid.layers.tensor.fill_constant(shape=[1], dtype='int64', value=1) + y_label_reshape = fluid.layers.cast(fluid.layers.reshape(params['labels'], [-1]), dtype='int32') + correct_prediction = fluid.layers.equal(probs, y_label_reshape) + accuracy = fluid.layers.reduce_mean(fluid.layers.cast(correct_prediction, dtype='float32')) + ce_loss = fluid.layers.softmax_with_cross_entropy(logits=logits, \ + label=fluid.layers.reshape(params['labels'], [-1, 1])) + loss = fluid.layers.reduce_mean(input=ce_loss) + + loss.persistable = True + probs.persistable = True + accuracy.persistable = True + num_seqs.persistable = True + results = {"loss": loss, + "probs": probs, + "accuracy": accuracy, + "num_seqs": num_seqs} + return results + + def paradigm(self, transformer_inst, params): + """ + run paradigm + """ + results = None + if self.task_name == 'udc': + results = self.create_cls(transformer_inst, params) + elif self.task_name == 'swda': + results = self.create_cls(transformer_inst, params) + elif self.task_name == 'mrda': + results = self.create_cls(transformer_inst, params) + elif self.task_name == 'atis_intent': + results = self.create_cls(transformer_inst, params) + elif self.task_name == 'atis_slot': + results = self.create_sequence_tagging(transformer_inst, params) + elif self.task_name == 'dstc2': + results = self.create_multi_cls(transformer_inst, params) + return results + + diff --git a/PaddleNLP/models/language_model/__init__.py b/PaddleNLP/models/language_model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PaddleNLP/language_model/lstm/lm_model.py b/PaddleNLP/models/language_model/lm_model.py similarity index 100% rename from PaddleNLP/language_model/lstm/lm_model.py rename to PaddleNLP/models/language_model/lm_model.py diff --git a/PaddleNLP/models/matching/__init__.py b/PaddleNLP/models/matching/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PaddleNLP/models/matching/bow.py b/PaddleNLP/models/matching/bow.py new file mode 100644 index 0000000000000000000000000000000000000000..c07250e13fc07f70c55850dce33a787fb1432ee3 --- /dev/null +++ b/PaddleNLP/models/matching/bow.py @@ -0,0 +1,52 @@ +""" +bow class +""" + +import paddle_layers as layers + + +class BOW(object): + """ + BOW + """ + + def __init__(self, conf_dict): + """ + initialize + """ + self.dict_size = conf_dict["dict_size"] + self.task_mode = conf_dict["task_mode"] + self.emb_dim = conf_dict["net"]["emb_dim"] + self.bow_dim = conf_dict["net"]["bow_dim"] + + def predict(self, left, right): + """ + Forward network + """ + # embedding layer + emb_layer = layers.EmbeddingLayer(self.dict_size, self.emb_dim, "emb") + left_emb = emb_layer.ops(left) + right_emb = emb_layer.ops(right) + # Presentation context + pool_layer = layers.SequencePoolLayer("sum") + left_pool = pool_layer.ops(left_emb) + right_pool = pool_layer.ops(right_emb) + softsign_layer = layers.SoftsignLayer() + left_soft = softsign_layer.ops(left_pool) + right_soft = softsign_layer.ops(right_pool) + # matching layer + if self.task_mode == "pairwise": + bow_layer = layers.FCLayer(self.bow_dim, "relu", "fc") + left_bow = bow_layer.ops(left_soft) + right_bow = bow_layer.ops(right_soft) + cos_sim_layer = layers.CosSimLayer() + pred = cos_sim_layer.ops(left_bow, right_bow) + return left_bow, pred + else: + concat_layer = layers.ConcatLayer(1) + concat = concat_layer.ops([left_soft, right_soft]) + bow_layer = layers.FCLayer(self.bow_dim, "relu", "fc") + concat_fc = bow_layer.ops(concat) + softmax_layer = layers.FCLayer(2, "softmax", "cos_sim") + pred = softmax_layer.ops(concat_fc) + return left_soft, pred diff --git a/PaddleNLP/models/matching/cnn.py b/PaddleNLP/models/matching/cnn.py new file mode 100644 index 0000000000000000000000000000000000000000..6b36e5797ca307def19b1f2e42dab077744c0e5e --- /dev/null +++ b/PaddleNLP/models/matching/cnn.py @@ -0,0 +1,52 @@ +""" +cnn class +""" + +import paddle_layers as layers + + +class CNN(object): + """ + CNN + """ + + def __init__(self, conf_dict): + """ + initialize + """ + self.dict_size = conf_dict["dict_size"] + self.task_mode = conf_dict["task_mode"] + self.emb_dim = conf_dict["net"]["emb_dim"] + self.filter_size = conf_dict["net"]["filter_size"] + self.num_filters = conf_dict["net"]["num_filters"] + self.hidden_dim = conf_dict["net"]["hidden_dim"] + + def predict(self, left, right): + """ + Forward network + """ + # embedding layer + emb_layer = layers.EmbeddingLayer(self.dict_size, self.emb_dim, "emb") + left_emb = emb_layer.ops(left) + right_emb = emb_layer.ops(right) + # Presentation context + cnn_layer = layers.SequenceConvPoolLayer( + self.filter_size, self.num_filters, "conv") + left_cnn = cnn_layer.ops(left_emb) + right_cnn = cnn_layer.ops(right_emb) + # matching layer + if self.task_mode == "pairwise": + relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu") + left_relu = relu_layer.ops(left_cnn) + right_relu = relu_layer.ops(right_cnn) + cos_sim_layer = layers.CosSimLayer() + pred = cos_sim_layer.ops(left_relu, right_relu) + return left_relu, pred + else: + concat_layer = layers.ConcatLayer(1) + concat = concat_layer.ops([left_cnn, right_cnn]) + relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu") + concat_fc = relu_layer.ops(concat) + softmax_layer = layers.FCLayer(2, "softmax", "cos_sim") + pred = softmax_layer.ops(concat_fc) + return left_cnn, pred diff --git a/PaddleNLP/models/matching/gru.py b/PaddleNLP/models/matching/gru.py new file mode 100644 index 0000000000000000000000000000000000000000..2d7f3e7150f06136857d6379469993f4dc649439 --- /dev/null +++ b/PaddleNLP/models/matching/gru.py @@ -0,0 +1,53 @@ +""" +gru class +""" + +import paddle_layers as layers + + +class GRU(object): + """ + GRU + """ + + def __init__(self, conf_dict): + """ + initialize + """ + self.dict_size = conf_dict["dict_size"] + self.task_mode = conf_dict["task_mode"] + self.emb_dim = conf_dict["net"]["emb_dim"] + self.gru_dim = conf_dict["net"]["gru_dim"] + self.hidden_dim = conf_dict["net"]["hidden_dim"] + + def predict(self, left, right): + """ + Forward network + """ + # embedding layer + emb_layer = layers.EmbeddingLayer(self.dict_size, self.emb_dim, "emb") + left_emb = emb_layer.ops(left) + right_emb = emb_layer.ops(right) + # Presentation context + gru_layer = layers.DynamicGRULayer(self.gru_dim, "gru") + left_gru = gru_layer.ops(left_emb) + right_gru = gru_layer.ops(right_emb) + last_layer = layers.SequenceLastStepLayer() + left_last = last_layer.ops(left_gru) + right_last = last_layer.ops(right_gru) + # matching layer + if self.task_mode == "pairwise": + relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu") + left_relu = relu_layer.ops(left_last) + right_relu = relu_layer.ops(right_last) + cos_sim_layer = layers.CosSimLayer() + pred = cos_sim_layer.ops(left_relu, right_relu) + return left_relu, pred + else: + concat_layer = layers.ConcatLayer(1) + concat = concat_layer.ops([left_last, right_last]) + relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu") + concat_fc = relu_layer.ops(concat) + softmax_layer = layers.FCLayer(2, "softmax", "cos_sim") + pred = softmax_layer.ops(concat_fc) + return left_last, pred diff --git a/PaddleNLP/models/matching/losses/__init__.py b/PaddleNLP/models/matching/losses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PaddleNLP/models/matching/losses/hinge_loss.py b/PaddleNLP/models/matching/losses/hinge_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..664a959957e968ea281e58e30ecf40b59b41a1a6 --- /dev/null +++ b/PaddleNLP/models/matching/losses/hinge_loss.py @@ -0,0 +1,37 @@ +""" +hinge loss +""" + +import sys + +sys.path.append("../../../") +import models.matching.paddle_layers as layers + + +class HingeLoss(object): + """ + Hing Loss Calculate class + """ + + def __init__(self, conf_dict): + """ + initialize + """ + self.margin = conf_dict["loss"]["margin"] + + def compute(self, pos, neg): + """ + compute loss + """ + elementwise_max = layers.ElementwiseMaxLayer() + elementwise_add = layers.ElementwiseAddLayer() + elementwise_sub = layers.ElementwiseSubLayer() + constant = layers.ConstantLayer() + reduce_mean = layers.ReduceMeanLayer() + loss = reduce_mean.ops( + elementwise_max.ops( + constant.ops(neg, neg.shape, "float32", 0.0), + elementwise_add.ops( + elementwise_sub.ops(neg, pos), + constant.ops(neg, neg.shape, "float32", self.margin)))) + return loss diff --git a/PaddleNLP/models/matching/losses/log_loss.py b/PaddleNLP/models/matching/losses/log_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..a62abdfc40e675e6c6d109a8277edcb6c6517480 --- /dev/null +++ b/PaddleNLP/models/matching/losses/log_loss.py @@ -0,0 +1,29 @@ +""" +log loss +""" + +import sys + +sys.path.append("../../../") +import models.matching.paddle_layers as layers + + +class LogLoss(object): + """ + Log Loss Calculate + """ + + def __init__(self, conf_dict): + """ + initialize + """ + pass + + def compute(self, pos, neg): + """ + compute loss + """ + sigmoid = layers.SigmoidLayer() + reduce_mean = layers.ReduceMeanLayer() + loss = reduce_mean.ops(sigmoid.ops(neg - pos)) + return loss diff --git a/PaddleNLP/models/matching/losses/softmax_cross_entropy_loss.py b/PaddleNLP/models/matching/losses/softmax_cross_entropy_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..e97c1dc3d4e98aa30792109dd1ddc6b8a1e59b1d --- /dev/null +++ b/PaddleNLP/models/matching/losses/softmax_cross_entropy_loss.py @@ -0,0 +1,30 @@ +""" +softmax loss +""" + +import sys + +sys.path.append("../../../") +import models.matching.paddle_layers as layers + + +class SoftmaxCrossEntropyLoss(object): + """ + Softmax with Cross Entropy Loss Calculate + """ + + def __init__(self, conf_dict): + """ + initialize + """ + pass + + def compute(self, input, label): + """ + compute loss + """ + softmax_with_cross_entropy = layers.SoftmaxWithCrossEntropyLayer() + reduce_mean = layers.ReduceMeanLayer() + cost = softmax_with_cross_entropy.ops(input, label) + avg_cost = reduce_mean.ops(cost) + return avg_cost diff --git a/PaddleNLP/models/matching/lstm.py b/PaddleNLP/models/matching/lstm.py new file mode 100644 index 0000000000000000000000000000000000000000..3a656da74053fe3e1d159204c89f131409e9361c --- /dev/null +++ b/PaddleNLP/models/matching/lstm.py @@ -0,0 +1,52 @@ +""" +lstm class +""" +import paddle_layers as layers + + +class LSTM(object): + """ + LSTM + """ + + def __init__(self, conf_dict): + """ + initialize + """ + self.dict_size = conf_dict["dict_size"] + self.task_mode = conf_dict["task_mode"] + self.emb_dim = conf_dict["net"]["emb_dim"] + self.lstm_dim = conf_dict["net"]["lstm_dim"] + self.hidden_dim = conf_dict["net"]["hidden_dim"] + + def predict(self, left, right): + """ + Forward network + """ + # embedding layer + emb_layer = layers.EmbeddingLayer(self.dict_size, self.emb_dim, "emb") + left_emb = emb_layer.ops(left) + right_emb = emb_layer.ops(right) + # Presentation context + lstm_layer = layers.DynamicLSTMLayer(self.lstm_dim, "lstm") + left_lstm = lstm_layer.ops(left_emb) + right_lstm = lstm_layer.ops(right_emb) + last_layer = layers.SequenceLastStepLayer() + left_last = last_layer.ops(left_lstm) + right_last = last_layer.ops(right_lstm) + # matching layer + if self.task_mode == "pairwise": + relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu") + left_relu = relu_layer.ops(left_last) + right_relu = relu_layer.ops(right_last) + cos_sim_layer = layers.CosSimLayer() + pred = cos_sim_layer.ops(left_relu, right_relu) + return left_relu, pred + else: + concat_layer = layers.ConcatLayer(1) + concat = concat_layer.ops([left_last, right_last]) + relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu") + concat_fc = relu_layer.ops(concat) + softmax_layer = layers.FCLayer(2, "softmax", "cos_sim") + pred = softmax_layer.ops(concat_fc) + return left_last, pred diff --git a/PaddleNLP/models/matching/mm_dnn.py b/PaddleNLP/models/matching/mm_dnn.py new file mode 100644 index 0000000000000000000000000000000000000000..8c5b68fe65dae05cdff4ebb702643a0a4232aea9 --- /dev/null +++ b/PaddleNLP/models/matching/mm_dnn.py @@ -0,0 +1,162 @@ +""" +MMDNN class +""" +import numpy as np +import paddle.fluid as fluid +import logging + + +class MMDNN(object): + """ + MMDNN + """ + + def __init__(self, config): + """ + initialize + """ + self.vocab_size = int(config['dict_size']) + self.emb_size = int(config['net']['embedding_dim']) + self.lstm_dim = int(config['net']['lstm_dim']) + self.kernel_size = int(config['net']['num_filters']) + self.win_size1 = int(config['net']['window_size_left']) + self.win_size2 = int(config['net']['window_size_right']) + self.dpool_size1 = int(config['net']['dpool_size_left']) + self.dpool_size2 = int(config['net']['dpool_size_right']) + self.hidden_size = int(config['net']['hidden_size']) + self.seq_len1 = int(config['max_len_left']) + self.seq_len2 = int(config['max_len_right']) + self.task_mode = config['task_mode'] + + if int(config['match_mask']) != 0: + self.match_mask = True + else: + self.match_mask = False + + if self.task_mode == "pointwise": + self.n_class = int(config['n_class']) + self.out_size = self.n_class + elif self.task_mode == "pairwise": + self.out_size = 1 + else: + logging.error("training mode not supported") + + def embedding_layer(self, input, zero_pad=True, scale=True): + """ + embedding layer + """ + emb = fluid.layers.embedding( + input=input, + size=[self.vocab_size, self.emb_size], + padding_idx=(0 if zero_pad else None), + param_attr=fluid.ParamAttr(name="word_embedding", + initializer=fluid.initializer.Xavier())) + if scale: + emb = emb * (self.emb_size ** 0.5) + return emb + + def bi_dynamic_lstm(self, input, hidden_size): + """ + bi_lstm layer + """ + fw_in_proj = fluid.layers.fc(input=input, + size=4 * hidden_size, + param_attr=fluid.ParamAttr(name="fw_fc.w"), + bias_attr=False) + forward, _ = fluid.layers.dynamic_lstm( + input=fw_in_proj, size=4 * hidden_size, is_reverse=False, + param_attr=fluid.ParamAttr(name="forward_lstm.w"), + bias_attr=fluid.ParamAttr(name="forward_lstm.b")) + + rv_in_proj = fluid.layers.fc(input=input, + size=4 * hidden_size, + param_attr=fluid.ParamAttr(name="rv_fc.w"), + bias_attr=False) + reverse, _ = fluid.layers.dynamic_lstm( + input=rv_in_proj, size=4 * hidden_size, is_reverse=True, + param_attr=fluid.ParamAttr(name="reverse_lstm.w"), + bias_attr=fluid.ParamAttr(name="reverse_lstm.b")) + return [forward, reverse] + + def conv_pool_relu_layer(self, input, mask=None): + """ + convolution and pool layer + """ + # data format NCHW + emb_expanded = fluid.layers.unsqueeze(input=input, axes=[1]) + # same padding + conv = fluid.layers.conv2d( + input=emb_expanded, + num_filters=self.kernel_size, + stride=1, + padding=(int(self.seq_len1 / 2), int(self.seq_len2 // 2)), + filter_size=(self.seq_len1, self.seq_len2), + bias_attr=fluid.ParamAttr( + initializer=fluid.initializer.Constant(0.1))) + + if mask is not None: + cross_mask = fluid.layers.stack(x=[mask] * self.kernel_size, axis=1) + conv = cross_mask * conv + (1 - cross_mask) * (-2 ** 32 + 1) + # valid padding + pool = fluid.layers.pool2d( + input=conv, + pool_size=[ + int(self.seq_len1 / self.dpool_size1), + int(self.seq_len2 / self.dpool_size2) + ], + pool_stride=[ + int(self.seq_len1 / self.dpool_size1), + int(self.seq_len2 / self.dpool_size2) + ], + pool_type="max", ) + + relu = fluid.layers.relu(pool) + return relu + + def get_cross_mask(self, left_lens, right_lens): + """ + cross mask + """ + mask1 = fluid.layers.sequence_mask( + x=left_lens, dtype='float32', maxlen=self.seq_len1 + 1) + mask2 = fluid.layers.sequence_mask( + x=right_lens, dtype='float32', maxlen=self.seq_len2 + 1) + + mask1 = fluid.layers.transpose(x=mask1, perm=[0, 2, 1]) + cross_mask = fluid.layers.matmul(x=mask1, y=mask2) + return cross_mask + + def predict(self, left, right): + """ + Forward network + """ + left_emb = self.embedding_layer(left, zero_pad=True, scale=False) + right_emb = self.embedding_layer(right, zero_pad=True, scale=False) + + bi_left_outputs = self.bi_dynamic_lstm( + input=left_emb, hidden_size=self.lstm_dim) + left_seq_encoder = fluid.layers.concat(input=bi_left_outputs, axis=1) + bi_right_outputs = self.bi_dynamic_lstm( + input=right_emb, hidden_size=self.lstm_dim) + right_seq_encoder = fluid.layers.concat(input=bi_right_outputs, axis=1) + + pad_value = fluid.layers.assign(input=np.array([0]).astype("float32")) + left_seq_encoder, left_lens = fluid.layers.sequence_pad( + x=left_seq_encoder, pad_value=pad_value, maxlen=self.seq_len1) + right_seq_encoder, right_lens = fluid.layers.sequence_pad( + x=right_seq_encoder, pad_value=pad_value, maxlen=self.seq_len2) + + cross = fluid.layers.matmul( + left_seq_encoder, right_seq_encoder, transpose_y=True) + if self.match_mask: + cross_mask = self.get_cross_mask(left_lens, right_lens) + else: + cross_mask = None + conv_pool_relu = self.conv_pool_relu_layer(input=cross, mask=cross_mask) + relu_hid1 = fluid.layers.fc(input=conv_pool_relu, + act="tanh", + size=self.hidden_size) + + pred = fluid.layers.fc(input=relu_hid1, size=self.out_size) + + return left_seq_encoder, pred diff --git a/PaddleNLP/models/matching/optimizers/__init__.py b/PaddleNLP/models/matching/optimizers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PaddleNLP/models/matching/optimizers/paddle_optimizers.py b/PaddleNLP/models/matching/optimizers/paddle_optimizers.py new file mode 100644 index 0000000000000000000000000000000000000000..ad3276fa152e044e379c67d0ff16f662bcdebbd0 --- /dev/null +++ b/PaddleNLP/models/matching/optimizers/paddle_optimizers.py @@ -0,0 +1,47 @@ +""" +optimizer calss +""" + +import paddle.fluid as fluid + + +class SGDOptimizer(object): + """ + SGD + """ + + def __init__(self, conf_dict): + """ + initialize + """ + self.learning_rate = conf_dict["optimizer"]["learning_rate"] + + def ops(self, loss): + """ + SGD optimizer operation + """ + sgd = fluid.optimizer.SGDOptimizer(self.learning_rate) + sgd.minimize(loss) + + +class AdamOptimizer(object): + """ + Adam + """ + + def __init__(self, conf_dict): + """ + initialize + """ + self.learning_rate = conf_dict["optimizer"]["learning_rate"] + self.beta1 = conf_dict["optimizer"]["beta1"] + self.beta2 = conf_dict["optimizer"]["beta2"] + self.epsilon = conf_dict["optimizer"]["epsilon"] + + def ops(self, loss): + """ + Adam optimizer operation + """ + adam = fluid.optimizer.AdamOptimizer( + self.learning_rate, beta1=self.beta1, beta2=self.beta2, epsilon=self.epsilon) + adam.minimize(loss) diff --git a/PaddleNLP/models/matching/paddle_layers.py b/PaddleNLP/models/matching/paddle_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..9c5baa18ad97ac022c54d69657d967583c756948 --- /dev/null +++ b/PaddleNLP/models/matching/paddle_layers.py @@ -0,0 +1,428 @@ +""" +network layers +""" + +import paddle.fluid as fluid +import paddle.fluid.param_attr as attr + + +class EmbeddingLayer(object): + """ + Embedding Layer class + """ + + def __init__(self, dict_size, emb_dim, name="emb"): + """ + initialize + """ + self.dict_size = dict_size + self.emb_dim = emb_dim + self.name = name + + def ops(self, input): + """ + operation + """ + emb = fluid.layers.embedding(input=input, size=[ + self.dict_size, self.emb_dim], is_sparse=True, + param_attr=attr.ParamAttr(name=self.name)) + return emb + + +class SequencePoolLayer(object): + """ + Sequence Pool Layer class + """ + + def __init__(self, pool_type): + """ + initialize + """ + self.pool_type = pool_type + + def ops(self, input): + """ + operation + """ + pool = fluid.layers.sequence_pool( + input=input, pool_type=self.pool_type) + return pool + + +class FCLayer(object): + """ + Fully Connect Layer class + """ + + def __init__(self, fc_dim, act, name="fc"): + """ + initialize + """ + self.fc_dim = fc_dim + self.act = act + self.name = name + + def ops(self, input): + """ + operation + """ + fc = fluid.layers.fc(input=input, size=self.fc_dim, param_attr=attr.ParamAttr( + name="%s.w" % self.name), + bias_attr=attr.ParamAttr(name="%s.b" % self.name), act=self.act, name=self.name) + return fc + + +class DynamicGRULayer(object): + """ + Dynamic GRU Layer class + """ + + def __init__(self, gru_dim, name="dyn_gru"): + """ + initialize + """ + self.gru_dim = gru_dim + self.name = name + + def ops(self, input): + """ + operation + """ + proj = fluid.layers.fc(input=input, size=self.gru_dim * 3, + param_attr=attr.ParamAttr(name="%s_fc.w" % self.name), + bias_attr=attr.ParamAttr(name="%s_fc.b" % self.name)) + gru = fluid.layers.dynamic_gru(input=proj, size=self.gru_dim, + param_attr=attr.ParamAttr(name="%s.w" % self.name), + bias_attr=attr.ParamAttr(name="%s.b" % self.name)) + return gru + + +class DynamicLSTMLayer(object): + """ + Dynamic LSTM Layer class + """ + + def __init__(self, lstm_dim, name="dyn_lstm"): + """ + initialize + """ + self.lstm_dim = lstm_dim + self.name = name + + def ops(self, input): + """ + operation + """ + proj = fluid.layers.fc(input=input, size=self.lstm_dim * 4, + param_attr=attr.ParamAttr(name="%s_fc.w" % self.name), + bias_attr=attr.ParamAttr(name="%s_fc.b" % self.name)) + lstm, _ = fluid.layers.dynamic_lstm(input=proj, size=self.lstm_dim * 4, + param_attr=attr.ParamAttr(name="%s.w" % self.name), + bias_attr=attr.ParamAttr(name="%s.b" % self.name)) + return lstm + + +class SequenceLastStepLayer(object): + """ + Get Last Step Sequence Layer class + """ + + def __init__(self): + """ + initialize + """ + pass + + def ops(self, input): + """ + operation + """ + last = fluid.layers.sequence_last_step(input) + return last + + +class SequenceConvPoolLayer(object): + """ + Sequence convolution and pooling Layer class + """ + + def __init__(self, filter_size, num_filters, name): + """ + initialize + Args: + filter_size:Convolution kernel size + num_filters:Convolution kernel number + """ + self.filter_size = filter_size + self.num_filters = num_filters + self.name = name + + def ops(self, input): + """ + operation + """ + conv = fluid.nets.sequence_conv_pool(input=input, filter_size=self.filter_size, + num_filters=self.num_filters, + param_attr=attr.ParamAttr(name=self.name), act="relu") + return conv + + +class DataLayer(object): + """ + Data Layer class + """ + + def __init__(self): + """ + initialize + """ + pass + + def ops(self, name, shape, dtype, lod_level=0): + """ + operation + """ + data = fluid.layers.data( + name=name, shape=shape, dtype=dtype, lod_level=lod_level) + return data + + +class ConcatLayer(object): + """ + Connection Layer class + """ + + def __init__(self, axis): + """ + initialize + """ + self.axis = axis + + def ops(self, inputs): + """ + operation + """ + concat = fluid.layers.concat(inputs, axis=self.axis) + return concat + + +class ReduceMeanLayer(object): + """ + Reduce Mean Layer class + """ + + def __init__(self): + """ + initialize + """ + pass + + def ops(self, input): + """ + operation + """ + mean = fluid.layers.reduce_mean(input) + return mean + + +class CrossEntropyLayer(object): + """ + Cross Entropy Calculate Layer + """ + + def __init__(self, name="cross_entropy"): + """ + initialize + """ + pass + + def ops(self, input, label): + """ + operation + """ + loss = fluid.layers.cross_entropy(input=input, label=label) + return loss + + +class SoftmaxWithCrossEntropyLayer(object): + """ + Softmax with Cross Entropy Calculate Layer + """ + + def __init__(self, name="softmax_with_cross_entropy"): + """ + initialize + """ + pass + + def ops(self, input, label): + """ + operation + """ + loss = fluid.layers.softmax_with_cross_entropy(logits=input, label=label) + return loss + + +class CosSimLayer(object): + """ + Cos Similarly Calculate Layer + """ + + def __init__(self): + """ + initialize + """ + pass + + def ops(self, x, y): + """ + operation + """ + sim = fluid.layers.cos_sim(x, y) + return sim + + +class ElementwiseMaxLayer(object): + """ + Elementwise Max Layer class + """ + + def __init__(self): + """ + initialize + """ + pass + + def ops(self, x, y): + """ + operation + """ + max = fluid.layers.elementwise_max(x, y) + return max + + +class ElementwiseAddLayer(object): + """ + Elementwise Add Layer class + """ + + def __init__(self): + """ + initialize + """ + pass + + def ops(self, x, y): + """ + operation + """ + add = fluid.layers.elementwise_add(x, y) + return add + + +class ElementwiseSubLayer(object): + """ + Elementwise Add Layer class + """ + + def __init__(self): + """ + initialize + """ + pass + + def ops(self, x, y): + """ + operation + """ + sub = fluid.layers.elementwise_sub(x, y) + return sub + + +class ConstantLayer(object): + """ + Generate A Constant Layer class + """ + + def __init__(self): + """ + initialize + """ + pass + + def ops(self, input, shape, dtype, value): + """ + operation + """ + constant = fluid.layers.fill_constant_batch_size_like( + input, shape, dtype, value) + return constant + + +class SigmoidLayer(object): + """ + Sigmoid Layer class + """ + + def __init__(self): + """ + initialize + """ + pass + + def ops(self, input): + """ + operation + """ + sigmoid = fluid.layers.sigmoid(input) + return sigmoid + + +class SoftsignLayer(object): + """ + Softsign Layer class + """ + + def __init__(self): + """ + initialize + """ + pass + + def ops(self, input): + """ + operation + """ + softsign = fluid.layers.softsign(input) + return softsign + +# class MatmulLayer(object): +# def __init__(self, transpose_x, transpose_y): +# self.transpose_x = transpose_x +# self.transpose_y = transpose_y + +# def ops(self, x, y): +# matmul = fluid.layers.matmul(x, y, self.transpose_x, self.transpose_y) +# return matmul + + +# class Conv2dLayer(object): +# def __init__(self, num_filters, filter_size, act, name): +# self.num_filters = num_filters +# self.filter_size = filter_size +# self.act = act +# self.name = name + +# def ops(self, input): +# conv = fluid.layers.conv2d(input, self.num_filters, self.filter_size, param_attr=attr.ParamAttr(name="%s.w" % self.name), bias_attr=attr.ParamAttr(name="%s.b" % self.name), act=self.act) +# return conv + + +# class Pool2dLayer(object): +# def __init__(self, pool_size, pool_type): +# self.pool_size = pool_size +# self.pool_type = pool_type + +# def ops(self, input): +# pool = fluid.layers.pool2d(input, self.pool_size, self.pool_type) +# return pool diff --git a/PaddleNLP/models/neural_machine_translation/transformer/__init__.py b/PaddleNLP/models/neural_machine_translation/transformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PaddleNLP/models/neural_machine_translation/transformer/desc.py b/PaddleNLP/models/neural_machine_translation/transformer/desc.py new file mode 100644 index 0000000000000000000000000000000000000000..5eeb0dbfdacd09325913a182bfbbb7a557b6a72d --- /dev/null +++ b/PaddleNLP/models/neural_machine_translation/transformer/desc.py @@ -0,0 +1,92 @@ +# The placeholder for batch_size in compile time. Must be -1 currently to be +# consistent with some ops' infer-shape output in compile time, such as the +# sequence_expand op used in beamsearch decoder. +batch_size = -1 +# The placeholder for squence length in compile time. +seq_len = 256 +# The placeholder for head number in compile time. +n_head = 8 +# The placeholder for model dim in compile time. +d_model = 512 +# Here list the data shapes and data types of all inputs. +# The shapes here act as placeholder and are set to pass the infer-shape in +# compile time. +input_descs = { + # The actual data shape of src_word is: + # [batch_size, max_src_len_in_batch, 1] + "src_word": [(batch_size, seq_len, 1), "int64", 2], + # The actual data shape of src_pos is: + # [batch_size, max_src_len_in_batch, 1] + "src_pos": [(batch_size, seq_len, 1), "int64"], + # This input is used to remove attention weights on paddings in the + # encoder. + # The actual data shape of src_slf_attn_bias is: + # [batch_size, n_head, max_src_len_in_batch, max_src_len_in_batch] + "src_slf_attn_bias": [(batch_size, n_head, seq_len, seq_len), "float32"], + # The actual data shape of trg_word is: + # [batch_size, max_trg_len_in_batch, 1] + "trg_word": [(batch_size, seq_len, 1), "int64", + 2], # lod_level is only used in fast decoder. + # The actual data shape of trg_pos is: + # [batch_size, max_trg_len_in_batch, 1] + "trg_pos": [(batch_size, seq_len, 1), "int64"], + # This input is used to remove attention weights on paddings and + # subsequent words in the decoder. + # The actual data shape of trg_slf_attn_bias is: + # [batch_size, n_head, max_trg_len_in_batch, max_trg_len_in_batch] + "trg_slf_attn_bias": [(batch_size, n_head, seq_len, seq_len), "float32"], + # This input is used to remove attention weights on paddings of the source + # input in the encoder-decoder attention. + # The actual data shape of trg_src_attn_bias is: + # [batch_size, n_head, max_trg_len_in_batch, max_src_len_in_batch] + "trg_src_attn_bias": [(batch_size, n_head, seq_len, seq_len), "float32"], + # This input is used in independent decoder program for inference. + # The actual data shape of enc_output is: + # [batch_size, max_src_len_in_batch, d_model] + "enc_output": [(batch_size, seq_len, d_model), "float32"], + # The actual data shape of label_word is: + # [batch_size * max_trg_len_in_batch, 1] + "lbl_word": [(batch_size * seq_len, 1), "int64"], + # This input is used to mask out the loss of paddding tokens. + # The actual data shape of label_weight is: + # [batch_size * max_trg_len_in_batch, 1] + "lbl_weight": [(batch_size * seq_len, 1), "float32"], + # This input is used in beam-search decoder. + "init_score": [(batch_size, 1), "float32", 2], + # This input is used in beam-search decoder for the first gather + # (cell states updation) + "init_idx": [(batch_size, ), "int32"], +} + +# Names of word embedding table which might be reused for weight sharing. +word_emb_param_names = ( + "src_word_emb_table", + "trg_word_emb_table", ) +# Names of position encoding table which will be initialized externally. +pos_enc_param_names = ( + "src_pos_enc_table", + "trg_pos_enc_table", ) +# separated inputs for different usages. +encoder_data_input_fields = ( + "src_word", + "src_pos", + "src_slf_attn_bias", ) +decoder_data_input_fields = ( + "trg_word", + "trg_pos", + "trg_slf_attn_bias", + "trg_src_attn_bias", + "enc_output", ) +label_data_input_fields = ( + "lbl_word", + "lbl_weight", ) +# In fast decoder, trg_pos (only containing the current time step) is generated +# by ops and trg_slf_attn_bias is not needed. +fast_decoder_data_input_fields = ( + "trg_word", + "init_score", + "init_idx", + "trg_src_attn_bias", ) + +# Set seed for CE +dropout_seed = None diff --git a/PaddleNLP/models/neural_machine_translation/transformer/model.py b/PaddleNLP/models/neural_machine_translation/transformer/model.py new file mode 100644 index 0000000000000000000000000000000000000000..8ac9850dc2f26c821fec8816e6a3b76401044735 --- /dev/null +++ b/PaddleNLP/models/neural_machine_translation/transformer/model.py @@ -0,0 +1,909 @@ +from functools import partial +import numpy as np + +import paddle.fluid as fluid +import paddle.fluid.layers as layers + +from desc import * + + +def wrap_layer_with_block(layer, block_idx): + """ + Make layer define support indicating block, by which we can add layers + to other blocks within current block. This will make it easy to define + cache among while loop. + """ + + class BlockGuard(object): + """ + BlockGuard class. + + BlockGuard class is used to switch to the given block in a program by + using the Python `with` keyword. + """ + + def __init__(self, block_idx=None, main_program=None): + self.main_program = fluid.default_main_program( + ) if main_program is None else main_program + self.old_block_idx = self.main_program.current_block().idx + self.new_block_idx = block_idx + + def __enter__(self): + self.main_program.current_block_idx = self.new_block_idx + + def __exit__(self, exc_type, exc_val, exc_tb): + self.main_program.current_block_idx = self.old_block_idx + if exc_type is not None: + return False # re-raise exception + return True + + def layer_wrapper(*args, **kwargs): + with BlockGuard(block_idx): + return layer(*args, **kwargs) + + return layer_wrapper + + +def position_encoding_init(n_position, d_pos_vec): + """ + Generate the initial values for the sinusoid position encoding table. + """ + channels = d_pos_vec + position = np.arange(n_position) + num_timescales = channels // 2 + log_timescale_increment = (np.log(float(1e4) / float(1)) / + (num_timescales - 1)) + inv_timescales = np.exp(np.arange( + num_timescales)) * -log_timescale_increment + scaled_time = np.expand_dims(position, 1) * np.expand_dims(inv_timescales, + 0) + signal = np.concatenate([np.sin(scaled_time), np.cos(scaled_time)], axis=1) + signal = np.pad(signal, [[0, 0], [0, np.mod(channels, 2)]], 'constant') + position_enc = signal + return position_enc.astype("float32") + + +def multi_head_attention(queries, + keys, + values, + attn_bias, + d_key, + d_value, + d_model, + n_head=1, + dropout_rate=0., + cache=None, + gather_idx=None, + static_kv=False): + """ + Multi-Head Attention. Note that attn_bias is added to the logit before + computing softmax activiation to mask certain selected positions so that + they will not considered in attention weights. + """ + keys = queries if keys is None else keys + values = keys if values is None else values + + if not (len(queries.shape) == len(keys.shape) == len(values.shape) == 3): + raise ValueError( + "Inputs: quries, keys and values should all be 3-D tensors.") + + def __compute_qkv(queries, keys, values, n_head, d_key, d_value): + """ + Add linear projection to queries, keys, and values. + """ + q = layers.fc(input=queries, + size=d_key * n_head, + bias_attr=False, + num_flatten_dims=2) + # For encoder-decoder attention in inference, insert the ops and vars + # into global block to use as cache among beam search. + fc_layer = wrap_layer_with_block( + layers.fc, fluid.default_main_program().current_block() + .parent_idx) if cache is not None and static_kv else layers.fc + k = fc_layer( + input=keys, + size=d_key * n_head, + bias_attr=False, + num_flatten_dims=2) + v = fc_layer( + input=values, + size=d_value * n_head, + bias_attr=False, + num_flatten_dims=2) + return q, k, v + + def __split_heads_qkv(queries, keys, values, n_head, d_key, d_value): + """ + Reshape input tensors at the last dimension to split multi-heads + and then transpose. Specifically, transform the input tensor with shape + [bs, max_sequence_length, n_head * hidden_dim] to the output tensor + with shape [bs, n_head, max_sequence_length, hidden_dim]. + """ + # The value 0 in shape attr means copying the corresponding dimension + # size of the input as the output dimension size. + reshaped_q = layers.reshape( + x=queries, shape=[0, 0, n_head, d_key], inplace=True) + # permuate the dimensions into: + # [batch_size, n_head, max_sequence_len, hidden_size_per_head] + q = layers.transpose(x=reshaped_q, perm=[0, 2, 1, 3]) + # For encoder-decoder attention in inference, insert the ops and vars + # into global block to use as cache among beam search. + reshape_layer = wrap_layer_with_block( + layers.reshape, + fluid.default_main_program().current_block() + .parent_idx) if cache is not None and static_kv else layers.reshape + transpose_layer = wrap_layer_with_block( + layers.transpose, + fluid.default_main_program().current_block(). + parent_idx) if cache is not None and static_kv else layers.transpose + reshaped_k = reshape_layer( + x=keys, shape=[0, 0, n_head, d_key], inplace=True) + k = transpose_layer(x=reshaped_k, perm=[0, 2, 1, 3]) + reshaped_v = reshape_layer( + x=values, shape=[0, 0, n_head, d_value], inplace=True) + v = transpose_layer(x=reshaped_v, perm=[0, 2, 1, 3]) + + if cache is not None: # only for faster inference + if static_kv: # For encoder-decoder attention in inference + cache_k, cache_v = cache["static_k"], cache["static_v"] + # To init the static_k and static_v in cache. + # Maybe we can use condition_op(if_else) to do these at the first + # step in while loop to replace these, however it might be less + # efficient. + static_cache_init = wrap_layer_with_block( + layers.assign, + fluid.default_main_program().current_block().parent_idx) + static_cache_init(k, cache_k) + static_cache_init(v, cache_v) + else: # For decoder self-attention in inference + cache_k, cache_v = cache["k"], cache["v"] + # gather cell states corresponding to selected parent + select_k = layers.gather(cache_k, index=gather_idx) + select_v = layers.gather(cache_v, index=gather_idx) + if not static_kv: + # For self attention in inference, use cache and concat time steps. + select_k = layers.concat([select_k, k], axis=2) + select_v = layers.concat([select_v, v], axis=2) + # update cell states(caches) cached in global block + layers.assign(select_k, cache_k) + layers.assign(select_v, cache_v) + return q, select_k, select_v + return q, k, v + + def __combine_heads(x): + """ + Transpose and then reshape the last two dimensions of inpunt tensor x + so that it becomes one dimension, which is reverse to __split_heads. + """ + if len(x.shape) != 4: + raise ValueError("Input(x) should be a 4-D Tensor.") + + trans_x = layers.transpose(x, perm=[0, 2, 1, 3]) + # The value 0 in shape attr means copying the corresponding dimension + # size of the input as the output dimension size. + return layers.reshape( + x=trans_x, + shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]], + inplace=True) + + def scaled_dot_product_attention(q, k, v, attn_bias, d_key, dropout_rate): + """ + Scaled Dot-Product Attention + """ + product = layers.matmul(x=q, y=k, transpose_y=True, alpha=d_key**-0.5) + if attn_bias: + product += attn_bias + weights = layers.softmax(product) + if dropout_rate: + weights = layers.dropout( + weights, + dropout_prob=dropout_rate, + seed=dropout_seed, + is_test=False) + out = layers.matmul(weights, v) + return out + + q, k, v = __compute_qkv(queries, keys, values, n_head, d_key, d_value) + q, k, v = __split_heads_qkv(q, k, v, n_head, d_key, d_value) + + ctx_multiheads = scaled_dot_product_attention(q, k, v, attn_bias, d_model, + dropout_rate) + + out = __combine_heads(ctx_multiheads) + + # Project back to the model size. + proj_out = layers.fc(input=out, + size=d_model, + bias_attr=False, + num_flatten_dims=2) + return proj_out + + +def positionwise_feed_forward(x, d_inner_hid, d_hid, dropout_rate): + """ + Position-wise Feed-Forward Networks. + This module consists of two linear transformations with a ReLU activation + in between, which is applied to each position separately and identically. + """ + hidden = layers.fc(input=x, + size=d_inner_hid, + num_flatten_dims=2, + act="relu") + if dropout_rate: + hidden = layers.dropout( + hidden, dropout_prob=dropout_rate, seed=dropout_seed, is_test=False) + out = layers.fc(input=hidden, size=d_hid, num_flatten_dims=2) + return out + + +def pre_post_process_layer(prev_out, out, process_cmd, dropout_rate=0.): + """ + Add residual connection, layer normalization and droput to the out tensor + optionally according to the value of process_cmd. + This will be used before or after multi-head attention and position-wise + feed-forward networks. + """ + for cmd in process_cmd: + if cmd == "a": # add residual connection + out = out + prev_out if prev_out else out + elif cmd == "n": # add layer normalization + out = layers.layer_norm( + out, + begin_norm_axis=len(out.shape) - 1, + param_attr=fluid.initializer.Constant(1.), + bias_attr=fluid.initializer.Constant(0.)) + elif cmd == "d": # add dropout + if dropout_rate: + out = layers.dropout( + out, + dropout_prob=dropout_rate, + seed=dropout_seed, + is_test=False) + return out + + +pre_process_layer = partial(pre_post_process_layer, None) +post_process_layer = pre_post_process_layer + + +def prepare_encoder_decoder(src_word, + src_pos, + src_vocab_size, + src_emb_dim, + src_max_len, + dropout_rate=0., + bos_idx=0, + word_emb_param_name=None, + pos_enc_param_name=None): + """Add word embeddings and position encodings. + The output tensor has a shape of: + [batch_size, max_src_length_in_batch, d_model]. + This module is used at the bottom of the encoder stacks. + """ + src_word_emb = layers.embedding( + src_word, + size=[src_vocab_size, src_emb_dim], + padding_idx=bos_idx, # set embedding of bos to 0 + param_attr=fluid.ParamAttr( + name=word_emb_param_name, + initializer=fluid.initializer.Normal(0., src_emb_dim**-0.5))) + + src_word_emb = layers.scale(x=src_word_emb, scale=src_emb_dim**0.5) + src_pos_enc = layers.embedding( + src_pos, + size=[src_max_len, src_emb_dim], + param_attr=fluid.ParamAttr( + name=pos_enc_param_name, trainable=False)) + src_pos_enc.stop_gradient = True + enc_input = src_word_emb + src_pos_enc + return layers.dropout( + enc_input, dropout_prob=dropout_rate, seed=dropout_seed, + is_test=False) if dropout_rate else enc_input + + +prepare_encoder = partial( + prepare_encoder_decoder, pos_enc_param_name=pos_enc_param_names[0]) +prepare_decoder = partial( + prepare_encoder_decoder, pos_enc_param_name=pos_enc_param_names[1]) + + +def encoder_layer(enc_input, + attn_bias, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd="n", + postprocess_cmd="da"): + """The encoder layers that can be stacked to form a deep encoder. + This module consits of a multi-head (self) attention followed by + position-wise feed-forward networks and both the two components companied + with the post_process_layer to add residual connection, layer normalization + and droput. + """ + attn_output = multi_head_attention( + pre_process_layer(enc_input, preprocess_cmd, + prepostprocess_dropout), None, None, attn_bias, d_key, + d_value, d_model, n_head, attention_dropout) + attn_output = post_process_layer(enc_input, attn_output, postprocess_cmd, + prepostprocess_dropout) + ffd_output = positionwise_feed_forward( + pre_process_layer(attn_output, preprocess_cmd, prepostprocess_dropout), + d_inner_hid, d_model, relu_dropout) + return post_process_layer(attn_output, ffd_output, postprocess_cmd, + prepostprocess_dropout) + + +def encoder(enc_input, + attn_bias, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd="n", + postprocess_cmd="da"): + """ + The encoder is composed of a stack of identical layers returned by calling + encoder_layer. + """ + for i in range(n_layer): + enc_output = encoder_layer( + enc_input, + attn_bias, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, ) + enc_input = enc_output + enc_output = pre_process_layer(enc_output, preprocess_cmd, + prepostprocess_dropout) + return enc_output + + +def decoder_layer(dec_input, + enc_output, + slf_attn_bias, + dec_enc_attn_bias, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, + cache=None, + gather_idx=None): + """ The layer to be stacked in decoder part. + The structure of this module is similar to that in the encoder part except + a multi-head attention is added to implement encoder-decoder attention. + """ + slf_attn_output = multi_head_attention( + pre_process_layer(dec_input, preprocess_cmd, prepostprocess_dropout), + None, + None, + slf_attn_bias, + d_key, + d_value, + d_model, + n_head, + attention_dropout, + cache=cache, + gather_idx=gather_idx) + slf_attn_output = post_process_layer( + dec_input, + slf_attn_output, + postprocess_cmd, + prepostprocess_dropout, ) + enc_attn_output = multi_head_attention( + pre_process_layer(slf_attn_output, preprocess_cmd, + prepostprocess_dropout), + enc_output, + enc_output, + dec_enc_attn_bias, + d_key, + d_value, + d_model, + n_head, + attention_dropout, + cache=cache, + gather_idx=gather_idx, + static_kv=True) + enc_attn_output = post_process_layer( + slf_attn_output, + enc_attn_output, + postprocess_cmd, + prepostprocess_dropout, ) + ffd_output = positionwise_feed_forward( + pre_process_layer(enc_attn_output, preprocess_cmd, + prepostprocess_dropout), + d_inner_hid, + d_model, + relu_dropout, ) + dec_output = post_process_layer( + enc_attn_output, + ffd_output, + postprocess_cmd, + prepostprocess_dropout, ) + return dec_output + + +def decoder(dec_input, + enc_output, + dec_slf_attn_bias, + dec_enc_attn_bias, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, + caches=None, + gather_idx=None): + """ + The decoder is composed of a stack of identical decoder_layer layers. + """ + for i in range(n_layer): + dec_output = decoder_layer( + dec_input, + enc_output, + dec_slf_attn_bias, + dec_enc_attn_bias, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, + cache=None if caches is None else caches[i], + gather_idx=gather_idx) + dec_input = dec_output + dec_output = pre_process_layer(dec_output, preprocess_cmd, + prepostprocess_dropout) + return dec_output + + +def make_all_inputs(input_fields): + """ + Define the input data layers for the transformer model. + """ + inputs = [] + for input_field in input_fields: + input_var = layers.data( + name=input_field, + shape=input_descs[input_field][0], + dtype=input_descs[input_field][1], + lod_level=input_descs[input_field][2] + if len(input_descs[input_field]) == 3 else 0, + append_batch_size=False) + inputs.append(input_var) + return inputs + + +def make_all_py_reader_inputs(input_fields, is_test=False): + reader = layers.py_reader( + capacity=20, + name="test_reader" if is_test else "train_reader", + shapes=[input_descs[input_field][0] for input_field in input_fields], + dtypes=[input_descs[input_field][1] for input_field in input_fields], + lod_levels=[ + input_descs[input_field][2] + if len(input_descs[input_field]) == 3 else 0 + for input_field in input_fields + ]) + return layers.read_file(reader), reader + + +def transformer(src_vocab_size, + trg_vocab_size, + max_length, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, + weight_sharing, + label_smooth_eps, + bos_idx=0, + use_py_reader=False, + is_test=False): + if weight_sharing: + assert src_vocab_size == trg_vocab_size, ( + "Vocabularies in source and target should be same for weight sharing." + ) + + data_input_names = encoder_data_input_fields + \ + decoder_data_input_fields[:-1] + label_data_input_fields + + if use_py_reader: + all_inputs, reader = make_all_py_reader_inputs(data_input_names, + is_test) + else: + all_inputs = make_all_inputs(data_input_names) + + enc_inputs_len = len(encoder_data_input_fields) + dec_inputs_len = len(decoder_data_input_fields[:-1]) + enc_inputs = all_inputs[0:enc_inputs_len] + dec_inputs = all_inputs[enc_inputs_len:enc_inputs_len + dec_inputs_len] + label = all_inputs[-2] + weights = all_inputs[-1] + + enc_output = wrap_encoder( + src_vocab_size, + max_length, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, + weight_sharing, + enc_inputs, ) + + predict = wrap_decoder( + trg_vocab_size, + max_length, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, + weight_sharing, + dec_inputs, + enc_output, ) + + # Padding index do not contribute to the total loss. The weights is used to + # cancel padding index in calculating the loss. + if label_smooth_eps: + label = layers.label_smooth( + label=layers.one_hot( + input=label, depth=trg_vocab_size), + epsilon=label_smooth_eps) + + cost = layers.softmax_with_cross_entropy( + logits=predict, + label=label, + soft_label=True if label_smooth_eps else False) + weighted_cost = cost * weights + sum_cost = layers.reduce_sum(weighted_cost) + token_num = layers.reduce_sum(weights) + token_num.stop_gradient = True + avg_cost = sum_cost / token_num + return sum_cost, avg_cost, predict, token_num, reader if use_py_reader else None + + +def wrap_encoder(src_vocab_size, + max_length, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, + weight_sharing, + enc_inputs=None, + bos_idx=0): + """ + The wrapper assembles together all needed layers for the encoder. + """ + if enc_inputs is None: + # This is used to implement independent encoder program in inference. + src_word, src_pos, src_slf_attn_bias = make_all_inputs( + encoder_data_input_fields) + else: + src_word, src_pos, src_slf_attn_bias = enc_inputs + enc_input = prepare_encoder( + src_word, + src_pos, + src_vocab_size, + d_model, + max_length, + prepostprocess_dropout, + bos_idx=bos_idx, + word_emb_param_name=word_emb_param_names[0]) + enc_output = encoder( + enc_input, + src_slf_attn_bias, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, ) + return enc_output + + +def wrap_decoder(trg_vocab_size, + max_length, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, + weight_sharing, + dec_inputs=None, + enc_output=None, + caches=None, + gather_idx=None, + bos_idx=0): + """ + The wrapper assembles together all needed layers for the decoder. + """ + if dec_inputs is None: + # This is used to implement independent decoder program in inference. + trg_word, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, enc_output = \ + make_all_inputs(decoder_data_input_fields) + else: + trg_word, trg_pos, trg_slf_attn_bias, trg_src_attn_bias = dec_inputs + + dec_input = prepare_decoder( + trg_word, + trg_pos, + trg_vocab_size, + d_model, + max_length, + prepostprocess_dropout, + bos_idx=bos_idx, + word_emb_param_name=word_emb_param_names[0] + if weight_sharing else word_emb_param_names[1]) + dec_output = decoder( + dec_input, + enc_output, + trg_slf_attn_bias, + trg_src_attn_bias, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, + caches=caches, + gather_idx=gather_idx) + # Reshape to 2D tensor to use GEMM instead of BatchedGEMM + dec_output = layers.reshape( + dec_output, shape=[-1, dec_output.shape[-1]], inplace=True) + if weight_sharing: + predict = layers.matmul( + x=dec_output, + y=fluid.default_main_program().global_block().var( + word_emb_param_names[0]), + transpose_y=True) + else: + predict = layers.fc(input=dec_output, + size=trg_vocab_size, + bias_attr=False) + if dec_inputs is None: + # Return probs for independent decoder program. + predict = layers.softmax(predict) + return predict + + +def fast_decode(src_vocab_size, + trg_vocab_size, + max_in_len, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, + weight_sharing, + beam_size, + max_out_len, + bos_idx, + eos_idx, + use_py_reader=False): + """ + Use beam search to decode. Caches will be used to store states of history + steps which can make the decoding faster. + """ + data_input_names = encoder_data_input_fields + fast_decoder_data_input_fields + + if use_py_reader: + all_inputs, reader = make_all_py_reader_inputs(data_input_names) + else: + all_inputs = make_all_inputs(data_input_names) + + enc_inputs_len = len(encoder_data_input_fields) + dec_inputs_len = len(fast_decoder_data_input_fields) + enc_inputs = all_inputs[0:enc_inputs_len] + dec_inputs = all_inputs[enc_inputs_len:enc_inputs_len + dec_inputs_len] + + enc_output = wrap_encoder( + src_vocab_size, + max_in_len, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, + weight_sharing, + enc_inputs, + bos_idx=bos_idx) + start_tokens, init_scores, parent_idx, trg_src_attn_bias = dec_inputs + + def beam_search(): + max_len = layers.fill_constant( + shape=[1], + dtype=start_tokens.dtype, + value=max_out_len, + force_cpu=True) + step_idx = layers.fill_constant( + shape=[1], dtype=start_tokens.dtype, value=0, force_cpu=True) + cond = layers.less_than(x=step_idx, y=max_len) # default force_cpu=True + while_op = layers.While(cond) + # array states will be stored for each step. + ids = layers.array_write( + layers.reshape(start_tokens, (-1, 1)), step_idx) + scores = layers.array_write(init_scores, step_idx) + # cell states will be overwrited at each step. + # caches contains states of history steps in decoder self-attention + # and static encoder output projections in encoder-decoder attention + # to reduce redundant computation. + caches = [ + { + "k": # for self attention + layers.fill_constant_batch_size_like( + input=start_tokens, + shape=[-1, n_head, 0, d_key], + dtype=enc_output.dtype, + value=0), + "v": # for self attention + layers.fill_constant_batch_size_like( + input=start_tokens, + shape=[-1, n_head, 0, d_value], + dtype=enc_output.dtype, + value=0), + "static_k": # for encoder-decoder attention + layers.create_tensor(dtype=enc_output.dtype), + "static_v": # for encoder-decoder attention + layers.create_tensor(dtype=enc_output.dtype) + } for i in range(n_layer) + ] + + with while_op.block(): + pre_ids = layers.array_read(array=ids, i=step_idx) + # Since beam_search_op dosen't enforce pre_ids' shape, we can do + # inplace reshape here which actually change the shape of pre_ids. + pre_ids = layers.reshape(pre_ids, (-1, 1, 1), inplace=True) + pre_scores = layers.array_read(array=scores, i=step_idx) + # gather cell states corresponding to selected parent + pre_src_attn_bias = layers.gather( + trg_src_attn_bias, index=parent_idx) + pre_pos = layers.elementwise_mul( + x=layers.fill_constant_batch_size_like( + input=pre_src_attn_bias, # cann't use lod tensor here + value=1, + shape=[-1, 1, 1], + dtype=pre_ids.dtype), + y=step_idx, + axis=0) + logits = wrap_decoder( + trg_vocab_size, + max_in_len, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + preprocess_cmd, + postprocess_cmd, + weight_sharing, + dec_inputs=(pre_ids, pre_pos, None, pre_src_attn_bias), + enc_output=enc_output, + caches=caches, + gather_idx=parent_idx, + bos_idx=bos_idx) + # intra-beam topK + topk_scores, topk_indices = layers.topk( + input=layers.softmax(logits), k=beam_size) + accu_scores = layers.elementwise_add( + x=layers.log(topk_scores), y=pre_scores, axis=0) + # beam_search op uses lod to differentiate branches. + topk_indices = layers.lod_reset(topk_indices, pre_ids) + # topK reduction across beams, also contain special handle of + # end beams and end sentences(batch reduction) + selected_ids, selected_scores, gather_idx = layers.beam_search( + pre_ids=pre_ids, + pre_scores=pre_scores, + ids=topk_indices, + scores=accu_scores, + beam_size=beam_size, + end_id=eos_idx, + return_parent_idx=True) + layers.increment(x=step_idx, value=1.0, in_place=True) + # cell states(caches) have been updated in wrap_decoder, + # only need to update beam search states here. + layers.array_write(selected_ids, i=step_idx, array=ids) + layers.array_write(selected_scores, i=step_idx, array=scores) + layers.assign(gather_idx, parent_idx) + layers.assign(pre_src_attn_bias, trg_src_attn_bias) + length_cond = layers.less_than(x=step_idx, y=max_len) + finish_cond = layers.logical_not(layers.is_empty(x=selected_ids)) + layers.logical_and(x=length_cond, y=finish_cond, out=cond) + + finished_ids, finished_scores = layers.beam_search_decode( + ids, scores, beam_size=beam_size, end_id=eos_idx) + return finished_ids, finished_scores + + finished_ids, finished_scores = beam_search() + return finished_ids, finished_scores, reader if use_py_reader else None diff --git a/PaddleNLP/models/reading_comprehension/__init__.py b/PaddleNLP/models/reading_comprehension/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PaddleNLP/machine_reading_comprehension/rc_model.py b/PaddleNLP/models/reading_comprehension/bidaf_model.py similarity index 100% rename from PaddleNLP/machine_reading_comprehension/rc_model.py rename to PaddleNLP/models/reading_comprehension/bidaf_model.py diff --git a/PaddleNLP/models/representation/__init__.py b/PaddleNLP/models/representation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PaddleNLP/models/representation/ernie.py b/PaddleNLP/models/representation/ernie.py new file mode 100644 index 0000000000000000000000000000000000000000..4adb7660278e0985de2493abfe20d570f17e0d75 --- /dev/null +++ b/PaddleNLP/models/representation/ernie.py @@ -0,0 +1,268 @@ +""" +This module provides ErnieModel and ErnieConfig +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import json + +import six +import paddle.fluid as fluid + +from models.transformer_encoder import encoder, pre_process_layer + + +def ernie_pyreader(args, pyreader_name): + """define standard ernie pyreader""" + pyreader = fluid.layers.py_reader( + capacity=50, + shapes=[[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1], + [-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1], [-1, 1], + [-1, 1]], + dtypes=['int64', 'int64', 'int64', 'float32', 'int64', 'int64'], + lod_levels=[0, 0, 0, 0, 0, 0], + name=pyreader_name, + use_double_buffer=True) + + (src_ids, sent_ids, pos_ids, input_mask, labels, seq_lens) = fluid.layers.read_file(pyreader) + + ernie_inputs = { + "src_ids": src_ids, + "sent_ids": sent_ids, + "pos_ids": pos_ids, + "input_mask": input_mask, + "seq_lens": seq_lens + } + return pyreader, ernie_inputs, labels + + +def ernie_encoder(ernie_inputs, ernie_config): + """return sentence embedding and token embeddings""" + + ernie = ErnieModel( + src_ids=ernie_inputs["src_ids"], + position_ids=ernie_inputs["pos_ids"], + sentence_ids=ernie_inputs["sent_ids"], + input_mask=ernie_inputs["input_mask"], + config=ernie_config) + + enc_out = ernie.get_sequence_output() + unpad_enc_out = fluid.layers.sequence_unpad(enc_out, length=ernie_inputs["seq_lens"]) + cls_feats = ernie.get_pooled_output() + + embeddings = { + "sentence_embeddings": cls_feats, + "token_embeddings": unpad_enc_out, + } + + for k, v in embeddings.items(): + v.persistable = True + + return embeddings + + +class ErnieConfig(object): + """ErnieConfig""" + def __init__(self, config_path): + self._config_dict = self._parse(config_path) + + def _parse(self, config_path): + try: + with open(config_path) as json_file: + config_dict = json.load(json_file) + except Exception: + raise IOError("Error in parsing Ernie model config file '%s'" % + config_path) + else: + return config_dict + + def __getitem__(self, key): + return self._config_dict[key] + + def print_config(self): + """print config""" + for arg, value in sorted(six.iteritems(self._config_dict)): + print('%s: %s' % (arg, value)) + print('------------------------------------------------') + + +class ErnieModel(object): + """ErnieModel""" + def __init__(self, + src_ids, + position_ids, + sentence_ids, + input_mask, + config, + weight_sharing=True, + use_fp16=False): + + self._emb_size = config['hidden_size'] + self._n_layer = config['num_hidden_layers'] + self._n_head = config['num_attention_heads'] + self._voc_size = config['vocab_size'] + self._max_position_seq_len = config['max_position_embeddings'] + self._sent_types = config['type_vocab_size'] + self._hidden_act = config['hidden_act'] + self._prepostprocess_dropout = config['hidden_dropout_prob'] + self._attention_dropout = config['attention_probs_dropout_prob'] + self._weight_sharing = weight_sharing + + self._word_emb_name = "word_embedding" + self._pos_emb_name = "pos_embedding" + self._sent_emb_name = "sent_embedding" + self._dtype = "float16" if use_fp16 else "float32" + + # Initialize all weigths by truncated normal initializer, and all biases + # will be initialized by constant zero by default. + self._param_initializer = fluid.initializer.TruncatedNormal( + scale=config['initializer_range']) + + self._build_model(src_ids, position_ids, sentence_ids, input_mask) + + def _build_model(self, src_ids, position_ids, sentence_ids, input_mask): + # padding id in vocabulary must be set to 0 + emb_out = fluid.layers.embedding( + input=src_ids, + size=[self._voc_size, self._emb_size], + dtype=self._dtype, + param_attr=fluid.ParamAttr( + name=self._word_emb_name, initializer=self._param_initializer), + is_sparse=False) + position_emb_out = fluid.layers.embedding( + input=position_ids, + size=[self._max_position_seq_len, self._emb_size], + dtype=self._dtype, + param_attr=fluid.ParamAttr( + name=self._pos_emb_name, initializer=self._param_initializer)) + + sent_emb_out = fluid.layers.embedding( + sentence_ids, + size=[self._sent_types, self._emb_size], + dtype=self._dtype, + param_attr=fluid.ParamAttr( + name=self._sent_emb_name, initializer=self._param_initializer)) + + emb_out = emb_out + position_emb_out + emb_out = emb_out + sent_emb_out + + emb_out = pre_process_layer( + emb_out, 'nd', self._prepostprocess_dropout, name='pre_encoder') + + if self._dtype == "float16": + input_mask = fluid.layers.cast(x=input_mask, dtype=self._dtype) + self_attn_mask = fluid.layers.matmul( + x=input_mask, y=input_mask, transpose_y=True) + + self_attn_mask = fluid.layers.scale( + x=self_attn_mask, scale=10000.0, bias=-1.0, bias_after_scale=False) + n_head_self_attn_mask = fluid.layers.stack( + x=[self_attn_mask] * self._n_head, axis=1) + n_head_self_attn_mask.stop_gradient = True + + self._enc_out = encoder( + enc_input=emb_out, + attn_bias=n_head_self_attn_mask, + n_layer=self._n_layer, + n_head=self._n_head, + d_key=self._emb_size // self._n_head, + d_value=self._emb_size // self._n_head, + d_model=self._emb_size, + d_inner_hid=self._emb_size * 4, + prepostprocess_dropout=self._prepostprocess_dropout, + attention_dropout=self._attention_dropout, + relu_dropout=0, + hidden_act=self._hidden_act, + preprocess_cmd="", + postprocess_cmd="dan", + param_initializer=self._param_initializer, + name='encoder') + + def get_sequence_output(self): + """Get embedding of each token for squence labeling""" + return self._enc_out + + def get_pooled_output(self): + """Get the first feature of each sequence for classification""" + next_sent_feat = fluid.layers.slice( + input=self._enc_out, axes=[1], starts=[0], ends=[1]) + next_sent_feat = fluid.layers.fc( + input=next_sent_feat, + size=self._emb_size, + act="tanh", + param_attr=fluid.ParamAttr( + name="pooled_fc.w_0", initializer=self._param_initializer), + bias_attr="pooled_fc.b_0") + return next_sent_feat + + def get_pretraining_output(self, mask_label, mask_pos, labels): + """Get the loss & accuracy for pretraining""" + + mask_pos = fluid.layers.cast(x=mask_pos, dtype='int32') + + # extract the first token feature in each sentence + next_sent_feat = self.get_pooled_output() + reshaped_emb_out = fluid.layers.reshape( + x=self._enc_out, shape=[-1, self._emb_size]) + # extract masked tokens' feature + mask_feat = fluid.layers.gather(input=reshaped_emb_out, index=mask_pos) + + # transform: fc + mask_trans_feat = fluid.layers.fc( + input=mask_feat, + size=self._emb_size, + act=self._hidden_act, + param_attr=fluid.ParamAttr( + name='mask_lm_trans_fc.w_0', + initializer=self._param_initializer), + bias_attr=fluid.ParamAttr(name='mask_lm_trans_fc.b_0')) + # transform: layer norm + mask_trans_feat = pre_process_layer( + mask_trans_feat, 'n', name='mask_lm_trans') + + mask_lm_out_bias_attr = fluid.ParamAttr( + name="mask_lm_out_fc.b_0", + initializer=fluid.initializer.Constant(value=0.0)) + if self._weight_sharing: + fc_out = fluid.layers.matmul( + x=mask_trans_feat, + y=fluid.default_main_program().global_block().var( + self._word_emb_name), + transpose_y=True) + fc_out += fluid.layers.create_parameter( + shape=[self._voc_size], + dtype=self._dtype, + attr=mask_lm_out_bias_attr, + is_bias=True) + + else: + fc_out = fluid.layers.fc(input=mask_trans_feat, + size=self._voc_size, + param_attr=fluid.ParamAttr( + name="mask_lm_out_fc.w_0", + initializer=self._param_initializer), + bias_attr=mask_lm_out_bias_attr) + + mask_lm_loss = fluid.layers.softmax_with_cross_entropy( + logits=fc_out, label=mask_label) + mean_mask_lm_loss = fluid.layers.mean(mask_lm_loss) + + next_sent_fc_out = fluid.layers.fc( + input=next_sent_feat, + size=2, + param_attr=fluid.ParamAttr( + name="next_sent_fc.w_0", initializer=self._param_initializer), + bias_attr="next_sent_fc.b_0") + + next_sent_loss, next_sent_softmax = fluid.layers.softmax_with_cross_entropy( + logits=next_sent_fc_out, label=labels, return_softmax=True) + + next_sent_acc = fluid.layers.accuracy( + input=next_sent_softmax, label=labels) + + mean_next_sent_loss = fluid.layers.mean(next_sent_loss) + + loss = mean_next_sent_loss + mean_mask_lm_loss + return next_sent_acc, mean_mask_lm_loss, loss diff --git a/PaddleNLP/models/sequence_labeling/__init__.py b/PaddleNLP/models/sequence_labeling/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PaddleNLP/models/sequence_labeling/nets.py b/PaddleNLP/models/sequence_labeling/nets.py new file mode 100644 index 0000000000000000000000000000000000000000..77db87bae5005d8de7aedbec4f650da1ed33532a --- /dev/null +++ b/PaddleNLP/models/sequence_labeling/nets.py @@ -0,0 +1,108 @@ +""" +The function lex_net(args) define the lexical analysis network structure +""" +import sys +import os +import math + +import paddle.fluid as fluid +from paddle.fluid.initializer import NormalInitializer + + +def lex_net(word, target, args, vocab_size, num_labels): + """ + define the lexical analysis network structure + """ + word_emb_dim = args.word_emb_dim + grnn_hidden_dim = args.grnn_hidden_dim + emb_lr = args.emb_learning_rate + crf_lr = args.crf_learning_rate + bigru_num = args.bigru_num + init_bound = 0.1 + IS_SPARSE = True + + def _bigru_layer(input_feature): + """ + define the bidirectional gru layer + """ + pre_gru = fluid.layers.fc( + input=input_feature, + size=grnn_hidden_dim * 3, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.Uniform( + low=-init_bound, high=init_bound), + regularizer=fluid.regularizer.L2DecayRegularizer( + regularization_coeff=1e-4))) + gru = fluid.layers.dynamic_gru( + input=pre_gru, + size=grnn_hidden_dim, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.Uniform( + low=-init_bound, high=init_bound), + regularizer=fluid.regularizer.L2DecayRegularizer( + regularization_coeff=1e-4))) + + pre_gru_r = fluid.layers.fc( + input=input_feature, + size=grnn_hidden_dim * 3, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.Uniform( + low=-init_bound, high=init_bound), + regularizer=fluid.regularizer.L2DecayRegularizer( + regularization_coeff=1e-4))) + gru_r = fluid.layers.dynamic_gru( + input=pre_gru_r, + size=grnn_hidden_dim, + is_reverse=True, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.Uniform( + low=-init_bound, high=init_bound), + regularizer=fluid.regularizer.L2DecayRegularizer( + regularization_coeff=1e-4))) + + bi_merge = fluid.layers.concat(input=[gru, gru_r], axis=1) + return bi_merge + + def _net_conf(word, target): + """ + Configure the network + """ + word_embedding = fluid.layers.embedding( + input=word, + size=[vocab_size, word_emb_dim], + dtype='float32', + is_sparse=IS_SPARSE, + param_attr=fluid.ParamAttr( + learning_rate=emb_lr, + name="word_emb", + initializer=fluid.initializer.Uniform( + low=-init_bound, high=init_bound))) + + input_feature = word_embedding + for i in range(bigru_num): + bigru_output = _bigru_layer(input_feature) + input_feature = bigru_output + + emission = fluid.layers.fc( + size=num_labels, + input=bigru_output, + param_attr=fluid.ParamAttr( + initializer=fluid.initializer.Uniform( + low=-init_bound, high=init_bound), + regularizer=fluid.regularizer.L2DecayRegularizer( + regularization_coeff=1e-4))) + + crf_cost = fluid.layers.linear_chain_crf( + input=emission, + label=target, + param_attr=fluid.ParamAttr( + name='crfw', + learning_rate=crf_lr)) + crf_decode = fluid.layers.crf_decoding( + input=emission, param_attr=fluid.ParamAttr(name='crfw')) + avg_cost = fluid.layers.mean(x=crf_cost) + return avg_cost, crf_decode + + avg_cost, crf_decode = _net_conf(word, target) + + return avg_cost, crf_decode diff --git a/PaddleNLP/models/transformer_encoder.py b/PaddleNLP/models/transformer_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..5c20735bf47f9b68eefd7bad00229cddb77fd2a2 --- /dev/null +++ b/PaddleNLP/models/transformer_encoder.py @@ -0,0 +1,329 @@ +"""Transformer encoder.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from functools import partial + +import paddle.fluid as fluid +import paddle.fluid.layers as layers + + +def multi_head_attention(queries, + keys, + values, + attn_bias, + d_key, + d_value, + d_model, + n_head=1, + dropout_rate=0., + cache=None, + param_initializer=None, + name='multi_head_att'): + """ + Multi-Head Attention. Note that attn_bias is added to the logit before + computing softmax activiation to mask certain selected positions so that + they will not considered in attention weights. + """ + keys = queries if keys is None else keys + values = keys if values is None else values + + if not (len(queries.shape) == len(keys.shape) == len(values.shape) == 3): + raise ValueError( + "Inputs: quries, keys and values should all be 3-D tensors.") + + def __compute_qkv(queries, keys, values, n_head, d_key, d_value): + """ + Add linear projection to queries, keys, and values. + """ + q = layers.fc(input=queries, + size=d_key * n_head, + num_flatten_dims=2, + param_attr=fluid.ParamAttr( + name=name + '_query_fc.w_0', + initializer=param_initializer), + bias_attr=name + '_query_fc.b_0') + k = layers.fc(input=keys, + size=d_key * n_head, + num_flatten_dims=2, + param_attr=fluid.ParamAttr( + name=name + '_key_fc.w_0', + initializer=param_initializer), + bias_attr=name + '_key_fc.b_0') + v = layers.fc(input=values, + size=d_value * n_head, + num_flatten_dims=2, + param_attr=fluid.ParamAttr( + name=name + '_value_fc.w_0', + initializer=param_initializer), + bias_attr=name + '_value_fc.b_0') + return q, k, v + + def __split_heads(x, n_head): + """ + Reshape the last dimension of inpunt tensor x so that it becomes two + dimensions and then transpose. Specifically, input a tensor with shape + [bs, max_sequence_length, n_head * hidden_dim] then output a tensor + with shape [bs, n_head, max_sequence_length, hidden_dim]. + """ + hidden_size = x.shape[-1] + # The value 0 in shape attr means copying the corresponding dimension + # size of the input as the output dimension size. + reshaped = layers.reshape( + x=x, shape=[0, 0, n_head, hidden_size // n_head], inplace=True) + + # permuate the dimensions into: + # [batch_size, n_head, max_sequence_len, hidden_size_per_head] + return layers.transpose(x=reshaped, perm=[0, 2, 1, 3]) + + def __combine_heads(x): + """ + Transpose and then reshape the last two dimensions of inpunt tensor x + so that it becomes one dimension, which is reverse to __split_heads. + """ + if len(x.shape) == 3: + return x + if len(x.shape) != 4: + raise ValueError("Input(x) should be a 4-D Tensor.") + + trans_x = layers.transpose(x, perm=[0, 2, 1, 3]) + # The value 0 in shape attr means copying the corresponding dimension + # size of the input as the output dimension size. + return layers.reshape( + x=trans_x, + shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]], + inplace=True) + + def scaled_dot_product_attention(q, k, v, attn_bias, d_key, dropout_rate): + """ + Scaled Dot-Product Attention + """ + scaled_q = layers.scale(x=q, scale=d_key ** -0.5) + product = layers.matmul(x=scaled_q, y=k, transpose_y=True) + if attn_bias: + product += attn_bias + weights = layers.softmax(product) + if dropout_rate: + weights = layers.dropout( + weights, + dropout_prob=dropout_rate, + dropout_implementation="upscale_in_train", + is_test=False) + out = layers.matmul(weights, v) + return out + + q, k, v = __compute_qkv(queries, keys, values, n_head, d_key, d_value) + + if cache is not None: # use cache and concat time steps + # Since the inplace reshape in __split_heads changes the shape of k and + # v, which is the cache input for next time step, reshape the cache + # input from the previous time step first. + k = cache["k"] = layers.concat( + [layers.reshape( + cache["k"], shape=[0, 0, d_model]), k], axis=1) + v = cache["v"] = layers.concat( + [layers.reshape( + cache["v"], shape=[0, 0, d_model]), v], axis=1) + + q = __split_heads(q, n_head) + k = __split_heads(k, n_head) + v = __split_heads(v, n_head) + + ctx_multiheads = scaled_dot_product_attention(q, k, v, attn_bias, d_key, + dropout_rate) + + out = __combine_heads(ctx_multiheads) + + # Project back to the model size. + proj_out = layers.fc(input=out, + size=d_model, + num_flatten_dims=2, + param_attr=fluid.ParamAttr( + name=name + '_output_fc.w_0', + initializer=param_initializer), + bias_attr=name + '_output_fc.b_0') + return proj_out + + +def positionwise_feed_forward(x, + d_inner_hid, + d_hid, + dropout_rate, + hidden_act, + param_initializer=None, + name='ffn'): + """ + Position-wise Feed-Forward Networks. + This module consists of two linear transformations with a ReLU activation + in between, which is applied to each position separately and identically. + """ + hidden = layers.fc(input=x, + size=d_inner_hid, + num_flatten_dims=2, + act=hidden_act, + param_attr=fluid.ParamAttr( + name=name + '_fc_0.w_0', + initializer=param_initializer), + bias_attr=name + '_fc_0.b_0') + if dropout_rate: + hidden = layers.dropout( + hidden, + dropout_prob=dropout_rate, + dropout_implementation="upscale_in_train", + is_test=False) + out = layers.fc(input=hidden, + size=d_hid, + num_flatten_dims=2, + param_attr=fluid.ParamAttr( + name=name + '_fc_1.w_0', initializer=param_initializer), + bias_attr=name + '_fc_1.b_0') + return out + + +def pre_post_process_layer(prev_out, out, process_cmd, dropout_rate=0., + name=''): + """ + Add residual connection, layer normalization and droput to the out tensor + optionally according to the value of process_cmd. + This will be used before or after multi-head attention and position-wise + feed-forward networks. + """ + for cmd in process_cmd: + if cmd == "a": # add residual connection + out = out + prev_out if prev_out else out + elif cmd == "n": # add layer normalization + out_dtype = out.dtype + if out_dtype == fluid.core.VarDesc.VarType.FP16: + out = layers.cast(x=out, dtype="float32") + out = layers.layer_norm( + out, + begin_norm_axis=len(out.shape) - 1, + param_attr=fluid.ParamAttr( + name=name + '_layer_norm_scale', + initializer=fluid.initializer.Constant(1.)), + bias_attr=fluid.ParamAttr( + name=name + '_layer_norm_bias', + initializer=fluid.initializer.Constant(0.))) + if out_dtype == fluid.core.VarDesc.VarType.FP16: + out = layers.cast(x=out, dtype="float16") + elif cmd == "d": # add dropout + if dropout_rate: + out = layers.dropout( + out, + dropout_prob=dropout_rate, + dropout_implementation="upscale_in_train", + is_test=False) + return out + + +pre_process_layer = partial(pre_post_process_layer, None) +post_process_layer = pre_post_process_layer + + +def encoder_layer(enc_input, + attn_bias, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + hidden_act, + preprocess_cmd="n", + postprocess_cmd="da", + param_initializer=None, + name=''): + """The encoder layers that can be stacked to form a deep encoder. + This module consits of a multi-head (self) attention followed by + position-wise feed-forward networks and both the two components companied + with the post_process_layer to add residual connection, layer normalization + and droput. + """ + attn_output = multi_head_attention( + pre_process_layer( + enc_input, + preprocess_cmd, + prepostprocess_dropout, + name=name + '_pre_att'), + None, + None, + attn_bias, + d_key, + d_value, + d_model, + n_head, + attention_dropout, + param_initializer=param_initializer, + name=name + '_multi_head_att') + attn_output = post_process_layer( + enc_input, + attn_output, + postprocess_cmd, + prepostprocess_dropout, + name=name + '_post_att') + ffd_output = positionwise_feed_forward( + pre_process_layer( + attn_output, + preprocess_cmd, + prepostprocess_dropout, + name=name + '_pre_ffn'), + d_inner_hid, + d_model, + relu_dropout, + hidden_act, + param_initializer=param_initializer, + name=name + '_ffn') + return post_process_layer( + attn_output, + ffd_output, + postprocess_cmd, + prepostprocess_dropout, + name=name + '_post_ffn') + + +def encoder(enc_input, + attn_bias, + n_layer, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + hidden_act, + preprocess_cmd="n", + postprocess_cmd="da", + param_initializer=None, + name=''): + """ + The encoder is composed of a stack of identical layers returned by calling + encoder_layer. + """ + for i in range(n_layer): + enc_output = encoder_layer( + enc_input, + attn_bias, + n_head, + d_key, + d_value, + d_model, + d_inner_hid, + prepostprocess_dropout, + attention_dropout, + relu_dropout, + hidden_act, + preprocess_cmd, + postprocess_cmd, + param_initializer=param_initializer, + name=name + '_layer_' + str(i)) + enc_input = enc_output + enc_output = pre_process_layer( + enc_output, preprocess_cmd, prepostprocess_dropout, name="post_encoder") + + return enc_output diff --git a/PaddleNLP/neural_machine_translation/transformer/README.md b/PaddleNLP/neural_machine_translation/transformer/README.md index 6fea167b5e7c3e9dd759ef30d9225b451350e889..ebbfd243d05c51d7c34adc4919b5dee7f49f4f39 100644 --- a/PaddleNLP/neural_machine_translation/transformer/README.md +++ b/PaddleNLP/neural_machine_translation/transformer/README.md @@ -1,23 +1,190 @@ -The minimum PaddlePaddle version needed for the code sample in this directory is the lastest develop branch. If you are on a version of PaddlePaddle earlier than this, [please update your installation](http://www.paddlepaddle.org/docs/develop/documentation/en/build_and_install/pip_install_en.html). +## 简介 ---- +### 任务说明 -# Attention is All You Need: A Paddle Fluid implementation +机器翻译（machine translation, MT）是利用计算机将一种自然语言(源语言)转换为另一种自然语言(目标语言)的过程，输入为源语言句子，输出为相应的目标语言的句子。本示例是机器翻译主流模型 Transformer 的实现和相关介绍。 -This is a Paddle Fluid implementation of the Transformer model in [Attention is All You Need]() (Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, arxiv, 2017). +### 效果说明 -If you use the dataset/code in your research, please cite the paper: +我们使用公开的 [WMT'16 EN-DE 数据集](http://www.statmt.org/wmt16/translation-task.html)训练 Base、Big 两种配置的Transformer 模型后，在相应的测试集上进行评测，效果如下所示： + +| 测试集 | newstest2014 | newstest2015 | newstest2016 | +|-|-|-|-| +| Base | 26.35 | 29.07 | 33.30 | +| Big | 27.07 | 30.09 | 34.38 | + +## 快速开始 + +### 安装说明 + +1. paddle安装 + + 本项目依赖于 Paddlepaddle Fluid 1.3，请参考安装指南进行安装。 + +2. 安装代码 +3. 环境依赖 + +### 开始第一次模型调用 + +1. 数据准备 + + 运行 `gen_data.sh` 脚本进行 WMT'16 EN-DE 数据集的下载和预处理（时间较长，建议后台运行）。数据处理过程主要包括 Tokenize 和 [BPE 编码（byte-pair encoding）](https://arxiv.org/pdf/1508.07909)。运行成功后，将会生成文件夹 `gen_data`，其目录结构如下： + + ```text + . + ├── wmt16_ende_data # WMT16 英德翻译数据 + ├── wmt16_ende_data_bpe # BPE 编码的 WMT16 英德翻译数据 + ├── mosesdecoder # Moses 机器翻译工具集，包含了 Tokenize、BLEU 评估等脚本 + └── subword-nmt # BPE 编码的代码 + ``` + + 另外我们也整理提供了一份处理好的 WMT'16 EN-DE 数据以供[下载](https://transformer-res.bj.bcebos.com/wmt16_ende_data_bpe_clean.tar.gz)使用（包含训练所需 BPE 数据和词典以及预测和评估所需的 BPE 数据和 tokenize 的数据） + +2. 模型下载 + + 我们提供了基于 WMT'16 EN-DE 数据训练好的模型以供使用：[base model](https://transformer-res.bj.bcebos.com/base_model.tar.gz) 、[big model](https://transformer-res.bj.bcebos.com/big_model.tar.gz) + +3. 模型预测 + + 使用以上提供的数据和模型，可以按照以下代码进行预测，翻译结果将打印到标准输出: + ```sh + # base model + python -u infer.py \ + --src_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \ + --trg_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \ + --special_token '' '' '' \ + --test_file_pattern gen_data/wmt16_ende_data_bpe/newstest2014.tok.bpe.32000.en-de \ + --token_delimiter ' ' \ + --batch_size 32 \ + model_path trained_models/iter_100000.infer.model \ + beam_size 5 \ + max_out_len 255 + + + # big model + python -u infer.py \ + --src_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \ + --trg_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \ + --special_token '' '' '' \ + --test_file_pattern gen_data/wmt16_ende_data_bpe/newstest2014.tok.bpe.32000.en-de \ + --token_delimiter ' ' \ + --batch_size 32 \ + model_path trained_models/iter_100000.infer.model \ + n_head 16 \ + d_model 1024 \ + d_inner_hid 4096 \ + prepostprocess_dropout 0.3 \ + beam_size 5 \ + max_out_len 255 + ``` +4. 模型评估 + + 预测结果中每行输出是对应行输入的得分最高的翻译，对于使用 BPE 的数据，预测出的翻译结果也将是 BPE 表示的数据，要还原成原始的数据（这里指 tokenize 后的数据）才能进行正确的评估。评估过程具体如下（BLEU 是翻译任务常用的自动评估方法指标）： + + ```sh + # 还原 predict.txt 中的预测结果为 tokenize 后的数据 + sed -r 's/(@@ )|(@@ ?$)//g' predict.txt > predict.tok.txt + # 若无 BLEU 评估工具，需先进行下载 + # git clone https://github.com/moses-smt/mosesdecoder.git + # 以英德翻译 newstest2014 测试数据为例 + perl gen_data/mosesdecoder/scripts/generic/multi-bleu.perl gen_data/wmt16_ende_data/newstest2014.tok.de < predict.tok.txt + ``` + + 完成后可以看到类似如下的结果： + ``` + BLEU = 26.35, 57.7/32.1/20.0/13.0 (BP=1.000, ratio=1.013, hyp_len=63903, ref_len=63078) + ``` + +## 进阶使用 + +### 模型原理介绍 + +Transformer 是论文 [Attention Is All You Need](https://arxiv.org/abs/1706.03762) 中提出的用以完成机器翻译（machine translation, MT）等序列到序列（sequence to sequence, Seq2Seq）学习任务的一种全新网络结构。其同样使用了 Seq2Seq 任务中典型的编码器-解码器（Encoder-Decoder）的框架结构，但相较于此前广泛使用的循环神经网络（Recurrent Neural Network, RNN），其完全使用注意力（Attention）机制来实现序列到序列的建模，整体网络结构如图1所示。 + +

+
+图 1. Transformer 网络结构图 +

+ +Encoder 由若干相同的 layer 堆叠组成，每个 layer 主要由多头注意力（Multi-Head Attention）和全连接的前馈（Feed-Forward）网络这两个 sub-layer 构成。 +- Multi-Head Attention 在这里用于实现 Self-Attention，相比于简单的 Attention 机制，其将输入进行多路线性变换后分别计算 Attention 的结果，并将所有结果拼接后再次进行线性变换作为输出。参见图2，其中 Attention 使用的是点积（Dot-Product），并在点积后进行了 scale 的处理以避免因点积结果过大进入 softmax 的饱和区域。 +- Feed-Forward 网络会对序列中的每个位置进行相同的计算（Position-wise），其采用的是两次线性变换中间加以 ReLU 激活的结构。 + +此外，每个 sub-layer 后还施以 [Residual Connection](http://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf) 和 [Layer Normalization](https://arxiv.org/pdf/1607.06450.pdf) 来促进梯度传播和模型收敛。 + +

+
+图 2. Multi-Head Attention +

+ +Decoder 具有和 Encoder 类似的结构，只是相比于组成 Encoder 的 layer ，在组成 Decoder 的 layer 中还多了一个 Multi-Head Attention 的 sub-layer 来实现对 Encoder 输出的 Attention，这个 Encoder-Decoder Attention 在其他 Seq2Seq 模型中也是存在的。 + +### 代码结构说明 + +以下是本例的简要目录结构及说明: ```text -@inproceedings{vaswani2017attention, - title={Attention is all you need}, - author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia}, - booktitle={Advances in Neural Information Processing Systems}, - pages={6000--6010}, - year={2017} -} +. +├── images # README 文档中的图片 +├── config.py # 训练、预测以及模型参数配置 +├── infer.py # 预测脚本 +├── reader.py # 数据读取接口 +├── README.md # 文档 +├── train.py # 训练脚本 +└── gen_data.sh # 数据生成脚本 +``` + +### 数据格式说明 + +本示例程序中支持的数据格式为制表符 `\t` 分隔的源语言和目标语言句子对，句子中的 token 之间使用空格分隔 +。如需使用 BPE 编码，亦可以使用类似 WMT'16 EN-DE 原始数据的格式，参照 `gen_data.sh` 进行处理。 + +### 如何训练 + +数据准备完成后，可以使用 `train,py` 脚本进行训练。以提供的 WMT'16 EN-DE 数据为例，具体如下： + +```sh +python -u train.py \ + --src_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \ + --trg_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \ + --special_token '' '' '' \ + --train_file_pattern gen_data/wmt16_ende_data_bpe/train.tok.clean.bpe.32000.en-de \ + --token_delimiter ' ' \ + --use_token_batch True \ + --batch_size 4096 \ + --sort_type pool \ + --pool_size 200000 +``` + +上述命令中设置了源语言词典文件路径（`src_vocab_fpath`）、目标语言词典文件路径（`trg_vocab_fpath`）、训练数据文件（`train_file_pattern`，支持通配符）等数据相关的参数和构造 batch 方式（`use_token_batch` 指定了数据按照 token 数目或者 sequence 数目组成 batch）等 reader 相关的参数。有关这些参数更详细的信息可以通过执行以下命令查看： + +```sh +python train.py --help ``` -### TODO +更多模型训练相关的参数则在 `config.py` 中的 `ModelHyperParams` 和 `TrainTaskConfig` 内定义；`ModelHyperParams` 定义了 embedding 维度等模型超参数，`TrainTaskConfig` 定义了 warmup 步数等训练需要的参数。这些参数默认使用了 Transformer 论文中 base model 的配置，如需调整可以在该脚本中进行修改。另外这些参数同样可在执行训练脚本的命令行中设置，传入的配置会合并并覆盖 `config.py` 中的配置，如可以通过以下命令来训练 Transformer 论文中的 big model ： + +```sh +python -u train.py \ + --src_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \ + --trg_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \ + --special_token '' '' '' \ + --train_file_pattern gen_data/wmt16_ende_data_bpe/train.tok.clean.bpe.32000.en-de \ + --token_delimiter ' ' \ + --use_token_batch True \ + --batch_size 3200 \ + --sort_type pool \ + --pool_size 200000 \ + n_head 16 \ + d_model 1024 \ + d_inner_hid 4096 \ + prepostprocess_dropout 0.3 +``` + +注意，如训练时更改了模型配置，使用 `infer.py` 预测时需要使用对应相同的模型配置；另外，训练时默认使用所有 GPU，可以通过 `CUDA_VISIBLE_DEVICES` 环境变量来设置使用指定的 GPU。 + +## 其他 + +### 如何贡献代码 -This project is still under active development. +如果你可以修复某个issue或者增加一个新功能，欢迎给我们提交PR。如果对应的PR被接受了，我们将根据贡献的质量和难度进行打分（0-5分，越高越好）。如果你累计获得了10分，可以联系我们获得面试机会或者为你写推荐信。 diff --git a/PaddleNLP/neural_machine_translation/transformer/config.py b/PaddleNLP/neural_machine_translation/transformer/config.py index 823341ed9084e80b5fe74655bf8db897d72175f0..b0943b016f44f715081af1c0f26adcaff13d6a9a 100644 --- a/PaddleNLP/neural_machine_translation/transformer/config.py +++ b/PaddleNLP/neural_machine_translation/transformer/config.py @@ -109,93 +109,3 @@ def merge_cfg_from_list(cfg_list, g_cfgs): pass setattr(g_cfg, key, value) break - - -# The placeholder for batch_size in compile time. Must be -1 currently to be -# consistent with some ops' infer-shape output in compile time, such as the -# sequence_expand op used in beamsearch decoder. -batch_size = -1 -# The placeholder for squence length in compile time. -seq_len = ModelHyperParams.max_length -# Here list the data shapes and data types of all inputs. -# The shapes here act as placeholder and are set to pass the infer-shape in -# compile time. -input_descs = { - # The actual data shape of src_word is: - # [batch_size, max_src_len_in_batch, 1] - "src_word": [(batch_size, seq_len, 1), "int64", 2], - # The actual data shape of src_pos is: - # [batch_size, max_src_len_in_batch, 1] - "src_pos": [(batch_size, seq_len, 1), "int64"], - # This input is used to remove attention weights on paddings in the - # encoder. - # The actual data shape of src_slf_attn_bias is: - # [batch_size, n_head, max_src_len_in_batch, max_src_len_in_batch] - "src_slf_attn_bias": [(batch_size, ModelHyperParams.n_head, seq_len, - seq_len), "float32"], - # The actual data shape of trg_word is: - # [batch_size, max_trg_len_in_batch, 1] - "trg_word": [(batch_size, seq_len, 1), "int64", - 2], # lod_level is only used in fast decoder. - # The actual data shape of trg_pos is: - # [batch_size, max_trg_len_in_batch, 1] - "trg_pos": [(batch_size, seq_len, 1), "int64"], - # This input is used to remove attention weights on paddings and - # subsequent words in the decoder. - # The actual data shape of trg_slf_attn_bias is: - # [batch_size, n_head, max_trg_len_in_batch, max_trg_len_in_batch] - "trg_slf_attn_bias": [(batch_size, ModelHyperParams.n_head, seq_len, - seq_len), "float32"], - # This input is used to remove attention weights on paddings of the source - # input in the encoder-decoder attention. - # The actual data shape of trg_src_attn_bias is: - # [batch_size, n_head, max_trg_len_in_batch, max_src_len_in_batch] - "trg_src_attn_bias": [(batch_size, ModelHyperParams.n_head, seq_len, - seq_len), "float32"], - # This input is used in independent decoder program for inference. - # The actual data shape of enc_output is: - # [batch_size, max_src_len_in_batch, d_model] - "enc_output": [(batch_size, seq_len, ModelHyperParams.d_model), "float32"], - # The actual data shape of label_word is: - # [batch_size * max_trg_len_in_batch, 1] - "lbl_word": [(batch_size * seq_len, 1), "int64"], - # This input is used to mask out the loss of paddding tokens. - # The actual data shape of label_weight is: - # [batch_size * max_trg_len_in_batch, 1] - "lbl_weight": [(batch_size * seq_len, 1), "float32"], - # This input is used in beam-search decoder. - "init_score": [(batch_size, 1), "float32", 2], - # This input is used in beam-search decoder for the first gather - # (cell states updation) - "init_idx": [(batch_size, ), "int32"], -} - -# Names of word embedding table which might be reused for weight sharing. -word_emb_param_names = ( - "src_word_emb_table", - "trg_word_emb_table", ) -# Names of position encoding table which will be initialized externally. -pos_enc_param_names = ( - "src_pos_enc_table", - "trg_pos_enc_table", ) -# separated inputs for different usages. -encoder_data_input_fields = ( - "src_word", - "src_pos", - "src_slf_attn_bias", ) -decoder_data_input_fields = ( - "trg_word", - "trg_pos", - "trg_slf_attn_bias", - "trg_src_attn_bias", - "enc_output", ) -label_data_input_fields = ( - "lbl_word", - "lbl_weight", ) -# In fast decoder, trg_pos (only containing the current time step) is generated -# by ops and trg_slf_attn_bias is not needed. -fast_decoder_data_input_fields = ( - "trg_word", - "init_score", - "init_idx", - "trg_src_attn_bias", ) diff --git a/PaddleNLP/neural_machine_translation/transformer/infer.py b/PaddleNLP/neural_machine_translation/transformer/infer.py index 96b8e0a14fcc225e11e63a7604c8bf8e6db8e45e..858c81038fc81e985c1e16856860f2d4a276c565 100644 --- a/PaddleNLP/neural_machine_translation/transformer/infer.py +++ b/PaddleNLP/neural_machine_translation/transformer/infer.py @@ -3,16 +3,16 @@ import ast import multiprocessing import numpy as np import os +import sys +sys.path.append("../../models/neural_machine_translation/transformer/") from functools import partial import paddle import paddle.fluid as fluid -import model import reader from config import * -from model import wrap_encoder as encoder -from model import wrap_decoder as decoder +from desc import * from model import fast_decode as fast_decoder from train import pad_batch_data, prepare_data_generator @@ -206,6 +206,7 @@ def fast_infer(args): ModelHyperParams.weight_sharing, InferTaskConfig.beam_size, InferTaskConfig.max_out_len, + ModelHyperParams.bos_idx, ModelHyperParams.eos_idx, use_py_reader=args.use_py_reader) @@ -281,10 +282,10 @@ def fast_infer(args): if feed_dict_list is not None else None, return_numpy=False, use_program_cache=True) - seq_ids_list, seq_scores_list = [ - seq_ids - ], [seq_scores] if isinstance( - seq_ids, paddle.fluid.LoDTensor) else (seq_ids, seq_scores) + seq_ids_list, seq_scores_list = [seq_ids], [ + seq_scores + ] if isinstance( + seq_ids, paddle.fluid.core.LoDTensor) else (seq_ids, seq_scores) for seq_ids, seq_scores in zip(seq_ids_list, seq_scores_list): # How to parse the results: # Suppose the lod of seq_ids is: diff --git a/PaddleNLP/neural_machine_translation/transformer/train.py b/PaddleNLP/neural_machine_translation/transformer/train.py index 4313f8b441ee194935c7c47abc52271589c7765d..b423e5e3be2c8d58745f9d5fbaabb7b78eff07eb 100644 --- a/PaddleNLP/neural_machine_translation/transformer/train.py +++ b/PaddleNLP/neural_machine_translation/transformer/train.py @@ -6,6 +6,7 @@ import multiprocessing import os import six import sys +sys.path.append("../../models/neural_machine_translation/transformer/") import time import numpy as np @@ -13,6 +14,7 @@ import paddle.fluid as fluid import reader from config import * +from desc import * from model import transformer, position_encoding_init @@ -661,6 +663,7 @@ def train(args): ModelHyperParams.postprocess_cmd, ModelHyperParams.weight_sharing, TrainTaskConfig.label_smooth_eps, + ModelHyperParams.bos_idx, use_py_reader=args.use_py_reader, is_test=False) diff --git a/PaddleNLP/preprocess/__init__.py b/PaddleNLP/preprocess/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PaddleNLP/preprocess/ernie/__init__.py b/PaddleNLP/preprocess/ernie/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PaddleNLP/preprocess/ernie/task_reader.py b/PaddleNLP/preprocess/ernie/task_reader.py new file mode 100644 index 0000000000000000000000000000000000000000..209ac795a454ba695a41062dcdead31035e7648f --- /dev/null +++ b/PaddleNLP/preprocess/ernie/task_reader.py @@ -0,0 +1,360 @@ +""" +This module provides reader for classification and sequence labing +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from collections import namedtuple +import csv +import json + +import numpy as np + +from preprocess.ernie import tokenization +from preprocess.padding import pad_batch_data + + +class BaseReader(object): + """BaseReader for classify and sequence labeling task""" + def __init__(self, + vocab_path, + label_map_config=None, + max_seq_len=512, + do_lower_case=True, + in_tokens=False, + random_seed=None): + self.max_seq_len = max_seq_len + self.tokenizer = tokenization.FullTokenizer( + vocab_file=vocab_path, do_lower_case=do_lower_case) + self.vocab = self.tokenizer.vocab + self.pad_id = self.vocab["[PAD]"] + self.cls_id = self.vocab["[CLS]"] + self.sep_id = self.vocab["[SEP]"] + self.in_tokens = in_tokens + + np.random.seed(random_seed) + + self.current_example = 0 + self.current_epoch = 0 + self.num_examples = 0 + + if label_map_config: + with open(label_map_config) as f: + self.label_map = json.load(f) + else: + self.label_map = None + + def get_train_progress(self): + """Gets progress for training phase.""" + return self.current_example, self.current_epoch + + def _read_tsv(self, input_file, quotechar=None): + """Reads a tab separated value file.""" + with open(input_file, "r") as f: + reader = csv.reader(f, delimiter="\t", quotechar=quotechar) + headers = next(reader) + Example = namedtuple('Example', headers) + + examples = [] + for line in reader: + example = Example(*line) + examples.append(example) + return examples + + def _truncate_seq_pair(self, tokens_a, tokens_b, max_length): + """Truncates a sequence pair in place to the maximum length.""" + + # This is a simple heuristic which will always truncate the longer sequence + # one token at a time. This makes more sense than truncating an equal percent + # of tokens from each, since if one sequence is very short then each token + # that's truncated likely contains more information than a longer sequence. + while True: + total_length = len(tokens_a) + len(tokens_b) + if total_length <= max_length: + break + if len(tokens_a) > len(tokens_b): + tokens_a.pop() + else: + tokens_b.pop() + + def _convert_example_to_record(self, example, max_seq_length, tokenizer): + """Converts a single `Example` into a single `Record`.""" + + text_a = tokenization.convert_to_unicode(example.text_a) + tokens_a = tokenizer.tokenize(text_a) + tokens_b = None + if "text_b" in example._fields: + text_b = tokenization.convert_to_unicode(example.text_b) + tokens_b = tokenizer.tokenize(text_b) + + if tokens_b: + # Modifies `tokens_a` and `tokens_b` in place so that the total + # length is less than the specified length. + # Account for [CLS], [SEP], [SEP] with "- 3" + self._truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) + else: + # Account for [CLS] and [SEP] with "- 2" + if len(tokens_a) > max_seq_length - 2: + tokens_a = tokens_a[0:(max_seq_length - 2)] + + # The convention in BERT/ERNIE is: + # (a) For sequence pairs: + # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] + # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 + # (b) For single sequences: + # tokens: [CLS] the dog is hairy . [SEP] + # type_ids: 0 0 0 0 0 0 0 + # + # Where "type_ids" are used to indicate whether this is the first + # sequence or the second sequence. The embedding vectors for `type=0` and + # `type=1` were learned during pre-training and are added to the wordpiece + # embedding vector (and position vector). This is not *strictly* necessary + # since the [SEP] token unambiguously separates the sequences, but it makes + # it easier for the model to learn the concept of sequences. + # + # For classification tasks, the first vector (corresponding to [CLS]) is + # used as as the "sentence vector". Note that this only makes sense because + # the entire model is fine-tuned. + tokens = [] + text_type_ids = [] + tokens.append("[CLS]") + text_type_ids.append(0) + for token in tokens_a: + tokens.append(token) + text_type_ids.append(0) + tokens.append("[SEP]") + text_type_ids.append(0) + + if tokens_b: + for token in tokens_b: + tokens.append(token) + text_type_ids.append(1) + tokens.append("[SEP]") + text_type_ids.append(1) + + token_ids = tokenizer.convert_tokens_to_ids(tokens) + position_ids = list(range(len(token_ids))) + + if self.label_map: + label_id = self.label_map[example.label] + else: + label_id = example.label + + Record = namedtuple( + 'Record', + ['token_ids', 'text_type_ids', 'position_ids', 'label_id', 'qid']) + + qid = None + if "qid" in example._fields: + qid = example.qid + + record = Record( + token_ids=token_ids, + text_type_ids=text_type_ids, + position_ids=position_ids, + label_id=label_id, + qid=qid) + return record + + def _prepare_batch_data(self, examples, batch_size, phase=None): + """generate batch records""" + batch_records, max_len = [], 0 + for index, example in enumerate(examples): + if phase == "train": + self.current_example = index + record = self._convert_example_to_record(example, self.max_seq_len, + self.tokenizer) + max_len = max(max_len, len(record.token_ids)) + if self.in_tokens: + to_append = (len(batch_records) + 1) * max_len <= batch_size + else: + to_append = len(batch_records) < batch_size + if to_append: + batch_records.append(record) + else: + yield self._pad_batch_records(batch_records) + batch_records, max_len = [record], len(record.token_ids) + + if batch_records: + yield self._pad_batch_records(batch_records) + + def get_num_examples(self, input_file): + """return total number of examples""" + examples = self._read_tsv(input_file) + return len(examples) + + def data_generator(self, + input_file, + batch_size, + epoch, + shuffle=True, + phase=None): + """return generator which yields batch data for pyreader""" + examples = self._read_tsv(input_file) + + def _wrapper(): + for epoch_index in range(epoch): + if phase == "train": + self.current_example = 0 + self.current_epoch = epoch_index + if shuffle: + np.random.shuffle(examples) + + for batch_data in self._prepare_batch_data( + examples, batch_size, phase=phase): + yield batch_data + + return _wrapper + + +class ClassifyReader(BaseReader): + """ClassifyReader""" + def _read_tsv(self, input_file, quotechar=None): + """Reads a tab separated value file.""" + with open(input_file, "r") as f: + reader = csv.reader(f, delimiter="\t", quotechar=quotechar) + headers = next(reader) + text_indices = [ + index for index, h in enumerate(headers) if h != "label" + ] + Example = namedtuple('Example', headers) + + examples = [] + for line in reader: + for index, text in enumerate(line): + if index in text_indices: + line[index] = text.replace(' ', '') + example = Example(*line) + examples.append(example) + return examples + + def _pad_batch_records(self, batch_records): + batch_token_ids = [record.token_ids for record in batch_records] + batch_text_type_ids = [record.text_type_ids for record in batch_records] + batch_position_ids = [record.position_ids for record in batch_records] + batch_labels = [record.label_id for record in batch_records] + batch_labels = np.array(batch_labels).astype("int64").reshape([-1, 1]) + + # padding + padded_token_ids, input_mask, seq_lens = pad_batch_data( + batch_token_ids, pad_idx=self.pad_id, return_input_mask=True, return_seq_lens=True) + padded_text_type_ids = pad_batch_data( + batch_text_type_ids, pad_idx=self.pad_id) + padded_position_ids = pad_batch_data( + batch_position_ids, pad_idx=self.pad_id) + + return_list = [ + padded_token_ids, padded_text_type_ids, padded_position_ids, + input_mask, batch_labels, seq_lens + ] + + return return_list + + +class SequenceLabelReader(BaseReader): + """SequenceLabelReader""" + def _pad_batch_records(self, batch_records): + batch_token_ids = [record.token_ids for record in batch_records] + batch_text_type_ids = [record.text_type_ids for record in batch_records] + batch_position_ids = [record.position_ids for record in batch_records] + batch_label_ids = [record.label_ids for record in batch_records] + + # padding + padded_token_ids, input_mask, batch_seq_lens = pad_batch_data( + batch_token_ids, + pad_idx=self.pad_id, + return_input_mask=True, + return_seq_lens=True) + padded_text_type_ids = pad_batch_data( + batch_text_type_ids, pad_idx=self.pad_id) + padded_position_ids = pad_batch_data( + batch_position_ids, pad_idx=self.pad_id) + padded_label_ids = pad_batch_data( + batch_label_ids, pad_idx=len(self.label_map) - 1) + + return_list = [ + padded_token_ids, padded_text_type_ids, padded_position_ids, + input_mask, padded_label_ids, batch_seq_lens + ] + return return_list + + def _reseg_token_label(self, tokens, labels, tokenizer): + assert len(tokens) == len(labels) + ret_tokens = [] + ret_labels = [] + for token, label in zip(tokens, labels): + sub_token = tokenizer.tokenize(token) + if len(sub_token) == 0: + continue + ret_tokens.extend(sub_token) + ret_labels.append(label) + if len(sub_token) < 2: + continue + sub_label = label + if label.startswith("B-"): + sub_label = "I-" + label[2:] + ret_labels.extend([sub_label] * (len(sub_token) - 1)) + + assert len(ret_tokens) == len(ret_labels) + return ret_tokens, ret_labels + + def _convert_example_to_record(self, example, max_seq_length, tokenizer): + tokens = tokenization.convert_to_unicode(example.text_a).split(u"") + labels = tokenization.convert_to_unicode(example.label).split(u"") + tokens, labels = self._reseg_token_label(tokens, labels, tokenizer) + + if len(tokens) > max_seq_length - 2: + tokens = tokens[0:(max_seq_length - 2)] + labels = labels[0:(max_seq_length - 2)] + + tokens = ["[CLS]"] + tokens + ["[SEP]"] + token_ids = tokenizer.convert_tokens_to_ids(tokens) + position_ids = list(range(len(token_ids))) + text_type_ids = [0] * len(token_ids) + no_entity_id = len(self.label_map) - 1 + labels = [label if label in self.label_map else u"O" for label in labels] + label_ids = [no_entity_id] + [ + self.label_map[label] for label in labels + ] + [no_entity_id] + + Record = namedtuple( + 'Record', + ['token_ids', 'text_type_ids', 'position_ids', 'label_ids']) + record = Record( + token_ids=token_ids, + text_type_ids=text_type_ids, + position_ids=position_ids, + label_ids=label_ids) + return record + + +class ExtractEmbeddingReader(BaseReader): + """ExtractEmbeddingReader""" + def _pad_batch_records(self, batch_records): + batch_token_ids = [record.token_ids for record in batch_records] + batch_text_type_ids = [record.text_type_ids for record in batch_records] + batch_position_ids = [record.position_ids for record in batch_records] + + # padding + padded_token_ids, input_mask, seq_lens = pad_batch_data( + batch_token_ids, + pad_idx=self.pad_id, + return_input_mask=True, + return_seq_lens=True) + padded_text_type_ids = pad_batch_data( + batch_text_type_ids, pad_idx=self.pad_id) + padded_position_ids = pad_batch_data( + batch_position_ids, pad_idx=self.pad_id) + + return_list = [ + padded_token_ids, padded_text_type_ids, padded_position_ids, + input_mask, seq_lens + ] + + return return_list + + +if __name__ == '__main__': + pass diff --git a/PaddleNLP/preprocess/ernie/tokenization.py b/PaddleNLP/preprocess/ernie/tokenization.py new file mode 100644 index 0000000000000000000000000000000000000000..f906b537177dce430888fbc1738cd4b33906d705 --- /dev/null +++ b/PaddleNLP/preprocess/ernie/tokenization.py @@ -0,0 +1,370 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization classes.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import unicodedata +import six + + +def convert_to_unicode(text): + """Converts `text` to Unicode (if it's not already), assuming utf-8 input.""" + if six.PY3: + if isinstance(text, str): + return text + elif isinstance(text, bytes): + return text.decode("utf-8", "ignore") + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + elif six.PY2: + if isinstance(text, str): + return text.decode("utf-8", "ignore") + elif isinstance(text, unicode): + return text + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + else: + raise ValueError("Not running on Python2 or Python 3?") + + +def printable_text(text): + """Returns text encoded in a way suitable for print or `tf.logging`.""" + + # These functions want `str` for both Python2 and Python3, but in one case + # it's a Unicode string and in the other it's a byte string. + if six.PY3: + if isinstance(text, str): + return text + elif isinstance(text, bytes): + return text.decode("utf-8", "ignore") + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + elif six.PY2: + if isinstance(text, str): + return text + elif isinstance(text, unicode): + return text.encode("utf-8") + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + else: + raise ValueError("Not running on Python2 or Python 3?") + + +def load_vocab(vocab_file): + """Loads a vocabulary file into a dictionary.""" + vocab = collections.OrderedDict() + fin = open(vocab_file) + for num, line in enumerate(fin): + items = convert_to_unicode(line.strip()).split("\t") + if len(items) > 2: + break + token = items[0] + index = items[1] if len(items) == 2 else num + token = token.strip() + vocab[token] = int(index) + return vocab + + +def convert_by_vocab(vocab, items): + """Converts a sequence of [tokens|ids] using the vocab.""" + output = [] + for item in items: + output.append(vocab[item]) + return output + + +def convert_tokens_to_ids(vocab, tokens): + return convert_by_vocab(vocab, tokens) + + +def convert_ids_to_tokens(inv_vocab, ids): + return convert_by_vocab(inv_vocab, ids) + + +def whitespace_tokenize(text): + """Runs basic whitespace cleaning and splitting on a peice of text.""" + text = text.strip() + if not text: + return [] + tokens = text.split() + return tokens + + +class FullTokenizer(object): + """Runs end-to-end tokenziation.""" + + def __init__(self, vocab_file, do_lower_case=True): + self.vocab = load_vocab(vocab_file) + self.inv_vocab = {v: k for k, v in self.vocab.items()} + self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) + self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) + + def tokenize(self, text): + split_tokens = [] + for token in self.basic_tokenizer.tokenize(text): + for sub_token in self.wordpiece_tokenizer.tokenize(token): + split_tokens.append(sub_token) + + return split_tokens + + def convert_tokens_to_ids(self, tokens): + return convert_by_vocab(self.vocab, tokens) + + def convert_ids_to_tokens(self, ids): + return convert_by_vocab(self.inv_vocab, ids) + + +class CharTokenizer(object): + """Runs end-to-end tokenziation.""" + + def __init__(self, vocab_file, do_lower_case=True): + self.vocab = load_vocab(vocab_file) + self.inv_vocab = {v: k for k, v in self.vocab.items()} + self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) + + def tokenize(self, text): + split_tokens = [] + for token in text.lower().split(" "): + for sub_token in self.wordpiece_tokenizer.tokenize(token): + split_tokens.append(sub_token) + + return split_tokens + + def convert_tokens_to_ids(self, tokens): + return convert_by_vocab(self.vocab, tokens) + + def convert_ids_to_tokens(self, ids): + return convert_by_vocab(self.inv_vocab, ids) + + +class BasicTokenizer(object): + """Runs basic tokenization (punctuation splitting, lower casing, etc.).""" + + def __init__(self, do_lower_case=True): + """Constructs a BasicTokenizer. + + Args: + do_lower_case: Whether to lower case the input. + """ + self.do_lower_case = do_lower_case + + def tokenize(self, text): + """Tokenizes a piece of text.""" + text = convert_to_unicode(text) + text = self._clean_text(text) + + # This was added on November 1st, 2018 for the multilingual and Chinese + # models. This is also applied to the English models now, but it doesn't + # matter since the English models were not trained on any Chinese data + # and generally don't have any Chinese data in them (there are Chinese + # characters in the vocabulary because Wikipedia does have some Chinese + # words in the English Wikipedia.). + text = self._tokenize_chinese_chars(text) + + orig_tokens = whitespace_tokenize(text) + split_tokens = [] + for token in orig_tokens: + if self.do_lower_case: + token = token.lower() + token = self._run_strip_accents(token) + split_tokens.extend(self._run_split_on_punc(token)) + + output_tokens = whitespace_tokenize(" ".join(split_tokens)) + return output_tokens + + def _run_strip_accents(self, text): + """Strips accents from a piece of text.""" + text = unicodedata.normalize("NFD", text) + output = [] + for char in text: + cat = unicodedata.category(char) + if cat == "Mn": + continue + output.append(char) + return "".join(output) + + def _run_split_on_punc(self, text): + """Splits punctuation on a piece of text.""" + chars = list(text) + i = 0 + start_new_word = True + output = [] + while i < len(chars): + char = chars[i] + if _is_punctuation(char): + output.append([char]) + start_new_word = True + else: + if start_new_word: + output.append([]) + start_new_word = False + output[-1].append(char) + i += 1 + + return ["".join(x) for x in output] + + def _tokenize_chinese_chars(self, text): + """Adds whitespace around any CJK character.""" + output = [] + for char in text: + cp = ord(char) + if self._is_chinese_char(cp): + output.append(" ") + output.append(char) + output.append(" ") + else: + output.append(char) + return "".join(output) + + def _is_chinese_char(self, cp): + """Checks whether CP is the codepoint of a CJK character.""" + # This defines a "chinese character" as anything in the CJK Unicode block: + # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) + # + # Note that the CJK Unicode block is NOT all Japanese and Korean characters, + # despite its name. The modern Korean Hangul alphabet is a different block, + # as is Japanese Hiragana and Katakana. Those alphabets are used to write + # space-separated words, so they are not treated specially and handled + # like the all of the other languages. + if ((cp >= 0x4E00 and cp <= 0x9FFF) or # + (cp >= 0x3400 and cp <= 0x4DBF) or # + (cp >= 0x20000 and cp <= 0x2A6DF) or # + (cp >= 0x2A700 and cp <= 0x2B73F) or # + (cp >= 0x2B740 and cp <= 0x2B81F) or # + (cp >= 0x2B820 and cp <= 0x2CEAF) or + (cp >= 0xF900 and cp <= 0xFAFF) or # + (cp >= 0x2F800 and cp <= 0x2FA1F)): # + return True + + return False + + def _clean_text(self, text): + """Performs invalid character removal and whitespace cleanup on text.""" + output = [] + for char in text: + cp = ord(char) + if cp == 0 or cp == 0xfffd or _is_control(char): + continue + if _is_whitespace(char): + output.append(" ") + else: + output.append(char) + return "".join(output) + + +class WordpieceTokenizer(object): + """Runs WordPiece tokenziation.""" + + def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100): + self.vocab = vocab + self.unk_token = unk_token + self.max_input_chars_per_word = max_input_chars_per_word + + def tokenize(self, text): + """Tokenizes a piece of text into its word pieces. + + This uses a greedy longest-match-first algorithm to perform tokenization + using the given vocabulary. + + For example: + input = "unaffable" + output = ["un", "##aff", "##able"] + + Args: + text: A single token or whitespace separated tokens. This should have + already been passed through `BasicTokenizer. + + Returns: + A list of wordpiece tokens. + """ + + text = convert_to_unicode(text) + + output_tokens = [] + for token in whitespace_tokenize(text): + chars = list(token) + if len(chars) > self.max_input_chars_per_word: + output_tokens.append(self.unk_token) + continue + + is_bad = False + start = 0 + sub_tokens = [] + while start < len(chars): + end = len(chars) + cur_substr = None + while start < end: + substr = "".join(chars[start:end]) + if start > 0: + substr = "##" + substr + if substr in self.vocab: + cur_substr = substr + break + end -= 1 + if cur_substr is None: + is_bad = True + break + sub_tokens.append(cur_substr) + start = end + + if is_bad: + output_tokens.append(self.unk_token) + else: + output_tokens.extend(sub_tokens) + return output_tokens + + +def _is_whitespace(char): + """Checks whether `chars` is a whitespace character.""" + # \t, \n, and \r are technically contorl characters but we treat them + # as whitespace since they are generally considered as such. + if char == " " or char == "\t" or char == "\n" or char == "\r": + return True + cat = unicodedata.category(char) + if cat == "Zs": + return True + return False + + +def _is_control(char): + """Checks whether `chars` is a control character.""" + # These are technically control characters but we count them as whitespace + # characters. + if char == "\t" or char == "\n" or char == "\r": + return False + cat = unicodedata.category(char) + if cat.startswith("C"): + return True + return False + + +def _is_punctuation(char): + """Checks whether `chars` is a punctuation character.""" + cp = ord(char) + # We treat all non-letter/number ASCII as punctuation. + # Characters such as "^", "$", and "`" are not in the Unicode + # Punctuation class but we treat them as punctuation anyways, for + # consistency. + if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or + (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)): + return True + cat = unicodedata.category(char) + if cat.startswith("P"): + return True + return False diff --git a/PaddleNLP/preprocess/padding.py b/PaddleNLP/preprocess/padding.py new file mode 100644 index 0000000000000000000000000000000000000000..630a4c180ef16c60434b139fa6ba787ea468b761 --- /dev/null +++ b/PaddleNLP/preprocess/padding.py @@ -0,0 +1,65 @@ +""" +Mask, padding and batching. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + + +def pad_batch_data(insts, + pad_idx=0, + return_pos=False, + return_input_mask=False, + return_max_len=False, + return_num_token=False, + return_seq_lens=False): + """ + Pad the instances to the max sequence length in batch, and generate the + corresponding position data and input mask. + """ + return_list = [] + max_len = max(len(inst) for inst in insts) + # Any token included in dict can be used to pad, since the paddings' loss + # will be masked out by weights and make no effect on parameter gradients. + + inst_data = np.array( + [inst + list([pad_idx] * (max_len - len(inst))) for inst in insts]) + return_list += [inst_data.astype("int64").reshape([-1, max_len, 1])] + + # position data + if return_pos: + inst_pos = np.array([ + list(range(0, len(inst))) + [pad_idx] * (max_len - len(inst)) + for inst in insts + ]) + + return_list += [inst_pos.astype("int64").reshape([-1, max_len, 1])] + + if return_input_mask: + # This is used to avoid attention on paddings. + input_mask_data = np.array([[1] * len(inst) + [0] * + (max_len - len(inst)) for inst in insts]) + input_mask_data = np.expand_dims(input_mask_data, axis=-1) + return_list += [input_mask_data.astype("float32")] + + if return_max_len: + return_list += [max_len] + + if return_num_token: + num_token = 0 + for inst in insts: + num_token += len(inst) + return_list += [num_token] + + if return_seq_lens: + seq_lens = np.array([len(inst) for inst in insts]) + return_list += [seq_lens.astype("int64").reshape([-1, 1])] + + return return_list if len(return_list) > 1 else return_list[0] + + +if __name__ == "__main__": + pass diff --git a/PaddleNLP/preprocess/tokenizer/README b/PaddleNLP/preprocess/tokenizer/README new file mode 100644 index 0000000000000000000000000000000000000000..fcdf464f6aafa1af4b9f88a4ccc3bcc9759b62f5 --- /dev/null +++ b/PaddleNLP/preprocess/tokenizer/README @@ -0,0 +1 @@ +fluid_1.2 tokenizer.py --test_data_dir ./test.txt.utf8 --batch_size 1 > res diff --git a/PaddleNLP/preprocess/tokenizer/conf/customization.dic b/PaddleNLP/preprocess/tokenizer/conf/customization.dic new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PaddleNLP/preprocess/tokenizer/conf/customization.dic.example b/PaddleNLP/preprocess/tokenizer/conf/customization.dic.example new file mode 100644 index 0000000000000000000000000000000000000000..22ebf8a4b15ec51870c7b0d683963e1f9e75d673 --- /dev/null +++ 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mode 100644 index 0000000000000000000000000000000000000000..46b88a1c63bb97c9fdf73f54ce9ca7d3f5aa3ac3 --- /dev/null +++ b/PaddleNLP/preprocess/tokenizer/reader.py @@ -0,0 +1,123 @@ +""" +The file_reader converts raw corpus to input. +""" +import os +import __future__ +import io + +def file_reader(file_dir, + word2id_dict, + label2id_dict, + word_replace_dict, + filename_feature=""): + """ + define the reader to read files in file_dir + """ + word_dict_len = max(map(int, word2id_dict.values())) + 1 + label_dict_len = max(map(int, label2id_dict.values())) + 1 + def reader(): + """ + the data generator + """ + index = 0 + for root, dirs, files in os.walk(file_dir): + for filename in files: + if not filename.startswith(filename_feature): + continue + for line in io.open(os.path.join(root, filename), 'r', encoding='utf8'): + index += 1 + bad_line = False + line = line.strip("\n") + if len(line) == 0: + continue + seg_tag = line.rfind("\t") + word_part = line[0:seg_tag] + label_part = line[seg_tag + 1:] + word_idx = [] + words = word_part + for word in words: + if ord(word) < 0x20: + word = ' ' + if word in word_replace_dict: + word = word_replace_dict[word] + if word in word2id_dict: + word_idx.append(int(word2id_dict[word])) + else: + word_idx.append(int(word2id_dict["OOV"])) + target_idx = [] + labels = label_part.strip().split(" ") + for label in labels: + if label in label2id_dict: + target_idx.append(int(label2id_dict[label])) + else: + target_idx.append(int(label2id_dict["O"])) + if len(word_idx) != len(target_idx): + continue + yield word_idx, target_idx + return reader + + +def test_reader(file_dir, + word2id_dict, + label2id_dict, + word_replace_dict, + filename_feature=""): + """ + define the reader to read test files in file_dir + """ + #print (word2id_dict) + word_dict_len = max(map(int, word2id_dict.values())) + 1 + label_dict_len = max(map(int, label2id_dict.values())) + 1 + #print word_dict_len + #print label_dict_len + def reader(): + """ + the data generator + """ + index = 0 + for line in io.open(file_dir, 'r', encoding='utf8'): + index += 1 + bad_line = False + line = line.strip("\n") + if len(line) == 0: + continue + word_part = line + word_idx = [] + words = word_part + for word in words: + if ord(word) < 0x20: + word = ' ' + if word in word_replace_dict: + word = word_replace_dict[word] + if word in word2id_dict: + word_idx.append(int(word2id_dict[word])) + else: + word_idx.append(int(word2id_dict["OOV"])) + yield word_idx, words + return reader + + +def load_dict(dict_path): + """ + Load a dict. The first column is the key and the second column is the value. + """ + result_dict = {} + for line in io.open(dict_path, "r", encoding='utf8'): + terms = line.strip("\n").split("\t") + if len(terms) != 2: + continue + result_dict[terms[0]] = terms[1] + return result_dict + + +def load_reverse_dict(dict_path): + """ + Load a dict. The first column is the value and the second column is the key. + """ + result_dict = {} + for line in io.open(dict_path, "r", encoding='utf8'): + terms = line.strip("\n").split("\t") + if len(terms) != 2: + continue + result_dict[terms[1]] = terms[0] + return result_dict diff --git a/PaddleNLP/preprocess/tokenizer/test.txt.utf8 b/PaddleNLP/preprocess/tokenizer/test.txt.utf8 new file mode 100644 index 0000000000000000000000000000000000000000..cda707a9b0c839af5a7cb43fbf60ef2036f9287a --- /dev/null +++ b/PaddleNLP/preprocess/tokenizer/test.txt.utf8 @@ -0,0 +1,5 @@ +我是中国人 +百度是一家人工智能公司 +国家博物馆将闭关 +巴萨5-1晋级欧冠八强 +c罗帽子戏法，尤文实现史诗级逆转 diff --git a/PaddleNLP/preprocess/tokenizer/tokenizer.py b/PaddleNLP/preprocess/tokenizer/tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..d1b00d1c72f79a65ca65877a1039dc75a2d8c24d --- /dev/null +++ b/PaddleNLP/preprocess/tokenizer/tokenizer.py @@ -0,0 +1,171 @@ +""" +This module provides wordseg tools +""" +from __future__ import print_function +import numpy as np +import reader +import paddle.fluid as fluid +import paddle +import argparse +import time +import sys +import io + +if sys.version_info > (3,): + sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8') +else: + reload(sys) + sys.setdefaultencoding("utf8") + +def parse_args(): + """ + Arguments Parse + """ + parser = argparse.ArgumentParser("Run inference.") + parser.add_argument( + '--batch_size', + type=int, + default=5, + help='The size of a batch. (default: %(default)d)' + ) + parser.add_argument( + '--model_path', + type=str, + default='./conf/model', + help='A path to the model. (default: %(default)s)' + ) + parser.add_argument( + '--test_data_dir', + type=str, + default='./data/test_data', + help='A directory with test data files. (default: %(default)s)' + ) + parser.add_argument( + "--word_dict_path", + type=str, + default="./conf/word.dic", + help="The path of the word dictionary. (default: %(default)s)" + ) + parser.add_argument( + "--label_dict_path", + type=str, + default="./conf/tag.dic", + help="The path of the label dictionary. (default: %(default)s)" + ) + parser.add_argument( + "--word_rep_dict_path", + type=str, + default="./conf/q2b.dic", + help="The path of the word replacement Dictionary. (default: %(default)s)" + ) + args = parser.parse_args() + return args + + +def print_arguments(args): + """ + Print the arguments + """ + print('----------- Configuration Arguments -----------') + for arg, value in sorted(vars(args).items()): + print('%s: %s' % (arg, value)) + print('------------------------------------------------') + + +def get_real_tag(origin_tag): + """ + Get real tag + """ + if origin_tag == "O": + return "O" + return origin_tag[0:len(origin_tag) - 2] + + +def to_lodtensor(data, place): + """ + Trans to lodtensor + """ + seq_lens = [len(seq) for seq in data] + cur_len = 0 + lod = [cur_len] + for l in seq_lens: + cur_len += l + lod.append(cur_len) + flattened_data = np.concatenate(data, axis=0).astype("int64") + flattened_data = flattened_data.reshape([len(flattened_data), 1]) + res = fluid.LoDTensor() + res.set(flattened_data, place) + res.set_lod([lod]) + return res + + +def infer(args): + """ + Tokenize + """ + id2word_dict = reader.load_dict(args.word_dict_path) + word2id_dict = reader.load_reverse_dict(args.word_dict_path) + + id2label_dict = reader.load_dict(args.label_dict_path) + label2id_dict = reader.load_reverse_dict(args.label_dict_path) + q2b_dict = reader.load_dict(args.word_rep_dict_path) + test_data = paddle.batch( + reader.test_reader(args.test_data_dir, + word2id_dict, + label2id_dict, + q2b_dict), + batch_size = args.batch_size) + place = fluid.CPUPlace() + #place = fluid.CUDAPlace(0) + exe = fluid.Executor(place) + #print("aaa") + inference_scope = fluid.core.Scope() + with fluid.scope_guard(inference_scope): + [inference_program, feed_target_names, + fetch_targets] = fluid.io.load_inference_model(args.model_path, exe) + #print('bbb') + for data in test_data(): + full_out_str = "" + word_idx = to_lodtensor([x[0] for x in data], place) + #print(word_idx) + word_list = [x[1] for x in data] + (crf_decode, ) = exe.run(inference_program, + feed={"word":word_idx}, + fetch_list=fetch_targets, + return_numpy=False) + lod_info = (crf_decode.lod())[0] + np_data = np.array(crf_decode) + assert len(data) == len(lod_info) - 1 + for sen_index in range(len(data)): + assert len(data[sen_index][0]) == lod_info[ + sen_index + 1] - lod_info[sen_index] + word_index = 0 + outstr = "" + cur_full_word = "" + cur_full_tag = "" + words = word_list[sen_index] + for tag_index in range(lod_info[sen_index], + lod_info[sen_index + 1]): + cur_word = words[word_index] + cur_tag = id2label_dict[str(np_data[tag_index][0])] + if cur_tag.endswith("-B") or cur_tag.endswith("O"): + if len(cur_full_word) != 0: + outstr += cur_full_word + " " + cur_full_word = cur_word + cur_full_tag = get_real_tag(cur_tag) + else: + cur_full_word += cur_word + word_index += 1 + #print(cur_full_word) + #outstr += cur_full_word + "/" + cur_full_tag + " " + outstr += cur_full_word + ' ' + outstr = outstr.strip() + full_out_str += outstr + "\n" + print(full_out_str.strip()) + #print(full_out_str.strip(), file=sys.stdout) + + +if __name__ == "__main__": + args = parse_args() + #print_arguments(args) + infer(args) diff --git a/PaddleNLP/reading_comprehension/LICENSE b/PaddleNLP/reading_comprehension/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..d645695673349e3947e8e5ae42332d0ac3164cd7 --- /dev/null +++ b/PaddleNLP/reading_comprehension/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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问题来自于真实的搜索日志 + - 文章内容来自于真实网页 + - 答案由人类生成 + - 面向真实应用场景 + - 标注更加丰富细致 + +更多关于DuReader数据集的详细信息可在[DuReader官网](https://ai.baidu.com//broad/subordinate?dataset=dureader)找到。 + +## DuReader基线系统 + +DuReader基线系统利用[PaddlePaddle](http://paddlepaddle.org)深度学习框架，针对[DuReader阅读理解数据集](https://ai.baidu.com//broad/subordinate?dataset=dureader)实现并升级了一个经典的阅读理解模型 —— [BiDAF](https://arxiv.org/abs/1611.01603). 该基线系统相较于DuReader论文中的基线，效果上有了大幅提升(在DuReader2.0验证集、测试集的表现见下表) + +| Model | Dev ROUGE-L | Test ROUGE-L | +| :------------- | :---------: | :----------: | +| BiDAF (原始[论文](https://arxiv.org/abs/1711.05073)基线) | 39.29 | 45.90 | +| 本基线系统 | 47.68 | 54.66 | + + +# 快速开始 + +## 安装 +### 环境依赖 +经测试，基线系统可在以下环境正常运行 + + * 系统：CentOS 6.3, cuda 9.0, CuDNN 7.0 + * python 2.7.13 + * PaddlePaddle 1.3.1 + +### 安装PaddlePaddle +关于PaddlePaddle框架的安装教程，详见[PaddlePaddle官方网站](http://www.paddlepaddle.org/#quick-start)。 + +### 安装代码 +克隆工具集代码库到本地 + +```shell +git clone https://github.com/PaddlePaddle/models.git +cd models/PaddleNLP/reading_comprehension/ +``` + + +### 下载第三方依赖 +在本基线系统中，我们采用了Bleu以及Rouge-L指标作为模型的评估标准。这些指标的计算脚本位于"https://github.com/tylin/coco-caption"，可以通过运行以下命令进行下载 + +``` +cd utils && bash download_thirdparty.sh +``` + +## 运行 + +### 下载数据集以及模型 +为了方便开发者进行测试，我们提供了预处理(分词、计算answer span等)过后的DuReader 2.0数据集、训练好的模型参数以及词表。通过运行以下命令即可下载： + +``` +cd data && bash download.sh +``` +此外，用户还可以利用[paddlehub](https://github.com/PaddlePaddle/PaddleHub)的方式下载模型参数，例如 + +``` +hub download dureader_machine_reading-bidaf +``` + +### 段落抽取 +我们采用了一种新的段落抽取策略以提升模型在DuReader 2.0数据集上的表现(策略内容详见src/UPDATES.md)。该段落抽取策略可通过运行以下命令执行: + +``` +sh run.sh --para_extraction +``` + +请注意，在运行上面命令之前，需要先下载预处之后的DuReader 2.0数据 (见”下载数据集以及模型“章节)。段落抽取得到的结果会存放在 +`data/extracted/`文件夹中。 + +### 评估 +通过运行以下命令，开发者可以利用上面提供的模型在DuReader 2.0验证集进行评估： + +``` +sh run.sh --evaluate --load_dir ../data/saved_model --devset ../data/extracted/devset/zhidao.dev.json ../data/extracted/devset/search.dev.json +``` +在评估结束后，程序会自动计算ROUGE-L指标并显示最终结果。 + + +### 预测 +通过运行以下命令，开发者可以利用上面提供的模型在DuReader 2.0测试集进行预测： + +``` +sh run.sh --predict --load_dir ../data/saved_model --testset ../data/extracted/testset/zhidao.test.json ../data/extracted/testset/search.test.json +``` +模型预测的答案将被保存在`data/results`文件夹中。 + + +### 训练 +如果开发者希望重新训练模型参数，可以参考本章节步骤。 + +在模型训练开始之前，需要先运行以下命令来生成词表以及创建一些必要的文件夹，用于存放模型参数等： + +``` +sh run.sh --prepare --trainset ../data/extracted/trainset/zhidao.train.json ../data/extracted/trainset/search.train.json --devset ../data/extracted/devset/zhidao.dev.json ../data/extracted/devset/search.dev.json --testset ../data/extracted/testset/zhidao.test.json ../data/extracted/testset/search.test.json +``` +建立好的词表会存放在`data/vocab`文件夹中。 + +然后运行下面的命令，即可开始训练: + +``` +sh run.sh --train --pass_num 5 --trainset ../data/extracted/trainset/zhidao.train.json ../data/extracted/trainset/search.train.json --devset ../data/extracted/devset/zhidao.dev.json ../data/extracted/devset/search.dev.json +``` +以上参数配置会对模型进行5轮训练，并在每轮结束后利用验证集自动进行评估。每轮过后，程序会自动将模型参数保存到`data/models`文件夹当中，并以该轮的ID命名。 + +如果开发者需要改变模型训练时的超参数， 例如初始学习率、隐层维度等，可以通过指定以下参数来实现： + +``` +sh run.sh --train --pass_num 5 --learning_rate 0.00001 --hidden_size 100 --trainset ../data/extracted/trainset/zhidao.train.json ../data/extracted/trainset/search.train.json --devset ../data/extracted/devset/zhidao.dev.json ../data/extracted/devset/search.dev.json +``` + +更多参数配置可在`paddle/args.py`中找到。 + + +### 提交测试集结果 +当开发者通过调参、修改模型结构得到更好的结果后，可以将DuReader 2.0测试集的预测结果提交到[官网](http://ai.baidu.com/broad/submission?dataset=dureader)来进行评测。在提交结果之前，请确保以下几点： + +1. 训练已经全部结束； +2. 通过训练日志在`data/models`文件夹中选择在验证集表现最佳的模型； +3. 通过上面章节描述的方法在测试集上进行预测，并得到完整结果。 + + +# 进阶使用 +## 任务定义与建模 +阅读理解任务的输入包括： + + - 一个问题Q (已分词)，例如：["明天"， "的", "天气", "怎么样", "？"]； + - 一个或多个段落P (已分词)，例如：[["今天", "的", "天气", "是", "多云", "转", "晴", "，", "温度", "适中", "。"], ["明天", "气温", "较为", "寒冷", "，", "请", "注意", "添加", "衣物", "。"]]。 + +模型输出包括： + + - 段落P中每个词是答案起始位置的概率以及答案结束位置的概率 (boundary model)，例如：起始概率=[[0.01, 0.02, ...], [0.80, 0.10, ...]]，结束概率=[[0.01, 0.02, ...], [0.01, 0.01, ...]]，其中概率数组的维度和输入分词后的段落维度相同。 + +模型结构包括： + + - 嵌入层 (embedding layer)：输入采用one-hot方式表示的词，得到词向量； + - 编码层 (encoding layer)：对词向量进行编码，融入上下文信息； + - 匹配层 (matching layer)：对问题Q和段落P之间进行匹配； + - 融合层 (fusion layer)：融合匹配后的结果； + - 预测层 (output layer)：预测得到起始、结束概率。 + +## 模型原理介绍 +下图显示了原始的[BiDAF](https://arxiv.org/abs/1611.01603)模型结构。在本基线系统中，我们去掉了char级别的embedding，在预测层中使用了[pointer network](https://arxiv.org/abs/1506.03134)，并且参考了[R-NET](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf)中的一些网络结构。 +

+
+

+ + +## 数据格式说明 +DuReader数据集中每个样本都包含若干文档(documents)，每个文档又包含若干段落(paragraphs)。有关数据的详细介绍可见[官网](https://ai.baidu.com//broad/subordinate?dataset=dureader)、[论文](https://arxiv.org/abs/1711.05073)以及数据集中包含的说明文件，下面是一个来自训练集的样本示例 + +``` +{ + "documents": [ + { + "is_selected": true, + "title": "板兰根冲剂_百度百科", + "most_related_para": 11, + "segmented_title": ["板兰根", "冲剂", "_", "百度百科"], + "segmented_paragraphs": [ + ["板兰根", "冲剂", "，", "药", "名", ":", ... ], + ["【", "功效", "与", "主治", "】", ...], + ... + ], + "paragraphs": [ + "板兰根冲剂，药名...", + "【功效与主治】...", + ... + ], + "bs_rank_pos": 0 + }, + { + "is_selected": true, + "title": "长期喝板蓝根颗粒有哪些好处和坏处", + "most_related_para": 0, + "segmented_title": ["长期", "喝", "板蓝根", "颗粒", "有", "哪些", "好处", "和", "坏处"], + "segmented_paragraphs": [ + ["板蓝根", "对", "感冒", "、", "流感","、", ...], + ... + ], + "paragraphs": [ + "板蓝根对感冒、流感、流脑、...", + ... + ], + "bs_rank_pos": 1 + }, + ... + ], + "answer_spans": [[5, 28]], + "fake_answers": ["清热解毒、凉血;用于温热发热、发斑、风热感冒、咽喉肿烂、流行性乙型脑炎、肝炎、腮腺炎。"], + "question": "板蓝根颗粒的功效与作用", + "segmented_answers": [ + ["清热解毒", "、", "凉血", "；", "用于", "温", "热", "发热", ...], + ["板蓝根", "的", "用途", "不仅", "是", "治疗", "感冒", ...], + ... + ], + "answers": [ + "清热解毒、凉血；用于温热发热、发斑、风热感冒、咽喉肿烂、流行性乙型脑炎、肝炎、 腮腺炎 。", + "板蓝根的用途不仅是治疗感冒，板蓝根的功效与作用多，对多种细菌性、病毒性疾病都有较好的预防与治疗作用。", + ... + ], + "answer_docs": [0], + "segmented_question": ["板蓝根颗粒", "的", "功效", "与", "作用"], + "question_type": "DESCRIPTION", + "question_id": 91161, + "fact_or_opinion": "FACT", + "match_scores": [ + 0.9583333333333334 + ] +} +``` + + +## 目录结构 + +```text +. +├── data # 包含数据、词表、模型 (需下载) +├── README.md # 本文档 +├── src # 源码 +└── utils # 包含评测脚本 (需下载) +``` + +## 如何组建自己的模型 +开发者可以通过修改src/rc_model.py中的网络结构来组建自己的模型。 + +# 其他 + +## Copyright and License +Copyright 2017 Baidu.com, Inc. All Rights Reserved + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +## 如何贡献代码 + +我们欢迎开发者向DuReader基线系统贡献代码。如果您开发了新功能，发现了bug……欢迎提交Pull request与issue到Github。 diff --git a/PaddleNLP/reading_comprehension/data/BiDAF.png b/PaddleNLP/reading_comprehension/data/BiDAF.png new file mode 100644 index 0000000000000000000000000000000000000000..37ec1f5d39acc7993619385b1198a76ba5994b1d Binary files /dev/null and b/PaddleNLP/reading_comprehension/data/BiDAF.png differ diff --git a/PaddleNLP/reading_comprehension/data/demo/devset/search.dev.json b/PaddleNLP/reading_comprehension/data/demo/devset/search.dev.json new file mode 100644 index 0000000000000000000000000000000000000000..7a2f949bf95e3f05338a4a661a2c8fbf44094186 --- /dev/null +++ b/PaddleNLP/reading_comprehension/data/demo/devset/search.dev.json @@ -0,0 +1,100 @@ +{"documents": [{"is_selected": true, "title": "2017年好看小说排行榜_小说推荐吧_百度贴吧", "most_related_para": 0, "segmented_title": ["2017", "年", "好看", "小说", "排行榜", "_", "小说", "推荐", "吧", "_", "百度贴吧"], "segmented_paragraphs": [["2017", "年", "好书", "排行榜", "。", "读书", "十", "几", "年", ",", "晒", "晒", "让", "我", "沉醉", "的", "那些", "书单", "本", "书单", "没有", "主次", ",", "不要", "在意", "序号", "。", "1", ".", "《", "将夜", "》", "作者", ":", "猫腻", "(", "起点", "白金", "作家", ")", "简介", ":", "与天斗", ",", "其乐无穷", "。", "故事", "概要", ":", "主角", "宁缺", "带", "着", "从", "死人", "堆", "中", "挖", "出来", "的", "小", "侍女", "桑桑", ",", "报仇雪恨", "的", "故事", "。", "推荐理由", ":", "一", ".", "文笔", "牛逼", ":", "猫腻", "的", "文", "读者", "很舒服", ",", "不喜欢", "小白文", "的", "人", "无法", "错过", "的", "好书", "。", "二", ".", "剧情", "牛逼", ":", "猫腻", "对", "文章", "的", "构思", "非常", "巧妙", ",", "不", "看到", "最后", ",", "你", "根本", "不知道", "结局", "会", "怎样", "。", "三", ".", "人物", "牛逼", ":", "莫山山", "与", "叶红鱼", ",", "是", "故事", "中", "宁缺", "的", "两位", "红颜知己", "。", "四", ".", "最后", "她们", "没有", "和", "宁缺", "走", "在一起", ",", "是", "一", "件", "自", "认为", "很遗憾", "的", "事", "。", "(", "嗯", "…", "…", "这", "是", "以", "男人", "的", "角度", "。", ")", "无法", "让", "人", "忘怀", "的", "地方", ":", "书院", "。", "书院", "有", "夫子", ",", "喜欢", "书", "中", "一句话", ",", "世间", "无", "夫子", ",", "万古", "如", "长夜", "。", "(", "希望", "没有", "记", "错", "。", ")", "书院", "有", "大师兄", ",", "大师兄", "李慢慢", "为人", "仁爱", ",", "曾", "因为", "君子", "可以", "欺", "之", "以", "方", "困扰", ",", "后", "在", "宁缺", "受到", "危险", "时", ",", "终于", "学会", "了", "杀人", "。", "书院", "有", "二师兄", ",", "二师兄", "君", "陌", ",", "一直", "以", "小", "师叔", "为", "榜样", "的", "君陌", ",", "一直", "想", "把", "号称", "天下第一", "剑", "的", "柳", "白", "斩", "于", "剑", "下", "。", "书院", "有", "三", "师姐", ",", "三", "师姐", "余帘", ",", "额", "…", "…", "突然想到", "既然", "是", "推", "书", ",", "就", "不能", "写", "太", "详细", ",", "毕竟", "透", "剧", "会", "被", "人", "弹", "丁丁", "。", "咳咳", "总而言之", ",", "这", "是", "一", "本", "不容错过", "的", "好书", "。", "2", ".", "《", "择天记", "》", "作者", ":", "猫腻", "too", "(", "嗯", "…", "…", "别误会", ",", "我", "只是", "想", "表达", "也是", "猫腻", "。", ")", "相对", "将夜", ",", "这", "是", "一", "本", "正在", "写", "的", "新书", ",", "值得一看", "。", "最近", "还在", "影视", "改编", ",", "据说", "主角", "陈长生", ",", "饰演", "是", "鹿晗", ",", "我", "也", "很", "喜欢", "鹿晗", "的", "小", "清新", ",", "但是", "我", "觉得", "演", "陈长生", "气质", "方面", "两种", "美", ",", "有点", "毁", "剧", ",", "当然", "还", "没", "上映", ",", "如此", "谈论", "未免", "为", "时尚", "早", "。", "3", ".", "《", "冒牌大英雄", "》", "作者", ":", "七", "十", "二", "编", "。", "这本书", ",", "猥琐", "流", "的", "神", "书", ",", "背景", "机甲", "宇宙", "什么", "的", "。", "写", "这本书", "的", "作者", "一", "本", "封神", ",", "所以", "这本书", "真", "的", "非常", "牛逼", "。", "而且", "这本书", "已经", "完本", "。", "喜欢", "猥琐", "流", "的", ",", "本人", "强烈推荐", "看", "这本书", ",", "超赞", "。", "4", ".", "《", "无限恐怖", "》", "作者", ":", "zhttty", "无限", "流", "开山", "之", "祖", "的", "神", "书", "。", "喜欢", "无限", "流", "的", ",", "不容错过", "。", "5", ".", "《", "恐怖", "搞", "校", "》", "作者", ":", "大宋", "红", "福", "坊", "这本书", "将", "无限", "流", "和", "校园", "相结合", ",", "非常", "有创意", "。", "6", ".", "《", "大国医", "》", "作者", ":", "美味", "宋罗汤", "这", "是", "一", "部", "历史", "类", "的", "书", ",", "也是", "我", "唯一", "看", "过", "的", "历史", "类", "。", "当然", "如果", "《", "寻秦记", "》", "算", "历史", "类", "的", ",", "那么", "这", "本", "是", "第", "二", "本", "。", "讲述", "主角", "洗", "了", "糊涂", "成为", "一", "代", "国", "医", "的", "故事", "。", "文风", "幽默风趣", ",", "很好看", "。", "红颜知己", "也", "多", ",", "啧", "啧", "。", "7", ".", "《", "龙", "魔导", "》", "作者", ":", "想", "屠神", "的", "疯子", "作品", "简洁", ":", "什么", "?", "你", "要", "考", "哈弗", "?", "当", "其他", "童鞋", "正", "忙", "备战高考", "的", "时候", "。", "李山", "已经", "学会", "了", "精神力", "的", "发散", "运用", "。", "李山", ":", "“", "高考", "很难", "么", "?", "这么多", "答案", "让", "我", "筛选", "…", "…", "”", "什么", "?", "你", "要", "结婚", "了", "?", "当", "其他", "童鞋", "正", "为", "求", "得", "白富美", "沾沾自喜", "时", "。", "李山", "在", "头疼", ",", "今天", "去", "找", "精灵", "女友", "还是", "猫女", "又", "或者", "…", "…", "李", "山", ":", "“", "不要", "这么", "看着我", ",", "我", "很", "纯洁", "的", ",", "就", "拉拉", "小", "手", "。", "”", "故事", "概述", ":", "主角", "李山", "在", "一款", "叫做", "凯撒", "大陆", "的", "游戏", "中", "赢得", "了", "一", "台", "虚拟现实", "游戏", "仓", "。", "但是", "却", "发现", "使用", "这", "台", "游戏", "仓", "后", ",", "在", "游戏", "中", "获得", "的", "属性", "加成", "竟然", "影响", "着", "现实", "世界", "。", "后来", "在", "主角", "的", "一", "再", "证实", "下", ",", "终于", "证明", "他", "参加", "的", "并", "不是", "游戏", ",", "而是", "另一个", "现实", "世界", "。", "至此", ",", "主角", "潇洒", "的", "一生", "就", "此", "起航", "。", "推荐理由", ":", "1", ".", "文笔", "不错", ",", "刻画", "生", "动", ",", "有", "将夜", "之", "风", "。", "2", ".", "人物", "不错", ",", "主角", "是", "一", "位", "拥有", "正义感", "的", "猥琐男", "。", "(", "丫", "的", "这个", "评论", "我", "笑", "了", "。", ")", "3", ".", "剧情", "不错", ",", "进展", "有些", "慢", ",", "但是", "也有", "一", "种", "娓娓道来", "的", "感觉", ",", "很喜欢", "。", "先", "推荐", "这么多", "(", "未完待续", ")"], ["大家", "喜欢", "这个", "书单", "的", "话", "就", "顶", "高高", ",", "我", "继续", "更新", ",", "哇哈哈", "哈", "。", "(", "还有许多", "珍藏", "的", "说", "~", ")"], ["我靠", ",", "我", "怎么", "看到", "写", "的", "书", "了", ",", "楼主", "慧眼", "如", "炬", ",", "我", "也", "觉得", "《", "将夜", "》", "写的不错", "。"], ["书名", ":", "《", "第", "十三", "座", "消失", "的", "城", "》", "作者", ":", "城", "渡", "文案", ":", "她", "是", "瘾", "城", "的", "守护者", ",", "也是", "半生", "花店", "的", "甩手掌柜", ",", "她", "带", "着", "花店", "四处", "游历", ",", "走", "走", "停停", ",", "为", "瘾城", "招揽", "能", "人", "异士", ",", "也", "借此", "从", "无名", "转", "而", "盛名", ",", "想起", "了", "万年", "前", "的", "记忆", ",", "重", "遇", "了", "那", "方", "幽静", "。", "她", "的", "半生", "花店", "有", "无数", "世间", "买不到", "或", "不曾", "见过", "的", "花", "种", ",", "她", "的", "花", "只", "卖给", "有缘人", "。", "来到", "她", "花店", "的", "人", "从不知", "她", "叫", "什么", ",", "只是", "世", "人", "皆", "称", "小妖", ",", "便", "以", "小妖", "之", "名", "而", "盛", ",", "皆", "不知", "她", "本为妖", ",", "数", "以", "万年", "。", "她", "总是", 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书院有夫子,喜欢书中一句话,世间无夫子,万古如长夜。(希望没有记错。) 书院有大师兄,大师兄李慢慢为人仁爱,曾因为君子可以欺之以方困扰,后在宁缺受到危险时,终于学会了杀人。 书院有二师兄,二师兄君陌,一直以小师叔为榜样的君陌,一直想把号称天下第一剑的柳白斩于剑下。书院有三师姐,三师姐余帘,额……突然想到既然是推书,就不能写太详细,毕竟透剧会被人弹丁丁。咳咳总而言之,这是一本不容错过的好书。 2.《择天记》 作者:猫腻too(嗯……别误会,我只是想表达也是猫腻。) 相对将夜,这是一本正在写的新书,值得一看。 最近还在影视改编,据说主角陈长生,饰演是鹿晗,我也很喜欢鹿晗的小清新,但是我觉得演陈长生气质方面两种美,有点毁剧,当然还没上映,如此谈论未免为时尚早。 3.《冒牌大英雄》 作者:七十二编。这本书,猥琐流的神书,背景机甲宇宙什么的。写这本书的作者一本封神,所以这本书真的非常牛逼。而且这本书已经完本。喜欢猥琐流的,本人强烈推荐看这本书,超赞。 4.《无限恐怖》 作者:zhttty 无限流开山之祖的神书。 喜欢无限流的,不容错过。 5.《恐怖搞校》作者:大宋红福坊这本书将无限流和校园相结合,非常有创意。 6.《大国医》 作者:美味宋罗汤 这是一部历史类的书,也是我唯一看过的历史类。 当然如果《寻秦记》算历史类的,那么这本是第二本。 讲述主角洗了糊涂成为一代国医的故事。 文风幽默风趣,很好看。 红颜知己也多,啧啧。 7.《龙魔导》 作者:想屠神的疯子 作品简洁:什么?你要考哈弗? 当其他童鞋正忙备战高考的时候。 李山已经学会了精神力的发散运用。李山:“高考很难么?这么多答案让我筛选……” 什么?你要结婚了? 当其他童鞋正为求得白富美沾沾自喜时。 李山在头疼,今天去找精灵女友还是猫女又或者…… 李山:“不要这么看着我,我很纯洁的,就拉拉小手。”故事概述:主角李山在一款叫做凯撒大陆的游戏中赢得了一台虚拟现实游戏仓。但是却发现使用这台游戏仓后,在游戏中获得的属性加成竟然影响着现实世界。后来在主角的一再证实下,终于证明他参加的并不是游戏,而是另一个现实世界。至此,主角潇洒的一生就此起航。 推荐理由:1.文笔不错,刻画生动,有将夜之风。2.人物不错,主角是一位拥有正义感的猥琐男。(丫的这个评论我笑了。)3.剧情不错,进展有些慢,但是也有一种娓娓道来的感觉,很喜欢。 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",", "程", "举", "等", "许多", "个", "表哥", "…", "…", "这", "是", "个", "我", "与", "程", "家", "不得不说的故事", "!", "只", "看", "内容简介", "便", "知道", "此", "书", "又", "是", "一", "个", "重生", "的", "故事", ",", "重生", "的", "故事", "不管", "如何", "描述", "总是", "引来", "好奇", "的", "读者", "一", "观", "。"]], "paragraphs": ["近年来,穿越小说、穿越剧以时尚、前卫的姿态进入到公众的视野当中,并且席卷了中国的电视荧屏,形成一股强烈的“穿越”之风。这么多的穿越小说中,好看的完结穿越小说有哪些?以下是小编推荐给大家的关于2017最好看的穿越小说,来看看有没有你喜欢的吧!", "一: 【我们住在一起】 投资银行的简称,并不是真的银行,是券商的说,一般就是给一些发展还不错的公司做做上市啦、再融资啦、发行债券啦、并购重组啦之类的玩意儿,包括了北漂,合租,职场(投行),隐身富二代,女菜鸟变业务精英等职业。 二: 【月都花落,沧海花开】 作者:君子以泽 此书古风韵味十足,像是一部“诗作”。结局中性,不喜也不悲,却让人哭得眼肿。明月和沧海相互倾慕,却只能遥遥相望,真真让人唏嘘。薇薇最后闭眼前看到自己变年轻又见到师尊的幻境,兴许也是薇薇和胤泽互相的思念而产生的。期待薇薇胤泽来世再续前缘,期待沧海的花快开。 三: 【天定风华】 作者:天下归元 【天定风华】1、2、3讲述的是君珂的故事,又名【天定风流】,【千金笑】。【天定风华】4、5、6讲述的是太史阑的故事,又名【凤倾天阑】。君珂与太史阑为好友,同时穿越,降落地点不同,穿越后10年两人才相遇。典型的穿越剧,精彩又好看。 四: 【寻找爱情的邹小姐】 作者: 匪我思存 重量级言情作家匪我思存的大作【寻找爱情的邹小姐】延续其一贯的“虐恋”风格,讲述了一堆都市爱人的苦情恋。作品中的故事经历跌宕起伏,主人公更是命运多舛,其中的爱情故事更是有很多传奇的结局。每一对恋人的爱情都是不一样的,都是独一无二的,每个人只有在自己的感情里才能有自己真实的感受。只要心中有爱,爱就无处不在。就像书中表达的,我们为了爱奔波劳碌,甚至终其一生的追求爱情,然而在百转千回、峰回路转之后,我们期许的爱情其实一直在那里。", "五: 【应许之日】", "作者: 辛夷坞", "【应许之日】是以封澜这一大龄女起伏不断的生活为主线展开的描述。封澜和她的男友分手之前夕迎来的是男友和其他女人的结婚请柬。然而作为一个餐厅的老板娘,封澜竟然喜欢上餐厅里的服务员丁小野,然而丁小野也不爱她,他是一个猎手,亲手捕获鲜活的猎物,封澜就是他的猎物。而读完【应许之日】,并没有阴险,没有所谓的捕获,而是有一种很暖的感觉。作者辛夷坞尝试过多种风格的写作,而此部【应许之日】更是一部献给每个为爱执着人的一大力作。", "六: 【星光的彼端】", "作者: 青罗扇子", "范冰冰说,一个人的诋毁和赞美同样多;千颂伊,不会陷入嫉妒的深渊,不会自己落水了还要拉上别人共同踏入泥潭。深谙电影与文学中国女作家青罗扇子,文风以大气励志,时尚深刻,【星光的彼端】也正是延续了这样的文风,并且文中多体现的是如何真正理解爱,如何去爱别人。文中主人公宋微与黄锦立两人之间爱恨纠结,涉及名利、事业、女性自立自强。“一个女人只会伤心三次。第一次,她想要的是他的爱,但不被公平对待;第二次,她退而求其次,只想要一份尊重,却被践踏努力;到了第三次,她已经什么都想不要了——因为这次,她只为自己加冕为王。多么富有哲理。一个不懂得如何去爱的人,是否还能有机会再爱一次?青罗扇子说:有时爱一个人,不是因为他给了你幻想,而是你明白了自己的尊严与骄傲。", "七: 【他来了,请闭眼】", "在继【如果蜗牛有爱情】之后,此部作品是丁墨再次创作出的超人气甜蜜爱情故事,作为网络写手,他独特的甜宠悬爱风格吸引了一大批读者,其文思巧妙、其开篇就给人一种【神探夏洛克】的感觉,因为薄靳言和傅子遇丝毫不逊于卷福和花生。作品中对大神薄小猫才华横溢与智慧过人的描写以及其骄傲、盛气凌人的描写固然入木三分,情绪与性格等内在描写非常到位,但是,在看过作品后最让人动容的是大神对瑶瑶毫无保留的、纯真热烈的、至死不渝的爱。", "八: 【何以笙箫默】", "电视剧的热播让这部剧面向了更多人群,此剧的原著小说感觉名字深奥而又极富玄机,蕴含着何以琛对默笙没有说出口的爱恋。在小说里,默笙的名字来自徐志摩的【再别康桥】 ,而默笙,可理解为沉默的箫笙,也代表着默默的爱意。小说从头看到尾或许会感觉这个故事很老套,但是却真正体现了有些事情只能意会不能言传。只有真正理解了主人公之间的感情,才觉得即便老套的故事也能体现出不一样的内心深处的感情。", "九: 【岁月是朵双生花】", "作者: 唐七公子", "读唐七的小说,往往在一开始找不到感觉,似乎它并不是多么的引人入胜,还感觉作者的描写非常啰嗦,但是,伴随着阅读的深入却会感觉此书越来越有意思,是一部非常不错的作品。该小说通过讲述女主角怀孕以后,由于车祸问题失忆了的故事。女主人公在失忆以后有了新的喜欢的人,但并没有得到。她有一个非常可爱的孩子,但是在发生车祸失忆之后却不记得孩子的爸爸。经过很多波折,小说有了一个完满的结局那便是主人公原来的爱人和记忆都回来了。", "十: 【夏有乔木 雅望天堂】", "籽月总是把爱情写得如此之抵死纠缠,甚至感觉残忍不堪,正因为其残忍,其结局的不完美,才容易引起人们的深思。在小说中,每一个人都付之一炬,最后全部坍塌。正因为此,所谓的爱坍塌了、信仰坍塌了,甚至付出了生命。一直以来,籽月作为国内一线青春畅销书的新锐作家,最擅长用最朴实的文字来塑造丰满的人物形象,她让我们感觉青春要用最真挚的情感来纪念。此小说非常值得一遍遍的去阅读,因为每一遍都有不同的感受,甚至读过后的感动会让人泣不成声。", "十一: 【忘川】", "此时在经过作者时隔六年的创作之后中终于完结,这是一部持续了六年的武林传奇,而作者顶着的“武侠言情天后”的标签,笑言:“纯粹是为了图书上架时方便分类而已。【忘川】承载了太多作者与读者的期待,该作品的诞生也极具戏剧性。", "十二: 【满满都是我对你的爱】", "作者: 顾西爵", "【满满都是我对你的爱】里的一些小段子便足以让网友为之疯狂,被万千网友誉为\"最萌最有爱,百看而不厌\"。【满满都是我对你的爱】最早是在2013年7月中旬第一次登载,便产生了超强的口碑效应,所以辛夷坞在读了本书后欢笑推荐,和安东尼【这些都是你给我的爱】一样温暖有爱。", "十三: 【欢喜记】", "作者: 石头与水", "与【千金记】是姐妹篇,此小说说明了一个很重要的道理便是可怜之人必有可恨之处。因为憋屈而死的赵长卿在重生之际,下定决心决定不再活的那样令人可怜可恨了。此文依然是深受读者喜爱的重生文,依然是以东穆江山为背景。", "十四: 【金陵春】", "周少瑾重生了,前世背叛她的表哥程辂自然被三振出局了,可她还有程许,程诣,程举等许多个表哥……这是个我与程家不得不说的故事!只看内容简介便知道此书又是一个重生的故事,重生的故事不管如何描述总是引来好奇的读者一观。"], "bs_rank_pos": 4}, {"is_selected": true, "title": "2017年第一季,超好看的小说大盘点", "most_related_para": 16, "segmented_title": ["2017", "年", "第一季", ",", "超", "好看", "的", "小说", "大盘点"], "segmented_paragraphs": [["文章", "阅读"], ["2017", "年", "的", "第", "一季度", "刚刚", "过去", ",", "大家", "在", "这", "三个月", "里", "有", "读", "到", "什么", "好书", "吗", "?", "培根", "曾", "说过", ":", "“", "孤独寂寞", "时", ",", "阅读", "可以", "消遣", "。", "高谈阔论", "时", ",", "知识", "可", "供", "装饰", "。", "处世行", "事", "时", ",", "知识", 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"一年的52周里,即使每周坚持阅读一本书,总共也不过52本而已,更遑论现代人一直有着拖延症的通病,我们阅读的书籍实在太少了。在浩如烟海的出版物之中,小编为你精心挑选了几本极富趣味性、又不乏内涵的小说,希望能够让各位在有限的阅读时间里享受到更多的乐趣。", "《大唐悬疑录:长恨歌密码》 by:唐隐", "作品简介:杨贵妃究竟死于马嵬驿,还是逃生日本?", "大诗人白居易根据杨贵妃的秘闻写就名篇《长恨歌》,不想竟引来杀身之祸。各路僧、道、官、匪先后出动,齐齐盯上了《长恨歌》中的明文暗码……", "推荐理由:《大唐悬疑录》系列中,女神探裴玄静踏入了一个关乎国家兴衰的阴谋杀局之中,她历经艰辛,只为找到最终的真相。她曾说“身为大唐的子民,我知道大唐的荣光从来不是幻觉。我相信,并且愿意用生命去维护它”。", "这个故事中有为了天下人至死不渝的信仰,有为了爱人出生入死的爱情,有为了友人以一己之力对抗朝廷的大义,也有一个繁华落尽、残阳如血的大唐,推理解谜的同时,也展现了说不尽的人间百态、道不完的苦乐辛酸。", "《风雪追击》 by:东野圭吾", "作品简介:以暴风雪中的滑雪场为背景,讲述一个突然被指认为杀人凶手的男孩,为了洗清嫌疑而进行自救的故事。作者将缜密的故事逻辑与快节奏的滑板运动结合起来,带来一种奇特的、充满紧张感的阅读体验。没有血腥暴力,却让人陷入人性的思考,直戳人心:如果有一天,毫无征兆地被指控为凶手,你该怎么办?", "推荐理由:东野圭吾毫无争议是中国读者最为喜爱的悬疑大师,但看图书销售网站排行榜上永远挂着他一本又一本的小说便可下此结论。也许东野圭吾的文笔不是最佳,也许他的推理剧情显得过于简单,但他着实是一个讲故事的高手,他的书简单易看,真真正正称得上是让人想一口气看完的小说,无疑会给读者带来畅快淋漓的阅读体验。", "《草原动物园》 by:马伯庸", "作品简介:这是一位传教士带着“半个动物园”勇闯蒙古草原的奇幻故事。光绪末年,在京城的美国传教士柯罗威,突发奇想要去赤峰修建一座草原动物园。他带着雄狮、大象、鹦鹉、蟒蛇以及一对虎纹马、五只狒狒一起奔赴草原。草原上,盗梦少女预知未来、通晓动物语言的少年涅槃重生、神秘的狼变月夜现身。这是一部关于信仰的动物寓言,一场巨大的变革在勇士之地上演,一段史诗般的全新冒险自此展开……", "推荐理由:这是一场奇幻的梦境。梦境被心理学家视为对现实的逃避与反击,其本身仿佛就带着某种奇幻又虚无缥缈的意味,梦境也是文人想象力的源泉,在梦境之上构筑起的小说王国亦能够带领我们逃离现实世界。这种描写寻梦之旅的小说外国有《牧羊少年奇幻之旅》,我们有马伯庸的新书《草原动物园》,且看看马伯庸如何带我们走入这场梦境。", "《有匪2:离恨楼》 by:priest", "作品简介:周翡没有等来段九娘,却一头撞上青龙主郑罗生。\"南北双刀\"传人、\"山川剑\"后人、芙蓉神掌……竟全在衡山脚下这一间小小的\"三春客栈\"凑齐了。\"山川剑\"亡故,一把剑鞘却被多方争夺;忠武将军死后,家眷南渡时遭北斗追杀;齐门生变,至今下落不明;而衡山这样大的门派,竟也人去楼空……然而洗墨江边冲天的火光,已经让周翡来不及细想这一切了。二十年了,风雨飘摇的夹缝里,这一隅的桃源,真能长久吗?", "推荐理由:也许由于对坚强勇敢的女孩子的偏爱,《大唐悬疑录》中的裴玄静与《有匪》中的周翡都曾让小编为之欢呼与落泪。这是一个江湖故事,也是一个浑身傲骨的女孩子如何在乱世之中摸爬滚打的故事。那个曾经不知天高地厚的水草精,终究长成了一个鬓似鸦羽、眉目宛然的红衣女侠,也终是在冷铁卷刃前得以窥见了天光。", "声明:本文由入驻搜狐公众平台的作者撰写,除搜狐官方账号外,观点仅代表作者本人,不代表搜狐立场。"], "bs_rank_pos": 6}, {"is_selected": false, "title": "2017年最好看的小说。推荐下。_百度知道", "most_related_para": 0, "segmented_title": ["2017", "年", "最", "好看", "的", "小说", "。", "推荐", "下", "。", "_", "百度", "知道"], "segmented_paragraphs": [["女人", "30", "+", "保鲜", "攻略", "作者", ":", "蓝", "一", "薰", "简介", ":", "有人", "说", ",", "30", "几", "岁", "是", "女人", "最具", "风采", "、", "最", "懂", "生活", "的", "年龄", "。", "因为", "她们", "大", "多", "已经", "为", 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["爬行垫根据中间材料的不同可以分为:XPE爬行垫、EPE爬行垫、EVA爬行垫、PVC爬行垫;其中XPE爬行垫、EPE爬行垫都属于PE材料加保鲜膜复合而成,都是无异味的环保材料,但是XPE爬行垫是品质较好的爬行垫,韩国进口爬行垫都是这种爬行垫,而EPE爬行垫是国内厂家为了减低成本,使用EPE(珍珠棉)作为原料生产的一款爬行垫,该材料弹性差,易碎,开孔发泡防水性弱。EVA爬行垫、PVC爬行垫是用EVA或PVC作为原材料与保鲜膜复合的而成的爬行垫,或者把图案转印在原材料上,这两款爬行垫通常有异味,如果是图案转印的爬行垫,油墨外露容易脱落。 当时我儿子爬的时候,我们也买了垫子,但是始终有味。最后就没用了,铺的就的薄毯子让他爬。", "您好,爬行垫一般色彩鲜艳,能吸引宝宝的注意力,当宝宝刚会爬的时候,趴在上面玩,相对比较安全,不存在从床上摔下来的危险。对宝宝的爬行还是很有好处的。还有就是妈妈选择爬行垫时可以选择无害的PE棉,既防潮又隔冷隔热。外有要有一层塑料膜,能隔绝液体进入垫子内部,而且方便清洗。宝宝每次爬行,一定要记得把宝宝的手擦干净。", "棉的不好吧,吸灰尘,容易有螨虫,爬行垫好多种,PVC的比较好一些,选择环保一些的,容易擦洗的,弹性好的,不要选太厚的,5mm-8mm左右的,太厚了宝宝容易摔跤,尤其是学走路的宝宝。安全性第一,看产品的证书有没有权威机构的检验,重金属和甲醛含量是重要考虑因素。", "你宝宝什么时候出生啊。一般的小孩要四个月以后才开始爬。如果你宝宝四个月的时候是冬天了,根本就不用爬行垫了,多冷啊。到10个月左右,宝宝有欲望学习站立了,就根本不会在爬行垫上耍了", "PVC的比较好一些,但是PVC也分等级的,选质量好的,弹性好的", "有一款,可以推荐,艾高,德国的牌子。。你可以去看看。。", "您好,爬行垫一般色彩鲜艳,能吸引宝宝的,当宝宝刚会爬的时候,趴在上面玩,相对比较安全,不存在从床上摔下来的危险。对宝宝的爬行还是很有好处的。还有就是妈妈选择爬行垫时可以选择无害的PE棉,既防潮又隔冷隔热。外有要有一层塑料膜,能隔绝液体进入垫子内部,而且方便清洗。宝宝每次爬行,一定要记得把宝宝的手擦干净。"], "bs_rank_pos": 0}, {"is_selected": false, "title": "宝宝爬行垫什么材质最好_百度知道", "most_related_para": 0, "segmented_title": ["宝宝", "爬行垫", "什么", "材质", "最好", "_", "百度", "知道"], "segmented_paragraphs": [["爬行垫", "我", "觉得", "还是", "泡沫", "的", "材质", "会", "更好", "一些", "的", "哦", ",", "这样", "宝宝", "摔倒", "的", "时候", "就", "不容易", "摔倒", "宝宝", "的", "哦", ",", "更安全", "一些", "的", "。"], ["类似", "瑜伽垫", "那种"]], "paragraphs": ["爬行垫我觉得还是泡沫的材质会更好一些的哦,这样宝宝摔倒的时候就不容易摔倒宝宝的哦,更安全一些的。", "类似瑜伽垫那种"], "bs_rank_pos": 1}, {"is_selected": false, "title": "【爬爬垫】爬爬垫什么材质好 四种材质爬爬垫对比_知识频道_品牌网", "most_related_para": 27, "segmented_title": ["【", "爬爬垫", "】", "爬爬垫", "什么", "材质", "好", "四种", "材质", "爬爬垫", "对比", "_", "知识", "频道", "_", "品牌", "网"], "segmented_paragraphs": [["摘要", ":", "婴儿", "爬爬垫", "起源于", "日韩", ",", "并", "在", "日韩", "市场", "上", "趋于", "成熟", ",", "中国", "市场", "的", "认知", "度", "也", "正", "逐步", "形成", "。", "婴儿", "爬爬垫", "有很多", "别名", ",", "婴儿", "爬行垫", ",", "宝宝", "游戏", "垫", ",", "宝宝", "活动", "垫", ",", "其实", "都是", "指", "婴儿", "爬行垫", "。"], ["【", "爬爬垫", "】", "爬爬垫", "什么", "材质", "好", "四种", "材质", "爬爬垫", "对比"], ["爬行垫", "作用"], 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"都是", "一", "整", "张", "的", "爬行垫", ",", "其", "优点", ":"], ["1", ".", "价格", "一般", "比比", "同", "材质", "同", "面积", "同", "厚度", "的", "拼接", "垫", "便宜", "。", "2", ".", "孩子", "在", "上面", "翻滚", "爬", "窜", "也", "不愁", "会", "裂开", "。", "3", ".", "方便", "外出", "携带", "。"], ["因此", "只要", "根据", "需要", "来", "选购", "适宜", "大小", "的", "爬行垫", "就", "可以", "了", "。"]], "paragraphs": ["摘要:婴儿爬爬垫起源于日韩,并在日韩市场上趋于成熟,中国市场的认知度也正逐步形成。婴儿爬爬垫有很多别名,婴儿爬行垫,宝宝游戏垫,宝宝活动垫,其实都是指婴儿爬行垫。", "【爬爬垫】爬爬垫什么材质好 四种材质爬爬垫对比", "爬行垫作用", "0-9个月:当宝宝开始爬行时,爬行垫的柔软缓冲有效保护宝宝的手腕和膝盖。", "9-24个月:宝宝学步时容易摔倒撞伤头部,爬行垫保护宝宝的头部、颈部。", "2-4岁:当宝宝开始坐在地上玩玩具时,爬行垫能够防止宝宝坐在冰冷的地面上而着凉,同时益智图案可以培养宝宝的认知能力,让宝宝赢在起跑线上。", "4-7岁:当宝宝蹦蹦跳跳,开始学习运动跳舞时,爬行垫可以缓解脚部与地面的冲击力,保护宝宝又防止噪音扰民。(摘自网络)", "除此之外爬行垫还很好打理,宝宝弄脏了只要用布轻轻一擦就焕然一新,席子毯子棉被之类就没这么好伺候了,带孩子也可以省力一点。", "爬爬垫什么材质好", "爬爬垫材质:PVC地垫", "PVC为学名聚氯乙烯,本身无毒,但PVC经过加工做成地垫后,无法再重复使用,也无法自己然降解,只能通过焚烧来处理,但它燃烧时会产生大量的氯化氢气体,对人体有极大的伤害,在欧盟,日本,美国以禁止在室内装修上用PVC来做原料(像以前的PVC电线,改用了PE电线)。所以PVC的使用会给以后的环境带来沉重的负担。", "PVC地垫,柔软舒适,但是有其不好的地方,", "2、吸水,表面膜破坏后就基本上不能用了(吸水后,又不能直接在阳光下晒,会褪色,变硬)。", "3、对环境带来沉重负担,为了我们以后的环境,我们应向先进国家学习,禁止使用。", "4、表面印刷,宝宝直接接触油墨,而油墨是最不稳定的物质长期使用会对人体产生伤害。", "5、价格很贵。", "PVC一定要有添加剂或增塑剂才可用,但这些添加剂或增塑剂大多有害,可能渗出或气化。部份添加剂会干扰生物内分泌(影响生殖机能),部份可增加致癌风险。焚化PVC垃圾,会产生致癌的二噁英(Dioxin)而污染空气。在高温下的燃烧过程中会释放出氯化氢、氯气和其他有毒气体。", "天然树脂PVC(有待商榷)", "不过,这种进口PVC之所以如此自命不凡,是因为在其介绍之中认为:", "1.拥有不添加发泡剂的技术专利,使其密度更大,能达到更大的缓冲力。 2.爬行垫的发明国——韩国已经使用PVC十几年的时间,据称其已经达到了最环保的级别,连比较严格苛刻的北美和欧洲也都在使用此PVC。", "爬爬垫材质:XPE爬行垫", "是最早进入中国的爬行垫,在韩国及其他国家都是用的这种材料做爬行垫,他也是做爬行垫最好的材料!环保,安全,抗菌,不吸水,平整,色彩表现丰富!其材料生产进入门槛高,最成熟的生产工艺在日韩。", "XPE回弹力好,舒适性,安全性好,对宝宝柔嫩的皮肤不会造成任何副作用。具有很好的保暖性和柔软性,可随意调整软硬度和厚度,重量轻等特点。", "防滑性好:是闭孔发泡不吸水,清洗简单,防止宝宝滑倒摔伤。在零下五十度的情况下,该垫子还能保持原来的物理状态,即较软的舒适感.即使在潮湿的环境下,也不会滑倒。", "防潮抗菌独立闭孔结构,紧密有效的阻止水分子渗透,使吸水率超级底(几乎为零),同时,因电子的照射改性,表面抗菌处理,让细菌无生存环境。保障孩子的健康,也能防止孩子的衣服受潮弄脏。", "如何选择爬行垫厚度", "常见的厚度有0.5cm,1cm,1.5cm,2cm,4cm及以上", "刚学爬的宝宝不建议选择太薄的,例如0.5cm,1.5cm及以上的爬行垫会比较适合月龄较小的宝宝;如果宝宝月龄较大(8个月或以上),可以选择相对不会太贵的1cm,收藏起来也方便点。", "4cm及以上价格比较贵,比如彩虹垫至少也要一千以上。", "如何选择爬行垫大小", "主要是根据家里空间大小来决定,当然越大越好,但是越大也越贵,而且太占地方。", "拼接垫、整张垫哪个好?一般现在都是一整张的爬行垫,其优点:", "1.价格一般比比同材质同面积同厚度的拼接垫便宜。 2.孩子在上面翻滚爬窜也不愁会裂开。 3.方便外出携带。", "因此只要根据需要来选购适宜大小的爬行垫就可以了。"], "bs_rank_pos": 2}], "answer_spans": [[0, 144]], "answer_docs": [0], "fake_answers": ["爬行垫根据中间材料的不同可以分为:XPE爬行垫、EPE爬行垫、EVA爬行垫、PVC爬行垫;其中XPE爬行垫、EPE爬行垫都属于PE材料加保鲜膜复合而成,都是无异味的环保材料,但是XPE爬行垫是品质较好的爬行垫,韩国进口爬行垫都是这种爬行垫,而EPE爬行垫是国内厂家为了减低成本,使用EPE(珍珠棉)作为原料生产的一款爬行垫,该材料弹性差,易碎,开孔发泡防水性弱。EVA爬行垫、PVC爬行垫是用EVA或PVC作为原材料与保鲜膜复合的而成的爬行垫,或者把图案转印在原材料上,这两款爬行垫通常有异味,如果是图案转印的爬行垫,油墨外露容易脱落。"], "question": "爬行垫什么材质的好", "segmented_answers": [["爬行垫", "根据", "中间", "材料", "的", "不同", "可以", "分为", ":", "XPE", "爬行垫", "、", "EPE", "爬行垫", "、", "EVA", "爬行垫", "、", "PVC", "爬行垫", "；", "其中", "XPE", "爬行垫", "、", "EPE", "爬行垫", "都", "属于", "PE", "材料", "加", "保鲜膜", "复合", "而", "成", "，", "都是", "无异味", "的", "环保", "材料", "，", "但是", "XPE", "爬行垫", "是", "品质", "较好", "的", "爬行垫", "，", "韩国", "进口", "爬行垫", "都是", "这种", "爬行垫", "，", "而", "EPE", "爬行垫", "是", "国内", "厂家", "为了", "减低", "成本", "，", "使用", "EPE", "作为", "原料", "生产", "的", "一款", "爬行垫", "，", "该", 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"的", "比较好", "一些", "，", "选择", "环保", "一些", "的", "，", "容易", "擦洗", "的", "，", "弹性", "好", "的", "，", "不要", "选", "太", "厚", "的", "，", "5mm", "-", "8mm", "左右", "的", "，", "太", "厚", "了", "宝宝", "容易", "摔跤", "，", "尤其", "是", "学", "走路", "的", "宝宝", "。", "安全性", "第", "一", "，", "看", "产品", "的", "证书", "有", "没有", "权威", "机构", "的", "检验", "，", "重金属", "和", "甲醛含量", "是", "重要", "考虑", "因素", "。"]], "answers": ["爬行垫根据中间材料的不同可以分为:XPE爬行垫、EPE爬行垫、EVA爬行垫、PVC爬行垫；其中XPE爬行垫、EPE爬行垫都属于PE材料加保鲜膜复合而成，都是无异味的环保材料，但是XPE爬行垫是品质较好的爬行垫，韩国进口爬行垫都是这种爬行垫，而EPE爬行垫是国内厂家为了减低成本，使用EPE作为原料生产的一款爬行垫，该材料弹性差，易碎，开孔发泡防水性弱。EVA爬行垫、PVC爬行垫是用EVA或PVC作为原材料与保鲜膜复合的而成的爬行垫，或者把图案转印在原材料上，这两款爬行垫通常有异味，如果是图案转印的爬行垫，油墨外露容易脱落。", "爬行垫一般色彩鲜艳，能吸引宝宝的注意力，当宝宝刚会爬的时候，趴在上面玩，相对比较安全，不存在从床上摔下来的危险。对宝宝的爬行还是很有好处的。还有就是妈妈选择爬行垫时可以选择无害的PE棉，既防潮又隔冷隔热。外有要有一层塑料膜，能隔绝液体进入垫子内部，而且方便清洗。宝宝每次爬行，一定要记得把宝宝的手擦干净。", "棉的不好吧，吸灰尘，容易有螨虫，爬行垫好多种，PVC的比较好一些，选择环保一些的，容易擦洗的，弹性好的，不要选太厚的，5mm-8mm左右的，太厚了宝宝容易摔跤，尤其是学走路的宝宝。安全性第一，看产品的证书有没有权威机构的检验，重金属和甲醛含量是重要考虑因素。"], "entity_answers": [["XPE"], ["PE棉"], ["PVC"]], 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",", "与", "其他", "品牌", "电视", "相比", "是", "天地", "之", "别", "。"], ["snx", "clb", "|", "回答", "时间", ":", "07", ".", "06", "10", ":", "34"], ["不会", "再买", "海信", "的", "产品", "了", ",", "什么", "最大", "的", "液晶", "生产商", ",", "是", "最差", "的", "液晶", "生产商", "吧", "!", "一", "次", "管", "够", "呀", "!", "一", "锤子", "买卖", "的", "企业", "怎么", "会", "有", "发展", ",", "不", "注重", "质量", "的", "企业", "应该", "会", "很快", "破产", "的"], ["zhang", "liping", "|", "回答", "时间", ":", "07", ".", "08", "22", ":", "59"], ["我", "本人", "也是", "买", "的", "海信", "电视", ",", "才", "不到", "3", "个", "月", "电视", "就", "坏", "了", ",", "而且", "都", "没", "怎么看", "。", "维修人员", "说", "只", "在", "15", "天", "内", "可以", "换", ",", "一年内", "保修", ",", "可是", "都", "过去", "10", "天", "了", "还", "不见", "人", "来", "修", ",", "真是", "郁闷", "了", "!", "鄙视", "海信"], ["yl", "121314", "|", "回答", "时间", ":", "07", ".", "30", "15", ":", "13"]], "paragraphs": ["常听说一旦坏了,修的钱比买还要贵!", "但液晶电视的由于系统集成度较高,坏机的可能性 相对降低,", "如果发生怀机,一般维修费用较高,这就如同起初的电脑市场一样", "x8y2kg | 回答时间:2007-12-08 20:11:12", "寿命最多十年,修一次少则几百,多则上千.高清背投的寿命最多五年.据说上海北京一带修这种电视的修理工都开小车去修电视了,可以想象修这种电视有多赚钱.", "i8aylg | 回答时间: 12.10 02:50", "不行以后咱也去海信售后去,咱不就成暴发户了,哈哈哈,感谢海信能给我门提供这个好机会.", "7936 | 回答时间: 02.23 10:31", "说起海信深有体会 那就是好哇 我家还有亲属家用的全是海信好极啦 画质清晰 音响好 还支持1080P 建议就买国产海信 海信", "459512336 | 回答时间: 10.16 11:55", "海信是中国最大的液晶生产商 难道你们都不知道吗 真无知 可叹哪", "459512336 | 回答时间: 10.16 12:07", "不会再买海信的产品了,什么最大的液晶生产商,是最差的液晶生产商吧!一次管够呀!一锤子买卖的企业怎么会有发展,不注重质量的企业应该会很快破产的。", "l_71ab | 回答时间: 04.19 15:07", "海信的电视不是一般的差,我结婚买的电视不到二年,就修了好几次,维修价格贵不说,影响家人的心情,三天两头坏,真烦人,海信的质量怎么是这样呢,价格也不便宜,本想支持国产货的,但每次,国产货真是让人心凉呀!我认为海信的领导是不是也应该看看用户的心声呢!", "limin312300 | 回答时间: 05.04 09:22", "我家买的液晶电视才一年就坏了,给售后服务打电话说保修过期了,要修得花钱好像好几百,难道海信电视就只能寿命一年吗?", "QING | 回答时间: 05.08 17:19", "我是买海信电视的在我看来 有人说他 好也有人 说他不好, 你们怎样来评价一个品牌?不是说他用一辈子不出问题撒 ! 我相信任何品牌都出现过质量问题的, 我举个简单的例子, 东芝电视 他还出现过 《竖线门》事件。包括索尼用台湾屏出问题的都多, 还不要说其他国产品牌, 问题更多。 我看到过的康佳电视卖5台,退回4台有问题的! 如果,你们朋友懂电器你可以问问他买国产 卖什么品牌! 海信是中国的索尼, 我做过对比拿海信TLM32E29对比三星32A350 效果不比三星差! 我需要说明的是TLM32E29他还是一个2008年产的型号.", "tianhuailong | 回答时间: 05.10 21:07", "我是一名海信的营业员,虽然我接触电器这个行业时间短,但是好与不好是你们个人的一个观点,其实你们做出这个评论之前先看看其他的品牌和海信这个品牌做比较,海信其实有很多的专利技术,从外观到芯片,包括屏源,就向别人有说,海信是中国的索尼,的确,其实国产里,我觉得海信真的是数一,不是我是海信人就说海信好,效果的确非常清晰。", "wangmengjiao | 回答时间: 05.12 10:56", "海信还能维持多久,现在应该注意质量问题了,最可恨的就是现在的服务了,我家07年10月份买的海信37寸液晶电视,本人一直不在家电视也不怎么看,没想到刚满一年电视就打不开了,开机时间也就几百个小时,打电话找维修的什么没问就说出门就的要钱,零件另算,真不知道海信是不是就只能看一年啊!这就事中国产业吗?!用一年坏就是你们的责任吗?如果这样下去我想海信一年也就快关门了!\"", "youjidui | 回答时间: 05.12 19:39", "我是海信电视的促销员,我要向各位澄清的是:海信40寸液晶的座架收费标准是300元,请问上面的朋友你的电视是在哪那儿买的,200元能买下40寸型号是LZ033的底座吗?", "liuxiaobao | 回答时间: 06.23 23:52", "海信做电视做的真系好好的 电视有问题一定上会有点的 你不要说机器拉 人也是有样啊 不但是海信有问题 每个牌子的电视都会出现这样的情况啊 有谁敢说么问题的 创维 康佳 TCL 长虹 那个电视没出现过爆炸事件啊 我怎么就没听说过海信有这样的现象啊 你们真是身在福中不知福啊 嗨......", "weiguanjun | 回答时间: 06.24 22:08", "看问题不能以点盖面,任何产品都会有故障率,有哪个厂家敢说他们的产品不会坏?那他们就可以取消售后部了。我经销家电10多年了,相比较而言,海信的销量最大,故障率最低;至于售后服务,要看具体的操作人员,我们这儿的售后就做的很好,非常及时,收费合理,基本无投诉。", "WA2008 | 回答时间: 06.26 12:42", "我刚买1个月的海信TLM55V88GP就坏了!电视拉走维修10多天了,服务电话打了N次都说零件没到!不买 不知道,买了就后悔!1个月的 质量,海信啊,你这样做会引起公愤的。要好 好整整服务理念了!说的 再好不如实际 行动!", "HAITIANYISEW | 回答时间: 07.02 00:04", "以前的家电都是国外的,爱国一次买还海信,质量太差,维修费太高,说板子坏了也不检查,直接就让买新的,这是什么修理呀,简直是抢钱?!海信你太让我失望了!", "BBBBBB | 回答时间: 07.05 16:12", "海信电视质量太差,售后服务更差,本人07年7月购买一台32cm短管电视,到2008年6月电视图像变大,找海信维修人员维修,但效果不佳,屏幕四周字体或图像任然不是很正常,如果图像所想为16:9能看到黑屏与图像联接的线条是弯曲的,不是整体形状,维修人员说:只能修到这样,我们当时没有追究,到2009年4月20日电视打不开,,并且电线与电视连接处冒火花,找海信维修人员维修后,交了120元维修费,到2009年6月11日电视又出故障了,又找海信维修人员维修,问我们要维修费30元,我们没付,说看段时间,没问题后再付款,到6月23日,前后12天时间,累计看电视时间不到5个小时,电视又坏了,故障同2008年6月份情况相似,与维修人员联系,维修人员说付款给修不付款不修,6月25日至6月30日连续6天与海信总部联系,才给了个答复是修理电视的配件必须收钱,我们要求对方保证维修后3个月内电视不再出现问题,对方不同意。如此的质量,如此的售后服务让人寒心,与其他品牌电视相比是天地之别。", "snxclb | 回答时间: 07.06 10:34", "不会再买海信的产品了,什么最大的液晶生产商,是最差的液晶生产商吧!一次管够呀!一锤子买卖的企业怎么会有发展,不注重质量的企业应该会很快破产的", "zhangliping | 回答时间: 07.08 22:59", "我本人也是买的海信电视,才不到3个月电视就坏了,而且都没怎么看。维修人员说只在15天内可以换,一年内保修,可是都过去10天了还不见人来修,真是郁闷了!鄙视海信", "yl121314 | 回答时间: 07.30 15:13"], "bs_rank_pos": 0}, {"is_selected": true, "title": "海信55寸液晶电视怎么样_好吗_质量怎么样-万维家电网口碑", "most_related_para": 2, "segmented_title": ["海信", "55", "寸", "液晶电视", "怎么样", "_", 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"钱多有钱多的做法,钱少有钱激的做法,像外汇投资,2000美金以上就可以操作的很少,假设一天盈利20美金,那么一个月盈利也在440美金,也是一笔很可观收入,投资理财最重要的就是有这个理念并且付诸实际行动中,如果有兴趣可以继续沟通,希望可以让你少走弯路,给你一些比较靠谱的学习资料。", "50年当中,有风华雪月的时光,有儿女成双的成就,有留恋麻将桌的悠闲,怎么就没有开源节流的意识呢?50岁知天命了,现在想起要理财也为时未晚。现在一份好工作的门槛很高,建议你到人寿保险公司去应聘,那里有你的梦想。即使做不成功,你也没有损唬,不是吗?不要听别人怎么说,你去试试才知道。", "因为凡是替人理财的都是自己不会赚钱的,否则他去银行贷款自己赚钱去了,为什么会让你占便宜??", "零钱宝是第三方支付平台易付宝为个人用户打造的现金理财产品,用户存放在零钱宝中的资金不仅能够得到较高收益,还能随时消费和快速转出,方便灵活且不收取任何手续费。", "不好意思,对这个不太清楚,不过你可以去中金 国泰看看啦。口碑特别好。", "百度搜索“就爱阅读”,专业资料,生活学习,尽在就爱阅读网92to.com,您的在线图书馆", "分享此文"], "bs_rank_pos": 1}, {"is_selected": false, "title": "建行理财产品到期后怎么没到账,理财产品信息也没有_百度知道", "most_related_para": 0, "segmented_title": ["建行", "理财产品", "到期后", "怎么", "没", "到账", ",", "理财产品", "信息", "也", "没有", "_", "百度", "知道"], "segmented_paragraphs": [["建行", "的", "理财产品", "今天", "到期", ",", "次日", "下午", "六", "点", "才", "到账", ",", "这", "是", "两", "个", "概念", "。"]], "paragraphs": ["建行的理财产品今天到期,次日下午六点才到账,这是两个概念。"], "bs_rank_pos": 2}, {"is_selected": true, "title": "建设银行的理财产品为什么到期了,钱还没有到账?-网贷之家问答", 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"到账"]], "paragraphs": ["建行理财到期几天到账 P2P理财是最新出现的网上理财产品,基本定义是个人对个人的借贷理财,目前国内P2P理财产品较多,公司鱼目混杂,利息高,但是对于理财产品的安全性是现在最为关注的,在高利息风险大的平台获取收益也是P2P理财的一大特点。", "文章内容部分来自网络转载,如有侵权,请联系删除,QQ:2939274365,邮箱:shaozhenlin#rong360.com(请将#换成@)", "想问贷款、信用卡问题?扫一扫关注微信公众号(qianniuer888)获取更多帮助。", "相关百科 建行虚拟信用卡 建行速盈 大众龙卡 春秋龙卡信用卡 安徽etc龙卡信用卡 助业贷", "相关攻略 工行信用卡退款几天到账? 工行信用卡退款几天到账?在日常生活中,我们常会遇到买到的商品不符合心意又需要退货的情况。如果消费时使用的现金或储蓄卡,退款过程相对较为简单,到账时间也很短,但如果是用信用卡支付货款的,那么退款时就需要... 余额宝转账到建行要多久? 余额宝的流动性好,转账到银行卡快速方便,那么到底余额宝转账到建行要多久? 余额宝转账到建行不是实时到账,具体分两种情况。 在6:00-21:30的建行服务时间内,通过手机支付宝余额宝操作... 建行信用卡可以转账吗? 随着信用卡的普及,信用卡消费已经成为众多持卡者的生活习惯。信用卡的便捷,让更多人办理信用卡和使用信用卡。但是,有持卡者疑问地表示,信用卡可以转账吗? 答案是肯定的,但是会有...", "相关回答 提问: 几天可以到账 回答: 你好,同行转账当天就到账,跨行转账,次日到账。 提问: 2016建行转账多久到账? 回答: 2016建设银行网上银行转账,到账时间在2-24小时。 提问: 建行给中行转账多久? 回答: 银行转账一般是在24小时之内完成。银行卡跨行转账可以通过以下方式转账:支付宝转账;网银转账;银行柜台转账等。 提问: 建行转招行多久到账? 回答: 建设银行到账时间:1、系统内9:00-16:30实时到帐。2、跨行1-3个工作日。具体的建议可以问下银行的客服。 提问: 恒易融到期多久到账? 回答: 提现是T+1天打到您银行卡里,也就是说您今天提现,最迟明晚24点就能到账。(前提是必须是工作日)", "大家还在搜 理财到期后几天到账 交行理财到期几天到账 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"自2014年10月1日起,每年1月1日开始办理上一年度《营业执照》网上年度报告(以下简称网上年报,网上年报可理解为手续简便的营业执照年检)。为避免临近6月30日截止期限网上年报系统用户激增,而可能出现的网络拥堵或被踢出系统的现象,请提前尽快办理。", "根据个人操作截图,以河南省为例说明。", "未按规定办理网上年告的,将会被企业信用信息系统列入经营异常名录(俗称黑名单),可能给公司、企业及经营者在多个领域带来不必要的麻烦。 依据2014年10月1日起实施的《企业信息公示暂行条例》和《个体工商户年度报告暂行办法》等法规、规章的规定,2013年底前注册的,应当办理2013和2014年度的网上年报;2014年底前注册的,应办理2014年度的网上年报;以此类推。另外,企业重要信息发生变化的,应在20日内上网公示。 实在不会办理的,可以携带下列材料到辖区工商所学习: 1、《营业执照》副本;2、所有的许可证副本;3、法定代表人或负责人身份证;4、手机现场接收短信,办理过网上年报的或注册过联络员的带注册登记的手机;5、公司、企业等法人单位另需资产负债表、损益表和公司章程。", "一、准备材料和数据;二、进入网页;三、未注册的请注册; 四、公示信息填报:1、登录; 2、填报信息:A、个体工商户;B、有限责任公司;", "五、注意事项;六、如何修改联络员信息;七、如何查询是否年报成功。", "受百度经验图片最高数量所限制,完整图片可以百度搜索“营业执照网上年报0379”", "百度经验:jingyan.baidu.com", "1、营业执照副本或者正本; 2、法定代表人或负责人身份证; 3、能够现场收短信的常用手机; 4、所有许可证副本或者正本; 5、全年营业额、纳税额; 6、党员人数、高校毕业生人数、退役士兵人数、残疾人人数、失业人员再就业人数; 7、公司等具备法人资格的单位,需年度资产负债表、损益表,股东及股东出资情况。建议财务人员在场。 8、能够上网的电脑", "百度经验:jingyan.baidu.com", "1 二、进入网页: 1、百度搜索“信用公示系统”,进入系统((图2-2、图2-3、图2-1) 2、选择“河南” (图2-4) 步骤阅读 2 三、未注册的请注册: 1、点击“企业联络员注册” (图3-1) 2、填写内容并保存(图3-2) ①注册号只有15位,副本上注册号只填前15位数字。 ②注册时使用的是老身份证号码的,填新号码无法通过。一般情况下,新身份证号去掉第8、9位和最后一位数字就是老身份证号。也可以到营业执照原工商注册部门备案新身份证号。 ③联络员可以不填写负责人。但是为了便于记忆,推荐填写负责人。 ④联络员手机号可以不填写负责人的。必须能够接收短信。 ⑤联络员证件号和手机号切勿填错。如果填错并忘记了造成无法登录,目前尚无法解决。 3、保存后显示“恭喜您,注册成功!” (图3-3) 步骤阅读 步骤阅读 3 四、公示信息填报: 1、登录: ①在“全国企业信用信息公示系统(河南)”页面,点击“企业公示信息填报” (图4-1-1) ②在“企业联络员登陆”界面,填写“营业执照注册号”、“联络员身份证” (图4-1-2、 图4-1-3) ③点击“获取验证码”,按照手机短信中的一次性“动态密码”填写“备案手机验证码” (图4-1-4) ④点击“登录”。 步骤阅读 步骤阅读", "百度经验:jingyan.baidu.com", "1 ③进入“基本信息”页面。“资金数额”和“从业人数”输入注册时登记的数字。内容填写完毕,点击“保存并下一步” (图4-2-4) 步骤阅读 2 ④进入“行政许可情况”页面。如果有网站或者网店的,输入相关信息,可参考下面有限责任公司的B④项说明。 a、如果有许可证的,点击“添加”(图4-2-5)。 在弹出的页面填写“许可文件名称”,点击“有效期至”选择时间,“保存”。 (图4-2-6) 多个许可证的,继续添加输入。全部添加完毕,点击“保存并下一步”(图4-2-7) b、没有许可证的,直接点击“保存并下一步”。 (图4-2-8) 步骤阅读 步骤阅读 步骤阅读 3 ⑤进入“资产状况信息”页面。输入全年“营业额或营业收入”,输入全年“纳税总额”,可以选择“公式”或“不公示”,点击“保存并下一步” (图4-2-9、图4-2-10) 步骤阅读 4 ⑥进入“党建信息”页面。输入和选择党员情况,点击“保存并下一步” (图4-2-11、图4-2-12) 步骤阅读 5 ⑦进入“预览并公示”页面。检查数据,无误后点击点击“提交并公式”,在弹出页面点击“确定”,完成。(图4-2-13、图4-2-14、图4-2-15) 步骤阅读", "百度经验:jingyan.baidu.com", "1 七、如何查询是否年报成功: 1、进入系统页面在“搜索”中输入注册号或者名称,点击“搜索”,输入验证码点击“搜索”(图4-2-43、图4-2-44) 2、在弹出的页面,点击搜索出来的名称(图4-2-45) 3、点击“个体工商户公示信息”或者“企业公示信息” (图4-2-46) 4、在“公示信息”页面查看“报送年度”和“发布日期”,确定否年报成功,图中是2013年度报送成功。(图4-2-47) 步骤阅读 步骤阅读 步骤阅读 END", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:多方面了解成为知道分子。", "怎么使用种子文件下载 测试网速的方法 毕业照拍怎么最漂亮 极品飞车9怎么改中文? 怎么写百度经验才最划算呢"], "bs_rank_pos": 3}, {"is_selected": false, "title": "营业执照网上申请流程_图文_百度文库", "most_related_para": 3, "segmented_title": ["营业执照", "网上申请", "流程", "_", "图文", "_", "百度", "文库"], "segmented_paragraphs": [["营业执照", "网上申请", "流程", "_", "计划", "/", "解决方案", "_", "实用", "文档", "。", "该", "营业执照", "网上申请", "流程", "适用于", "甘肃省", "内", "个体", "、", "企业", "申请", "登记"], ["(", "新", "名称", "申请", ")", "1", "、", "登陆", ":", "甘肃", "企业", "登记", "网", "(", "域名", ":", "http", ":", "/", "/", "qydj", ".", "gsaic", ".", "gov", ".", "cn", "/", ")", "。", "2", "、", "查找", ":", "企业", "网上", "办理", "大厅", "(", "网页", "右侧", ")", "?", "点击", ":", "名称", "申请", "。", "3", "、", "迚", "入", ":", "甘肃", "省", "工商", 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"—", "联系", "电", "话", "(", "如", "上", "图", "所示", ")", "填写说明", ":", "⒈", "企业", "机构名称", "项", "、", "字号", "、", "企业", "机构", "类型", "为", "系统", "自动", "生成", "。"]], "paragraphs": ["营业执照网上申请流程_计划/解决方案_实用文档。该营业执照网上申请流程适用于甘肃省内个体、企业申请登记", "(新名称申请) 1、登陆:甘肃企业登记网(域名:http://qydj.gsaic.gov.cn/)。 2、查找:企业网上办理大厅(网页右侧)?点击:名称申请。 3、迚入:甘肃省工商行政管理局网上业务办理系统(域名: http://nj.gsaic.gov.cn/gsaicEn/ )。 4、点击未登记企业登录?新用户注册?按要求填写内容后?注册。 5、登陆后迚入下图界面。", "6、点击名称预登记?新名称申请?选择自己所要申请名称的企业类型 (例:个体工商户新名称申请或私营企业新名称申请)?双击点击迚 入。", "7、名称登记(以个体工商户为例)。 ●依次填写内容分三部分: (1) 名称信息。包括: 名称行政区划——请选择名称地域——行业 信息——组成形式——申请字号——申请名称——备用字号——备用名 称。(如图所示) 填写说明: ①、行政区划冠“甘肃”选择省名、冠“酒泉”选择市州名、冠“阿克塞”选择区县名。 ②、名称地域选择“阿克塞哈萨克族自治县”。③、行业信息指主要从事行业的大类信息。如从事餐饮业→行业信息可以填写为“饮食”、“饭馆”: 从事商业→行业信息可以填写“商贸”、“销售”。④、组成形式指从事市场经济的类型。如个体工商户一般选择“店、部、超市、公司一般选择“有限 责任公司、股份有限责任公司”、企业一般选择 “厂、中心、医院” 。⑤、字号指同一行政区划用于区别市场主体行业不同类型的标示,它应由2个以上字(汉字)组成。 如“康师傅”、“汇源”、“鄂尔多斯”等均为字号,【注意驰名商标、著名商标、禁用字号不能作为被 申请使用的名称】,在申请时如无法确认字号是否可以通过可以填写备用字号。 ⑥、名称由行政区划+字号+行业信息+组成形式四个基本要素构成(此项为系统自动生成可以按照自 己的需要迚行修正)。 (2)个体工商户(企业)信息。包括:资金数额——经营场所(住所)所在地——经营场所(住所)所在行政区划——拟受理单位——行 业门类——行业代码——经营范围及方式——联系人——联系电话。 (如图所示) 填写说明: ①、资金数额指从事经营活动所需的必要资金数额。包括薪金(人民币)和固定资产。 ②、经营场所所在地指从事经营活动具体的处所不包括住所(主要针对个体工商户)。住所所在地指 从事业务、经营活动的处所包括住所(主要针对企业)。③、行政区划指从事经营活动所在地点隶属的区域区划,如在阿克塞县从事经营活动的行政区划即为 阿克塞哈萨克族自治县。 ④、行业门类指主要从事的经营活动所属于《国家行业分类》的那一类。如:房屋建筑属于建筑业、 开采属于采矿业等。 ⑤、行业代码指按照《国家行业分类》迚行分类后详细至具体的类别子项。如小型商店属于批发零售 业中零售业里的小型商业。 ⑥、联系人及联系电话填写负责人的姓名及常用电话。 (3)经营者信息。此项限于个体工商户基本信息(如图所示) (4)投资人信息。此项限于投资身份信息(如图所示) 填写说明: ①、经营者信息仅限于申请营业执照的当事人按身份填写即可。 ②、投资人信息指所有投资人的身份信息,属自然人投资的的以身份证为准;属企业法人、个人独资 企业的以营业执照为准;属机关法人、事业单位的以组织机构代码证为准;属社团组织的以社团登记证为 准。 以上既是新名称申请的操作流程,切记完成操作后不要忘记点击提交预审 个体工商户及私营企业 设立登记 1、点击未登记企业登录?●依次填写内容分三部分: (1)基本信息。包括:名称——从业人数——组成形式——资金 数额——经营范围及方式——行业门类——行业代码——许可经营项 目——一般经营项目——经营场所——住所——预审受理机关——申请 人——申请日期——联系人——联系电话。(如上图所示)填写说明: ①、名称项为系统自动生成。", "●依次填写内容分三部分: (1)基本信息。包括:名称——从业人数——组成形式——资金 数额——经营范围及方式——行业门类——行业代码——许可经营项 目——一般经营项目——经营场所——住所——预审受理机关——申请 人——申请日期——联系人——联系电话。(如上图所示)填写说明: ①、名称项为系统自动生成。 ②、从业人员指除经营者以外的其他人员,如服务员、雇工等。 ③、组成形式为选择项,一般选择个人经营。 ④、经营范围指申请人开展经营活动所有涉及商品的类别、品种及服务项目。 ⑤ 、行业门类指主要从事的经营活动所属于《国家行业分类》的那一类。如:房屋建筑属于建筑业、开采属于采矿业等。 ⑥ 、行业代码指按照《国家行业分类》迚行分类后详细至具体的类别子项。如小型商店属于批发零售业中零售业里的小型商业。 ⑦、许可经营项目为选择项。许可经营项目是指在申请登记前依据法律、行政法规、国务院决定应当报经有关部门批准的项目。 ⑧ 、一般经营项目为填写项。 一般经营项目是指不需批准,可以自主申请的项目。 ⑨、经营场所指从事经营活动具体的处所。 ⑩、住所指负责人的居住场所,原则上要与经营场所分离。 (页面中红色星号标志处为设立信息必填内容) (2) 经营者信息。此页面按照申请人身份信息填写,所涉及内容 多以身份证信息为准。 (3)许可信息。此页面主要是指经营范围中涉及许可项目的范围 必须填写许可证信息。 填写说明: 点击“添加许可信息”工具,在弹出页面内填写已取得的许可证或行政许可批准文件基本信息即可,如涉及多个行政许可可依次 点击“添加许可信息”工具添加许可信息至填写完毕。 以上既是个体户设立的操作流程,切记完成操作后不要忘记点击提交预审 4、迚入如下界面。迚行操作(内/私企业)。 ●依次填写内容分六部分: (1)基本信息。包括:企业机构名称——字号——企业机构类型—— 住所——住所产权——房屋使用期至——经营面积(平方米)——联系 电话——邮政编码——电子邮箱——注册资本(万元)——经营期限— —行业门类——行业代码——经营类别——经营婴幼儿乳制品标示—— 拟登记机关——设立方式——副本数——审批机关——批准文号——批 准日期——开户银行——银行账号——许可经营项目——一般经营项 目——经营范围及方式——申请人——申请日期——联系人——联系电 话(如上图所示) 填写说明: ⒈企业机构名称项、字号、企业机构类型为系统自动生成。"], "bs_rank_pos": 4}], "answer_spans": [[49, 209]], "fake_answers": 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】。4.选择【新用户注册】。5.按要求填写信息，点击【验证】，验证通过后再旁边设置密码，然后选择登陆。6.登陆需要填写手机号接受验证码，验证码来得比较慢。7.登陆后选择【年底报告在线填报】，选择【2014年度】。8.按要求填写，可以选择是否对外公示。9.填写完毕保存并下一步，来到出资情况这块点击【添加】将各个股东的出资时间填上。10.【保存并下一步】资产状况这块让贵公司会计出具上一年度的资产负债表及损益表，按要求填写，可选择是否公示。11.【保存并下一步】来到对外提供保证担保信息，如果没有可以【保存并下一步】。12.下一步来到党建信息。13.再下一步到了预览并公示，查看下如果没有错误就选择【提交并公司】。14.提交以后再到企业信用网查询你公司信息就能查看年报信息了。", "进入网页：1、百度搜索“信用公示系统”，进入系统。 2、选择“河南” 。三、未注册的请注册。1、点击“企业联络员注册” 。2、填写内容并保存。 ①注册号只有15位，副本上注册号只填前15位数字 ②注册时使用的是老身份证号码的，填新号码无法通过。一般情况下，新身份证号去掉第8、9位和最后一位数字就是老身份证号。也可以到营业执照原工商注册部门备案新身份证号。 ③联络员可以不填写负责人。但是为了便于记忆，推荐填写负责人 ④联络员手机号可以不填写负责人的。必须能够接收短信。 ⑤联络员证件号和手机号切勿填错。如果填错并忘记了造成无法登录，目前尚无法解决。3、保存后显示“恭喜您，注册成功”。"], "answer_docs": [3], "segmented_question": ["营业执照", "网上申报"], "question_type": "DESCRIPTION", "question_id": 181578, "fact_or_opinion": "FACT", "match_scores": [0.8614457831325302]} +{"documents": [{"is_selected": false, "title": "打开游戏时显示CSGO.EXE已停止工作。。。【csgo吧】_百度贴吧", "most_related_para": 0, "segmented_title": ["打开", "游戏", "时", "显示", "CSGO", ".", "EXE", "已", 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"崩溃", "。"], ["一直", "被", "困扰", ",", "TMD", "一次", "升级后", "就", "老是", "闪退"], ["显示", "缺少", "文件", "怎么回事", ",", "vpk", "的", "文件"], ["这个问题", "是不是", "到", "现在", "都", "没有", "一", "个", "解决办法", "。", "。"]], "paragraphs": ["这个问题,苦了我好久,问过吧友,看过台湾的论坛。吧友提示的方法都用过,什么做系统,检查文件完整性,删了csgo重下,删了steam重下,什么更新显卡驱动,都试过,结果是没用还是那样。但是我成功解决了,在此分享一下其实原因可能出在steam云功能上,查了外国steam的论坛是得出这样的结论大致是这个意思,把从workshop下载的地图全部删掉。玩过cs的都知道有那么个map文件夹,我自己的解决方式就是在map文件夹里有个文件夹叫workshop,选中后直接删除。然后验证文件完整性,自动下载验证失败的文件,等待下载安装。然后你就可以进入游戏开虐吧,不会再停止工作了自此之后我就再也没从workshop下载过任何地图……因为这个问题貌似困扰不少人了,反正我是这么解决 ,感觉还是有道理的", "取消创意工房订阅,你怎么不早点遇到我呢", "卧槽,大师,你好。我这几天一直被这个问题快整崩溃了,换个图5次里最少有1,2次这样,我还以为是我的有问题没想到是通病。操。有机会试试你的方法。", "昨天的更新后就出现了这样的问题 在游戏中退出界面就停止工作了 可是你现在的办法不可行了....", "控制台输入 disconnect 就行了", "没订阅创意工妨有时也遇到,,不过不频繁", "楼主啊,表示不懂,那个map文件夹在哪里!!", "什么狗屁方法,系统换成64位,内存到8G,我再 也没停止工作过", "运行图标右键→属性→只读,搞定", "LZ什么系统多少位?这个问题困扰我很久了,很崩溃。", "一直被困扰,TMD一次升级后就老是闪退", "显示缺少文件怎么回事,vpk的文件", "这个问题是不是到现在都没有一个解决办法。。"], "bs_rank_pos": 2}, {"is_selected": true, "title": "csgo 第一次运行就出现 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4、兼容模式运行 5、管理员运行 6、网上删除创意地图、修改程序代码等,因为还没进过游戏,都不能操作=! 都不行,怎么破?", "dx10修复工具试试,我就是这么弄好的。重装dx10好像没用。", "下载知乎客户端 与世界分享知识、经验和见解"], "bs_rank_pos": 3}, {"is_selected": true, "title": "玩CSGO老是开始游戏的时候老是退出到桌面并显示CSGO已停止工作是", "most_related_para": 1, "segmented_title": ["玩", "CSGO", "老是", "开始游戏", "的", "时候", "老是", "退出", "到", "桌面", "并", "显示", "CSGO", "已", "停止", "工作", "是"], "segmented_paragraphs": [["查看", "下", "是不是", "兼容性", "问题", "你", "什么", "系统"], ["你", "关闭", "输入法", "之后", "再", "玩", "看看", "一般", "都是", "因为", "这"], ["兄弟", "。", "。", "能", "玩", "就", "已经", "很好", "了", "。", "。", "很多人", "都", "下载", "不到", "你", "的", "这种", "能", "玩", "的", "破解版", "呢", "。", "。", "csgo", "是", "和", "steam", "绑定", "的", "。", "。", "经常", "升级", "。", "。", "里面", "有很多", "东西", "一直", "在", "改变", "。", "。", "所以", "基本", "不会", "有", "最新", "的", "破解版", "。", "。", "而且", "只能", "自己", "跟", "机器人", "k", "了", "。", "。", "所以", "破解版", "会", "点", "问题", "是", "很正常", "的"], ["喜欢", "这个游戏", "就", "不要", "破解", "了", ",", "正版", "购买", "一次", ",", "永久", "使用", ",", "没有", "烦恼", "。", 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"从2004《超级女声》出道的张含韵,至今已经过去了十多年。在这些年里,小花多以歌手身份出镜。《兰陵王妃》是张含韵首次独挑大梁,担纲女主角的古装大剧。为了演好角色,她做了充分准备,学骑马、学跳舞、学武术、背古诗词等。", "张含韵表示,她在剧中有多达四十套的戏服,自己最期待大婚的服装“全手绣,价值几十万”。而据透露,张含韵将在剧中选出自己的荧幕初吻。", "对于其在剧中即将上演兰陵王妃的史诗绝恋,粉丝表示期待,在超长片花放出后,网友也对其演技表示肯定“张小花演技在线!”。而导演叶昭仪认为张含韵“漂亮,够清纯”,符合他心目中女主的形象。", "《兰陵王妃》网络播出回应粉丝期待", "该剧于2014年年初杀青后,即转入后期制作流程,而原计划的定档上映时间是2014年年中。", "在当时,《兰陵王》热度尚未褪尽,本剧的主演阵容也非常青春亮眼。仅在前期宣传阶段,《兰陵王妃》的关注度和粉丝热度就一路高企,备受瞩目。", "此次《兰陵王妃》化身网剧登陆芒果TV视频平台,以会员尊享的模式回归。该平台也于日前公布了该剧超长预告片片花。", "《西游伏妖篇》总票房过20亿才", "争议虽大《大闹天竺》票房预测", "过年的时候香蕉都有哪些好的吃", "兰陵王妃张含韵如何处理三角恋关系 很多感情不是那么容易受自己控制,很少人能那么大方分享自己爱人,但是很多爱情中也存在着三角恋的关系,除了小三,出轨等一...更多 2016-12-21 09:24:09", "驴得水任素汐刘帅良倾情加盟 电影《驴得水》上映以来备受好评,网友对电影《驴得水》名字的由来比较好奇,《驴得水》电影名字是怎么来的又是什么意思呢?...更多 2016-10-30 16:34:26", "兰陵王妃阿史那竟是宇文邕的最爱 电视剧《兰陵王妃》开播之后好评如潮,剧中倾国倾城、深明大义的阿史那公主也引起了网友们的关注!那么阿史那公主的历史原型...更多 2016-10-18 11:39:33", "白羊男彻底分手的表现有哪些 白羊座男人思想活跃,热情似火,周身洋溢着不竭的创造力。白羊男喜欢一个女人的时候会想尽一切办法的去呵护帮助喜欢的女人,...更多 2017-01-28 18:00:03", "小品真情永驻结尾歌曲大揭秘 春晚小品《真情永驻》最后响起的歌曲是什么?2017年的央视春晚迎来许多熟悉面孔,其中,孙涛、闫学晶、刘仪伟表演的小品《真...更多 2017-01-27 22:40:11", "《西游伏妖篇》总票房过20亿才能回本? 电影《西游伏妖篇》的预售票房是多少,累计总票房预测多少亿能保底俨然成为了新春佳节大家热议的话题的质疑,接下来快跟尚之...更多 2017-01-27 22:55:56", "争议虽大《大闹天竺》票房预测15亿封顶 电影《大闹天竺》片方预测能有多少让大家十分关心,早前有网友根据王宝强离婚案事件分析,《大闹天竺》票房预测能达30亿,但...更多 2017-01-27 19:39:18", "除夕的14大习俗和禁忌一定有你不知道的! 今天是除夕,对于还奋战在编辑第一线的小编来说是一个忙碌而充实的除夕,坐等春晚的空隙还研究了下除夕的习俗,发现各个地方...更多 2017-01-27 19:38:59", "2017央视春晚靳东为何缺席 靳东为何缺席2017年央视春晚?2015年一部《伪装者》让靳东、胡歌、王凯组成的“明家三兄弟”形象深入人心,近日,2017年央视...更多 2017-01-27 13:58:19", "《大闹天竺》竟是一个当代版西游记故事 王宝强执导的电影《大闹天竺》即将在大年初一上映,备受网友们的期待。但是令网友好奇的是电影《大闹天竺》讲了什么故事,背...更多 2017-01-26 19:06:06", "《西游伏妖篇》台词周星驰式无厘头风格明显 电影《西游伏妖篇》将于大年初一上映,单是周星驰和徐克这个黄金组合就万众瞩目了,而高颜值的小鲜肉演员阵容更是令影迷们期...更多 2017-01-26 17:04:26", "《西游伏妖篇》的物理特效可不仅值6毛 《西游伏妖篇》的特效在电影上映前就备受网友关注,那么《西游伏妖篇》真的没有特效吗,物理特效又是什么鬼呢?有网友吐槽《...更多 2017-01-26 17:09:51", "《大闹天竺》票房大热马蓉究竟能不能分到钱 电影《大闹天竺》将映,王宝强前期马蓉是否能分到票房钱让网友们关心不已,那么王宝强离婚判决结果如何了,最新消息是怎么宣...更多 2017-01-26 16:08:37", "《功夫瑜伽》迪拜王子竟出借世界最贵超跑 成龙喜剧电影《功夫瑜伽》即将上映,据悉该片主打异域风情,前往多个国家取景拍摄,展现了各具特色的风景和风情。令网友好奇...更多 2017-01-26 15:36:28", "青云志第三季什么时候播揭晓 <鬼怪>大结局编剧脑洞开大了! 鬼吹灯精绝古城Shirley杨身世揭秘 鬼怪刘德华前世曝光令人震惊 网剧<鬼吹灯>系列一共有几部 精绝古城陈教授结局竟然疯了 鬼吹灯Shirley杨父亲死了吗 网剧无间道15年后致敬经典 还没看过西部世界?你out了! 震撼!陈二狗父亲背景太恐怖", "2017年支付宝敬业福不求人的秘诀在这里", "《鬼吹灯》胡八一身世来历父亲是谁介绍", "王昱珩能再创不败奇迹碾压人工智能吗", "这款跑车不仅便宜竟然还有剪刀门?", "老公日记惊现他与初恋情人的偷情丑事"], "bs_rank_pos": 3}, {"is_selected": true, "title": "兰陵王妃大结局剧情介绍 兰陵王妃结局是什么 兰陵王妃最后结局", "most_related_para": 13, "segmented_title": ["兰陵王妃", "大", "结局", "剧情", "介绍", "兰陵王妃", "结局", "是", "什么", "兰陵王妃", "最后", "结局"], "segmented_paragraphs": [["学习", "啦", ">", "励志", ">", "励志", "大全", ">", "励志", "电视剧", ">", "《", "兰陵王妃", "大", "结局", "剧情", "介绍", "兰陵王妃", "结局", "是", "什么", "兰陵王妃", "最后", "结局", "》", "正文"], ["兰陵王妃", "大", "结局", "是什么", "?", "《", "兰陵王妃", "》", "元清锁", "、", "宇文邕", "、", "高长恭", "的", "结局", "分别", "是", "什么", "?", "电视剧", "《", "兰陵王妃", "》", "正在热播", ",", "兰陵王妃", "开播", "之后", "各种", "关于", "兰陵王妃", "的", "话题", "就", "不断", "的", "接踵而至", ",", "那么", "兰陵王妃", "电视剧", "结局", 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"在此期间", "元清锁", "分别", "结识", "了", "兰陵王", "高长恭", "和", "北周", "宇文邕", ",", "各自", "发生", "了", "一", "段", "感情", "纠葛", ",", "元清锁", "最终", "帮", "宇文邕", "成就", "了", "帝王", "霸业", ",", "而", "选择离开", "北周", "与", "兰陵王", "高长恭", "共", "生死", "。"], ["下", "页", "查看", "电视剧", "《", "兰陵王妃", "》", "分集", "剧情", "介绍"], ["上", "一篇", ":", "张含韵", "林依晨", "兰陵王妃", "谁", "更胜一筹", "张含韵", "能否", "超", "林依晨", "下一篇", ":", "兰陵王妃", "最后", "结局", "兰陵王妃", "结局", "是", "什么", "兰陵王妃", "大", "结局"], ["【", "励志", "电视剧", "】", "精华", "文章"], ["【", "励志", "电视剧", "】", "相关文章"]], "paragraphs": ["学习啦>励志>励志大全>励志电视剧>《兰陵王妃大结局剧情介绍 兰陵王妃结局是什么 兰陵王妃最后结局》正文", "兰陵王妃大结局是什么?《兰陵王妃》元清锁、宇文邕、高长恭的结局分别是什么?电视剧《兰陵王妃》正在热播,兰陵王妃开播之后各种关于兰陵王妃的话题就不断的接踵而至,那么兰陵王妃电视剧结局是什么呢?一起来看看下文关于电视剧《兰陵王妃》的大结局及分集剧情介绍。", "电视剧《兰陵王妃》海报", "从2004《超级女声》出道的张含韵,至今已经过去了十多年。在这些年里,小花多以歌手身份出镜。《兰陵王妃》是张含韵首次独挑大梁,担纲女主角的古装大剧。为了演好角色,她做了充分准备,学骑马、学跳舞、学武术、背古诗词等。", "张含韵表示,她在剧中有多达四十套的戏服,自己最期待大婚的服装“全手绣,价值几十万”。而据透露,张含韵将在剧中选出自己的荧幕初吻,对于其在剧中即将上演兰陵王妃的史诗绝恋,粉丝表示期待,在超长片花放出后,网友也对其演技表示肯定“张小花演技在线!”。而导演叶昭仪认为张含韵“漂亮,够清纯”,符合他心目中女主的形象。", "该剧于2014年年初杀青后,即转入后期制作流程,而原计划的定档上映时间是2014年年中。在当时,《兰陵王》热度尚未褪尽,本剧的主演阵容也非常青春亮眼。仅在前期宣传阶段,《兰陵王妃》的关注度和粉丝热度就一路高企,备受瞩目。", "此次《兰陵王妃》化身网剧登陆芒果TV视频平台,以会员尊享的模式回归。该平台也于日前公布了该剧超长预告片片花。有不少粉丝表示,作为国内少有的精制Ip古装剧,《兰陵王妃》“值得花钱去看,终于等到你,还好我没放弃!”", "刚刚翻微博看到粉丝说的,是金鹰访谈里含韵剧透的,最后没有和王爷在一起,和宇文邕在一起了。相信很多小伙伴早就知道了。或许女主改名叫李娥姿,就注定了结局。", "如果是这样感觉我王好虐呀!不是有人说兰陵王会死,是真的吗?", "电视剧《兰陵王妃》剧照", "《兰陵王妃》中,青鸾镜、离殇剑被视为宝物,如果持有这两件宝物者,可以一统天下,正是因为如此,所以引来 争夺,剧中女主人公名为元清锁,是由张含韵所扮演,元清锁聪明活泼,本名叫端木怜,从小在天罗地宫长大,并且得到紫魅的真传,元清锁为了寻觅宝物,在路途中也是遇到了兰陵王高长恭和北周宇文邕,上演了一场三角恋情 。", "根据小说描述,元清锁帮助宇文邕成就了霸业,随后 就和兰陵王高长恭离开了,而电视剧版的是根据小说改编,所以在大结局中元清锁并没有死,而是和高长恭在一起了。", "文邕成就了帝王霸业,却未能与心爱的人在一起。", "高长恭最终抱的美人归,与元清锁共度生死。", "电视剧《兰陵王妃》剧照", "东晋末年,王朝动荡,诸侯割据。相传只有左持青鸾镜,右握离殇剑者,方可统一天下。青鸾镜与离殇剑本为龙教宝物,却因为战乱遗失。龙教圣女紫魅因练功入魔,被自己的内力反噬重伤,命悬一线。紫魅临死前,前交龙教唯一代传人元清锁,告知她青鸾镜与离殇剑的秘密。紫魅最后叮嘱元清锁,这两件宝物切不可落入奸人之手,只有真龙天子才配拥有,否则将天下大乱。元清锁为了寻找宝物,假扮北周司空夫人元氏的外甥女李娥姿,以家道中落为名,带着家族信物,投奔北周司空府。在此期间元清锁分别结识了兰陵王高长恭和北周宇文邕,各自发生了一段感情纠葛,元清锁最终帮宇文邕成就了帝王霸业,而选择离开北周与兰陵王高长恭共生死。", "下页查看电视剧《兰陵王妃》分集剧情介绍", "上一篇:张含韵林依晨兰陵王妃谁更胜一筹 张含韵能否超林依晨 下一篇:兰陵王妃最后结局 兰陵王妃结局是什么 兰陵王妃大结局", "【励志电视剧】精华文章", "【励志电视剧】相关文章"], "bs_rank_pos": 4}, {"is_selected": false, "title": "兰陵王妃小说结局_兰陵王妃大结局是什么 - 你知道吗", "most_related_para": 2, "segmented_title": ["兰陵王妃", "小说", "结局", "_", "兰陵王妃", "大", "结局", "是", "什么", "-", "你知道吗"], "segmented_paragraphs": [["拍摄", "了", "很久", "的", "电视剧", "兰陵王妃", "终于", "要", "不", "出", "了", ",", "这部", "电视剧", "有", "由", "同名", "小说改编", "而", "成", "的", ",", "据悉", "蛮", "好看", "的", "。", "那么", "兰陵王妃", "小说", "的", "结局", "是", "什么", "呢", "?", "兰陵王妃", "大", "结局", "是什么", "呢", "?", "接下来", "小", "编", "来", "和", "大家", "探讨", "一", "下", "吧", "。"], ["兰陵王妃", "剧情简介"], ["东晋", "末", "年", ",", "王朝", "动荡", ",", "诸侯", "割据", "。", "相", "传", "只有", "左", "持", "青鸾镜", ",", "右", "握", "离殇", "剑", "者", ",", "方", "可", "统一", "天下", "。", "青鸾镜", "与", "离殇", "剑", "本", "为龙", "教", "宝物", ",", "却", "因为", "战乱", "遗失", "。", "龙", "教", "圣女", "紫魅", "因", "练功", "入魔", ",", "被", "自己", "的", "内力", "反噬", "重伤", ",", "命悬一线", "。", "紫魅", "临死前", ",", "前", "交", "龙", "教", "唯一", "代", "传", "人", "元清锁", ",", 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"东晋末年,王朝动荡,诸侯割据。相传只有左持青鸾镜,右握离殇剑者,方可统一天下。青鸾镜与离殇剑本为龙教宝物,却因为战乱遗失。龙教圣女紫魅因练功入魔,被自己的内力反噬重伤,命悬一线。紫魅临死前,前交龙教唯一代传人元清锁,告知她青鸾镜与离殇剑的秘密。紫魅最后叮嘱元清锁,这两件宝物切不可落入奸人之手,只有真龙天子才配拥有,否则将天下大乱。元清锁为了寻找宝物,假扮北周司空夫人元氏的外甥女李娥姿,以家道中落为名,带着家族信物,投奔北周司空府。在此期间元清锁分别结识了兰陵王高长恭和北周宇文邕,各自发生了一段感情纠葛,元清锁最终帮宇文邕成就了帝王霸业,而选择离开北周与兰陵王高长恭共生死。", "兰陵王妃小说大结局:", "宇文邕灭了齐国统一了天下,但他之前服了毒所以统一之后不久也死了,高纬先杀了已经成为皇后的郑儿,然后自己自杀,然后四爷抱着已经快死的雪舞跑了出去。清锁最终帮宇文邕成就了帝王霸业,而选择离开北周与兰陵王高长恭共生死。", "基本信息", "中文名 兰陵王妃", "出品时间 2014", "出品公司 东阳欣尚新影视文化传播有限公司", "制片地区 中国大陆", "发行公司 东阳欣尚新影视文化传播有限公司", "导 演叶昭仪/林峰", "编剧 张林楠", "主 演张含韵,彭冠英,陈奕,林韦辰,王洁曦,张子文,华娇,田丽", "类型 古代传奇、爱情", "制片人 成钏", "开机时间 2013年(癸巳年)9月8日", "播出情况 即将播出", "小编结语:兰陵王妃确实是一部不俗的小说,但是电视剧拍的怎么样,还未播出我们无从所知。", "看过《兰陵王妃小说结局》的人还看了以下文章", "编辑推荐", "最新文章"], "bs_rank_pos": 5}], "answer_spans": [[0, 10]], "fake_answers": ["高长恭最终抱的美人归,与元清锁共度生死。"], "question": "兰陵王妃大结局是什么", "segmented_answers": [["女", "主", "选择", "了", "兰陵王", "高长恭", "，", "离开", "了", "北周", "，", "但是", "女", "主", "最后", "被", "萧洛云", "杀", "了", "，", "当时", "她", "和", "兰陵王", "高长恭", 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"paragraphs": ["你可以先把手伸到 最上方的进纸口 然后把这个掀起来 然后可以看到下面的板旁边有两个凸出的部分 然后拿住那两个地方 把这下面的板也掀起来 掀起来以后 你可以看到一块白色的小板 你拍一下 然后再改好 然后再看下这个面板有么有显示正常", "硬件故障要送修才可以。", "8成可能扫描电机损坏!1成是相关扫描齿轮,另1成是扫描架"], "bs_rank_pos": 3}, {"is_selected": false, "title": "hp一体机提示扫描仪错误22是什么意思_百度知道", "most_related_para": 0, "segmented_title": ["hp", "一体机", "提示", "扫描仪", "错误", "22", "是什么", "意思", "_", "百度", "知道"], "segmented_paragraphs": [["扫描仪", "错误", "22", "意思", "是", "扫描", "头", "出现故障", ",", "请", "通过", "以下", "方法", "解决", ":", "液晶屏", "扳手", "按键", "—", "服务", "—", "恢复", "默认值", "—", "OK", ",", "测试", "能否", "使用", ",", "如", "问题", "没", "解决", ",", "请", "通过", "以下", "方法", "将", "一体机", "复位", "至", "初始", "状态", ":", "组合键", ":", "关机", "-", "按住", "“", "取消", "”", "和", "“", "向右", "”", "按钮", "-", ">", "开机", ",", "如", "以上", "没有", "解决", "你", "的", "问题", "请", "送修", "。"]], "paragraphs": ["扫描仪错误22意思是扫描头出现故障,请通过以下方法解决:液晶屏扳手按键—服务—恢复默认值—OK,测试能否使用,如问题没解决,请通过以下方法将一体机复位至初始状态:组合键:关机-按住“取消”和“向右”按钮->开机,如以上没有解决你的问题请送修。"], "bs_rank_pos": 4}], "answer_spans": [[15, 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进入手机绑定页面:1、输入您要绑定的手机;2、获取验证短信,输入6位数验证码,点击确定 步骤阅读 6 手机绑定成功 步骤阅读 END", "百度经验:jingyan.baidu.com", "1 进入账户中心——账户安全后,可以看到目前所绑定的手机,点击后面的更改手机 步骤阅读 2 在更改手机页面,输入新的手机号,进行验证即可 步骤阅读 END", "百度经验:jingyan.baidu.com", "希望对亲们有所帮助", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:来来去去,认认真真"], "bs_rank_pos": 0}, {"is_selected": true, "title": "B站上绑定的电话号码注销了,怎么解绑? - 知乎", "most_related_para": 3, "segmented_title": ["B站", "上", "绑定", "的", "电话号码", "注销", "了", ",", "怎么", "解", "绑", "?", "-", "知乎"], "segmented_paragraphs": [["原来", "的", "电话", "号码", "早", "就", "注销", "了", ",", "但是", "b站", "改", "绑", "还", "需要", "原来", "的", "手机号码", "的", "验证码", ",", "晕", "啊", ",", "怎么", "搞", "啊", "显示全部"], ["谢", "邀", "的", "确", "没有", "其他", "的", "途径", "可以", "改", "绑", ",", "不过", "B站", "帐号", "题", "主", "需要", "记住", "一", "点", ",", "它", "最看重", "的", "是", "邮箱", ",", "邮箱", "才是", "最", "最", "优先", "最", "重要", "的", "保险", ",", "所以", "你", "手机号", "被", "注销", "了", "的", "话", ",", "保证", "你", "绑定", "的", "邮箱", "是", "正常", "的", "就", "可以", "了", ",", "手机号", "不", "绑", "也", "没关系", "。"], ["谢", "邀", "。", "联系", "客服", ",", "提交", "个人", "信息", ",", "绑定", "邮箱", ",", "原先", "手机号", ",", "登录", "地点", "等", "信息", "即可", "。"], ["申请", "解", "绑", "手机", ",", "请", "您", "使用", "账号", "绑定", "的", "邮箱", "发送", "申请", "邮件", "到", "help", "@", "bilibili", ".", "com", ",", "标题", "请", "注明", "解", "绑", "手机", ",", "并", "提供", "下列", "账号", "信息", ":", "用户名", ";", "昵称", ";", "原", "手机号", ";", "注册时间", "和", "注册", "地点", ";", "常用", "登陆", "地点", ";"], ["现在", "可以", "用", "认证", "邮箱", "解", "绑", "了", ",", "不需要", "老", "手机", "认证"], ["下载", "知乎", "客户端", "与", "世界", "分享", "知识", "、", "经验", "和", "见解"], ["相关", "Live", "推荐"]], "paragraphs": ["原来的电话号码早就注销了,但是b站改绑还需要原来的手机号码的验证码,晕啊,怎么搞啊显示全部", "谢邀的确没有其他的途径可以改绑,不过B站帐号题主需要记住一点,它最看重的是邮箱,邮箱才是最最优先最重要的保险,所以你手机号被注销了的话,保证你绑定的邮箱是正常的就可以了,手机号不绑也没关系。", "谢邀。联系客服,提交个人信息,绑定邮箱,原先手机号,登录地点等信息即可。", "申请解绑手机,请您使用账号绑定的邮箱发送申请邮件到 help@bilibili.com,标题请注明 解绑手机,并提供下列账号信息:用户名;昵称;原手机号;注册时间和注册地点;常用登陆地点;", "现在可以用认证邮箱解绑了,不需要老手机认证", "下载知乎客户端 与世界分享知识、经验和见解", "相关Live 推荐"], "bs_rank_pos": 1}, 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"的", "。"]], "paragraphs": ["可以 用你绑定的邮箱给help@bilibili.com发邮件说明用户名,原手机号,更改手机号。然后半天左右就会邮件通知你改好了^ω^", "在个人中心-安全设置相关的。"], "bs_rank_pos": 3}, {"is_selected": false, "title": "我在b站绑定了手机号,现在换手机了怎么解除绑定?_百度知道", "most_related_para": 1, "segmented_title": ["我", "在", "b站", "绑定", "了", "手机号", ",", "现在", "换", "手机", "了", "怎么", "解除", "绑定", "?", "_", "百度", "知道"], "segmented_paragraphs": [["方法", "1", ":", "通过", "手机", "编辑", "短信", "“", "QXBD", "”", "发送", "到", "服务", "代码", ":", "10661700", "(", "移动用户", ")", ",", "10661700", "山东", "用户", "请", "发送", "指令", "到", "10625700", "湖北", ",", "广西", ",", "江苏", ",", "福建", ",", "浙江", ",", "黑龙江", ",", "广东", ",", "上海", ",", "陕西", ",", "四川", ",", "河南", "请", "发送", "指令", "到", "10621700", "其它", "联通", "请", "发送", "指令", "到", "10661700", "(", "联通用户", ")", ",", "10621700", "(", "电信", "、", "网通", "用户", ")", "重庆", "用户", "请", "发送", "指令", "到", "10625700", "山东", "用户", "请", "发送", "指令", "到", "10625700", "吉林", "用户", "请", "发送", "指令", "到", "10628700", "辽宁", "用户", "请", "发送", "指令", "到", "10661700", 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山东用户请发送指令到10625700湖北,广西,江苏,福建,浙江,黑龙江,广东,上海,陕西,四川,河南请发送指令到10621700其它联通请发送指令到10661700 (联通用户),10621700(电信、网通用户) 重庆用户请发送指令到10625700山东用户请发送指令到10625700吉林用户请发送指令到10628700辽宁用户请发送指令到10661700青海,宁夏,甘肃,新疆,西藏用户请发送指令到10661700其它小灵通请发送指令到10621700 ,可解除绑定。方法2:您还可以登录QQ客户端,选择菜单->无线QQ,进入绑定相关页面,选择解除绑定操作。方法3:在绑定网页( http://bd.qq.com/ )登录,点击解除绑定按钮进行解绑操作。", "请以“xxx【账号昵称】-邮箱【或手机号】失效”为标题,发至邮箱help@bilibili.com请在邮件中说明具体是绑定的邮箱还是手机号失效,工作人员会根据申诉内容向您索要相关信息进行验证", "找B站客服,然后按照客服说得做"], "bs_rank_pos": 4}], "answer_spans": [[0, 31]], "fake_answers": ["手机注册的号可以更改手机号。登陆,进入账户安全中心,更改手机号,然后找一个不用但可以收验证短信的手机号绑定,完成"], "question": "b站手机解绑", "segmented_answers": [["申请", "解", "绑", "手机", "，", "请", "您", "使用", "账号", "绑定", "的", "邮箱", "发送", "申请", "邮件", "到", "help", "@", "bilibili", ".", "com", "，", "标题", "请", "注明", "解", "绑", "手机", "，", "并", "提供", "下列", "账号", "信息", "：", "用户名", "；", "昵称", "；", "原", "手机号", "；", "注册时间", "和", "注册", "地点", "；", "常用", "登陆", "地点", "。"], ["手机", "注册", "的", "号", "可以更改", "手机号", "。", "登陆", "，", "进入", "账户", "安全", "中心", 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"此后十个有效工作日后,买房买方拿身份证原件取房证。", "注意事项", "卖方提供资料:", "1.房屋所有权证(复印件1份、产籍图4份);", "3.身份证(夫妻双方)(复印件2份);", "4.夫妻双方户口簿(复印件1份);", "5.结婚证(如丢失到原婚姻登记的民政部门补办,其他证明方式不被承认);", "6.单身、离婚者提供离婚证、离婚协议书或民事调节书或法院判决书,离婚未再婚证明;丧偶者提供购房证明、丧偶未再婚证明;未婚者提供未婚证明(复印件1份)。", "买方提供资料:身份证(复印件2份)。", "1.房地产赠与、继承、委托、涉外以及涉及港澳台地区的提供《公证书》。 2.被拆迁户购房登记提供《拆迁协议书》。 3.五年内买卖提供相应房地产转让审批书原件及抵税专用表原件。 4.验人签字时卖方夫妻及买方必须本人到场。", "5.办理贷款请先预留资料,原房证(包括产籍图)、契税证、买卖双方身份证复印件。", "6.如该房屋为产权人和其配偶先购买的,之后产权人配偶去世的情况,需要其所有子女及合法继承人到场,产权人先办理继承公证后才可以办理转让。"], "bs_rank_pos": 0}, {"is_selected": true, "title": "成都二手房交易流程及材料- 成都本地宝", "most_related_para": 2, "segmented_title": ["成都", "二手房", "交易流程", "及", "材料", "-", "成都", "本地宝"], "segmented_paragraphs": [["【", "导语", "】", ":", "成都", "本地宝", "为您", "带来", "成都", "二手房", "交易", "信息", "。", "包含", "成都", "二手房交易", "步骤", ",", "需要", "提交", "的", "材料", "以及", "流程", "等", "信息", "。"], ["成都", "二手房", "交易流程", "及", "材料", ":"], ["一", "、", "买卖双方", "交易", "之", "前", "协商", "沟通", "后", "共同", "同意", "。", "二", "、", "卖方", "提供", "房屋", "的", "所有", "合法", "证件", 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"银行", "对", "房屋", "审核", "之后", ",", "再", "确定", "可以", "贷款", "的", "额度", "。", "等", "取", "领取", "了", "房屋所有权证", "之后", ",", "银行", "就会", "一次性", "发放", "贷款", "。"], ["八", "、", "买方", "拿到", "新", "房屋所有权证", ",", "付清", "所有", "的", "房款", ",", "卖方", "再", "将", "物业费", "结清", "之后", ",", "二手房交易", "就", "可以", "到", "此", "结束", "了", "。"], ["本地宝", "郑重声明", ":", "本", "文", "仅", "代表", "作者", "个人观点", ",", "与", "本地宝", "无关", "。", "其", "原创", "性", "及", "文", "中", "陈述", "内容", "未经", "本站", "证实", ",", "本地宝", "对", "本", "文", "及其", "中", "全部", "或者", "部分", "内容", "的", "真实性", "、", "完整性", "、", "及时", "性", "不", "作", "任何", "保证", "和", "承诺", ",", "请", "网友", "自行", "核实", "相关内容", "。"]], "paragraphs": ["【导语】:成都本地宝为您带来成都二手房交易信息。包含成都二手房交易步骤,需要提交的材料以及流程等信息。", "成都二手房交易流程及材料:", "一、买卖双方交易之前协商沟通后共同同意。 二、卖方提供房屋的所有合法证件,买方就可以交纳一定的购房定金,然后买卖双方根据协商的意见,签订正规的合法买卖合同,合同内必须包括对房屋坐落位置、产权状况及成交价格、房屋交付时间、房屋交付、产权办理等具体事项,合同需要一式三份。 三、然后向房地产交易管理部门提交申请,然后房地产管理部门会将申请和有关资料进行审查,对符合上市条件的房屋会给予批准过户的回复。 四、房产部门根据交易房屋的产权和购买对象,按交易部门设定的审批权限进行逐级的审报,得到批准之后,交易的双方可以进行办理立契的手续。", "五、由买卖双方交纳各项税费。", "六、在房地产交易管理部门办理变更之后,买方就可以凭领取房屋所有权证的通知单,到发证部门领取新的产权证。", "七、如买方有贷款,等银行对房屋审核之后,再确定可以贷款的额度。等取领取了房屋所有权证之后,银行就会一次性发放贷款。", "八、买方拿到新房屋所有权证,付清所有的房款,卖方再将物业费结清之后,二手房交易就可以到此结束了。", "本地宝郑重声明:本文仅代表作者个人观点,与本地宝无关。其原创性及文中陈述内容未经本站证实,本地宝对本文及其中全部或者部分内容的真实性、完整性、及时性不作任何保证和承诺,请网友自行核实相关内容。"], "bs_rank_pos": 1}, {"is_selected": false, "title": "成都二手房交易流程、所需材料和税费_百度文库", "most_related_para": 1, "segmented_title": ["成都", "二手房", "交易流程", "、", "所需材料", "和", "税费", "_", "百度", "文库"], "segmented_paragraphs": [["成都", "二手房", "交易流程", "、", "所需材料", "和", "税费", "_", "社会民生", "_", "生活", "休闲", "。", "成都", "二手房", "交易流程", "、", "所需材料", "和", "税费", "一", "、", "流程", "1", ".", "领取", "《", "房地产", "转让", "申请", "审批", "书", "》", "、", "《", "房地产", "买卖", "合同", "》", "、", "《", "买方", "确认", "单", "》", "表格", "并", "填表", ";", "2", ".", "交易", "—", "—", "买卖双方", "验", "人", "签字", ";", "3", ".", "查", "档", "—", "—", "查", "房屋产权", "是否", "清晰", "、"], ["成都", "二手房", "交易流程", "、", "所需材料", "和", "税费", "一", "、", "流程", "1", ".", "领取", "《", "房地产", "转让", "申请", 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"的", "家", "年", "的", "普通住宅", "免征", "庭", "唯一", "住房", ",", "免征", "营业税", ")", "个人所得税", ")", "2", "元", "每平米", "/", "注", ":", "以", "60", "万", "计", ",", "契税", "6000", "元", ",", "交易手续费", "240", "元", ",", "过户", "登记费", "80", "元", ",", "工本费", "5", "元", "。"]], "paragraphs": ["成都二手房交易流程、所需材料和税费_社会民生_生活休闲。成都二手房交易流程、所需材料和税费一、流程 1.领取《房地产转让申请审批书》 、 《房地产买卖合同》 、 《买方确认单》表格并填表; 2.交易——买卖双方验人签字; 3.查档——查房屋产权是否清晰、", "成都二手房交易流程、所需材料和税费一、流程 1.领取《房地产转让申请审批书》 、 《房地产买卖合同》 、 《买方确认单》表格并填表; 2.交易——买卖双方验人签字; 3.查档——查房屋产权是否清晰、有无抵押、查封(上午查下午出结果,下午查次日出结果) ; 4.鉴定——做《房屋安全鉴定报告》 ; 5.核价——财政部门根据《房屋买卖(置换)合同》的成交金额评估房价; 6.地税——缴纳卖方房产营业税 (免税的地税部门开具免税确认单, 5 月 1 日后改到国税局缴纳增值税) ; 7.当以上的工作完成后,第二天或第三天交件,七天后交契税,领取契证交手续费,交纳交易手续费封 卷。 (注意,一定要领取证回执。 ) 8.十个有效工作日后,买房买方拿身份证原件取房证。 二、材料 卖方需提供: 1.房屋所有权证(复印件 1 份、产籍图 4 份) ; 2.契税证; 3.身份证(夫妻双方) (复印件 2 份) ; 4.夫妻双方户口簿(复印件 1 份) ; 5.结婚证(如丢失到原婚姻登记的民政部门补办,其他证明方式不被承认) ; 6.单身、离婚者提供离婚证、离婚协议书或民事调节书或法院判决书,离婚未再婚证明;丧偶者提供购 房证明、丧偶未再婚证明;未婚者提供未婚证明(复印件 1 份) 。 买方提供资料: 身份证(复印件 2 份) 。三、税费 商品房 契税 1%(90 平米以 下普通住房) ; / 3% 个人所得税 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"三、龙泉驿过户流程 1、 查档:申请人持身份证、产权证到政务 一楼A区房管局6-7号窗口办理。 2、 买方持身份证、户口簿、婚姻证明到政务 一楼A区房管局6-7号窗口打印房屋登记信息表。 3、 打印网签合同:买卖双方持身份证及房产证到政务 一楼A区房管局自助网签机发布房源并打印网签合同。 4、 核税:买卖双方持网签合同及所需证件及资料到政务 一楼A区地税局20号窗口预审核税资料,16-19号窗口核税。 5、 缴纳税费:政务 二楼B区1-10号窗口缴纳税费。 6、 登记受理:买卖双方带齐过户所需证件及资料到政务 一楼A区房管局9-10号窗口办理过户登记。 7、 缴费、登簿、领证:1个工作日后(受理当天不算,节假日除外),由受理时约定的领证人携身份证按照受理单上面的流程办理。", "四、 郫县过户流程 1. 发布房源:到政务 B区二楼房管局在网签自助机上录入房源信息(包括产权人及共有人姓名、身份证号码、联系电话、产权证权字号)并取得告知单。(★办理贷款的客户,房东应在查档当天录入房源) 2. 打印网签合同:当天(长时效一个月内)凭告知单,买卖双方带齐过户所需证件到政务 B区二楼房管局在网签自助机上打印网签。 3. 查档:卖方持身份证、产权证到政务 B区二楼房管局21号窗口办理。 4. 核税:买卖双方持网签合同及所需证件及资料到政务 B区四楼地税窗口核税并缴纳税费(只能刷卡),下午三点半不核税。 5. 登记受理:买卖双方带齐过户所需证件及资料到政务 B区二楼房管局27-36号窗口办理过户登记。 6. 登记受理后,5个工作日(受理当天不算,节假日除外)后本人持身份证原件及缴费通知单到政务 B区二楼缴费窗口缴费(登记费必须交现金,其余可以在二楼刷卡,现金在三楼成都银行交),政务 B区二楼18-19号窗口领证。 7、 缴费、登簿、领证:1个工作日后(受理当天不算,节假日除外),由受理时约定的领证人携身份证按照受理单上面的流程办理。"], "bs_rank_pos": 4}], "answer_spans": [[0, 149]], "fake_answers": 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对这个问题西医尚未有很好的解释,所以有的医生可能说这不算什么病,但中医对此有详细的解释。 手足心发热而未有其它症状相伴的是极少的,绝大多数手足心发热的多同时伴有未引起注意的或被看作是正常的表现。中医将手足心发热分为疳积脾虚和血虚阴亏两种。 1、疳积脾虚,手足心发热 其原因一是饮食不节,即有的饮食无度,食不定时,常吃零食,长期下去可损伤脾胃功能,引起运化失常,形成积滞,积滞日久,水谷精微无能吸收,形成疳积而发热。 二是患其它疾病后,如吐泻,痢疾、寄生虫病等治疗不当,迁延日久,损伤气血,导致营养不良而形成疳积发热。这部分常见为手足心发热,面黄肌瘦,毛发干枯,腹部胀大,食欲不佳,常食异物,夜睡不宁,大便较稀,有不消化食物,小便黄浊如米泔。 2、血虚阴亏,手足发热 其原因多由于平素体质虚弱,或大病、热病后,失于调理,阴血耗伤,正气尚未恢复而致。这部分常表现为手足心发热,形体消瘦,精神萎靡,咳嗽少痰,目眩耳鸣,口干舌燥,午后潮热,颧红盗汗,小便频数,大便秘结。 手足心发热,以疳积脾虚引起的多见,绝大多数是3岁以下的婴幼儿。 在药物治疗的同时,还要注意补充一定数量的富有营养的食品,多晒太阳,呼吸新鲜空气,以促进体质的恢复。 从中医角度看,手足心热是阴虚证以及阴虚火旺证常见的临床表现,除了手足心热,阴虚证还可以有潮热盗汗,烦躁,心烦不寐,舌红少苔,脉细数等表现,治疗方面主要是使用一些滋阴清热的药物,比如知柏地黄丸之类的。", "手心脚心特别的热,混身一点力气都没有,就向一摊泥是的,是什么病啊?应该吃点什么药呢.", "我也是手心脚心发热,不知道怎么回事,特别是夏天的时候,特别的烫", "阴虚血热,吃知柏地黄丸滋阴降火有效。", "多为营分学分热邪炽盛。"], "bs_rank_pos": 0}, {"is_selected": true, "title": "女性手心发热是什么原因?_百度知道", "most_related_para": 0, "segmented_title": ["女性", "手心", "发热", "是什么", "原因", "?", "_", "百度", "知道"], "segmented_paragraphs": [["手心", "热", "证", "名", "。", "两", "手掌", "心", "有", "发热", "感觉", "。", "一名", "掌中热", "。", "手三阴经", "是", "动", "、", "所", "生病", "证", "之", "一", "。", "详见", "掌", "中", "热", "条", "。", "又", "为", "劳倦", "内伤", "及", "五心烦热", "的", "症状", "之", "一", "。", "《", "内外伤辨惑论", "》", "卷", "上", ":", "“", "内伤", "及", "劳役", "饮食", "不", "节", ",", "病", "手心热", ",", "手背", "不热", ";", "外伤", "风寒", "则", "手背", "热", ",", "手心", "不热", ",", "此", "辨", "至", "甚", "皎然", "。", "”", "参见", "掌中热", "、", "五心烦热", "条", "。", "应", "请", "①", "从", "饮食", "上", "注意", ",", "少", "食", "油炸", ",", "油腻", "食品", "含", "脂肪", "和", "胆固醇", "高", "的", "动物", "内脏", "和", "肥肉", "等", ",", "多", "食", "蔬菜", "、", "水果", "和", "清淡", "的", "饮食", ",", "如", "绿豆", "百合", "汤", ",", "忌", "酒", "和", "烟", "。", "②", "可用", "金银花", "、", "野菊花", "、", "芦根", "泡水", "代", "茶", "有", "清", "火", "作用", ",", "少", "吃", "可口可乐", "及", "奶制品", "冷饮", ".", "此外", ",", "梨", "、", "葡萄", "吃着", "正当时", ":", "梨", ",", "含有", "丰富", "的", "维生素", ",", "具有", "祛痰", "止咳", "、", "利咽", ",", "保护心脏", ",", "减轻", "疲劳", "和", "通便", "的", "作用", ";", "葡萄", ",", "有", "抑制", "癌变", "细胞", "扩散", "、", "抗", "动脉硬化", "、", "调节", "心", "搏", "次数", "的", "功能", "。", "但是", "对于", "老人", "孩子", ",", "食用", "的", "分量", "不宜过多", "。", "便秘", "、", "痔疮", "者", "可", "多", "食", "菠菜", "、", "芹菜", "、", "竹笋"]], "paragraphs": ["手心热证名。两手掌心有发热感觉。一名掌中热。手三阴经是动、所生病证之一。详见掌中热条。又为劳倦内伤及五心烦热的症状之一。《内外伤辨惑论》卷上:“内伤及劳役饮食不节,病手心热,手背不热;外伤风寒则手背热,手心不热,此辨至甚皎然。”参见掌中热、五心烦热条。 应请①从饮食上注意,少食油炸,油腻食品含脂肪和胆固醇高的动物内脏和肥肉等,多食蔬菜、水果和清淡的饮食,如绿豆百合汤,忌酒和烟。②可用金银花、野菊花、芦根泡水代茶有清火作用,少吃可口可乐及奶制品冷饮. 此外, 梨、葡萄吃着正当时:梨,含有丰富的维生素,具有祛痰止咳、利咽,保护心脏,减轻疲劳和通便的作用;葡萄,有抑制癌变细胞扩散、抗动脉硬化、调节心搏次数的功能。但是对于老人孩子,食用的分量不宜过多。 便秘、痔疮者可多食菠菜、芹菜、竹笋"], "bs_rank_pos": 2}, {"is_selected": true, "title": "女人手心热的原因_百度文库", "most_related_para": 0, "segmented_title": ["女人", "手心热", "的", "原因", "_", "百度", "文库"], "segmented_paragraphs": [["很多人", "常", "有", "这样", "的", "感觉", "，", "手心", "经常", "发", "烫", "，", "贴", "在", "其他", "部位", "的", "皮肤", "上", "，", "会", "有", "热", "热", "的", "感觉", "；", "有", "的", "人", "还有", "夏季", "手心", "热", "，", "冬季", "反而", "手心", "冷", "的", "特点", "；", "还有", "一些人", "手心", "、", "脚", "心", "都", "感觉", "热", "，", "甚至", "心里", "老是", "烦", "烦", "的", "，", "容易", "发脾气", "。", "在", "中医学", "中", "，", "这", "叫", "“", "五心烦热", "”", "，", "多", "见于", "阴虚", "、", "血虚", "和", "食积", "。", "前", "二者", "多发", "于", "女性", "，", "后者", "多", "为", "儿童", "。", "阴虚", "所致", "的", "手心热", "多", "见于", "肾阴", "不足", "，", "或", "肝", "肾阴虚", "。", "表现", "为", "腰膝酸软", "，", "手足", "心", "热", "，", "咽干", "口", "燥", "，", "舌", "质", "红", "少", "津", "，", "严重", "者", "可", "出现", "性机能", "虚", "性", "亢奋", "。", "这样", "的", "患者", "可", "选用", "知柏地黄丸", "、", "六味地黄丸", "这", "一类", "滋阴", "清热", "的", "药物", "。", "血虚", "导致", "的", "手心热", "，", "大", "多", "见于", "年轻女性", "，", "多", "伴有", "痛经", "。", "最", "典型", "的", "表现", "就是", "夏", "天", "手心热", "、", "冬天", "反倒", "手脚", "冰凉", "。", "这", "类", "患者", "，", "平", "日", "可", "服用", "当归", "补血", "丸", "这", "类", "补", "养", "气血", "的", "药物", "，", "或者", "用", "当归", "6", "g", "、", "黄芪", "30g", "，", "配合", "来", "熬汤", "。", "食积", "也称", "“", "停食", "”", "，", "是", "指", "小儿", "由于", "脾胃虚弱", "，", "饮食", "不", "节", "，", "引起", "小儿", "脾胃", "功能", "失常", "，", "造成", "小儿", "消化", "功能", "紊乱", "导致", "食积", "症", "。", "多", "表现", "为", "食欲不振", "、", "两", "颧", "潮红", "、", "手足", "心", "热", "、", "腹胀", "嗳气", "。", "小孩", "手足心热", "，", "既", "不能", "用", "滋阴", "的", "方法", "，", "也", "不能", "用", "养血", "的", "方法", "，", "当", "以", "健脾", "消", "积", "为", "治", "法", "，", "或", "以", "捏脊", "疗法", "缓解", "。", "的", "来", "说", "，", "手心", "热", "的", "成人", "，", "不管", "哪", "种", "症", "状", "，", "生活", "中", "一", "要", "忌", "生气", "，", "二", "要", "忌食", "辛辣", "。", "这", "是因为", "“", "气", "有余", "便是", "火", "”", "，", "火热", "损伤", "阴", "血", "，", "会", "发生", "手心热"]], "paragraphs": ["很多人常有这样的感觉，手心经常发烫，贴在其他部位的皮肤上，会有热热 的感觉；有的人还有夏季手心热，冬季反而手心冷的特点；还有一些人手心、脚 心都感觉热，甚至心里老是烦烦的，容易发脾气。在中医学中，这叫“五心烦热”， 多见于阴虚、血虚和食积。前二者多发于女性，后者多为儿童。 阴虚所致的手心热多见于肾阴不足，或肝肾阴虚。表现为腰膝酸软，手足心 热，咽干口燥，舌质红少津，严重者可出现性机能虚性亢奋。这样的患者可选用 知柏地黄丸、六味地黄丸这一类滋阴清热的药物。 血虚导致的手心热，大多见于年轻女性，多伴有痛经。最典型的表现就是夏 天手心热、冬天反倒手脚冰凉。这类患者，平日可服用当归补血丸这类补养气血 的药物，或者用当归 6g、黄芪 30g，配合来熬汤。 食积也称“停食”，是指小儿由于脾胃虚弱，饮食不节，引起小儿脾胃功能 失常，造成小儿消化功能紊乱导致食积症。多表现为食欲不振、两颧潮红、手足 心热、腹胀嗳气。小孩手足心热，既不能用滋阴的方法，也不能用养血的方法， 当以健脾消积为治法，或以捏脊疗法缓解。的来说，手心热的成人，不管哪种症 状，生活中一要忌生气，二要忌食辛辣。这是因为“气有余便是火”，火热损伤 阴血，会发生手心热"], "bs_rank_pos": 12}], "answer_spans": [[0, 7]], "fake_answers": ["手心脚心发热是典型的肾阴虚症状"], "question": "女人手心烧是什么原因", "segmented_answers": [["手心", "脚心", "发热", "是", "典型", "的", "肾阴虚", "症状", "。"], ["结缔组织", "疾病", "：", "风湿热", "、", "系统性红斑狼疮", "、", "类风湿性关节炎", "等", "可", "引起", "手心", "发热", ",", "但", "发生", "率", "不", "高", ",", "而且", "一般", "伴有", "明显", "全身", "发热", "症状", "。"], ["手心", "经常", "发", "烫", "，", "贴", "在", "其他", "部位", "的", "皮肤", "上", "，", "会", "有", "热", "热", "的", "感觉", "；", "有", "的", "人", "还有", "夏季", "手心", "热", "，", "冬季", "反而", "手心", "冷", "的", "特点", "；", "还有", "一些人", "手心", "、", "脚心", "都", "感觉", "热", "，", "甚至", "心里", "老是", "烦", "烦", "的", "，", "容易", "发脾气", "。", "在", "中医学", "中", "，", "这", "叫", "\"", "五心烦热", "\"", "，", "多", "见于", "阴虚", "、", "血虚", "和", "食积", "。", "前", "二者", "多发", "于", "女性", "，", "后者", "多", "为", "儿童", "。", "阴虚", "所致", "的", "手心热", "多", "见于", "肾阴", "不足", "，", "或", "肝", "肾阴虚", "。", "血虚", "导致", "的", "手心热", 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"手心经常发烫，贴在其他部位的皮肤上，会有热热的感觉；有的人还有夏季手心热，冬季反而手心冷的特点；还有一些人手心、脚心都感觉热，甚至心里老是烦烦的，容易发脾气。在中医学中，这叫\"五心烦热\"，多见于阴虚、血虚和食积。前二者多发于女性，后者多为儿童。阴虚所致的手心热多见于肾阴不足，或肝肾阴虚。血虚导致的手心热，大多见于年轻女性，多伴有痛经。中医将手心发热是怎么回事分为疳积脾虚和血虚阴亏两种。1、疳积脾虚，手足心发热其原因一是饮食不节，即有的饮食无度，食不定时，常吃零食，长期下去可损伤脾胃功能，引起运化失常，形成积滞，积滞日久，水谷精微无能吸收，形成疳积而发热。二是患其它疾病后，如吐泻，痢疾、寄生虫病等治疗不当，迁延日久，损伤气血，导致营养不良而形成疳积发热。这部分常见为手足心发热，面黄肌瘦，毛发干枯，腹部胀大，食欲不佳，常食异物，夜睡不宁，大便较稀，有不消化食物，小便黄浊如米泔。2、血虚阴亏，手足发热其原因多由于平素体质虚弱，或大病、热病后，失于调理，阴血耗伤，正气尚未恢复而致。这部分常表现为手足心发热，形体消瘦，精神萎靡，咳嗽少痰，目眩耳鸣，口干舌燥，午后潮热，颧红盗汗，小便频数，大便秘结。手足心发热，以疳积脾虚引起的多见，绝大多数是3岁以下的婴幼儿。在药物治疗的同时，还要注意补充一定数量的富有营养的食品，多晒太阳，呼吸新鲜空气，以促进体质的恢复。"], "answer_docs": [0], "segmented_question": ["女人", "手心", "烧", "是什么", "原因"], "question_type": "DESCRIPTION", "question_id": 181587, "fact_or_opinion": "OPINION", "match_scores": [0.9411764705882353]} +{"documents": [{"is_selected": true, "title": "比熊和泰迪能配种吗_百度知道", "most_related_para": 1, "segmented_title": ["比熊", "和", "泰迪", "能", "配种", "吗", "_", "百度", "知道"], "segmented_paragraphs": [["很多", "比熊", "卖家", "为了", "让", 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贵宾犬,Poodle,也称“贵妇犬”,又称“卷毛狗”,泰迪是贵宾犬的美容造型之一。贵宾犬起源于德国,以水中捕猎而著称,属于非常聪明的且喜欢狩猎的犬种,多年以来,它一直被认为是法国的国犬。贵宾犬根据体型大小分为巨型犬、标准犬、迷你犬、玩具犬(含茶杯犬)四种,它们的主要区别是体型,看起来有种高贵的气质,给人一种喜悦的、聪明的感觉,能给人带来愉悦的心情。 比熊犬(法语:Bichon Frisé,意指“白色卷毛的玩赏用小狗”)原产于地中海地区,是一种小型犬品种。是常见宠物,外表类似于马尔济斯。它们不会自然脱毛,因此毛发需要整理。颜色一般白色。原称巴比熊犬,后缩为比熊犬。怀疑是巴比特犬和", "可以,比熊和白色的泰迪,除了尾巴和耳朵,从外型上基本上是特征一致的两个品种。但是,泰迪的智商要比比熊高很多呢,而且性格更稳定,所以,你自己考虑喽。。。", "可以,比熊和白色的泰迪,除了尾巴和耳朵,从外型上基本上是特征一致的两个品种。但是,泰迪的智商要比比熊高很多呢,而且性格更稳定 贵宾犬,Poodle,也称“贵妇犬”,又称“卷毛狗”,泰迪是贵宾犬的美容造型之一。贵宾犬起源于德国,以水中捕猎而著称,属于非常聪明的且喜欢狩猎的犬种,多年以来,它一直被认为是法国的国犬。贵宾犬根据体型大小分为巨型犬、标准犬、迷你犬、玩具犬(含茶杯犬)四种,它们的主要区别是体型,看起来有种高贵的气质,给人一种喜悦的、聪明的感觉,能给人带来愉悦的心情。 比熊犬(法语:Bichon Frisé,意指“白色卷毛的玩赏用小狗”)原产于地中海地区,是一种小型犬品种。是常见宠物,外表类似于马尔济斯。它们不会自然脱毛,因此毛发需要整理。颜色一般白色。原称巴比熊犬,后缩为比熊犬。怀疑是巴比特犬和", "千万别!会后悔的!生出来的狗狗是浅土黄色的!超难看!不纯!", "这个得问它们自己愿不愿意了?如果愿意,品种不是问题,咳咳!", "可以,但是生出来的狗狗可能不会好看。"], "bs_rank_pos": 1}], "answer_spans": [[4, 18]], "yesno_answers": ["Yes"], "fake_answers": ["。只要体型差距别大的太过分。和任何品种都可以配种"], "question": "比熊跟泰迪能配吗", "segmented_answers": [["只要", "体型", 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"难道", "王宝强", "其实", "有", "170", "?", "却", "自称", "159", "水仙", "和", "小青", ":", "举报", "2016", "-", "10", "-", "07", "02", ":", "53", ":", "36", "评论", "评论", "馒头", "逗", "面条", ":", "认为", "AB", "168", "的", "感觉", "是", "智障"]], "paragraphs": ["字体:", "边距:", "背景:", "还原:", "范冰冰和baby的身高真如其百科所写,都是168? 好,我们来看图!!!", "旁边的熊黛琳感觉鞋跟差不多,baby和冰冰的鞋跟也都蛮高,私以为冰冰的鞋跟略高一些,可是都是这种落差是怎么回事???", "KITTY斌斌: 举报 2016-10-07 02:58:52 评论 评论小马甲的想法:感觉你说的是对的", "然后我又看到了这张图,所以他们真的身高差不多???", "黑幕基地: 举报 2016-08-22 19:06:15 评论 范冰冰体态这么宽? ty_127714596: 举报 2017-04-23 17:34:25 评论 评论 黑幕基地 裙子宽", "然后想起他们都跟黄晓明有交集,就去找了他们各自的合影", "他们分别长成这样。。。。", "逗比天天有: 举报 2017-02-14 15:41:02 评论 这个没法比,还要看教主鞋子里塞了啥 丁希希: 举报 2017-04-24 10:12:36 评论 评论逗比天天有:被你的评论笑喷", "好吧,看了这两张图我又迷思了。。冰冰的鞋跟比较高,这一点毋庸置疑,可是为什么他们都是比小明稍微矮一点点!!!!!为什么!!!!", "好像真的差不多～～～跑男里好像也差不多", "那么,让我们从头来看,熊黛琳的百科身高178厘米,黄晓明的百科身高179厘米。因为熊黛琳的鞋跟并不是很高,姑且算她实际增高5厘米,也就是183厘米,黄晓明的皮鞋和帆布鞋看起来没有很夸张地增高,就算他穿鞋182厘米,那么接下来我们来比对一下!!!!", "大岛大岛君: 举报 2016-02-18 10:25:44 评论 黄晓明没有179 水仙和小青: 举报 2016-10-07 02:48:07 评论 评论馒头逗面条:黄晓明和黄海波身高对比,黄晓明170左右", "看起来冰冰和baby的身高如此相似,究竟是我们误以为冰冰比较高,以为她有168???还是冤枉了baby,其实她不止传说中的159呢???", "更正,因为晓明的身高水分太大,无从比较,所以我们从冰冰和baby与其他人的合影来八一八!!", "jishutie技术贴留名 举报 | 12楼 | 打赏 | 回复 | 评论 | 赞", "冰冰都是穿恨天高,可能她只是骨架比较大,胳膊比较长,腿短,所以都误以为她很高,个人猜测,天涯扒了这么多年,都没扒出来,很没面子啊", "找了找图,迷思更重了,没什么标杆,实在八不好,算了,弃贴而逃。", "大家集思广益,有什么靠谱的标杆吗?给我一个标杆,我给你们八出真相!!!可是现在好丢脸啊!", "看到有人支持,我再努力一把!!!", "转着弯来坑教主,,,,", "更正,因为晓明的身高水分太大,无从比较,所以我们从冰冰和baby与其他人的合影来八一八!! ----------------------------- 小明170 那个什么波嫖妓入狱服刑照片170 有人拿上海滩八过了 然后杜海涛那个军官 参照的军人172 有人也八了 总之 监狱和部队两个地方不能zuojia的", "范冰冰,ab都有好多替身的。有时候拍到的根本就不是她们本人的。", "只知道高跟鞋是看鞋跟的高度,而不是看鞋尖的防水台高度。", "其实两人差不多,都是160左右,只是某冰整天不离恨天高,让人觉得她好像个子高", "其实两人差不多,都是160左右,只是某冰整天不离恨天高,让人觉得她好像个子高 ————————————————— 我觉得你说得很有道理", "humei201515: 举报 2016-02-17 12:20:09 评论 李治廷长得不错,个矮可惜了了,ab和fbb大概都165cm iseisei: 举报 2016-09-09 03:22:51 评论 单说这两张图 明显图二的李治廷比图一的内增高要高 头发也要高一些 而且虽然视觉上二女逗比李治廷猛那么一丢丢 但ab在李治廷的前位 bb在李治廷的后位 估计还是bb高一些吧 ps图二李治廷的鞋里面是不是根本没有脚呀", "只知道高跟鞋是看鞋跟的高度,而不是看鞋尖的防水台高度。 ————————————————— 鞋跟差不多的,防水台高的那个实际增高会多一点,不信的话自己可以做个实验", "范冰冰没那么高北鼻也没159那么矮啊不过北鼻应该比范冰冰矮估计范冰冰165北鼻163这样吧我猜", "呼呼忽忽忽: 举报 2015-12-13 14:25:23 评论 AB,158. 范160 水仙和小青: 举报 2016-10-07 02:51:57 评论 评论 呼呼忽忽忽 :你的这个差不多AB158-159冰冰161-162大概。", "╮(╯_╰)╭ AB要是有168cm早跳出来量给你看啪啪啪打脸了", "我们都知道李治廷不高,他的百科身高175,从真人秀以及各种照片看来是没有的,暂算他172.按照雪林的说法,冰冰和baby的实际身高 应当差不多是160左右", "冰冰都是穿恨天高,可能她只是骨架比较大,胳膊比较长,腿短,所以都误以为她很高,个人猜测,天涯扒了这么多年,都没扒出来,很没面子啊 ————————————— 加一,此人属于显高,实不高", "有个朋友在某机场贵宾室工作,曾经我们一起聊天时候问过她见过的女明星哪个素颜好看,她说baby素颜很美,我们异口同声说还不是整出来的。然后我就问了困惑很久的问题,baby到底多高。朋友说baby不矮,168左右的,矮她一头(朋友178是个高妹)。", "好吧,看了这两张图我又迷思了。。冰冰的鞋跟比较高,这一点毋庸置疑,可是为什么他们都是比小明稍微矮一点点!!!!!为什么!!!! ————————————— 小明第二张内增了吧", "好吧,看了这两张图我又迷思了。。冰冰的鞋跟比较高,这一点毋庸置疑,可是为什么他们都是比小明稍微矮一点点!!!!!为什么!!!! ————————————— 小明第二张内增了吧 ----------------------------- 貌似是的", "有个朋友在某机场贵宾室工作,曾经我们一起聊天时候问过她见过的女明星哪个素颜好看,她说baby素颜很美,我们异口同声说还不是整出来的。然后我就问了困惑很久的问题,baby到底多高。朋友说baby不矮,168左右的,矮她一头(朋友178是个高妹)。 ————————————————— 矮她一头是多少,一头难道不是25厘米???", "呼呼忽忽忽: 举报 2015-12-13 14:26:31 评论 168......笑喷了, 王宝强自称身高159. 王宝强和AB同穿平底, 王宝强比AB高了一点点, 难道王宝强其实有170? 却自称159 水仙和小青: 举报 2016-10-07 02:53:36 评论 评论馒头逗面条:认为AB168的感觉是智障"], "bs_rank_pos": 1}, {"is_selected": true, "title": "回复:范冰冰真实身高,多图对比_身高吧_百度贴吧", "most_related_para": 6, "segmented_title": ["回复", ":", "范冰冰", "真实", "身高", ",", "多", "图", "对比", "_", "身高", "吧", "_", "百度贴吧"], "segmented_paragraphs": [["如果", "她", "真", "的", "高", ",", "干嘛", "还", "每次", "撑", "那么", "高", "的", "鞋子", "。", "都是", "女人", ",", "都", "知道", "穿", "高跟", "的", "痛苦", ",", "何况", "她", "穿", "那么", "高", ",", "而且", "几乎", "都", "穿", ",", "那", "就", "证明", "她", "内心", "清楚", "自己", "这一点"], ["范冰冰", "163", ".", "刘亦菲", "165", ".", "韩雪", "169", "～"], ["赵薇", "比", "169", "的", "谢霆锋", "还要", "矮", "一", "截", ",", "估计", "160", ",", "苏有朋", "是", "很", "诚实", "的", "人", ",", "小虎队", "都是", "170", ".", "比", "冰冰", "高一", "截", "呢", "。", "范冰冰", "161", "."], ["以", "李斯羽", "181", ".", "5", "韩雪", "169", "蒋梦婕", "159", ".", "7", "作为", "标杆", ",", "考证", "娱乐圈", "女星", "身高", "作者", ":", "杨晓旭", "181", ".", "5", "李斯羽", "[", "绝对", "标杆", "]", "181", 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"(", "Ella", ")", "=", "郭羡妮", "=", "桂纶镁", ".", ".", ".", ".", ".", "=", "高圆圆", "=", "袁姗姗", "=", "杨紫琼", "=", "陈紫函", "=", "俞飞鸿", "=", "陈玉莲", "=", "郑", "爽", "=", "李", "湘", "159", ".", "7", "蒋梦婕", "[", "绝对", "标杆", "]", "(", "可以", "算", "作", "160", "的", "吧", ")", "159", "安以轩", "=", "霍思燕", "=", "温碧霞", "=", "张嘉倪", "=", "黎", "姿", "=", "张", "庭", "=", "厉", "娜", "158", "张柏芝", "=", "杨丞琳", "=", "萧亚轩", "=", "周笔畅", "=", "徐若瑄", "=", "林忆莲", "=", "刘诗诗", "=", "马伊琍", "=", "佘诗曼", "=", "关", "咏"], ["我", "见过", "林心如", "和", "赵薇", "本人", "绝对", "不到", "160", "(", "净高", ")", ",", "也", "就", "158", "!", "推测", "范冰冰", "也", "就", "161", "左右"], ["艾玛", "大家", "不要吵", "了", "好", "吗", "。", "一般来说", "我", "的", "平底鞋", "身高", "别人", "目测", "都是", "165", "左右", ",", "我", "不", "算", "高", "个", "的", "人", ",", "但", "绝对", "不是", "个", "矮", "的", "人", "。", "前", "天", "去", "体检", ",", "红外线", "测", "身高", ",", "光", "脚", ",", "纯", "量", "身高", ",", "其实", "我", "是", "160", "。"], ["大家", "的", "真实", "身高", "真", "没有", "你们", "说", "的", "那么", "玄乎", ",", "净高", "的", "话", "男", "的", "哪有", "那么多", "180", "啊", ",", "女", "的", "哪有", "那么多", "165", "啊", "。", "我", "排队", "体检", "观察", "的", "时候", ",", "大部分", "身高", "可以", "的", "女", "的", ",", "全", "是", "158", "左右", ",", "162", "看起来", "就是", "非常", "高挑", "。", "好", "几个", "声称", "172", "的", "高妹", ",", "一", "测", "全", "现", "原型", "了", ",", "不是", "165", "就是", "166", ".", "人", "的", "真实", "身高", "真", "没有", "你们", "想象", "得", "高", ",", "女人", "160", "以上", "真", "的", "就是", "高个", ",", "信不信由你", "。", "下面", "说说", "男", "的", "。"], ["好多", "咱们", "平常", "感觉", "起来", "172", "左右", "的", "男", "的", ",", "其实", "就是", "和", "女", "的", "一样", "165", "左右", ",", "不到", "170", "的", "男", "的", "不要太多", "好不好", "。", "红外线", "测", "身高", "以后", "我", "才", "觉得", "男", "的", "不到", "170", "其实", "不", "丢人", "。", "体检", "时候", "看见", "一", "个", "很高", "的", "男", "的", ",", "绝对", "是", "咱们", "平常", "感觉", "180", "多", "的", "人", ",", "其实", "是", "177", "。", "大家", "不要", "被", "现在", "宣传", "的", "东西", "迷惑", "了", "。", "多看", "看", "科学", "文献", ",", "中国", "女性", "评论", "身高", "158", ",", "确实", "是", "真", "的", "。", "所以", "范", ",", "还有", "众多", "女明星", ",", "160", "左右", "一点", "都", "不", "奇怪", ",", "难道", "她们", "不是", "人", "?"], ["我", "平常", "报", "身高", "是", "162", ",", "虽然", "现在", "知道", "了", "也是", "虚报", ",", "但", "最", "起码", "没", "瞎", "报", "。", "哪有", "那么多", "168", ",", "开玩笑", "。", "男", "的", "哪有", "那么多", "180", "。", "数字", "并", "不重要", ",", "气质", "好", "比例", "好", ",", "给", "人", "感觉", "挺拔", "精神", ",", "就", "显", "个", ",", "也", "好看", "。", "。", "综上所述", ",", "范冰冰", "是", "个", "会", "打扮", ",", "气质", "好", ",", "很", "挺拔", ",", "身高", "不会", "超出国", "人", "平均", "水平", "的", "女子", "。"], ["帖子", "不错", ",", "又", "长见识", "了", ",", "以前", "一直", "认为", "范冰冰", "真", "有", "168", ",", "因为", "看上去", "的", "确", "很高", ",", "不过", "现实", "终", "归", "是", "残酷", "的", "。", "我", "一直", "都", "坚持", "的", "观点", "是", ",", "真正", "长", "得", "高", "的", "人", "是", "不会", "经常", "穿", "如此", "之", "高", "的", "鞋子", "的", ",", "既然", "自己", "长", "得", "够", "高", "就", "没", "必要", "通过", "其他", "的", "方式", "承", "托", ",", "别人", "自然", "也", "就", "不会", "议论", "。"], ["不", "说", "恨天高", ",", "真", "那么", "高", "绝对不会", "穿", "恨天高", "掩饰", "自己", "身高", ",", "162", "绝对", "的"], ["感觉", "她", "胸", "其实", "也", "不大", ",", "光", "靠", "挤", ",", "挺", "虚伪", "的", "一个人", ",", "搞", "得", "自己", "像", "波霸", "似", "的"], ["感觉", "她", "胸", "其实", "也", "不大", ",", "光", "靠", "挤", ",", "挺", "虚伪", "的", "一个人", ",", "搞", "得", "自己", "像", "波霸", "似", "的"]], "paragraphs": ["如果她真的高,干嘛还每次撑那么高的鞋子。都是女人,都知道穿高跟的痛苦,何况她穿那么高,而且几乎都穿,那就证明她内心清楚自己这一点", "范冰冰163.刘亦菲165.韩雪169～", "赵薇 比169 的谢霆锋还要矮一截 ,估计 160 ,苏有朋是 很诚实的人,小虎队都是 170. 比冰冰高一截呢。 范冰冰161 .", "以李斯羽181.5 韩雪169 蒋梦婕159.7 作为标杆,考证娱乐圈女星身高作者:杨晓旭181.5 李斯羽[绝对标杆]181 张梓琳(和李斯羽基本同高)178 倪景阳[标杆] =蒋 怡[标杆] =林嘉绮176 熊黛林 =马艳丽 175-176 李艾(自爆177.56)175 艾尚真[标杆] =孟广美174 于娜173 林志玲[标杆] =周韦彤172 王祖贤[标杆] =梁咏琪 =瞿 颖170 关晓彤[标杆] =郑希怡 =乐基儿 =全智贤 =王 菲169 韩雪[标杆] =韩雨芹[标杆] =李宇春168 袁咏仪[标杆] =宣 萱[标杆] =梅艳芳(已故) =陈慧琳 =钟嘉欣 =蔡少芬 =小宋佳 =吴佩慈.....=李嘉欣 =唐 嫣 =叶 童167 周海媚 =吴美珩 =王思懿166 叶倩文[标杆] =容祖儿 =关之琳 =陈法拉 =孙耀琦 =徐静蕾 =胡杏儿165 刘亦菲 =刘力扬 =叶一茜 =李小冉 =杨恭如 =郭可盈 =李彩桦 =张可颐 =邓丽君(已故).....=胡 静 =曹 颖 =姚 晨 =巩 俐 =萧 蔷 =汤 唯 =闫 妮 =陈 红(演员)164 戚薇[标杆] =张曼玉 =林青霞 =谭维维 =尚雯婕 =莫文蔚 =周慧敏 =应采儿 =钟丽缇.....=蒋雯丽 =张馨予 =罗美薇 =郭蔼明 =弦 子163 刘嘉玲 =陈法蓉 =周秀娜 =杨丽菁 =曾华倩 =金喜善 =刘 涛 =那 英 =张 敏 =舒 淇 .....=翁 虹 =叶 璇 =蔡 琳162 范冰冰 =林心如 =李冰冰 =陈乔恩 =李若彤 =钟楚红 =邱淑贞 =赵雅芝 =吴倩莲 =李英爱.....=陈 好 =王 艳 =秦 岚 =馨 子161 陈慧珊 =陈德容 =蒋勤勤 =金巧巧 =杨 幂 =赵 薇 =黄 奕 =梅 婷 =高 洋160 郑秀文 =章子怡 =徐熙媛(大S) =任家萱(Selina) =陈嘉桦(Ella) =郭羡妮 =桂纶镁.....=高圆圆 =袁姗姗 =杨紫琼 =陈紫函 =俞飞鸿 =陈玉莲 =郑 爽 =李 湘159.7 蒋梦婕[绝对标杆](可以算作160的吧)159 安以轩 =霍思燕 =温碧霞 =张嘉倪 =黎 姿 =张 庭 =厉 娜158 张柏芝 =杨丞琳 =萧亚轩 =周笔畅 =徐若瑄 =林忆莲 =刘诗诗 =马伊琍 =佘诗曼 =关咏", "我见过林心如和赵薇本人绝对不到160(净高),也就158!推测范冰冰也就161左右", "艾玛大家不要吵了好吗。一般来说我的平底鞋身高别人目测都是165左右,我不算高个的人,但绝对不是个矮的人。前天去体检,红外线测身高,光脚,纯量身高,其实我是160。", "大家的真实身高真没有你们说的那么玄乎,净高的话男的哪有那么多180啊,女的哪有那么多165啊。我排队体检观察的时候,大部分身高可以的女的,全是158左右,162看起来就是非常高挑。好几个声称172的高妹,一测全现原型了,不是165就是166.人的真实身高真没有你们想象得高,女人160以上真的就是高个,信不信由你。下面说说男的。", "好多咱们平常感觉起来172左右的男的,其实就是和女的一样165左右,不到170的男的不要太多好不好。红外线测身高以后我才觉得男的不到170其实不丢人。体检时候看见一个很高的男的,绝对是咱们平常感觉180多的人,其实是177。大家不要被现在宣传的东西迷惑了。多看看科学文献,中国女性评论身高158,确实是真的。所以范,还有众多女明星,160左右一点都不奇怪,难道她们不是人?", "我平常报身高是162,虽然现在知道了也是虚报,但最起码没瞎报。哪有那么多168,开玩笑。男的哪有那么多180。数字并不重要,气质好比例好,给人感觉挺拔精神,就显个,也好看。。综上所述,范冰冰是个会打扮,气质好,很挺拔,身高不会超出国人平均水平的女子。", "帖子不错,又长见识了,以前一直认为范冰冰真有168,因为看上去的确很高,不过现实终归是残酷的。我一直都坚持的观点是,真正长得高的人是不会经常穿如此之高的鞋子的,既然自己长得够高就没必要通过其他的方式承托,别人自然也就不会议论。", "不说恨天高,真那么高绝对不会穿恨天高掩饰自己身高,162绝对的", "感觉她胸其实也不大,光靠挤,挺虚伪的一个人,搞得自己像波霸似的", "感觉她胸其实也不大,光靠挤,挺虚伪的一个人,搞得自己像波霸似的"], "bs_rank_pos": 2}, {"is_selected": false, "title": "范冰冰身高162cm封顶 范冰冰真实身高多图对比_男人窝", "most_related_para": 25, "segmented_title": ["范冰冰", "身高", "162", "cm", "封顶", "范冰冰", "真实", "身高", "多", "图", "对比", "_", "男人", "窝"], "segmented_paragraphs": [["编辑", ":", "小", "男", "2015", "-", "12", "-", "01", "16", ":", "32", ":", "08"], ["无论", "何种", "场合", ",", "范冰冰", "从来", "都是", "高跟鞋", "不", "离", "脚", "。", "这样", "给", "大家", "造成", "了", "一种错觉", ",", "那", "就是", "范冰冰", "身高", "很高", "。", "实际上", ",", "有", "网友", "发出", "范冰冰", "和", "其他", "明星", "站在一起", "的", "对比", "图", ",", "范冰冰", "身高", "多少", "立马", "见", "分晓", "。"], ["范冰冰", "身高", "多少", "?", "先", "看看", "个人资料"], ["范冰冰", "身高", 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"无论何种场合,范冰冰从来都是高跟鞋不离脚。这样给大家造成了一种错觉,那就是范冰冰身高很高。实际上,有网友发出范冰冰和其他明星站在一起的对比图,范冰冰身高多少立马见分晓。", "范冰冰身高多少?先看看个人资料", "范冰冰身高多少", "中文名:范冰冰", "身高:168cm", "体重:56.9kg", "星座:处女座", "出生地:山东青岛", "出生日期:1981年9月16日(农历:八月十九)", "职业:演员、歌手、影视工作室老板", "范冰冰真实身高,多图对比", "众所周知,范冰冰是不论场合的防水台恨天高,所以她的身高特别不好算。", "还珠里和赵、林一般高,比苏有朋矮很多。苏有朋的身高有没有170,大家都懂。", "刘若英一直报160,范冰冰多高,自己看", "和160的章子怡。范冰冰鞋子防水台还高点。", "除了范冰冰,清一色运动鞋。田亮168,运动员身高难做假。范冰冰防水台高跟鞋和田亮一样高。", "范冰冰真实身高162cm封顶 有图有真相", "10月中旬,范冰冰亮相柳州捞金,一双枚红色恨天高可谓是亮相了前来围观的民众。继而导致#范冰冰12寸高跟鞋#一度被顶至最热搜索,范爷的魅力真可谓不一般呢。同时,网友对范冰冰的身高也提出质疑,穿那么高的恨天高究竟是为啥呢?", "范冰冰黄晓明", "两人一同宣传电影,黄晓明这次没有穿“汗血宝马”喔,范爷还是穿着恨天高。", "范冰冰黄晓明鞋子对比", "黄晓明的平底凉鞋,范冰冰的恨天高有多恐怖大家感受下,这鞋跟10cm少不了!范冰冰真实身高真的差不多就162cm左右。", "范冰冰脱鞋和郭德纲站一起", "早期跟相声演员郭德纲合作《三笑》的时候一同亮相发布会,范冰冰为了迁就郭德纲身高,贴心地脱下了高跟鞋站在郭德纲旁边,真实身高一下子就曝光了。", "范冰冰身高官方报是168cm,不过往郭德纲身旁一站显得这报的数据也虚假过头了点。根据网友反映,郭德纲最多160封顶,而发布会上的这张图来看,范冰冰也没比郭德纲高出许多,郭德纲还没完全站直的说。范冰冰真实身高大概162cm左右。", "再来感受下穿平底鞋的范爷", "范冰冰身高如果真的是162封顶的话,那么她给人造成的手长脚长的错觉,只能说明她身形好。不过娱乐圈中,很多女明星都给人很高的感觉,就像小S、蔡依林等人。有些是因为身材比例好,有些是因为会穿搭。", 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"paragraphs": ["百度经验 > 生活/家居 > 生活常识", "百度经验:jingyan.baidu.com", "游标卡尺,作为常用的度量工具,以其精度为名,一般可以精确到0.01cm,但是其结构比起卷尺直尺相对要复杂很多,刚刚接触的人,很可能不能一下子摸到门路。", "所以在此介绍一下它的使用方法。", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "1 测量外径 如图,红框内部分,钳住物品,得出测量数据。 步骤阅读 2 测量内径 如图,红框内部分,在物品内径部分,两端张开,撑住物品,得出测量数据。 步骤阅读 3 测量深度 如图,红框内部分,探入后,固定标尺,得出测量数据。 步骤阅读 END", "百度经验:jingyan.baidu.com", "1 无论测量方法如何,数据都便现在标尺上。 所以我们以下图测量内径的图示为例: 步骤阅读 2 首先,看副尺“0”的位置,它决定了头两个数位。图中0在2.3cm的后面。即为测量物体的内径为2.3xxcm. 步骤阅读 3 然后观察副尺分度(精确度),就是有多少个格图中为20分度,即精确度为0.05mm。(每分度的单位=1毫米/分度) 步骤阅读 4 然后看副尺和主尺完全重合的数位,看红框内,重合部分与二差3格。即重合处为17。每单位为0.05mm,得出最后的数位,0.85mm(0.085cm) 步骤阅读 5 最后测量出,目标的内径为2.385cm END", "百度经验:jingyan.baidu.com", "还有一种带电子显示屏的游标卡尺,请及时清零。有必要的时候,一定人工读数确保精度。", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:爱好广泛,是个快乐的人"], "bs_rank_pos": 0}, {"is_selected": true, "title": "教你如何正确读出游标卡尺的数值及读数方法_百度经验", "most_related_para": 9, "segmented_title": ["教", "你", "如何", "正确", "读出", "游标卡尺", "的", "数值", "及", "读数", "方法", "_", "百度", "经验"], 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"解决", "具体", "问题", "(", "尤其", "法律", "、", "医学", "等", "领域", ")", ",", "建议", "您", "详细", "咨询", "相关", "领域", "专业人士", "。"]], "paragraphs": ["百度经验 > 生活/家居 > 生活常识", "百度经验:jingyan.baidu.com", "游标卡尺作为一种高精度测量工具,被广泛的应用于生产和生活中。作为经常用到的一种量具,我们应当熟练掌握其读数方法。", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "1 常见游标卡尺按其精度有:0.1毫米、0.05毫米和0.02毫米。应该根据自己的需要选择合适的精度等级。下图所示为一精度为0.02mm的游标卡尺 步骤阅读 END", "百度经验:jingyan.baidu.com", "1 将被测物体置于测量爪之间,移动测量爪,夹紧物体。 2 测量完毕之后应将紧固螺钉拧紧,以防数值在量具移动过程中发生变化。 步骤阅读 END", "百度经验:jingyan.baidu.com", "1 读数时首先以游标零刻度线为准,读取尺身上的毫米整数。如图所示,游标零刻度位于主尺的14mm与15mm之间,所以读取其整数毫米为14mm 步骤阅读 2 再读取游标所指示的数值。观察游标上第几条刻度线与尺身的刻度线对齐,如图所示,取最接近对齐的线,下图取游标8数值处刻度线与主尺刻度线对齐。则读数结果为:40格*0.02mm(精度)=0.8mm 步骤阅读 步骤阅读 3 将两次读数相加即为此次测量的数值。即最终读数为14mm+0.8mm=14.8mm END", "百度经验:jingyan.baidu.com", "根据自己的需要选择合适的精度等级很重要", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "bs_rank_pos": 1}, {"is_selected": false, "title": "游标卡尺的读数方法_百度文库", "most_related_para": 2, "segmented_title": ["游标卡尺", "的", "读数", "方法", "_", "百度", "文库"], 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3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 0 1 B 2 0 1 2 答案:11.14mm 游标卡尺读数练习 答案:10.55mm 游标卡尺读数练习 6 5 0 5 10 答案:54.5mm 游标卡尺读数练习 答案:41.20mm", "160份文档", "2014年各行业从业资格考试", "80份文档"], "bs_rank_pos": 2}, {"is_selected": false, "title": "游标卡尺的读数方法_图文_百度文库", "most_related_para": 1, "segmented_title": ["游标卡尺", "的", "读数", "方法", "_", "图文", "_", "百度", "文库"], "segmented_paragraphs": [["游标卡尺", "的", "读数", "方法", "_", "机械", "/", "仪表", "_", "工程", "科技", "_", "专业", "资料", "。", "让", "你", "轻松", "学会", "游标卡尺", "的", "读数", "。"], ["游标卡尺", "的", "读数", "原理", "1", ".", "游", "标尺", "的", "刻度", "0", "0", "10", "1", "2", "cm", "游标", "尺", "的", "每", "一", "最小", "刻度", "0", ".", "9", "mm", ",", "它", "与", "主", "尺", "上", "的", "最小", "刻度", "相差", "0", ".", "1mm", "2", ".", "游标卡尺", "的", "读数", "原理", "(", "10", "个", "小", "刻度", ")", "0", "0", "10", "1", "2", "cm", "游", "标尺", "向右", "移动", "0", ".", "1", "mm", ",", "游标", "上", "的", "第", "1", "刻度", "与", "主", "尺", "上", "的", "某", "一", "刻度", "对齐", ",", "其", "读数", "为", "0", "+", "1", "×", "0", ".", 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["无论测量方法如何，数据都便现在标尺上。所以我们以下图测量内径的图示为例：首先，看副尺“0”的位置，它决定了头两个数位。图中0在2.3cm的后面。即为测量物体的内径为2.3xxcm.然后观察副尺分度（精确度），就是有多少个格图中为20分度，即精确度为0.05mm。（每分度的单位=1毫米/分度）然后看副尺和主尺完全重合的数位，看红框内，重合部分与二差3格。即重合处为17。每单位为0.05mm，得出最后的数位，0.85mm（0.085cm）最后测量出，目标的内径为2.385cm。", "读数时首先以游标零刻度线为准，读取尺身上的毫米整数。如图所示，游标零刻度位于主尺的14mm与15mm之间，所以读取其整数毫米为14mm。再读取游标所指示的数值。观察游标上第几条刻度线与尺身的刻度线对齐，如图所示，取最接近对齐的线，下图取游标8数值处刻度线与主尺刻度线对齐。则读数结果为：40格*0.02mm（精度）=0.8mm。将两次读数相加即为此次测量的数值。即最终读数为14mm+0.8mm=14.8mm。"], "answer_docs": [1], "segmented_question": ["游标卡尺", "读数"], "question_type": "DESCRIPTION", "question_id": 181592, "fact_or_opinion": "FACT", "match_scores": [0.8814814814814814]} +{"documents": [{"is_selected": true, "title": "四月是你的谎言结局是悲剧吗_百度知道", "most_related_para": 5, "segmented_title": ["四月", "是", "你的谎言", "结局", "是", "悲剧", "吗", "_", "百度", "知道"], "segmented_paragraphs": [["悲剧", ",", "并", "算", "不", "上", "。", "只是", ",", "会", "让", "人", "难受", "!", "最初", ",", "天才少年", "公", "生", "堕落", "凡尘", ",", "这个", "并没有", "让", "人", "感到", "意外", "。", "关系", ":", 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"就", "哭", "了", ",", "但", "十分", "值得", "我们", "去", "感受", ",", "去", "体会", "。"]], "paragraphs": ["悲剧,并算不上。只是,会让人难受! 最初,天才少年公生堕落凡尘,这个并没有让人感到意外。关系:一非常受欢迎的男朋友 + 一感情方面有点迟钝的和男主青梅竹马的女朋友。常规套路。 然后,女主出现,“喜欢”男主朋友。按照套路,外加“谎言”,肯定是女主喜欢男主。毕竟谁叫她是女主,他是男主。 不过这毕竟是动漫、而且猪脚才LV14。所以,根据套路来说,肯定是夹杂了悲伤的旋律。然后最后有“努力、前进”的意义在内。 类似《fate stay night》,saber死了,但是不在后悔、得到了救赎,而且士郎虽然失去了喜欢的saber,但是曾经的过往未曾忘却。这是成长中的宝贵财富、一生的珍藏。 最后,剧中点了好几次“在失去中成长”,所以已奠定“悲”的色彩在内。 但是呢,说是悲剧也不尽然,毕竟这不是末世剧,男女主都死光光、毁三观", "以前我回答过这个问题,现在只是完善与修改。 漫画结局在不久前就已经出来了,熏死了,还留给了公生一封信,具体结局参见漫画。 评论里面我也有回复:……看OP里面钢琴上信封的画面死的可能性也比较大了,还是放低期待比较好。”大部分人都希望熏可以活下来,若改变看法的话熏不在了也是一种好发展。 TV里也出了结局,少年节哀吧。", "据说会在明年的5月左右完结 结局虽然不清楚,但是既然前面已经带给我们这么多正能量 最后肯定会让我们看到奇迹 贴吧有最新的漫画更新哦", "这是个很难回答的问题。单纯从结局上来说的话,刚刚互相确认心意的二人自此阴阳两隔,这的确很像是悲剧吧,但却又不仅仅是那样。众人都在这场由一个四月到下一个四月的纷繁青春中,有了新的认知和憧憬。尽管迷茫的依旧迷茫,偏执的仍然偏执,但是就像不再低下头的公生一样,他们最终不再停留在静止的时间里,纷纷踏上了旅途,就连最终逝去的薰也一样。从这层意义上说,其实也不那么悲剧吧。", "怎么说呢,非要说悲喜,那确实是个悲剧结局,相爱的人并没有走到一起,年轻的生命刚刚绽放就凋谢了。女主说了一个关于爱情的谎言,来接近男主,最后男主重新拾起了钢琴,女主实现了愿望,总的来说虽然悲伤但并不遗憾。", "我个人认为楼主的观点是片面的。说是悲剧,因为薰最终是死了,而两情相悦的公生和薰并没有在一起。这是停留在表象的理解。 深入其中,四月是你的谎言本身就是一部励志漫画。在最后的比赛上,公生也接受到了来自于薰的情感,而进行了改变。怎么说呢,我自己学音乐的体会就是一个人的心情会直接影响到你的演奏情感,所以说公生最后的演出是成功的,这对于公生来说也是一次成长,所以说也不能算是悲剧。但这只是停留在结局而言。 如果我们把全部都联系起来,那么我们又可以有新的理解。这个动漫中的谎言其实不止一个。 第一个谎言:薰为了接近公生而说自己喜欢渡亮太,结果被渡识破。 第二个谎言:九年的等待,两年的跟踪生涯终于结束,薰想到终于能和自少最憧憬的人说话忍不住泪流满面,成功引起公生的注意。 第三个谎言:强装笑容和镇定", "怎么说呢 悲剧比喜剧更有震撼力 就像罪恶王冠 我看过很多喜剧类型的动漫 但大多都记不太清了 唯独罪恶王冠 我记得特别清楚 悲剧不一定是坏事情 有些东西不是我们能够掌控的 就像FATE ZERO 中我最喜欢的大帝 伊斯坎达尔 虽然我知道随着剧情的发展 大帝之死无法避免 但是 大帝带给人们的勇气和力量 却是无与伦比的 而一味的否认自己历史的亚瑟王 连最靠近她的人的心都无法看清 这才是真正的悲剧吧!", "并不是悲剧,女主收获了真正的爱情,也坚持追逐了自己的梦想,完事后就去领便了。男主也找回了自己的音色,开始了自己新的生活。女主曾经邀请男主一起去领便当的,但也许是不满意剧组的伙食,并没有打算和女主一起去领便当(详情在第16集),女主对此也表示理解,虐了吗?虐了吗?悲剧吗?不悲剧啊。", "四月是你的谎言的漫画已经完结了,结果有些不尽人心。但是其中的那些熏与公生仍值得我们回念,不愧为2014年最强十月番啊!!!如今也是四月,就让我们重温那让我们感动的动漫吧!", "最后那段,明白了薰的谎言也可能是美好的发展吧,因为有了这个谎言才能让有马的成长多了份希望,伴它一起走下去!让每个人获得了成长,即使失去,也要向前看会有人一直伴你。也许这样的结局也是十分美好的!不过结局是挺悲的,不由自主就哭了,但十分值得我们去感受,去体会。"], "bs_rank_pos": 0}], "answer_spans": [[51, 72]], "fake_answers": 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"四", "角", "星", "的", "折", "法", "立体", "六角星", "的", "折", "法", "四", "角", "星", "怎么", "折"], ["简易", "的", "幼儿", "立体", "花朵", "贺卡制作", "幼儿", "纸风筝", "的", "制作方法", "幼儿", "手工", "小燕子", "的", "制作方法", "简单", "的", "幼儿", "圣诞树", "制作方法", "幼儿园", "纸袋", "猫头鹰", "手工制作", "幼儿", "树叶贴画", "长颈鹿", "的", "做法"]], "paragraphs": ["导语:相信很多朋友的窗台或者阳台都会喜欢挂上一个风铃,一般风铃都是会发出呤呤的声音,美丽的风铃就随着风儿这样飘呀飘,很是惬意,接下来我们就把这种惬意带到自己的家中如何呢?小风铃怎么做小编今天就为大家简单介绍下。", "生活中可以有很多机会接触到塑料勺,它们基本上是一次性使用,扔进垃圾筒未免有些可惜,现在就把它们收集起来,巧手利用一下。怎样用这些废弃物做小风铃呢?想不到吧,接下来我们就说说小风铃怎么做。下面是手工制作的纸风铃DIY图解,大家一起学习吧。", "小风铃怎么做材料准备:大瓶盖、一次性勺子、珠子、皮筋、羽毛、铃铛、锥子等。", "1、首先把瓶盖顶部用裁纸刀去掉,四周用锥子扎空,如图所示。 2、在用彩色尼龙绳装饰勺子。 3、在勺子把用尼龙线捆绑。 4、然后再拿出事先准备好的一些装饰风铃的材料。", "5、在瓶盖上绑一个十字。", "6、把我们准备好的各种风铃挂坠捆绑好。", "7、看看效果吧,是不是觉得很好看,那就赶紧动手做一做。", "淡蓝色和紫色搭配的花朵,这些都是整个花朵风铃的主色调。在这个风铃制作上我们还搭配了一些粉色丝带喔!几个简单的步骤,我们就可以制作出无声的花朵风铃,女孩们都喜欢花朵,将这个放在姑娘们的卧室里,很是合适呢,接下来我们就去了解这款小风铃怎么做。", "材料准备:色带 木材花形 喷漆 热胶枪 绣花箍。", "第一步:清点一下事先准备好使用的材料(色带 木材花形 喷漆 热胶枪 绣花箍 )。 第二步:用喷漆将木材花形喷上颜色。 第三步:在花朵中心粘贴一个闪闪发光的水钻。 第四步:在绣花箍的内侧,粘贴好色带。", "第五步:将色带在绣花箍上围绕一圈,在木制花朵的反面涂上胶水,将色带粘贴在木制花朵胶水位置。", "第六步:间隔一个换另一种颜色的花朵。", "最后美丽的花朵风铃就制作完成了,生活处处皆艺术,如果能有父母的言传身教,变废为宝是让孩子觉得最有成就感的事,因为它不需要任何成本,只需要付出一些时间和精力,就能把家中的各种垃圾废物重新组合,做成实用、环保的工艺品,有趣的小制作或者美好的家居用品。", "结语:说起小风铃,既有视觉造型之美,又有听觉悦耳的享受,既是居家的小摆饰,也是宗教的重要器物,它可以是轻松休闲,也可以是庄严宁静。对于今天小编介绍的小风铃怎么做你学会了吗?学会了就赶紧分享给你的小伙伴们,大家一起做美丽的小风铃。", "更多精彩手工制作推荐:", "怎么折小星星最简单 空心立体五角星的折法 镂空五角星怎么折", "怎么折小星星最简单 空心立体五角星的折法 镂空五角星怎么折 立体五角星怎么叠 立体五角星的折法图解 立体四角星的折法 立体六角星的折法 四角星怎么折", "简易的幼儿立体花朵贺卡制作 幼儿纸风筝的制作方法 幼儿手工小燕子的制作方法 简单的幼儿圣诞树制作方法 幼儿园纸袋猫头鹰手工制作 幼儿树叶贴画长颈鹿的做法"], "bs_rank_pos": 2}, {"is_selected": false, "title": "【手工制作风铃】儿童手工制作价格_diy手工风铃图片 - 阿里巴巴", "most_related_para": 12, "segmented_title": ["【", "手工制作", "风铃", "】", "儿童", "手工制作", "价格", "_", "diy", "手工", "风铃", "图片", "-", "阿里巴巴"], "segmented_paragraphs": [["一", "件", "代发", "手工", "diy", "材料", "包益智玩具", "儿童", "手工制作", "无纺布", "涂鸦", "画画"], ["儿童", "手工", "DIY", "制作", "花束", "捧花", "带", "花瓶", "彩色", "纽扣", "创意", "饰品", "玩具", "女孩", "礼物"], ["甲乙丙丁", "diy", "儿童", "串珠", "益智", "手工", "穿", "珠子", "女孩", 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上穿根线; 2. 用硬纸片剪个圆盘,中间穿上线,然后把药瓶的线固定在圆盘的周围; 3", "废旧物品制作风铃手工制作: 1. 你要想办法找到 9 个小药瓶,然后在里面装上各种颜色的水,之后还要在每个瓶的盖子 上穿根线; 2. 用硬纸片剪个圆盘,中间穿上线,然后把药瓶的线固定在圆盘的周围; 3. 用胶泥和电光纸做出各种好看的图案粘在瓶上就可以了 哈!太好看了!又可以保护环 境。", "材料很简单,剪刀,直尺,铅笔,当然还少不了干净的玻璃罐子和你喜欢的相片。对于粘有 恼人的商品标签的罐子,告诉你一个简易的解决方法。用风油精、活络油等有机溶剂即可轻 易的将牢牢粘附在物体表面的商品标签去除,适用于任何情况。", "用尺子大致量出罐子内壁可容纳相片的高度, 如果罐子比相片略矮一些, 可以用铅笔在相片 上做出标记并用剪刀稍作裁剪。", "尽量为对应的相片选择高度适中的罐子, 毕竟相片是可以修 剪的,而玻璃罐子就……", "当然也有一些例外,比如你想给相片中的脸部来一个特写,那就剪下脸部的那一部分,选择 一个相对矮小的瓶子,能给你带来意料之外的艺术效果。", "废弃瓜子壳制作的手工-鱼 西瓜子 南瓜子 葵花子,动手创意吧。", "收集够了 50 个牛奶盒,就赶快开始动手做一个吧。", "很结实的,小朋友可以真的坐上去呢 毛线制作的娃娃-幼儿教师手工制作 好可爱哦,身体是用铁丝做的,所以还可以摆出各种各样的造型哦。", "准备材料: 松球、纸杯、彩色纸、双面胶、剪刀 1 把纸杯在大约二分之一处剪开。 2 把剪下的杯底部分包上彩色纸。 3 剪掉多余的边。 4 杯口部分插在杯底内。一只小花盆做好了。 5 用记号笔在花盆上画喜欢的花纹。红花盆是曹宇鹏画的,蓝花盆是妈妈画的。", "6 先用废纸揉成纸团,再用彩色纸包住。 7 纸包当做泥土装进花盆。 8 挑一个形状完整的松球种在花盆中。(用双面胶粘贴) 9 用绿色纸对折,剪出铁树的叶子。 10 叶子要有大有小、有长有短。 11 最小的叶子长在中间。 12 中间的叶子可以保持对折的姿态, 13 越往下叶子越大。大叶子要舒展些。 14 铁树做好了。", "纸杯手工作品“灯笼”的制作过程 1 纸杯手工作品“灯笼”的制作过程 2 纸杯手工作品“灯笼”的制作过程 3 纸杯手工作品“灯笼”的制作过程 4 纸杯手工作品“灯笼”的制作过程 5 纸杯手工作品“灯笼”的制作过程 6 纸杯手工作品“灯笼”的制作过程 7 纸杯手工作品“灯笼”的制作过程 8 纸杯手工作品“灯笼”的制作过程 9 准备材料: 1、鸡蛋或其他蛋类 2、丙烯颜料 3、毛笔 步骤: 1、在鸡蛋或其他蛋类的顶端凿开一个小洞,将蛋清和蛋黄道出,清洗干净,注意要手脚轻 巧点哦,蛋壳可是很脆弱的哦。", "适用游戏:娃娃家游戏 适用年龄段:中、大班 制作材料:废旧纸盒、及时贴、废报纸 制作要点:先用剪刀将纸盒剪开两斜角,将剪开的部分塞进纸盒内压条。用报纸搓成筒状, 将彩条绕上。将彩棒粘贴到簸箕上即可。 设计思路:在日常生活中,簸箕、笤帚是我们的日用品,幼儿对此都不会感到陌生,在家里 经常会看见父母打扫卫生,会对打扫卫生的过程发生兴趣。在中大班娃娃家游戏中,经常会 有许多幼儿到模仿爸爸妈妈一样打扫卫生,因此在娃娃家游戏中增添了簸箕、笤帚的玩具, 这个自制玩具可以丰富幼儿的游戏内容。 使用说明:放在娃娃家里当日用品,让幼儿通过模仿成人劳动,体会劳动的艰辛和快乐。 玩具特色:利用废旧的包装盒制作,取材方便,废物利用,节约能源。直接在盒子切割,不 需要复杂的工艺,制作过程简单、快速,可以节约教师的时间。", "适用游戏:娃娃家游戏 适用年龄段:中、大班 制作材料:废旧的长方体纸盒、彩色纸、黑色、红色、黄色的及时贴、小刀、塑封纸 制作要点:选一个长方体的纸盒,把纸盒的上下、前后四个面用颜色比较鲜艳的红色即时贴 贴住, 左右两侧则选用黄色的及时贴, 然后直接在盒子的正面挖一个微波炉的门, 进行切割, 中间被切割掉的地方用塑封纸贴住,再装上门的把手,最后在门的旁边贴上一些蔬菜、荤菜 和使用按钮。 设计思路:在日常生活中,每家每户都有一个微波炉,孩子每次会看到爸爸妈妈会用微波炉 把饭菜加热, 所以对于它的使用方法已经有了一定的经验, 所以在娃娃家游戏中放入微波炉, 使游戏真正的与现实生活联系起来了,孩子在这过程中也会得到很多快乐。 使用说明:孩子在玩游戏时可以扮演妈妈的角色,比如一家人吃饭时,如果饭菜凉了,就可 以拿到微波炉里进行加热,在加热前要看好按钮,荤菜和蔬菜加热的按钮是不一样的,还要 注意加热的时间,等饭菜加热好了之后,打开微波炉的门,把它们拿出来,还要注意不要烫 到手。", "玩具特色:利用废旧纸盒制作,取材方便,废物利用,节约能源。在制作过程中能直接在盒 子上挖洞、切割,不需要复杂的工艺,制作过程简单,快速。玩具也比较形象、便操作,适 合中、大班孩子在角色游戏中使用。", "作品照片: 适用游戏:超市收银台 适用年龄段:中班 制作材料:长方体扁形月饼盒、九分裤盒子、娃哈哈酸奶瓶一个、小药盒、约 15 厘米长、 10 厘米宽的盒子和另一个 15 厘米长、5 厘米宽的盒子各一个、即时贴包装纸、数字贴纸。 制作要点:月饼盒的银色边比较漂亮,不需要包装,可直接利用。盒子的一条宽边抠出一个 九分裤盒子大小的空间做收银台的抽屉。 月饼盒的桌面部分用绿色正方形即时贴盖住原来图 案,即时贴的上面贴上数字键,这样收银台的台面就做好了。接下来做收银台的屏幕,用即 时贴将一个约 15 厘米长、10 厘米宽的盒子(做屏幕)和另一个 15 厘米长、5 厘米宽的盒 子(做台面和屏幕之间的撑杆)包装起来,将屏幕、台面、撑杆三部分组合起来。最后做收 银台刷条形码的机器。 先将娃哈哈瓶口系一根绿色绳子, 绳子另一头塞到台面里面后打结使 其不掉出来。再用娃哈哈底部和小药盒组合,用绿色即时贴包装。 设计思路:游戏之初,我们仅提供一个小钱盒作为超市收银台,收银员在超市里没有可操作 的材料,经常因无所事事而脱离岗位。我们想:到如何增强孩子的角色意识,让孩子喜欢做 超市收银员?应该如何调整材料, 使收银员有事可做?在这样的思考中, 以及与现实生活中 的超市情景结合,我们就发明了这样的“超市收银台”。"], "bs_rank_pos": 5}], "answer_spans": [[8, 80]], "fake_answers": ["你要想办法找到9个小药瓶,然后在里面装上各种颜色的水,之后还要在每个瓶的盖子上穿根线;2.用硬纸片剪个圆盘,中间穿上线,然后把药瓶的线固定在圆盘的周围;3.用胶泥和电光纸做出各种好看的图案粘在瓶上就可以了"], "question": "儿童手工风铃制作方法", "segmented_answers": [["一", "个", "护发素", "的", "内盖", "（", "没有", "的", "话", "可以", "用", "光盘", "）", "，", "在", "上面", "钻", "上", "洞", "，", "在", "洞", "里", 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患上,可真是让人痛苦万分。", "儿童口腔溃疡的原因: 1、手足口病——这种病也有与疱疹性口炎相类似的症状,好发于 5 岁以下幼儿,具有 流行性。患病后口腔损害遍布于唇、颊、舌、腭等处,为很多小水疱,极易破裂,破后形成 多个小溃疡,除口腔部位外,在手掌、足底、臀部皮肤上亦可出现分散的小水疱,所以称之 为手足口病。 2、 药物过敏——特殊体质的宝宝可能会因为药物或感染等不明原因, 而引起所谓的 “多 形性红斑疾病” ,这时宝宝身上会出现靶形红斑,眼睛、嘴唇、口腔、生殖泌尿道均有发炎、 溃烂的情况。 3、口疮——口疮比较常见,但是病因不一。有的是因为口腔黏膜有不明显的伤口造成 的,有的是因为压力过大造成的,还有的是因为食物中缺少维生素 B。初期口腔黏膜会有灼 烧感,接着会发红,并形成许多小溃疡,相当疼痛。常见部位在舌侧黏膜、口腔底部和舌头 部位。 4、外伤——凡是烫伤、刺伤、误食有腐蚀性的东西等,都会引起口腔黏膜受伤,继而 引发溃疡。", "儿童口腔溃疡怎么办? 1、找到溃疡部位:在儿童口腔有溃疡时,要仔细观察孩子的口腔,找到口腔溃疡的具 体部位。如果溃疡在颊黏膜处,就要进一步找到造成溃疡的原因:比如看看患处附近的牙齿 是否有尖锐不光滑的缺口,如果有这种缺口,就应当带孩子去医院处理。 新稀宝官方商城 www.shinsbo.cn 2、转移注意力:多关心一下孩子,多和孩子谈心,转移他的注意力,给宝宝创造一个 轻松、愉快的生活环境。 3、饮食镇痛:不要给宝宝吃酸、辣或咸的食物,否则宝宝的溃疡处会更痛。应当给宝 宝吃流食,以减轻疼痛,也有利于溃疡处的愈合。 治疗儿童口腔溃疡小偏方: 1、维生素 C 药片 1-2 片压碎,撒于溃疡面上,让孩子闭口片刻,每日 2 次。这个方法 虽然很有效,但是会引起一定的疼痛,小宝宝可能会不太配合。 2、用全脂奶粉,每次 1 汤韪并加少许白糖,用开水冲服,每天 2-3 次,临睡前冲服效 果最佳。通常服用 2 天后溃疡即可消失。 3、西红柿列切挤汁,然后把西红柿汁含在口中,每次含数分钟,一日多次。 4、柿饼上刮取柿霜,用开水冲服或加入粥中服用。 5、西瓜瓤挤取瓜汁后含于口中,约 2-3 分钟后咽下,再含服西瓜汁,反复数次,每天 2-3 次。 6、蛋打入碗内拌成糊状,绿豆适量放陶罐内用冷水浸泡 10 多分钟,放火上煮沸约 2 分钟(不宜久煮),这时绿豆未熟,取绿豆水冲鸡蛋花饮用,每日早晚各 1 次。 7、采鲜芭蕉叶适量,将其用火烤热贴敷于口腔溃疡处,每日 2-3 次。 如何预防儿童口腔溃疡? 1、想让孩子远离烂嘴巴的苦恼,最重要的是在平时注意调整饮食,多给孩子吃一些富 含核黄素的食物,如牛奶、动物肝脏、菠菜、胡萝卜、白菜等;还有一些富锌的新稀宝片帮 助提高免疫力。", "新稀宝:专注营养保健品 20 年"], "bs_rank_pos": 0}, {"is_selected": false, "title": "儿童口腔溃疡的原因和治疗方法_百度文库", "most_related_para": 2, "segmented_title": ["儿童", "口腔溃疡", "的", "原因", "和", "治疗方法", "_", "百度", "文库"], "segmented_paragraphs": [["儿童", "口腔溃疡", "的", "原因", "和", "治疗方法", "_", "育儿", "理论", "经验", "_", "幼儿教育", "_", "教育", "专区", "。", "儿童", "口腔溃疡", "的", "原因", "和", "治疗方法", "你的", "宝宝", "是不是", "三天两头", "就会", "发生", "口腔溃疡", "的", "情况", ",", "我们", "都", "知道", ",", "口腔溃疡", "是", "人们", "日常", "最", "易", "患", "的", "一", "种", "口腔黏膜", "疾病", ",", "家长", "在", "发现", "孩子", "口腔溃疡", "后", ",", "通常", "认为", "孩子", "是", "上火", "等", "问题", "导致", ","], ["儿童", "口腔溃疡", "的", "原因", "和", "治疗方法", "你的", "宝宝", "是不是", "三天两头", "就会", "发生", "口腔溃疡", "的", "情况", ",", "我们", "都", "知道", ",", "口腔溃疡", "是", "人们", "日常", "最", "易", "患", "的", "一", "种", "口腔黏膜", "疾病", ",", "家长", "在", "发现", 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"paragraphs": ["小孩口腔溃疡令妈妈们很是担心,下面教妈妈们如何给宝宝治疗口腔溃疡。", "大家都不想患上一些口腔上面的疾病,因为它的出现也是有可能造成疼痛的问题,因此当儿童患上了口腔溃疡要采取好的办法,积极进行选择好的方法进行治疗是解决疾病的唯一途径,那么,治疗儿童口腔溃疡的小妙方?下面和专家来解答吧。", "1、找到溃疡部位: 在宝宝口腔有溃疡时,要仔细观察宝宝的口腔,找到溃疡的具体部位。如果溃疡在颊黏膜处,就要进一步找到造成溃疡的原因:比如看看患处附近的牙齿是否有尖锐不光滑的缺口,如果有这种缺口,就应当带宝宝去医院处理。 2、饮食镇痛: 不要给宝宝吃酸、辣或咸的食物,否则宝宝的溃疡处会更痛。应当给宝宝吃流食,以减轻疼痛,也有利于溃疡处的愈合。 3、转移注意力: 多关心一下宝宝,多和宝宝谈心,转移他的注意力,给宝宝创造一个轻松、愉快的生活环境。 此外,还有一些小偏方能促进宝宝溃疡愈合,爸爸妈妈不妨试试: 1、维生素c药片1-2片压碎,撒于溃疡面上,让宝宝闭口片刻,每日2次。这个方法虽然很有效,但是会引起一定的疼痛,小宝宝可能会不太配合。", "1、维生素c药片1-2片压碎,撒于溃疡面上,让宝宝闭口片刻,每日2次。这个方法虽然很有效,但是会引起一定的疼痛,小宝宝可能会不太配合。 2、用全脂奶粉,每次1汤韪并加少许白糖,用开水冲服,每天2-3次,临睡前冲服效果最佳。通常服用2天后溃疡即可消失。 3、西瓜瓤挤取瓜汁后含于口中,约2-3分钟后咽下,再含服西瓜汁,反复数次,每天2-3次。 4、蛋打入碗内拌成糊状,绿豆适量放陶罐内用冷水浸泡10多分钟,放火上煮沸约2分钟(不宜久煮),这时绿豆未熟,取绿豆水冲鸡蛋花饮用,每日早晚各1次。", "5、采鲜芭蕉叶适量,将其用火烤热贴敷于口腔溃疡处,每日2-3次。", "6、西红柿列切挤汁,然后把西红柿汁含在口中,每次含数分钟,一日多次。", "7、柿饼上刮取柿霜,用开水冲服或加入粥中服用。", "tips:", "有些宝宝的溃疡症状会反复发作,对此情况,给宝宝适当补锌会有所好转。锌在肌红蛋白里含量最高,比如牛肉、猪肉、动物肝脏,还有硬坚果,但宝宝不能吃硬坚果,爸爸妈妈可以把坚果磨成粉,冲泡给宝宝喝。", "预防口腔溃疡", 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"这点我们从小儿口腔溃疡的最主要原因中就可以看到,多数幼儿之所以易发口腔溃疡就是因为没有做好口腔的清洁工作。", "所以平常不仅要养成口腔的清洁还要做到饭后漱口,注意不要让宝宝嘴里含有着奶而进入睡眠。在每次的喂奶之后可以选择用清水帮宝宝漱口一下。", "其次就要选择药物治疗的方法来缓解宝宝口腔溃疡的难受感了,临床认为宝宝的口腔和维生素B2不足,文森氏螺旋体和梭状杆菌的混合感染有着很大的关系,因此可以采用补充维生素B等增加营养等方式进行治疗。建议治疗方法多补充给生素C和维生素b。", "如果口腔溃疡处有伤口的话还要用青霉素控制感染,避免宝宝的口腔溃疡更加严重化。如果有条件的话也可选择用3%的双氧水和2%的硼酸水进行清洗伤口,清洗之后可涂抹碘甘油。", "另外也可选择一些偏方治疗小儿口腔溃疡", "偏方一:喝蜜汁法 经常给患儿喝10%的蜜蜂汁或者是漱口,因为很多口腔溃疡都是由于上火引起的,而蜜汁水就不仅具有消炎,同时还具有止痛的作用,对促进伤口的愈合也有一定的帮助。 偏方二:木耳疗法 使用黑白木耳两种混合,山楂各10克,然后加上三碗水煎至一碗,然后吃木耳,喝汤。坚持每天多喝几次就可以一定的缓解口腔溃疡症状。 偏方三:可可疗法 用可可粉和蜂蜜一起调成糊状,然后经常含咽就可。而且可可粉和蜂蜜的味道都是孩子最为喜欢的,相信这样的治疗方法对防治宝宝口腔溃疡效果是最好的。 偏方四:白菜根疗法 用白菜的根部,同芥菜籽,葱白一起弄破碎然后用纱布将这些菜贴于足心就可。 偏方五:全脂奶粉法 用全脂奶粉加适量的白糖,晚间休息的时候冲服就可了,正常情况下两天就可痊愈了。", "偏方六:维生素C", "将维生素C弄碎了之后,然后敷在伤口处就可了,当然了如果宝宝不小心吃了的话也是不要紧的。", "偏方七:苹果法", "将苹果去皮之后加冷水一起煮沸,然后取苹果同少量酒一起含在口中片刻就可。不过这个方法不是一般宝宝都可以接受的。", "偏方八:云南白药法", "直接将云南白药敷在宝宝溃疡的表面就可以了,在敷的时候注意不要让宝宝吃下了。", "以上这些偏方都是民间偏方,妈妈要用最好咨询下医生。宝宝口腔溃疡本身就很难受了,所以宝妈们就更要细心的护理了,要知道口腔溃疡可是有传染性的如果护理变成疱疹毒性口疮的话,那么护理起来就更难了。", "+ 加载更多新闻", "众泰请交广告费 所谓官图都是\"PS造车\"? 江西小伙砸100万自建农村别墅 安幕墙玻璃豪华似宫廷 实用的商务舱 网易试驾东风风行CM7旗舰型", "日专家称出云号可半小时击沉辽宁舰 中方霸气回应 文化部关停12家网络表演平台 处理3万多人次 习近平抵达香港 将出席庆香港回归祖国20周年大会", "网易商城,您身边24小时的联通电信移动营业厅 随时随地为您提供手机话费充值服务。"], "bs_rank_pos": 4}], "answer_spans": [[155, 397]], "fake_answers": ["。治疗儿童口腔溃疡小偏方:1、维生素C药片1-2片压碎,撒于溃疡面上,让孩子闭口片刻,每日2次。这个方法虽然很有效,但是会引起一定的疼痛,小宝宝可能会不太配合。2、用全脂奶粉,每次1汤韪并加少许白糖,用开水冲服,每天2-3次,临睡前冲服效果最佳。通常服用2天后溃疡即可消失。3、西红柿列切挤汁,然后把西红柿汁含在口中,每次含数分钟,一日多次。4、柿饼上刮取柿霜,用开水冲服或加入粥中服用。5、西瓜瓤挤取瓜汁后含于口中,约2-3分钟后咽下,再含服西瓜汁,反复数次,每天2-3次。6、蛋打入碗内拌成糊状,绿豆适量放陶罐内用冷水浸泡10多分钟,放火上煮沸约2分钟(不宜久煮),这时绿豆未熟,取绿豆水冲鸡蛋花饮用,每日早晚各1次。7、采鲜芭蕉叶适量,将其用火烤热贴敷于口腔溃疡处,每日2-3次"], "question": "儿童口腔溃疡的治疗方法", "segmented_answers": [["治疗", "儿童", "口腔溃疡", "小", "偏方", "：", "1", "、", "维生素", "C", "药片", "1", "-", "2", "片", "压碎", "，", "撒", "于", "溃疡", "面上", "，", "让", "孩子", "闭口", "片刻", "，", "每日", "2", "次", "。", "这个方法", "虽然", "很", "有效", "，", "但是", "会引起", "一定", "的", "疼痛", "，", "小宝宝", "可能会", "不太", "配合", "。", "2", "、", "用", "全脂奶粉", "，", "每次", "1", "汤", "韪", "并", "加", "少许", "白糖", "，", "用", 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6、蛋打入碗内拌成糊状，绿豆适量放陶罐内用冷水浸泡10多分钟，放火上煮沸约2分钟（不宜久煮），这时绿豆未熟，取绿豆水冲鸡蛋花饮用，每日早晚各1次。 7、采鲜芭蕉叶适量，将其用火烤热贴敷于口腔溃疡处，每日2-3次。", "偏方一：喝蜜汁法。经常给患儿喝10%的蜜蜂汁或者是漱口，因为很多口腔溃疡都是由于上火引起的，而蜜汁水就不仅具有消炎，同时还具有止痛的作用，对促进伤口的愈合也有一定的帮助。偏方二：木耳疗法。使用黑白木耳两种混合，山楂各10克，然后加上三碗水煎至一碗，然后吃木耳，喝汤。坚持每天多喝几次就可以一定的缓解口腔溃疡症状。偏方三：可可疗法。用可可粉和蜂蜜一起调成糊状，然后经常含咽就可。而且可可粉和蜂蜜的味道都是孩子最为喜欢的，相信这样的治疗方法对防治宝宝口腔溃疡效果是最好的。偏方四：白菜根疗法。用白菜的根部，同芥菜籽，葱白一起弄破碎然后用纱布将这些菜贴于足心就可。偏方五：全脂奶粉法。用全脂奶粉加适量的白糖，晚间休息的时候冲服就可了，正常情况下两天就可痊愈了。偏方六：维生素C。将维生素C弄碎了之后，然后敷在伤口处就可了，当然了如果宝宝不小心吃了的话也是不要紧的。偏方七：苹果法。将苹果去皮之后加冷水一起煮沸，然后取苹果同少量酒一起含在口中片刻就可。不过这个方法不是一般宝宝都可以接受的。偏方八：云南白药法。直接将云南白药敷在宝宝溃疡的表面就可以了，在敷的时候注意不要让宝宝吃下了。"], "answer_docs": [0], "segmented_question": ["儿童", "口腔溃疡", "的", "治疗方法"], "question_type": "DESCRIPTION", "question_id": 181595, "fact_or_opinion": "OPINION", "match_scores": [0.8824742268041237]} +{"documents": [{"is_selected": true, "title": "为什么网络测速很快,但实际上网很慢?_百度知道", "most_related_para": 3, "segmented_title": ["为什么", "网络", "测速", "很快", ",", "但实际", "上网", "很", "慢", "?", "_", "百度", "知道"], 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打开网页速度慢解决方案金山毒霸的系统清理中痕迹清理功能,可以解决打开网页速度慢/网页打开慢的问题。一键清理将您习惯清理的范围设置成默认,一键搞定清理垃圾清理电脑垃圾文件,节省磁盘空间清理痕迹清除使用记录,保护个人隐私清理注册表定期", "首先,请查看电脑本身配置,以及内存大小,辨明是电脑本身运行速度慢还是只是上网慢, 如果只是上网慢,可以考虑查下网速,看有没ARP攻击的情况。 建议可以用腾讯电脑管家工具箱里面的流量监控查查。要是上传的流量特别大就有问题了。建议开启防火墙(打开腾讯电脑管家——首页——工具箱——ARP防火墙)还有开启网速保护,打开腾讯电脑管家——工具箱——网速保护——浏览器。 然后清理下浏览器的垃圾和缓存, 在资源管理器中看一下什么东西占用资源很多,可以把一些不必要的软件先停了。 最后一个可能,就是中毒,那就杀毒吧,用杀毒软件彻底把电脑扫描一遍。", "网速慢原因 1.病毒木马原因导致的 ,会导致导致网速慢。 2.由于使用了p2p下载软件导致占用带宽资源很大,导致网速变慢了 3.多台电脑共享上网,可能共享网络中的电脑有中arp病毒 ,解决办法就是找到攻击源,使用金山卫士对该电脑杀毒 4.还有一种是就是你所处的宽带环境,有可能宽带线路老化,路由器坏了原因导致网速变慢 。 5. 你的电脑和网站处在不同网络环境 ,例如网通用户与电信网站之间的访问,也会出现网速变慢。 6.还有一种就是网站自身的问题,网站所在的服务器质量很差,带宽有限导致的网速变慢。网速慢的解决办法首先排除是不是外部因素造成的网速变慢了,例如网站自身的问题,宽带环境设备老化或者破损。如果排除外部因素,那就解决内部因素造成的。 一,金山卫士可以修复病毒木马导致的ie组件被破坏的疑难杂症 1、进入主界面进"], "bs_rank_pos": 0}, {"is_selected": true, "title": "网络测速很快,但实际上网却很慢。_百度知道", "most_related_para": 0, "segmented_title": ["网络", "测速", "很快", ",", "但实际", "上网", "却", "很", "慢", "。", "_", "百度", "知道"], "segmented_paragraphs": [["我", "估计", "的", "原因", "有", 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["我估计的原因有几个:1、同时使用的人多,拉慢了网速;2、对方在下载一些资源很多的东西,占用了大部分网速;3、路由器出现了问题,导致WIFI信号没以前那么好了;4、对方使用类似路由器管理器的软件对你进行限速了。最后一点我深有体会,我之前破解链接了一个邻居的WIFI,刚开始使用也很快,后面估计被发现了,链接的信号也很好,但是速度却非常的慢,连一个手机的网页都打不开,估计就是使用了路由器管理器限速了,360路由器就有这个限速的功能。", "无线路由信号范围有限的。。。估计对面换路由器了吧", "可能是他家在用吧"], "bs_rank_pos": 1}, {"is_selected": false, "title": "100m宽带测速很快,实际下载很慢为什么_百度知道", "most_related_para": 0, "segmented_title": ["100m", "宽带", "测速", "很快", ",", "实际", "下载", "很", "慢", "为什么", "_", "百度", "知道"], "segmented_paragraphs": [["首先", "100M", "宽带", ",", "理论", "峰值", "可以", "过", "12M", "/", "S", "。", "实际", "带宽", "要", "小", "一些", ",", "主要", "的", "影响因素", "有", ":", "1", "、", "网络", "提供商", "的", "网络终端", ",", "到", "家庭", "用户", "的", "连接", "介质", "的", "区别", ",", "目前有", "电话线", "、", "5", "类", "网线", "、", "光纤", "等", "。", "2", "、", "路由", "或", "猫", "到", "电脑", "间", "的", "连接", "网线", ",", "大", "多用户", "使用", "的", "是", "5", "类", "双绞线", ",", "上限", "100M", ",", "还有", "路由", "和", "猫", "的", "自身", "数据", "吞吐量", "的", "限制", ",", "市", "面", "已经", "有", "千元", "级", "的", 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1:防止大的固体侵入 2:防止中等大小的固体侵入 3:防止小固体进入侵入 4:防止物体大于1mm的固体进入 5:防止有害的粉尘堆积 6:完全防止粉尘进入 第二个X表示防水等级(数值越高,防水表现越好) 0:没有保护 1:水滴滴入到外壳无影响 2:当外壳倾斜到15度时,水滴滴入到外壳无影响 3:水或雨水从60度角落到外壳上无影响 4:液体由任何方向泼到外壳没有伤害影响 5:用水冲洗无任何伤害 6:可用于船舱内的环境 7:可于短时间内耐浸水(1m) 8:于一定压力下长时间浸水 如果说小米的防水等级是IP68,那么表示小米6能够在一定的水深环境下,长时间浸水。因此还在顾虑小米6无法防水的朋友,不需要过多的担心。只要你不是刻意把手机放入水中,日常生活的防水是完全没有问题的。", "提示:支持键盘“←→”键翻页 阅读全文"], "bs_rank_pos": 0}, {"is_selected": true, "title": "小米6的防水级别是多少?【小米m6吧】_百度贴吧", "most_related_para": 1, "segmented_title": ["小米", "6", "的", "防水", "级别", "是", "多少", "?", "【", "小米", "m6", "吧", "】", "_", "百度贴吧"], "segmented_paragraphs": [["数据线", "接口", "没有", "防水", "处理", ",", "所以", "最好", "别", "手", "贱"], ["水", "冲", "没问题", ",", "下水", "五分钟", "左右", "也", "就", "那样", "了", "。"], ["登录", "百度", "帐号"], ["下载", "贴吧", "APP", "看", "高清", "直播", "、", "视频", "!"], ["贴吧", "页面", "意见反馈", "违规", "贴吧", "举报", "反馈", "通道", "贴吧", "违规", "信息处理", "公示"]], "paragraphs": ["数据线接口没有防水处理,所以最好别手贱", "水冲没问题,下水五分钟左右也就那样了。", "登录百度帐号", "下载贴吧APP看高清直播、视频!", "贴吧页面意见反馈 违规贴吧举报反馈通道 贴吧违规信息处理公示"], "bs_rank_pos": 1}, {"is_selected": false, "title": "小米6防水等级多少能浸泡吗_百度知道", "most_related_para": 0, "segmented_title": ["小米", "6", "防水等级", "多少", "能", "浸泡", "吗", "_", "百度", "知道"], "segmented_paragraphs": [["注意", "这个", "是", "防泼溅", ",", "不是", "较", "高", "等级", "的", "防水", "的", ",", "仅限于", "意外", "被", "泼洒", "了", "少量", "的", "水", ",", "并且", "也", "不能", "再", "开", "孔", "的", "地方", "进水", "。"], ["该", "机", "的", "方式", "仅限于", "泼溅", ",", "也就是说", "有", "少量", "的", "水", "泼洒", "在", "手机", "上", ",", "不能", "进行", "浸泡", "的", "。"]], "paragraphs": ["注意这个是防泼溅,不是较高等级的防水的,仅限于意外被泼洒了少量的水,并且也不能再开孔的地方进水。", "该机的方式仅限于泼溅,也就是说有少量的水泼洒在手机上,不能进行浸泡的。"], "bs_rank_pos": 2}, {"is_selected": false, "title": "小米6的防水级别是多少_百度知道", "most_related_para": 0, "segmented_title": ["小米", "6", "的", "防水", "级别", "是", "多少", "_", "百度", "知道"], "segmented_paragraphs": [["很", "低", "及", "的", "防泼溅", ",", "也就是说", "有", "少量", "的", "水", "泼洒", "在", "手机", "上", ",", "不能", "进行", "浸泡", "的", ",", "开", "孔", "处", "也", "不能", "浸水", ",", "不", "然", "就", "坏", "了"]], "paragraphs": ["很低及的防泼溅,也就是说有少量的水泼洒在手机上,不能进行浸泡的,开孔处也不能浸水,不然就坏了"], "bs_rank_pos": 3}, {"is_selected": false, "title": "小米6防水等级多少能浸泡吗 - 小米社区官方论坛", "most_related_para": 1, "segmented_title": ["小米", "6", "防水等级", "多少", "能", "浸泡", "吗", "-", "小米", "社区", "官方", "论坛"], "segmented_paragraphs": [["扫描", "二维码", ",", "手机", "查看", "本", "帖"], ["总", "评分", ":", "经验", "+", "1"]], "paragraphs": ["扫描二维码,手机查看本帖", "总评分: 经验 +1"], "bs_rank_pos": 4}], "answer_spans": [[114, 121]], "answer_docs": [0], "fake_answers": ["小米6是支持IP68级的防水"], "question": "小米6防水等级", "segmented_answers": [["小米", "6", "是", "支持", "IP68", "级", "的", "防水", "。"], ["IP56", "吧", "。"]], "answers": ["小米6是支持IP68级的防水。", "IP56吧。"], "entity_answers": [["IP68级"], ["IP56"]], "segmented_question": ["小米", "6", "防水等级"], "question_type": "ENTITY", "match_scores": [0.9411764705882353], "fact_or_opinion": "OPINION", "question_id": 181597} +{"documents": [{"is_selected": true, "title": "平安银行贷款拔打10100000可以贷款吗?都要什么程序,谢_百度知道", "most_related_para": 1, "segmented_title": ["平安银行", "贷款", "拔", "打", "10100000", "可以", "贷款", "吗", "?", "都", "要", "什么", "程序", ",", "谢", "_", "百度", "知道"], "segmented_paragraphs": [["(", "1", ")", "10100000", "是", "平安", "直通", "贷款", "服务", "的", "咨询电话", ",", "(", "2", ")", "可以", "向", "客服专员", "说明", "贷款", "意向", ",", "客服", "会", "协助", "完成", "贷款", "申请", "流程", "办理", "平安银行", "的", "个人", "信用", "贷款", "流程", ":", "　", "第一步", ",", "申请人", "向", "平安", "提交", "申请", "资料", "、", "贷款", "申请表", "及", "授权书", "。", "　　", "1", "、", "主", "贷", "人", "及", "配偶", "的", "身份证复印件", ";", "　　", "2", "、", "主", "贷", "人", "及", "配偶", "户口本", "复印件", "(", "第", "1", "、", "2", "、", "3", "联", "必须", "要", "有", ")", ";", "　　", "3", "、", "结婚证", "复印件", ",", "单身", "证明", "原件", ",", "离婚证", "及", "离婚协议", "书", "或", "法院", "判决书", "复印件", ";", "　　", "4", "、", "主", "贷", "人", "及", "配偶", "个人", "收入证明", "原件", "(", "单位", "盖公章", "有效", ")", ";", "　　", "5", "、", "房产", "产权", "证明", "文本", "复印件", "(", "房产证", "、", "购房合同", "及", "发票", "、", "预售合同", "及", "发票", ")", ";", "　　", "6", "、", "近期", "水电", 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"public static void main(String[] args) { String s = UUID.randomUUID().toString(); System.out.println(s); }", "关注 kijioy kijioy 本版等级: 本版专家分:361 结帖率:0%", "你放心吧, 用到了 MAC、时间戳、随机数等因素生成", "关注 mp19901204 mp19901204 本版等级: 本版专家分:69 结帖率:94.74%", "不会重复,我们公司有时候数据库的主键就是这么用的,嘿嘿!", "关注 lrbyantai lrbyantai 本版等级: 本版专家分:7812 结帖率:100%", "为什么会有这种担心呢"], "bs_rank_pos": 1}, {"is_selected": false, "title": "java 生成UUID重复_百度知道", "most_related_para": 0, "segmented_title": ["java", "生成", "UUID", "重复", "_", "百度", "知道"], "segmented_paragraphs": [["java", "是", "可以", "做到", "的", "。", "先", "导入", "uuid", "包", ":", "import", "java", ".", "util", ".", "UUID", ";", "然后", ":", "String", "uuid", "=", "UUID", ".", "randomUUID", "(", ")", ".", "to", "String", "(", ")", ".", "replace", "All", "(", "\"", "-", "\"", ",", "\"", "\"", ")", ";"]], "paragraphs": ["java是可以做到的。先导入uuid包:importjava.util.UUID;然后:Stringuuid=UUID.randomUUID().toString().replaceAll(\"-\",\"\");"], "bs_rank_pos": 2}, {"is_selected": false, "title": 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"随即生成字符串UUID.randomUUID().toString().replace(\"-\", \"\"); 随即生成0~n之间的数字:Math.random()*n; 生成的都是不重复的字符串或者数字,一般UUID.randomUUID().toString()用作表的主键。"], "bs_rank_pos": 3}, {"is_selected": false, "title": "使用UUID解决重复提交问题 - 博客频道 - CSDN.NET", "most_related_para": 0, "segmented_title": ["使用", "UUID", "解决", "重复", "提交", "问题", "-", "博客", "频道", "-", "CSDN", ".", "NET"], "segmented_paragraphs": [["2015", "一月", "21", "原", "使用", "UUID", "解决", "重复", "提交", "问题", "分类", ":", "jee", "(", "1257", ")", "(", "0", ")", "1", "UUID", "UUID", "是", "1.5", "中", "新增", "的", "一", "个", "类", ",", "在", "java", ".", "util", "下", ",", "用", "它", "可以", "产生", "一", "个", "号称", "全球", "唯一", "的", "ID", "。", "UUID", "(", "Universally", "Unique", "Identifier", ")", "全局", "唯一", "标识", "符", ",", "是", "指", "在", "一", "台", "机器", "上", "生成", "的", "数字", ",", "它", "保证", "对", "在", "同", "一", "时空", "中", "的", "所有", "机器", "都是", "唯一", "的", "。", "按照", "开放", "软件", "基金会", "(", "OSF", ")", "制定", "的", "标准", "计算", ",", "用", "到", "了", "以太网卡", "地址", "、", 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Identifier)全局唯一标识符,是指在一台机器上生成的数字,它保证对在同一时空中的所有机器都是唯一的。按照开放软件基金会(OSF)制定的标准计算,用到了以太网卡地址、纳秒级时间、芯片ID码和许多可能的数字。由以下几部分的组合:当前日期和时间(UUID的第一个部分与时间有关,如果你在生成一个UUID之后,过几秒又生成一个UUID,则第一个部分不同,其余相同),时钟序列,全局唯一的IEEE机器识别号(如果有网卡,从网卡获得,没有网卡以其他方式获得),UUID的唯一缺陷在于生成的结果串会比较长。 
import java.util.UUID; public class UUIDTest { public static void main(String[] args) { for(int i = 0 ; i < 10; i++){ UUID uuid = UUID.randomUUID(); System.out.println(uuid); } } }  \"\"  2 使用UUID解决提交两次问题(通过“返回上一页”,把缓存中的数据重复提交) 1)在进入“提交”页面的控制层中,生成一个UUID放进session中 2)在“提交”页面中,把session中的UUID传到保存数据控制层 3)在保存数据的控制层,判断session中的UUID和传过来的UUID,如果相等,保存并删除session中的UUID;如果不相等,不保存。", "文章分类 《轻量级JavaEE企业应用实战》(33) spring实战笔记(33) 《JavaScript权威指南》(51) 信息安全(1) jse(19) jee(26) eclipse(3) maven(12) 《maven实战》(10) spring(2) html+css(2) 工具(1) 《Apache CXF构建SOA》(5) apache cxf(4) Spring_Security(8) 《Spring Security 3》(1) apache shiro(1) mybatis(5) ActiveMQ(4) 《Java并发编程实战》(8) 架构(9) 《大型网站技术架构》(9) 好书收藏(1) 《HTML5权威指南》(2) linux(46) mysql(1) 《Java 网络编程 第四版》(13) 《Java RESTful Web Service实战》(3) 《Java设计模式》(8) js(9) Nginx/LVS/HAProxy(1) redis(0) netty(0) 《Java 8 in Action》(14) 《Netty in Action》(8) 《Netty权威指南》(16) 《Spring Boot实战》(15) MongoDB(1) 《Git版本控制管理》第2版(3) idea(2) 《DOCKER技术入门与实战》(2) 《Vue.js权威指南》(9)"], "bs_rank_pos": 4}], "answer_spans": [[0, 12]], "yesno_answers": ["No"], "fake_answers": ["不会重复,我们公司有时候数据库的主键就是这么用的"], "question": "uuid.randomuuid 会重复吗", "segmented_answers": [["不会", "重复", "，", "我们", "公司", "有时候", "数据库", "的", "主键", "就是", "这么", "用", "的"]], "answers": ["不会重复，我们公司有时候数据库的主键就是这么用的"], "yesno_type": "FACT", "answer_docs": [1], "segmented_question": ["uuid", ".", "randomuuid", "会", "重复", "吗"], "question_type": "YES_NO", "question_id": 181600, "fact_or_opinion": "FACT", "match_scores": [0.9230769230769231]}
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"美甲不同于化妆美发,是个很精细的工作,所以选择美甲学校,一定要选择专业的美甲学校(学校只教美甲。)南京格林彩妆学校李萨老师曾今在一个综合学校学过美甲,别提有多后悔了,屋里面教美甲,屋外面一股头发的味道(美发班同学在练习),简直快晕了,所以一定要选择专业的美甲学校,而非美甲,化妆美发等综合学校。", "专业的美甲学校一般是有资质的,比如格林彩妆美甲学校,在南京专业教学四年,这样的专业学校是肯定有美甲教学资质的,而综合学校一般是拿着化妆,美发的资质在美甲,从侧面说,这些学校根本不正归。", "看师资力量与环境,有很多学校都刚刚起步,学校环境不好,在地下室里,而美甲的老师也只有一个,你觉的在这样的学校能学好吗?", "学美甲要多少钱?", "在南京有很多美甲机构,当然从教学质量来分,学费也是不同的。有的人学美甲可能只花了一千,而有的人却花了一万,这里面有什么区别?当然有区别!", "美甲的重点在所用的工具与材料,一千学费的,一般配的材料都是很差的产品,而且有时候还有假货。前段时间央视才刚刚暴光因为假货弄的截肢的新闻,在普通的美甲学校一般学完美甲差不多需要1800-3000左右,如果太低了,各位就要小心了。", "学美甲去哪里?", "学美甲去哪个学校最好?南京格林彩妆学校,建校十余年,期间培训了数万多名优秀美甲人才,做为国内的美甲学校,南京格林彩妆美甲学校从未停止过前进的脚步,南京格林彩妆美甲学校希望携手所有向往健康美丽的朋友,时尚美甲的朋友,实现创业梦想。南京格林彩妆美甲学校与你共同向世界展示美甲的美丽和精髓!南京格林彩妆美甲学校与您一同走向美甲世界的最高峰!", "上海艾艾贴总代 苏州光芒售后电话 溧阳针扎化纤地毯公司 溧阳纯毛地毯价格", "溧阳塑料地毯哪家好 天河哪里有生煎包制作 昆山5083铝批发生 广州天河好的杭州小笼"], "bs_rank_pos": 5}, {"is_selected": false, "title": "南京格远建材_njgyjc【今日推荐网-认证】电话手机联系方式_可信度、", "most_related_para": 11, "segmented_title": ["南京", "格", "远", "建材", "_", "njg", "yjc", "【", "今日推荐", "网", "-", "认证", "】", "电话", 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"大便", ",", "永远", "健康", ",", "年轻", ",", "貌", "美", "!", "文章", "关键词", ":", "大便", "疾病", "性福", "课堂", "NO", "54", ":", "男人", "越", "吃", "辣", "性能力", "越", "强", "吗", "2014", "-", "12", "-", "23", "17", ":", "19", "水中分娩", "并非", "人人", "皆宜", "2014", "-", "12", "-", "23", "10", ":", "57", "石榴", "有", "伟哥", "功效", "吗", "2014", "-", "12", "-", "23", "10", ":", "27", "老", "吃", "太饱", "撑", "出", "10", "种", "病", "2014", "-", "12", "-", "22", "15", ":", "06", "办公室", "久坐", "易", "引", "6", "病", "如何", "防", "2014", "-", "12", "-", "22", "10", ":", "49"]], "paragraphs": ["您体检时留大便了吗?可别小瞧这个臭烘烘的东西,大便的颜色、性状、潜血、细胞等与疾病密切相关。便常规检查作用大,被称为消化道疾病的“警报器”。所以,如果关注自身健康,千万不要怕麻烦,每次体检一定要留下宝贵的“便便”。", "便常规也包括物理学检查(性状、颜色、不消化物等)、化学检查(隐血)以及显微镜检查(血细胞、寄生虫、寄生虫虫卵、结晶和细菌等)如解毒便常规的化验报告,可参考如下程序和办法。", "(一) 观“色”又察“形” 大便的颜色是平时就可经常观察额,到了一定的年龄,就要养成便后观察看看自己大便的颜色和性状,既不麻烦,有时还能帮大忙。 大便颜色:正常的大便呈黄色或棕黄色(大便中含有胆道排下来的胆黄素,所以才呈现这种颜色)。绿色多事因为混杂色素的缘故,无大碍。至于棕色、黑色和红色,就要注意胃肠道疾病的信号灯(如大便呈现红色但镜显下午红细胞,这是正常的,可能是吃了含有色素的食品;如果镜显下有红细胞,就是消化道可能真的出现了问题)。 白陶土样便:一般提示有胆道梗阻,如结石、肿瘤或蛔虫等引起的(大便里没有胆黄素) 柏油样便:一般提示上消化道出血,可能是良性疾病,也可能是恶性疾病所致。一单发现,一定要及时到医院就诊 小贴士:不仅要观察大便里是否有血,还要观察血和大便的关系,无论是上消化道(柏油样便)还是下消化道出(血便)都有一个特点,那就是血和大便是混在一起的(便里有血,血里有便)如果发现血只是附着在大便表面,或是便后才有滴血,多半是患了痔疮。 大便性状:通过观察大便的性(性质)与状(形状)也能了解一些疾病信息,一般“软、成形”为正常,便秘者为“硬”便,习惯性便秘为“羊粪样”是否呈液状,则可用来判断肠道疾病的轻重 柏油样便:见于上消化道出血。 黏液稀便:见于肠壁受刺激或者发炎时,如肠炎、痢疾和急性血吸虫病等。 黏液脓性血便:多见于细菌痢疾。 酱色黏液便:多见于阿米巴痢疾。 水样、蛋花样便:多见于急性肠胃炎,大量见于假膜性肠炎。 糊状便:多见于小肠疾患。 油花便:粪便中浮有“油花”,多由于脂肪类进食过多,不消化所致。 大便变细:这时要小心关注是否患有消化道肿瘤。 (二)“隐血试验”是重点 要关注“便隐血试验”,这是小异常预示大问题的典型代表,它对消化道出血诊断有重要价值,现常作为消化道恶性肿瘤早期诊断的一个筛选指标,一般要求40岁以上的健康人每年做一次隐血试验,作为健康筛查。便隐形式样间断性阳性提示消化道溃疡性出血,持续性阳性提示消化道恶性肿瘤,应进一步做消化内镜检查。 小贴士:在检测隐血试验是,检查前三日起必须禁食肉类和含血的食物、铁剂、叶绿素(如菠菜)等食物,以避免“假阳性”和“假阴性” 假阳性:服用阿司匹林、皮质类固醇、非类固醇抗炎药。 假阴性:摄入大量的维生素C。 (三)镜下观察仔细看 显微镜下查出红细胞,提示多半患了结肠炎、肿瘤、息肉、肠结核和痔疮出血等;如红细胞多余白细胞,多为阿米巴痢疾;如白细胞多于红细胞,多为细菌性痢疾;显微镜下见到多个白细胞而无红细胞,提示患有肠炎;显微镜下见到虫卵及成虫,证明有寄生虫感染。 (四)推荐反复检查 一次便常规异常不一定就说明了消化系统有问题,必要时应当反复化验协助诊断,出现一次不可怕,可怕的是,它经常出现而被忽视。如果,大便习惯在一定时间内发生明显的变化,比如反复出现腹泻或便秘,或便秘和腹泻交替出现,一定要去关注。 希望朋友们在体检中来辨识“便便”的真面目,不正常的现象背后,一定有不正常的健康因素,望大家都能够拥有五星级大便,永远健康,年轻,貌美!文章关键词: 大便疾病性福课堂NO54:男人越吃辣性能力越强吗2014-12-23 17:19 水中分娩并非人人皆宜2014-12-23 10:57 石榴有伟哥功效吗2014-12-23 10:27 老吃太饱撑出10种病2014-12-22 15:06 办公室久坐易引6病如何防2014-12-22 10:49"], "bs_rank_pos": 1}, {"is_selected": true, "title": "大便常规能检查出什么_百度拇指医生", "most_related_para": 1, "segmented_title": ["大便", "常规", "能", "检查", "出", "什么", "_", "百度", "拇指", "医生"], "segmented_paragraphs": [["?", "大便", "常规", "能", "检查", "出", "什么"], ["正常", "粪便", "由", "已", "消化", "和", "消化", "未尽", "的", "食物残渣", "、", "消化道", "分泌物", ",", "大量", "细菌", "和", "水分", "组成", ",", "通过", "全", "肠道", ",", "特别", "是", "结肠", "直肠", "而", "逐渐形成", ",", "它", "不", "但", "能", "反映", "消化道", "的", "功能", "状态", "及", "病理", "情况", ",", "也有", "助", "于", "某些", "全身", "疾病", "的", "诊断", "。", "大便", "常规", "检查", "属于", "三大", "常规", "之", "列", "。", "1", ".", "偶发", "或", "长期", "腹泻", "的", "患者", "。", "2", ".", "较", "长时间", "便秘", "的", "患者", ",", "包括", "用药", "后", "便秘", "。", "3", ".", "如果", "您", "有", "脐", "周围", "原因不明", "的", "腹痛", "、", 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"正常粪便由已消化和消化未尽的食物残渣、消化道分泌物,大量细菌和水分组成,通过全肠道,特别是结肠直肠而逐渐形成,它不但能反映消化道的功能状态及病理情况,也有助于某些全身疾病的诊断。大便常规检查属于三大常规之列。1.偶发或长期腹泻的患者。2.较长时间便秘的患者,包括用药后便秘。3.如果您有脐周围原因不明的腹痛、腹胀、消化不良,大便常规检查是必不可少的一步。4.发现大便颜色出现异常,呈黑色、陶土色、绿色、酱红色等,大便性状改变,稀水、黏液便、脓血便等。5.大便中明显可见未消化的食物、菜叶、油花等。6.不明原因的发热。7.临床上疑有胃癌、结肠癌、胰腺癌等。8.有便虫史者,可以确定肠道寄生虫种类。9.各种原因的黄疸患者应查大便常规,可助临床诊断。1.了解消化道及通向消化道的胆管有无炎症、感染存在,包括寄生虫感染。如肠炎时镜检可查出脓细胞,溃疡性结肠炎时有较多红细胞,胆道中蛔虫症及肠道蛔虫症可查出蛔虫卵。2.了解消化道及通向消化道的胆管有无病变引起的出血,以便进一步细查。如肝硬化食管静脉曲张、溃疡病、胆管出血,出血量较大时,肉眼可见明显血便或黑便;而结肠肿瘤,溃疡性结肠炎,血便更是主要症状。3.粪便隐血试验(化学法或免疫法)可以查肉眼不能辨明的出血。隐血阳性除提示消化道有出血应细查原因外,由于消化道良、恶性肿瘤常发生小量出血,肉眼不能辨明,故隐血试验亦可作为消化道肿瘤普查的初筛手段。4.通过细菌培养或镜检,可以检查肠道有无致病菌(如伤寒杆菌、沙门杆菌、痢疾杆菌)以防治肠道传染病。5.协助诊断某些肝胆疾病,如阻塞性黄疸时大便呈陶土色、胆石症时粪中可检出小结石。6.了解食物在胃肠道内消化情况的参考。粪便见油珠说明脂肪消化吸收不良;见肉食纤维说明蛋白质消化不良等。三、送检粪便标本注意事项:1.粪便中不能混有尿液,因为有些被检成分会被尿液的成分破坏,出现假阴,陛。2.最好按医嘱停服某些药物与食物,否则可出现某些假阳性结果(如服用中药或食用蔬菜、羊血等会导致粪便隐血阳性)。3.挑送您认为不正常的成分,如脓、血、像鼻涕样的黏液,不要挑送外观正常便送检。4.留取蚕豆大小一块就够用了。5.要用不吸水的蜡纸袋或塑料纸袋送检,最好事先在化验室取容器。6.不要忘记贴上您的名字、日期。7.大便要新鲜,最好是立解立送(尤其是冬天),至少也要是当天早上的。"], "bs_rank_pos": 2}, 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"你好,大便常规不能检查出肠癌,也不能作为肠癌的确诊依据,需要进行肠镜检查等,取病变部位的组织进行病理检查,才能确诊,建议描述清楚你现在有哪些不适的症状,尽快的给于早发现,早诊断,早治疗,早手术,祝你幸福健康。", "2017-04-19 09:59 投诉", "大肠癌能活多久", "大肠癌在我国的发病率已经占至肠道肿瘤的第一位,也是一种恶性肿瘤,对患者的危害很大,很多患病的人都想知道自己还能活多久。下", "直肠癌的早期症状图片 直肠癌能活多久 直肠癌的早期症状及表现是什么 大肠癌筛查阳性什么意思", "大肠癌是指大肠粘膜上皮在环境或遗传等多种致癌因素作用下发生的恶性病变,预后不良,死亡率较高,是我国常见的恶性肿瘤之一。其中由粘膜上皮发生的恶性肿瘤统称为大肠癌,为胃肠道最常见的恶性肿瘤。起源于间叶组织者称为肉瘤,约占大肠恶性病变的1%。手术切除后的5年生存率平均可达40%～60%,早期发现、早期诊断、早期治疗以及开展规范化的手术治疗仍是提高大肠癌疗效的关键。", "全国三甲医院,主任级名医在线坐诊已有124家三甲医院,828位主任医师在线答疑"], "bs_rank_pos": 3}, {"is_selected": false, "title": "检查大便能检查出什么_百度拇指医生", "most_related_para": 1, "segmented_title": ["检查", "大便", "能", "检查", "出", "什么", "_", "百度", "拇指", "医生"], "segmented_paragraphs": [["?", "检查", "大便", "能", "检查", "出", "什么"], ["大便", "常规", "分享", "一", "、", "一般", "性状", ":", "正常", "为", "黄色", "软便", "。", "颜色", "异常", "有", "以下", "几种情况", ":", "1", ".", "黑色", "或", "柏油", "样", ":", "见于", "上消化道出血", ",", "如", "溃疡病", "出血", "、", "食道", "静脉曲张", "破裂", "、", "消化道", "肿瘤", "等", "。", "如", "服", "铁剂", "、", "铋剂", "或", "进食", 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缩略语;", "d. 本文档适用的系统计算机软件配置项(CSCI)。此外,还应包括在本报告中记录的每个正式合格性测试的名称和编号。", "1.2 系统概述", "概述本报告所适用的系统和CSCI 的用途。", "1.3 文档概述", "概述本报告的用途和内容。", "按文档号和标题列出本文档引用的所有文档。", "分节描述本报告所覆盖的每项正式合格性测试的结果。", "3.1 (正式合格性测试名称及项目的唯一标识号)", "按名称和编号来说明正式合格性测试,并分小节概述测试结果。", "3.1.1 (正式合格性测试名称)小结 总结正式合格性测试的结果。若失败,则要说明产生错误结果的测试步骤和问题报告。这些内容可参考表1 的测试结果一览表进行概括。 3.1.2 (正式合格性测试名称)测试记录 按时间顺序记录所有测试前、进行测试、分析、说明以及正式合格性测试结果等有关事件。同时,还庆提供测试日志,按时间顺序记录正式合格性测试中的工作,包括:", "a.测试时间、地点、软硬件的配置。需要时,测试配置项的描述还要记录软件版本号、研制单位、升级号、批准日期及所有硬件型号和软件部件使用的名 b.每一个测试相关活动的日期和时间、测试操作人员和参加人员; c.测试过程中对所出现和产生的问题所采取的测试步骤,包括对问题的改 进的次数和每一次结果; d.恢复重新测试的备份点或测试步骤。 分节详述每个正式合格性测试的细节。 4.X (正式合格性测试的名称和项目的唯一标识号)测试结果", "从4.1 节开始编号。按名称和项目唯一标识号标识正式合格性测试,并分小节详细描述每一正式合格性测试用例的结果。", "表1 测试结果一览表示例", "1) 如果测试过程出现一个故障或错误,则记录发生故障或错误的各个步骤。 2) PR=问题报告。", "4.X.Y (测试用例名称和项目的唯一标识号)", "从4.1.1 节开始编号,按名称和项目的唯一标识号标识每一测试用例,并分小节详细说明测试用例的结果。", "4.X.Y.1 (测试用例名称)测试结果", "说明测试用例的测试结果。对测试过程的每一步都要记录测试结果和在测试", "过程中出现的各种异常和矛盾情况。记录或引用有助于杜绝和纠正矛盾情况的信息(如存储器转储、寄存器记录、显示流程图),并分析导致矛盾的原因和改进的方法。", "4.X.Y.2 (测试用例名称)测试过程中的差异情况", "详细说明相应的软件测试说明中描述的测试过程中的差异情况(例如,所需设备的替换,支持软件的改变,测试计划的偏差)。对每一种差异情况,必须说明导致差异的原因和它对测试有效性的影响。", "5 CSCI 评估和建议", "5.1 CSCI 评估", "全面分析测试结果,对CSCI 的能力作出评估。通过分析标出存在的缺陷、局限性和CSCI 的约束等,并写入软件问题/更改报告。对每一种偏差,局限性和约束应包括:", "a. 说明它对于CSCI 及系统运行的影响; b. 说明它对于CSCI 及为纠正偏差的系统设计的影响; c. 提供改必的方法和建议。", "5.2 改进建议", "对系统设计、操作和CSCI 测试提出改进建议,并分析每一建议对CSCI 的影响。若无建议,则写“无”。...余下全文>>", "先谢需求分析,提出概要设计,在进行详细设计、单元测试、系统测试检验、设计验证、综合测试和确认即可", "请采纳,谢谢", "应该不是一种语言编写的吧,单论安全性C没C++、java高。", "跟你的导师联系,他会指导你!", "如果你已经开发完了软件系统,就更好写了。硕士论文的套路基本一样的,不同学校间模版会有差别。", "国内外研究现状", "以上是一级标题,其他的自己列", "写清楚项目目的、业务流程、组织机构、功能要求、非功能要求(支持的用户数、响应时间等)就可以了。可以用UML图,但是做不好还不如不用,关键恭描述准确。", "你好,这属于软件类的可行性研究报告哦。", "建议您找有资质的专业咨询公司进行编制。您可以将相关的材料发给他们的工程师,由他们给您提出合理性的建议后,您在做采纳。", "因为专注,所以专业。(点我名字)", "软件系统(Software Systems)是指由系统软件、支撑软件和应用软件组成的计算机软件系统,它是计算机系统中由软件组成的部分。它包括操作系统、语言处理系统、数据库系统、分布式软件系统和人机交互系统等。", "软件系统作用", "操作系统用于管理计算机的资源和控制程序的运行。语言处理系统是用于处理软件语言等的软件,如编", "译程序等。数据库系统是用于支持数据管理和存取的软件,它包括数据库、数据库管理系统等。数据库是常驻在计算机系统内的一组数据,它们之间的关系用数据模式来定义,并用数据定义语言来描述;数据库管理系统是使用户可以把数据作为轴象项进行存取、使用和修改的软件。分布式软件系统包括分布式操作系统、分布式程序设计系统、分布式文件系统、分布式数据库系统等。人机交互系统是提供用户与计算机系统之间按照一定的约定进行信息交互的软件系统,可为用户提供一个友善的人机界面。操作系统的功能包括处理器管理、存储管理、文件管理、设备管理和作业管理。其主要研究内容包括:操作系统的结构、进程(任务)调度、同步机制、死锁防止、内存分配、设备分配、并行机制、容错和恢复机制等。", "编辑本段软件系统功能", "语言处理系统的功能是各种软件语言的处理程序,它把用户用软件语言书写的各种源程序转换成为可为计算机识别和运行的目标程序,从而获得预期结果。其主要研究内容包括:语言的翻译技术和翻译程序的构造方法与工具,此外,它还涉及正文编辑技术、连接编辑技术和装入技术等。 数据库系统的主要功能包括数据库的定义和操纵、共享数据的并发控制、数据的安全和保密等。按数据定义模块划分,数据库系统可分为关系数据库、层次数据库和网状数据库。按控制方式划分,可分为集中式数据库系统、分布式数据库系统和并行数据库系统。数据库系统研究的主要内容包括:数据库设计、数据模式、数据定义和操作语言、关系数据库理论、数据完整性和相容性、数据库恢复与容错、死锁控制和防止、数据安全性等。 分布式软件系统的功能是管理分布式计算机系统资源和控制分布式程序的运行,提供分布式程序设计语言和工具,提供分布式文件系统管理和分布式数据库管理关系等。分布式软件系统的主要研究内容包括分布式操作系统和网络操作系统、分布式程序设计、分布式文件系统和分布式数据库系统。 人机交互系统的主要功能是在人和计算机之间提供一个友善的人机接口。", "编辑本段分布式软件系统"], "bs_rank_pos": 3}, {"is_selected": false, "title": "论文系统实现_论文系统实现怎么写", "most_related_para": 2, "segmented_title": ["论文", "系统", "实现", "_", "论文", "系统", "实现", 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"宝", "的", "炒作", "水平", "竟然", "超过", "了", "支付宝", "?"], ["赶紧", "查查", "看", ",", "一看", "才", "知道", ",", "原来", "是", "回锅饭", ",", "图片", "还是", "上次", "裸条", "时候", "的", "图片", ",", "只是", "又", "被", "打包", "炒", "了", "一次", ",", "看到这里", ",", "小", "编", "感觉", "这次", "借贷", "宝", "又", "被黑了", ",", "而且", "这次", "被", "黑", "有点", "连环计", "的", "味道", ",", "借", "着", "某", "媒体", "批评", "借贷", "宝", "之后", ",", "马上", "来", "黑", "借贷", "宝", ",", "应该", "是", "有人", "在", "幕后", "故意策划", "的", ",", "否则", "它", "的", "热度", "不可能", "一夜之间", "超过", "支付宝", ",", "要知道", "支付宝", "成名", "已", "久", "了", "啊", "。", "借贷", "宝", "纵使", "是", "非常", "强悍", "的", "独角兽", ",", "但", "目前", "也", "不能", "与", "支付宝", "匹敌", "啊", "。"], ["接着", ",", "就是", "大量", "的", "媒体", "曝光", "说", "借贷", "宝", "已经", "报警", "了", ",", "我", "搜索", "了", "一", "下", ",", "发现", "竟然", "有", "926", "个", "媒体报道", "过", "借贷", "宝", "澄清", "裸条", "的", "事件", ",", "然后", ",", "事件", "出乎意料", "的", "平息", "下来", ",", "大家", "都", "当成", "了", "一", "场", "闹剧", "。"]], "paragraphs": ["为什么借贷宝倒闭不了? 借贷宝一线员工泄密!!", "为什么借贷宝倒闭不了?借贷宝一线员工泄密!!", "不知道从什么时候开始,有不少网友开始质疑:为什么借贷宝倒闭不了?据悉,“为什么借贷宝倒闭不了”这个词已经进入了百度的相关搜索好长一段时间了,要知道进入这里的相关搜索并不容易,那么,问题来了,为什么借贷宝倒闭不了呢?本文并不想做盖棺定论,只是想把正反两方面的观点亮出来,抛砖引玉,请各位看官自行判断。", "为什么借贷宝倒闭不了", "《借贷宝的最大后台被曝光,怪不得屡陷危机安然无恙》", "自借贷宝创立以来,先后多次被抹黑为传销、裸照等,甚至被某权威媒体批评,像这样被反复打击却依然屹立不倒的P2P平台,在国内恐怕只有借贷宝一家了。那么,借贷宝背后的靠山是什么人?借贷宝最大的后台是谁?这令我们非常感兴趣。", "全球互金视角下的借贷宝:盛世繁荣还是危机四伏?", "全球互金视角下的借贷宝:盛世繁荣还是危机四伏?", "什么?借贷宝的后台是王璐?别开玩笑了!", "根据公开资料显示,王璐出生于1988年,是北京大学金融数学系本科,北京大学经济学硕士学历。据说王璐是被保送到北大的,当初因为喜欢数学,他和北大招生办的人员约定:如果高考数学满分,就让他进数学系,结果他的高考数学真的满分。要知道北大的数学可是国内最厉害的地方了,高考数学满分,全国又有几个?所以他的智商绝对不低。", "又有资料显示:王璐是2012年加入九鼎的,期间参与负责过多个金融投资、并购、大型战略合作项目,在大数据分析、战略合作研究方面有丰富的项目经验。虽然王璐的履历非常辉煌,但如果说他就是拯救屡陷舆论危机的借贷宝的后台,可能不能令人信服,因为没有哪一家P2P平台可以经受住如此多的攻击,那么,我想这个后台一定非常强大。王璐毕竟是一家公司的掌门人,与我们想象的躲在幕后操盘的靠山不太相符。那么,借贷宝幕后真正的靠山究竟是谁呢?", "什么,借贷宝的最大后台是吴刚?虽然他是新三板首富,然并卵", "提起吴刚这个人,绝对不简单,吴刚是中国P2P行业中的一个传奇人物。公开资料显示:吴刚最初只有中专学历、负债百万起步、从地下室的2个人开始干起,到身家180亿,成为新三板首富。在2015年胡润研究院发布《2015胡润百富榜》的上榜名单中,有14位是来自新三板挂牌公司。其中财富涨幅最快的就是九鼎投资董事长吴刚,在过去一年中财富增长8倍,以180亿身家位列百富榜第120位,同时也是新三板首富。据悉,九鼎投资被誉为新三版老大,2014年4月上市,市值超过1000亿。", "但即便聪敏如吴刚,也只是一个企业家,最多是企业家领袖,想那借贷宝的负面事件闹得满城风雨,显然不是一个企业家能够平息下来的。那么,借贷宝背后更大的后台到底是谁?很显然,他应该是从未被曝光的人物,这令小编更加急切的想知道真相。", "在透露借贷宝的最大后台之前,我们先回顾一下借贷宝备受攻讦的几次事件,透过这些事件,有助于我们发觉借贷宝神秘靠山的“蛛丝马迹”。", "事件回顾之——借贷宝被抹黑为传销", "2015年8月,借贷宝发起了一场轰动全国的拉好友有奖励营销活动,拉一个好友注册借贷宝,双方均可获得20元奖励,如果你拉的好友再拉了另一个好友,那么,你还可以从中赚取10元。正是这个设计,让借贷宝瞬间被扣上传销的帽子。众所周知,传销企业犹如过街老鼠,是人人喊打的,但我们冷静分析一下才知道,借贷宝似乎并未让我们交钱,也未让我们买产品,所谓的传销从何而来?俗谚曰:谣言止于智者,这场关于借贷宝“传销”的弥天大谎,仅仅存在了几天便偃旗息鼓了。如此规模的谣言,如此短时间内悄无声息的平息下来,如果我说借贷宝背后没有大人物做靠山,你会相信吗?", "事件回顾之——借贷宝被裹挟上裸条", "众所周知,借贷宝是一个熟人借贷的平台,他采用的是单向匿名模式,平台上也没有上传图片,何来裸条?仔细分析一下,原来是有极个别的用户,不遵守借贷宝的规定,在线下进行交易,并用裸照作为按时还款证据。事件被某些媒体扩大之后,一些不明真相的吃瓜群众将裸条与借贷宝挂上钩。但是这与借贷宝有何干系呢?我举个例子:你可以在淘宝上进行买卖交易,如果遵守淘宝的约定,用支付宝交易,那么,淘宝就会保护你的财产安全;但如果你违背淘宝的规定,与卖家线下交易并直接打款给卖家,你看看淘宝会不会保护你的财产?所以,我感觉这样的事情,问题在于那些极其个别的不遵守规则的人,而不在于平台本身。后来,听说借贷宝设立了专门的小组来协助当事人处理相关事宜,但是幕后黑手似乎依然想给借贷宝裹挟上裸照,企图让借贷宝声名扫地。", "这个裸条事件,放在国内任何一家民营企业身上,可能都会让它万劫不复,但奇怪的是借贷宝不仅没有受到多大影响,反而在不久之后便宣布撮合交易额已突破800亿,如果我说借贷宝背后没有真正的大人物撑腰,你会相信吗?反正我是不信。", "事件回顾之——某权威媒体点名批评", "树大招风,借贷宝仅仅用了一年多就将交易额做到了1000多亿,这在P2P行业可谓一骑绝尘。恰逢相关监管部门正在整顿P2P行业,在整顿期间内(12个月),还没有听到哪个P2P平台已经整改完毕,在这个节骨眼上,幕后黑手又开始抹黑借贷宝了! 而且这次连权威媒体都被蒙蔽了,这让借贷宝多少有些措手不及,但是,借贷宝当晚好像就回过神来了,据说当晚就发出相关整改回应,例如取消赚利差、限额20万、银行托管等等,动作非常迅疾,虽然整改后的借贷宝是P2P行业首家完全合规的平台,但是,这起事件中,借贷宝被抹黑却又是不争的事实。", "相关人士表示:凡是被该媒体平台报道过的P2P企业,还没有哪家能挺过一周的,但借贷宝简直是个奇葩,它不仅挺过了一周,更进一步,它反而变成了P2P行业首家合规的平台! 看到没有,是首家合规!! 这将借贷宝从一个被抹黑为典型的负面形象,直接反转为行业首家合规!!!是的,你没看错,它现在已经是整个中国的P2P行业首家合规的平台,是样板,是榜样!!如果说借贷宝背后没人,或者说背后没有绝对实力的靠山,你会相信吗?(假如智商正常的话)", "事件回顾之——再次被曝裸照事件", "就在借贷宝被贴上行业首家合规的P2P平台的时候,一场支付宝被喊为支付鸨的事件传播开来。然而,这不是重点,重点是第二天的头条竟然不是支付宝!第二天的头条是借贷宝!是借贷宝!是借贷宝!(重要的事情说三遍)这令小编一时惊掉了下巴!莫非借贷宝的炒作水平竟然超过了支付宝?", "赶紧查查看,一看才知道,原来是回锅饭,图片还是上次裸条时候的图片,只是又被打包炒了一次,看到这里,小编感觉这次借贷宝又被黑了,而且这次被黑有点连环计的味道,借着某媒体批评借贷宝之后,马上来黑借贷宝,应该是有人在幕后故意策划的,否则它的热度不可能一夜之间超过支付宝,要知道支付宝成名已久了啊。借贷宝纵使是非常强悍的独角兽,但目前也不能与支付宝匹敌啊。", "接着,就是大量的媒体曝光说借贷宝已经报警了,我搜索了一下,发现竟然有926个媒体报道过借贷宝澄清裸条的事件,然后,事件出乎意料的平息下来,大家都当成了一场闹剧。"], "bs_rank_pos": 2}, {"is_selected": false, "title": "为什么借贷宝倒闭不了的最新相关信息_百度知道", "most_related_para": 0, "segmented_title": ["为什么", "借贷", "宝", "倒闭", "不", "了", "的", "最新", "相关", "信息", "_", "百度", "知道"], "segmented_paragraphs": [["只要", "民间借贷", "的", "利率", "居高不下", ",", "就", "不会", "倒闭", "的", "。"], ["炒", "得", "这么", "火", ",", "能", "倒闭", "吗"]], "paragraphs": ["只要民间借贷的利率居高不下,就不会倒闭的。", "炒得这么火,能倒闭吗"], "bs_rank_pos": 3}, {"is_selected": false, "title": "2016借贷宝逾期不还案例怎么办 借贷宝爆发大面积逾期不还_尚之潮", "most_related_para": 10, "segmented_title": ["2016", "借贷", "宝", "逾期", "不", "还", "案例", "怎么办", "借贷", "宝", "爆发", "大面积", "逾期", 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"跑车", "不仅", "便宜", "竟然", "还有", "剪刀门", "?"], ["老公", "日记", "惊现", "他", "与", "初恋情人", "的", "偷情", "丑事"]], "paragraphs": ["首页 > 理财 > 资讯 > 2016借贷宝逾期不还案例怎么办", "2016借贷宝逾期不还案例怎么办?近日,有媒体曝光借贷宝爆发大面积逾期不还。近日,一批自称“借贷宝受害者”的投资人陆续赶至北京,聚集在一起维权。有投资人透露,预计规模将超1000人。他们相互之间称为“宝友”,多位“宝友”表示,从今年5月份开始,借贷宝爆发大面积逾期,他们借出的贷款全部逾期,每天只能看着管理费不断增加,无法收回。", "\"裸条\"风波刚过,\"赚利差\"业务又被爆出风险,借贷宝可谓一波刚平一波又起。多位希望通过\"赚利差\"赚钱的借贷宝用户,10月20日,对新京报记者表示,由于借出去的钱不能按时收回,导致其在借贷宝平台逾期,除了丧失本金外,还要向借贷宝支付高额的逾期管理费。", "律师认为,借贷宝收取逾期管理费是没有法律依据的,并认为这涉嫌变相违背国家关于民间借贷的最高利息的限制,也违背合同法的公平原则。", "但借贷宝方面告诉新京报记者,\"从法律关系上说,赚利差者与上游借款人与下游出借人之间均构成独立的借贷关系,法律关系清晰。\"", "据了解,20日,多位借贷宝用户在北京东城区某小区的招待所门前挂出条幅,横幅上写道:“九鼎借贷宝虚假宣传坑害万人家破人亡”、“借贷宝还我们血汗钱”等。", "据网易财经,在北京东城区某小区的招待所内,来自山东、江西、重庆等地的借贷宝用户聚集在这里,这个地下室中的招待所,成了他们在京的汇合点,几天之内,有人离开,也有人陆续到来。", "据金融舆情了解,在借贷宝的平台上,有很多人是做“中介”,希望通过“低进高出”的方式挣利差。有多位“宝友”表示,自己是通过地推的推荐下,注册了借贷宝。地推人员称,扫二维码,下载软件注册后,就能有20元的返利。或者可以用牛奶等等价物作为奖励。", "公开资料显示,借贷宝由人人行科技股份有限公司运营,注册资本55000万元,股东是思运科(上海)科技股份有限公司和思运科(北京)科技有限公司,后者由前者投资,而前者的股东之一是同创九鼎投资控股有限公司(简称九鼎投资)。此外,在2015年5月25日变更前,人人行公司的唯一股东是九鼎投资,之后投资人又经过了几次变更,直到2015年11月27日,投资人变更为唯一股东思运科(上海)科技股份有限公司,不再出现九鼎系。", "也有学生表示,自己也曾是借贷宝的用户。为了“20块钱的返利,按照要求绑定了银行卡,提现了之后就没有再用了。”该学生还表示,在借贷宝每邀请一个好友成功注册使用借贷宝并进行认证,自己就可以获得20元的返利,还在学校流行一时。该学生表示,自己的一个同学通过邀请自己班级的同学赚到大约300元。", "事实上,借贷宝自上线以来,质疑的声音便一直不断。早在2015年8与8日,人人行公司就发布过声明,表示借贷宝‘拉好友抢现金’的营销活动与传销完全不是一个概念,并且称“借贷宝诈骗、套取用户钱财”是凭空捏造的谣言。同年8月12日,借贷宝宣布完成20亿元的首轮融资,刷新了当时互联网金融首轮融资额的新纪录。", "《西游伏妖篇》总票房过20亿才", "争议虽大《大闹天竺》票房预测", "过年的时候香蕉都有哪些好的吃", "白羊男彻底分手的表现有哪些 白羊座男人思想活跃,热情似火,周身洋溢着不竭的创造力。白羊男喜欢一个女人的时候会想尽一切办法的去呵护帮助喜欢的女人,...更多 2017-01-28 18:00:03", "小品真情永驻结尾歌曲大揭秘 春晚小品《真情永驻》最后响起的歌曲是什么?2017年的央视春晚迎来许多熟悉面孔,其中,孙涛、闫学晶、刘仪伟表演的小品《真...更多 2017-01-27 22:40:11", "《西游伏妖篇》总票房过20亿才能回本? 电影《西游伏妖篇》的预售票房是多少,累计总票房预测多少亿能保底俨然成为了新春佳节大家热议的话题的质疑,接下来快跟尚之...更多 2017-01-27 22:55:56", "争议虽大《大闹天竺》票房预测15亿封顶 电影《大闹天竺》片方预测能有多少让大家十分关心,早前有网友根据王宝强离婚案事件分析,《大闹天竺》票房预测能达30亿,但...更多 2017-01-27 19:39:18", "除夕的14大习俗和禁忌一定有你不知道的! 今天是除夕,对于还奋战在编辑第一线的小编来说是一个忙碌而充实的除夕,坐等春晚的空隙还研究了下除夕的习俗,发现各个地方...更多 2017-01-27 19:38:59", "2017央视春晚靳东为何缺席 靳东为何缺席2017年央视春晚?2015年一部《伪装者》让靳东、胡歌、王凯组成的“明家三兄弟”形象深入人心,近日,2017年央视...更多 2017-01-27 13:58:19", "《大闹天竺》竟是一个当代版西游记故事 王宝强执导的电影《大闹天竺》即将在大年初一上映,备受网友们的期待。但是令网友好奇的是电影《大闹天竺》讲了什么故事,背...更多 2017-01-26 19:06:06", "《西游伏妖篇》台词周星驰式无厘头风格明显 电影《西游伏妖篇》将于大年初一上映,单是周星驰和徐克这个黄金组合就万众瞩目了,而高颜值的小鲜肉演员阵容更是令影迷们期...更多 2017-01-26 17:04:26", "《西游伏妖篇》的物理特效可不仅值6毛 《西游伏妖篇》的特效在电影上映前就备受网友关注,那么《西游伏妖篇》真的没有特效吗,物理特效又是什么鬼呢?有网友吐槽《...更多 2017-01-26 17:09:51", "《大闹天竺》票房大热马蓉究竟能不能分到钱 电影《大闹天竺》将映,王宝强前期马蓉是否能分到票房钱让网友们关心不已,那么王宝强离婚判决结果如何了,最新消息是怎么宣...更多 2017-01-26 16:08:37", "《功夫瑜伽》迪拜王子竟出借世界最贵超跑 成龙喜剧电影《功夫瑜伽》即将上映,据悉该片主打异域风情,前往多个国家取景拍摄,展现了各具特色的风景和风情。令网友好奇...更多 2017-01-26 15:36:28", "适合农村低投资的项目有哪些? 2017养殖什么最能赚钱 如何才能让欠了钱的人乖乖还钱呢 做什么生意有前景2017 月薪过万十大冷门职业你造吗 99版100元人民币暴涨真假 千元手机排行榜2016前十名性价比高 十年后最便宜的东西是房子? 如果中国房价暴跌会有那些后果 在家开什么加工厂不愁销路", "2017年支付宝敬业福不求人的秘诀在这里", "《鬼吹灯》胡八一身世来历父亲是谁介绍", "王昱珩能再创不败奇迹碾压人工智能吗", "这款跑车不仅便宜竟然还有剪刀门?", "老公日记惊现他与初恋情人的偷情丑事"], "bs_rank_pos": 4}], "answer_spans": [[124, 217]], "fake_answers": ["的财产安全;但如果你违背淘宝的规定,与卖家线下交易并直接打款给卖家,你看看淘宝会不会保护你的财产?所以,我感觉这样的事情,问题在于那些极其个别的不遵守规则的人,而不在于平台本身。后来,听说借贷宝设立了专门的小组来协助当事人处理相关事宜,但是幕后黑手似乎依然想给借贷宝裹挟上裸照,企图让借贷宝声名扫地。"], "question": "为什么借贷宝倒闭不了", "segmented_answers": [["一", "、", "国内外", "P2P", "平台", "林立", "，", "但", "国内", "缺乏", "真正", "意义", "上", "的", "P2P", "平台", "。", "二", "、", "监管", "的", "加强", "肃清", "了", "行业", "的", "风气", "，", "带来", "了", "新", "的", "发展", "契机", "。", "三", "、", "大", "数据", "之", "声", "的", "喧嚣", "，", "推动", "着", "新", "的", "征信", "平台", "的", "诞生", "。", "四", "、", "中国", "民间借贷", "规模", "庞大", "但", "缺乏", "法律", "保障", "、", "回款", "困难", "。", "五", "、", "中国", "5600", "万", "小微", "企业", "存在", "的", "融资难", "、", "融资", "贵", "等", "问题", "。"]], "answers": ["一、国内外 P2P 平台林立，但国内缺乏真正意义上的 P2P 平台。二、监管的加强肃清了行业的风气，带来了新的发展契机。三、大数据之声的喧嚣，推动着新的征信平台的诞生。四、中国民间借贷规模庞大但缺乏法律保障、回款困难。五、中国5600万小微企业存在的融资难、融资贵等问题。"], "answer_docs": [2], "segmented_question": ["为什么", "借贷", "宝", "倒闭", "不", "了"], 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你好,你的情况我已知晓,你是13号来的月经,排卵期大概在30号左右,所以你房事不在排卵期。 指导意见: 这只是个大概,排卵期会因人而异,所以现在还不能完全排除你是否会怀孕?怀孕最早的标志就是停经,检查一般在月经推迟10天左右即可测的出,你可以自测也可以到医院检查,好吧?", "这位朋友你好,女性出现妊娠反应一般是从6-12周左右,也就是女性怀孕1个多月就会开始出现反应,第3个月的时候,妊辰反应基本结束。 而大部分女性怀孕初期都会出现恶心、呕吐的感觉,这些症状都是因人而异的,除非恶心、呕吐的非常厉害,才需要就医,否则这些都是刚怀孕的的正常症状。1-3个月的时候可以观察一下自己的皮肤,一般女性怀孕初期可能会产生皮肤色素沉淀或是腹壁产生妊娠纹,特别是在怀孕的后期更加明显。 还有很多女性怀孕初期会出现疲倦、嗜睡的情况。怀孕三个月的时候,膀胱会受到日益胀大的子宫的压迫,容量会变小,所以怀孕期间也会有尿频的现象出现。月经停止也是刚怀孕最容易出现的症状,只要是平时月经正常的女性,在性行为后超过正常经期两周,就有可能是怀孕了。 如果你想判断自己是否怀孕,可以看看自己有没有这些反应。当然这也只是多数人的怀孕表现,也有部分女性怀孕表现并不完全是这样,如果你无法确定自己是否怀孕,最好去医院检查一下。", "您好,一般停经6周左右,就会出现畏寒、流涎、恶心、食欲不振、厌恶油腻、头晕、乏力、嗜睡、喜食酸物、晨起呕吐等早孕反应。孕12周会自行消失,当然早孕反应是因人而异的,时间可能有所差别。一般来说月经周期正常的女性,月经推迟一周可以用试纸检查怀孕。 如果月经一直很规律,一旦到期不来,超过一周以上,就应该考虑是怀孕了。这是怀孕的最早信号,过期时间越长,妊娠的可能性就越大。白带也会有增多现象,其颜色、气味、粘稠度无变化。停经以后孕妇还会逐渐感到一些异常现象,最早出现的反应是怕冷,以后逐渐感到疲乏、嗜睡、头晕、食欲不振、挑食、怕闻油腻味、早起恶心、甚至呕吐,安全时还有头晕,疲乏,倦怠等症状。 因此,受孕后出现早孕反应时,应保持身心的愉快与充分休息,早晨起床前可吃固体食物,半小时内不要喝水,少量多餐,以清淡饮食为主,少吃产气的食物,养成每天排便的习惯,适当地运动。一般是孕12周左右,早孕反应会消失。值得注意的是,如果孕妇妊娠期间,反应太过激烈的话,则应该就医。在专业医生的指导下缓解症状,避免对胎儿造成影响。 早孕反应症状因人而异,快则怀孕5周会出现,完全没有早孕反应的大有人在。多数人会持续3个月,通常3个月后,早孕反应即自动停止。除非是恶心,呕吐的非常厉害,才需要就医,否则这些都是怀孕初期的正常现象。 以上是对“怀孕后最快多久才有反应?”这个问题的建议,希望对您有帮助,祝您健康!", "你好,一般在怀孕最初的3个月,身体变化还不会太明显,此时完全不必把自己当作一个特殊的人来看待,平时想吃什么就吃什么,做些开心的事情,忘掉身体的不舒服。身体不适时,就躺下休息;尽量保持原来的生活节奏。 而怀孕最初的3个月是最容易失去宝宝的3个月,为了留住宝宝,孕妇的一举一动一定要格外当心。妊娠反应的发生时间早晚也是与孕妇的个人体质有关的,但一般都会在停经40天左右会出现妊娠反应。妊娠反应与体内激素水平有关,部分孕妇可能不会出现症状。常见的妊娠反应有恶心、呕吐、嗜睡、疲倦等。所以不管有没有妊娠反应,如果没有做避孕措施,月经延期超过了10天后,就要及时的去做一下妊娠检测。 此外,还可以用验孕试纸检测是否怀孕,这种检测的准确率很高,但不能作为确诊妊娠的最终手段。因此,确定是否怀孕还是要到医院接受正规检查,医院里的早孕确诊检查主要有尿检和B超检查,只有全面检查才更放心。", "病情分析: 你好,根据描述的情况来看,是出现避孕失败的情况,一般出现这种情况就有怀孕的可能性存在。 指导意见: 不过在月经来临之前的三天之内,属于绝对安全期,这个时间段还是不会怀孕的,若出现怀孕的话,一般在怀孕的第六周左右,可能会出现早孕反应。", "病情分析: 您好,根据您描述的情况,您是在于男朋友做爱时,避孕套破裂了,担心会因此怀上孩子的事宜。 指导意见: 只要是在排卵期,即使有很少的精子进入阴道都有可能导致怀孕的,怀孕一般最早月经推迟一周到两周就会出现的。仅供参考祝您如意!", "病情分析: 你好,这样的情况下如果月经周期正常的情况下当然你性生活考虑是在排卵期 指导意见: 内的一个情况的。所以应该是不至于怀孕不需要那么紧张的。以后避孕建议用好避孕套建议事前要检查下避孕套是否破损。等待月经来即可别自己过于紧张 以上是对“怀孕后最快多久才有反应?”这个问题的建议,希望对您有帮助,祝您健康!", "您好: 从月经来的那天算超第11-25天都属于排卵期,所以您的时间正好在排卵期,怀孕的可能性比较大。同房时间没有超过72小时还可以服用紧急避孕药,但这个药不能拿来当成常规避孕措施。祝您健康!D", "病情分析: 怀孕的初期反应:1、停经;2、早孕反应:约半数妇女在停经6周前后出现头晕、乏力、嗜睡、流涎、食欲不振或恶心、晨起呕吐等现象;3、尿频。 指导意见: 建议你在月经推迟1周后,使用早孕试纸或到医院进行B超、血HCG检查,明确是否怀孕。", "病情分析: 你好,如果平时月经规律的话,排卵期在下次月经前十四天左右,如果你的月经周期是三十天左右的话,你的排卵期在四月一号左右。你怀孕的机会比较小。 指导意见: 你好,如果不放心的话,可以服用紧急避孕药,另外,怀孕后7天就可以有人绒毛膜促性腺激素的升高,十天左右可以从血中测出,早孕试纸一般是半月左右才能测出,祝你好运", "病情分析: 以排卵日(一般是月经前14天)为基数,其前4天和后五天都是危险期。以你的推算,25到5号都不安全,都有怀孕的可能。 指导意见: 建议你还是赶快吃紧急事后避孕药,否则怀孕的可能性比较大。", "病情分析: 你好。你的排卵期是在你月经来潮的前14天。没有做好避孕措施是很容易怀孕的 指导意见: 避孕套破了的话,精液就会进入阴道。就会有怀孕的可能。最快你可以在时候的10天买验孕纸来测试!祝你健康!", "病情分析: 您好!排卵日一般在下次月经前14天,排卵日的前2天到后1天为排卵期,根据您介绍的情况,排卵期大概在27--30号左右 指导意见: 这几天正在排卵期附近,虽然精子有流出,但不能保证没有残留,还是有可能怀孕的,在性生活后7天左右,若是怀孕差不多就可以查出来了,血HCG肯定能查出来,早孕试纸可能能查出来"], "bs_rank_pos": 0}, {"is_selected": false, "title": "【怀孕多久会有反应】怀孕多长时间有反应_怀孕反应_亲子百科_太平洋", "most_related_para": 15, "segmented_title": 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"的", "。", "从", "受精卵", "着", "壁", "的", "那一刻", "起", ",", "女性", "体", "内", "的", "内分泌系统", "就", "开始", "发生变化", "。", "其中", ",", "最", "明显", "的", "征兆", "之", "一", "就是", "乳房", "敏感", "、", "胀痛", "。", "这", "是", "由于", "孕妇", "体", "内", "的", "激素", "水平", "提高", "所", "引起", "的", "。", "这种", "胀痛", "感", "与", "女性", "在", "经期", "前", "的", "感觉", "很相似", ",", "只是", "更", "强烈", "一些", "。"], ["胸部", "胀痛", "的", "情况", "会", "在", "孕妇", "怀孕", "3", "个", "月", "后", "有", "明显好转", ",", "因为", "孕", "中期", "开始", ",", "孕妇", "的", "身体", "就", "已经", "适应", "孕期", "荷尔蒙", "的", "改变", "了", "。"], ["对于", "怀孕后", "乳房", "出现", "的", "胀痛", "症状", ",", "孕妇", "可以", "采用", "热敷", "、", "按摩", "等", "乳房", "护理", "方式", "来", "缓解", "。", "千万", "要", "注意", "的", "是", ",", "这", "一", "时期", "乳房", "是", "非常", "脆弱", "的", ",", "孕妇", "在", "护理", "过程", "中", "应该", "动作", "轻柔", ",", "避免", "损伤", "乳头", "。"], ["孕妇", "可以", "吃", "青", "芒果", "吗", "孕妇", "可以", "吃", "猕猴桃", "吗", "孕妇", "可以", "吃", "山楂", "卷", "吗", "孕妇", "能", "吃", "孜然", "羊肉", "吗", "孕妇", "能", "吃", "橄榄", "吗", "孕妇", "能", "吃", "黄油", "吗"], ["怀孕", "多久", "有", "胎动"], ["一般", "孕妇", ",", "在", "孕", "5", "个", "月", "左右", "能", "感觉", "到", "比较", "明显", "的", "胎动", ",", "但", "具体", "时间", "因人而异", ",", "有", "的", "人", "可能", "稍", "早", "在", "16", "周", "或", "17", "周", "的", "时候", "就能", "感觉", "到", "第一次", "胎动", ",", "有", "的", "人", "可能", "稍", "晚", "一些", ",", "在", "20", "周", "左右", "感觉", "到", "第一次", "胎动", ",", "这", "都", "很", "正常", "。"], ["胎动", ",", "是", "指", "胎儿", "在", "母体", "子宫", "内", "的", "主动性", "运动", ",", "比如", "呼吸", "、", "张嘴", "运动", "、", "翻滚", "运动", "等", "。", "如果", "是", "受到", "妈妈", "咳嗽", "、", "呼吸", "等", "动作", "影响", "所", "产生", "的", "被动", "性", "运动", ",", "就", "不", "算", "胎动", "。", "胎动", "是", "胎儿", "生命", "的", "最", "客观", "的", "征兆", "之", "一", ",", "它", "不仅", "能", "与", "准妈妈", "建立", "起", "紧密", "的", "亲情", "联系", ",", "而且", "能", "表达", "胎儿", "在", "子宫", "内", "的", "成长", "发育", "状况", ",", "是", "胎儿", "给母亲", "发出", "的", "信号", ",", "胎动", "次数", "多少", "、", "快慢", "、", "强弱", "等", ",", "常", "预示", "着", "胎儿", "的", "安", "危", "。"], ["怀孕早期", ",", "胚胎", "还", "很", "小", ",", 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"就要", "到", "17", "-", "18", "周", "才能", "追踪", "到", "胎儿", "的", "心跳", "声", "。"], ["还有", "一", "种", "情况", "是", ",", "妊娠", "初期", ",", "由于", "胎儿", "的", "位置", "关系", ",", "或者", "其他", "种", "种", "干扰", "因素", ",", "比如", "母体", "的", "脂肪", "过", "厚", "等", "原因", ",", "即", "使用", "极", "精密", "的", "仪器", "也", "无法", "听到", "胎心音", "。"], ["相关", "专家解答"], ["您好", ",", "一般", "在", "房事", "后", "的", "7", "-", "10", "天", "就", "可以", "检测", ",", "当然", "时间", "越", "长", "就", "越", "准确", "。", "早孕", "试纸", "工作原理", "是", "检测", "HCG", "(", "人体", "绒毛", "膜", "促性腺激素", ")", "的", "值", ",", "这种", "激素", "是", "由", "胎盘", "制造", "的", ",", "一般", "在", "怀孕", "几天后", "就会", "出现", "在", "尿液", "里", ",", "但是", "由于", "量", "比较", "少", ",", "开始", "不易", "检测", "出来", ",", "直到", "10", "-", "14", "天才", "日益", "明显", ",", "(", "通过", "验血", "也", "可以", "测", "得", "HCG", "值", ",", "而且", "要", "准确", "得", "多", ")", "。", "虽然", "许多", "种", "早孕", "试纸", "都", "表明", "女性", "在", "正常", "经期", "推迟", "1", "天", "之后", "便", "可做", "怀孕", "自测", ",", "但是", "实际上", ",", "不是", "所有", "怀孕", "的", "女性", "排放", "HCG", "激素", "的", "速度", "和", "数量", "都", "相同", ",", "这", "是", "因人而异", ",", "过早", "地", "做", "尿液", "测试", ",", "所", "呈现", "的", "阴性", "反应", "有", "可能", "是", "错误", "的", ",", "所以", ",", "最好", "在", "月经期", "迟", "来", "两", "周", "后", "再", "做", "怀孕", "自测", ",", "这样", "结果", "会", "更", "可信", "些", "。"], ["缪缙", "暨南大学", "附属", "第", "一", "医院", "产科", "副主任医师"], ["您好", ",", "一般", "情况", "下", ",", "用", "早孕", "试纸", "在", "同房", "后", "10", "天", "左右", "就", "可以", "测", "出来", ",", "但是", "时间", "越", "久", "就", "越", "准确", ",", "最好", "是", "到", "医院", "做", "血", "检", "检查", ",", "能", "更准确", "。", "根据", "您", "的", "描述", ",", "来", "了", "例假", "的", "话", "是", "没有", "怀孕", "的", ",", "应该", "等", "下次", "的", "排卵期", "再", "同房", ",", "在", "排卵期", "同房", ",", "怀孕", "的", "几率", "会", "大一点"], ["宋鹤兰", "广州", "伊丽莎白", "妇产", "医院", "产科", "主任医师"]], "paragraphs": ["孕期百科 / 怀孕多久会有反应", "怀孕多久会有反应 收藏", "怀孕多久能用试纸测出来", "使用早孕试纸怀孕自测的工作原理是检测HCG值,即人体绒毛膜促性腺激素的值。这种激素是由胎盘制造的,在受孕后10-14天后人体开始分泌绒毛膜促性腺激素。分泌后先会出现在血液中,然后随着循环系统出现在尿液里,但由于量少,开始不易测验出来,直到妊娠1-2周后才日益明显。时间太早和太晚效果都不好。", "1、晨尿检测对于刚刚怀孕的女性,结果更准确。但对于已怀孕一段时间的女性,一天中的任何时刻的尿液均可用于检测。 2、受精卵着床一般需要7天左右的时间,也就是说,怀孕当天(相当于同房后7天)即可检测是否怀孕。 3、月经周期推迟7天以后或同房10天至14天或最早可在停经后的33天左右使用,如果早早孕测试不出是否怀孕的话可以在停经后37天左右再做一次测试。", "关注宝宝辅食跟我学(baobaofood),提前学习宝宝喂养知识,轻松做厨神妈妈!", "孕妇能吃鹌鹑蛋吗 孕妇能吃辛辣食物吗 孕妇能吃狗肉煲吗 孕妇可以吃山楂糕吗 孕妇可以吃味精吗 孕妇可以吃香菜吗", "怀孕多久有反应", "女性在怀孕六周左右会出现怀孕症状,如月经推迟、基础体温持续上升、恶心呕吐、尿频、乳房和皮肤变化等,这些早孕症状通常会持续到怀孕3个月。但并不是每个人在怀孕初期都会有反应。女性若怀疑自己怀孕了,可以用验孕棒、验孕试纸等工具自我检测一下,最好到医院进行检查,结果更准确。", "小编汇总了1200个孕妈的真实经验,教你分辨孕早期身体症状,准确率高达90%!还有权威的专家视频,轻松应对怀孕后的不适!", "需要明确的是,不同人群的怀孕反应差异非常大,仅仅通过怀孕初期出现的症状来确定自己是否怀孕是很不科学的。女性出现以上身体变化很可能是由于疾病、身体内环境变化等因素引起,并不一定是怀孕了。女性一定要到医院进行HCG尿检或者HCG血检、B超等检查,这样才能准确地判定自己是否怀孕,从而做出正确的处理", "如何测出怀孕  10大怀孕初期症状  早孕反应大全", "【图】早孕测试常识大科普 【图】怀孕多久能测出来? 【图】早孕试纸怎么用 【图】读懂验孕棒怎么用 怀孕以后 大脑发生的惊人变化 怀孕后 乳房不可避免的3个变化", "怀孕多久会呕吐", "许多女性在怀孕的时候,有着不同程度的早孕反应症状,如呕吐、乳房胀痛、体温变化等等,其中呕吐最为常见。那么怀孕多久会呕吐呢?", "每个人的妊娠反应不一样,呕吐的时间也不一样,有些孕妇在整个孕期都不会有呕吐的症状。约50%的女性在在停经6周前后就会有恶心呕吐、头晕、乏力、食欲不振、偏食等症状出现,到妊娠12周左右症状会自行消失。但不排除有个别情况,有的人反应厉害,可能从怀孕初期至分娩前一直吃什么吐什么,有的人则反应较轻,基本没有怎么呕吐。", "需要注意的是,怀孕后是否有孕吐因人而异。如果女性在停经六周左右出现恶心呕吐的症状,需要考虑到是否怀孕。为了缓解妊娠呕吐带来的不是,女性可以选择一些新鲜易于消化的的食物进食,可以多吃一些水果蔬菜,避免进食过于油腻和刺激性的食物。", "女人必知 怀孕初期症状有哪些 孕5周:什么时候有早孕现象? 7大身体特征预示着你怀孕了! 孕6周:孕妈咪呕吐好坏面面观 幸福的煎熬 孕期反应经历多少 怀孕40天就会发生早孕反应吗", "怀孕多久胸部会胀痛", "乳房是宝宝出生之后的粮仓。因此,在整个孕期里,乳房的发育都是随着胎儿的发育一起变化的,其目的是为了适应分娩后哺乳的需要。一般来说,女性怀孕后的第4-6周胸部胀痛的情况会比较严重。", "怀孕后胸部胀痛是由于激素水平改变所引起的。从受精卵着壁的那一刻起,女性体内的内分泌系统就开始发生变化。其中,最明显的征兆之一就是乳房敏感、胀痛。这是由于孕妇体内的激素水平提高所引起的。这种胀痛感与女性在经期前的感觉很相似,只是更强烈一些。", "胸部胀痛的情况会在孕妇怀孕3个月后有明显好转,因为孕中期开始,孕妇的身体就已经适应孕期荷尔蒙的改变了。", "对于怀孕后乳房出现的胀痛症状,孕妇可以采用热敷、按摩等乳房护理方式来缓解。千万要注意的是,这一时期乳房是非常脆弱的,孕妇在护理过程中应该动作轻柔,避免损伤乳头。", "孕妇可以吃青芒果吗 孕妇可以吃猕猴桃吗 孕妇可以吃山楂卷吗 孕妇能吃孜然羊肉吗 孕妇能吃橄榄吗 孕妇能吃黄油吗", "怀孕多久有胎动", "一般孕妇,在孕5个月左右能感觉到比较明显的胎动,但具体时间因人而异,有的人可能稍早在16周或17周的时候就能感觉到第一次胎动,有的人可能稍晚一些,在20周左右感觉到第一次胎动,这都很正常。", "胎动,是指胎儿在母体子宫内的主动性运动,比如呼吸、张嘴运动、翻滚运动等。如果是受到妈妈咳嗽、呼吸等动作影响所产生的被动性运动,就不算胎动。胎动是胎儿生命的最客观的征兆之一,它不仅能与准妈妈建立起紧密的亲情联系,而且能表达胎儿在子宫内的成长发育状况,是胎儿给母亲发出的信号,胎动次数多少、快慢、强弱等,常预示着胎儿的安危。", "怀孕早期,胚胎还很小,孕妇是无法感觉到胎动的,只有通过B超才能见到胎儿的细微活动,准妈妈要到孕中期才能察觉到第一次胎动。最初的胎动也是很轻微的,可能不易察觉,不过胎动会越来越强烈,但到临产前几周,由于胎儿发育得很大,宫腔内空间相对狭小,孕晚期胎动反而会减少。", "怀孕几个月会出现胎动?", "孕妇可以吃红提吗 孕妇可以吃鲜枣吗 孕妇可以吃血橙吗 孕妇能吃生鸡蛋吗 孕妇能吃金枪鱼寿司吗 孕妇能吃酸木瓜吗", "怀孕多久有胎心", "一般来说,胎儿的胎心最早在孕10周就可以通过仪器测出来,目前,比较普遍运用到的多普勒的高灵敏度仪器,可以在胎儿10周或者12周的时候,便可以听到像马蹄声一样的心跳。不过,也有些医院可能采用一般的听诊器,这样的话,就要到17-18周才能追踪到胎儿的心跳声。", "还有一种情况是,妊娠初期,由于胎儿的位置关系,或者其他种种干扰因素,比如母体的脂肪过厚等原因,即使用极精密的仪器也无法听到胎心音。", "相关专家解答", "您好,一般在房事后的7-10天就可以检测,当然时间越长就越准确。早孕试纸工作原理是检测HCG(人体绒毛膜促性腺激素)的值,这种激素是由胎盘制造的,一般在怀孕几天后就会出现在尿液里,但是由于量比较少,开始不易检测出来,直到10-14天才日益明显,(通过验血也可以测得HCG值,而且要准确得多)。虽然许多种早孕试纸都表明女性在正常经期推迟1天之后便可做怀孕自测,但是实际上,不是所有怀孕的女性排放HCG激素的速度和数量都相同,这是因人而异,过早地做尿液测试,所呈现的阴性反应有可能是错误的,所以,最好在月经期迟来两周后再做怀孕自测,这样结果会更可信些。", "缪缙 暨南大学附属第一医院 产科 副主任医师", "您好,一般情况下,用早孕试纸在同房后10天左右就可以测出来,但是时间越久就越准确,最好是到医院做血检检查,能更准确。根据您的描述,来了例假的话是没有怀孕的,应该等下次的排卵期再同房,在排卵期同房,怀孕的几率会大一点", "宋鹤兰 广州伊丽莎白妇产医院 产科 主任医师"], "bs_rank_pos": 1}, {"is_selected": false, "title": "一般怀孕多久会有反映_39健康问答_39健康网", "most_related_para": 22, "segmented_title": ["一般", "怀孕", "多久", "会", "有", "反映", "_", "39", "健康", "问答", "_", "39", "健康网"], "segmented_paragraphs": [["一般", "怀孕", "多久", "会", "有", "反映"], ["一般", "怀孕", "多久", "会", "有", "反映"], ["精选", "回答", "(", "4", ")"], ["你好", "注意观察", "月经", ",", "如果", "是", "该", "来", "月经", "的", "时候", "没有", "来", ",", "超过", "7", "-", "10", "天", "没有", "来", "就要", "考虑", "怀孕", ".", "建议", "做", "早", "试纸", "检查", ".", "而", "不是", "以", "同房", "时间", "计算", "的", ",", "临床", "上", "一般", "是", "从", "末", "次", "月经", "开始", "计算", ".", "检查", "如果", "是", "2", "根", "红线", "表示", "阳性", ",", "表示", "怀孕", "的", "可能", ".", "但是", "同房", "后", "7", "天后", "也", "可以", "到", "医院", "做", "血", "hcg", "检查", ".", "如果", "检查", "阴性", "或", "不", "明显", ",", "还", "需要", "7", "天", "以后", "再次", "复查", "早孕", "实验", "啊", ".", 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"怀疑", "是否", "怀孕", ",", "到", "医院", "做", "尿", "HCG", "检查", "以", "确定", "是否", "怀孕", "。", "如果", "过期", "1", "个", "月", ",", "医生", "大致", "能", "查出", "怀孕", "征象", ",", "怀孕", "就", "比较", "容易", "肯定", "了", "。", "有", "极少数", "女性", ",", "虽然", "已经", "怀", "了", "孕", ",", "但是", "在", "该", "来", "月经", "的", "时候", ",", "仍然", "行", "经", "一", "两次", ",", "不过", ",", "来", "的", "经血", "比", "平常", "要", "少", ",", "日期", "也", "短", "些", ",", "这", "在", "中医", "上", "称为", "“", "漏", "经", "”", ",", "真正原因", "尚", "不", "十分", "清楚", "。"], ["2015", "-", "05", "-", "30", "17", ":", "32", "投诉"], ["怀孕", "初期", "的", "症状", "一般", "在", "40", "天", "左右", "出现", "有", ":", "月经", "停止", ";", "乳房发育", ",", "乳头", "增大", ",", "乳房", "皮下", "的", "静脉", "明显", ",", "还", "可能", "乳房", "有", "刺痛", "、", "膨胀", "和", "瘙痒", "感", ",", "而", "乳头", ",", "乳晕", "颜色", "加深", ";", "以后", "逐渐", "感到", "疲乏", ",", "嗜睡", ",", "头晕", ",", "恶心", ",", "反胃", ",", "食欲不振", ",", "挑食", ",", "喜欢", "吃", "酸", "食", ",", "怕", "闻", "油腻", "味", ",", "唾液", "的", "分泌", "量", "也", "会", "增加", ";", "常", "有", "恶心", ",", "呕吐", "的", "感觉", "。"], ["2016", "-", "01", "-", "19", "15", ":", "01", "投诉"]], "paragraphs": ["一般怀孕多久会有反映", "一般怀孕多久会有反映", "精选回答(4)", "你好注意观察月经,如果是该来月经的时候没有来,超过7-10天没有来就要考虑怀孕.建议做早试纸检查.而不是以同房时间计算的,临床上一般是从末次月经开始计算.检查如果是2根红线表示阳性,表示怀孕的可能.但是同房后7天后也可以到医院做血hcg检查.如果检查阴性或不明显,还需要7天以后再次复查早孕实验啊.晨尿检查最好.", "2010-04-10 21:21 投诉", "怀孕后一般需要停经6-8周才会出现妊娠反应,恶心 呕吐 嗜睡 乏力等。", "2010-04-10 21:21 投诉", "你好: 一般情况下,怀孕40天左右,可出现早孕反应,如晨起恶心呕吐,食欲不振,困乏,尿频等临床表现。早孕反应因人而宜,有的反应轻,有的反应重。一般在怀孕3个后症状会减轻或消失。怀孕3个月后腹部可略见增大;18—20周左右可出现胎动。分娩时是有疼痛感的,但大多数女性都可忍受。", "2010-04-10 21:22 投诉", "你好,怀孕初期的症状一般在40天左右出现有:月经停止;乳房发育,乳头增大,乳房皮下的静脉明显,还可能乳房有刺痛、膨胀和瘙痒感,而乳头,乳晕颜色加深;以后逐渐感到疲乏,嗜睡,头晕,恶心,反胃,食欲不振,挑食,喜欢吃酸食,怕闻油腻味,唾液的分泌量也会增加;常有恶心,呕吐的感觉。在性生活10天以后可到医院进行血HCG检查,这是检查怀孕最准确的方法", "2010-04-10 21:22 投诉", "医生回答(11)", "你好,据了解,女性出现妊辰反应的一般是从第6周开始到12周左右,也就是说,女性怀孕1个多月就会开始出现反应,第三个月的时候,妊辰反应基本结束,当然了,也不排除个别反应期较长的案例。", "2015-01-21 15:20 投诉", "受孕后出现早孕反应时,应保持身心愉快与充分休息,早晨起床前可吃固体食物如苏打饼干,半小时内暂不喝水,少量多餐,以清淡饮食为主,少吃产气的食物 如豆类,洋葱等,养成每天排便的习惯,适当地运动。一般是孕12周左右,早孕反应会消失。值得注意的是,如果孕妇妊娠期间,反应太过激烈的话,则应该就医。在专业医生的指导下缓解症状,避免对胎儿造成影响。停经以后孕妇会逐渐感到一些异常现象,这是由于呕吐中枢对增多的雌激素暂时不能适应,或与精神因素有关,做早孕反应。最早出现的反应是怕冷,以后逐渐感到疲乏,嗜睡,头晕,恶心,反胃,食欲不振,挑食,喜欢吃酸食,怕闻油腻味,唾液的分泌量也会增加。早起甚至呕吐,严重时还有头晕,疲乏无力,倦怠等症状。早孕反应症状因人而异,快则怀孕5周会出现,完全没有早孕反应的大有人在。多数人会持续3个月,通常3个月后,早孕反应即自动停止。除非是恶心,呕吐的非常厉害,才需要就医,否则这些都是怀孕初期的正常现象。早孕期间食欲和饮食习惯常有改变,例如对烟,酒,茶和油炸食物兴趣减退,而喜食咸,酸,冰食物和水果,甚至偏食。", "2015-01-22 09:31 投诉", "擅长:从事计划生育临床工作20余年,临床经验丰富,对终止妊娠,放取宫内节育器及避孕节育指导方面有独特见解。擅长运用薇薇保 宫无痛技术针对大月份引产、早早孕、未生育年轻女性、宫颈狭小患者进行终止妊娠", "孕妇大部份在前三个月都难免有不同程度的恶心、呕吐经验,常发生在早上。具体反应的时间和程度因人而异。性生活后最早七天一般十四天左右少数二十多天能查出来怀孕,可自用试纸或者医院HCG检查确诊。", "2015-12-30 14:05 投诉", "擅长:妇产科常见病、多发病、无痛人流、私密整形手术等。", "每个人的体质不同,所以出现孕期反应的时间也会不同,有些体质的人在怀孕一周内就会出现一些孕期反应,比如恶心、呕吐、嗜睡等等,而还有些体质的人在整个孕期可能都不会出现妊娠反应。", "2015-12-31 17:22 投诉", "你好,一般怀孕多久会有反映呢?通常临床上计算怀孕的时间是从末次月经的第一天算起的,停经6周左右,出现畏寒、流涎、恶心、食欲不振、厌恶油腻、头晕、乏力、嗜睡、喜食酸物、晨起呕吐等早孕反应,乳房有刺痛、膨胀和瘙痒感;容易疲倦,嗜睡;频尿等。孕12周自行消失,当然早孕反应是因人而异的,时间可能有所差别。一般来说月经周期正常的女性,月经推迟一周可以用试纸检查怀孕,或者是同房10天到医院做血hcg检查确诊怀孕。", "2015-05-29 22:29 投诉", "1、感觉反胃吗? 你可能会觉得想吐或反胃,虽然害喜也叫“晨吐”,但你可能整天都觉得反胃想吐。你的嗅觉会变得灵敏,以前喜欢的味道现在可能引起呕吐。 2、乳房胀痛吗? 如果你的乳房感到刺痛、柔软又肿胀,你可能怀孕了。在你的身体适应大量的荷尔蒙之后,这种疼痛会消退,你的乳房就不会再感到不舒服了。 3、感觉困倦吗? 原本精力充沛的你,突然感到困倦乏力,黄体素的大量分泌,会让你觉得筋疲力尽。你可以再观察其他的症状,以确定你的判断。", "2015-05-30 09:00 投诉", "准妈一般会在停经40天左右会出现妊娠反应。所以不管有没有妊娠反应,如果没有做避孕措施,月经延期超过了10天后,就建议去做一下妊娠检测。 妊娠反应的发生时间早晚与孕妇的个人体质有关,但一般都会在停经40天左右会出现妊娠反应。妊娠反应与体内激素水平有关,部分孕妇可能不会出现症状,而常见的妊娠反应有:恶心、呕吐、嗜睡、疲倦等。所以不管有没有妊娠反应,如果没有做避孕措施,月经延期超过了10天后,就建议去做一下妊娠检测。", "2015-05-30 12:57 投诉", "你好,妊娠反应一般在孕42天以后才会出现。你需要对比一下这次例假跟以前有没有不同。 根据你的情况,如果这次是例假跟以前没有不同,例假刚过去一周,是不可能会出现早孕反应的。胃难受的话,需要看下是否是胃里边的本身的病,需要进一步检查。", "2015-05-30 14:52 投诉", "在受孕的第一个月,孕妈咪一般感觉不到新生命的开始。但是,有一些重要的征兆,会提醒育龄女性,恭喜你,你可能怀孕了! 看懂怀孕信号 对于月经规则的妇女,怀孕比较容易判断。 怀孕信号1――停经 停经是怀孕的第1信号。所有的有性生活的女性都应该记住自己的月经日期,可用日历做记号。 一般来说,如果月经过了一个星期,就应该怀疑是否怀孕,到医院做尿HCG检查以确定是否怀孕。如果过期1个月,医生大致能查出怀孕征象,怀孕就比较容易肯定了。 有极少数女性,虽然已经怀了孕,但是在该来月经的时候,仍然行经一两次,不过,来的经血比平常要少,日期也短些,这在中医上称为“漏经”,真正原因尚不十分清楚。", "2015-05-30 17:32 投诉", "怀孕初期的症状一般在40天左右出现有:月经停止;乳房发育,乳头增大,乳房皮下的静脉明显,还可能乳房有刺痛、膨胀和瘙痒感,而乳头,乳晕颜色加深;以后逐渐感到疲乏,嗜睡,头晕,恶心,反胃,食欲不振,挑食,喜欢吃酸食,怕闻油腻味,唾液的分泌量也会增加;常有恶心,呕吐的感觉。", "2016-01-19 15:01 投诉"], "bs_rank_pos": 2}, {"is_selected": false, "title": "一般怀孕多久会有反应_百度宝宝知道", "most_related_para": 0, "segmented_title": ["一般", "怀孕", "多久", "会", "有", "反应", "_", "百度", "宝宝", "知道"], "segmented_paragraphs": [["怀孕", "症状", "及", "表现", "1", ".", "月经", "停止", "这", "是", "一般人", "最", "常", "注意到", "的", "怀孕", "徵兆", ",", "只要", "是", "一般", "正", "值", "生育", "年龄", "的", "妇女", ",", "月经", "正常", ",", "在", "性行为", "后", "超过", "正常", "经期", "两", "周", ",", "就", "有", "可能", "是", "怀孕", "了", ",", "这", "是", "怀孕", "的", "最早", "信号", ",", "过期时间", "越", "长", ",", "妊娠", "的", "可能性", "就", "越", "大", "。", "不过", ",", "月经", "没有", "来", "的", "原因", "有很多", ",", "可能", "因为", "卵巢", "机", "能", "不佳", ",", "可能", "因为", "荷尔蒙分泌", "不", "正常", ",", "工作", "忙碌", "等等", "。", "所以", "最", ".", ".", ".", "好", "还是", "要", "经过", "医师", "的", "诊断", ",", "才是", "最安全", "的", "。", "过", "正常", "夫妻生活", "的", "女性", ",", 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过正常夫妻生活的女性,一旦月经迟到,首先就要考虑自己是否怀孕,不过除了月经没来之外,观察自身生理的变化也很重要。尤其是在已经怀孕而不自知的情况下,误将出血当作月经来潮。在怀孕初期,有时会出现类似月经,但并非月经的经样出血症状,还有些女性会因受精卵着床而下体出血。经样出血虽是量少色淡的出血,但有时是流产的先兆,最好赶紧找医生诊断。 2.恶心呕吐 孕妇大部份在前三个月都难免有不同程度的恶心、呕吐经验,老人家说这就是“害喜”,常发生在早上。治疗方式可以食物疗法,即避免油腻及油炸的食物。如早晨下床前先吃几片苏打饼干,另外喝果汁也可以改善症状。如果还是很严重,则可以服用“止吐药”及“维生素B6”来改善;严重到引起低血糖、脱水及肝功能异常,则需要住院治疗。 3.频尿及便意感 怀孕后由于子宫渐渐的变大,往前压迫到膀胱,膀胱的内容量愈来愈小,所以当尿液积到某一程度时,便有尿意感以致于勤跑洗手间,可是却仅涓涓细流,不像以前澎湃。", "体质不同,时间也不一样,因人而异 百度知道用户 2013-08-24 13:50", "我个人认为怀孕后一般是二个星期以后慢慢就会有反应了,只不过刚开始轻微些,不容易查觉", "百度知道用户 2013-08-24 13:53"], "bs_rank_pos": 4}, {"is_selected": false, "title": "怀孕后多久才会有反应 6种征兆告诉你怀孕了_网易亲子", "most_related_para": 8, "segmented_title": ["怀孕", "后", "多久", "才会", "有", "反应", "6", "种", "征兆", "告诉", "你", "怀孕", "了", "_", "网易", "亲子"], "segmented_paragraphs": [["2015", "-", "04", "-", "27", "10", ":", "31", ":", "00", "来源", ":", "亲", "贝", "网", "(", "北京", ")"], ["怀孕", "多久", "会", "有", "反应", "呢", "?", "今天", "小", "编", "就", "来", "为你", "介绍", "怀孕", "多久", "会", "有", "反应", "和", "出现", "怀孕", "反应", 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"早孕反应出现的时间、停止的时间,都和体内的一种激素有很大的关系,这个激素就叫绒毛膜促性腺激素,它从六周左右开始出现。", "HCG出现比这个更早一点,一般四五周就开始出现了。", "有的人早孕反应是从四五周开始出现,大部分人是从六周左右开始表现得比较强烈一点。到十周左右达到高峰,一般要到三个月左右,也就是十二周左右,HC剂才会下降。", "所以一般的早孕反应出现的时间可以从四周或者五周就开始出现,但是大部分人从六周左右开始出现,高峰的在十周左右,一般要持续到十二周左右。", "出现怀孕反应后要做哪些检查呢?", "根据病史、临床表现及妇科检查进行诊断,hCG测定,可明确早孕诊断。症状严重,化验尿中有酮体,则可诊断为妊娠剧吐。注意与妊娠合并消化系统疾病鉴别。根据病史、临床表现及妇科检查进行诊断,hCG测定,可明确早孕诊断。症状严重,化验尿中有酮体,则可诊断为妊娠剧吐。注意与妊娠合并消化系统疾病鉴别。", "+ 加载更多新闻", "最时尚的越野车 丰田将推小号\"酷路泽\" 入住日本公寓傻瓜指南 细节多到让外国人不知所措 80后姑娘受雇设计装修山居 房主看完后交给她整座山", "\"广场舞抢地盘\"大妈遭留学女儿痛斥:你们干的啥事 陕西宝鸡\"最长名称公司\"火了:名字长达39个字 \"五毒局长\"笔记:葡萄美酒夜光杯 金钱美女一大堆", "网易商城,您身边24小时的联通电信移动营业厅 随时随地为您提供手机话费充值服务。"], "bs_rank_pos": 5}], "answer_spans": [[3, 13]], "answer_docs": [0], "fake_answers": ["女性出现妊娠反应一般是从6-12周左右"], "question": "怀孕多久会有反应", "segmented_answers": [["女性", "出现", "妊娠反应", "一般", "是", "从", "在", "6", "-", "12", "周", "左右", "。"]], "answers": ["女性出现妊娠反应一般是从在6-12周左右。"], "entity_answers": [["6-12周左右"]], "segmented_question": ["怀孕", "多久", "会", "有", "反应"], "question_type": 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"唇形", "的", "女人", "桃花运", "特别", "旺", "!"], ["男人", "嘴唇", "可以看出", "他", "的", "性格", ",", "长", "这样", "的", "薄情", "又", "冷血", ",", "女人", "千万", "别", "嫁", "!"]], "paragraphs": ["首页 > 汽车 > 用车 > 白色的汽车出现划痕怎么处理", "日常使用汽车的时候,由于自己的粗心大意,很多车辆出现了划痕,今天小编给大家说说白色汽车划痕怎么处理?怎么样可以让车恢复原貌,下面就赶紧的看看,希望这些小妙招对你有用。", "白色汽车划痕怎么处理", "1、补漆笔可处理小划痕 如果锈蚀不很严重,可先用极细的水砂纸蘸水轻轻磨去锈斑,但切忌无方向地乱磨,要同向直线打磨。等完全擦净后,涂上一层底漆。 若是新的刮伤,可擦净后直接涂上底漆。之后,如果车辆配有小罐原厂漆,就可以等底漆干了后,再用水砂纸将其磨平,在底漆之上把原厂漆涂上。 如果没有原厂漆,可以购买一支补漆笔,用补漆笔进行上色处理。这样的处理虽然没有去美容店处理效果好,但如果是小的或者不醒目处的伤痕,也可以起到防止锈迹扩大、加重的作用。 2、涂牙膏可临时防锈 还有就是用普通的牙膏来处理。发现汽车车身有新的小伤痕时,把牙膏轻轻抹圆圈涂在划痕处。下雨或洗车后,别忘了再涂一下。 这样做一般可减轻划痕印记,也可以简单地起到隔绝作用,防止生锈,短期内没问题。特别是白色车漆,效果最为明显。做好汽车轻微划痕小处理后,等下次进行汽车全面保养的时候在彻底的补好划痕。 九个自己动手处理划痕的小绝招 1、不论什么样的汽车都会有或大或小的伤痕。伤痕对表漆造成多大的损伤?怎样才能修补?都必须考虑正确的处置方法。如果是不太醒目的伤痕,聪明的方法是不要去管它。", "1、不论什么样的汽车都会有或大或小的伤痕。伤痕对表漆造成多大的损伤?怎样才能修补?都必须考虑正确的处置方法。如果是不太醒目的伤痕,聪明的方法是不要去管它。 2、车有擦伤,最好的修补方法是伤痕部分填上与车身相同的镀膜剂。镀膜剂可涂一次、二次、三次,次数越多,会涂得越干净。但整个车身的均匀处理也很重要。", "《欢乐颂2》可一点也不欢乐!安", "《欢乐颂2》曲筱绡赵医生第二", "刷牙流血千万别轻视 可能是大", "白色车轮毂喷哪种颜色最好看  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"猪哥亮生前最后照片曝光,被病魔折磨成纸片人但笑容依旧  猪哥亮近日病逝,很多人悲痛,但也有很多的人迷茫,因为不认识猪哥亮到底是谁,其实猪哥亮已经是一个台...更多 2017-05-15 13:38:53", "小胸妹纸这样选泳衣,飞机场照样秒变大胸辣妹! 夏天去泳池游泳,可是胸小穿泳衣不好看怎么办?那是你没选对泳衣,只要选对泳衣平胸照样变波霸!荷叶边...更多 2017-05-14 21:46:45", "这4种常见发型最伤身,连理发师都不知道,你还在留吗? 4种发型留不对可能伤身,这几种常见发型最伤身你还在留吗?高马尾、丸子头等,是一种青春洋溢又清爽利落...更多 2017-05-14 20:59:12", "小个子女生演绎百变风格穿搭,娇小女生必看! 今年夏天虽然出了很多新款的衣服,但是对于矮个子女生和胖女生来说如何通过穿衣搭配改变自己的风格是个...更多 2017-05-14 10:16:55", "奔跑吧第五期收视率回到解放前,鹿晗热巴零互动是根源? 《奔跑吧兄弟》第五季第五期的节目收视率已经新鲜出炉了,只不过本期节目的收视率却并不理想,在上期节...更多 2017-05-13 12:03:22", "倒车入库怎么找最准确的点 新手开车去车管所验车一定要拍照 简单分析suv和旅行车的优缺点 2017年最高逼格的汽车坐垫你造吗,亲们? 铁轮毂与合金轮毂的优缺点分析 揭秘上坡起步要想做到绝不会熄火诀窍 10万元的汽车车险第一年多少钱 教你如何判断节气门是否脏了 传祺gs4自动挡挡位是什么意思 车门凹陷修复吸盘工具怎么用", "女人只需这样做,让男人着迷上瘾一辈子,超准的!", "脾气像母老虎,却能让男人一辈子上瘾的4个生肖女,风情万种惹人爱!", "这生肖从今天起到8月运势最佳,但是有钱未必能存住", "超准!女人的嘴唇可以看出她的性格,这种唇形的女人桃花运特别旺!", "男人嘴唇可以看出他的性格,长这样的薄情又冷血,女人千万别嫁!"], "bs_rank_pos": 0}, {"is_selected": true, "title": "白色汽车划痕怎么修补_百度经验", "most_related_para": 3, "segmented_title": ["白色", "汽车", "划痕", "怎么", "修补", "_", "百度", "经验"], 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然后就是要沿着划痕的两侧仔细贴两层美纹纸,让美纹纸正好露出划痕。美纹纸最好多贴几层。贴的时候要更加仔细才行,一定要把划痕完全框住,保证后期的补漆效果。 步骤阅读 5 如果买车的时候带有小罐的原厂漆,就可以把原厂漆均匀涂在底漆上面,涂得时候要仔细一点。那如果没有原厂漆的话,可以买补漆笔,用补漆笔进行车漆的处理。 步骤阅读 6 用补漆笔之前一定要仔细摇匀,补漆的时候要多次反复得刷,让凹槽慢慢凭证,每两次刷漆之间要间隔5-10分钟。等完全干了之后,用砂纸仔细打磨平整即可。 步骤阅读 END", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "bs_rank_pos": 1}, {"is_selected": false, "title": "白色汽车划痕处理妙招 白色车小划痕怎么处理?-百科大全-就爱阅读网", "most_related_para": 6, "segmented_title": ["白色", "汽车", "划痕", "处理", "妙招", "白色", "车", "小", "划痕", "怎么", "处理", "?", "-", "百科", "大全", "-", "就爱阅读", "网"], "segmented_paragraphs": [["看", "痕", "深", "不", "深", "罗", ",", "如果", "不", "深", "打", "打蜡", "就", "可", "修复", "。", "如果", "深", "了", ",", "还", "没", "露底", ",", "你", "又", "可", "忍", "得", "住", ",", "就", "还是", "打", "上蜡", ",", "防锈", "。", "。", "。", "。", "不行", "就", "走", "保险", "修车", "。"], ["轻微", "刮花", "的", "漆面", "损伤", "部位", "是", "在", "面漆", "的", "表层", ",", "可以通过", "打蜡", "完全", "修复", "。"], ["不", "明显", "的", "话", "就", "去", "4S店", "叫", "他们", "给", "你", "描", "点", "漆", "得", "了", ",", 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",", "生活", "学习", ",", "尽", "在", "就", "爱", "阅读", "网", "92to", ".", "com", ",", "您", "的", "在线", "图书馆"]], "paragraphs": ["看痕深不深罗,如果不深打打蜡就可修复。如果深了,还没露底,你又可忍得住,就还是打上蜡,防锈。。。。不行就走保险修车。", "轻微刮花的漆面损伤部位是在面漆的表层,可以通过打蜡完全修复。", "不明显的话就去4S店叫他们给你描点漆得了,这样不管漏没漏铁皮也不至于生锈。", "深纹腻子粉打磨调漆喷!细纹鹿皮绒布擦!在简单点贴一帖小屁孩图片!!", "先抛光,处理不了就喷油漆", "有一种真皮修复的东西,抹上之后待一会儿就能好了!", "牛奶喝剩的牛奶不要倒掉,它可是皮鞋的防皱液,用它能滋润皮鞋的皮质,防止干裂。醋挤出鞋油先别急着擦鞋,在里面加几滴食用醋试试,它能使鞋子光亮还不容易沾灰。还有,如果白色皮鞋脏了,先用食醋擦一擦,用干布擦净,再上白鞋油,去污效果特别好。 丝袜不小心钩了丝的丝袜,套在鞋刷上,擦出的皮鞋光泽度很高。蜡笔皮鞋上的轻微划痕,可以用孩子的蜡笔来消除,找一支和皮鞋同色的蜡笔,涂在划痕处,然后轻轻擦拭,划痕就几乎看不出来了。 报纸被雨淋湿的皮鞋最让人心疼了,让报纸来帮你吧。先用干布擦拭皮鞋,趁半干的时候,涂上鞋油,然后把报纸 塞入鞋中。这样既可吸收水分,也有校正鞋形的作用,最后阴干就行了。 橡皮翻毛皮鞋弄脏后最难办了,这时橡皮就该出场了。用普通的白橡皮在鞋面上轻轻擦拭,然后再用干净软刷刷掉 橡皮屑,看看,不用费多大力气,再难去的污渍也能除去。 涂改液白皮鞋一不小心,鞋尖和后跟就会被磕破皮面,变得很难看。把涂改液涂在磕破的地方,抹平后晾干,基本 就能恢复原样了。 牛皮或羊皮鞋:用优质鞋膏和鞋布把污垢去除,用鞋布稍微用力在鞋面上来回擦拭令鞋面光亮。 翻毛反绒或马毛鞋:先用胶擦或铜丝刷把鞋面上的毛顺着一个方向梳理好,用翻毛鞋翻新剂距离鞋20cm处垂直喷向鞋面,再用胶刷朝一个方向梳理,反复多次梳理后自然阴干就会翻旧如新。", "牙膏清理或者高乐氏SOS万能去污刷,我觉得这个洗的效果特别好。因为刷子里面是内含可降解洗涤剂的,一擦就像洗过一样。因为是由软性纤维构成,比头发丝还要细1/3。所以不会对表面留下划痕。", "用白色修正液涂上就行了,近看可能有点区别,但是在人眼所及高度是看不出的", "找细砂纸打打 再刷点清漆", "百度搜索“就爱阅读”,专业资料,生活学习,尽在就爱阅读网92to.com,您的在线图书馆"], "bs_rank_pos": 2}, {"is_selected": false, "title": "请教,白色汽车轻微划痕怎么处理_百度知道", "most_related_para": 0, "segmented_title": ["请教", ",", "白色", "汽车", "轻微", "划痕", "怎么", "处理", "_", "百度", "知道"], "segmented_paragraphs": [["轻度", "损伤", ",", "可以", "先", "在家", "进行", "预防", "处理", ",", "具体如下", ":", "　　", "1", "、", "轻微", "损伤", ":", "建议", "不", "去", "管", "它", "　　", "日常", "使用", "肯定", "不能", "避免", "被", "一些", "硬", "物体", "划过", "车漆", "留下", "痕迹", ",", "比如", "路人", "背包", "上", "的", "拉链", "或", "行驶", "时", "迸溅", "的", "小", "石子", "都会", "给", "车漆", "留下", "轻微", "的", "痕迹", "。", "往往", "这种", "轻微", "的", "划痕", "在", "车身", "上", "是", "最多", "的", ",", "但", "他们", "并", "不", "影响", "车身", "的", "美观", ",", "对于", "这种", "情况", "来", "说", "我", "一般", "不", "去", "管", "它", "。", "　　", "另外一个", "不花钱", "的", "小窍门", "就是", "用", "牙膏", "涂抹", "在", "这些", "轻微", "划痕", "上面", ",", "然后", "慢慢", "擦拭", "也", "可以", "去掉", "划痕", ",", "因为", "牙膏", "里面", "有", "很", "细小", "的", "微粒", ",", "慢慢", "的", "擦拭", "相当于", "把", "明显", "的", "划痕", "研磨", "没", "。", "　　", "对于", "这种", "轻微", "损伤", "除了", "被动", "接受", "外", "还", "可以", "主动出击", "进行", "预防", ",", "1", "-", "2", "个", "月", "打", "一", "次", "蜡", "或者", "在", "经常", "容易", "受到", "磨损", "的", "地方", "贴上", "防", "磨损", "贴片", "可", "在", "一定", "程度", "上", "的", "避免", "轻微", "划痕", "的", "产生", "。", "　　", "2", "、", "轻度", "损伤", ":", "可以", "使用", "补漆笔", "　　", "这种情况", "的", "损伤", "多数", "情况", "是", "已经", "能", "非常", "清楚", "的", "看到", "漆面", "上", "的", "划"]], "paragraphs": ["轻度损伤,可以先在家进行预防处理,具体如下: 　　1、轻微损伤:建议不去管它 　　日常使用肯定不能避免被一些硬物体划过车漆留下痕迹,比如路人背包上的拉链或行驶时迸溅的小石子都会给车漆留下轻微的痕迹。往往这种轻微的划痕在车身上是最多的,但他们并不影响车身的美观,对于这种情况来说我一般不去管它。 　　另外一个不花钱的小窍门就是用牙膏涂抹在这些轻微划痕上面,然后慢慢擦拭也可以去掉划痕,因为牙膏里面有很细小的微粒,慢慢的擦拭相当于把明显的划痕研磨没。 　　对于这种轻微损伤除了被动接受外还可以主动出击进行预防,1-2个月打一次蜡或者在经常容易受到磨损的地方贴上防磨损贴片可在一定程度上的避免轻微划痕的产生。 　　2、轻度损伤:可以使用补漆笔 　　这种情况的损伤多数情况是已经能非常清楚的看到漆面上的划"], "bs_rank_pos": 3}, {"is_selected": false, "title": "白色汽车划痕怎么处理_百度知道", "most_related_para": 0, "segmented_title": ["白色", "汽车", "划痕", "怎么", "处理", "_", "百度", "知道"], "segmented_paragraphs": [["汽车", "划痕", "程度", "判断", "处理", "划痕", "首先", "要", "看", "这些", "划痕", "的", "深浅", "程度", ",", "有", "微度", "划痕", "、", "中度", "划痕", "和", "深度", "划痕", "。", "金属漆", "从", "外", "到", "里", "分别", "为", "清漆", "层", "、", "色", "漆", "层", "和", "底漆", "层", "。", "微度", "划痕", "指", "表面", "漆", "被", "轻微", "刮", "伤", ",", "划痕", "穿过", "清漆", "层", "并", "已经", "伤", "及", "色", "漆", "层", ",", "但", "色", "漆", "层", "还", "未", "被", "刮", "透", "。"]], "paragraphs": ["汽车划痕程度判断 处理划痕首先要看这些划痕的深浅程度,有微度划痕、中度划痕和深度划痕。金属漆从外到里分别为清漆层、色漆层和底漆层。 微度划痕指表面漆被轻微刮伤,划痕穿过清漆层并已经伤及色漆层,但色漆层还未被刮透。"], "bs_rank_pos": 4}], "answer_spans": [[1, 229]], "fake_answers": ["白色汽车身上有了新的划痕,先要用鹿皮布仔细将划痕及其周围车漆擦拭干净,然后将日常用的牙膏涂抹在划痕“伤口”处!一定要涂抹均匀,并且在下雨或者是洗车之后也要涂抹一遍。步骤阅读2但是需要注意的是,涂抹了牙膏的划痕伤患处,只是能起到临时保护的作用,并且能防止划痕处生锈,只是暂时的权宜之计,不想自己动手的话,可以等到爱车下次进行保养的时候,让4S店进行彻底得修补。步骤阅读3想自己动手操作的话,我们可以先用颗粒极细的水砂纸先进行打磨,沿着直线进行打磨,打磨掉锈斑以及牙膏之后,然后均匀得涂上薄薄的一层底漆。等到底漆干了之后,还要进行砂纸打平。步骤阅读4然后就是要沿着划痕的两侧仔细贴两层美纹纸,让美纹纸正好露出划痕。美纹纸最好多贴几层。贴的时候要更加仔细才行,一定要把划痕完全框住,保证后期的补漆效果。"], 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1)分组:012 345 672)转成二进制:012→0000001100 345→0101011001 67 →10000113)转成序列:0000001100 0101011001 10000114)字符数 转成二进制:8→00000010005)加入模式指示符(上图数字)0001:0001 0000001000 0000001100 0101011001 1000011对于字母、中文、日文等只是分组的方式、模式等内容有所区别,基本方法是一致的。二维码虽然比起一维条码具有更强大的信息记载能力,但也是有容量限制,通过下面这个表格小草带你了解二维码的容量到底有多大。3、纠错编码:按需要将上面的码字序列分块,并根据纠错等级和分块的码字,产生纠错码字,并把纠错码字加入到数据码字序列后面,成为一个新的序列。在二维码规格和纠错等级确定的情况下,其实它所能容纳的码字总数和纠错码字数也就确定了,比如:版本10,纠错等级时H时,总共能容纳346个码字,其中224个纠错码字。就是说二维码区域中大约1/3的码字时冗余的。对于这224个纠错码字,它能够纠正112个替代错误(如黑白颠倒)或者224个据读错误(无法读到或者无法译码),这样纠错容量为:112/346=32.4% 4、构造最终数据信息:在规格确定的条件下,将上面产生的序列按次序放如分块中按规定把数据分块,然后对每一块进行计算,得出相应的纠错码字区块,把纠错码字区块 按顺序构成一个序列,添加到原先的数据码字序列后面。如:D1, D12, D23, D35, D2, D13, D24, D36, … D11, D22, D33, D45, D34, D46, E1, E23,E45, E67, E2, E24, E46, E68,…5 、构造矩阵:在构造矩阵之前,我们先来了解一个普通二维码的基本结构。", "位置探测图形、位置探测图形分隔符、定位图形:用于对二维码的定位,对每个QR码来说,位置都是固定存在的,只是大小规格会有所差异; 校正图形:规格确定,校正图形的数量和位置也就确定了; 格式信息:表示改二维码的纠错级别,分为L、M、Q、H; 版本信息:即二维码的规格,QR码符号共有40种规格的矩阵(一般为黑白色),从21×21(版本1),到177×177(版本40),每一版本符号比前一版本 每边增加4个模块。 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“我们之所以对二维码进行扫描能读出那么多信息,就是因为这些信息被编入了二维码之中。”黄海平说,“制作二维码输入的信息可以分成三类,文本信息,比如名片信息;字符信息,比如网址、电话号码;还有图片信息,甚至还可以包括简短的视频。”数据信息是怎么被编入的呢?信息输入后,首先要选择一种信息编码的码制。现在常见的二维码都是以QR码作为编码的码制。QR码是矩阵式二维码,它是在一个矩形空间内,通过黑、白像素在矩阵中的不同分布,来进行编码的。我们知道电脑使用二进制(0和1)数来贮存和处理数据,而在二维码中,用黑白矩形表示二进制数据我们肉眼能看到的黑色表示的是二进制“1”,白色表示二进制的“0”,黑白的排列组合确定了矩阵式二维条码的内容,以便于计算机对二维码符号进行编码和分析。", "百度经验:jingyan.baidu.com", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "bs_rank_pos": 2}, {"is_selected": false, "title": "二维码的生成细节和原理 - 51CTO.COM", "most_related_para": 0, "segmented_title": ["二维码", "的", "生成", "细节", "和", "原理", "-", "51", "CTO", ".", "COM"], "segmented_paragraphs": [["二维码", "又", "称", "QR", "Code", ",", "QR", "全称", "Quick", "Response", ",", "是", "一", "个", "近几年来", "移动", "设备", "上", "超", "流行", "的", "一", "种", "编码方式", ",", "它", "比", "传统", "的", "Bar", "Code", "条形码", "能", "存", "更多", "的", "信息", ",", "也", "能", "表示", "更多", "的", "数据类型"], ["微信", "二维码", "登录", "原理", "二维码", "的", "生成", "细节", "和", "原理"], ["编程", "领域", "在", "这", "几", "年", "里", "迎来", 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"干海参涨发即食海参操作方法:1.先将干海参用清水浸泡20-30小时,漂洗除盐,清理内脏。2.沸水煮两边,每次煮15-20分钟,每次煮完捞出放入凉水降温透彻。3.将海立美化开(糯脆型的用温水,爽脆型的用凉水),加入冰水中,然后放入煮好的海参,再按冰与水的比例2:1加入冰块,(也可不加冰块,直接放入冰箱冷藏)浸泡2-3天,将发好的海参检出,没发好的海参继续涨发,发好为止。", "干海参发制方法:1、干海参用自来水直接冲洗干净;2、凉纯净水泡24小时左右,中间换水2次;3、将泡好的海参从腹部纵向剖开,去掉海参前端牙状物也就是沙嘴;4、添纯净水上无油锅加盖煮沸,改用中火35-50分钟,关火后加盖焖至自然凉透;5、换新的凉纯净水,泡48小时左右,中间换水2-3次,发好后即可食用。一斤好的干海参可以发制出好几倍的水发海参,譬如国内的知名品牌福临门海参,其涨发往往可以达到8倍以上。", "【必读】干海参发泡方法指导: 1、将干海参用自来水直接冲洗1分钟,洗掉表面少许微尘。 2、置于1-10度凉纯净水中24小时左右,中间换水2次直至将海参泡软。 3、将泡软的海参从腹部纵向剖开,去掉海参前端牙状物和体内白筋。 4、添纯净水上无油锅加盖煮沸,用中火煮15-25分钟。 5、换新的凉纯净水,泡24小时左右,中间换水2次直至发泡到2倍左右长度。 6、泡好后,即可食用。可把多余的单独零度以下冷冻,建议2周内用完。 7、如有个别海参没有发大,属于正常现象,可重复4、5步骤。 友情提示: 海参的大小不是统一的,会出现厚薄不一的情况,所以在海参发制时时间的长短也应该不同。小而薄的海参发制时间可短些,固而厚的海参发制时间应长些。有的同样大小、同样厚薄、同样品种,涨发时也有先发透的和后发透的。先发透的应先拣出来,没发透的继续发", "第一步;回软将干海参用凉水泡24小时直至海参变软。第二步清洁剪掉海参的沙嘴,切断筋,清洗干净。第三步;将海参放入无油的,装入凉水的干净锅内,大火煮开改用小火煮50至60分钟左右,将海参捞出,用海参掐海参侧壁肉,能掐透或者稍变软即可,如没有则继续煮。第四步;水发,将煮好的海参捞出来,自然凉透之后,用凉水泡制【最好用纯净水】2,3天,并将水中放入冰块,保持每天换一次水,加冰块。第五步;保存将发好的海参捞出来,用干净的保鲜袋逐个装好,每支装一个袋,放入冰块中冷冻。纯干海参的涨发及保存方法", "第一步;回软将干海参用凉水泡24小时直至海参变软。第二步清洁剪掉海参的沙嘴,切断筋,清洗干净。第三步;将海参放入无油的,装入凉水的干净锅内,大火煮开改用小火煮50至60分钟左右,将海参捞出,用海参掐海参侧壁肉,能掐透或者稍变软即可,如没有则继续煮。第四步;水发,将煮好的海参捞出来,自然凉透之后"], "bs_rank_pos": 2}, {"is_selected": false, "title": "如何发海参快又好_百度知道", "most_related_para": 0, "segmented_title": ["如何", "发", "海参", "快", "又", "好", "_", "百度", "知道"], "segmented_paragraphs": [["如何", "发", "海参", "快", "又", "好", "两种方法", ":", "一", "、", "热", "泡", "法", "先", "用", "热水", "将", "海参", "泡", "24", "小时", "(", "可", "直接", "随", "冷水", "装入", "锅", "内", "煮", "开", ",", "再", "加盖", "焖", "泡", "4", "-", "5", "小时", ")", ",", "再", "从", "腹", "下", "开口", "取出", "内脏", ",", "然后", "换上", "新", "水", ",", "上火", "煮", "50", "分钟", "左右", ",", "用", "原", "汤", "泡", "起来", ",", "过", "24", "小时", "后", "即可", "。", "　　", "二", "、", 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"方法", "三", "、", "水煮", ":", "将", "清洗干净", "的", "海参", "倒入", "无油", "的", "锅", "中", ",", "加入", "凉", "的", "纯净水"]], "paragraphs": ["如何发海参快又好 两种方法: 一、热泡法先用热水将海参泡24小时(可直接随冷水装入锅内煮开,再加盖焖泡4-5小时),再从腹下开口取出内脏,然后换上新水,上火煮50分钟左右,用原汤泡起来,过24小时后即可。 　　二、冷水发泡法,将海参浸入清水内,约3天即泡发;取出剖腹去肠杂、腹膜,然后再换清水浸泡,待泡软后即可加工食用。此法在热天要多换几次水,并经常注意是否已变软。 　　泡发海参时,切莫沾染油脂、碱、盐,否则会妨碍海参吸水膨胀,降低出品率,甚至会使海参溶化,腐烂变质。发好的海参不能再冷冻,否则会影响海参的质量,故一次不宜发得太多。 海参是一种营养价值较高的海鲜产品,在国内海参产品市场,海参养殖、加工企业鱼目混珠,各类产品良莠不齐,行业缺乏统一标准,难辨优劣。消费者如果不懂得如何鉴别海参", "现在的自然环境大部分破坏,生态环境极其恶劣,造成了人们的发病率比以往增高很多,人们为了健康选择养生,好多人选择海参滋补身体,但好多人对淡干海参的发制方法还不是那么了解,那么淡干海参怎么发制呢?下面给您介绍一下宫品海参推荐干海参的发制方法。 海参的发制方法一、回软:将淡干海参全部用清水洗净表面浮尘,将洗净海参放入无油器皿中,加入凉的纯净水,水一定要淹没过淡干海参,将装有海参的器皿放入0-5度冰箱冷藏室内24-36小时(每天换一次纯净水),能将海参掐透为止。 海参的发制方法二、清洗:先将手和剪刀洗干净,将回软好的海参用剪刀在腹部开口处剪掉海参前端白状物(海参沙嘴),在用剪刀将海参筋剪断(不是剪掉),将剪好的海参清洗干净。 海参的发制方法三、水煮:将清洗干净的海参倒入无油的锅中,加入凉的纯净水"], "bs_rank_pos": 3}], "answer_spans": [[9, 160]], "fake_answers": 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"1、将干海参用自来水直接冲洗1分钟，洗掉表面少许微尘；2、置于1-10度凉纯净水中24小时左右，中间换水2次直至将海参泡软；3、将泡软的海参从腹部纵向剖开，去掉海参前端牙状物和体内白筋；4、添纯净水上无油锅加盖煮沸，用中火煮15-25分钟；5、换新的凉纯净水，泡24小时左右，中间换水2次直至发泡到2倍左右长度；6、泡好后，即可食用。可把多余的单独零度以下冷冻，建议2周内用完；7、如有个别海参没有发大，属于正常现象，可重复4、5步骤。"], "answer_docs": [2], "segmented_question": ["如何", "发", "海参"], "question_type": "DESCRIPTION", "question_id": 181611, "fact_or_opinion": "OPINION", "match_scores": [0.898360655737705]}
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400-664-4881", "今日港币汇率最新查询(2017年5月19日) 今日港币汇率最新查询(2017年5月18日) 今日港币汇率最新查询(2017年5月17日) 今日港币汇率最新查询(2017年5月16日) 今日港币汇率最新查询(2017年5月15日) 今日港币汇率最新查询(2017年5月12日) 今日港币汇率最新查询(2017年5月11日) 今日港币汇率最新查询(2017年5月10日) 今日港币汇率最新查询(2017年5月9日) 今日港币汇率最新查询(2017年5月8日)", "今日人民币对港币汇率走势利好(2017年6月1日) 今日港币汇率最新查询(2017年6月1日) 今日港币对人民币汇率持续波动(2017年5月31日) 今日港币汇率最新查询(2017年5月31日) 今日港币汇率最新查询(2017年5月27日) 穆迪降级影响发酵!港币创15个月新低 今日人民币对港币汇率持续震荡(2017年5月26日) 今日港币汇率最新查询(2017年5月26日) 今日港币对人民币汇率走势震荡(2017年5月25日) 今日港币汇率最新查询(2017年5月25日) 今日人民币对港币汇率持续震荡(2017年5月24日) 今日港币汇率最新查询(2017年5月24日) 今日港币对人民币汇率走势震荡(2017年5月23日) 今日港币汇率最新查询(2017年5月23日)", "名称 最新价 涨跌值 涨跌幅 开盘价", "欧元美元 6.3741 1.22 1.22↓ 6.3741", "英镑美元 6.3741 1.22 1.22↓ 6.3741", "美元加元 6.3741 1.22 1.22↓ 6.3741", "美元日元 6.3741 1.22 1.22↓ 6.3741", "美元港币 6.3741 1.22 1.22↓ 6.3741", "澳元美元 6.3741 1.22 1.22↓ 6.3741", "瑞郎美元 6.3741 1.22 1.22↓ 6.3741", "[外汇基本面]外汇基本面中的浮动汇率制是什么? [外汇基本面]外汇基本面中的货币供给是什么意思? [外汇基本面]外汇基本面中的货币扩张是什么? [外汇实战策略]如何设置外汇交易头寸? [外汇实战策略]外汇套利交易该怎样开始? [外汇实战策略]如何确定你适合日内交易还是波段交易? [外汇实战策略]如何在外汇市场低波动期进行交易? 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"说一个既不用翻墙又不要切换地区的注册方法吧,下载一个网易邮箱大师,然后添加账号,先随便敲一个@gmail的地址,在跳转的页面里直接注册账号就行了。", "更新一下哈,这个方法直接在手机端下载App按照步骤来做就行了(我用的是iPhone版的,Android 版的没试过)。", "尝试N种方法,换N个地方的VPN,最后发现,打开QQ邮箱,选择“添加账户”-》gmail-》然后正常注册就可以使用中国电话收短信。毫无障碍可言,不用vpn,快的一B。第一次觉得企鹅也不是只看人民币玩家。。。。", "苹果手机(挂VPN)--设置--邮件、同学录、日历---添加账户---选“google”--- 进到google登录界面选“其他选项”---填写姓名---到填写电话那里了!!高潮来了!!! 往下看!!", "不管你挂哪里的VPN!!啥都不用改!!!", "直接在后边写 +86你的手机号码!!", "直接在后边写 +86你的手机号码!!", "直接在后边写 +86你的手机号码!!", "直接在后边写 +86你的手机号码!!", "直接在后边写 +86你的手机号码!!", "直接在后边写 +86你的手机号码!!", "别改前边儿的自动选区号!!!自己在后边儿加 “+86手机号”!!!", "别改前边儿的自动选区号!!!自己在后边儿加 “+86手机号”!!!", "别改前边儿的自动选区号!!!自己在后边儿加 “+86手机号”!!!", "后边儿就正常填了,能受到验证码就一马平川了!", "一次成功!!!亲试有用!!!苹果手机可以通过以下步骤解决注册谷歌账号时手机号码无法通过验证的方法:1、成功连接vpn;2、设置➡️邮件➡️添加账号➡️Google➡️更多选项➡️创建新账号;3、填完资料➡️填完手机号即可收取验证码➡️注册成功!!! 本人之前网页注册一直显示手机号码无法通过验证,用了此方法,一次注册成功!!!", "一个偏方,先谷歌“gmail香港”,然后点击进入 “登入- Google 帳戶” (应该是搜索结果页面的第二个),然后从这里开始注册,流程和页面和直接用谷歌注册一样,但是字体自动变成了繁体。这个办法也不是100%成功,大家可以尝试一下。", "换一个别人的手机号试试看…这一步只是起到类似验证码的作用,不会把号码绑定到你账号上的。", "都想玩pokemon吧。。", "并不知道題主为何会发生这种情况,但是纠正轮子哥,中国大陆手机是可以用的,我一直在用...", "我来说一个非常简单的方法,你在电脑上用chromium注册,手机验证可以跳过。chromium下载地址 https://download-chromium.appspot.com/ 下载后无需安装,解压运行 chrome.", "2016.2.28补充说明:我是使用客户端注册的,至于网页行不行没试过,如果有人成功请回复。", "我按照以上朋友的回答,全部没起作用。我说说我自己的方法。", "首先,我也是手机号没法验证,所以我就不管google去用facebook了。", "然后,我在用facebook客户端的时候,打开youtube,在那里登陆,登陆时注册一个新的账户。虽然是全英文不过应该没多大影响≧∇≦在那里注册的大陆手机号可以收到短信。", "注意:请在facebook客户端内打开注册页面,不知为何,我用uc浏览器打开该链接时依然无法注册。", "希望有效或无效的盆友都在此回答下评论~\\(≧▽≦)/~", "注册,手机号选中国,所在地选香港,然后用电话验证,在手机号那里面 输入+86 xxxxxx", "被虐了两天,网上说的方法全试了一遍都不行。最终自己摸索出一个没有人用过的方法,用chrome浏览器,在设置那里,先把高级选项里的语言设置成英文(其他语言不知道)然后应用,关掉浏览器再打开接下来的步骤就是之前有人说过的,在“设置”那里选择sign in--create new account---接下来的事情就很简单了,到了验证电话那一步多了一个选项“skip\",尼玛,等的就是这个,还有什么难度?", "上午挂VPN进谷歌网站直接注册,每次到了手机号那一步都失败。试了好多网上说的方法都没用,最后受到某个知友(忘记了名字,抱歉啊)的启发,我决定用YouTube注册(因为实在是懒得再进网站了,刚好手机上有个谷歌旗下的油管),先挂上VPN走日本线路,油管登录的时候选择注册新账号,到了手机号这一步,我没看到能跳过的选项,选择CN(中国),然后填了自己的手机号,果然不行,提示无法验证之类的。然后改成HK(香港),还是不行,不过提示变了一个,不是无法验证了。我灵机一动输入我妈的手机号,提示正在发送验证码,但是等了好久都没有收到,在短信拦截里面也没找到。然后我返回VPN走香港线路,再回到油管选HK填自己的手机号,不行!填我妈的手机号,验证码还是发不过来!", "啊啊啊,想起来了,忘记在手机号前加86……………………", "一秒钟收到短信,我快被自己感动哭了", "当我跟我妈要验证码的时候,她是不愿意的,都怪现在的骗子,害我妈连亲闺女都不相信了,直到我再三说明不干坏事,才肯上交验证码。", "啊,对了,我妈的手机号是130开头。", "苹果手机 1⃣️开vpn 2⃣️设置_邮件_添加账号_谷歌_更多选项_创建新账号,3⃣️填资料就可以注册成功,填写手机号秒收验证码,亲测有效,已经注册两个!", "_(:з」∠)_ ios系统一枚。", "PS 实在注册不成功的。。可以私信我帮你。但是不保证百分百成功。私信吧要用的信息都写上", "---------------------接到很多私信 都是二半夜啊啊!你们都是时差党吗!答主目前产假在家 要当奶牛 还要去学车考驾照 建议大家先换我说的 whale 威屁恩 (怕查水表 实在不行答主会帮注册 谁叫答主我又善良又瘦呢(微笑 but 注册有一点就是我们要同时在线!so 这个真要 猿粪!--------------20160712--------//", "-----以下是原文——————————————亲们赶紧用youtube啊啊啊啊啊啊啊 我把以上所有方法都试了 只有这个可以用!", "首先appstore里搜YouTube 下载", "然后 打开YouTube 有个登录第一次进去很好找的 我这是退出了必须点省略号才行", "然后 点用google账户登录 会看到创建新账户 就这样一直点下一步就可以了 刚刚又注册了个 小伙伴们快去吧! ps 全程要翻墙。 -///-------------------------/--------------———————————我去 知乎又抽了 我的图应该是在第一个然后后面 都给我放前面了 不过就这样吧!懒癌晚期表示不想改了～", "--------------------------20160710有小伙伴反映手机号还是无法验证 解决办法就是", "~申请此电话号码无法用于进行验证码 11/8留~~~ 真NMB累~"], "bs_rank_pos": 0}, {"is_selected": true, "title": "google账号注册显示此手机无法用于验证_百度经验", "most_related_para": 4, "segmented_title": ["google", "账号", "注册", "显示", "此", "手机", "无法", "用于", "验证", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["大家", "注册", "google", "账号", "的", "都会", "遇到", "“", "此", "手机", "无法", "用于", "验证", "”", "的", "难题", "。", "小", "编", "像", "大家", "一样", "试", "了", "网上", "的", "很多", "办法", "都", "无效", "。", "最终", ",", "历", "经", "千辛万苦", ",", "找到", "了", "一", "个", "非常", "简便", "的", "方法", "。", "当然", "好", "方法", "我们", "就", "应该", "拿来", "共享", ",", "所以", "特", "地", "分享", "给", "大家", "。"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["1", "进入", "app", "后", ",", "随便", "填写", 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"大家注册google账号的都会遇到“此手机无法用于验证”的难题。小编像大家一样试了网上的很多办法都无效。最终,历经千辛万苦,找到了一个非常简便的方法。当然好方法我们就应该拿来共享,所以特地分享给大家。", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "1 进入app后,随便填写一个以@gmail结尾的邮箱账号 步骤阅读 2 下一步就到了google账号登录界面,在这里点击选择账号输入框下的“创建账户”链接。 步骤阅读 3 在创建账户页面,填写相应信息。(在这里,要注意手机号码要选择中国+86的。)然后点击继续,不用输入什么验证码,就可以成功创建google账号了。 步骤阅读 步骤阅读 4 Google账号创建成功之后,Google的gmail小组会给你发一封邮件。 步骤阅读 END", "百度经验:jingyan.baidu.com", "请一定选择手机号码为中国+86的! 如果帮到你,请投我一票。", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "bs_rank_pos": 1}, {"is_selected": false, "title": "google账号申请最后一步手机验证怎么办!? - 知乎", "most_related_para": 29, "segmented_title": ["google", "账号", "申请", "最后", "一", "步", "手机", "验证", "怎么办", "!", "?", "-", "知乎"], "segmented_paragraphs": [["如", "题", ".", ".", ".", "中国", "手机号", "就是", "不", "让", "验证", "!"], ["刚", "注册", "成功", ",", "分享", "一", "下", "。", "下一个", "谷歌", "安装", "器", "app", ",", "这个", "APP", "会", "自动", "将", "谷歌", "全家桶", "都", "下", "好", "。", "点击", "play", "商店", "注册", ",", "就", "不", "需要", "手机号", "了", ",", 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"1", ":", "升级", "跟", "不", "升级", "有", "什么", "影响", "?", ")"]], "paragraphs": ["如题...中国手机号就是不让验证!", "刚注册成功,分享一下。下一个谷歌安装器app,这个APP会自动将谷歌全家桶都下好。点击play商店注册,就不需要手机号了,只需要按照步骤注册一个谷歌的邮箱就OK。更新play商店,然后大功告成。", "下一个hosts解析器,连接成功后,登录gmail,然后按正常程序注册申请,不用验证码就申请成功了,但是登录小精灵的时候就会要你验证,几秒钟就能收到验证短信,很快,亲测有效!!!", "可以啊,头个月我还绑定手机号来着。要不换个路线。", "Google的帐号和密码其实就是Gmail的帐号和密码 你可以去这个网站注册", "我是这样的1.开始填完姓名2.用户名那里开始点击我想使用我目前的电子邮件地址3.密码性别生日(最好大于18岁)填完后4.手机那里选中国5.位置那里选美国6.最后开始验证号码,选中国,输入号码验证.打开邮件继续完成.但是好像是个辅助账号,不过油管也算是上去了", "几年前注册的Google账号弄丢了,然后今天申请了一上午没搞定。下午七上八下各种尝试,终于注册成功。总结一下:", "1.开一个vpn 2.手机中Google浏览器申请账号 3.注册的时候不要选Gmail的邮箱 选自己已有的邮箱注册 4.国家统一选中国 号码不用加任何东西 提示你输什么就输什么", "然后就可以发送验证码到手机 并且可以收到 注册成功", "这个时候注册的是没有Gmail邮箱账号的 如果要Gmail邮箱账号 还要填手机号发验证码再申请,到现在也没有发送成功验证码。。。。话说这个可能就是Gmail的原因 导致之前申请不了 。。。", "好了希望这个方法对大家有用", "注册的时候,电话都要填中国的,就是那个+86的(下滑找了我半天)", "上淘宝买一个", "刚刚成功太激动来给大家分享一下!", "在chrome搜索 “google香港”", "点击“登入-google賬戶”页面注册就行", "我挂的香港vpn,第一个页面号码和地区都是中国,我自己邮箱没有填,", "刚成功线路换成日本的秒发我自己都不敢相信就酱", "用手机下载谷歌浏览器,然后登录谷歌香港,都填好了之后,最后所在地选择香港,可以收到验证码。自己刚注册了", "在网页上注册成功了,方法如下:", "1、VPN选择节点为香港(关键) 2、位置选择为香港(第二个红框),电话号码就直接填写国内的,形式如+86 13x xxxx xxxx,然后进行下一步 3、填写待验证的手机号码,此时的号码的形式为 13x xxxx xxxx(注意没有+86) 4、然后点继续就能收到短信了", "这个必须来回答下,昨晚注册Google账号,前面都是好好的,最后一步验证手机号的时候,死活无法验证,换了手机号,换了VPN地点都无果,折腾了10来次,真的想放弃了,但是又必须要注册,才能使用Google Analysis,所以最后只能沉下心来,每个选项不断验证。最后, 奇迹发生了!!!!我来总结几点吧(或许还有更好的方法):", "将chrome浏览器改为IE浏览器 前面手机号写+86的 位置随便选,千万不要选“中国” 验证短信的时候不需要+86,直接填手机号就好了 验证完成就大功告成啦!!! Google小组还会给你的备用邮箱发送一封邮件O(∩_∩)O", "通过以上方法,我同事也顺利注册完成了~~~希望对正在被Google注册账号折磨的童鞋们也有帮助~~~", "我在电脑上用所有的浏览器试了一个遍都没弄好,不管是用哪里的VPN还是什么。然后用手机下载一个谷歌浏览器的APP,搜Google官网创建账号,填了手机号但没用上,验证的信息直接发到了邮箱里然后就可以了!!真心累人啊。", "手机下载谷歌安装器,一个应用,可以帮助装好框架什么的。", "之后这个app里可以申请谷歌账号,我就这么莫名其妙收到了短信", "原申请了一天,最后采用题内@卜之的方法,也莫名的收到验证码,搞定之", "都忘了当时是怎么申请成功的,就记得验证码是浙江的一个号码发过来的....笑CRY....", "说一个我成功申请谷歌账号的经历,但不确定是否对其他人有用。我是在(香港)谷歌YouTube网站申请成功的。1.电脑连上vpn,打开浏览器,先打开香港谷歌网站,在没有账号的情况下打开YouTube,并随意选一内容订阅,便会出现申请谷歌账号的页面;2.依次填写,另外邮箱需写明确;手机号,地区均选中国大陆;3.填写完成后,点击,而后并不需要手机接收验证码,因为此时谷歌便会发一账号注册激活链接于自己所填写的邮箱中;4.在电脑端打开邮箱,点击链接,便可成功注册谷歌账号。", "手机挂vpn 就可以了", "我是在电脑端安装lantrn翻墙的。虽然说流量用完了,但是还能翻墙,所以就没升级。(问题1:升级跟不升级有什么影响?)"], "bs_rank_pos": 2}, {"is_selected": false, "title": "google谷歌账号注册显示此手机无法用于验证怎么办?_浏览下载_软件", "most_related_para": 5, "segmented_title": ["google", "谷歌", "账号", "注册", "显示", "此", "手机", "无法", "用于", "验证", "怎么办", "?", "_", "浏览", "下载", "_", "软件"], "segmented_paragraphs": [["大家", "注册", "google", "账号", "的", "都会", "遇到", "“", "此", "手机", "无法", "用于", 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"输入", "什么", "验证码", ",", "就", "可以", "成功", "创建", "google", "谷歌", "账号", "了", "。"], ["Google", "谷歌", "账号", "创建", "成功", "之后", ",", "Google", "谷歌", "的", "gmail", "小组", "会", "给", "你", "发", "一封", "邮件", "。"], ["请", "一定", "选择", "手机号码", "为", "中国", "+", "86", "的", "!"]], "paragraphs": ["大家注册google账号的都会遇到“此手机无法用于验证”的难题。小编像大家一样试了网上的很多办法都无效。最终,历经千辛万苦,找到了一个非常简便的方法。当然好方法我们就应该拿来共享,所以特地分享给大家。", "google谷歌账号注册显示此手机无法用于验证怎么办?", "在电脑官网上注册账号验证手机号码时显示是这样的,“此电话号码无法用于进行验证”。", "首先点击进入手机版邮件app,点击添加Gmail账号。(不同手机型号界面会有所不同)进入app后,随便填写一个以@gmail结尾的邮箱账号", "下一步就到了google谷歌账号登录界面,在这里点击选择账号输入框下的“创建账户”链接。", "在创建账户页面,填写相应信息。(在这里,要注意手机号码要选择中国+86的。)然后点击继续,不用输入什么验证码,就可以成功创建google谷歌账号了。", "Google谷歌账号创建成功之后,Google谷歌的gmail小组会给你发一封邮件。", "请一定选择手机号码为中国+86的!"], "bs_rank_pos": 3}, {"is_selected": true, "title": "注册Gmail,验证手机号码的时候提示“此号码不能用于验证”,怎么解决", "most_related_para": 3, "segmented_title": ["注册", "Gmail", ",", "验证", "手机号码", "的", "时候", "提示", "“", "此", "号码", "不能", "用于", "验证", "”", ",", "怎么", "解决"], "segmented_paragraphs": [["听", "着", 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"了"], ["基本上", "按照", "这", "几点", "来", "排查", ",", "就", "没有问题", "了", ",", "看到", "下面", "这个", "页面", ",", "恭喜你", "!", "你", "成功", "了", "!"], ["用", "chrome", "浏览器", "注册", "邮箱", "不需要", "验证", "手机", "也", "能", "成功", "注册", "亲", "测", "有效"]], "paragraphs": ["听着,列位跟vpn都没关系,我一直挂着美国vpn但是请从chrome换成IE。。。就好了!!一脸惊恐。。。。。。", "这个问题我们也是试了很久才解决的。", "一开始,无论填什么号码都不行,换了好多手机号码,固定电话号码,都不行。", "后来终于找到了窍门:关键在于你注册的第一步,选国家的时候一定要根据你的VPN的国家来选,不是选中国的。到了第二步,用中国的手机号就能注册了。", "我刚刚遇到同样的问题,VPN选用的是美国服务器,无论试多少次都是提示此号码无法验证,切换到日本服务器后一次就好了", "用网易邮箱大师或者QQ邮箱客户端添加gmail邮箱,然后跳转到一个登录的网页,就可以注册了,最后手机验证可以通过。(有一定成功率,看人品的时候到了 )", "1.打开谷歌浏览器(得是最新版本的),点击设置后,再点登录Chrome 2.点击更多选项,创建新账号。", "之后就水到渠成了。", "刚刚注册成功。折腾了好久,最后换了Chrome浏览器就成功了。手机号在注册的第一个界面时就填+86 xxxxxxxx,第二个界面会自动显示出你在第一页填的号码,然后选语音验证,立马加拿大的电话打过来,中文男声,先一句开场白,然后就是六位数字验证码,输入后验证成功!十分快捷,赞一个。", "花了七个点注册Gmail,最后终于成功了。手机下载chrome app,连接美国vpn,打开app进入Gmail网页版,然后注册,我是手机号和地区都没改也没填写,然后直接注册成功了,之后在电脑上打开chrome浏览器自带的登录,进去之后输入Gmail地址,然后会让验证手机号,前边的地区不改,手机号码+86 xxx xxxx xxxx格式写,然后就会发送验证短信,马上就过来了,最后终于成功了,花了七个小时。。。", "刚刚亲测有效。首先需要挂上vpn。然后打开谷歌官网注册。所在地区选择VPN所用的服务器的地区。在注册的这个表格里手机号码是不必须的哈哈哈 。然后就可以提交了。提交之后他会告诉你需要验证。你再输手机号就好了。好像手机号码的地区不重要。我就没有改。还是美国国旗。但是我的手机号码格式为+86 xxx xxxx xxxx. 就是要有空格!!!!一共三个空格哦! 开心!折腾了一晚终于注册好了!注册成功的记得要回来点赞啊哈哈哈!", "我用的VPN是美国服务器。所以最下面位置一栏我选了美国。提交表格时手机不是必须的,我就先空下来了。", "谷歌验证太恶心了,简直是崩溃了,用注册时的手机号码无论如何都验证不了,垃圾,浪费我大把的时间", "把上面方法都试了还是不行,然后用chrome浏览器成功了。到chrome浏览器的设置里面找到一个绑定谷歌账号的选项,然后从那里注册,验证手机的时候用默认的地区,手机号前面加“+86”,然后就验证成功了。", "B了狗了,用Chrome试了十多次都不行,用microsoft edge一次成功!谷歌和微软之间肯定有什么肮脏的PY交易!", "有毒的Google啊 换了好几个浏览器 google不行 换edge 最后换了ie 轻松搞定", "具体原因我也不太清楚,不过题主可以试试换一个 VPN 服务器或换一台设备注册试试,应该是没问题的,即使你还是使用这个手机号码注册。(格式没有错,直接输手机号码就可以了,既不用加 +86,也不用按照 xxx-xxxx-xxxx 这样的格式来;如果是次数超过验证,会直接提示。)", "激活时候国家选择VPN所在地国家,在你的电话号码前加+86然后短信验证", "我按照你们说的,都试过了,,还是不可以验证,手机号码无法验证", "用谷歌浏览器没成功,换了ie没翻墙就收到验证码了", "按照前面给的", "地区选择vpn的地区,", "电话号码按照 +86 XXX XXXX XXXX格式填了,", "试了搜狗浏览器、Chrome、Firefox、IE", "均以失败告终,", "看到有人说是vpn的事,", "后来又测试了各种组合,结果如下:", "vpn 浏览器 成功/失败", "美国IE 失败", "美国 搜狗 失败", "美国Chrome 失败", "日本Chrome 失败", "日本 搜狗 失败", "日本IE 成功", "虽然可能每个人遇到的情况会略有差异,但是结合前面的答案,给出以下几点建议:", "Life is short,don't waste yout time!", "直接用IE,准没错!", "貌似美国的不好用,日本的好用,多换几个不同的vpn,说不定就好了", "但是,值得指出的是,当我在验证手机的地方去掉 +86 时(采用上面最后一种组合),同样成功了,像下面这样:", "所以可能+86不是必须的,但是加上是肯定没有问题的,所以还是加上好了", "基本上按照这几点来排查,就没有问题了,看到下面这个页面,恭喜你!你成功了!", "用chrome浏览器注册邮箱 不需要验证手机 也能成功注册 亲测有效"], "bs_rank_pos": 4}], "answer_spans": [[6, 43]], "fake_answers": ["关键在于你注册的第一步,选国家的时候一定要根据你的VPN的国家来选,不是选中国的。到了第二步,用中国的手机号就能注册了。"], "question": "谷歌账号注册手机无法验证", "segmented_answers": [["进入", "http", ":", "/", "/", "google", ".", "com", ".", "hk", "首页", "后", "把", "下面", "的", "中文简体", "换", "成", "繁体", "，", "大陆", "手机号", "就能", "验证", "成功", "了", "。"], ["进入", "app", "后", "，", "随便", "填写", "一", "个", "以", "@", "gmail", "结尾", "的", "邮箱", "账号", "，", "下一步", "就", "到", "了", "google", "账号", "登录界面", "，", "在这里", "点击", "选择", "账号", "输入框", "下", "的", "“", "创建", "账户", "”", "链接", "。", "在", "创建", "账户", "页面", "，", "填写", "相应", "信息", "。", "（", "在这里", "，", "要", "注意", "手机号码", "要", "选择", "中国", "+", "86", "的", "。", "）", 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"关于前人说过的我就不说了,主要是我的一些观点。这是第二幅动画中的截图,大家注意到没有,自从这个长官进到城里去了以后,就再也没有看到过他出现,这说明什么,一定有猫腻!我们看游戏中有个动画是关于楼顶飞船出现的,可是,从飞船里出来的人呢?同样也是警官他又跑到哪去了?还有飞船里的小萝莉。我记得我们上飞船的时候飞船楼梯是关着的,是谁关的,为什么要关。还有,在游戏开始时叼走主人翁一只手臂的老鼠和囚牢里叼走扫把组成的工具的老鼠是不是同一只。此外,在温室幻灯片中的生物(上图)是在我们从乐队到广场中间的道路上能看到的,这意味着什么。有人说,当我们用雨伞过了贵妇那里,就不能回去,我想了,这水是从哪来的,难道没有什么阀门,同志们可以搜搜看。我记得扳手那一关有个钩子,如果先放下音响,再用大嘴扳手放水,尽管此后可以在“恐龙”那里得到第三个扳手,那钩子呢?BUG?不是吧。有什么想法,请同志们在后面留言。", "我们看游戏中有个动画是关于楼顶飞船出现的,可是,从飞船里出来的人呢?同样也是警官他又跑到哪去了?还有飞船里的小萝莉。不好意思,这个找到了,他们看守着电梯的入口。", "又有发现新,在守卫那里滑下来后其实是可以原路返回再爬上去的!", "在广场的中那里贴着月亮的,摇出来是一个阿拉伯人,很奇怪是吧。按纸条上说的,如果钟走的话他一天出来三次,再看老妇人的纸条,加一块的话,只有那个像太阳的图像没有出现过,等老妇人进入钟楼后看到的纸条,关于太阳的被划掉了!为什么?有点诡异哦!", "我记得那两个坏蛋在装炸弹的时候是直接从外壁上爬上去,可是是从哪儿爬的呢,分析一下,如果从电梯入口那里沿着外壁往上爬,但是胖警官可是在那里,他俩吃了胸袭豹子胆了?!如果说他们不怕胖警官的话,开始也就没必要卷起女主角跑了。难道还有路?大胆假想一下,假如还有路,会不会是广场那个弄不开的门,到底缺什么呢,踌躇……", "说一个小细节,同志们有没有注意,机器人不需要食物,只需要油,而所有与油相关的东西上面都有一个黑色小水滴一样的标志,就如我发的第一张图片。甚至包括所谓的酒吧,按我说就不该叫酒吧,而是该叫油吧,没看见各式各样的油壶摆在架子上吗!真是搞笑。", "这是在吧里找的一张图片下面三幅小图中的第二幅,是从门里出来,可是问题是没必要啊,从电梯里是能下来可是下来干啥?一定有猫腻!!", "再来看看这儿,也有个门,打不开会连到哪儿呢?和广场的门同样打不开。我装过补丁了,也就是说这不是BUG,何解?", "同样是上面的场景,左边有个拉杆,拉了之后会换一辆有煤的车,煤可以点,却没有用处,为什么,如果没用,那设计的时候好要他干啥,完全没必要嘛,反常必有妖,古怪。", "还记得这个筒状物吗?他也是可激发的。", "终于知道了,那俩人是从大棚上面爬的。想到大棚我又会联想到大棚左边的像卷闸门发动机似的东西。", "从监狱出来后,再点水管居然能激发。总感觉火房和牢房之间似乎还有个房间。", "哦,对了,LZ那些截图,貌似是你在不做任何操作的时候,主角的幻想...没什么隐藏结局吧。", "再来看看这儿,也有个门,打不开会连到哪儿呢?和广场的门同样打不开。 我装过补丁了,也就是说这不是BUG,何解? 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"首先我们看一下故事的发生地,我就简单的叫它主城。这是一个由机械与植物构成的复杂系统,各种建筑缠缠绕绕,城市结构错综复杂。不过,大体上看,它是由基本的垂直结构组成,也就是说,城市的上下层次分明,水平结构简单。", "附主城外观图(转自网络):", "之后我们的主角出现了。", "主角的样子平平无奇,这是我们的第一印象。不过,当你在进城之后,才会发现,主角的样子是最最特殊的。", "第一,他没有任何额外的颜色修饰,全身灰色铁皮。第二,他拥有独一无二的伸缩功能。第三,他拥有强大的DIY能力。第三点我们撇开不提,单单是前两点就足以体现他的主角光环。那么主角的出场呢?被垃圾车扔在离主城遥远无比的显然是垃圾场的地方,身体支离破碎。这让我觉得,主角一定是做了什么不该做的事,或者根本不属于那个宏伟的城市,所以被丢了下来。这仅仅是我的第一感觉,于是我们继续这段旅程。", "下一个场景,断崖闸口。主角被警察踢开,警察拉铃入内,主角模仿警察的装扮,并蒙混过关。这是我要讲的第一点。仔细观察大家会发现,很多机器人并没有正常的视觉。", "可以明显地看出,部分机器人完全按照流程图行事,以上图看守为例,首先判断是否为高个机器人,之后判断是否戴蓝色帽子,之后判断帽子上有没有灯,若三者都通过,则落下闸门。", "然而这会导致城市防御体系的破坏,人们可以使用类似主角的方法以瞒天过海,让任何人物都可以进入城市,进而引发安全危机与身份混乱。我们走过了闸门,不想有一摊油挡在面前,主角变高之后无法自如行走,所以掉了下去。", "此处略过一个场景,进入下一个场景:", "这三个人分别体现了社会中的不同阶层,他们三个聚集在一起打牌,还图谋炸掉高塔又说明了什么呢?说明这个城市中,已经没有真正的守护力量了,人人为自己,只要智商足够(说实话,主角,女主,三反派是全城最聪明的五个人了,这点一会儿再提。)就可以为所欲为。", "我们看一下他们最大的的阴谋:炸地标高塔。", "可以明显看出,这个塔贯穿全局,是最最重要的东西。这也在一个角度加强了我关于机械迷城垂直结构的说法。", "那么有人可能要问,这塔有什么用吗?不能因为它是地标就这么重视它吧。请你想一想,在我们生活的正常城市,一个地标型建筑是怎样的存在(以下图片转自网络,转侵删):", "玩游戏的过程中你会发现,这个塔由两部分组成。第一部分是地下室,我们不管它;", "重点来了:这个大头机器是什么?", "我们先观察一下周围环境:控制台,监控器*n,其他的机器可享受不了这个待遇。", "再看它所处的位置:高塔顶端正中,全城围绕它成众星拱月之势。", "懂了吧,这是城主。亦或者说,中央控制台。", "但是这个城主过的显然不是很舒服。主角进来时,他正在咬着什么哭泣。点一下他,主角玩了一段回忆杀。不放这段回忆杀还好,我们会一直以为主角是偷渡的难民,这一放可不得了,原来主角和女主就住在这座塔上,每天跟城主同志相处融洽。那么主角的身份就十分明显了,城主助理,或是实际的城市监管者(更有力的证明在文章底部)。", "之后我们知道了,一切都是三反派搞的鬼。这就引发了第三个问题。在动画中,我们可以注意到一个细节:报警叫来真正管用的警察之后,本来警察想把反派警察用垃圾飞艇吸走,却因为反派太大吸不动,反而弄走了主角,这也是故事的开端。", "问题来了:1.为什么警察会带着小孩来执法;2.为什么垃圾处理飞艇会带不走反派;3.为什么反派有胆量挑战城主的威严,难道城主没有防御手段/制裁方法吗?", "再看看这些居民吧:", "作者用心良苦,这岂不就是目前社会阴暗面的突出放大吗?", "这是废土,这是末日后的世界啊!", "不管怎样,这个游戏的目的达到了:让我们游玩,并且让人思考。", "————7/14更新————", "下载知乎客户端 与世界分享知识、经验和见解"], "bs_rank_pos": 3}, {"is_selected": true, "title": "机械迷城结局是什么_百度知道", "most_related_para": 0, "segmented_title": ["机械迷城", "结局", "是什么", "_", "百度", "知道"], "segmented_paragraphs": [["结局", "是", "男主角", "把", "女主角", "救", "了", "出来", ",", "两个人", "(", "哦", ",", "机器人", ")", "乘坐", "飞行器", "飞走了", ",", "在空中", "又", "撞", "到", "了", "东西", ",", "飞行器", "坏", "了", ",", "男女主角", "又", "分离", "了", ",", "这", "估计", "还有", "下", 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WannaCry 的勒索软件。一旦中毒,电脑就会显示“你的电脑已经被锁,文件已经全部被加密,除非你支付等额价值 300 美元的比特币,否则你的文件将会被永久删除” 。", "目前,尚未有黑客组织认领这次袭击。但业界人士的共识是,这次大规模网络攻击采用了美国国家安全局(NSA)开发的黑客工具。几个私立网络安全公司表示,发动网络袭击的黑客用 NSA 开发的黑客工具“永恒之蓝(Eternal Blue)”与 WannaCry 勒索软件捆绑,“永恒之蓝”可以利用 Windows 系统漏洞,强制“中招”的电脑运行 WannaCry 勒索软件。", "下一页更多精彩的“勒索病毒文件恢复的方法”", "上一篇:2017年金砖国家峰会习近平讲话内容_习近平在2017金砖国家峰会上的主旨讲话全文  下一篇:如何预防勒索病毒方法_勒索病毒补丁下载地址_预防勒索病毒通知2017", "【热点】精华文章", "【热点】相关文章"], "bs_rank_pos": 1}, {"is_selected": false, "title": "在Windows 2003 下关闭135/139/445端口 - 专注HA架构集群 - 51CTO", "most_related_para": 0, "segmented_title": ["在", "Windows", "2003", "下", "关闭", "135", "/", "139", "/", "445", "端口", "-", "专注", "HA", "架构", "集群", "-", "51", "CTO"], "segmented_paragraphs": [["在", "Windows", "2003", "下", "关闭", "135", "/", "139", "/", "445", "端口"], ["2016", "-", "12", "-", "23", "15", ":", "01", ":", "47"], ["这个方法", "绝对", "是", "错", "的", ",", "楼主", "没有", "实验", "吧", "。"], ["2017", "-", "05", "-", "16", "09", ":", "32", ":", "46"], ["确实", "不好", "使", "。"], ["昵称", ":", 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"不能", "连", "上", "这些", "端口", ",", "从而", "保护", "了", "你", "的", "电脑", "。"], ["经验", "内容", "仅供参考", ",", "如果", "您", "需", "解决", "具体", "问题", "(", "尤其", "法律", "、", "医学", "等", "领域", ")", ",", "建议", "您", "详细", "咨询", "相关", "领域", "专业人士", "。"]], "paragraphs": ["百度经验:jingyan.baidu.com", "默认情况下,Windows有很多端口是开放的,在你上网的时候,网络病毒和黑客可以通过这些端口连上你的电脑。为了让你的系统变为铜墙铁壁,应该封闭这些端口,主要有:TCP 135、139、445、593、1025 端口和 UDP 135、137、138、445 端口,一些流行病毒的后门端口(如 TCP 2745、3127、6129 端口),以及远程服务访问端口3389。下面介绍如何在WinXP/2000/2003下关闭这些网络端口:第一步,点击“开始”菜单/设置/控制面板/管理工具,双击打开“本地安全策略”,选中“IP安全策略,在本地计算机”,在右边窗格的空白位置右击鼠标,弹出快捷菜单,选择“创建 IP安全策略”(如右图),于是弹出一个向导。在向导中点击“下一步”按钮,为新的安全策略命名;再按“下一步”,则显示“安全通信请求”画面,在画面上把“激活默认相应规则”左边的钩去掉,点击“完成”按钮就创建了一个新的IP 安全策略。第二步,右击该IP安全策略,在“属性”对话框中,把“使用添加向导”左边的钩去掉,然后单击“添加”按钮添加新的规则,随后弹出“新规则属性”对话框,在画面上点击“添加”按钮,弹出IP筛选器列表窗口;在列表中,首先把“使用添加向导”左边的钩去掉,然后再点击右边的“添加”按钮添加新的筛选器。第三步,进入“筛选器属性”对话框,首先看到的是寻址,源地址选“任何 IP 地址”,目标地址选“我的 IP 地址”;点击“协议”选项卡,在“选择协议类型”的下拉列表中选择“TCP”,然后在“到此端口”下的文本框中输入“135”,点击“确定”按钮(如左图),这样就添加了一个屏蔽 TCP 135(RPC)端口的筛选器,它可以防止外界通过135端口连上你的电脑。点击“确定”后回到筛选器列表的对话框,可以看到已经添加了一条策略,重复以上步骤继续添加 TCP 137、139、445、593 端口和 UDP 135、139、445 端口,为它们建立相应的筛选器。重复以上步骤添加TCP 1025、2745、3127、6129、3389 端口的屏蔽策略,建立好上述端口的筛选器,最后点击“确定”按钮。第四步,在“新规则属性”对话框中,选择“新 IP 筛选器列表”,然后点击其左边的圆圈上加一个点,表示已经激活,最后点击“筛选器操作”选项卡。在“筛选器操作”选项卡中,把“使用添加向导”左边的钩去掉,点击“添加”按钮,添加“阻止”操作(右图):在“新筛选器操作属性”的“安全措施”选项卡中,选择“阻止”,然后点击“确定”按钮。第五步,进入“新规则属性”对话框,点击“新筛选器操作”,其左边的圆圈会加了一个点,表示已经激活,点击“关闭”按钮,关闭对话框;最后回到“新IP安全策略属性”对话框,在“新的IP筛选器列表”左边打钩,按“确定”按钮关闭对话框。在“本地安全策略”窗口,用鼠标右击新添加的 IP 安全策略,然后选择“指派”。于是重新启动后,电脑中上述网络端口就被关闭了,病毒和黑客再也不能连上这些端口,从而保护了你的电脑。", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "bs_rank_pos": 5}], "answer_spans": [[0, 93]], "fake_answers": ["、单击始——运行输入regedit单击确定按钮打注册表二、找注册表项HKEY_LOCAL_MACHINE\\System\\Controlset\\Services\\NetBT\\Parameters三、选择Parameters项右键单击选择新建——DWORD值orQWORD值(32位系统选择DWORD值64位系统选择QWORD值)四、DWORD值命名SMBDeviceEnabled五、右键单击SMBDeviceEnabled值选择修改六、现编辑DWORD值框数值数据输入0单击确定按钮完设置"], "question": "2003如何关闭445端口", "segmented_answers": [["1", "、", 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"回复"], ["2", "楼", "·", "2016", ".", "08", ".", "05", "00", ":", "18"], ["3", "楼", "·", "2016", ".", "08", ".", "05", "03", ":", "50"], ["4", "楼", "·", "2016", ".", "08", ".", "10", "16", ":", "11"]], "paragraphs": ["2016.08.03 05:19 打开App", "-- 作者 谢恩铭 转载请注明出处", "这年头不知道Git, 还真不好意思出门跟人家打招呼!不过假如您不知道, 或者不是太清楚, 那也没关系。可以先跟我打个招呼, 看完这篇文章, 就可以跟别人去打招呼了。:P", "Git是一个版本控制系统(Version Control System,VCS)。", "版本控制是一种记录一个或若干文件内容变化,以便将来查阅特定版本修订情况的系统。", "多年前,我在法国做第一个实习时(2011年),那时候自己编程还在入门阶段,根本没听过版本控制系统。", "那时候真是蠢(现在也没好到哪里去),每次修改多一点的代码,就要复制、黏贴整个项目目录。因此,公司的电脑里有着几十个项目的副本(简直了)。", "前不久,我看到公司的一个实习生也在做和我多年前同样的事。而且我们问他,他说没听说过版本控制系统...", "现在,我用Git也有几年了吧。绝对是越用越爽,不用不爽;一朝使用,爱不释手。", "每天到公司第一件事,开电脑;第二件事,在终端中切换到项目目录,然后输入Git命令。", "有了版本控制系统,就可以不用担心文件丢失,不小心误修改文件等等“事故”,而且你可以随便回到历史记录的某个时刻。", "SVN, CVS这类早期的集中式版本控制系统,都有一个单一的集中管理的服务器,保存所有文件的修订版本,而协同工作的人们都通过客户端连到这台服务器,取出最新的文件或者提交更新。", "它们的原理类似下图:", "而Git或Mercurial这类分布式版本控制系统, 才是现代的首选。因为分布式的优势绝对显著。", "在分布式版本控制系统里,客户端并不只提取最新版本的文件快照,而是把代码仓库完整地镜像下来。", "这么一来,任何一处协同工作用的服务器发生故障,事后都可以用任何一个镜像出来的本地仓库恢复。因为每一次的提取操作,实际上都是一次对代码仓库的完整备份。", "现在,Google, Facebook, Microsoft等世界级公司每天在用Git。", "众所周知,IT界大牛很多, 但是大牛中的大牛稀少。", "Linus Torvalds就是其中之一。", "Linus Torvalds不但开创了Linux操作系统(当然了, 开源社区对Linux贡献极大, 参看我的【Linux探索之旅】开宗明义+第一部分第一课:什么是Linux?), 而且他发明了Git版本控制系统。", "现在的Android系统底层就是基于更改过的Linux系统,你说Linus Torvalds是不是贡献极大。", "Git的诞生很有趣:", "2005年Linux 内核开发社区正面临严峻的挑战:他们不能继续使用 BitKeeper(一个分布式版本控制系统) 了(原因是当时Bitkeeper 著作权所有者决定收回授权,内核开发团队与其协商无果),而又没有其他的 SCM (Software Configuration Management)可满足他们的分布式系统的需求。", "Linux 之父 Linus Torvalds 接受了这个挑战,决定开发一个新的版本控制系统。周末他消失了,新的一周,Git 问世了。LT就是威武。", "Atlassian为Git做了一个10年的纪录图。", "安装Git很简单,在Ubuntu等Debian族的Linux系统上,只要输入下面命令:", "关于安装Git,可以看这个链接", "安装完Git就可以对其做一些配置。", "Git有一个工具被称为git config,它允许你获得和设置配置变量;这些变量可以控制Git的外观和操作的各个方面。这些变量可以被存储在三个不同的位置:", "/etc/gitconfig 文件:包含了适用于系统所有用户和所有库的值。如果你传递参数选项’--system’ 给 git config,它将明确的读和写这个文件。  ~/.gitconfig 文件 :具体到你的用户。你可以通过传递--global 选项使Git 读或写这个特定的文件。 位于git目录的config文件 (也就是 .git/config) :无论你当前在用的库是什么,特定指向该单一的库。每个级别重写前一个级别的值。因此,在.git/config中的值覆盖了在/etc/gitconfig中的同一个值。", "我的Git config(配置)列表:", "这个配置就在你的个人目录的.gitconfig(有一个点哦, 不用ls -al是看不到的)文件中, 你可以用vim来打开编辑内容 (vim ~/.gitconfig), 我的内容如下", "因为我比较喜欢偷懒(程序员需要学会偷懒好么), git命令有几个太长的, 我就用alias(别名)来替代了, 这样平时输入git命令就快了很多, 简直飞起!", "我最常用的Git命令(全名,没用alias之前):", "Pro Git这本书(也可以看其电子版)绝对是Git入门和进深的不二选择。", "我的同桌说:他进公司时,被要求先读两遍这本书。", "Github 和 Git 是两回事。", "Git是版本控制系统,Github是在线的基于Git的代码托管服务。", "GitHub是2008年由Ruby on Rails编写而成。GitHub同时提供付费账户和免费账户。这两种账户都可以创建公开的代码仓库,但是付费账户也可以创建私有的代码仓库。", "为什么现在Github这么火,以至于世界顶级公司和项目的源码很多都托管在Github上,首先:", "现在这世代还是得看看颜值的。你说Sourceforge等代码托管网站也年岁很久了,为什么没有Github那么火呢?", "关键是Github长得好看(你长这么好看,说什么都是对的)。", "对比Github和Sourceforge", "反正我看了Github再来看Sourceforge的主页,我已经不想用Sourceforge了。", "软件的颜值体现了设计者的审美观以及设计能力。", "2011年,Github已经取代Sourceforge,成为最活跃的代码交流社区。这意味着在Open Source Community(开源社区),Git取代了SVN已经成为事实。", "而且,Github现在都快成为程序员简历的一个重要加分条件了。", "如果你的简历里放上你的Github链接,招聘者通过你对开源项目的贡献和你自己的项目代码,就可以对你的水准了解大概(我还没把我的项目都放到Github上,慢慢来吧)。", "Linus Torvalds的Github页面", "Github有个小缺陷 (也不能算是缺陷吧), 就是你的repo(repository的缩写,表示“仓库”)都需要public(公开), 如果你想要创建private(私人)的repo, 那得付钱。", "不过, 幸好, Gitlab解决了这个问题, 可以在上面创建免费的私人repo。", "比如我们公司就是用Gitlab来管理项目的(【程序员】项目经理如何调动组员积极性)。", "可以看到,我是从2015年10月12日在Ercom公司工作的,一直活跃度还不错(必须为中国人争光么,我可是组里最努力的)。", "再看我个人的Gitlab账户页面,至今只有几个项目,因为大部分都开源在Github上。", "要创建Gitlab的新项目很简单,和Github类似,只要点击New Project按钮,填写基本信息即可。", "可以看到,项目的默认属性是Private(私人的),当然,你也可以选择Public(公开的)或Internal(内部的)。", "怎么样,Github和Gitlab很酷吧。", "快去试试Git吧, 再不赶紧, \"女神\"都老了 (没关系, 用git reset或者revert或者git rebase可以重回美好时光)!", "我是谢恩铭,在巴黎奋斗的软件工程师。热爱生活,喜欢游泳,略懂烹饪。人生格言:「向着标杆直跑」", "gitlab没有下定义啊,它和github有什么区别?", "5楼 · 2016.08.19 15:25", "2016.08.19 15:55 回复", "2016.11.23 17:58 回复", "2016.11.23 18:16 回复", "2楼 · 2016.08.05 00:18", "3楼 · 2016.08.05 03:50", "4楼 · 2016.08.10 16:11"], "bs_rank_pos": 3}, {"is_selected": true, "title": "gitlab - 阿里云code git和那个github的git有什么区别? - Segment", "most_related_para": 2, "segmented_title": ["gitlab", "-", "阿里", "云", "code", "git", "和", "那个", "github", "的", "git", "有", "什么", "区别", "?", "-", "Segment"], "segmented_paragraphs": [["1", ".", "如", "题", "!", "公司", "马上", "要", "用", "git", "代替", "svn", "作为", "代码", "的", "版本控制", "工具", "了", ",", "说", "是", "用", "阿里", "云", "的", "code", "git", ",", "回来", "把", "廖雪峰", "的", "git", "教程", "看", "了", "一遍", ",", "大致", "命令", "都", "熟悉", "了", ",", "以及", "从", "github", "远程", "库", "克隆", "什么", "也", "都", "操作", "了", "一遍", ",", "但", "问题", "来", "了", ",", "阿里", "云", "的", "code", "git", "和", "github", "的", "git", ",", "还有什么", "是", "gitlab", "有", "什么", "区别", "?", "操作方式", "有", "什么", "区别"], ["阿里", "云", "的", "git", "和", "github", "的", "git", "是", "没有", "什么", "区别", "的", "。"], ["但", "阿里", "云", "这个", "基于", "git", "的", "代码", "托管", "平台", "(", "应该", "是", "基于", "gitlab", "的", ")", "和", "github", "这个", "基于", "git", "的", "托管", "平台", "是", "有", "一定", "区别", "的", "。", "他们", "的", "共同点", "就是", ":", "都是", "基于", "git", "提供", "的", "服务", "。", "他们", "的", "区别", "你", "可以", "搜索", "gitlab", "和", "github", "的", "区别"], ["国内", 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"您", "详细", "咨询", "相关", "领域", "专业人士", "。"], ["个性签名", ":", "我喜欢", ",", "我", "分享", "。", "。"]], "paragraphs": ["该经验图片、文字中可能存在外站链接或电话号码等,请注意识别,谨防上当受骗!", "百度经验:jingyan.baidu.com", "刚入行的新手淘宝卖家有时会因为一点小疏忽而填错发货单号,导致顾客看不到物流动态被质问从而对店铺产生影响,这对一位新手卖家的打击是比较大的,其实处理这种问题很不难。", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "1 登陆淘宝卖家账户,进入卖家中心一已卖出的宝贝。 步骤阅读 2 找到填错单号的订单,点击查看物流。 步骤阅读 3 发货单号填好后的24小时内是可以更改的,点击修改。 可以修改成任意快递与快递单号,点击确认。 步骤阅读 步骤阅读 4 超过24小时才发现单号填错了是无法修改的,这时候要第一时间联系顾客,把正确的单号与发给顾客并且报以歉意,一般顾客都会理解的,不第一时间处理好的话,容易被顾客中差评,得不偿失。 步骤阅读 5 遇到某些因为你填错单号从而为难你要退款的顾客也不怕,因为此时货已经在路上,他此刻要退款你就会钱货双失,不要被顾客唬一下就退款了,这个时候一定要耐心安抚好顾客,并且把单号底单拍给顾客看,让顾客放心。即使顾客投诉你要求客服介入退款,因为你有底单,也不用怕因为单号填错而造成什么损失。 步骤阅读 END", "百度经验:jingyan.baidu.com", "单号填错的情况不要出现太多次,不然会被淘宝系统看成刷信誉而导致删除宝贝或被扣分。", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:我喜欢,我分享。。"], "bs_rank_pos": 1}, {"is_selected": true, "title": "淘宝退货物流公司物流单号填错怎么办_百度经验", "most_related_para": 6, "segmented_title": ["淘宝", "退货", "物流", "公司", "物流", "单号", "填", "错", "怎么办", "_", "百度", 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"今天上午我在淘宝退货,然后去快递把东西邮回去。拿着快递单号在淘宝后台,那个订单的退货处理中填写快递信息,结果我把快递单号填错了,这样运费保险不会赔偿了。怎么办呢? 在退货的东西没发出的情况下,快递物流单号填错了,就马上撤销退货,在重新申请,卖家同意后在添加一遍物流号,这样投的运费保险会生效的,要不然运费保险没跟踪到快递物流单号是不会生效的。", "我去快递公司把要邮的东西要回来,然后淘宝后台尝试了下面方法解决了问题。", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "1 打开浏览器,输入淘宝的网址,进入淘宝网站。 步骤阅读 2 把鼠标移到【我的淘宝】点击进入【已买到的宝贝】。 步骤阅读 3 找到你已退货的商品,点击【退货/退款】按钮。 步骤阅读 4 申请后会变成退货处理,点退货处理再点击【修改退款申请】。然后重点是点取消退货,这样我们错填的快递单号就不会收到影响。 步骤阅读 5 取消了退货后,再重新申请 退货/退款,等卖家审核后,就可以再重新填写快递物流单号了,这次不要填写错了哦。 步骤阅读 END", "百度经验:jingyan.baidu.com", "在淘宝买到的东西有质量问题或不喜欢,一定先别点确认收货。要点击【退货/退款】。 在退货的东西没发出的情况下,快递物流单号填错了,就马上撤销退货,在重新申请,卖家同意后在添加一遍物流号,这样投的运费保险会生效的,要不然运费保险没跟踪到快递物流单号是不会生效的。 最好找离家近的物流公司,或记住物流公司的电话,发生这种情况先告诉物流公司别发出货物,在马上重新撤销退货,等卖家通过后,告诉物流公司可以寄出,然后这是就可以重新填写快递物流单号了,没有错误的快递物流单号,运费险才会生效呢。", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:做个漂亮又优秀的美食达人"], "bs_rank_pos": 2}, {"is_selected": false, "title": "淘宝快递单号写错了有两天了 怎么修改_百度知道", "most_related_para": 1, "segmented_title": ["淘宝", "快递", "单号", "写错", "了", "有", "两天", "了", 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"。"]], "paragraphs": ["淘宝的快递单号填错了,超过两天了就不可以修改的。 淘宝只能在填写快递单号不超过24小时的情况下在查询物流处进行修改。 卖家只能在旺旺上和买家联系,给买家提供单号即可。", "我也是做淘宝的,也出过几次运单号填错的事。严格的说24小时后就无法更改了,只有好好和买家沟通,必要的话想办法查实宝贝真正已在哪里了,当然把正确的单号告诉买家是必须的。 然后,然后就祈祷吧。。。。", "淘宝的快递单号填错了,超过两天了就不可以修改的。 淘宝只能在填写快递单号不超过24小时的情况下在查询物流处进行修改。 卖家只能在旺旺上和买家联系,给买家提供单号即可。"], "bs_rank_pos": 3}, {"is_selected": false, "title": "淘宝卖家填错快递公司 能更改快递公司么_百度知道", "most_related_para": 2, "segmented_title": ["淘宝", "卖家", "填", "错", "快递公司", "能", "更改", "快递公司", "么", "_", "百度", "知道"], "segmented_paragraphs": [["刚好", "我", "也是", "淘宝", "卖家", ",", "这个", "是", "完全", "可以修改", "的", "。", "具体操作", "步骤", "是", ":", "进入", "淘宝", "卖家", "平台", ",", "点击", "已经", "发货", "的", "订单", ",", "找到", "你", "要", "修改", "快递", "号", "的", "商品", ",", "点击", "“", "订单", "详情", "”", "就", "可以看到", "修改", "快递", "单号", "的", "提示", "。", "我", "也是", "做", "淘宝", "的", ",", "做", "淘宝", "店", "很辛苦", ",", "要", "装修", "店面", ",", "每天", "还", "得", "守店", ",", "讨价还价", ",", "还要", "发货", "。", "新", "开张", "的", "淘宝", "店", "更", "是", "无人问津", "!", "做", "淘宝", 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"▲糖糖也在微博上表达了对靓颖的祝福,还晒出了和刘亦菲、张靓颖三人的合影,看来这三人的感情确实颇深呢。", "▲是的,刘亦菲又再次展示了一次什么叫“自拍不如他拍”的反人类定律。", "“自拍不如他拍”的神仙姐姐,", "群众呼吁请重视自拍!", "我们先来看看刘亦菲微博上的自拍往往是迷之角度,迷之画风!", "而在路人毫无PS的镜头里,她却是长这样的!", "▲我们发现,神仙姐姐就是神仙姐姐,即使日常生活根本不注重打扮的她在路人的镜头下依然美得惊人。", "▲立体的五官和白皙的皮肤就算不启动PS大法也是颜值碾压级别。", "大美女伴娘辣么多,拒绝撕X,友情绝对大过天!", "娱乐圈里给人当伴娘的大美女们其实不少,比如陈乔恩和阿娇同时出现给陈妍希当伴娘!陈乔恩更是把人生中第一次伴娘献给了陈妍希。", "李小冉的婚礼上也是云集了各大美女军团,阿娇、应采儿、朱丹·······", "baby和黄晓明的世纪婚礼更是请到了倪妮和李冰冰,这个级别的伴娘也是没sei了……", "刘诗诗的伴娘团曾被称为“史上最美伴娘团”,全都是和诗诗一卦的“清水出芙蓉”的姑娘们。步步惊心姐妹团郭晓婷、叶青、刘心悠、诗诗的后宫谢楠、于莎莎全齐。", "最美伴娘当然不能忘了唯一一位成为杨幂伴娘的唐嫣,这对“中国好闺蜜”当时在婚礼上也是感动过了不少人。不知到时唐嫣大婚时,大幂幂会不会出现呢?", "最近放飞自我的小爽之前也把第一次伴娘献给了谢楠,原来是在电影《画壁》拍摄时,两人因戏结缘成为好朋友。谢楠和吴京大婚,郑爽以伴娘的身份出席了婚礼。现场郑爽多次激动泪崩,最后还接到了闺蜜的捧花。", "伴娘都已经那么美了,好期待看到她们自己当新娘时的美照,第一个就是期待刘天仙,你们同意不?以下附赠一张至今无法超越的史上最帅伴郎团!", "六旬保洁冒雨工作学生为其撑伞40分钟 小伙被“美女”搭讪玩一下对方却是男儿身", "凤凰资讯官方微信", "1我们能做什么来帮助孩子预防近视?  2 德国红点设计至尊奖 超薄智能键盘  3源自美国 Fitkid菲宝智能护眼台灯  4 凤凰卫视独家定制礼品  5西域果园五彩葡萄干10包(50g每包)包邮"], "bs_rank_pos": 2}, {"is_selected": false, "title": "刘亦菲再当伴娘 找个天仙做伴娘究竟是抢镜还是抢婚|伴娘|刘亦菲|", 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"是", "颜值", "相当", ",", "伴娘", "也", "要", "记住", "不能", "艳压群芳", ",", "不", "然", "好姐妹", "一秒", "变", "仇家", "了", "。"]], "paragraphs": ["导语:你有想过在你结婚的时候,穿着美美的租来的或者是买来的婚纱,准备做全场女主角的时候,你隔壁那个拖着裙摆的伴娘却闪闪发光的场景?这种场景也不是没有,最近不是就上演了这么一出好戏吗?", "刘亦菲再当伴娘 找个天仙做伴娘究竟是抢镜还是抢婚", "天仙攻刘亦菲身穿水蓝色伴娘裙第二次成为了闺蜜的伴娘,转过头看看这位新娘,颜值就不多说了,而刘亦菲自带的女神光环就算是站在一边还是难挡光芒,所以这也是透露了她们的姐妹情分,不然谁有勇气请个比自己好看,还比自己红得人当伴娘。", "而上一次刘亦菲当伴娘是为闺蜜张靓颖,在一排的伴娘中,刘亦菲身穿淡紫色的长裙宛若古希腊的女神雅典娜。据说这位仙女还充当了一把天使,把尊贵的戒指拿给了好闺蜜,如此这般的场景更加期待当刘亦菲做新娘的时候会是怎样的盛况。", "新娘大作战:angelababy PK 倪妮", "说到了伴娘团我们这边看看angelababy的伴娘团倪妮和李冰冰,巧的是angelababy和倪妮共同演绎了《新娘大作战》,故事围绕着一对好闺蜜巧合地把婚礼安排在了同一家酒店的同一天,于是原本各自想邀请对方成为伴娘的她们大打出手,成就一番闹剧。", "李冰冰+倪妮", "在angelababy的婚礼中倪妮亮相了,两位都很美的姑娘在颜值上难分伯仲,而一旁的李冰冰也成为伴娘之一,恨嫁心可想而知,不过人家现在有小鲜肉在手。", "倪妮+李冰冰", "倪妮+李冰冰", "杨幂与唐嫣成就好闺蜜", "杨幂与唐嫣成就好闺蜜", "都说演艺圈尔虞我诈,但唐嫣和杨幂的交情那可就深了,在杨幂婚礼中唐嫣成为了伴娘,一身蓝色伴娘长裙站在闺蜜身边依然光彩,不知道唐嫣婚礼上好友杨幂会不会也成为伴娘。", "陈乔恩为闺蜜当伴娘", "在好友陈妍希的婚礼上,陈妍希作为伴娘之一,虽然和其他人穿着一样的衣服,但是陈乔恩的美就是透着一股仙气儿,粉红色的裙装带有精致的刺绣,露出长腿的细节展示了优雅身材。", 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["您现在的位置:首页 > 娱乐", "娱乐圈当中的明星结婚,伴娘团队往往是非常强大的,但是有一个人确实很少出席别人的婚礼,也没给别人当过伴娘,她就是刘亦菲。而在今天张靓颖冯柯婚礼上,刘亦菲竟然出席了,而且还", "娱乐圈当中的明星结婚,伴娘团队往往是非常强大的,但是有一个人确实很少出席别人的婚礼,也没给别人当过伴娘,她就是刘亦菲。而在今天张靓颖冯柯婚礼上,刘亦菲竟然出席了,而且还是首次当伴娘。实在是太让人觉得不可思议了。", "刘亦菲破天当伴娘", "今日,张靓颖与冯轲将在意大利举行婚礼,冯轲贴心牵手张靓颖登场,两人上台致辞,更和唐嫣举杯合影。", "据悉,除了唐嫣、周笔畅等好友特意前往助阵外,张靓颖今日大婚的伴娘团阵容也相当豪华,同处音乐圈的谭维维和潘辰均在列,而刘亦菲和王珞丹竟也在伴娘名单中。", "这场婚礼的请柬也于早前曝光,带有音符标志的紫色请柬上,印着张靓颖和冯轲二人名字的缩写(Jane和Michael)。请柬打开后,则呈现一组立体的城堡模样。", "刘亦菲都参加过谁的婚礼?", "伴娘团的阵容非常有看点,不少网友表示,刘亦菲的出现确实是一个惊喜!其实张靓颖和刘亦菲能成为好姐妹,这一点都不意外,早在某期快本舞台上,两人就同台过,现场关系也非常融洽。而在婚礼举行之前,一张张靓颖刘亦菲唐嫣三人的合照曝光,搞笑的是,刘亦菲卖萌嘟嘴时变脸,乍一看竟和曾轶可有点撞脸,真是有点小惊吓啊!", "说来这只是刘亦菲第二次出席好友婚礼,第一次出席女性朋友的婚礼,而此前她只参加过朱亚文的婚礼,且是素颜出席,当时她低调现身未发任何通稿。", "另外,为什么说张靓颖伴娘团阵容强大,其中还有一个名叫章元一的人物在时尚圈也是大名鼎鼎。章元一是范冰冰的合作伙伴,几乎为她的御用摄影师,能把章元一请来,看来张靓颖的朋友圈够强大。", "而章元一其实是个男孩子,只是他平常酷爱女性打扮,长相一点儿也不比范冰冰逊色。张靓颖真是胆大,请了好几个颜值颇佳的人当伴娘,她就不怕被抢风头吗?", "转载请带上本文地址:http://www.gaochengnews.net/yule/19359.html本文来自藁城新闻网", "相关热词搜索:", "高伟光和热巴两人是什么关系?高伟光女朋友 青云志2鬼厉复活碧瑶了吗?青云志2什么时候 尉迟恭是怎么成为门神的?尉迟恭最后是怎么 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"后", "拍照", "即可", "查看", "照片", "时", "按压", "照片", "即可", "播放", "live", "照片"]], "paragraphs": ["LivePhotos这新特性的功能作用是在照片拍摄前后录制一段1.5秒的动态视频,当用户在照片上深按一下,照片就会自动播放动态效果。用户还可以将LivePhotos设置为锁屏墙纸,随时重现动态瞬间。在拍照的同时可以附加一段声音。使用LivePhotos拍照的操作为:第一步:在桌面点击“相机”图标。第二步:进入相机后,点击界面上方的“LivePhotos”这个功能。第三步:按下拍照键后,再注意看LIVE这几个字,会在1.5秒之后自动消失。这样拍下来的照片就是LivePhotos有声音也会动,就如短视频。关闭该功能:在相机照相模式中,点击如图所示的地方,如果提示LIVEOFF说明就关闭了LivePhotos功能。", "Live Photos 这新特性的功能作用是在照片拍摄前后录制一段1.5秒的动态视频,当用户在照片上深按一下,照片就会自动播放动态效果。 用户还可以将Live Photos设置为锁屏墙纸,随时重现动态瞬间。 在拍照的同时可以附加一段声音。 使用Live Photos拍照的操作为: 第一步:在桌面点击“相机”图标。 第二步:进入相机后,点击界面上方的“Live Photos”这个功能。 第三步:按下拍照键后,再注意看LIVE这几个字,会在1.5秒之后自动消失。这样拍下来的照片就是LivePhotos 有声音也会动,就如短视频。 关闭该功能: 在相机照相模式中,点击如图所示的地方,如果提示LIVE OFF 说明就关闭了Live Photos 功能。", "开启live后拍照即可 查看照片时按压照片即可播放live照片"], "bs_rank_pos": 3}, {"is_selected": false, "title": "上手视频: 教你如何拍摄Live Photos_互联网头条-中关村在线", "most_related_para": 2, "segmented_title": ["上", "手", "视频", ":", "教", "你", "如何", "拍摄", "Live", "Photos", "_", "互联网", "头条", "-", "中关村在线"], 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"拿到新设备的用户中,有尝试了几次之后就把这个特性关闭了的人,但是也有认真体验发现它的好和不好的用户。比如有一名用户指出,“如果你是有心想要拍摄 Live Photos 的话,那它就真的是非常不错的功能。但是我希望苹果能够支持,允许用户将照片中不小心拍下的‘视频’部分删除掉,因为我不是每一次拍照的时候都会坚持按快门键 3 秒钟。” 可惜只有 iPhone 6s 和 iPhone 6s Plus 能够拍摄 Live Photos,不过所有运行iOS 9的移动设备都能够查看 Live Photos。如上所述,在 iPhone 6s 和 iPhone 6s Plus 上查看 Live Photos 的话,用户只需要一个 3D 手势,照片就会变模糊,随后再开始清晰地播放出来。在不支持 3D Touch 的 iOS 9 设备上,用户则需要长按来播放 Live Photos。Mac 设备在 9 月 30 日 OS X El Capitan 系统正式发布之后就可以在照片应用中查看这些照片。 另外用户还可以将 Live Photos 共享到社交平台上,未来等到支持这个特性的应用越来越多,相信它的潜力能够得到进一步发挥,给用户带来更多乐趣。 如果你要分享 Live Photos,找到你想分享的那一张点击分享按键,点击照片上的live按键,那么你就可以分享视频和照片结合在一起的 Live Photos,如果你只是需要分享一张静态照片,那么你就不需要打开live键。最后再选择你想要通过哪个平台来分享即可。 如果你想把 Live Photos 设置为壁纸,同样先找到你需要的照片然后点击分享按键,找到用作壁纸项,然后调整大小,预览看看效果如何,然后选择 Live Photos 键,点击设定,你就可以选择将 Live Photos 设定为壁纸了。 也许你在使用Live Photos的过程中还发现了其他问题,但此前苹果已经表示他们会进行更新,以解决这些问题。"], "bs_rank_pos": 4}], "answer_spans": [[6, 141]], "fake_answers": 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这些车型感觉都不太好,然后又跟他分析了一些比较常见的案例又推荐了他几款车型有日系有国产。。。。。。下面是重点来了,我说了半天之后,他总结了一句:不要管那么多,你去看看吧,大街上这么多大众的车,说明大众是好车,国产车不行,日系车铁皮太薄不安全啊!我:呵呵。。。后来我不禁在思考了:我朋友是一个受过高等教育而且没有任何迂腐思想,平时也是很喜欢接触新鲜事物,而且喜欢自己独立思考的一个人,竟然也被大众忽悠成了坚定的大众粉,我在想那些不接触网络不接触一些新鲜事物的中老年人到底中大众的毒中了有多深???大众,真的是很神奇的一个公司,尤其是在中国。。。", "大众牛逼关键在于是个德国车企,中国人普遍崇拜德国。一模一样的车,本田要是个德国企业,我估计在华销量能逆天。", "楼主,你不觉得太自作聪明了? 有些时候觉得万人皆醉我独醒是说明你钻牛角尖了", "合资都坑 如果大众优惠够给力 也不是不能买 我的优先选择是别克本田 其次丰田大众 再次福特日产 再次我就看国产了……或者低端韩系", "我印象中的大众也不错,不怪我,因为打从我记事开始,见的最多的就是大众。", "我就想知道路上那么多大众怎么破,这不是事实么", "说归说路上大众车确实是最多的消费者也不是傻子啊毕竟", "中国人骨子里信仰“中庸”。大众没有亮点,也没有明显的缺点,对于国人来说,汽车是个大件,大部分人面对汽车的态度是不求有功但求无过。大众保有量大是事实,这么多的销量没有出现过大问题,品牌中庸、外观中庸、质量中庸,这就足够了。当然我说的是现在,以后大众什么样就不知道了。几年前谁又能想到诺基亚摩托罗拉会倒闭呢", "只有进口大众才能算是德国车,进口德国车质量确实好,国产没法评价,只能呵呵", "我跟她分手了 第一天,她没有起床,把自己用被子捂的严严实实的,她宿舍的人都不敢去安慰她,她一天都没有吃饭,连刷牙洗脸都没有,晚上睡觉的时候我听到她在被子里抽泣. 第二天,今天她吃饭了,是她的宿舍同学强制性的让她吃的,她的眼眶红红的,我总说她是个爱哭鬼,她每次都噘着小嘴说她不是. 第三天,今天她穿的很妖艳,走进一家酒吧,喝了好多酒,用一种很诱惑的眼光环视全场,好多人上来搭腔“小姐,你好漂亮啊”.她喝了很多,当一个年纪可以做她爸爸的男人对她说“小姐,我送你回家吧”的时候她把手中的酒全泼在他的脸上,那个该死的老头扬起他的手掌就要打下去的时候,凌来了,救了她,这一切我都知道,我就在酒吧的一个角落里看着. 第四天,今天她早早就起床了,忙忙碌碌一上午,然后把自己关在浴室里好久,当舍友们踹门进去的时候都惊呼到:好干净啊. 第五天,她开始学习了,其实她原来学习很好,我们开始后受我影响她的成绩也退步了,这也好,转移一下注意力,恢复的也快. 三个月后……她做了学生会主席,她越来越能干,也开朗了不少,马上她就要靠研了. 一年后……在她身边的男人很多,比我优秀的也很多,可她根本没在意过,不过她和凌很好,校园里传他们的关系很暧昧,她只是把他当哥哥,可是流言是挡不住的. 三年后……她要结婚了,新郎是凌,她在写结婚请贴,一张,两张,三张……写到第十二张的时候她哭了,趴在桌上眼泪完全抑制不住,我上前一看,所有的喜贴新郎写的都是我的名字. 我也很想哭,可是鬼魂是不能哭的,我没有眼泪. 三年前,我横穿马路,遇上车祸,手里提着要给她庆祝生日的蛋糕.然后我就醒了,感动哭了,哭着哭着发现自己根本没有女朋友", "虽然他不懂车,但是他很聪明。小白买车最好的方式就是买销量最多的。", "其实不管大众到底好不好,但普通消费者肯定是信销量的。把一些冷门品牌吹上天,但别人一看路上都没个影,也不会买。另外,你朋友虽然是高学历高智商,但这就一定代表他懂车吗?就比如你十分懂车,但把你放到一个陌生的领域,你也不懂,你也只能看那些别人都买的品牌,能因此说你愚昧,被洗脑了吗?只不过术业有专攻而已。", "你朋友是受到高等教育的人,而中国的高等教育就是抹杀个性,灌输平庸的教育", "我跟小明一起喜欢上了贴吧,直到有一天 他都11 级了,我才4级,就向小明讨教有 没有 什么秘籍。小明憋了半天语重心长地告诉 我:”你每到一个帖子就粘贴这句话, 十五天 就到了11级”......我彻底恍然大悟, 吧主再 也不用担心我的经验了!!!假如 你每天签到 拿4经验,18级300000/4=750 00 天,如果 从1岁开始签到,那100 年=3 650 0天,你差 不多要活200年保持每天签 到(谁知道200 年后尼玛还有没有签到这玩意),如果你每 天再水4经验,时间减半 ,但考虑现实,你不 可能再活100年,取5 0年吧,你就要每天水1 6经验,可能你是 个勤快的人,每天水32经 验,那就需要25 年!!!再如果你是个大水怪 ,每天水64 经验,那就只要12.5年!!!还如 果你个心急的人,每天水128经验,你只要6 .25 年!!!!假如你已经急不可耐了,每天 水 256经验,那你碉堡了,只要3.125年! !!当然,你会觉得3年还是太远了,每 天你闲的 蛋疼,忙忙碌碌的水512经验, 碉堡了,你只 需要1.5625年,只比1年半 多一点!!!什么! !你还不满意?那你 觉得你可能一天水1024 经验吗,可能吗? 可能吗??可能!!!据说回复 100字或 者一百字以上可以得到11~30经验 ,真的 很棒......那么,按照队形,点击复制, 把我的话复制一片,拿经验妥妥的啊", "一般人买车不都是路上哪个多买哪个么", "他并不关注汽车,关注学习和赚钱", "如果丰田本田不是日本品牌,估计内地销量会翻倍,而且丰田全球第一汽车商会更牢固。", "给我的感觉的确大众多····但是自主品牌多,尤其是吉利自己就有好几个不同的标志。而合资大致是奔驰宝马奥迪大众,具体看看标志就知道了3个BBA都是低端中端型号做为家用而大众有很多分支品牌、全是VW标志,然后各个品牌下才是型号。其他合资一般都是一个车标分支就是型号配置了。而大众是车标分支是车的名字,其次是型号配置。"], "bs_rank_pos": 0}, {"is_selected": true, "title": "为什么大众车质量那么差,销量那么好? 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"大众汽车在国内如此成功,不是一年两年了。我注意到本题目的许多答案,对VW的成功之处视而不见。我丝毫不否认日系车的优点被部分国人忽视了,但我们不要走到另一个极端。", "因为很多回答已经把大众批的够惨了,本人专门来写点软文。如有酥骨沁心之效,不谢!", "关于题主的问题,我要吹毛求疵一下。", "1 贴出的销量榜单是2015年1月单月的,单月的数据时间太短,不是有力数据。看起来有些夸大大众的销量优势。尽管数据源是没错的。(大众把去年年底的部分销量藏到1月释放,造成1月的优势过于明显。至于为什么大众可以藏销量?因为14年车市颇为艰难,只有大众品牌举重若轻,轻松达成目标。) 题主如果引用14年全年数据,会更好。 2 关于题主对大众的一些问题的描述。 加价,是供求关系的市场化体现,并非厂家蓄谋。大家有没有想过,对于厂家指导价,经销商为何只能降低不能增加呢?在一个自由竞争的市场,无论是厂家还是经销商,都不掌握商品的定价权,价格是在市场上自然形成的。加价是优质紧缺商品的特权,是其稀缺程度和供求失衡的合理量化体现。普遍加价的车型,如上一代CRV,国产前3年的Q5,国产初期的汉兰达,这一代途观、上一代高尔夫。无不是因为具备几个特点中的一个或者全部:产品力强大,缺乏直接竞争对手,供应量有限,需求旺盛。", "举Q5的例子来说明一下,Q5国产之前,也进口过,并无波澜。当时他和X3,GLK一起,定价在50w-60w的区间。一夜之间,Q5国产了,38万起。如果你是一名想要购买中型豪华品牌SUV的消费者,突然Q5比X3和GLK便宜了15万左右,你怎么想?当然是,市场需求一拥而上,自然抬高了国产Q5的终端售价。", "大众经销商服务态度不好?我怕这是个例。经销商是加盟的玩法,个体差异很大,参差不齐。如果走访较大数量的门店,你会发现,大众经销商总体服务水平不算糟糕。", "大众产品的质量问题?这个大众确实不是最好的,也不是那么差。质量确实是日系品牌的强项。因为大众有最多的销量,所以关于大众的正面和负面的新闻一定也是最多的,大家要注意这一点。", "最近两年,大众出了两个比较严重的质量问题。", "一个是7速干式DSG,一个是新速腾断轴。DSG sucks,断轴sucks,这些都是大众应该被批评,也应该负起责任的。(监管部门是不是也该负起责任呢?真有问题,那你说出来呀? 不能总靠消费者扯横幅,厂家自证其罪吧。)", "但是这些就是大众汽车在中国市场的全部吗?未免太小看广大也许广大选择了大众汽车的朴素的消费者。", "事实上,即使是在汽车知识程度不高的市场,产品在市场上优胜劣汰的法则依然是有效的。经济学原理从来不曾说过:一个自由的市场经济市场的运转,有赖于消费者对产品有足够的认知能力。", "让我们来看看断轴和DSG故障之外的大众汽车。", "1 大众来的最早。 中国人谈及感情时,最爱排先来后到。先者,堂堂正房,后者,无耻小三。当福特和通用都没有拥抱那个曾经一穷二白的社会主义红色中国的时候,是大众犹犹豫豫的抱着试试看的心态率先投资了中国市场。外资投资都是有风险的,谁知道你会经济腾飞还是回归文化革命呢?标致,菲亚特,克莱斯勒的初次冒险都失败了,但大众的投资则带来了丰厚的回报,它率先占据了市场份额,完成了产能和营销渠道布局,奠定了极为强大的品牌基础,与政府建立了良好的合作关系,并潜在的影响了中国汽车行业法规的建立,以及培养和储备了大量专业的人才(中国汽车法规主要参考欧洲,这对大众有利;很多国内汽车从业者受大众乃至德国汽车的影响也很深远) 如果你有两个朋友来找你借钱,一个跟你认识了30年,一个跟你认识了3天,在别的因素都一样的情况下,你更可能会借钱给谁? 2 大众集团本身是全球列强之一。 这一点不需要太多解释吧。德国有优质的制造传统,汽车工业实力强大。大众集团上有兰博基尼,Porsche,奥迪,中有大众,下有斯柯达,西亚特,商用车也有涉足。走多元化品牌战略,全球销量最近几年一直是前三,目前在销量上无限逼近丰田集团。 3 强大伙伴。 中国法律要求外资在华造车需要找到不低于50%股份的中资伙伴。大众汽车在国内有两个合作伙伴,都非常强大:一汽集团和上汽集团。 这叫强强联合,相比之下福建汽车和三菱的合作、广汽与菲亚特的合作,基本可以定义为弱弱联合。 4 品牌力也产品的知名度优势。 目前产能和销量接近400万/年。(包括奥迪)比(丰田+本田)的两倍还要多。 保有量我没有数据,但显然是单一品牌的绝对第一,保守估计是第二名的三倍以上。(上海通用的销量是建立在多品牌的基础上的,雪佛兰和别克品牌要拆开) 销量和保有量最多,意味着无数身边的活广告,意味着最高的知名度。 不是有英菲尼迪被认作奇瑞嘛?不是有凯迪拉克被认做帝豪嘛?你见过大众被认做东风嘛? 你可以选取中国的10个代表性城市,做100次线下消费者品牌调研,VW,Toyota,Honda,Buick,Ford,Hyundai,100次都会是大众第一。不管是知名度还是美誉度。", "丰田是很强大的企业,混合动力也是很不错的技术。但是传播是有极大的成本的,尤其对于汽车这么复杂的产品。希望知乎上参与讨论的朋友们注意到这一点。", "5 渠道优势", "跟随大众成长起来的是中国汽车经销商中最早,经验最丰富,获益最多,因为获益而把自己发展的最健壮的经销商体系之一。", "他们就像经销商中的精英战马,受到精细的照顾和培养,有强大的战斗力。", "超过2000家大众经销商分布在五湖四海的各个角落,他们比电视机里的广告更能代表一个品牌的形象。(弱小的品牌只有100-300家,无法覆盖幅员辽阔的中国市场)", "一家典型的上海大众4S展厅的客户体验,要比一家长安马自达强很多,比奇瑞强更多。", "典雅明亮的展厅,高品质的内装设计,西装笔挺的销售员,漂亮的展车,这些对于隔壁老王是有视觉听觉触觉的综合感官说服力的。", "为何大众的经销商能够做好这些表面功夫而别的品牌也许不能?因为卖大众比较赚钱,只有持续赚钱的生意才能做成健康的生意。如果一个生意已经不赚钱,不要去指望经销商的职业道德。(我在上海的家附近有一家Fiat的城市展厅,可怜巴巴的一块极小的地方,放着1-2台车,以及一个只能做快保的狭小售后工位,最近终于关闭了。我走访过不同城市的不同品牌的经销商,很多时候,弱小品牌的经销商也是弱小的,无论接待人员的服务素养、展厅装修等等)"], "bs_rank_pos": 1}, {"is_selected": true, "title": "【图】大众口碑这么差,为何销量这么好?_速腾论坛_汽车之家论坛", "most_related_para": 3, "segmented_title": ["【", "图", "】", "大众", "口碑", "这么", "差", ",", "为何", "销量", "这么", "好", "?", "_", "速腾", "论坛", "_", "汽车", "之", "家", "论坛"], "segmented_paragraphs": [["现在", "还是", "销量", "第", "一", "?", "?", "不可能", "吧", "。"], ["傻逼", "是", "慢慢", "变", "少", "的", "。", "不是", "一蹴而就", "。"], ["看", "谁", "笑到最后", "吧", ",", "好", "口碑", "不是", "一蹴而就", ",", "坏", "口碑", "也", "非", "一日", "之", "寒"], ["签名", ":", "曾经", "的", "大众", "粉", ",", "如今", "的", "速腾", "车主", "+", "大众", "黑", ",", "宣传", "大众", "垃圾", "是", "我", "的", "义务"], ["请", "拖", ",", "公关", ",", "控制", "舆论", ",", "洗脑", "式", "营销", "方式", "的", "功劳", "。"], ["车子", "好不好", "主要", "还是", "自己去", "试驾", "了", "知道"], ["签名", ":", "速腾", ",", "很好", ",", "很", "强大", "."], ["总", "有", "一些人", "认为", "自己", "运气好", ",", "买", "的", "车", "不会", "出", "问题", "。"]], "paragraphs": ["现在还是销量第一??不可能吧。", "傻逼是慢慢变少的。不是一蹴而就。", "看谁笑到最后吧,好口碑不是一蹴而就,坏口碑也非一日之寒", "签名:曾经的大众粉,如今的速腾车主+大众黑,宣传大众垃圾是我的义务", "请拖,公关,控制舆论,洗脑式营销方式的功劳。", "车子好不好主要还是自己去试驾了知道", "签名:速腾,很好,很强大.", "总有一些人认为自己运气好,买的车不会出问题。"], "bs_rank_pos": 2}, {"is_selected": false, "title": "为什么大众车质量那么差 销量那么好_汽车频道_东方头条", "most_related_para": 12, "segmented_title": ["为什么", "大众", "车", "质量", "那么", "差", "销量", "那么", "好", "_", "汽车频道", "_", "东方", "头条"], "segmented_paragraphs": [["亲", ",", "暂时", "无法", "评论", "!"], ["哪里", "看", "的", ",", "没有", "百分百", "的", "车", ",", "豪车", "也", "不", "另外"], ["因为", "国", "人", "眼光", "问题"], ["国", "人", "买车", "不管", "现在", "这", "车", "怎么样", ","], ["只", "管", "以前", "这", "车", "名气", "大", "不大", "好不好", ","], ["大众", "十", "几", "年", "前", "捷达", "桑塔纳", "老牛", "X", "了", ","], ["所以", "现在", "大众", "再", "破", "也是", "好车"], ["国产车", "十年前", "就是", "垃圾", ","], ["所以", "现在", "国产车", "再", "好", "也是", "破车"], ["因为", "大众", "是", "靠", "信仰", "卖车", "的", ","], ["在", "中国", "除了", "信仰", "大众", ","], ["其余", "的", "都是", "无神论者", "。"], ["因为", "大众", "是", "最早", "进入", "中国", "市场", "的", "合资", "品牌", ","], ["最", "风光", "时", "占", "了", "中国", "市场", "一半以上", "份额", ","], ["以", "皮实", "耐用", ",", "经济", "省油", "著称", ","], ["在", "中国", "的", "消费者", "心中", "品牌", "号召力", "很", "强", ","], ["所以", "到处都有", "大众", "的", "车", "就", "不足为奇", "了"]], "paragraphs": ["亲,暂时无法评论!", "哪里看的,没有百分百的车,豪车也不另外", "因为国人眼光问题", "国人买车不管现在这车怎么样,", "只管以前这车名气大不大好不好,", "大众十几年前捷达桑塔纳老牛X了,", "所以现在大众再破也是好车", "国产车十年前就是垃圾,", "所以现在国产车再好也是破车", "因为大众是靠信仰卖车的,", "在中国除了信仰大众,", "其余的都是无神论者。", "因为大众是最早进入中国市场的合资品牌,", "最风光时占了中国市场一半以上份额,", "以皮实耐用,经济省油著称,", "在中国的消费者心中品牌号召力很强,", "所以到处都有大众的车就不足为奇了"], "bs_rank_pos": 3}, {"is_selected": false, "title": "【说客】时隔10年,为啥大众销量还是那么好?_汽车之家", "most_related_para": 9, "segmented_title": ["【", "说客", "】", "时", "隔", "10", "年", ",", "为啥", "大众", "销量", "还是", "那么", "好", "?", "_", "汽车", "之", "家"], "segmented_paragraphs": [["当前位置", ":", "汽车", "之", "家", ">", "说客", ">", "正文"], ["声明", ":", "本", "文", "由", "说客", "作者", "撰写", ",", "观点", "仅", "代表", "个人", ",", "不", "代表", "汽车", "之", "家", "。", "文", "中", "部分", "图片", "来自", "于", "网络", ",", "感谢", "原作者", "。"], ["[", "导读", "]", "为啥", "大众", "销量", "还是", "那么", "好", "?"], ["各", "大", "汽车", "厂商", "近日", "相继", "公布", "了", "2016", "年", "全年", "销售", "状况", ",", "作为", "中国", "市场", "上", "最大", "的", "合资", "品牌", ",", "大众", "(", "中国", ")", "2016", "年", "在", "中国", "市场", "(", "包括", "香港地区", ")", "取得", "了", "398", "万", "辆", "的", "销量", "成绩", ",", "要知道", "大众", "2016", "年", "的", "全球", "销量", "也", "不过", "1030", "万", "辆", 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"说", "汽车", "快", "知网", "CEO", ",", "资深", "汽车", "媒体", "人"], ["下半年", "不适合", "出手", "的", "车型", "盘点", "韩", "系", "车", "销量", "大跌", ",", "腹背受敌", "还是", "四", ".", ".", "."], ["发表", "我", "的", "评论"], ["说客", "是", "汽车", "人", "的", "自", "媒体", "平台", ",", "我", "有", "我", "的", "主张", ",", "申请成为", "说客", "作者"], ["快", "知网", "CEO", ",", "资深", "汽车", "媒体", "人"], ["什么", "是", "说客", "?", "说客", "有", "什么", "激励", "政策", "?", "如何", "成为", "说客", "作者", "?"]], "paragraphs": ["当前位置:汽车之家 > 说客 > 正文", "声明:本文由说客作者撰写,观点仅代表个人,不代表汽车之家。文中部分图片来自于网络,感谢原作者。", "[导读]为啥大众销量还是那么好?", "各大汽车厂商近日相继公布了2016年全年销售状况,作为中国市场上最大的合资品牌,大众(中国)2016年在中国市场(包括香港地区)取得了398万辆的销量成绩,要知道大众2016年的全球销量也不过1030万辆,大众在中国的销量已经占到了其全球总销量的38.64%,中国已经连续10年成为大众最重要的市场。", "中国市场首次成为大众最大的全球市场是在2006年。2007年1月的这个时候,大众同样发布了一份上一年度的销量报告,报告显示,2006年大众在中国市场的实际总销量达到了71万辆,较2005年增长24.3%。一年71万的总销量,也创下了大众进入中国市场22年以来的历史最高销量记录,同时,大众也成为中国市场上第一家年销量达到70万辆大关的国际车企。从2006年起,中国市场就一举超越德国,成为了大众在全球最大的市场。10年后的2016年,大众在中国的年销量达到了398万辆,较10年前增长了321万辆,同时也将老对手丰田远远甩在了身后,2016年丰田在中国的销量仅为121万辆,相比大众的398万还是有些小巫见大巫。我们不禁要问:10年过去了,为啥大众的销量还是那么好?", "关于大众销量好的问题,很多人都会提到一点,大众进入中国市场早。确实,大众进入中国市场要比其他品牌要早,但它其实不是第一个进入中国的国际汽车品牌。最早来中国合资办厂的是美国/德国的克莱斯勒,克莱斯勒和北京汽车合资办的厂叫北京吉普,克莱斯勒进入中国的时间比大众还要早一年。因此,并不能把进入中国市场早笼统归纳为大众销量好的原因,如果照这么说的话,吉普应该卖的和大众差不多才对,但很明显吉普混得并不如大众,就这说明,大众在中国卖得好还有其他原因,比如产品战略、市场定位、定价策略等等。", "产品战略就不多说了,刚开始大众靠着桑塔纳杀遍中国市场,因为那时可选的品牌不多,适合家用的轿车就更少了,大众几乎以垄断局面开始了大杀四方的生涯。后来合资品牌逐渐多了起来,大众也不怕,人无我有人有我多,什么?你说原厂车型不多?改还不会?桑塔纳、捷达、宝来……实在不行还可以开发特供车型,德原朗(朗逸)不就这么来的嘛,实在不行就换个牌子继续卖,POLO晶锐总有一款是你想要的吧?", "市场定位方面,大众几乎是高中低市场通吃,入门车型大众都有,中端市场大众也有,豪华市场有奥迪撑着,顶级市场还有保时捷、宾利、布加迪、兰博基尼,总之买车想绕开大众还是一件蛮难的事情。", "再说定价策略,大众原本就是给老百姓造车的,大众进入中国以来也没有忘记这一点,现在大众的POLO、宝来、桑塔纳、朗逸、捷达入门车型都在10万以内,价格在合资品牌中算比较低的,再加上品牌影响力,就不难理解为什么那么多人在同价位选车时选择大众了。", "真要说的话,其实大众比日系吃香还有一个历史因素,虽说德国也是二战轴心国,但德国没跟中国产生直接的冲突,日本就不同了,其实说实话,丰田的车耐用程度并不比大众差,为什么买丰田的人少呢?买卡罗拉可能会被砸,但买朗逸不会。", "图说汽车快知网CEO,资深汽车媒体人", "下半年不适合出手的车型盘点 韩系车销量大跌,腹背受敌还是四...", "发表我的评论", "说客是汽车人的自媒体平台,我有我的主张,申请成为说客作者", "快知网CEO,资深汽车媒体人", "什么是说客? 说客有什么激励政策? 如何成为说客作者?"], "bs_rank_pos": 4}], "answer_spans": [[0, 14]], "fake_answers": ["大众牛逼关键在于是个德国车企,中国人普遍崇拜德国。"], "question": "为什么大众车质量那么差 销量那么好", "segmented_answers": [["大众", "牛逼", "关键", "在于", "是", "个", "德国", "车", "企", "，", "中国人", "普遍", "崇拜", "德国", "。"], ["大众", "来", "的", "最早", "，", "大众", "集团", "本身就是", "全球", "列强", "之", "一", "，", "有", "强大", "的", "伙伴", "，", "品牌", "力", "也有", "产品", "的", "知名度", "优势", "，", "渠道", "优势", "，", "规模", "成本", "效应", "。"], ["因为", "大众", "是", "最早", "进入", "中国", "市场", "的", "合资", "品牌", "，", "国", "人", "买车", "不管", "现在", "这", "车", "怎么样", "，", "只", "管", "以前", "这", "车", "名气", "大", "不大", "好不好", "，", "大众", "十", "几", "年", "前", "捷达", "桑塔纳", "老牛", "X", "了", "，", "所以", "现在", "大众", "再", "破", "也是", "好车", "。"]], "answers": 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文字是记录语言的书写符号系统,是扩大语言在时间和空间上的交际功能的辅助工具。甲骨文作为文字中的一种,它是我们现在可以确认的最早的文字,并不是说它是最早的或最原始的文字,它只是我们现在所能看到的成体系的文字中最早的一种。今天我们所说的“甲骨文”,不仅指商代的也包括周代刻在(偶尔也有写的)在龟甲兽骨上的文字,在这里我们只讨论殷墟出土的甲骨文。 殷墟出土的甲骨,都是第二十个商王盘庚到末代商王帝辛时期的遗物。 甲骨文毕竟是离原始文字较近的字体,与后世文字相比,自然会保留不少早期文字的特征。象形成分仍相当重,在甲骨文中,大约保留百分之三十以上的直接象形字。孙诒让在《名原》中提出文字“本于图像”。", "范文三:甲骨文特点 甲骨文在殷商时期虽已有完备的体系,但文字处在不断创造发展中。通过对五期甲骨文字的分析可以看出,其文字形式尚未固定,有些字的构形在不同的发展时期存在着明显的不同,而且多种多样.。 由于甲骨文用刀刻在龟甲骨板上的,因此其刚劲有力, 瘦劲、峻峭,方笔多,圆笔少,露锋多,肥笔少,富有刀笔味。但也有一部分甲骨文刻辞与同时期的金文是很相像的,其结构体势、笔划粗细与同时期的金文大致相同。因此甲骨文作为一种书法形式,其表现力是十分丰富的,并不是单一的瘦、刚、方、细的特点,这就为我们进行甲骨文法创作留下很大的空 间。 那么,甲骨文字的构形究竟有什么特点呢?作为甲骨文书法爱好者,我们又不是文字学家,研究其特 点有什么用呢?一个书法家要创作出上佳的作品,必须透过甲骨文字的构形特点,了解文字的“神”、“韵”在哪里。只有写出它的神韵才能使甲骨文“形似”更“神 似”,才能把甲骨文写活用活。 概括地讲,甲骨文字最主要的特点是:结构不固定,形式多变,或正或反,或分或合,笔画或多或少,相当灵活,具有很大的随意性(但这并不说写甲骨文就可以随心所欲地自创字,毫无依据地“乱写”。)", "范文四:甲骨文的特点 甲骨文的特点: (一)图画性强,以象形字体为主,有的字体甚至颇逼真,尤以与人或动物相关的名词。 (二)合文性字体不少,(那就是把两个以上的字体组合成另一字体,以表达一个新涵意)合文性字为后来的部首偏旁的创造作了前驱性的实践与铺垫。 (三)某些字体在写法上尚未定型,例如车字有四种写法: ;又如人字有十二种写法,而且可以正写反写: 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["暂无评价|0人阅读|0次下载|举报文档", "6、甲骨文字形特点_哲学/历史_人文社科_专业资料。授课教师 题目 教学目标 教学重点 教学难点 赵霞 任教学科 授课班级 甲骨文字形特点 授课类型 让学生了解甲骨文文字的特点 掌握构字的方法 了解字形特点 教学过程 教师活动 学生活动 日", "授课教师 题目 教学目标 教学重点 教学难点 赵霞 任教学科 授课班级 甲骨文字形特点 授课类型 让学生了解甲骨文文字的特点 掌握构字的方法 了解字形特点 教学过程 教师活动 学生活动 日期 新授课 设计目的 预设 时间 举例讲解字形的特点 1.笔画纤细,一般为瘦笔 记录字形特点,谈论这 让学生熟悉甲骨 2 些特点有何妙处。", "午(杵) 2、笔画走势自然,随体诘屈 2 3.字形原始,图画色彩浓 5 4.字形简捷,多省略笔画子 獸甲克 5 5.字形未定,异体繁多 5 (1)使用的表义物体不同 1 1 (2)形体的方向不同 (3)物体的正视与侧视不同 5 (4)笔画多寡不同 4 (5)会意形体多寡不同 5 (6)会意形体的位置不同 3 6.存有大量合文 3 板书设计 教学反思 2 3", "2014下半年教师资格..."], "bs_rank_pos": 3}, {"is_selected": false, "title": "甲骨文的特点 【范文十篇】 fw.wenku1.com", "most_related_para": 0, "segmented_title": ["甲骨文", "的", "特点", "【", "范文", "十", "篇", "】", "fw", ".", "wenku", "1", ".", "com"], "segmented_paragraphs": [["范文", "一", ":", "甲骨文", "特点", "投稿", ":", "刘驏驐", "甲骨文", "在", "殷商", "时期", "虽", "已有", "完备", "的", "体系", ",", "但", "文字", "处", "在", "不断", "创造", "发展中", "。", "通过", "对", "五", "期", "甲骨", "文字", "的", "分析", "可以看出", ",", "其", "文字", "形式", "尚未", "固定", ",", "有些", 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",", "而是", "運用", "六書", "理論", "來", "分析", "甲骨文", ",", "結果", "認", "出", "許多", "與", "《", "說", "文", "》", "形體", "不同", "的", "字", "。"]], "paragraphs": ["范文一:甲骨文特点 投稿:刘驏驐 甲骨文在殷商时期虽已有完备的体系,但文字处在不断创造发展中。通过对五期甲骨文字的分析可以看出,其文字形式尚未固定,有些字的构形在不同的发展时期存在着明显的不同,而且多种多样.。 由于甲骨文用刀刻在龟甲骨板上的,因此其刚劲有力, 瘦劲、峻峭,方笔多,圆笔少,露锋多,肥笔少,富有刀笔味。但也有一部分甲骨文刻辞与同时期的金文是很相像的,其结构体势、笔划粗细与同时期的金文大致相同。因此甲骨文作为一种书法形式,其表现力是十分丰富的,并不是单一的瘦、刚、方、细的特点,这就为我们进行甲骨文法创作留下很大的空 间。 那么,甲骨文字的构形究竟有什么特点呢?作为甲骨文书法爱好者,我们又不是文字学家,研究其特 点有什么用呢?一个书法家要创作出上佳的作品,必须透过甲骨文字的构形特点,了解文字的“神”、“韵”在哪里。只有写出它的神韵才能使甲骨文“形似”更“神 似”,才能把甲骨文写活用活。 概括地讲,甲骨文字最主要的特点是:结构不固定,形式多变,或正或反,或分或合,笔画或多或少,相当灵活,具有很大的随意性(但这并不说写甲骨文就可以随心所欲地自创字,毫无依据地“乱写”。)范文二:甲骨文的特点 投稿:范栢栣 甲骨文的特点: (一)图画性强,以象形字体为主,有的字体甚至颇逼真,尤以与人或动物相关的名词。 (二)合文性字体不少,(那就是把两个以上的字体组合成另一字体,以表达一个新涵意)合文性字为后来的部首偏旁的创造作了前驱性的实践与铺垫。 (三)某些字体在写法上尚未定型,例如车字有四种写法: ;又如人字有十二种写法,而且可以正写反写: 。有些字体还可以侧写倒写;尤其是合文性字体的组成,其某字可有增有减的,到其演进为部首偏旁时则可作颇灵活的取舍了。 (四)行款没有定格:甲骨文的行款有从右到左;也有从左到右;有横写的,也有直写的。这种行款方式影响遗痕在现存的书画艺术的行文补白上还一直风行到如今。 书体行款进入到小篆、隶、楷以降,传统定为直书,从右到左; 有横写,从右至左,直至新中国成立之后未久,又确定为横书,从右至左。 偏旁部首是合文性字体日趋成熟后的衍生物,它更带规整性,更为规范化。行款的特点影响如今也可以在部首偏旁等汉字架构上找到甲骨文行款的遗痕,不过它们已经是经规整过规范化了的,成为以下系列:上中下,上下,左右,左中右,上下左中右等。范文三:甲骨文与金文的特点分析 投稿:洪氰氱 文字的发展是一个逐渐发展成熟的过程。汉字的起源可以追溯到距今六千年左右的仰韶文化,也就是刻在彩陶上的类似文字的早期刻画。甲骨文、金文作为早期的成系统的文字,有着共同的特点,即象形程度高、异体字多、具有装饰性笔画。但二者内部特点的强弱也有所不同。 众所周知,甲骨文是铸刻在龟甲或者兽骨上的文字。这些甲骨文大部分是商王用来占卜的,称为甲骨卜辞。除此之外,还包括一些记事刻辞。当然,甲骨文并没有包含殷朝所有的文字,依旧有没有铸刻在甲骨上的文字。所谓金文,是指在青铜器上铸刻的文字,先秦称青铜为“金”,故此青铜文又称金文。金文在商后期开始流行,盛于周。作为历史上已知出现最早的两种字体,我们有必要了解二者的特点。 第一,象形程度较高 所谓象形,就是指用描绘事物的形状的来创造文字的方法。象形字“表示语言中的词,用字形来记录词的声音和意义”。综观世界上各种文字,象形是造字之初的最基本的方法。古埃及象形字、苏美人的楔形文字、美洲的玛雅文字都是以象形字为基础的。我国的少数民族纳西族和水族的东巴文是世界最后的象形文字。象形与象事、象意、象声、转注、假借并称为“六书”,始见于《周礼・地官・保氏》。范文四:甲骨文的特点及发展变化 投稿:郝腀腁 甲骨文的特點及發展變化 7‧5‧1 甲骨文字與六書問題 六書,是漢代學者根據對小篆的分析而歸納出來的一種文字理論,它把古人造字方法歸結為象形、指事、會意、形聲、轉注、假借六種。班固《漢書‧藝文志》,鄭眾《周禮‧保氏》注都提到六書問題,但只是排列名稱,並沒有作理論上的說明。許慎《說文‧敘》則對六書的內涵作了扼要的分析。三家所謂六書的名稱與排列的次序也各有不同: 班固 象形 象事 象意 象聲 轉注 假借 鄭眾 象形 會意 轉注 處事 假借 諧聲 許慎 指事 象形 形聲 會意 轉注 假借 論名稱,以許說為勝,論次序,則是班說為優。許慎以後,「六書」使成了中國文字學的基礎理論,分析字形,總離不開它。甲骨文出土後,很自然地產生了六書理論是否適用於甲骨文的問題。 劉鶚在《鐵雲藏龜序》裡曾十分感慨地說:「以六書之恉推求鐘鼎多不合,再以鐘鼎體勢推求龜版之文又多不合,蓋去古愈遠愈難推求耳。」他是用小篆「推」金文,(即所謂鐘鼎),又用金文去「推」甲骨文,推來推去只覺得「不合」、「又多不合」。 羅振玉、王國維不是採取「推」(實即套)的辦法,而是運用六書理論來分析甲骨文,結果認出許多與《說文》形體不同的字。"], "bs_rank_pos": 4}, {"is_selected": true, "title": "甲骨文的字型特点_弘文博雅国学艺术馆_新浪博客", "most_related_para": 2, "segmented_title": ["甲骨文", "的", "字", "型", "特点", "_", "弘文", "博雅", "国学", "艺术馆", "_", "新浪", "博客"], "segmented_paragraphs": [["甲骨文", "的", "字", "型", "特点", "(", "2015", "-", "08", "-", "31", "16", ":", "54", ":", "20", ")", "转载", "▼"], ["关于", "殷商", "甲骨文", ",", "可", "稽考", "的", "贞", "人", "书法家", "就", "达", "120", "多", "位", "。", "他们", "性格", "各", "异", "、", "才华", "各有千秋", ",", "表现", "在", "书", "契", "风格", "上", "就", "有", "很大", "的", "差异", "。", "在这里", "简单", "介绍", "几", "种", ",", "以", "飨", "读者", "。"], ["一", "、", "瘦", "硬", "挺直", "、", "尖锐", "芳", "峻", ",", "属于", "劲", "峭", "型", "因为", "甲骨文", "是", "刻", "在", "坚硬", "的", "龟甲", "、", "兽", "骨", "上", "的", ",", "尽管", 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这类字的笔画中间粗、两头细,某些地方特别粗,与金文的肥笔相似,结体开阔,风格雄奇突出,对早期金文书风的影响较大。据观察:甲骨文实体的字形往往大小不一致,大都是笔画多的字形大,笔画少的字形小,这主要是由于受到当时刻写工具的制约。三、两字合写、偶有三连,属于雄浑型 这类字的笔画厚重圆润,结体为了避免因线条粗而产生的拥挤,将边线向外弯曲,呈现外拓状。甲骨文中的合文大概是当时的一些专有名词,两个字或三个字写在一起,如“小丁”等。还有一些合文的字和方式极为特别,很容易混淆,如“十二月”等。风格圆通雄浑,与后来的篆书有些接近。直至后来的金文中,仍有不少两字合文出现。四、一字多形、正反不拘,属于委婉型 甲骨文以点画劲峭、结体方折为主要特征,而这类字反其道而行之。由于视物的角度不同、状物的方式不同,一个字的笔画有多有少,形成一字多形,字的摆放也可横、可竖、可倒,合成结构字的偏旁可左可右,而且线条纤细、婉转流丽、结体颀长、布局参差,整个风格如仕女步春,飘摇多姿。这一现象的出现在文字还没有统一之前是可以理解的。五、行列不分、参差错落,属于疏放型", "形成这种现象的原因可能是由于龟甲、兽骨的硬度和表面不平整给刻写造成了难度,使得在一定技术条件下难以做到字与字之间的统一安排。因此,这类字笔画细劲、结体开阔,字心处留白很多,显得格外疏放。特别是初期的甲骨文字形偏大,且大小悬殊。字的中轴线也时左时右,纵不成列。字间距时大时小,横不成行。给人以“素手把芙蓉,步虚蹑太清”的境界,潇洒而又空灵。", "甲骨文书法的风格很多,上述几种只是一些比较突出的代表。如果再细分的话,它们之间还可以有端整、豪放、苍润、流美等风格,真可谓环肥燕瘦、绚丽多姿,显得极其朴素自然。", "评论加载中,请稍候...", "以上网友发言只代表其个人观点,不代表新浪网的观点或立场。", "< 前一篇甲骨文的特点 后一篇 >职场与围棋"], "bs_rank_pos": 5}], "answer_spans": [[278, 305]], "fake_answers": ["结构不固定,形式多变,或正或反,或分或合,笔画或多或少,相当灵活,具有很大的随意性"], "question": "简述甲骨文的字形特点.", "segmented_answers": [["结构", "不", "固定", "，", "形式", "多", 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四、一字多形、正反不拘，属于委婉型。五、行列不分、参差错落，属于疏放型。"], "answer_docs": [1], "segmented_question": ["简述", "甲骨文", "的", "字形", "特点", "."], "question_type": "DESCRIPTION", "question_id": 181625, "fact_or_opinion": "FACT", "match_scores": [0.7719298245614034]}
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["B2又名核黄素。1879年大不列颠及北爱尔兰联合王国化学家布鲁斯首先从乳清中发现,1933年美利坚合众国化学家哥尔倍格从牛奶中提取,1935年德国化学家柯恩合成了它。维生素B2是橙黄色针状晶体,味微苦,水溶液有黄绿色荧光,在碱性或光照条件下极易分解。熬粥不放碱就是这个道理。人体缺少它易患口腔炎、皮炎、微血管增生症等。成年人每天应摄入2～4mg,它大量存在于谷物、蔬菜、牛乳和鱼等食品中。 一、维生素B2的生理功能 1、 参与碳水化合物、蛋白质、核酸和脂肪的代谢可提高肌体对蛋白质的利用率,促进生长发育。 2、 参与细胞的生长代谢,是肌体组织代谢和修复的必须营养素。 3、 强化肝功能、调节肾上腺素的分泌。 4、 保护皮肤毛囊粘膜及皮脂腺的功能。 二、维生素B2缺乏所产生的症状及其毒性 与所有其它维生素不同,轻微缺乏维生素B", "有,但是危害不大! 摄取过多过,可能引起搔痒、麻痹、灼热感、刺痛等。 假如正在服用抗癌药,如氨甲喋呤(methotrexate)的话,则过量的B2会减低这些抗癌剂的效用.", "维生素分水溶性和脂溶性.而维生素B是属于水溶性的,它会随着人体的人体水分的排泄而随之减少.比如:汗液,尿液等等. 如果长期食用维生素B的人一般尿液都偏黄.这正是维生素B的颜色."], "bs_rank_pos": 1}, {"is_selected": true, "title": "维生素B2补充太多会有什么症状?_百度知道", "most_related_para": 1, "segmented_title": ["维生素B2", "补充", "太多", "会", "有", "什么", "症状", "?", "_", "百度", "知道"], "segmented_paragraphs": [["维生素", "是", "人体", "内部", "各种", "化学反应", "与", "代谢", "过程", "必需", "的", "多", "种", "辅酶", "的", "组成", "成分", ",", "且", "不能", "在", "体", "内", "合成", ",", "需要", "由", "食物", "供给", ",", "在", "饮食", "中", "自然", "补充维生素", "对", "人体", "有", "很大", "好处", "。", "但是", 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["主宰和黑暗暴君作为游戏中的重要野怪,击杀二者其中之一都将是对局中一个重要的转折点。优势方击杀将扩大自身优势,而劣势方击杀的话,则有可能带来翻盘的机会。而如果同时击杀主宰和黑暗暴君,更是基本奠定了胜利的基础。在之前版本中,虽然为主宰和黑暗暴君之间设定了羁绊关系,用于限制某个阵营同时击杀主宰和黑暗暴君的情况,但是如果玩家选择多人一起快速击杀主宰/黑暗暴君,之前的羁绊效果则会大打折扣。因此在此次更新中修改了羁绊的效果,下面就让我们一起看一下新的羁绊效果吧。", "带着主宰BUFF去和黑暗暴君作战,黑暗暴君对自己的每次攻击附带额外80%普攻伤害 → 带着主宰BUFF效果去和黑暗暴君作战,自己对黑暗暴君的输出降低50%", "带着黑暗暴君BUFF去和主宰作战,主宰对自己的每次攻击附带额外80%普攻伤害 → 带着黑暗暴君BUFF效果去和主宰作战,自己对主宰的输出降低50%", "新的羁绊效果,将之前携带主宰BUFF去攻击黑暗暴君会使黑暗暴君的攻击能力提升,修改为携带主宰BUFF去攻击黑暗暴君,会使自己对黑暗暴君的输出降低50%;反之携带黑暗暴君BUFF去攻击主宰,同样会使自己对主宰的输出降低50%。", "在之前的版本中,主宰和黑暗暴君之间的羁绊效果,是使得携带BUFF的玩家去攻击时,会增强主宰或暴君的输出能力,使得它们每次攻击都会附带额外80%普攻伤害。但是对于玩家来说,完全可以通过多人轮流承受伤害,快速将其击杀,从而获得主宰及黑暗暴君双BUFF的效果。同时,当游戏进行到后期,玩家装备完全成型后,主宰或黑暗暴君附带的额外伤害,面对高攻击高防御的玩家,已经很难造成影响,同一阵营获得双BUFF效果极易出现。", "针对此种情况,本次改版中将羁绊效果修改为降低玩家输出50%,这就在无形中拖慢了玩家在击杀完主宰后再去击杀黑暗暴君的速度,反之亦然。可能会有小伙伴感觉,即使降低了输出也只是减缓了击杀速度而已,同一阵营依然有机会获得双BUFF效果。但是需要注意的是,王者荣耀是一款竞技类游戏,双方无时无刻不在相互博弈之中。当一方击杀完主宰或黑暗暴君之后,对方肯定会有所察觉,而此时在地图上长期消失的话,对方肯定可以判断出敌方是转身攻击另一个大型野怪,从而抱团去抓。在之前版本中,我们可以通过快速击杀瓦解对方的反扑,但是改版之后,击杀速度的降低,势必会让敌方有机会前去骚扰,可能的后果便是打龙失败甚至在龙与敌方的双重夹击下导致团灭。而有的小伙伴又会考虑,那我们可以选择几人上线混淆敌方视野,另外一到两人前去偷龙。但是现在击杀速度大幅降低后,偷龙效率必然会降低很多。试问如果是你的话,敌方拥有主宰或黑暗暴君BUFF在身,结果只有3-4人佯装进攻,另外1-2人一直不出现在视野中,你是否会考虑到敌方在偷龙想要双BUFF在身呢。此时对方肯定会派人去龙坑查看,而带来的后果便可能是偷龙失败,偷龙人员被击杀甚至将BUFF拱手让人。所以,改版之后,想要双龙BUFF在身的难度有了极大的提升。", "此次改版调整主宰与黑暗暴君之间的羁绊关系后,想要获得双BUFF难度有所上升,羁绊关系也更能发挥出它的作用,让玩家能够更加重视战术打法的安排,双方之间的博弈与对抗也会越发的激烈与精彩。", "加载中,请稍候..."], "bs_rank_pos": 2}, {"is_selected": false, "title": "《王者荣耀》主宰和暴君有什么作用?_王者荣耀攻略站", "most_related_para": 19, "segmented_title": ["《", "王者", "荣耀", "》", "主宰", "和", "暴君", "有", "什么", "作用", "?", "_", "王者", "荣耀", "攻略", "站"], "segmented_paragraphs": [["新版本", "下", "主宰", "暴君", "对战", "局", "的", "剖析"], ["注", ":", "本", "位", "来自", "【", "任玩堂", "电竞", "站", "】", "作者", "为瑞斯", 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"弥补", "劣势", "!"], ["结", "语", ":", "好", "了", ",", "这一次", "的", "详细", "的", "战略", "和", "战术", "的", "分析", "就到这里", "了", ",", "相信", "大家", "只要", "能够", "掌握", "这", "几点", "就", "一定", "能够", "在", "颓势", "中", "翻盘", ",", "优势", "中", "一举拿下", "比赛", "!"], ["←", "【", "攻略", "】", "刺客", "一哥", "李白", "如何", "暴力", "拿", "五杀", "?", "王者", "荣耀", "KPL", "第", "4", "周", ":", "张良", "成", "法师", "新宠", "?", "→"]], "paragraphs": ["新版本下主宰暴君对战局的剖析", "注:本位来自【任玩堂电竞站】作者为瑞斯。", "《王者荣耀》主宰和暴君有什么作用?大家都知道在王者荣耀中有很多的野怪,而且野怪提供的经济是十分可观的!而且野怪可以说攻击力并不强,而且线上的资源往往并不能保证所有的英雄都能快速的发育,所以就诞生了在今天完全可以影响战局甚至可以说战斗前期的主要节奏带动者。尤其是暴君和主宰。在国庆节的版本更新之后,游戏中的主宰和暴君都进行了较大的改变,相信很多玩家都并不熟悉这一次的改动,下面我就给大家带来对局中暴君和主宰的作用和与之相关的详细分析。", "暴君,主宰的收益:", "主宰——击杀金币调整:击杀者150,其他队员90-> 全队100", "击杀经验保持原样。", "从这一条说明看来,游戏是一步步朝着更加均衡的方向发展的,增加了全队的金币收益,让所有位置的玩家都能够得到更多的收益,而并不是单纯的增加击杀者的收益,让队伍内的经济差距进一步拉大。", "从击杀主宰一刻开始,击杀方的接下来三波(三条路)士兵将全部被换成主宰先锋!主宰先锋非常强力,而且会在三路齐推,对对方带来巨大兵线压力。对手需要分路防守。", "另外,击杀主宰依然可以为击杀方获取一个持续90秒的BUFF,可以提升全队每2秒3%的生命回复和法力回复。", "主宰的刷新时间修改为:8分钟首次刷新,死亡后间隔5分钟再次刷新。", "这一条主要是为了加快游戏在中后期的节奏,因为王者荣耀是为了让所有的玩家都能在闲暇的时刻能够随时随地的享受与队友共同战斗的快感,而并不希望王者荣耀变成一个只有拥有大量时间的玩家才能够享受游戏的乐趣。在战略上,击杀主宰后并不是仅仅提供一个回复状态的buff,经过大家的体验,相信大家都能够体会到主宰先锋的强力之处,它和炮兵一样对防御塔拥有格挡机制和伤害加成,两条主宰先锋足以推掉一路所有的塔了,所以如果敌方击杀了主宰,一定要在第一时间击杀每条兵线上的主宰先锋再去集合团战。", "暴君:击杀经验奖励:全队固定500经验->全队固定300基础经验。获取的经验随着时间递增,每隔30秒击杀暴君可获得的经验会增加10点。", "暴君刷新时间仍然为开场2分钟,被击杀后间隔3分钟重新刷新。", "新版本中,暴君将会在第9分55秒遁入黑暗(如果这一时刻暴君未被击杀),新的黑暗暴君将会在第10分钟降临王者峡谷,它会出现在和暴君相同的位置,替换掉它。而队伍击杀黑暗暴君会获得强力BUFF:增加全队物理攻击(80+5%当前攻击),增加全队法术攻击(120+5%当前法术攻击),持续90S。", "黑暗暴君的战斗力会明显强于暴君,攻击力:360;血量18000。", "另外,主宰和黑暗暴君之间会有羁绊关系,玩家击杀主宰后,在主宰BUFF持续期间如果和黑暗暴君战斗,黑暗暴君的每次攻击都会造成基于他50%普攻攻击力的额外伤害。同样的,玩家击杀黑暗暴君后,在暴君BUFF持续期间如果和主宰战斗,主宰的每次攻击都会造成基于他50%普攻攻击力的额外伤害。", "这新版的暴君的前两条依旧是重要的战略资源,并且改变了暴君在后期无人问津的尴尬局面,可以说新版的黑暗暴君buff十分的强力,甚至强过了主宰buff,同时提高了攻击力和生命值,这也让一个人偷掉黑暗暴君更加的困难,应该是在一次团战胜利后组团拿掉暴君然后在下一波团战胜利后再推掉水晶。由于主宰buff于暴君的羁绊关系,所以应该尽量避免先拿暴君后拿主宰的情况,因为那50%的伤害加成很容易让所有队员都处于血量极低的状态,非常容易在一波埋伏中葬送掉所有优势", "解析:在新的版本中,主宰和暴君更加的强势,所以在排位中十分流行李元芳和刘备这种偷龙能力极强的英雄出现,所以如果对局中对方拥有刘备,李元芳时,应该时时注意暴君和主宰以免敌方偷掉buff从而让对方获得极大的优势。新版本的暴君,主宰这么的重要,那么问题来了,如果发现敌方正在打龙,我们到底应不应该去抢夺?或是我们应该如何针对拥有主宰buff的敌人?", "如果在团战后,敌方不顾自己的状态强势打龙,而己方打野是李白,露娜,赵云等等拥有众多位移技能的英雄,那么可以尝试性的进行抢龙,就算不能抢到也能够安然的脱身。如果己方少人,那么最好还是不要去强行抢龙,因为这十分有可能让敌方直接抓住机会导致我们被一波带走。如果我方少人,我们可以把己方的野区清光,防止敌方在我方野区拿到更多的经济,如果有可能,也要趁这个时候偷塔或者去反野,尽力缩小两方因为主宰或者暴君而拉开的经济差距。", "不论是主宰或者暴君的buff都十分的强力,特别是敌方击杀主宰后,一定要避免与敌方纠缠,因为主宰先锋十分的强力,很有可能在我们纠缠的时候就推掉我们的高地。还有一点要切记!那就是,龙buff很强!但是它不是万能的,并不能因为某次团战因为敌方的失误打赢了,顺势打掉主宰,然后就忘记了自己的经济还是落后于敌方的!这点很重要。很多时候就是因为这一点,本来稳扎稳打能翻盘的局而被浪费了机会,这种时候应该稳扎稳打,继续发育经济,只有这样才能弥补劣势!", "结语:好了,这一次的详细的战略和战术的分析就到这里了,相信大家只要能够掌握这几点就一定能够在颓势中翻盘,优势中一举拿下比赛!", "← 【攻略】刺客一哥李白如何暴力拿五杀? 王者荣耀KPL第4周:张良成法师新宠? →"], "bs_rank_pos": 3}, {"is_selected": false, "title": "王者荣耀主宰和暴君在哪 王者荣耀主宰和暴君属性-起点资讯", "most_related_para": 5, "segmented_title": ["王者", "荣耀", "主宰", "和", "暴君", "在", "哪", "王者", "荣耀", "主宰", "和", "暴君", "属性", "-", "起点", "资讯"], "segmented_paragraphs": [["您", "的", "位置", ":", "起点", "软件下载", 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"韦嫈", "离婚", ",", "艾轩", "和", "姐姐", "艾", "清明", "、", "哥哥", "艾端午", "判给", "了", "父亲", ",", "妹妹", "艾梅梅", "则", "归", "母亲", "抚养", "。"], ["1955", "年", ",", "艾青", "与", "刚", "调到", "中国作家协会", "的", "高瑛", "相识", "。", "1956", "年", "3", "月", "27", "日", ",", "艾青", "与", "高瑛", "结为", "夫妻", "。"], ["艾青", "与", "高瑛", "生", "有", "两", "子女", ":", "分别", "为", "当代", "著名", "艺术家", "艾", "未未", "及", "作家", "艾丹", "。"], ["认识", "艾青", "之", "前", ",", "高瑛", "爱", "艾青", "的", "诗", ";", "认识", "艾青", "之后", ",", "她", "更", "爱", "艾青", "这个人", "。", "艾青", "为人", "真诚", ",", "从", "不", "装腔作势", ",", "更", "不会", "说", "假话", ",", "高瑛", "说", ":", "“", "他", "比", "我", "还要", "真实", "。", "”", "艾青", "认识", "高瑛", "后", ",", "也", "为", "高瑛", "的", "率", "真", "、", "美丽", "所", "打动", "。", "高瑛", "是", "一", "个", "要", "强", "的", "人", ",", "天真", "、", "幼稚", ",", "不", "隐瞒", "任何事情", "。", "她", "的", "典雅", "独特", "的", "气质", ",", "使", "艾青", "深深地", "爱", "上", "了", "她", ",", "并", "开始", "追求", "她", "。"], ["那时", ",", "艾青", "同", "韦嫈", "感情", "已经", "破裂", "。"], ["高瑛", "已有", "过", "一次", "婚姻", ",", "但", "并", "不", "幸福", "。"], ["于是", ",", "共同", "的", "遭遇", "使", "他们", "相爱", "了", ",", "走", "到", "了", "一起", "。", "当时", ",", "高瑛", "比", "艾青", "小", "23岁", ",", "已", "是", "两个孩子", "的", "母亲", ",", "带", "着", "两个孩子", ",", "高瑛", "走进", "了", "艾青", "的", "生活", "中", ",", "四", "口", "人", "组成", "了", "一", "个", "新", "的", "家庭", "。"], ["1956", "年", "3", "月", "27", "日", ",", "既是", "艾青", "的", "生日", ",", "也是", "他", "同", "高瑛", "结婚", "的", "日子", "。", "尽管", "在这之前", ",", "艾青", "在", "文艺界", "已", "开始", "遭到", "批评", ",", "但", "深深", "爱着", "艾青", "的", "高瑛", "毅然", "决定", "宣布", "他们", "的", "婚事", "。"], ["然而", ",", "艾青", "和", "高瑛", "谁", "也", "没有", "想到", ",", "一", "场", "运动", "正在", "向", "他们", "逼近", ",", "他们", "爱情", "与", "婚姻", "的", "序幕", ",", "正是", "苦难", "生活", "的", "开端", ",", "并", "从此", "决定", "了", "他们", "将", "相依相伴", "熬过", "最", "苦难", "的", "岁月", "。"], ["1957", "年", ",", "反右斗争", "开始", "了", "。", "丁玲", "等", "被", "打", "成", "“", "反党", "集团", "”", "。"], ["作为", "丁玲", "的", "朋友", ",", "艾青", "想不通", "。", "虽然", "他", "没有", "写", "过", "一", "张", "大字报", ",", "没有任何", "攻击", "党", "的", "言论", ",", "虽然", "他", "只是", "说", "了", "“", "丁玲", "并没有", "反党", "嘛", ",", "她", "并没有", "跟", "国民党", "走", "嘛", ",", "还是", "到", "了", "延安", ",", "跟着共产党走", "嘛", "。", "”", "他", "也", "只", "在", "作家协会", "的", "党组织", "生活", "会", "上", "说过", "“", "不要", "一部分", "人", "总是", "整人", ",", "一部分", "人", "总是", "被", "整", "”", "的", "话", "。", "所以", ",", "艾青", "只是", "替", "丁玲", "说过", "话", ",", "他", "只是", "对", "文艺界", "的", "宗派", "主义", "发", "过", "牢骚", "。", "但", "他", "说", "的", "是", "真话", ",", "说真话", "的", "后果", "是", ":", "被", "责令", "检查", ",", "接着", "是", "一", "顶", "“", "大", "右派", "”", "的", "帽子", ",", "随着", "是", "一连串", "的", "惩罚", ":"], ["1957", "年", "12", "月", "被", "开除", "出", "党", ";", "1958", "年", "4", "月", "被撤消", "一切", "职务", "。", "这", "还", "不", "算", ",", "艾青", "一家人", "的", "厄运", "才", "开", "了", "头", ",", "作为", "“", "大", "右派", "”", "的", "妻子", ",", "高瑛", "开始", "受到", "迫害", "。", "单位", "有人", "找", "高", "瑛", "谈话", ",", "想", "通过", "她", "来", "检举", "、", "揭发", "艾青", "等人", "的", "反党", "反", "社会主义", "言论", 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"有", "什么", "反党", "反", "社会主义", "的", "言行", "。", "所以", ",", "我", "没法", "揭发", "。", "”"], ["会场", "一", "下", "乱", "起来", ",", "不少", "人", "冲着", "高瑛", "大喊大叫", ",", "各种", "难听", "的", "话", "迎面而来", "。"], ["高瑛", "思绪万千", "。", "她", "从小", "就", "参加", "了", "革命", "。", "党", "教育", "她", "实事求是", ",", "不能", "诬陷", "好人", ",", "也", "不", "包庇", "坏人", "。", "她", "没法", "和", "艾青", "划清界限", ",", "更", "不能", "和", "艾青", "离婚", "。", "“", "为了", "纯洁", "团", "的", "组织", ",", "我", "自动", "提出", "退团", "。", "”", "高瑛", "的", "决定", ",", "令", "大会", "所有", "的", "人", "目瞪口呆", "。", "但", "她说", "完", "就", "站起来", ",", "头", "也", "不", "回", "地", "逃离", "会场", "直奔", "家里", "。"], ["回到", "家中", ",", "高", "瑛", "抱着", "艾青", "大声痛哭", ":", "“", "艾青", ",", "我", "是", "从", "会场", "上", "逃", "出来", "的", "。", "”", "望", "着", "伤心", "的", "妻子", ",", "艾青", "深", "感", "内疚", ":", "“", "高瑛", "啊", ",", "跟着我", ",", "让", "你", "受苦", "呵", "!", "”"], ["“", "你", "上", "刀山", "下", "火海", ",", "我", "都", "跟着", "你", ";", "你", "到哪儿", ",", "我", "就", "到哪儿", "。", "我", "什么", "也", "不怕", ",", "要", "死", ",", "咱们", "死", "到", "一", "块", "。", "”", "高", "瑛", "说", "。"], ["在", "严酷", "的", "现实", "面前", ",", "艾青", "濒于", "崩溃", "。", "他", "想不通", ":", "自己", "被", "打", "成", "右派", ",", "简直", "是", "笑话", "!", "我", "怎么", "可能", "反党", "反", "社会主义", ",", "怎么", "可能成为", "右派", "?"]], "paragraphs": ["艾青(1910年3月27日—1996年5月5日),原名蒋海澄,号海澄,曾用笔名莪加、克阿、林壁等,浙江省金华人。成名作《大堰河——我的保姆》发表于1933年,这首诗奠定了他诗歌的基本艺术特征和他在现代文学史上的重要地位,被认为是中国现代诗的代表诗人之一。其作品被译成几十种文字,着有《大堰河》、《北方》、《向太阳》、《黎明的通知》、《湛江,夹竹桃》等诗集。在中国新诗发展史上,艾青是继郭沫若、闻一多等人之后又一位推动一代诗风、并产生过重要影响的诗人,在世界上也享有声誉。", "艾青于1910年阴历2月17日生于浙江金华的一个地主家庭。母亲生他时难产,生了三天三夜,一个算卦的又说他是“克父母”的,因此他成了一个不受欢迎的人,甚至不许他叫父母为“ 爸爸妈妈”,只能叫“叔叔婶婶”。由于家里不喜欢这个“克父母”的婴儿 ,就托付给大堰河收养,然而这个妇女却十分疼爱他。", "1910年出生于浙江省金华市金东区畈田蒋村的一个封建家庭。自幼由一位贫苦农妇养育到5岁。", "1917年就读于金师附小。", "1928年中学毕业后考入国立杭州西湖艺术院。", "1928年在林风眠校长的鼓励下到巴黎勤工俭学,学习绘画,接触欧洲现代派诗歌。", "1932年初加入中国左翼美术家联盟,从事革命文艺活动,不久被捕,在狱中写了不少诗,《大堰河——我的保姆》引起轰动。", "1933年第一次用艾青的笔名发表长诗《大堰河———我的保姆》,感情诚挚,诗风清新,轰动诗坛。以后陆续出版诗集《北风》、《大堰河》(1939)、《火把》(1941)、《向太阳》(1947)、《黎明的通知》《欢呼集》《宝石的红星》《春天》等,笔触雄浑,感情强烈,倾诉了对祖国和人民的情感。解放后的诗集有《欢呼集》、《光的赞歌》等。", "1935年出狱,翌年出版了第一本诗集《大堰河》。抗日战争爆发后,任《文艺阵地》编委、育才学校文学系主任等。", "1941年赴延安任《诗刊》主编。出版了《北方》《向太阳》《旷野》《火把》《黎明的通知》《雷地钻》等9部诗集。", "担任《人民文学》副主编。着有诗集《宝石的红星》《黑鳗》《春天》《海岬上》。", "1957年被错划为右派。曾赴黑龙江、新疆生活和劳动,创作中断了二十余年。直到1976年重又执笔,出现了创作的另一个高潮。", "1979年平反,任中国作家协会副主席。创作有诗集《彩色的诗》《域外集》,出版了《艾青叙事诗选》《艾青抒情诗选》。", "1985年获法国文学艺术最高勋章。", "其诗作《我爱这土地》被选入人教版中学语文教材(九年级下册)。《北方》中的《土地情诗》被编入鄂教版八年级下册语文书第十三课。《北方》被选入苏教版必修三语文书。《我的思念是圆的》被入选苏教版七年级上册语文书。《大堰河——我的保姆》被选入人教版高中语文必修一。另外,他的诗作《太阳的话》也被选入小学六上语文教材。", "1935年,25岁的艾青听从父母之命,与张竹茹结婚。", "1936年,艾青在常州武进女子师范当国文老师,与女学生韦嫈(真名张月琴),即艾轩的母亲相识、相爱。1939年,29岁的艾青与18岁的韦嫈结婚,相继生下女儿艾清明、儿子艾端午。1947年,艾轩在河北深县小李庄出生。", "1955年,艾青与韦嫈离婚,艾轩和姐姐艾清明、哥哥艾端午判给了父亲,妹妹艾梅梅则归母亲抚养。", "1955年,艾青与刚调到中国作家协会的高瑛相识。1956年3月27日,艾青与高瑛结为夫妻。", "艾青与高瑛生有两子女:分别为当代著名艺术家艾 未未及作家艾丹。", "认识艾青之前,高瑛爱艾青的诗;认识艾青之后,她更爱艾青这个人。艾青为人真诚,从不装腔作势,更不会说假话,高瑛说:“他比我还要真实。”艾青认识高瑛后,也为高瑛的率真、美丽所打动。高瑛是一个要强的人,天真、幼稚,不隐瞒任何事情。她的典雅独特的气质,使艾青深深地爱上了她,并开始追求她。", "那时,艾青同韦嫈感情已经破裂。", "高瑛已有过一次婚姻,但并不幸福。", "于是,共同的遭遇使他们相爱了,走到了一起。当时,高瑛比艾青小23岁,已是两个孩子的母亲,带着两个孩子,高瑛走进了艾青的生活中,四口人组成了一个新的家庭。", "1956年3月27日,既是艾青的生日,也是他同高瑛结婚的日子。尽管在这之前,艾青在文艺界已开始遭到批评,但深深爱着艾青的高瑛毅然决定宣布他们的婚事。", "然而,艾青和高瑛谁也没有想到,一场运动正在向他们逼近,他们爱情与婚姻的序幕,正是苦难生活的开端,并从此决定了他们将相依相伴熬过最苦难的岁月。", "1957年,反右斗争开始了。丁玲等被打成“反党集团”。", "作为丁玲的朋友,艾青想不通。虽然他没有写过一张大字报,没有任何攻击党的言论,虽然他只是说了“丁玲并没有反党嘛,她并没有跟国民党走嘛,还是到了延安,跟着共产党走嘛。”他也只在作家协会的党组织生活会上说过“不要一部分人总是整人,一部分人总是被整”的话。所以,艾青只是替丁玲说过话,他只是对文艺界的宗派主义发过牢骚。但他说的是真话,说真话的后果是:被责令检查,接着是一顶“大右派”的帽子,随着是一连串的惩罚:", "1957年12月被开除出党;1958年4月被撤消一切职务。这还不算,艾青一家人的厄运才开了头,作为“大右派”的妻子,高瑛开始受到迫害。单位有人找高瑛谈话,想通过她来检举、揭发艾青等人的反党反社会主义言论。但高瑛什么也交代不出来,她写道:“最近,我因为生孩子,不知道这个情况……”有人还不死心,想逼她就范。一天, 单位通知高瑛开会,到了会场,高瑛才知道是团总支大会。人很多。主持会议的人说:“今天这个会,是专门欢迎高瑛同志勇敢地站出来,同‘丁陈反党集团’划清界限,勇敢地检举揭发他们的反党反社会主义的罪行的。”", "听到这话,高瑛只觉脑子一片空白,耳中嗡嗡作响,她完全没有一点思想准备啊,她怎么可能出卖自己心爱的丈夫,而且真的从来没有听到过什么反党言论啊。安静下来后,高瑛发言说:“我首先声明,我不知道今天这个会是专门为我开的。我虽然生活在他们中间,但我确实没有看到和听到他们有什么反党反社会主义的言行。所以,我没法揭发。”", "会场一下乱起来,不少人冲着高瑛大喊大叫,各种难听的话迎面而来。", "高瑛思绪万千。她从小就参加了革命。党教育她实事求是,不能诬陷好人,也不包庇坏人。她没法和艾青划清界限,更不能和艾青离婚。“为了纯洁团的组织,我自动提出退团。”高瑛的决定,令大会所有的人目瞪口呆。但她说完就站起来,头也不回地逃离会场直奔家里。", "回到家中,高瑛抱着艾青大声痛哭:“艾青,我是从会场上逃出来的。”望着伤心的妻子,艾青深感内疚:“高瑛啊,跟着我,让你受苦呵!”", "“你上刀山下火海,我都跟着你;你到哪儿,我就到哪儿。我什么也不怕,要死,咱们死到一块。”高瑛说。", "在严酷的现实面前,艾青濒于崩溃。他想不通:自己被打成右派,简直是笑话!我怎么可能反党反社会主义,怎么可能成为右派?"], "bs_rank_pos": 3}, {"is_selected": false, "title": "艾青的诗_经典语录_名言名句赏析_句子迷", "most_related_para": 3, "segmented_title": ["艾青", "的", "诗", "_", "经典语录", "_", "名言", "名句", "赏析", "_", "句子", "迷"], "segmented_paragraphs": [["艾青", "的", "名句", "/", "诗", "/", "经典语录"], ["共", "收录", "24", "个", "艾青", "的", "句子", ":"], ["艾青"], ["艾青", "(", "1910", "年", "3", "月", "27", "日", "—", "1996", "年", "5", "月", "5", "日", ")", ",", "原名", "蒋海澄", ",", "曾", "用", "笔名", "莪", "加", "、", "克", "阿", "、", "林", "壁", "等", ",", "浙江省", "金华", 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"艾青(1910年3月27日—1996年5月5日),原名蒋海澄,曾用笔名莪加、克阿、林壁等,浙江省金华人。中国现代诗人。成名作《大堰河——我的保姆》发表于1933年,这首诗奠定了他诗歌的基本艺术特征和他在现代文学史上的重要地位。其作品被译成几十种文字,著有《大堰河》、《北方》、《向太阳》、《黎明的通知》、《湛江,夹竹桃》等诗集。 20世纪三四十年代他出版的诗集有《大堰河》《北方》《他死在第二次》《向太阳》《献给乡村的诗》《反法西斯》《旷野》《黎明的通知》《雪里钻》等...(更多)", "分享这些好句子到:", "喜欢艾青的用户 (296)", "您可能感兴趣的名人名句"], "bs_rank_pos": 4}], "answer_spans": [[4, 102]], "answer_docs": [3], "fake_answers": ["艾青的笔名发表长诗《大堰河———我的保姆》,感情诚挚,诗风清新,轰动诗坛。以后陆续出版诗集《北风》、《大堰河》(1939)、《火把》(1941)、《向太阳》(1947)、《黎明的通知》《欢呼集》《宝石的红星》《春天》等,笔触雄浑,感情强烈,倾诉了对祖国和人民的情感。解放后的诗集有《欢呼集》、《光的赞歌》"], "question": "艾青的诗", "segmented_answers": [["《", "大堰河", "》", "（", "诗集", "）", "1936", "；", "《", "北方", "》", "（", "诗集", "）", "1939", "；", "1942", "，", "文生", "；", "《", "他死在第二次", "》", "（", "诗集", "）", "1939", "，", "上", "杂", "；", "《", "向太阳", "》", "（", "长诗", "）", "1940", "，", "海燕", "读书", "；", "《", "吴满有", "》", "（", "长诗", "）", "1943", "，", "新华书店", "；", "1946", "，", "作家", "书屋", "；", "《", "黎明的通知", "》", "（", "诗集", "）", "1943", "，", "文化", "供应", "社", "；", "《", "愿春天", "早点来", "》", 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"的", ",", "看一下", "就", "懂了", ",", "牛", "气", "总", ":", "3", "页", "/", "24", "条", "评论", "1", "2", "3", "下一页"]], "paragraphs": ["在公司人力资源的同事们在管理人员时,经常需要创建公司的组织结构图,以便于主管或经理准确地评估部门组织结构的合理性,并且根据不断变化的市场需求对组织结构做出及时的调整。员工也需要了解公司的策略和组织结构,以及自己在达成公司目标的过程中所担当的角色。组织结构图模板可以帮助人力资源的管理人员直观地展现公司目前的组织结构、创建更改方案,以及在整个企业内传递企业信息起到了非常大的作用。 组织结构图是我们在公司办公中经常会用到的模板,我们应该使用什么软件来制作呢;又该如何制作这种模板呢?其实,Word中就能制作组织结构图模板,而且还拥有非常强大和专业的功能。前面我们介绍了流程图的制作,方法有点类似。今天Word联盟就以Word2007为例,详细分享一篇组织结构图制作全过程的教程,相信大家看完后一定会受益匪浅! 1、单击“插入”选项卡,在“插图”控件组中单击“SmartArt”,在“选择SmartArt图形”对话框中单击左侧的“层次结构”,在中间栏中选择一种样式,最后点“确定”。 2、在左侧“文本窗格”中从组织结构的最高级开始输入组织结构图中每个小方框中的文字。 3、如发现结构图中的小方框不够用,如想添加一个后勤副校长框,可以右键单击副书记框,在弹出的快捷菜单中单击“添加形状”,根据实际情况选择“在后面添加形状”或者是“在前面添加形状”、“在上边添加形状”、“在下边添加形状”。 在文本窗格中或直接单击插入的小方框,把其中输入文字。 右键单击后勤副校长框,在弹出的快捷菜单中单击“添加形状”→在下边添加形状,添加出其下级组织结构框。 把新插入的组织结构框中输入文字。 4、在组织结构图总框内空白处单击左键后按Ctrl+A(选择所有),然后单击“开始”选项卡,在“字体”控件组中设置字体、字号等。 5、可以拖动组织结构图总框周围的控制点,来调整组织结构图的大小。 6、在新出现的“SmartArt工具”下单击“设计”选项卡,在“SmartArt样式”控件组中可以选择“三维”组织结构图样式。 在“SmartArt样式”控件组中单击“更改颜色”可以选择预设的组织结构图颜色。 7、在“设计”选项卡下的“创建图形”控件组中可以通过选择“从右向左”来改变组织结构图的左右顺序。 8、如要删除组织结构图的某个小方框,可以右键单击该小方框的四框,在弹出的快捷菜单中选择“剪切”即可。 9、在“设计”选项卡下的“布局”控件组中选择“组织结构图”样式(先前的其实是层次结构图)来改换组织结构图样式,改为真正组织结构图后,就可以给某小方框添加“助理”了。 10、单击某小方框后,在“格式”选项卡下的“形状”控件组中单击“更改形状”,可单独更改组织结构图中的每个小方框的形状。", "上一篇:如何在Word文档中添加背景图片、背景颜色  下一篇:使用批处理轻松解决“Word遇到问题需要关闭”问题", "怎么样给Word2003文档设置密码? Word2007制作禁止吸烟的标志 Word中下划线怎么打出来 在Word2003文档中怎么分栏 Word2003怎么设置默认打印机", "XP与Win7打印机共享怎么设置(详细图文步骤) Office 2010详细安装指南 《Word页码设置全攻略》详细技巧教你Word文档中怎么设置页码? Office 2007专业版 安装使用详细步骤教程 Excel表格中如何使用高级筛选技巧 详细实例教程 “Word页码从第三页开始”详细的图文步骤教程 《Excel表格自动求和全攻略》单个公式求和,批量求和详细图解! Word2007怎么设置行距?行距设置详细解说 Excel2003中给表格画斜线表头 《Excel超级链接使用全攻略》设置超链接、取消超链接等技巧 Excel2003如何合并单元格 PowerPoint超链接字体颜色修改、怎么去掉超链接下划线", "支持[0] 反对[0] [引用] 匿名2016-11-14 非常清楚,赞 支持[0] 反对[0] [引用] 匿名2016-10-31 非常好,简单易明!谢谢! 支持[0] 反对[0] [引用] 匿名2016-10-31 好用 支持[0] 反对[0] [引用] 匿名2016-10-14 棒 支持[0] 反对[0] [引用] 匿名2016-08-01 超级棒,一学就会 支持[0] 反对[0] [引用] 匿名2016-04-17 谢谢 支持[0] 反对[0] [引用] 匿名2016-02-24 谢谢 支持[0] 反对[0] [引用] 匿名2016-01-07 很清晰 就明白了 支持[0] 反对[0] [引用] 匿名2015-12-20 很好,太实用了。 支持[0] 反对[0] [引用] 匿名2015-09-06 太赞了,不会的,看一下就懂了,牛气 总: 3 页/24 条评论 1 2 3 下一页"], "bs_rank_pos": 2}, {"is_selected": false, "title": "用word如何绘制组织结构图_百度经验", "most_related_para": 4, "segmented_title": ["用", "word", "如何", "绘制", "组织结构", "图", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["在", "文档", "中", "常", "需要", "创建", "工作", "流程图", "、", "组织结构", "图", "等", "图示", ",", "手动", "制作", "操作", "起来", "就", "很麻烦", "了", ",", "我们", "今天", "就", "用", "word", "本身", "自带", "的", "SmartArt", "图形", ",", "来", "为", "文档", 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"百度经验:jingyan.baidu.com", "1 选择SmartArt图形类型。这一步就是先把最初的组织结构图创建出来,我们启动一个新的文档。我们在文档中输入“企业组织机构图”,字号设置为“一号” 步骤阅读 2 单击“插入”/“SmartArt”,弹出“选择SmartArt图形”对话框,在该对话框左侧列表中选择“层次结构”,在中间区域选择“表层次结构”,右侧我们可以看他的一些说明,点击确,这样我们就做好了最初的组织机构图 步骤阅读 步骤阅读 步骤阅读 3 设置机构图的布局。我们可以根据需要改变一些布局;单击“设计”我们就可以看见基本栏中间区域“更改布局”我们在这里选择“组织结构图” 步骤阅读 4 这会默认的图形可能还不符合我们的要求,我们选中第一行的矩形图形,然后单击“设计”“添加形状”按钮,在下拉菜单中选择“添加助理”选项 步骤阅读 步骤阅读 5 上一步是在第二行添加。我们这一步是在第三行添加图形。选中第一行的矩形图形,然后单击“设计”“添加形状”按钮,在下拉菜单中选择“在下方添加形状”选项,这样新形状就会出现在第三行里面。 步骤阅读", "百度经验:jingyan.baidu.com", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "bs_rank_pos": 3}, {"is_selected": false, "title": "组织结构图制作方法_百度文库", "most_related_para": 2, "segmented_title": ["组织结构", "图", "制作方法", "_", "百度", "文库"], "segmented_paragraphs": [["组织结构", "图", "制作方法", "_", "图片", "/", "文字", "技巧", "_", "PPT", "制作技巧", "_", "实用", "文档", "。", "组织结构", "图", "制作方法", ",", "可", "参考", "制作", "出", "各种", "精美", "的", "组织结构", "图"], ["无锡", "永中", "软件", "有限公司", "2012", "年", "11", "月", "14", "日", "公司", "组织结构", "图", "制作", 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"杂豆肉粽是广东特色,配料通常有经过腌制的五花肉、咸蛋黄、去皮绿豆、红豆、眉豆、花生、板栗等。", "煮 其它口味 <数小时 较高热量", "糯米一小盆", "花生适量 赤小豆适量 粽叶20片 火腿一根 五花肉300克", "食盐5克 生抽半勺 料酒4克 白糖一大勺 五香粉一小勺 花椒粉少许 老抽半勺", "粽叶浸泡后刷洗干净,放入大锅煮约10分钟,棉线一同煮开消毒", "五花肉切大片用生抽、老抽、料酒、白糖、花椒粉和少量五香粉提前腌制一晚入味,火腿洗净后煮20分钟去除多余的盐味的油脂,切大块", "取粽叶卷起来,先放糯米,再放五花肉和火腿,再放满糯米,按自己喜欢的方式和形状包粽子(我是去年拍的照片,包的过程不详细)", "全部包好后,放入大锅中,加足没过粽子的冷水,上面盖一层篦子(蒸锅中间隔层的那种),再压一个碟子,盖上锅盖大火烧开后,转小火煮约3个小时左右", "糯米根据自己喜欢加不加盐、生抽和油都行。豆类最好浸泡六小时以上。五花肉可以根据自己喜欢的味道腌制,提前一晚腌制更入味。火腿肉很咸,可以先煮一下,去掉一些盐分。锅内压篦子和碟子是为煮的过程中粽子不会过度翻滚,避免煮散。煮粽子的时间根据粽子的大小和多少调整", "英国茶文化 6607浏览  过敏者应尽量少吃四类食物 1500浏览", "青瓜炒鸡蛋 3557浏览  童趣早餐盘 7418浏览  懒人电饭煲酸梅汤 5199浏览", "每天吃一个鸡蛋到底合适吗? 5332浏览  “儿童”食品未必更健康 3367浏览  你错吃了多少年的糖? 23188浏览", "小龙虾这些部位最好别吃 2463浏览  洋快餐是怎样诞生的 4730浏览  西芹作为保健蔬菜的功效 6889浏览  健康晚餐三要素,早吃素吃少吃! 9206浏览", "原来法餐是这样炼成的 3094浏览  6种酱油怎么吃更营养? 3924浏览  乙草胺事件后 草莓还能吃吗? 11948浏览", "黄豆的功效与副作用 14305浏览  春季养生明星抗癌食物 13407浏览  当心!5种食物不能微波炉反复加热 6502浏览"], "bs_rank_pos": 0}, {"is_selected": true, "title": "鲜肉粽子的做法,鲜肉粽子怎么做好吃,鲜肉粽子的家常做法_鱼小胃_好", "most_related_para": 2, 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"一定要扎紧,不然糯米松散口感不好。"], "bs_rank_pos": 5}], "answer_spans": [[61, 215]], "fake_answers": ["做好。2.把泡好的糯米用适量的盐、生抽、老抽拌匀。3.粽叶清洗干净4.把鲜肉切成1厘米见方的块,用生抽、老抽、盐拌匀腌制至少20分钟备用。5.取一张粽叶,折成漏斗状6.漏斗里放一点糯米,放一块瘦肉7.再放一些糯米盖住瘦肉,放一块肥肉8.放糯米将肉全部盖住,但是粽叶留出1厘米高的边以便“包扎”9.把边缘多出来的粽叶用自己的手法收起10.然后用自己的方法把粽子五花大绑,只要能绑结实就好。11.用砂锅高火煮两个小时,肥肉都煮得透明,特别香"], "question": "肉粽子怎么做好吃", "segmented_answers": [["1", "、", "粽叶", "浸泡", "后", "刷", "洗干净", "，", "放入", "大锅", "煮", "约", "10", "分钟", "，", "棉线", "一同", "煮", "开", "消毒", "。", "2", "、", "粽叶", "浸泡", "后", "刷", "洗干净", "，", "放入", "大锅", "煮", "约", "10", "分钟", "，", "棉线", "一同", "煮", "开", "消毒", "。", "3", "、", "红豆", "、", "花生", "浸泡", "六小时", "以上", "，", "加入", "糯米", "中", "拌匀", "。", "4", "、", "粽叶", "剪", "去", "两头", "，", "准备", "好", "所有", "用具", "。", "5", "、", "粽叶", "剪", "去", "两头", "，", "准备", "好", "所有", "用具", "。", "6", "、", "取", "粽叶", "卷", "起来", "，", "先", "放", "糯米", "，", "再", "放", "五花肉", "和", "火腿", "，", "再", "放", "满", "糯米", "，", "按", "喜欢", "的", "方式", "和", "形状", "包粽子", "。", "7", "、", "全部", "包", "好", "后", "，", 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"1.糯米在做粽子的前天晚上就泡上，第二天就可以随时包粽子了。2.把泡好的糯米用适量的盐、生抽、老抽拌匀。3.粽叶清洗干净。4.把鲜肉切成1厘米见方的块，用生抽、老抽、盐拌匀腌制至少20分钟备用。5.取一张粽叶，折成漏斗状。6.漏斗里放一点糯米，放一块瘦肉。7.再放一些糯米盖住瘦肉，放一块肥肉。8.放糯米将肉全部盖住，但是粽叶留出1厘米高的边以便“包扎”。9.把边缘多出来的粽叶用自己的手法收起。10.然后用方法把粽子五花大绑，只要能绑结实就好。11.用砂锅高火煮两个小时，肥肉都煮得透明，特别香。", "1、五花肉切成1寸左右的见方，加入酱油、老抽、糖、盐、白酒适量，白酒一定要多放，这样好吃，根据自己的口味调整咸甜。2、用手搓加入调料的肉，入味。最好提前一天腌制好。3、米淘洗，尽量不要让米吃水。4、把米里面的水控干，我用过滤网过滤了两遍，米放在盆里面，略微停一会，让米吃掉多余的水。5、米里面加酱油、糖、白酒适量，盐少许，可以多放酱油，根据自己的口味，拌匀，备用，用太太乐宴会酱油味道好，这酱油很香，也可以用普通的酱油。6、粽叶洗净，把根部剪刀掉一点，煮开水，把粽叶放进去煮软就可以了；泡一会，把粽叶泡软。7、2片粽子叶重叠。8、卷成圆锥形。9、放入糯米和肉，每个粽子都一定要放肥肉，不然不好吃。10、最后放满糯米。压一下。11、把上面的粽叶往下折盖住米，把长出来的粽叶窝成角，用棉线将粽子绑紧。包紧包实；只要结实，不漏米就行了。"], "answer_docs": [1], "segmented_question": ["肉", "粽子", "怎么", "做", "好吃"], "question_type": "DESCRIPTION", "question_id": 181633, "fact_or_opinion": "OPINION", "match_scores": [0.8787878787878788]}
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"很多", "。", "看到这里", "姐", "突然", "觉得", "整个", "人", "都", "精神", "多", "了", ",", "是不是", "已经", "得", "了", "精神病", "的", "缘故", "呢", "。", "。", "。"], ["虽说", "以上", "血型", "与", "健康", "的", "研究成果", "让", "某些", "人", "有点", "小", "沮丧", ",", "但", "姐", "坚信", "命运", "掌握", "在", "自己", "手", "中", ",", "先天不足", ",", "后天", "来", "补", ",", "跟着", "姐", "看看", "各种", "血型", "的", "先天", "劣势", "怎么", "破", "!"], ["根据", "血型", "来", "养生"], ["A型血", ":", "A型血", "的", "人大", "多", "是", "工作狂", ",", "他们", "喜欢", "尝试", "未知", ",", "承担", "风险", ",", "但", "与此同时", "也", "承担", "了", "繁重", "的", "压力", "。", "当", "压力", "增大", "时", ",", "神经系统", "会", "受到", "影响", ",", "人体", "生理机能", "失衡", ",", "继而", "导致", "心脏", "功能", "严重", "受损", "。", "因此", "A型血", "的", "人", "最", "重要", "的", "就是", "要", "学会", "减压", ",", "不", "给", "心脏病", "以", "生存", "空间", "。", "A型血", "在", "日常", "生活", "中", "可以", "常吃", "鳗鱼", ",", "适当", "吃", "猪肉", ",", "补充", "身体", "所需", "的", "脂肪", "、", "蛋白质", "等", "营养", "元素", ",", "还要", "少", "做", "剧烈运动", ",", "这样", "不仅", "不能", "锻炼身体", ",", "反而", "会", "加剧", "免疫力下降", "。", "不如", "每日", "练", "练", "瑜伽", ",", "在", "一招一式", "中", "舒缓", "疲劳", "身体", "。", "此外", ",", "在", "睡前", "30", "分钟", "可以", "喝一杯", "纯净水", ",", "让", "本来", "黏稠", "的", "血液", "变得", "顺畅", ",", "从而", "缓解", "紧张", "状态", ",", "达到", "减压", "的", "目的", ",", "促进", "安眠", "。", "B型血", ":", "B型血", "对于", "具有", "相同", "的", "B型", "抗原", "的", "细菌", "毫无", "防范", "之", "力", ",", "加", "之", "自身", "某些", "免疫系统", "功能", "失调", ",", "防卫", "能力", "最差", ",", "因此", "极其", "容易", "患", "上", "肺结核", "、", "流感", "等", "传染疾病", "。", "对于", "B型血", "的", "人", "来", "说", ",", "调节", "健康", "的", "重点", "在于", "提升", "自身", "对于", "外界", "环境", "变化", "的", "应变能力", ",", "才能", "减低", "患", "上", "传染病", "的", "几率", "。", "B型血", "在", "生活", "中", "要", "少", "吃", "猪肉", ",", "如果", "一次", "食用", "过", "多", "就会", "出现", "过敏", "现象", "。", "钙", "是", "B型血", "的", "重要", "元素", ",", "唯有", "身体", "内", "有", "足", "量", "的", "钙", ",", "新陈代谢", "才能", "正常运转", ",", "提升", "身体", "的", "应变能力", ",", "调节", "自身", "的", "免疫系统", "平衡", "。", "最佳", "的", "补钙", "方式", "就是", "享受", "日光浴", ",", "因此", "B型血", "的", "人", "不", "妨", "多", "去", "户外", "做", "做", "小步跑", "、", "高", "抬腿", "散步", "等", "力所能及", "的", "运动", "。", "O型血", ":", "O型血", "人", "的", "自身", "消化系统", "功能", "一般", "都", "强大", ",", "也", "增加", "了", "胃酸", "的", "分泌", "含量", ",", "如此一来", ",", "胃壁", "就", "更容易", "受到刺激", ",", "轻微", "的", "影响", "就会", "出现", "胃溃疡", "现象", "。", "想要", "避免", "胃酸", "分泌", "过", "多", ",", "最佳", "的", "方法", "就是", "在", "中餐", "和", "午餐", "过后", "切", "一", "片", "生姜", ",", "先", "含", "在", "嘴里", "3", "分钟", ",", "再", "细嚼", "咽下", "。", "需要", "注意", "的", "是", ",", "吃", "姜", "不宜", "贪", "多", ",", "只", "需", "薄", "薄", "一", "片", "即可", ",", "如果", "服用", "过", "多", ",", "反而", "会", "加重", "胃酸分泌", "。", "此外", ",", "每天", "的", "下午茶时间", ",", "用", "70", "%", "的", "热水", "和", "30", "%", "的", "凉开水", "勾兑", "出", "温开水", ",", "快速", "饮用", "后", ",", "努力", "有", "节奏", "地", "打嗝", ",", "然后", "用", "手掌", "心", "从", "上", "到", "下", "轻", "揉", "胃部", ",", "达到", "温暖", "、", "通气", "的", "目的", "。", "这样", "简单", "的", "小", "行动", "就能", "让", "胃部", "的", "浊气", "排出", ",", "不", "给", "过量", "胃酸", "提供", "条件", "。", "AB型血", ":", "AB型血", "是", "人类", "历史", "上", "最晚", "诞生", "的", "血型", ",", "对", "疾病", "的", "抵御", "能力", "也", "最", "弱", ",", "哪怕", "是", "微弱", "的", "影响", "也", "会", "让", "健康", "很受伤", "。", "不", "妨", "在", "每天", "上午", "10", "点", "、", "下午", "4", "点", "各", "饮用", "一大", "杯", "酸奶", ",", "改变", "体", "内", "生物", "环境", ",", "提高", "人体", "免疫", "能力", "。"], ["建议", "AB型血", "的", "人", "每星期", "吃", "两次", "凉菜", ",", "比如", "虾油", "炝", "拌", "萝卜丝", "就", "有", "促进", "消化", "、", "增强", "食欲", "的", "作用", ",", "为", "身体", "内部", "降降温", ",", "有效", "提升", "AB型血", "人", "的", "免疫力", "。"], ["最近", "日本", "有", "一", "份", "交通事故", "调查", "表明", ",", "AB型血", "的", "人", "经常", "因为", "睡眠不足", "而", "导致", "交通事故", ",", "尤其", "在", "深夜", "2", "点", "～", "凌晨", "6", "点", "之间", "。", "这", "与", "AB型血", "人", "免疫力", "低下", ",", "容易", "疲倦", "有", "很大", "关系", "。", "所以", ",", "请", "AB型血", "的", "你", "在", "需要", "深夜", "驾车", "前", "小憩", "15", "分钟", ",", "养", "足", "精神", "才能", "开车", "哦", "!"]], "paragraphs": ["O型血的人最长寿", "数学家及医生史蒂芬.M.魏斯贝格与知名的健身专家约瑟夫.克里斯蒂安奴在对5000人的问卷调查中得出了有趣的数据。其中有一个问题,不同血型的人的平均估计寿命是否存在差别呢?", "美国人的平均寿命为76岁。这个数字是命中注定的。而它又是让人欣喜的,因为只有少数人明显地更加长寿,相反其他人很早就去世了。", "拥有最长的估计寿命的人是O型血人,这些人的平均寿命几乎达到87岁。他们是养老金发放机构的噩梦。这个估计寿命之高原本并不令人吃惊,因为这些人身体强壮,有坚定的意志,心理上相对平衡,并且无忧无虑。如果你是O型血,又恰巧活过87岁,那么姐要恭喜你!你交的社保绝对都赚回来啦~", "B型血的人还是保持沉着冷静,即使他们会比O型血人早10年去世。他们顽固,并对一切事情过于执著。B型朋友们,放松一点好吗?", "估计寿命再少10年的是AB型血的人,平均达到70岁。(ˇ?ˇ) ～AB型的姐其实还没考虑好到底打算活多少岁~~#@*#*EY!@......", "而平均活得最短的是A型血的人,仅仅62岁!在心理学家看来,这其实是意料之中的,因为A型血的人脸皮薄而且非常敏感,他们会一直为自己以及身边的人操心。很遗憾,这往往会使心脏的负担过重,甚至出现耗损。A型血的亲们,你们都要好好的!脸皮厚一点没关系的,别太操心啊,要注意身体啊!", "但是A型血的人寿命相对于其他血型要短,还有一个完全不同的原因。医生及自然疗法医师彼特.J.达达默医学博士,发现了特定的食物与血型之间的对立关系。在他的一本著作中阐述了人与食物的搭配问题,因为饮食习惯受血型的影响。", "他认为O型血与A型血的人的主要区别在于对肉类食品的消化:O型血的人需要肉食,并且吸收得很好;而A型血的人不能很好地消化肉类,相对而言更需要谷物及蔬菜类食物。其根本原因在于胃酸分泌量的不同:O型血的人胃酸充足,因此消化肉类只是小菜一碟。很可惜,我们今天的食谱上几乎都是肉类快餐:烤肉串、汉堡包、烤香肠以及牛排,这对于A型血人以及他们那脆弱的消化系统来说,就好比毒药。错误的饮食习惯使这些人元气大伤,并且缩短了寿命。", "让我们来看一下日本的情况。这个国家的A型血人却有着最长的估计寿命,高于其他所有国家。谜底在于,他们能正确地对待饮食。日本人基本上食用大量的鱼、米及豆制品,并且视他们的绿茶为宝贝,就像巴伐利亚人对待他们的啤酒一样。这种饮食方式有利于A型血的人。谁想要长寿,就得按照血型合理地安排饮食。", "据俄罗斯《共青团真理报》报道,俄罗斯医学家们找到了许多血型和疾病之间相互关联的证据,以下是他们的研究成果:", "专家认为,O型血的人普遍长寿;A型血的人几乎没有对天花的免疫力,蚊子也更爱叮咬他们;B型血的人很少得癌症;而AB型血的人则有很高的肌体免疫力。", "O型血的人易患疾病包括胃溃疡和十二指肠疾病、肝硬化、胆囊炎、阑尾炎、支气管哮喘,脓肿等。虽然平常较易生病,但平均寿命明显较长。", "A型血的人容易患葡萄球菌化脓感染、沙门氏菌病、结核病、白喉、痢疾、流行性感冒、动脉粥样硬化、风湿病、心肌梗塞、癫痫、慢性酒精中毒等疾病。 B型血的人易患的疾病包括痢疾,流行性感冒、神经根炎、骨病、泌尿生殖系统、关节炎等。", "AB型血的人容易患脓毒性感染、急性呼吸道疾病、病毒性肝炎等疾病。据统计,AB型血的人患精神分裂症比其他血型高出3倍多,但AB型血在患结核病、妊娠贫血的比率上,则比其他血型的人低很多。看到这里姐突然觉得整个人都精神多了,是不是已经得了精神病的缘故呢。。。", "虽说以上血型与健康的研究成果让某些人有点小沮丧,但姐坚信命运掌握在自己手中,先天不足,后天来补,跟着姐看看各种血型的先天劣势怎么破!", "根据血型来养生", "A型血:A型血的人大多是工作狂,他们喜欢尝试未知,承担风险,但与此同时也承担了繁重的压力。当压力增大时,神经系统会受到影响,人体生理机能失衡,继而导致心脏功能严重受损。因此A型血的人最重要的就是要学会减压,不给心脏病以生存空间。 A型血在日常生活中可以常吃鳗鱼,适当吃猪肉,补充身体所需的脂肪、蛋白质等营养元素,还要少做剧烈运动,这样不仅不能锻炼身体,反而会加剧免疫力下降。不如每日练练瑜伽,在一招一式中舒缓疲劳身体。此外,在睡前30分钟可以喝一杯纯净水,让本来黏稠的血液变得顺畅,从而缓解紧张状态,达到减压的目的,促进安眠。 B型血:B型血对于具有相同的B型抗原的细菌毫无防范之力,加之自身某些免疫系统功能失调,防卫能力最差,因此极其容易患上肺结核、流感等传染疾病。对于B型血的人来说,调节健康的重点在于提升自身对于外界环境变化的应变能力,才能减低患上传染病的几率。 B型血在生活中要少吃猪肉,如果一次食用过多就会出现过敏现象。钙是B型血的重要元素,唯有身体内有足量的钙,新陈代谢才能正常运转,提升身体的应变能力,调节自身的免疫系统平衡。最佳的补钙方式就是享受日光浴,因此B型血的人不妨多去户外做做小步跑、高抬腿散步等力所能及的运动。 O型血:O型血人的自身消化系统功能一般都强大,也增加了胃酸的分泌含量,如此一来,胃壁就更容易受到刺激,轻微的影响就会出现胃溃疡现象。想要避免胃酸分泌过多,最佳的方法就是在中餐和午餐过后切一片生姜,先含在嘴里3分钟,再细嚼咽下。需要注意的是,吃姜不宜贪多,只需薄薄一片即可,如果服用过多,反而会加重胃酸分泌。 此外,每天的下午茶时间,用70%的热水和30%的凉开水勾兑出温开水,快速饮用后,努力有节奏地打嗝,然后用手掌心从上到下轻揉胃部,达到温暖、通气的目的。这样简单的小行动就能让胃部的浊气排出,不给过量胃酸提供条件。 AB型血:AB型血是人类历史上最晚诞生的血型,对疾病的抵御能力也最弱,哪怕是微弱的影响也会让健康很受伤。不妨在每天上午10点、下午4点各饮用一大杯酸奶,改变体内生物环境,提高人体免疫能力。", "建议AB型血的人每星期吃两次凉菜,比如虾油炝拌萝卜丝就有促进消化、增强食欲的作用,为身体内部降降温,有效提升AB型血人的免疫力。", "最近日本有一份交通事故调查表明,AB型血的人经常因为睡眠不足而导致交通事故,尤其在深夜2点～凌晨6点之间。这与AB型血人免疫力低下,容易疲倦有很大关系。所以,请AB型血的你在需要深夜驾车前小憩15分钟,养足精神才能开车哦!"], "bs_rank_pos": 0}, {"is_selected": false, "title": "哪种血型的人最长寿_新浪健康_新浪网", "most_related_para": 21, "segmented_title": ["哪", "种", "血型", "的", "人", "最长寿", "_", "新浪", "健康", "_", "新浪网"], "segmented_paragraphs": 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"拥有最长的估计寿命的人是O型血人,这些人的平均寿命几乎达到87岁。他们是养老金发放机构的噩梦。这个估计寿命之高原本并不令人吃惊,因为这些人身体强壮,有坚定的意志,心理上相对平衡,并且无忧无虑。", "B型血的人还是保持沉着冷静,即使他们会比O型血人早10年去世。他们顽固,并对一切事情过于执著。", "估计寿命再少10年的是AB型血的人,平均达到70岁。", "而平均活得最短的是A型血的人,仅仅62岁!在心理学家看来,这其实是意料之中的,因为A型血的人脸皮薄而且非常敏感,他们会一直为自己以及身边的人操心。很遗憾,这往往会使心脏的负担过重,甚至出现耗损。", "但是A型血的人寿命相对于其他血型要短,还有一个完全不同的原因。医生及自然疗法医师彼特.J。达达默医学博士,发现了特定的食物与血型之间的对立关系。在他的一本著作中阐述了人与食物的搭配问题,因为饮食习惯受血型的影响。", "他认为O型血与A型血的人的主要区别在于对肉类食品的消化:O型血的人需要肉食,并且吸收得很好;而A型血的人不能很好地消化肉类,相对而言更需要谷物及蔬菜类食物。其根本原因在于胃酸分泌量的不同:O型血的人胃酸充足,因此消化肉类只是小菜一碟。很可惜,我们今天的食谱上几乎都是肉类快餐:烤肉串、汉堡包、烤香肠以及牛排,这对于A型血人以及他们那脆弱的消化系统来说,就好比毒药。错误的饮食习惯使这些人元气大伤,并且缩短了寿命。", "让我们来看一下日本的情况。这个国家的A型血人却有着最长的估计寿命,高于其他所有国家。谜底在于,他们能正确地对待饮食。日本人基本上食用大量的鱼、米及豆制品,并且视他们的绿茶为宝贝,就像巴伐利亚人对待他们的啤酒一样。这种饮食方式有利于A型血的人。谁想要长寿,就得按照血型合理地安排饮食。", "据俄罗斯《共青团理报》报道,俄罗斯医学家们找到了许多血型和疾病之间相互关联的证据,以下是他们的研究成果。", "专家认为,O型血的人普遍长寿;A型血的人几乎没有对天花的免疫力,蚊子也更爱叮咬他们;B型血的人很少得癌症;而AB型血的人则有很高的肌体免疫力。", "O型血的人易患疾病包括胃溃疡和十二指肠疾病、肝硬化、胆囊炎、阑尾炎、支气管哮喘,脓肿等。虽然平常较易生病,但平均寿命明显较长。", "A型血的人容易患葡萄球菌化脓感染、沙门氏菌病、结核病、白喉、痢疾、流行性感冒、动脉粥样硬化、风湿病、心肌梗塞、癫痫、慢性酒精中毒等疾病。 B型血的人易患的疾病包括痢疾,流行性感冒、神经根炎、骨病、泌尿生殖系统、关节炎等。", "AB型血的人容易患脓毒性感染、急性呼吸道疾病、病毒性肝炎等疾病。据统计,AB型血的人患精神分裂症比其他血型高出3倍多,但AB型血在患结核病、妊娠贫血的比率上,则比其他血型的人低很多。", "让我们来看一下日本的情况。这个国家的A型血人却有着最长的估计寿命,高于其他所有国家。谜底在于,他们能正确地对待饮食。日本人基本上食用大量的鱼、米及豆制品,并且视他们的绿茶为宝贝,就像巴伐利亚人对待他们的啤酒一样。这种饮食方式有利于A型血的人。谁想要长寿,就得按照血型合理地安排饮食。", "据俄罗斯《共青团理报》报道,俄罗斯医学家们找到了许多血型和疾病之间相互关联的证据,以下是他们的研究成果。", "专家认为,O型血的人普遍长寿;A型血的人几乎没有对天花的免疫力,蚊子也更爱叮咬他们;B型血的人很少得癌症;而AB型血的人则有很高的肌体免疫力。", "O型血的人易患疾病包括胃溃疡和十二指肠疾病、肝硬化、胆囊炎、阑尾炎、支气管哮喘,脓肿等。虽然平常较易生病,但平均寿命明显较长。", "A型血的人容易患葡萄球菌化脓感染、沙门氏菌病、结核病、白喉、痢疾、流行性感冒、动脉粥样硬化、风湿病、心肌梗塞、癫痫、慢性酒精中毒等疾病。 B型血的人易患的疾病包括痢疾,流行性感冒、神经根炎、骨病、泌尿生殖系统、关节炎等。", "AB型血的人容易患脓毒性感染、急性呼吸道疾病、病毒性肝炎等疾病。据统计,AB型血的人患精神分裂症比其他血型高出3倍多,但AB型血在患结核病、妊娠贫血的比率上,则比其他血型的人低很多。", "爱唠叨的人长寿", "北京曾对1000多名“抗癌明星”进行调查,几乎所有人都认为能活下来的原因就是心态好,一旦精神崩溃,那就全完了。美国匹兹堡大学研究人员对9万名女性进行了8年跟踪调查,发现性格乐观的人死亡率低14%,罹患心脏病的几率低9%。挪威对2015名癌症患者的调查也发现,那些极具幽默感的人的生存率,要比那些幽默感极度贫乏的人高出70%。", "“保持一个阳光的心态,远胜过任何好医生。它不但有益大脑皮层和神经的协调,还能增强机体的免疫力,延缓衰老。”首都医科大学附属北京安贞医院心血管十二病区主任周玉杰教授表示,研究表明,开朗的人免疫力往往比敏感者强。平日里各种不良情绪都会使你的身体发生变化。比如,生气时会出现脉搏、心跳、呼吸加快等症状;忧伤时会使消化腺分泌的消化液减少,食欲减退;恐惧、说谎会使中枢神经紧张,随时导致血压升高。", "这是因为,人不高兴和开心时分泌的物质不同———不高兴时分泌的是肾上腺素,这是一种应激激素,会导致血管收缩,血压升高,长期过量分泌,会导致高血压、心脏病等;而开心时分泌的是内啡肽,它能使人心情愉悦。", "不管谁家有个爱唠叨的人,都会让人叫苦不迭。但在中国中医科学院研究员杨金生眼里,这也是一种长寿秘诀。他认为,唠叨能调动记忆功能和语言表达功能,还能锻炼脑细胞。经常说话的人,能使口腔肌肉和咽喉得到锻炼,有利于保持耳咽管的通畅,使耳朵内外的压力保持平衡,对于耳鸣、耳聋有保健作用,说话时带动眼肌和三叉神经运动,还可防止老花眼、老年性白内障和视力减退。", "揭秘14个长寿迹象", "1.出生时母亲还年轻。美国芝加哥大学科学家研究发现,一个人出生时母亲年龄如果不到25岁,他们活到100岁的几率是出生时母亲超过25岁的人的两倍。 2.爱喝茶。一项针对4.05万名日本男女的研究结果显示,每天喝至少5杯茶的人,他们死于心脏病和中风的几率最低。 3.每天步行30分钟。一项针对2603名男女的最新研究结果表明,每天坚持步行30分钟左右的人,不管其体内脂肪含量有多高,他们的长寿几率是那些每天步行少于30分钟的人的4倍。 4.少喝碳酸饮料。美国科学家发现,每天喝一次以上可乐会使你患心脏病、糖尿病等病的风险加倍。倘若实在想喝几口碳酸饮料,可以在里面加入一些果汁。"], "bs_rank_pos": 1}, {"is_selected": false, "title": "O型血的人长寿 血型与寿命有何神奇关系_网易健康", "most_related_para": 6, "segmented_title": ["O型血", "的", "人", "长寿", "血型", "与", "寿命", "有", "何", "神奇", "关系", "_", "网易", "健康"], 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"时至今日都还没有证据表明,美国是否在征兵时也运用了有关血型心理学的知识,来选拔适合于充当领导的人。据说日本早就推行了这种方法。无论如何,研究还是在继续进行、补充及计算,并得出了令人兴奋但又吃惊的结论。", "B型性格寿星最多", "“在心理学家眼里,性格不止决定命运,还可能决定你得什么病。”中国科学院心理所研究员、老年心理研究中心主任李娟表示,就像血型分A、B、O型一样,人的性格也分为A、B、C、D四种,并和人的得病几率密切相关。", "A型性格的人争强好胜,时间观念和上进心都很强,力求占据领导者的地位,这种人压力比较大,容易紧张、激动,他们患高血压、心脑血管疾病的风险是普通人的2—3倍;C型性格的人对人际沟通过分焦虑,总是忍气吞声,压抑情绪,这样的人最容易得癌症;D型性格又称“忧伤人格”,最明显的表现是消极忧伤和孤独压抑,这些正是心血管疾病的危险因素。而“没心没肺,有说有笑”则指的是B型性格的人,他们很满足于现状,知足常乐,生活张弛有度,没有大的情绪波动,所以这类人进入“寿星”行列的几率最高。 今年84岁高龄的国医大师陆广莘满头乌发,从相貌上看也就50岁左右,他总结自己的养生秘诀时说:“我就是个没心没肺的人,什么事情都不往心里去。”陆老笑言自己读书的时候从来不考第一名,但总是第一个交卷,因为不想给自己那么大的压力。他说:“当你有一个积极的心理状态,身体的免疫功能都会变好。”上海市闸北区最长寿的107岁老人包廷琴,最大的特点就是乐观开朗。老人耳朵不好,听不见别人说话,但一见对方开口,就一定会乐呵呵点头。包廷琴的女儿透露,老人没有“隔夜愁”,遇到烦心事,总是睡一觉起来就全消散了。 据中国老年学会调查,在百岁老人的长寿原因中,遗传基因占15%、社会因素占10%、医疗条件改善占8%、气候条件占7%,其余60%则取决于老人自己。其中排在第一位的秘诀就是心态。 O型血的人最长寿 数学家及医生史蒂芬.M.魏斯贝格(StevenM.Weissberg)与知名的健身专家约瑟夫.克里斯蒂安奴(JosephChristiano)在对5000人的问卷调查中得出了有趣的数据。其中有一个问题,不同血型的人的平均估计寿命是否存在差别呢? 美国人的平均寿命为76岁。这个数字是命中注定的。而它又是让人欣喜的,因为只有少数人明显地更加长寿,相反其他人很早就去世了。 拥有最长的估计寿命的人是O型血人,这些人的平均寿命几乎达到87岁。他们是养老金发放机构的噩梦。这个估计寿命之高原本并不令人吃惊,因为这些人身体强壮,有坚定的意志,心理上相对平衡,并且无忧无虑。 B型血的人还是保持沉着冷静,即使他们会比O型血人早10年去世。他们顽固,并对一切事情过于执著。 估计寿命再少10年的是AB型血的人,平均达到70岁。 而平均活得最短的是A型血的人,仅仅62岁!在心理学家看来,这其实是意料之中的,因为A型血的人脸皮薄而且非常敏感,他们会一直为自己以及身边的人操心。很遗憾,这往往会使心脏的负担过重,甚至出现耗损。 但是A型血的人寿命相对于其他血型要短,还有一个完全不同的原因。医生及自然疗法医师彼特.J.达达默医学博士,发现了特定的食物与血型之间的对立关系。在他的一本著作中阐述了人与食物的搭配问题,因为饮食习惯受血型的影响。 他认为O型血与A型血的人的主要区别在于对肉类食品的消化:O型血的人需要肉食,并且吸收得很好;而A型血的人不能很好地消化肉类,相对而言更需要谷物及蔬菜类食物。其根本原因在于胃酸分泌量的不同:O型血的人胃酸充足,因此消化肉类只是小菜一碟。很可惜,我们今天的食谱上几乎都是肉类快餐:烤肉串、汉堡包、烤香肠以及牛排,这对于A型血人以及他们那脆弱的消化系统来说,就好比毒药。错误的饮食习惯使这些人元气大伤,并且缩短了寿命。 让我们来看一下日本的情况。这个国家的A型血人却有着最长的估计寿命,高于其他所有国家。谜底在于,他们能正确地对待饮食。日本人基本上食用大量的鱼、米及豆制品,并且视他们的绿茶为宝贝,就像巴伐利亚人对待他们的啤酒一样。这种饮食方式有利于A型血的人。谁想要长寿,就得按照血型合理地安排饮食。 据俄罗斯《共青团理报》报道,俄罗斯医学家们找到了许多血型和疾病之间相互关联的证据,以下是他们的研究成果。 专家认为,O型血的人普遍长寿;A型血的人几乎没有对天花的免疫力,蚊子也更爱叮咬他们;B型血的人很少得癌症;而AB型血的人则有很高的肌体免疫力。 O型血的人易患疾病包括胃溃疡和十二指肠疾病、肝硬化、胆囊炎、阑尾炎、支气管哮喘,脓肿等。虽然平常较易生病,但平均寿命明显较长。 A型血的人容易患葡萄球菌化脓感染、沙门氏菌病、结核病、白喉、痢疾、流行性感冒、动脉粥样硬化、风湿病、心肌梗塞、癫痫、慢性酒精中毒等疾病。", "A型血的人容易患葡萄球菌化脓感染、沙门氏菌病、结核病、白喉、痢疾、流行性感冒、动脉粥样硬化、风湿病、心肌梗塞、癫痫、慢性酒精中毒等疾病。 B型血的人易患的疾病包括痢疾,流行性感冒、神经根炎、骨病、泌尿生殖系统、关节炎等。", "爱唠叨的人长寿", "北京曾对1000多名“抗癌明星”进行调查,几乎所有人都认为能活下来的原因就是心态好,一旦精神崩溃,那就全完了。美国匹兹堡大学研究人员对9万名女性进行了8年跟踪调查,发现性格乐观的人死亡率低14%,罹患心脏病的几率低9%。挪威对2015名癌症患者的调查也发现,那些极具幽默感的人的生存率,要比那些幽默感极度贫乏的人高出70%。", "“保持一个阳光的心态,远胜过任何好医生。它不但有益大脑皮层和神经的协调,还能增强机体的免疫力,延缓衰老。”首都医科大学附属北京安贞医院心血管十二病区主任周玉杰教授表示,研究表明,开朗的人免疫力往往比敏感者强。平日里各种不良情绪都会使你的身体发生变化。比如,生气时会出现脉搏、心跳、呼吸加快等症状;忧伤时会使消化腺分泌的消化液减少,食欲减退;恐惧、说谎会使中枢神经紧张,随时导致血压升高。", "这是因为,人不高兴和开心时分泌的物质不同———不高兴时分泌的是肾上腺素,这是一种应激激素,会导致血管收缩,血压升高,长期过量分泌,会导致高血压、心脏病等;而开心时分泌的是内啡肽,它能使人心情愉悦。", "揭秘14个长寿迹象", "1.出生时母亲还年轻。美国芝加哥大学科学家研究发现,一个人出生时母亲年龄如果不到25岁,他们活到100岁的几率是出生时母亲超过25岁的人的两倍。 2.爱喝茶。一项针对4.05万名日本男女的研究结果显示,每天喝至少5杯茶的人,他们死于心脏病和中风的几率最低。 3.每天步行30分钟。一项针对2603名男女的最新研究结果表明,每天坚持步行30分钟左右的人,不管其体内脂肪含量有多高,他们的长寿几率是那些每天步行少于30分钟的人的4倍。 4.少喝碳酸饮料。美国科学家发现,每天喝一次以上可乐会使你患心脏病、糖尿病等病的风险加倍。倘若实在想喝几口碳酸饮料,可以在里面加入一些果汁。"], "bs_rank_pos": 2}, {"is_selected": false, "title": "哪种血型的人容易长寿_百度知道", "most_related_para": 0, "segmented_title": ["哪", "种", "血型", "的", "人", "容易", "长寿", "_", "百度", "知道"], "segmented_paragraphs": [["我", "觉得", "这", "是", "要", "看", "个人", "是否", "爱惜", "自己", "的", "身体", "了", "。", "O型血", "的", "人", "容易", "患", "上", "糖尿病", "和", "肥胖", "A型血", "的", "人", "癌症", "发病率", "相对", "较", "高", "B型血", "的", "人", "能够", "抵抗", "许多", "最", "严重", "的", "疾病", "你知道吗", "?", "B型血", "的", "人", "能够", "适应", "各种", "食物", ",", "而且", "能够", "抵抗", "现代", "生活", "中", "许多", "最", "严重", "的", "疾病", ",", "例如", "心脏病", "和", "癌症", "。", "你知道吗", "?", "AB型血", "的", "人", "食用", "植物类", "食物", "新陈代谢", "快", "、", "效率", "高", ",", "而", "使用", "过", "多", "的", "肉食品", "会引起", "脂肪", "堆积", ",", "引发", "肥胖", "、", "心脏病", "和", "糖尿病", "等", "。", "你知道", "吗", "?", "不同", "血型", "的", "人", "具有", "不同", "的", "生理特征", ",", "同", "一", "种", "食物", "对", "不同", "血型", "的", "人", "可能", "会", "有", "不同", "的", "影响", "。", "你知道吗", "?", "最", "健康", "、", "最有效", "的", "减肥", "方法", "就是", "坚持", "适合", "你", "血型", "的", "饮食", "计划", "和", "体育运动", "。", "你知道吗", "?", "你", "的", "血型", "和", "你", "的", "健康", 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"叮咬", "他们", ";", "B型血", "的", "人", "很少", "得", "癌症", ";", "而", "AB型血", "的", "人", "则", "有", "很高", "的", "肌", "体", "免疫力", "。"], ["[", "责任编辑", ":", "王亚楠", "]"], ["这", "三种", "茶", "堪称", "糖尿病", "的", "克星", "嘴里", "这", "一", "症状", "是", "癌前病变", "!", "三伏天", "这样", "泡脚", ",", "活血", "补肾", "去", "湿气", "中医", "雄起", "穴位", "一分钟", "治病", "!", "83", "岁", "国", "医", "大师", "的", "5", "大", "养生", "箴言", "这些", "极速", "解酒", "食物", "你", "要知道", "饭后", "六", "个", "习惯", "堪比", "慢性自杀"], ["凤凰", "健康", "官方", "微信"]], "paragraphs": ["时至今日都还没有证据表明,美国是否在征兵时也运用了有关血型心理学的知识,来选拔适合于充当领导的人。据说日本早就推行了这种方法。无论如何,研究还是在继续进行、补充及计算,并得出了令人兴奋但又吃惊的结论。", "时至今日都还没有证据表明,美国是否在征兵时也运用了有关血型心理学的知识,来选拔适合于充当领导的人。据说日本早就推行了这种方法。无论如何,研究还是在继续进行、补充及计算,并得出了令人兴奋但又吃惊的结论。", "哪种血型的人寿命最长?(华盖供图)", "O型血的人最长寿?", "数学家及医生史蒂芬(StevenM.Weissberg)与知名的健身专家约瑟夫。克里斯蒂安奴(JosephChristiano)在对5000人的问卷调查中得出了有趣的数据。其中有一个问题,不同血型的人的平均估计寿命是否存在差别呢?", "美国人的平均寿命为76岁。这个数字是命中注定的。而它又是让人欣喜的,因为只有少数人明显地更加长寿,相反其他人很早就去世了。", "拥有最长的估计寿命的人是O型血人,这些人的平均寿命几乎达到87岁。他们是养老金发放机构的噩梦。这个估计寿命之高原本并不令人吃惊,因为这些人身体强壮,有坚定的意志,心理上相对平衡,并且无忧无虑。", "B型血的人还是保持沉着冷静,即使他们会比O型血人早10年去世。他们顽固,并对一切事情过于执著。", "估计寿命再少10年的是AB型血的人,平均达到70岁。", "而平均活得最短的是A型血的人,仅仅62岁!在心理学家看来,这其实是意料之中的,因为A型血的人脸皮薄而且非常敏感,他们会一直为自己以及身边的人操心。很遗憾,这往往会使心脏的负担过重,甚至出现耗损。", "但是A型血的人寿命相对于其他血型要短,还有一个完全不同的原因。医生及自然疗法医师彼特.J。达达默医学博士,发现了特定的食物与血型之间的对立关系。在他的一本著作中阐述了人与食物的搭配问题,因为饮食习惯受血型的影响。", "他认为O型血与A型血的人的主要区别在于对肉类食品的消化:O型血的人需要肉食,并且吸收得很好;而A型血的人不能很好地消化肉类,相对而言更需要谷物及蔬菜类食物。其根本原因在于胃酸分泌量的不同:O型血的人胃酸充足,因此消化肉类只是小菜一碟。很可惜,我们今天的食谱上几乎都是肉类快餐:烤肉串、汉堡包、烤香肠以及牛排,这对于A型血人以及他们那脆弱的消化系统来说,就好比毒药。错误的饮食习惯使这些人元气大伤,并且缩短了寿命。", "让我们来看一下日本的情况。这个国家的A型血人却有着最长的估计寿命,高于其他所有国家。谜底在于,他们能正确地对待饮食。日本人基本上食用大量的鱼、米及豆制品,并且视他们的绿茶为宝贝,就像巴伐利亚人对待他们的啤酒一样。这种饮食方式有利于A型血的人。谁想要长寿,就得按照血型合理地安排饮食。", "据俄罗斯《共青团理报》报道,俄罗斯医学家们找到了许多血型和疾病之间相互关联的证据,以下是他们的研究成果。", "专家认为,O型血的人普遍长寿;A型血的人几乎没有对天花的免疫力,蚊子也更爱叮咬他们;B型血的人很少得癌症;而AB型血的人则有很高的肌体免疫力。", "[责任编辑:王亚楠]", "这三种茶堪称糖尿病的克星 嘴里这一症状是癌前病变! 三伏天这样泡脚,活血补肾去湿气 中医雄起穴位一分钟治病! 83岁国医大师的5大养生箴言 这些极速解酒食物你要知道 饭后六个习惯堪比慢性自杀", "凤凰健康官方微信"], "bs_rank_pos": 5}], "answer_spans": [[298, 300]], "answer_docs": [0], "fake_answers": ["O型血人的"], "question": "什么血型的人长寿健康", "segmented_answers": [["O型血", "的", "人", "最长寿", "。"]], "answers": ["O型血的人最长寿。"], "entity_answers": [["O型"]], "segmented_question": ["什么", "血型", "的", "人", "长寿", "健康"], "question_type": "ENTITY", "match_scores": [0.7499999999999999], "fact_or_opinion": "OPINION", "question_id": 181635}
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",", "大家", "尝试", "一", "下", "就", "知道", "了", "~", "希望", "能", "帮", "到", "大家", "!"], ["?", "?", "你", "自己", "复制", "这个", "就是", "了", "你", "看看", "是不是", "你", "要", "的", ",", "你", "要", "复制", "到", "好友", "印象", "就能", "看得见", "了"]], "paragraphs": ["☑ 1.在word中, 点击插入,选择“符号”,在弹出的“符号”对话框中,最后一行的“字符代码”框中输入221A就可以了。当然也可以自己慢慢找“√”2.像字体加外框一样,选中已经打好的“√”,单击“格式”、“边框和底纹”在“边框”里选择“方框”。 OK, 完成了。 如果在excel里,就这么做:1.右键工具栏,调出绘图工具栏,选方框,在工作表上拖出一个文本框,点右键“添加文字”然后在文本框里输入√,再调整大小,OK,完成,或者,右键工具栏,调出窗体工具栏,选中复选框,在工作表上拖出一个复选框,鼠标点击就能出现√,OK,完成 当然你也可以直接把这个符号复制过去O(∩_∩)O~", "一个更为简单的方法: 在你要打勾的地方输入R,之后把R选中,然后单击 菜单栏→格式→字体。 会出现一个窗口,你会看到一个中文字体,西文字体,选择下拉框,拉到最下面。 选中Wingdings2,然后点击确定,就会变成你想要的方框里打勾的符号了。 此方法亲测有效,但有一些版本是不能使用此方法的,大家尝试一下就知道了~ 希望能帮到大家!", "??你自己复制这个就是了你看看是不是你要的,你要复制到好友印象就能看得见了"], "bs_rank_pos": 2}, {"is_selected": true, "title": "word中在方框内打勾符号怎么打_百度知道", "most_related_para": 0, "segmented_title": ["word", "中", "在", "方框", "内", "打", "勾", "符号", "怎么", "打", "_", "百度", "知道"], "segmented_paragraphs": [["工具", ":", "word", "2010", "通过", "插入", "-", 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"准备", "的", "人"]], "paragraphs": ["百度经验:jingyan.baidu.com", "方框内打的符号做为一种特殊符号,在日常办公中具有极高的使用频率,下面小编就教大家实现Word内插入方框打勾符号的方法。", "百度经验:jingyan.baidu.com", "1 打开Office Word,点击“插入”->“符号”->“其它符号”。 步骤阅读 2 接着在打开的“符号”窗口中,从“字体”下拉列表中选择“WingDings”,然后就会在符号列表的最下方找到“方框勾号”符号。 步骤阅读 3 点击“插入”按钮即可将方框打勾符号插入到文档中。 步骤阅读 4 同时为了以后输入方便,我们可以通过指定快捷键来实现快速插入操作。点击“快捷键”按钮。 步骤阅读 5 然后输入一组新的快捷键,并点击“指定”按钮完成设置。以后只需要按这一组快捷键即可快速完成输入操作。 步骤阅读 END", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:机遇总是留给有准备的人"], "bs_rank_pos": 4}, {"is_selected": false, "title": "word中怎么插入方框中打对勾的符号_百度知道", "most_related_para": 1, "segmented_title": ["word", "中", "怎么", "插入", "方框", "中", "打", "对勾", "的", "符号", "_", "百度", "知道"], "segmented_paragraphs": [["1", "、", "启动", "word", ",", "单击", "插入", "菜单", ",", "选择", "符号", "选项", "。", "2", "、", "单击", "符号", "选项", "中", "的", "其他", "符号", "按钮", "。", "3", "、", "在", "打开", "的", "符号", "对话框", "中", ",", "单击", "字体", "下拉框", "。", "4", "、", "在", "字体", "下拉框", "中", ",", "选择", "“", "Wing", "dings", "2", "”", "。", "5", 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"然后", "选择", "【", "勾", "选", "框", "】", "，", "点击", "插入", "即可", "。"]], "answers": ["第一种办法：打开一个word表格，然后单击工具栏中的“插入”。然后在下拉工具栏中找到符号，并在符号下拉框中找到√，单击√就可以直接插入到word文档。第二种方法是单击“插入”，“形状”。选择下拉框中的√，可以根据自己的需要调节√的大小、颜色和是否需要外边的方框。有的输入法可以直接在打出文字”勾“的时候会出现相应的 √，这是只是需要进行相应的选择即可。", "1.在word中， 点击插入，选择“符号”，在弹出的“符号”对话框中，最后一行的“字符代码”框中输入221A就可以了。当然也可以自己慢慢找“√”2.像字体加外框一样，选中已经打好的“√”，单击“格式”、“边框和底纹”在“边框”里选择“方框”。如果在excel里，就这么做：1.右键工具栏，调出绘图工具栏，选方框，在工作表上拖出一个文本框，点右键“添加文字”然后在文本框里输入√，再调整大小，OK，完成，或者，右键工具栏，调出窗体工具栏，选中复选框，在工作表上拖出一个复选框，鼠标点击就能出现√，OK，完成。", "工具：word2010。通过插入--符号--其他符号，可以插入【方框内打钩符号】。步骤1：打开word文档；步骤2：点击插入--符号--其他符号；步骤3：字体选择Wingdings，然后选择【勾选框】，点击插入即可。"], "answer_docs": [2], "segmented_question": ["框", "中", "打", "勾", "符号"], "question_type": "DESCRIPTION", "question_id": 181636, "fact_or_opinion": "FACT", "match_scores": [0.864]}
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"可以看到彩豆的获取和你没把比赛的名次是有关的,所以想获得更多的彩豆,可以找些人一起合作,这样容易获得好的排名。", "▍彩豆有什么用", "那么重点来了,彩豆到底能用来干嘛?在之前的版本里,彩豆可以在魔法屋中购买光环、孢子等皮肤,不过能买的数量,不如棒棒糖多。", "不过现在彩豆的主要作用在于说话,也就是在游戏中语音消耗,已经不能用来买皮肤了。", "更多相关攻略:", "【攻略】球球大作战彩豆能买吗 彩豆怎么得", "【攻略】球球大作战棒棒糖怎么获得 棒棒糖怎么刷", "【攻略】球球大作战棒棒糖抵用券怎么得 能换几个棒棒糖", "【攻略】球球大作战金蘑菇怎么得 金蘑菇有什么用", "加入4399球球大作战官方群!1群546297796(满)2群499491158", "4399手游官方微信(微信号:sj4399)要你好玩,要你好看!独家礼包,新鲜爆料!"], "bs_rank_pos": 3}, {"is_selected": false, "title": "球球大作战彩豆有什么用?球球大作战彩豆用途介绍_百度文库", "most_related_para": 1, "segmented_title": ["球", "球", "大作战", "彩", "豆", "有", "什么", "用", "?", "球", "球", "大作战", "彩", "豆", "用途", "介绍", "_", "百度", "文库"], "segmented_paragraphs": [["球", "球", "大作战", "彩", "豆", "有", "什么", "用", "?", "球", "球", "大作战", "彩", "豆", "用途", "介绍", "_", "游戏", "_", "生活", "休闲", "。", "球", "球", "大作战", "彩", "豆", "有", "什么", "用", "?", "球", "球", "大作战", "彩", "豆", "用途", "介绍", "球", "球", "大作战", "彩", "豆", "有", "什么", "用", "?", "获得", "了", "一", "堆", "球", "球", "大作战", "的", "道具", "彩", "豆", "但是", "不懂", "具体", "这个", "道具", "有", "什么", 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"最后", "的", "结局", "是", "什么"], ["【", "励志", "电视剧", "】", "精华", "文章"], ["【", "励志", "电视剧", "】", "相关文章"]], "paragraphs": ["学习啦>励志>励志大全>励志电视剧>《青云志第三季什么时候播出 青云志第三部上映时间 青云志3播出时间》正文", "诛仙青云志第三部刚传出开播消息,就有很多人问诛仙青云志第三部什么时候上映?诛仙青云志第三部还是原班人马吗?以下是学习啦小编分享给大家的关于青云志第三季什么时候播出,一起来看看青云志第三部上映时间吧!", "内容提要: 鬼厉重回中原,亲自去摧毁诛仙剑以复活碧瑶,在望月台重逢陆雪琪。雪琪劝鬼厉回归正道,鬼厉却对正邪之分提出质疑。鬼厉趁兽神进攻青云山打断诛仙剑,却被诛仙剑反噬重伤,后被天音阁所救,普智师傅点醒他初心,于无字玉璧取得第四卷天书。鬼厉进入十万大山斩杀兽神,和陆雪琪经历生死一战,忘情相拥,鬼厉意识到自己对碧瑶是恩,对雪琪才是爱。", "鬼厉打算重回青云门与雪琪相爱定亲,却发现道玄已被诛仙剑戾气反噬。陆雪琪为守护青云门剿灭道玄同时被迫杀死被控制的田不易,鬼厉无法接受而再次离开。鬼王野心爆发,导致狐岐山崩塌,碧瑶失踪。鬼厉十年宿愿落空,反而领悟真心真爱,重寻回雪琪。鬼王却将碧瑶复活,并注入可怕的修罗之力。鬼厉面对昔日爱人和天下苍生,终于明白正邪自在人心,在雪琪相助下重塑诛仙剑。", "并回归初心变回张小凡与雪琪一生相守。", "每个人都有心魔,每个人都有执念!可以为了一个人与整个世界为敌,可以为了一个人牺牲自己的生命!其实很喜欢秦无炎的那句话 凭什么你说的岸就是岸,我说的岸便是海,看似强词夺理,却也不无道理!天地无心人有心,人心给予了善恶生存的空间～无论是小凡还是鬼厉,都是为了他们心中的执念,那一份信念给了他足以战胜兽神的力量。", "鬼厉等人成功阻止兽神复活 碧瑶是否醒来成谜团", "鬼王指责鬼厉最终还是背叛了魔教,他清楚地知道鬼厉会拼劲最后一丝力气,阻止兽神复活。鬼厉表示鬼王当初答应自己要帮自己复活碧瑶,一样也是在骗自己,因为他只想复活兽神。鬼王辩解道自己复活兽神就是为了复活碧瑶,因为兽神一旦复活,会释放出巨大的能量,碧瑶也会因此获得新生。鬼厉一语言中要害,他指出,鬼王现在的行为正是碧瑶生前最不愿意看到的。终于,鬼厉和鬼王还是大打出手,鬼厉身受重伤,小白赶来帮助鬼厉,却被鬼王关在了结界中。", "李洵拼尽全力对抗上官策,上官策凭着残存的一丝意识,告诉李洵,自己已经魔性攻心,要李洵杀了自己,去阻止鬼王。李洵不愿动手,魔化的上官策要对李洵出招,李洵抵抗的时候,不下心了结了上官策的性命,李洵十分难过,但上官策却笑着对李洵说了声谢谢,随后,就羽化消失了。", "秦无炎因为获得了兽神给予的法力功力大增,书书苦苦抵抗,李洵赶来相助,他们两人一起对付了秦无炎,这对曾经彼此看不惯的表兄弟齐心协力,为外祖父报了仇。李洵要带着伤势惨重的燕虹回焚香谷,他对要去帮助雪琪的书书说了句小心。雪琪对抗幽姬,她本可置幽姬于死地,可她选择了收手,她告诉幽姬,在小池镇,碧瑶救过自己的命,碧瑶是一个十分善良的人,所以她一定不愿意看到复活兽神,伤及无辜这样的事。幽姬有所触动,离开了。", "林惊羽以一敌二,对付金瓶儿和青龙,关键时刻,金瓶儿倒戈,帮助了林惊羽,青龙受伤逃走,金瓶儿身受重伤倒在林惊羽的怀里,她笑着闭上了眼睛,丁玲及时赶到,给金瓶儿喂下了护心丹,奉三妙夫人之命带金瓶儿回去,寻求一线生机。", "冥灵峰的祭坛,小白看出了鬼王心里的一丝害怕,她直言小痴的死和碧瑶的死,其实都是硅烷造成的,而此时,身受重伤的鬼厉一步步艰难地向鬼王走去,在他要几乎要撑不住的时候,他感受到了碧瑶站在自己的面前,握着自己的手,告诉他自己会陪他一起走下去,鬼厉又充满了力量,一步步走向冥灵峰祭坛。", "兽神察觉到了鬼厉的到来,他知道鬼厉三番四次阻难自己复活,要鬼王杀了他。鬼王再次和鬼厉出手相向,此时,雪琪书书和林惊羽赶来,助鬼厉一臂之力。鬼王用玄火鉴的能量和鬼厉手中的噬血珠抵抗,鬼王看到了伤心花的法力,看到碧瑶的幻影对自己说,爹,你停手吧,一时恍惚失手丢了玄火鉴,小白拿到玄火鉴交给鬼厉,让他赶快进入血阵。鬼厉拿着玄火鉴进入血政,看到鬼先生正在对萧逸才施法,拼尽全力阻止了两人,鬼先生和萧逸才都被鬼厉带出了血阵。小白在一旁知道,要鬼厉快收集天地灵气。", "鬼王带着鬼先生离开,林惊羽陆雪琪带着萧逸才离开,突见远处一道又一道黑光,他们好奇鬼厉现在身在何处。此时的鬼厉,在镇魔古洞,他释放了刚刚收集的天地灵气,加上伤心花的力量,希望可以复活碧瑶。可是碧瑶没有醒,鬼厉绝望地闭上了双眼,可就在此时,碧瑶的眼里滴下一滴泪,落在了伤心花上,小凡诧异地睁大了眼睛....(完结)", "青云志第三季什么时候播出相关文章:", "上一篇:春风十里不如你主题曲/插曲/bgm/片尾曲所有歌曲mv歌词mp3下载  下一篇:青云志第三季结局是什么 诛仙青云志3张小凡碧瑶最后的结局是什么", "【励志电视剧】精华文章", "【励志电视剧】相关文章"], "bs_rank_pos": 0}, {"is_selected": false, "title": "青云志3将开拍 青云志第三季什么时候上映?播出时间2018年_银行信息", "most_related_para": 2, "segmented_title": ["青云", "志", "3", "将", "开拍", "青云", "志", "第", "三", "季", "什么时候", "上映", "?", "播出时间", "2018", "年", "_", "银行", "信息"], "segmented_paragraphs": [["银行", "信息港", "财经", "6", "月", "9", "日", "讯", ":", "由", "赵丽颖", "、", "李易峰", "、", "杨紫", "主演", "的", "《", "青云", "志", "》", "已经", "收官", "了", ",", "不管", "是", "第", "一", "部", "还是", "第二部", "都", "牵动", "众多", "观众", "的", "心", 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"初心", "变", "回", "张小凡", "与", "雪琪", "一生", "相守", "。"], ["上", "一篇", ":", "欧阳", "娜娜", "回应", "退学", ":", "做", "喜欢", "又", "有", "薪水", "拿", "的", "工作", "有", "错", "吗", "?", "下一篇", ":", "关爱", "八卦", "成长", "协会", "等", "多", "家", "微信", "公众", "号", "被", "封号", "事件", "详情", "网址", ":", "www", ".", "yinhang", "123", ".", "net", "/", "zixun", "/", "yule", "news", "/", "2017", "/", "0609", "/", "989232", ".", "html", "首发", ":", "青云", "志", "3", "将", "开拍", "青云", "志", "第", "三", "季", "什么时候", "上映", "?", "播出时间", "2018", "年"], ["相关文章", "推荐"]], "paragraphs": ["银行信息港财经6月9日讯:由赵丽颖、李易峰、杨紫主演的《青云志》已经收官了,不管是第一部还是第二部都牵动众多观众的心,如今第三部已经正式启动了。那青云志3还是原班人马吗?剧情是怎样的?什么时候播?", "《青云志》是李易峰、赵丽颖、杨紫、成毅、秦俊杰、茅子俊等主演,讲述了草庙村少年张小凡投身青云门下,与鬼王之女碧瑶、好友林惊羽,以及陆雪琪、曾书书等少年们一道,帮助良善,斩妖除恶,最终挫败鬼王阴谋的故事。", "该剧第一部于2016年7月31日播出,第二部也早已经在腾讯视频上线了,那有第三部吗?近日,关于青云志第三部的消息来了,预计今年都是开拍,总的有80集,应该明年就可以里看到了。", "《青云志3》讲的是鬼厉重回中原,亲自去摧毁诛仙剑以复活碧瑶,在望月台重逢陆雪琪。雪琪劝鬼厉回归正道,鬼厉却对正邪之分提出质疑。鬼厉趁兽神进攻青云山打断诛仙剑,却被诛仙剑反噬重伤,后被天音阁所救,普智师傅点醒他初心,于无字玉璧取得第四卷天书。鬼厉进入十万大山斩杀兽神,和陆雪琪经历生死一战,忘情相拥,鬼厉意识到自己对碧瑶是恩,对雪琪才是爱。鬼厉打算重回青云门与雪琪相爱定亲,却发现道玄已被诛仙剑戾气反噬。", "陆雪琪为守护青云门剿灭道玄同时被迫杀死被控制的田不易,鬼厉无法接受而再次离开。鬼王野心爆发,导致狐岐山崩塌,碧瑶失踪。鬼厉十年宿愿落空,反而领悟真心真爱,重寻回雪琪。鬼王却将碧瑶复活,并注入可怕的修罗之力。鬼厉面对昔日爱人和天下苍生,终于明白正邪自在人心,在雪琪相助下重塑诛仙剑。并回归初心变回张小凡与雪琪一生相守。", "上一篇:欧阳娜娜回应退学:做喜欢又有薪水拿的工作有错吗? 下一篇:关爱八卦成长协会等多家微信公众号被封号事件详情  网址:www.yinhang123.net /zixun/yulenews/2017/0609/989232.html 首发:青云志3将开拍 青云志第三季什么时候上映?播出时间2018年", "相关文章推荐"], "bs_rank_pos": 1}, {"is_selected": true, "title": "青云志第三季还有吗_青云志第三季什么时候播出_青云志3更新时间 - ", "most_related_para": 2, "segmented_title": ["青云", "志", "第", "三", "季", "还有", "吗", "_", "青云", "志", "第", "三", "季", "什么时候", "播出", "_", "青云", "志", "3", "更新时间", "-"], "segmented_paragraphs": [["《", "青云", "志", "》", "8", "月", "8", "日", "开播", "在即", ",", "昨天", "(", "7", "月", "14", "日", ")", ",", "该剧", "在京举行", "了", "媒体", "超前", "点映", "会", ",", "带", "大家", "提前", "领略", "了", "来自", "“", "青云门", 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"电视剧诛仙《青云志》第三季还是会有的,黄杰透露,腾讯视频已经和欢瑞世纪制定了长线IP开发的计划,即将联合打造第三季,具体的播出更新时间未定,让我们一起来期待吧。", "《青云志》第二季无缝登陆 会员福利享不停", "看完片子就要放大招了!腾讯视频电视剧内容运营中心总监黄杰透露,为了陪伴大家度过完美暑假,腾讯视频专门打造了“暑期奇幻季”的概念,以迎接暑期“大战”,其中《九州天空城》、《青云志》、《幻城》、《权力的游戏6》等大剧将率先发力。“《青云志》虽然还没有正式播出,但它的相关专辑点击已经破亿了,可见观众们非常热情。”", "黄杰表示,《青云志》第一季定档8月8日登陆湖南卫视,腾讯视频也将同步全网首播,“第一季播出完毕之后,频会立即独立播出第二季的17集,是原班人马,剧情也会有延续性。”此外,黄杰还透露,腾讯视频已经和欢瑞世纪制定了长线IP开发的计划,即将联合打造第三季,“因为原著《诛仙》本身的体量,是能够支撑长篇的。”", "而且,腾讯视频线还会上线会员尊享的独家纪录片、独家特别篇、独家番外和独家衍生栏目等,VIP会员真正精彩享不停!更多精彩尽情期待8月8日腾讯视频《青云志》,不见不散!", "来说两句吧...", "凡人修仙传会拍成电视剧吗 《凡人修仙传》会出电视剧吗?都有哪些明星出演?凡人修仙传剧情介绍,《凡人修仙......", "电视剧十宗罪第一季一共多少集 电视剧十宗罪第一季一共多少集?第一季每天更新几集?播放平台是什么?《十宗罪》......", "超少年密码插曲 超少年密码正在热播中,三小只热情的演绎着青春激昂的机器人故事,伴随着未来的进......", "诛仙青云志第三季一共多少集 看完片子就要放大招了!腾讯视频电视剧内容运营中心总监黄杰透露,为了陪伴大家度......", "诛仙青云志第二季一共多少集 看完片子就要放大招了!腾讯视频电视剧内容运营中心总监黄杰透露,为了陪伴大家度......", "诛仙青云志第二季什么时候播出 《青云志》8月8日开播在即,昨天(7月14日),该剧在京举行了媒体超前点映会......", "林心如怀孕 当年的紫薇格格林心如如今有喜啦,小编没图会乱讲吗?然后我们一起来看一下吧。 ......", "大鱼海棠片尾曲 此前,由吉田潔作曲,梁旋作词,徐佳莹演唱的电影片尾曲《湫兮如风》单曲版一经发......"], 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"据悉,青云志2碧瑶是复活了的,从伤心花开,合欢铃响。另外,网传青云志2原本还拍了碧瑶鬼厉结婚的戏份,但是后面被删掉了。有网友曝出了碧瑶与鬼厉的大婚图,称碧瑶死而复生,两人终成眷属,不过这个桥段的戏份被删掉了没有播出。", "大结局剧情中鬼厉与青云门众人齐聚十万大山,并肩作战历尽艰险,最终阻止了兽神复活,正魔两道归于平静。鬼厉来到镇魔古洞,封印和嗜血珠释放灵气,灵气逐渐凝聚到碧瑶身上,碧瑶终于站了起来。鬼厉满怀欣喜,想触摸碧瑶的面庞,却发现碧瑶毫无动静。正当他失望之际,碧瑶眼角留下一滴眼泪,落在伤心花花瓣上。此时,碧瑶腰间的金玲发出声响。", "《青云志》编剧姜磊表示,第三季的剧本已经差不多了,应该是2017年上半年开拍,演员方面,不一定全部用原来这些演员,但会尽量满足大家的愿望。", "本文相关新闻推荐:", "其他网友正在看", "日本放宽签证条件新政策 中国人赴日越来越", "上海林家宅37号灵异事件结果 无脑人叶先国"], "bs_rank_pos": 4}], "answer_spans": [[39, 41]], "answer_docs": [2], "fake_answers": ["具体的播出"], "question": "青云志3几月几日播出", "segmented_answers": [["具体", "的", "播出", "更新时间未定", "。"]], "answers": ["具体的播出更新时间未定。"], "entity_answers": [[]], "segmented_question": ["青云", "志", "3", "几", "月", "几", "日", "播出"], "question_type": "ENTITY", "match_scores": [0.7499999999999999], "fact_or_opinion": "FACT", "question_id": 181639}
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["公司倒闭开除员工属于合同劳动法第四十一条的第一款,根据第四十六条规定公司应该对员工进行赔偿,劳动合同法第四十七条对补偿标准进行了规定。劳动合同法第四十一条有下列情形之一,需要裁减人员二十人以上或者裁减不足二十人但占企业职工总数百分之十以上的,用人单位提前三十日向工会或者全体职工说明情况,听取工会或者职工的意见后,裁减人员方案经向劳动行政部门报告,可以裁减人员:(一)依照企业破产法规定进行重整的;(二)生产经营发生严重困难的;(三)企业转产、重大技术革新或者经营方式调整,经变更劳动合同后,仍需裁减人员的;(四)其他因劳动合同订立时所依据的客观经济情况发生重大变化,致使劳动合同无法履行的。裁减人员时,应当优先留用下列人员:(一)与本单位订立较长期限的", "首先公司因为经营不善倒闭员工离职那不叫“开除”开除是指有过错的辞退。公司倒闭员工离开应该叫辞退或是遣散,你们应该除工资\"五险一金\"外还要有额外的赔偿。如果说签2年只做了1年那应该给你们后一年的工资。或你们和公司谈判你们认为你们可以接受的赔偿。如果太少你们可以去劳动局仲裁.但大家要齐心谈不拢就不走不要怕时间晚了什么公司不理你们,不给认可的赔偿大家都别做事情你们就跟这和你谈的人他去那你们就去那。包括厕所但你们不要动手他就是报警也没用因为你们是因为合同纠纷不关警察管。团结就是力量", "公司辞人应该提前3个月通知,如果没有提前的话,需要赔偿一个月的工资 另外看工作年限,每满一年,再多赔一个月的工资 当然,你当月的实际工作日的工资也是需要发的。。 就是说,到你被辞那天为止,应该赔偿:未提前通知的1个月工资+年数相应的月份工资+该月你所工作的实际天数所对应的工资。。", "公司倒闭开除员工属于合同劳动法第四十一条的第一款,根据第四十六条规定公司应该对员工进行赔偿,劳动合同法第四十七条对补偿标准进行了规定。 劳动合同法 第四十一条　有下列情形之一,需要裁减人员二十人以上或者裁减不足二十人但占企业职工总数百分之十以上的,用人单位提前三十日向工会或者全体职工说明情况,听取工会或者职工的意见后,裁减人员方案经向劳动行政部门报告,可以裁减人员: 　　 (一)依照企业破产法规定进行重整的; 　　 (二)生产经营发生严重困难的; 　　 (三)企业转产、重大技术革新或者经营方式调整,经变更劳动合同后,仍需裁减人员的; 　　 (四)其他因劳动合同订立时所依据的客观经济情况发生重大变化,致使劳动合同无法履行的。 　　 裁减人员时,应当优先留用下列人员: 　　 (一)与本单位订立较", "公司都已经倒闭了还补偿什么啊,,,,就算是银行黄了,,你存到里面的钱也都没有了,,除非是国家养着的银行", "看你是属于什么单位了,有实力有影响力的公司么当然补偿会多点,可能个把月的工资补偿吧。 小公司、私人单位?随便找个理由把你开了,发足本月工资就不错了,还赔偿金?别想。"], "bs_rank_pos": 1}, {"is_selected": false, "title": "公司倒闭,怎么补偿员工工资?_百度知道", "most_related_para": 0, "segmented_title": ["公司", "倒闭", ",", "怎么", "补偿", "员工", "工资", "?", "_", "百度", "知道"], "segmented_paragraphs": [["经济补偿", "按", "劳动者", "在", "本", "单位", "工作", "的", "年限", ",", "每", "满", "一年", "支付", "一个月", "工资", "的", "标准", "向", "劳动者", "支付", "。", "六个月", "以上", "不满", "一年", "的", ",", "按", "一年", "计算", ";", "不满", "六个月", "的", ",", "向", "劳动者", "支付", "半个月", "工资", "的", "经济补偿", "。", "这里", "所称", "的", "月", "工资", "是", "指", "劳动者", "在", "劳动合同", "解除", "或者", "终止", "前", "十二", "个", "月", "的", "平均工资", "。"]], "paragraphs": ["经济补偿按劳动者在本单位工作的年限,每满一年支付一个月工资的标准向劳动者支付。六个月以上不满一年的,按一年计算;不满六个月的,向劳动者支付半个月工资的经济补偿。这里所称的月工资是指劳动者在劳动合同解除或者终止前十二个月的平均工资。"], "bs_rank_pos": 2}], "answer_spans": [[82, 122]], "fake_answers": ["一)破产企业所欠职工工资和劳动保险费用;　　(二)破产企业所欠税款;　　(三)破产债权。根据司法解释的规定,劳动者享有的经济补偿金可与工资、"], "question": "公司倒闭员工补偿标准", "segmented_answers": [["职工", "一", "次性", "安置", "费", "的", "计", "发", "，", "由", "破产", "企业", "安置", "工作", "机构", "根据", "在册", "职工", "本", "企业", "工作年限", "、", "再就业", "难度", "等", "因素", "确定", "标准", "，", "一般", "人均", "不", "超过", "本市", "企业", "职工", "上", "年", "平均工资", "的", "三", "倍", "。"]], "answers": ["职工一次性安置费的计发，由破产企业安置工作机构根据在册职工本企业工作年限、再就业难度等因素确定标准，一般人均不超过本市企业职工上年平均工资的三倍。"], "answer_docs": [0], "segmented_question": ["公司", "倒闭", "员工", "补偿", "标准"], "question_type": "DESCRIPTION", "question_id": 181641, "fact_or_opinion": "FACT", "match_scores": [0.2619047619047619]}
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"与淘宝网大型电子商务平台一样,京东商城一样有着庞大的客户群体,并且在推广难度上也好不容易。其中京东快车的使用,相当于淘宝直通车一样,虽然能快速带来直接的流量,但也需要付费,是种烧不起的推广工具。因此,在京东快车如何操作的问题上,不但是要会,而且是要精,才能给商家带来可观的效果。 要熟悉京东快车的操作方法,必须先了解京东商城快车的两种模式,这样操作起来才能得心应手。 (一) 京东商城快车的两种模式 第一步:先申请京东商城“快车系统”。店主可自行联系京东。", "(一) 京东商城快车的两种模式 第一步:先申请京东商城“快车系统”。店主可自行联系京东。 第二步:登录系统进入之后,会看到如下界面。(红框处是两种投放模式:“创建频道页投放申请”和“创建搜索页投放申请”。) 模式一(CPD):创建频道页投放申请  “创建频道页投放申请”就是使推广的产品展示在二级频道页的三个位置,按天买", "模式一(CPD):创建频道页投放申请  “创建频道页投放申请”就是使推广的产品展示在二级频道页的三个位置,按天买断位置付费,即Cost per day,简称CPD。举例来说,按类目点击“服饰鞋帽-男装-衬衫”这个类目进去,能看到如下三个图: 位置一:页面上“热卖推荐”,基准价￥100.00起,即竞价不低于100元,根据出价等因素预测排名; 位置二:左侧“推广商品”位置,基准价￥50.00起,即竞价不低于50元,根据自己出价预测排名; 位置三:页面底部“商品精选”位置,基准价￥50.00起,即竞价不低于50元,可以根据自己出价来预测排名; 模式二(CPC):创建搜索页投放申请 “创建搜索页投放申请”也就是搜索某个关键词后,你所推广的产品展示位置,按点击付费,即Cost per click,简称CPC。举例来说,当你选择“衬衫男”为关键词,若按此投放模式,在京东商城首页搜索框内输入“衬衫男”这个关键词,那么你投放产品的推广位置你将会在如下两个位置看到,如下图: 位置一:左侧“推广商品”位置。 位置二:搜索结果页面,下边“商品精选”页面。 (二)京东商城快车如何操作 第一步:填写投放申请名称 以关键词竞价模式为例,点击“创建搜索页投放申请” ,填写投放申请名称,这里就相当于淘宝直通车的推广计划名称一样,点击确定即可。会出现如下图界面:", "第二步:选择广告投放位置", "在广告投放位置方面,有两个位置可选,分别是左侧和底侧,如下截图,位置选择时,还要选择投放的日期。", "第三步:选择要推广的产品", "这里要注意的是输入产品的id,可以编辑推广标题,以及广告语,还有标签如新品等等,这里标题和标签的设置,往往会有很多人忽视掉。标题的设置对搜索的关键词相当大的影响,排名是其中一个。如下图:", "第四步:选择关键词出价", "在京东快车关键词出价的问题上,它跟淘宝直通车关键词选取和出价是一样的,点击预测可看到排名,选择好以后,完成就提交了。如图:", "京东快车如何操作?根据以上方法步骤,一步步来,也不会太难的。更多网络营销知识分享,请继续关注开淘网(http://www.kaitao.cn)。"], "bs_rank_pos": 3}, {"is_selected": false, "title": "京东快车商品推广操作流程_百度经验", "most_related_para": 3, "segmented_title": ["京东", "快车", "商品", "推广", "操作流程", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["建立", "推广计划", "是", "京", "准", "通", "数字", "营销", "的", "第", "一", "步骤", ",", "通过", "推广", "商品", "、", "推广", "时间", "、", "推广", "单元", "等", "在", "内", "的", "基础", "资料", "输入", ",", "不仅", "帮助", "商家", "准确", "定向", ",", "也", "进一步规范", "管理平台", "推广", "行为", "。"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["1", "创建", "推广计划", "1", ".", "路径", "一", ":", "登录", "进入", "京", "准", "通", "商家", "投放", "平台", ",", "系统", "默认", "跳转", "至", "“", "京东", 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"性", "、", "地域性", "等", "品牌", "个性", "因素", "选择", "本", "单元", "的", "地域", "定向", "。", "步骤", "阅读"], ["经验", "内容", "仅供参考", ",", "如果", "您", "需", "解决", "具体", "问题", "(", "尤其", "法律", "、", "医学", "等", "领域", ")", ",", "建议", "您", "详细", "咨询", "相关", "领域", "专业人士", "。"]], "paragraphs": ["百度经验:jingyan.baidu.com", "建立推广计划是京准通数字营销的第一步骤,通过推广商品、推广时间、推广单元等在内的基础资料输入,不仅帮助商家准确定向,也进一步规范管理平台推广行为。", "百度经验:jingyan.baidu.com", "1 创建推广计划 1.路径一:登录进入京准通商家投放平台,系统默认跳转至“京东快车--我的投放平台推广”页面,点击【创建投放平台推广】开始建立推广计划。 2.路径二:进入“京东快车--投放平台推广管理”页面,点击【新建推广计划】。 3.进入【新建推广计划】页面,按页面提示进行操作。 4.填写说明 ① 填写推广计划名称 ② 选择商品推广 ③ 填写每日预算金额(正整数) ④ 投放的起止日期 ⑤ 投放时段的设置(默认起止日期内全日程展示) ⑥ 点击下一步 步骤阅读 步骤阅读 2 建立推广单元 建立推广计划后,跳转至【推广单元】页面,并根据页面提示创建推广单元。 基础设置 ① 填写推广单元名称 ② 定向地域(选填),默认【不限地区】为全网地域,选择【特定区域】需勾选具体城市,数量不设上限。 特别说明 商家可针对商品特性、季节性、地域性等品牌个性因素选择本单元的地域定向。 步骤阅读", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "bs_rank_pos": 4}], "answer_spans": [[2, 45]], "fake_answers": ["登录京东快车-帮助页面,http://kuaiche.jd.com/help　　第二步:在下方的快捷入口界面-结算中心有个人账户充值和企业账户充值,选择对应的充值项目即可。"], 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"也", "要", "注意", "一些", "事项", "哦", "。"]], "paragraphs": ["1、银耳能提高肝脏解毒能力,起保肝作用;银耳对老年慢性支气管炎、肺原性心脏病有一定疗效。  2、银耳富含维生素D,能防止钙的流失,对生长发育十分有益;因富含硒等微量元素,它可以增强机体抗肿瘤的免疫力。  3、银耳富有天然植物性胶质,加上它的滋阴作用,长期服用可以润肤,并有祛除脸部黄褐斑、雀斑的功效。  4、银耳中的有效成分酸性多糖类物质,能增强人体的免疫力,调动淋巴细胞,加强白细胞的吞噬能力,兴奋骨髓造血功能;银耳多糖具有抗肿瘤作用。", "5、银耳中的膳食纤维可助胃肠蠕动,减少脂肪吸收,从而达到减肥的效果。", "6、银耳还能增强肿瘤患者对放疗、化疗的耐受力。", "银耳的营养:", "银耳的营养成分相当丰富,在银耳中含有蛋白质、脂肪和多种氨基酸、矿物质及肝糖。银耳蛋白质中含有17种氨基酸,人体所必需的氨基酸中的3/4银耳都能提供。银耳还含有多种矿物质,如钙、磷、铁、钾、钠、镁、硫等。此外,银耳中还含有海藻糖、多缩戊糖、甘露糖醇等肝糖,营养价值很高,具有扶正强壮的作用,是一种高级滋养补品。", "每100克银耳含有下面的营养素:", "怎样挑选好的银耳 冰糖银耳可以化痰吗 银耳汤能治咳嗽吗 银耳红枣汤能丰胸吗 银耳含有胶原蛋白吗 哪些人群不宜吃银耳", "木瓜银耳羹的做法", "木瓜维生素较高,有利于提高免疫力,还有很强的催乳功效,适合产后爱美的新妈咪。", "木瓜半个、银耳1朵、枸杞子25克、冰糖50克", "1、木瓜切一半备用。木瓜去皮去籽切成块,银耳撕成小块,所有原料备好。 2、银耳和枸杞放在冷水中浸泡30分钟左右。 3、锅中放入适量水,烧开后转小火,将银耳和冰糖放入,煮约30分钟。 4、放入木瓜快和枸杞再煮5分钟后关火即可。", "1、木瓜最好不要选熟得太透的,青皮的最好,因为青皮木瓜美容丰胸效果最好。 2、银耳要先煮,喜欢吃软烂的把银耳煮至快要溶化更软糯。 3、木瓜和枸杞要最后放,煮几分钟就好,时间长营养就流失了。 4、放点大枣和百合也不错,这个随个人口味。", "功效:木瓜经常被认为是热量很高的水果,其实木瓜的热量极低,比苹果、橙子、桃、梨等的热量都低,每100克里只含27卡路里,苹果则是52卡路里,而维生素C的含量苹果的48倍,维生素A在水果中也可以说是最高的,适合所有爱美的女性。", "孕妇能吃银耳吗", "银耳营养丰富,为滋补佳品,能起到益气清肠、滋阴润肺的作用,同时还能增强人体免疫力,补气和血,强心壮身,补脑提神,那么孕妇能吃银耳吗?", "1、银耳富有天然植物性胶质,加上它的滋阴作用,长期服用可以润肤,并有祛除脸部黄褐斑、雀斑的功效。很多孕妇都会发现自从怀孕以后,脸上就长出了黄褐斑,这主要是因为孕妇在怀孕期间荷尔蒙的变化促使体内黑色素暂时增加,因此,孕妇不妨食用些银耳来防治黄褐斑。 2、银耳具有润肺止咳、补益肺气的功效,可以提高肺组织的防御功能,提高机体的免疫能力,从而增强体质,减少孕妇生病的机率。 3、银耳有补脾安神的作用,是秋冬进补的上乘佳品。而且其富含维生素D,能防止钙的流失,对胎儿的生长发育十分有益。孕期维生素D缺乏,可影响胎儿的骨骼发育,并能导致新生儿的低钙血症和牙齿发育不良。 4、银耳90%是胶原蛋白,孕妇食用可以缓解皮肤干燥,嘴唇干裂,但是,不建议孕妇食用过多,要合理平衡搭配饮食,才有益于胎儿的健......", "孕妇能吃黑木耳吗 孕妇能吃紫菜吗 孕妇能吃海带吗 孕妇能吃蘑菇吗 孕妇能吃口蘑吗 孕妇能吃茶树菇吗", "宝宝能吃银耳吗", "宝宝能吃银耳吗?宝宝要慎吃银耳,主要是因为一岁以前的宝宝吞咽功能还没有发育完善,而银耳不容易煮烂,不利于宝宝吞咽。因此,不要给宝宝食用整块的银耳。", "宝宝冬天上火在北方相对更常见一些,因为北方冬季干燥,户外寒冷,很容易引起宝宝上火。再加上北方室内一般都有暖气,更容易加重上火症状。如果宝宝已经上火了,不妨为他们炖点冰糖银耳汤。银耳有滋阴润燥的功效,可以作为宝宝去火的辅助食疗。但是,一定要剪碎后再给宝宝食用,而且腹泻的宝宝不可食用,否则会加重病情。", "银耳可以作为肺热咳嗽、肺燥干咳的辅助食疗,因此如果宝宝秋季肺燥干咳,不妨服用银耳化痰。具体做法是,干的银耳先用水泡一两个小时,换水,煮沸三分钟,再用小火炖半小时以上,煮到很软或成黏稠状即可。吃时冷热均可,稍加冰糖孩子更容易接受,每天三至四次,每次一小碗(约100毫升)。太大块容易造成宝宝吸食呛着,因此,一定要煮烂了给宝宝吃,要不不消化。冰糖要最后放,早放冰糖......", "宝宝能吃黑木耳吗 宝宝能吃紫菜吗 宝宝能吃海带吗 宝宝能吃蘑菇吗 宝宝能吃口蘑吗 宝宝能吃茶树菇吗", "产妇能吃银耳吗", "江浙一带有个习俗,就是过年要喝红枣银耳汤,其口感滑润,香甜醇美,那么产妇能吃银耳吗?产妇能吃银耳,不少的医院还将银耳汤作为月子里的食谱呢。", "1、银耳富有天然植物性胶质,加上它的滋阴作用,长期服用可以润肤,并有祛除脸部黄褐斑、雀斑的功效。如果和苹果一起煮成汤,可使皮肤细腻、润滑、红润有光泽。  2、产妇产后比较虚弱,因此排便力量减弱,再加上蔬菜、水果吃得少,产后经常有便秘现象。银耳中的膳食纤维可助胃肠蠕动,从而帮助预防产妇便秘。如果和紫薯一起食用,润肠通便的效果更佳。  3、钙对产后妈妈仍然有十分重要的作用,如果不注意,产妇容易钙流失,对于自身身体的恢复和母乳喂养的宝宝均有影响。银耳富含维生素D,能防止钙的流失,有助于宝宝的生长发育。  4、近年来的医学研究还证明,从银耳中分离出来的多种糖类物质,对恶性肿瘤也有明显的抑制作用。常服银耳汤,还可起到嫩肤美容的效果。", "产妇能吃黑木耳吗 产妇能吃紫菜吗 产妇能吃海带吗 产妇能吃蘑菇吗 产妇能吃口蘑吗 产妇能吃茶树菇吗", "哺乳期能吃银耳吗", "银耳有“强精、补肾、润肺、生津、止咳、清热、养胃、补气、和血、强心、壮身、补脑、提神”之功。那么哺乳期能吃银耳吗?", "1、哺乳期的女性朋友很容易出现缺钙的现象,因为女性在哺乳期间,不管自己血液中含有多少钙,每天都会有一定量的钙通过乳汁给孩子,以满足孩子的营养需要。因此,哺乳期女性是骨质疏松的高发人群。银耳富含维生素D,能防止钙的流失,可以调节人体钙和磷的代谢,帮助肠道吸收钙,维持骨骼强度,预防骨质疏松。  2、哺乳妈妈因为要母乳喂养,因此,要提高自身的免疫力,减少生病的机率,以免会影响到宝宝。在平时,哺乳妈妈除了可以通过体育锻炼提高免疫力外,还可以吃些可以增强免疫的食物。银耳中的有效成分酸性多糖类物质,能增强人体的免疫力  3、人人都认为怀孕以及哺乳期需要增加热量摄入,为新妈妈的生产和哺乳提供充足的能量保证,但是很多新妈妈却高估了需要增加的热量,大量增加食物摄入,也就导致了肥胖。银耳中的膳食纤维可助胃肠......", "哺乳期能吃黑木耳吗 哺乳期能吃紫菜吗 哺乳期能吃海带吗 哺乳期能吃蘑菇吗 哺乳期能吃口蘑吗 哺乳期能吃茶树菇吗", "食用银耳的注意事项", "吃银耳有很多好处,但吃银耳时也要注意一些事项哦。"], "bs_rank_pos": 0}, {"is_selected": false, "title": "【孕妇能吃银耳吗】孕妇可以吃银耳吗_孕妇可以吃白木耳吗_亲子百科_", "most_related_para": 3, "segmented_title": ["【", "孕妇", "能", "吃", "银耳", "吗", "】", "孕妇", "可以", "吃", "银耳", "吗", "_", "孕妇", "可以", "吃", "白木耳", "吗", "_", "亲子", "百科", "_"], "segmented_paragraphs": [["美食", "百科", "/", "银耳", "/", "孕妇", "能", "吃", "银耳", "吗"], ["孕妇", "能", "吃", "银耳", "吗", "收藏"], ["银耳", "营养丰富", ",", "为", "滋补", "佳品", ",", "能", "起", "到", "益气", "清肠", "、", "滋阴", "润肺", "的", "作用", ",", "同时", "还能", "增强", "人体免疫力", ",", "补气", "和", "血", ",", "强心壮", "身", ",", "补脑", "提神", ",", "那么", "孕妇", "能", "吃", "银耳", "吗", "?"], ["1", "、", "银耳", "富有", "天然植物", "性", "胶质", ",", "加上", "它", "的", "滋阴", "作用", ",", "长期服用", 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["缪缙", "暨南大学", "附属", "第", "一", "医院", "产科", "副主任医师"], ["相关", "网友", "问答"], ["极", "\"", "智", "\"", "妈咪", "BOX", "开业", "优惠", "抢先", "购"]], "paragraphs": ["美食百科 / 银耳 / 孕妇能吃银耳吗", "孕妇能吃银耳吗 收藏", "银耳营养丰富,为滋补佳品,能起到益气清肠、滋阴润肺的作用,同时还能增强人体免疫力,补气和血,强心壮身,补脑提神,那么孕妇能吃银耳吗?", "1、银耳富有天然植物性胶质,加上它的滋阴作用,长期服用可以润肤,并有祛除脸部黄褐斑、雀斑的功效。很多孕妇都会发现自从怀孕以后,脸上就长出了黄褐斑,这主要是因为孕妇在怀孕期间荷尔蒙的变化促使体内黑色素暂时增加,因此,孕妇不妨食用些银耳来防治黄褐斑。 2、银耳具有润肺止咳、补益肺气的功效,可以提高肺组织的防御功能,提高机体的免疫能力,从而增强体质,减少孕妇生病的机率。 3、银耳有补脾安神的作用,是秋冬进补的上乘佳品。而且其富含维生素D,能防止钙的流失,对胎儿的生长发育十分有益。孕期维生素D缺乏,可影响胎儿的骨骼发育,并能导致新生儿的低钙血症和牙齿发育不良。 4、银耳90%是胶原蛋白,孕妇食用可以缓解皮肤干燥,嘴唇干裂,但是,不建议孕妇食用过多,要合理平衡搭配饮食,才有益于胎儿的健康成长。", "1、银耳红枣汤的做法", "食材:银耳15克,红枣6个,枸10克,冰糖30克。", "(1)将银耳放入温水,浸泡30分钟,撕成小片。  (2)放入锅内,加水,大火煮开。  (3)加入洗净的红枣、冰糖,一同沸煮10分钟左右,再改为小火,煲上2个小时,即可。", "功效:银耳红枣汤是由古医书《补养篇》中的“黑木耳红枣汤”演化而成,银耳红枣汤气味清润可口,具有活血润燥、洁肤除斑之功,有助于消化面部褐斑和气虚血淤引致的面色暗黄,对改善便秘、口渴等也有效果,是一款美容养颜的家庭靓汤。", "食材:银耳10克,莲子6克,红枣10个。冰糖适量。", "(1)银耳泡发后,除去根部泥沙及杂质,撕成片状;红枣洗净去核,备用。  (2)砂锅加入适量清水,放入银耳、莲子、红枣、冰糖,先大火烧开,再用小火慢炖。  (3)待银耳煮至熟烂,汤汁粘稠,盛入碗中即可。", "功效:银耳富有天然植物性胶质,加上它的滋阴作用,长期服用可以润肤,并有祛除脸部黄褐斑、雀斑的功效。", "孕妇能吃黑木耳吗 孕妇能吃紫菜吗 孕妇能吃海带吗", "相关专家解答", "可以,没什么问题,注意:别太多,别太甜!", "葛静 解放军总医院第一附属医院 妇产科 副主任医师", "孕妇可以吃银耳莲子汤。", "孕妇吃银耳莲子汤的好处:银耳莲子汤为以银耳和莲子为主要食材的家常汤品,味道香甜可口,可清热健胃,美容养颜,营养价值丰富。银耳能提高肝脏解毒能力,保护肝脏功能,它不但能增强机体抗肿瘤的免疫能力,还能增强肿瘤患者对放疗、化疗的耐受力。它也是一味滋补良药,特点是滋润而不腻滞,具有补脾开胃、益气清肠、安眠健胃、补脑、养阴清热、润燥之功,对阴虚火旺的孕妇是一种良好的补品。银耳富有天然特性胶质,加上它的滋阴作用,长期服用可以润肤,并有祛除脸部黄褐斑、雀斑的功效。银耳是和种含膳食纤维的减肥食品,它的膳食纤维可助胃肠蠕动,减少脂肪吸收。", "孕妇吃银耳莲子汤需注意:如若准妈妈感染风寒就最好不要食用。如果大便干结、腹部胀满的孕妇也最好忌食。", "吴佳子 太平洋亲子网 育儿频道 资深主编", "你好,根据你描述的情况,蟹黄比较寒凉,容易导致孕妇流产或早产,建议最好不要吃了。怀孕期间的饮食应清淡有营养,注意多休息,情绪不能过度紧张激动。", "汪星星 广州医学院荔湾医院 妇产科 主任医师", "你好,也是可以吃的,考虑影响不大,不知妈妈具体的情况,建议在医生的指导下进行治疗是比较好的,否则孕早期牙齿不适对胎儿也是有一定影响的,建议不要大意。", "冯力民 首都医科大学附属北京天坛医院 妇科 主任医师", "其实孕妇在这个期间,最好还是不要吃甲鱼,因为甲鱼有活血化瘀、滑胎的作用,因此在怀孕期间,最好还是不要吃为好。另外,对于孕妇的饮食,只要注意不要吃哪些容易上火、过于油腻,或者是过于寒凉的食物,就会好了。而对于平时的饮食上,应要多吃瘦肉、鸡肉、牛肉、青菜、水果等。", "缪缙 暨南大学附属第一医院 产科 副主任医师", "相关网友问答", "极\"智\"妈咪BOX  开业优惠抢先购"], "bs_rank_pos": 1}, {"is_selected": false, "title": "孕妇能吃银耳吗_百度文库", "most_related_para": 1, 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["怀孕可以吃银耳吗_游戏_生活休闲。怀孕可以吃银耳吗孕妇作为一种非常特殊的群体,有些食物自己在日常的生活中是一定要注意的,正确的饮食 习惯才能保证自己和胎儿的健康,那么怀孕可以吃银耳吗?答案是肯定的,银耳是一种性平的食物 ,现在很多的", "怀孕可以吃银耳吗孕妇作为一种非常特殊的群体,有些食物自己在日常的生活中是一定要注意的,正确的饮食 习惯才能保证自己和胎儿的健康,那么怀孕可以吃银耳吗?", "答案是肯定的,银耳是一种性平的食物 ,现在很多的医院都会为孕妇提供一碗银耳汤,而且90%的银耳都是含有胶原蛋白的,对于对于孕 妇的皮肤也是有着一定的帮助的。", "现在这种天气食用银耳的话,可以缓解皮肤出现干燥的情况,同时还能能到祛斑养颜的功效。 最关键的就是银耳具备了一定的止咳、补肾的功能,是一种非常理想的补气产物,所以孕妇食用 银耳是一种非常有意义的事情。 银耳擅长补益肺气,可以提高肺组织的防御功能,提高机体的免疫能力,从而增强体质,达到 抗衰老的作用,是秋天进补的上乘佳品。 还有滋阴润肺,补脾安神的作用。适用于干咳痰少、口干咽干、饮食减少、心烦失眠、手足 无力等症。健康人食用能消除疲劳,增进食欲,增强体质。", "什么样的银耳对孕妇最好呢?", "一看,质量好的银耳,耳花大而松散,耳肉肥厚,色泽呈白色或略带微黄。 二摸,质量好的银耳应干燥,无潮湿感。 三尝,质量好的银耳应无异味,如尝有辣味,则为劣质银耳。 四闻,银耳受潮会发霉变质,如能闻出酸味或其他气味,则不能再食用。 一些不法商贩要用硫磺来熏银耳,用硫磺熏漂白一方面警备干货发霉生虫,聪明的虫虫是不 会吃的。而且这样的银耳放几年也会变质,永远是那么的光鲜,随便扔到一个角落,很好保存, 这样就给不法商贩省下不少麻烦事了。另一方面,熏过的银耳,“卖相”更好,白白净净的像玉一样 ,晶莹剔透,甚至这样处理的银耳还可以加重,用硫磺加水泡过之后再晾干变更多精彩攻略访问gl.baidu.com 1 得沉甸甸的,未处理的一斤银耳处理之后就能变成二斤。", "更多精彩攻略访问gl.baidu.com 2"], "bs_rank_pos": 3}, {"is_selected": false, "title": "孕妇可以吃白木耳吗_孕期饮食_育儿_99健康网", "most_related_para": 6, 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"是", "不"], ["月经", "正常", "就能", "怀孕", "?", "这"], ["妇科", "最佳", "医院", "男科", "最佳", "医院", "眼科", "最佳", "医院", "儿科", "最佳", "医院", "心血管", "最佳", "医院", "皮肤科", "最佳", "医院", "肿瘤科", "最佳", "医院", "肝病", "最佳", "医院", "整形", "最佳", "医院", "综合", "最佳", "医院", "内科", "最佳", "医院", "性病", "科", "最佳", "医院", "上海市", "医院", "排行", "北京市", "医院", "排行", "妇产科", "医院", "排行", "儿科", "医院", "排行"], ["西安市", "医院", "排行", "厦门市", "糖尿病", "医院", "排行", "上海市", "医院", "排行榜", "北京市", "医院", "排行榜", "天津市", "医院", "排行榜", "重庆市", "医院", "排行榜", "武汉市", "医院", "排行榜", "南京市", "医院", "排行榜", "成都", "市", "医院", "排行榜", "广州市", "医院", "排行榜", "合肥市", "医院", "排行榜", "福州市", "医院", "排行榜", "大连市", "医院", "排行榜", "东莞市", "医院", "排行榜", "佛", "上市", "医院", "排行榜", "长沙市", "医院", "排行榜"], ["子宫肌瘤", "预防", "子宫肌瘤", "|", "子宫肌瘤", "自查", "|", "子宫肌瘤", "症状"], ["感冒", "伤风感冒", "|", "小儿感冒", "|", "孕妇", "感冒"], ["痛经", "什么", "是", "痛经", "|", "女性", "痛经", "|", "痛经", "的", "原因"], ["阴道炎", "女性", "阴道炎", "|", "阴道炎", "病因", "|", "治疗", "阴道炎"], ["水痘", "水痘", "饮食", "|", "水痘", "有", "哪些", "|", "什么", "是", "水痘"], ["包皮过长", "包皮过长", "手术", "|", "治疗", "包皮过长", "|", "包皮过长", "影响生育"], ["月经不调", "月经不调", "怎么办", "|", "月经不调", "症状", "|", "月经不调", "原因"], ["盆腔炎", "盆腔炎", "保健", "|", "盆腔炎", "病因", "|", "盆腔炎", "的", "症状"], ["宫颈炎", "治愈", "宫颈炎", "|", "产后", "宫颈炎", "|", "宫颈炎", "症状"], ["人流", "无痛人流", "|", "注意事项", "|", "如何", "预防", "人流"], ["口腔溃疡", "民间偏方", "|", "饮食", "预防", "|", "复发", "原因"], ["早泄", "早泄", "怎么办", "|", "假性", "早泄", "|", "怎么", "治疗", "早泄"], ["乳腺癌", "乳腺癌", "自查", "|", "乳腺癌", "原因", "|", "乳腺癌", "饮食"], ["宫外孕", "治疗", "宫外孕", "方法", "|", "警惕", "宫外孕", "|", "宫外孕", "初期"], ["外阴白斑", "外阴白斑", "生活", "|", "外阴白斑", "检测", "|", "外阴白斑", "症状"], ["阳痿", "认识", "阳痿", "|", "阳痿", "怎么办", "|", "阳痿", "的", "表现"], ["吃什么", "食物", "为", "身体", "补充", "营"], ["冬至", "吃什么", "食物", "好", "各地"], ["冬季", "心情", "忧郁", "该怎么办", "1"], ["冬季", "怎么", "泡脚", "推荐", "10", "个"]], "paragraphs": ["本文导读:怀孕后,很多准妈妈对于自己的日常饮食可以说是格外小心了。那么像白木耳这样一种具有滋阴润肺、补气益气,同时还可提高身体免疫力的食物,孕妇可以吃吗?", "便秘(constipation)是指排便频率减少,一周内大便次数少于2～3次,或者2～3天才大便1…[详细]", "可以肯定的是孕妇适当吃点白木耳对促进身体健康有很多好处的。", "增强免疫力", "白木耳中含有含有7种人体所需的氨基酸,特别是对于孕妇而言更具有很大的裨益作用。其不仅可增加人体细胞的免疫作用,同时还有很明显的抗氧化作用。再者白木耳也具有很好的润肺之咳的功效,因此多吃对于增强孕妇的抵抗力,减少生病的机会也会有很大的帮助的。", "预防钙质流失", "白木耳中含有丰富的维生素D,而维生素D可促进钙的吸收,所以在孕期适当的吃些白木耳对于预防缺钙,减少钙质的流失也是会有很大的帮助的。", "滋养成皮肤", "白木耳中含有丰富的植物性胶质,其对于孕期肤质突然变差的女性而言就具有很好的保健功效。特别是对于一些孕后长了黄褐班的女性更可多吃白木耳以减少黄褐斑的出现。", "安神", "孕期可能会荷尔蒙的变化,而使得自己心烦意燥,而白木耳素有安神的作用。因此孕妇适当的吃点白木耳对安度整个妊娠期也具有一定的帮助。特别是对于孕后期由于心理压力大,睡不着,或者失眠多梦者就具有很好的补益作用。", "所以说孕妇是可以吃白木耳的,孕妇适当吃点白木耳对促进优生优育也是有一定的帮助的。", "红枣白木耳汤", "做法:先将白木耳放到温水中泡发洗干净,然后撕成小片,放入锅中,加适量水,这个时候加点红枣,枸杞子一起煮开在煮10分钟,然后加入冰糖就可了。", "红枣白木耳汤具有滋阴润燥的功效,特别是对于孕期心烦意乱,脸上长斑的女性就具有很好的食疗作用。当然了其对于改善便秘,口渴的感觉也有一定的补益作用。", "莲子银耳汤", "做法:先将银耳和莲子分别用温水泡发后,去掉莲子的芯,把银耳撕成小片。", "取一锅,加入清水,将银耳,莲子一起放进去用大火烧开,然后改成小火慢炖两个小时,直到银耳的肉烂了之后,倒出加入冰糖就可食用了。", "这算得上是最适合孕妇在秋冬季进补的甜品之一了。", "当然了孕妇吃白木耳也要注意个量,特别是在孕早期,因为白木耳属凉,如果食用过多的话,容易引起胎动不安,甚至流产。", "再者隔夜的白木耳一定不能吃,隔夜的白木耳会产生很大的毒素,如果准妈妈吃了的话,对身体会造成很大的伤害的,甚至会产生畸形儿,因此一定要注意了。", "另外很多人在吃白木耳的时候喜欢加很多的糖,这样一来口感是有了,但是也会使得孕妇的血黏度增高,甚至有可能会引起妊娠高血压,因此在吃白木耳的时候也一定要注意这样的问题。(99健康网(www.99.com.cn)专稿,如需转载请注明出处。)", 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"操作太频繁,请稍后再试!其实,你稍后再试(包括:转天再试,退出再试,注销再试),只要超字数,群发时永远只会提示:操作太频繁,请稍后再试!(不会提示你其他原因的,从这一点不难看出tx一贯擅长的水深作风)。", "但我要说的是:为什么苹果的微信就没出现过这问题呢?是不是怕严格的审查不合格后被“下架”,不敢给苹果偷偷的穿小鞋呢?呵呵", "没办法!tx不想解决,你还是暂时精简文字后群发吧,谁叫咱依赖微信呢,谁叫微信是tx的呢。要么舆论让tx自己出面去解释,要么你就换成苹果,成本啊!!!", "用户使用群发功能20-30次后,就会变成有限字数的群发了。如再群发时,字数超过一定数量,将只提示用户: 操作太频繁,请稍后再试!其实,你稍后再试(包括:转天再试,退出再试,注销再试),只要超字数,群发时永远只会提示:操作太频繁,请稍...", "你准备群发多少客户,微信群发助手一次只能发200人,你看下人数是否超出,如果没问题的话,你就先不发呗,等过几个小时在发。", "群发消息的规则:", "1、群发消息条数: 公众订阅号(认证用户、非认证用户)在24小时内只能群发1条消息; 公众服务号(认证用户、非认证用户),1个月(按自然月)内可发送4条群发消息。 2、通过微信公众平台已经群发的消息内容(包括群发成功、或群发后审核中的内容),不支持修改或者删除消息内容。 3、用户第1次群发时,会自动弹出签署使用协议(服务协议)提示框,签署协议以后不会再提醒、也无法查看协议,请您了解。 4、被加入黑名单的关注用户,不会接收到群发信息。 5、为了保持绿色网络环境,请您不要发布色情、暴力、赌博、血腥、政治等违反国家法律法规的信息,避免引起其他用户投诉,导致您的微信公众号无法正常使用。", "我开发 微信群发消息", "你用无限还是自己流量?", "用户使用群发功能20-30次后,就会变成有限字数的群发了。如再群发时,字数超过一定数量,将只提示用户:", "操作太频繁,请稍后再试!其实,你稍后再试(包括:转天再试,退出再试,注销再试),只要超字数,群发时永远只会提示:操作太频繁,请稍后再试!(不会提示你其他原因的,从这一点不难看出tx一贯擅长的水深作风)。", "但我要说的是:为什么苹果的微信就没出现过这问题呢?是不是怕严格的审查不合格后被“下架”,不敢给苹果偷偷的穿小鞋呢?呵呵", 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"运营管理就是对运营过程的计划、组织、实施和控制,是与产品生产和服务创造密切相关的各项管理工作的总称。从另一个角度来讲,运营管理也可以指为对生产和提供公司主要的产品和服务的系统进行设计、运行、评价和改进。", "无论是制造型企业还是服务型企业或组织,怎样及时向顾客提供有价值的高质量的产品或服务是运营管理的核心。因此,运营管理就是为实现顾客价值而进行的系统设计、运行、评价和改进的管理过程。", "其实,最早“运营管理”这一术语是由“生产管理”演变而来的,近一二十年来,生产管理学界对于生产的理解逐渐深化:生产不仅指对有形产品的制造,同时还包含对无形产品——服务的提供;它是指将生产要素投入转换为有形产品和无形服务产出,通过创造效用而增加附加价值的过程。", "因此,“生产管理”这门课程的名称也从“生产管理”(production management)演变到“生产与运营管理”(production and operations management),或统称为“运营管理”(operations management)。运营管理所要研究的是如何对制造产品或提供服务的过程进行组织、计划、实施和控制。由于运营管理这门课程阐述的基本概念、方法和技术,不仅适用于制造业,同样也适用于服务业。", "随着科学技术的发展和人们生活水平的提高,服务业在GDP中所占比重日益提高,20世纪80年代末,纽约与伦敦的服务业占GDP比重已经达到80%以上,香港在1983年时,服务业的比重已67%,上海在2001--2005年服务业的平均占比也已达到51.5%,因此,现代“运营管理”要控制的主要目标不仅仅只是是质量,成本,还有时间、柔性和服务,它们是企业竞争力的根本源泉。", "因此,运营管理在企业经营中就具有了重要的作用。特别是近二三十年来,现代企业的生产经营规模不断扩大,产品本身的技术和知识密集程度不断提高,产品的生产和服务过程日趋复杂,市场需求日益多样化、多变化,世界范围内的竞争日益激烈,这些因素使运营管理本身也在不断发生变化。尤其是近十几年来,随着信息技术突飞猛进的发展,为运营增添了新的有力手段,也使运营学的研究进入了一个新阶段,使其内容更加丰富,范围更加扩大,体系更加完整。", "现代运营管理涵盖的范围越来越大。现代运营的范围已从传统的制造业企业扩大到非制造业。其研究内容也已不局限于生产过程的计划、组织与控制,而是扩大到包括运营战略的制定、运营系统设计以及运营系统运行等多个层次的内容。把运营战略、新产品开发、产品设计、采购供应、生产制造、产品配送直至售后服务看作一个完整的“价值链”,对其进行集成管理。", "信息技术也成为运营管理的重要手段。由信息技术引起的一系列管理模式和管理方法上的变革,成为运营的重要研究内容。近30年来出现的计算机辅助设计(CAD)、计算机辅助制造(CAM)、计算机集成制造系统(CIMS)、物料需求计划(MRP)、制造资源计划(MRPII)以及企业资源计划(ERP)等,在企业生产运营中得到广泛应用。", "对象和目标", "运营管理的对象是运营过程和运营系统。运营过程是一个投入、转换、产出的过程,是一个劳动过程或价值增值的过程,它是运营的第一大对象,运营必须考虑如何对这样的生产运营活动进行计划、组织和控制。运营系统是指上述变换过程得以实现的手段。它的构成与变换过程中的物质转换过程和管理过程相对应,包括一个物质系统和一个管理系统。", "企业运营管理要控制的主要目标是质量,成本,时间和柔性,它们是企业竞争力的根本源泉。因此,运营管理在企业经营中具有重要的作用。特别是近二三十年来,现代企业的生产经营规模不断扩大,产品本身的技术和知识密集程度不断提高,产品的生产和服务过程日趋复杂,市场需求日益多样化、多变化,世界范围内的竞争日益激烈,这些因素使运营管理本身也在不断发生变化。尤其是近十几年来,随着信息技术突飞猛进的发展,为运营增添了新的有力手段,也使运营学的研究进入了一个新阶段,使其内容更加丰富,范围更加扩大,体系更加完整。", "现代运营管理", "现代运营管理涵盖的范围越来越大。现代运营的范围已从传统的制造业企业扩大到非制造业。其研究内容也已不局限于生产过程的计划、组织与控制,而是扩大到包括运营战略的制定、运营系统设计以及运营系统运行等多个层次的内容。把运营战略、新产品开发、产品设计、采购供应、生产制造、产品配送直至售后服务看作一个完整的“价值链”,对其进行集成管理。", "上一篇:壹基金的运营管理分析  下一篇:企业管理和运营管理的知识点归纳", "【企业运营】精华文章", "【企业运营】相关文章"], "bs_rank_pos": 0}, {"is_selected": true, "title": "营运管理具体是做什么?_百度知道", "most_related_para": 2, "segmented_title": ["营运", "管理", "具体", "是", "做什么", "?", "_", "百度", "知道"], "segmented_paragraphs": [["营运", "管理", "(", "OPERATION", "MANAGEMENT", ")", ":", "是", "零售", "管理", "术语", ",", "是", "指", "零售商", "透过", "一些", "硬指标", "及", "软", "指标", "对其", "门店", "的", "各项", "作业", ",", "起", "到", "培训", "、", "督导", "、", "考核", "、", "奖惩", "的", "一系列", "经营管理", "活动", "。", "　　", "运营管理", "就是", "对", "运营", "过程", "的", "计划", "、", "组织", "、", "实施", "和", "控制", ",", "是", "与", "产品", "生产", "和", "服务", "创造", "密切相关", "的", "各项", "管理", "工作", "的", "总称", "。", "从", "另一个", "角度", "来", "讲", ",", "运营管理", "也", "可以", "指", "为", "对", "生产", "和", "提供", "公司", "主要", "的", "产品", "和", "服务", "的", "系统", "进行", "设计", "、", "运行", "、", "评价", "和", "改进", "。", "　", "在", "当今", "社会", ",", "不断", "发展", "的", "生产力", "使得", "大量", "生产要素", "转移", "到", "商业", "、", "交通运输", "、", "房地产", "、", "通讯", "、", "公共事业", "、", "保险", "、", "金融", "和", "其他", "服务", "性", "行业", "和", "领域", ",", "传统", 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　　因此,随着服务业的兴起,生产的概念进一步扩展,逐步容纳了非制造的服务业领域,不仅包括了有形产品的制造,而且包括了无形服务的提供", "所谓市场营销管理,其实就是在规划实施市场理念、商品劳务、设计、定价、促销等环节中,为满足顾客需求及组织目标,从而创造交换机会的一个过程,它是通过分析,计划实施与控制,通过一定的管理交换,从而达到目的。对于一个公司而言,管理的指导思想与生产、产品、市场营销及社会营销是分不开的,因此营销管理它又与公司内部行政是密不可分的。 在有了营销管理思想的同时,我认为它必须从各个方面着手进行: 一、基本目标 市场管理的任务与分类 :一些人认为,营销人员的工作任务是刺激顾客对企业产品的需求,以便尽量扩大生产和销售。我认为这是一种狭义的理解,营销管理应该通过分析、计划,实施和控制来谋求创造,建立及保持公司与客户之间的利益交换。从而达到营销的目的。在有任务的同时不可忘记的是营销分类。比如:刺激性营", "营运管理(OPERATION MANAGEMENT):是零售管理术语,是指零售商透过一些硬指标及软指标对其门店的各项作业,起到培训、督导、考核、奖惩的一系列经营管理活动。 　　运营管理就是对运营过程的计划、组织、实施和控制,是与产品生产和服务创造密切相关的各项管理工作的总称。从另一个角度来讲,运营管理也可以指为对生产和提供公司主要的产品和服务的系统进行设计、运行、评价和改进。 　在当今社会,不断发展的生产力使得大量生产要素转移到商业、交通运输、房地产、通讯、公共事业、保险、金融和其他服务性行业和领域,传统的有形产品生产的概念已经不能反映和概括服务业所表现出来的生产形式。 　　因此,随着服务业的兴起,生产的概念进一步扩展,逐步容纳了非制造的服务业领域,不仅包括了有形产品的制造,而且包括了无形服务的提供"], "bs_rank_pos": 1}, {"is_selected": false, "title": "运营管理主要是做什么的?_百度知道", "most_related_para": 1, "segmented_title": ["运营管理", "主要", "是", "做什么", "的", "?", "_", "百度", "知道"], "segmented_paragraphs": [["运营", "管理", "的", "对象", "是", 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------------------------------------------------分割线来惹-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Day 1 (上海长途客运南站—乌镇汽车站—西栅) 从上海到乌镇的途径很多,可以做高铁到桐乡再转车,也可以直接坐长途大巴。询过万能的度妈,我们果断选择了后者。在网上买的车票,很方便也不贵(50大洋)。1.5h车程后我们到达乌镇汽车站,然后乘K350公交车,几分钟就到了西栅景区(乌镇主要景区是西栅和东栅,东栅下午5点就闭馆了,并且不接受入住,西栅一直开放,且旅客可住景区内)。", "旅店我们是提前在乌镇官网上订好的,订的是水巷青旅(西栅景区内只有两家青旅:紫藤和水巷,都很火爆,旺季一般要提前1个月,因为是淡季我们很幸运地仅在提前10天就订到辣~景区内旅店相对会贵些,但住在里面的旅客可享受超值两日联票、免费电瓶车接送以及无限次免费西栅出入,性价比相对高,而且灰常舒服,很推荐)。在西栅景区门口服务大厅里办理好入住、两日联票以及出入证后我们被电瓶车送到了水巷青旅~~", "乌镇西栅水巷驿青年旅舍", "(去之前在刷别人的游记的时候看到这个青旅,去的人都说超nice,而且乌镇官网上也有推荐。真的到了发现果真棒棒哒~)", "乌镇西栅水巷驿青年旅舍", "乌镇西栅水巷驿青年旅舍", "(在铺床滴巴扎黑~~~)", "(介个是女生的活动室~男生活动室里好像是桌球啥的。。。) ------------------------------------------------来个分割线------------------------------------------------------------------ Dinner time 来惹~~ 前台MM倾情推荐我们去民宿里吃晚餐。我们随意走进一家,菜也是随意点的,期待并不高,但味道真的很棒,民宿大妈和老大爷也超热心的。。。乌镇是鱼米之乡也是酒乡,连我们的菜里都飘着淡淡的酒香跟我们拼桌的是两个北大女生,一上来就点了2斤白酒,想到我最多只能搞定一听啤酒真是觉得自己弱爆了", "(次饭滴民宿~)", "(快吃完才想起要照的,所以很丑,大家海涵海涵) --------------------------------------------华丽丽滴分割线------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ 吃完饭就开始游走辣~~那天因为下雨,花鼓戏、露天电影都没有开放,想起来有点小遗憾(评书总是开放的,时间一般都是19:30开始),所以我们更多的是散步,欣赏西栅的夜景。因为是淡季,而且晚上没有旅游团,在西栅里的人不多,基本都是住客,比较安静,氛围很闲适惬意。下过雨的空气也是湿湿的,很清新很干净。", "(乌镇特产-定胜糕,味道可以的~~ point是有大爱的糯米纸)", "( 唐朝甜品,马蜂窝上好像是必吃体验,不行的不行的,又贵又一般,大家不要被骗了) 西栅不大,全长也就1800多米,半个小时就能走完单程~~However,我们两个拍照狂人硬生生来回走了快2h。西栅里还有很多轻吧,喜欢来点小酒的可以进去慢摇一下。。。 ----------------------------------------------------------分割线又来惹------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Day 2 (西栅-东栅-乌镇汽车站) 早晨的西栅也是很安静的,店铺大多到10点才开始营业。到乌镇我最大的感受就是当地人生活的真的很随意,很自然。早上很晚才开始营业,晚上很早就收工回家。我想这也是我感觉乌镇比西塘好太多的原因,虽然乌镇旅游开发程度比西塘高,但规范的管理并没有掩盖太多朴实的民风,反而是号称没有被过度开发的西塘商业性欲盖弥彰。乌镇可贵的也在于它的创新,由于早期80后文艺青年入驻开店开客栈,微博微信二维码等网络营销给乌镇注入了互联网基因,使得乌镇成为早期几个得益于次的旅游地。乌镇也由此成为世界互联网大会的永久会址。 废话太多。。。来上图吧~~"], "bs_rank_pos": 0}, {"is_selected": true, "title": "乌镇三人行(两天一夜自由行),乌镇旅游攻略 - 蚂蜂窝", "most_related_para": 2, "segmented_title": ["乌镇", "三人行", "(", "两天一夜", "自由行", ")", ",", "乌镇", "旅游攻略", "-", "蚂", "蜂窝"], "segmented_paragraphs": [["头", "图", "加载", "中", ".", ".", "."], ["有", "202798", "张", "照片"], ["相关", "目的地", ":", "乌镇"]], "paragraphs": ["头图加载中...", "有202798张照片", "相关目的地:乌镇"], "bs_rank_pos": 1}, {"is_selected": false, "title": "乌镇旅游攻略两天一夜,吃喝玩乐2日1夜!", "most_related_para": 49, "segmented_title": ["乌镇", "旅游攻略", "两天一夜", ",", "吃喝玩乐", "2", "日", "1", "夜", "!"], "segmented_paragraphs": [["乌镇", "旅游攻略", "两天一夜", ",", "吃喝玩乐", "2", "日", "1", "夜", "!"], ["=", "WU", "ZHEN", "="], ["攻略", "概要", ":", "门票", "→", "两", "日游", "行程", "推荐", "→", "美食", "→", 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"但", "去", "乌镇", "一般", "先", "到", "上海", "、", "苏州", "或", "杭州", ",", "然后", "再", "坐", "汽车", "去", "乌镇", "。"], ["自驾", ":", "乌镇", "距", "桐乡", "市区", "13", "公里", ",", "距", "周围", "嘉兴", "、", "湖州", "、", "吴江", "三", "市", "分别", "为", "27", "公里", "、", "45", "公里", "和", "60", "公里", ",", "距", "杭州", "、", "苏州", "均为", "80", "公里", ",", "距", "上海", "140", "公里", "。", "自驾", "都是", "不错", "的", "选择", "噢", "!"], ["A", ".", "杭州", "自驾", "线路", ":", "全程", "约", "80", "公里", ",", "行程", "时间", "约", "1", "小时", "40", "分钟", "B", ".", "苏州", "出发", ":", "最好", "是", "自驾", ",", "沿", "路", "风光", "不错", ",", "全程", "88", "公里", ",", "行程", "时间", "约", "为", "1", "小时", "45", "分钟", "。", "C", ".", "上海", "自驾", "线路", ":", "以", "黄浦区", "为", "出发点", "全程", "约", "合", "120", "公里", "行程", ",", "耗时", "约", "合", "2", "小时", "30", "分钟", "。"], ["初", "到", "乌镇", ",", "你", "需要", "了解", "的", "几", "件", "小事"], ["1", "、", "请", "避免", "夏季", "正午", "时", "去", "乌镇", "游览", ",", "白晃晃", "的", "日光", "直", "直", "地", "晒", "下来", ",", "也", "没", "什么", "廊", "棚", "可", "遮挡", ",", "让", "人", "感觉", "非常", "不好", "。", "2", "、", "乌镇", "东栅", "景区", "与", "西栅", "景区", "之间", "有", "免费", "班车接送", ",", "营运", "时间", "通常", "为", "7", ":", "30", "至", "17", ":", "20", "。", "周一", "至", "周四", "的", "班车", "间隔时间", "为", "20", "分钟", ",", "周五", "至", "周日", "的", "班车", "间隔时间", "为", "10", "分钟", "。", "3", "、", "最好", "避开", "节假日", ",", "因为", "这段时间", "不仅", "住宿", "等", "价格", "很", "贵", ",", "而且", "人", "多", "就", "无法", "品味", "古镇", "的", "宁静", "美", "。", "4", "、", "去", "过", "的", "游客", "感觉", "东栅", "的", "景点", "一般", "半天", "就", "可以", "逛", "完", "了", ",", "而", "西栅", "内容", "比较", "丰富", "一些", ",", "可以", "花", "时间", "多一点", "在", "西栅"], ["本", "文", "仅", "代表", "作者", "观点", ",", "不", "代表", "百度", "立场", "。"], ["本", "文", "系", "作者", "授权", "百家", "号", "发表", ",", "未经许可", ",", "不得转载", "。"], ["还没有人", "评论", ",", "点击", "抢沙发", "～"]], "paragraphs": ["乌镇旅游攻略两天一夜,吃喝玩乐2日1夜!", "= WU ZHEN =", "攻略概要 :门票→两日游行程推荐→美食→住宿→交通→注意事项", "门票:东栅门票100元,西栅门票120元,东西栅联票150元(一日内有效)可以买联票,白天游东栅,夜晚住西栅景区内,第二天不用门票继续游西栅。营业时间:东栅景区/07:00-18:00,西栅景区/09:00-22:00。", "DAY1", "白天游东栅,夜宿西栅看夜景", "路线:逢源双桥→财神湾→江南百床馆→宏源泰染坊→余榴梁钱币馆→茅盾故居→乌镇古戏台→修真观→皮影戏馆→访卢阁→汇源当铺", "乌镇之所以这么有名,与一代文豪茅盾的关系很大,茅盾故居只在白天开放,到了乌镇如果没有去茅盾故居,等于没有去过乌镇。", "乌镇南派的皮影戏,名扬海内外,戏馆里收留下也许仅仅是老艺人对过去的一往情深,更是一种原生态生活的回归与渴望。", "夜晚的西栅是乌镇最美的景色;江南水乡的夜,犹如深闺女子一般,遮遮掩掩中流露出女人的柔情来,青石板路传来阵阵蹩音,远处是穿着旗袍的曼妙女人...", "坐着乌篷船泛舟河上,河面上是被灯光映照出彩色倒影的一座座古桥,新旧交替恍然如入两座古镇,一个是昨日的斑驳,一个是今日的绚烂。", "DAY2", "?", "西栅一日游", "路线:草木本色染坊→昭明书院→三寸金莲展馆→老邮局→乌镇大戏院→乌镇关帝庙→白莲塔→乌将军庙→灵水居→茅盾纪念馆→水上集市→叙昌酱园", "早起看看清晨的乌镇,泛着薄薄雾气的河流,湿润的青石板道理,少了游人如织,千年古镇的静谧之美此刻才能深刻体会。", "到了乌镇大戏院里听评弹,欣赏地方戏曲,老邮局里写一张明信片或者寄一封信,盖上乌镇的邮戳,才不枉此行。", "乌镇美食", "白水鱼", "一般白水鱼捕捞出水即死,但保存得法,其味不变。如若遇到活的白水鱼,则不要错失机会,可让店家活杀清蒸,尽品其鲜嫩原味。推荐在乌镇东栅景区百年老字号九江楼酒店里品尝。", "红烧羊肉", "乌镇的红烧羊肉选料十分讲究,一定要以当年的“花窠羊”即青年湖羊为原料,这种羊肉肉嫩脂肪少,皮细洁多膏,味道一级棒,同样可在九江楼酒店品尝。", "乌镇酱鸡酱鸭", "加工时整体原汁浸烧,三次出汤;再放入上等酱油、黄酒佐料浸烧。出锅后又得涂上一层麻油,方可完成。应家桥堍的三珍斋酱鸭,游客赞不绝口。", "特产", "姑嫂饼", "三白酒", "3、定胜糕", "青团", "臭豆干,茅老太臭豆腐店能找够尝到正宗的豆腐干", "抗白菊", "美食分布点", "1、在西栅的各所民宿内,房东均可单独经营餐饮,且都烧得一手好菜,但桌头不多,每家只有1到3桌。菜谱和价格都是大致统一的,土鸡煲35元,红烧羊肉48元。挑家靠水临阁的民宿,坐下来品尝房东的拿手好菜,也是很惬意实惠的。2、在几个作坊、酱园边上,都开有小吃店,卖炸臭豆腐(5元/份)等小吃,配料多得眼花缭乱,风味独特。3、在东栅小吃店更普遍,羊肉面随处可见,10元一份,红烧羊肉也只要35元,还有蚕花糕、桂花糕等特色糕点,但坏境不如西栅清幽干净,推荐江南水乡小吃店。", "乌镇住宿", "西栅景区:西栅的住宿条件非常发达。从高档会所到临水客栈应有尽有。其中最受推崇的是当地民宿,由景区统一管理房子都是当地人的民宅,很有特色,管理也很好。住宿在西栅景区内,可以办理西栅临时出入证。", "东栅景区:东栅虽然没有西栅那么繁华热闹,但是保留了很多当地原始的风情和韵味,价格要比西栅便宜,所以住在东栅也是不错的选择,但需要注意的是,东栅每天早上8点要清肠,如果你住在东栅景区内,最好8点以后出来。", "东西栅景区门口:附近也都还有很多的家庭旅馆和宾馆可供选择,价格比景区内便宜很多,交通也很方便,适合追求高性价比出游的朋友们。", "酒店:世界遗产级古镇与五星酒店配置的完美结合,推荐西栅景区枕水度假酒店,标间30元起。", "会所:成功商务人士的雅集之地,叱咤商场之余的禅意之园,推荐乌镇锦堂会所,高级大床房,1500元起。", "客栈:推窗临河,枕水而居,归家的感受,享不尽惬意与浪漫,推荐嘉兴月河客栈、乌镇万宝座客栈、两府客栈、通安客栈,100元至1000元不等。", "民宿:江南风情水乡人家,体验星级宾馆洁净与水上人家亲情,推荐乌镇民宿计阿姨、 殷阿姨家等,民宿多在400元左右。", "乌镇交通", "飞机:目前只有杭州萧山机场有直达乌镇的班车,其他地方都需要下机后周转汽车或火车前往乌镇。每天有多趟快客班车往返乌镇(桐乡)和杭州萧山国际机场,票价约为46/人。若从萧山机场直接打车去乌镇,费用在300元左右。", "汽车:目前,上海、杭州、南京、温州、绍兴、苏州已开通直达乌镇的旅游专线,旅客可到各旅游集散中心乘坐。到乌镇的一般路线是目的地——桐乡——乌镇,或者目的地——嘉兴——乌镇。其中,汽车是最方便的到达方式。", "火车:乌镇没有火车站,前往乌镇旅游,坐火车可到乌镇周边地区之后再到附近汽车站进行中转。到达桐乡火车站后,可直接换成k282路公交车,直接抵达乌镇汽车站,桐乡火车站发往乌镇的公交末班车时间为7:40和17:50,每隔30分钟一趟,票价5元,用时在半小时左右。出租车前往80元起。", "tips:乌镇虽是浙江桐乡市的一个镇,但去乌镇一般先到上海、苏州或杭州,然后再坐汽车去乌镇。", "自驾:乌镇距桐乡市区13公里,距周围嘉兴、湖州、吴江三市分别为27公里、45公里和60公里,距杭州、苏州均为80公里,距上海140公里。自驾都是不错的选择噢!", "A.杭州自驾线路:全程约80公里,行程时间约1小时40分钟B.苏州出发:最好是自驾,沿路风光不错,全程88公里,行程时间约为1小时45分钟。C.上海自驾线路:以黄浦区为出发点全程约合120公里行程,耗时约合2小时30分钟。", "初到乌镇,你需要了解的几件小事", "1、请避免夏季正午时去乌镇游览,白晃晃的日光直直地晒下来,也没什么廊棚可遮挡,让人感觉非常不好。2、乌镇东栅景区与西栅景区之间有免费班车接送,营运时间通常为7:30至17:20。周一至周四的班车间隔时间为20分钟,周五至周日的班车间隔时间为10分钟。3、最好避开节假日,因为这段时间不仅住宿等价格很贵,而且人多就无法品味古镇的宁静美。4、去过的游客感觉东栅的景点一般半天就可以逛完了,而西栅内容比较丰富一些,可以花时间多一点在西栅", "本文仅代表作者观点,不代表百度立场。", "本文系作者授权百家号发表,未经许可,不得转载。", "还没有人评论,点击抢沙发～"], "bs_rank_pos": 2}, {"is_selected": false, "title": "去乌镇两天一夜的行程安排-乌镇旅游问答 【携程攻略】", "most_related_para": 2, "segmented_title": ["去", "乌镇", "两天一夜", "的", "行程安排", "-", "乌镇", "旅游", "问答", "【", "携程", "攻略", "】"], "segmented_paragraphs": [["已", "解决", "问题", ":", "2857613"], ["6830168", "人", "贡献", "宝贵", "经验"], ["打算", 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"企业主导的技术研发已然成为科研创新的主要力量,科技型中小企业是其中最有活力的群体。相比于大型企业,激励科技型中小企业的技术创新活动就显得尤为重要。", "本报记者 何亮", "“研发费用加计扣除的政策红利我们已经享受4年,每年获得近30万的研发财税优惠。”听到研发税收新政发布,黑龙江省东安橡胶集团有限公司的财务部长许丽梅在电话一端激情流露,“如果研发费用加计扣除的比例还能提高,得到的实惠自然增多,作为科技企业群体中的‘婴幼儿’怎能不欢迎这顿‘营养餐’?”", "近日,财政部、国家税务总局、科技部(以下简称“三部委”)联合发布了《关于提高科技型中小企业研究开发费用税前加计扣除比例的通知》,将科技型中小企业研发费用加计扣除比例由50%提高到75%;随后,三部委联合印发《科技型中小企业评价办法》(以下简称《评价办法》),明确科技型中小企业的评价条件、评价程序和评价管理工作。", "新政出台后,正式通过政策核定的科技型中小企业,不仅能拿到更大的激励红包,还能获得全国统一的身份名片。", "提高扣除比例,中小企业研发提升“获得感”", "研发费用加计扣除的基本政策原理是,通过降低企业的应纳税所得额和相应税负,从而降低企业研发投入的风险。企业必须产生足够利润,在此前提下,研发投入越大,享受研发费用加计扣除政策扶植的力度也就越大。", "在世界范围内,科技型中小企业有两个共同特点。一方面规模较小,另一方面处于创业初期,发展不成熟。这样的企业,即便前期研发费用投入较大,但是市场规模小、利润少、甚至长期亏损,研发费用加计扣除的优惠政策相对于大企业,“获得感”大打折扣。", "科技部政策法规与监督司有关负责人在接受科技日报记者采访时表示,通过调研和对地方加计扣除政策评估发现,由于研发费用本身的政策专业性比较强,项目管理、经费使用要求比较高,企业需要一定的人力投入或者委托中介机构的经济投入。如果优惠力度小,小微企业没有积极性,导致政策落地动能不足、政策落实效果不好。", "一组数据显示,2015年,企业的研发经费投入占全社会研发经费投入的76.8%。同年,全国享受研发费用加计扣除优惠的企业数为5.36万户,优惠金额3035.46亿元,约等于减免税额758.86亿元。 “从总体上看,企业主导的技术研发已然成为科研创新的主要力量,科技型中小企业是其中最有活力的群体。相比于大型企业,激励科技型中小企业的技术创新活动就显得尤为重要。”该负责人表示,“将科技型中小企业实施研发费用加计扣除比例由原先的50%提高到75%,目的是落实国家大力支持‘双创’和发展新动能的总体部署,尽量加大科技型中小企业的税收优惠力度,对科技型中小企业进行精准支持,激励他们更多的去创新。” 突出技术先导,科技型中小企业认定是前提 要想实现对科技型中小企业研发费用加计扣除比例的提高,一个隐含的必要条件不可被忽视,即厘清哪些企业是“科技型中小企业”。 此前,科技部曾规定企业申请“科技型中小企业技术创新基金”需满足的条件,各地针对享受优惠政策的科技型中小企业也会有不同的要求,但全国并没有统一的科技型中小企业评价标准。此次《评价办法》的出台,使得科技型中小企业有了统一的评价条件和办法。 科技型中小企业评价指标具体包括科技人员、研发投入、科技成果三类,满分100分。企业根据科技型中小企业评价指标进行综合评价所得分值不低于60分,且科技人员指标得分不得为0分。 “在《评价办法》中,对科技型中小企业的定义突出‘技术先导’作用。”中国科学技术发展战略研究院科技体制与管理研究所副所长、研究员张赤东告诉科技日报记者,“办法中提到研究开发活动、自主知识产权、高新技术产品或服务转化等要素条件,明确了科技创新在科技型中小企业评定中的主导作用。” 张赤东进一步解释科技型中小企业准入与科技活动评分条件的特点。一是参照国际上通行的规则,对企业的规模如职工总数、销售收入和资产总额进行了规定;二则为促进产业转型升级,促进科技型中小企业健康发展,对企业的产品和服务范围进行了合理的界定;三是通过设置科学合理的企业科技活动评价指标,有效评价企业的科技和技术属性,聚焦科技创新这一核心;四是整个评价方法设计突出了科技创新和体制机制创新双轮驱动的有机结合,既精准施策又重体系建设。 科技部政策法规与监督司有关负责人表示,考虑到我国目前存在多层次的企业科技创新评价体系,《评价办法》规定,企业在满足基本准入条件的同时,如果符合“高技术”条件中的一项,可直接确认符合科技型中小企业条件。这一方面减轻了企业重复申报的工作量、提高了工作效率,另一方面也体现了技术先导在不同的企业科技创新评价体系中的连贯性。 落地优惠政策,还需具体操作和企业自律 对科技型中小企业给予更多适度的优惠政策,是国际社会的一种主流做法。 比如英国的中小企业研发费用税前加计扣除比例达到230%,大型企业只有130%;澳大利亚中小企业的税收抵免率为43.5%,大型企业为38.5%。 “相比于大型企业,国际上很多国家对中小企业的研发支出给以‘加强’的税收优惠,而且优惠力度在不断提高。”中国科学技术发展战略研究院研究员薛薇告诉科技日报记者,我国的政策调整正在与先进国家靠近。 值得注意的是,此次科技型中小企业研发费用加计扣除新政的执行期限是2017年1月1日至2019年12月31日。薛薇表示,“作为三年周期的一个阶段性政策,如果执行的效果比较好,建议变成一种长期稳定的政策支持。” 除此之外,如果企业的应纳税所得额不足抵扣,我国现行税法规定,可在以后纳税年度结转抵扣,但结转期限最长不得超过5年。薛薇建议,未来的政策完善应考虑很多科技型中小企业在研发投入前期长期处于亏损或微利,要经历超过5年时间才能逐步盈利,应延长亏损结转年限,更大地激励中小企业的创新研发积极性。 政策的执行体现“放管结合”,虽然政策主张各中小企业积极按照评价办法自主评价及信息更新,但是对于企业作假与失信行为也有相应的惩罚措施。北京高精尖科技开发院院长汪斌建议,各中小型企业应尊重规则、严格自律,以便充分利用政策提供的宽松环境提升自身研发实力。", "(责编:孙博洋、夏晓伦)", "连接东西南北,跨越万水千山的中欧班列上,除了满载的商品货物,就只剩下两名开火车的司机了,他们夙兴夜寐,拉着沉甸甸的货物奔跑在新时期的“丝绸之路”上,他们是现代的“丝路”人,更是中欧经济文化的使者。本期《财景》,走近中欧班列的80后“老司机”。 财景故事:走近中欧班列80后“老司机” …【详细】", "重庆抗洪演练 海陆空救援堪比大片 \"五分钱\"粉汤诠释地道\"定安味道\" 专家谈\"一带一路\":刷新视野 鼓舞人心"], "bs_rank_pos": 1}, {"is_selected": false, "title": "财税〔2017〕最新研究开发费用税前加计扣除比例政策_最新研究开发", "most_related_para": 7, "segmented_title": ["财税", "〔", "2017", "〕", "最新", "研究", "开发", "费用", "税前", "加计扣除", "比例", "政策", "_", "最新", "研究", "开发"], "segmented_paragraphs": [["财税", "〔", "2017", "〕", "34", "号", "最新", "研究", "开发", "费用", "税前", "加计扣除", "比例", "政策"], ["日前", ",", "税务总局", "、", "财政部", "以及", "科技部", "联合", "公布", "了", ",", "关于", "研究", "开发", "费用", "的", "最新", "通知", "。", "对", "研发", "费用", "的", "计", "扣除", "相关问题", "公布", "了", "新", "的", "规定", ",", "以", "进一步", "刺激", "中小企业", "加大", "研发", "投入", "。", "以下", "是", "相关", "通知", "公告", "的", "全文", ",", "需要", "对", "研究", "开发", "费用", "进行", "纳税申报", "等", "行为", "的", "企业", ",", "可", "详细", "了解", "该", "通知", "内容", ",", "以便", "更好", "处理", "企业", "相关", "税务", "问题", "。"], ["财政部", "税务总局", "科技部"], ["关于", "提高", "科技型", "中小企业", "研究", "开发", "费用", "税前", "加计扣除", "比例", "的", "通知"], ["财税", "〔", "2017", "〕", "34", "号"], ["各省", "、", "自治区", "、", "直辖市", "、", "计划单列市", "财政厅", "(", "局", ")", "、", "国家", "税务局", "、", "地方税务局", "、", "科技厅", "(", "局", ")", ",", "新疆", "生产", "建设", "兵团", "财务", "局", "、", "科技局", ":"], ["为", "进一步", "激励", "中小企业", "加大", "研发", "投入", ",", "支持", "科技创新", ",", 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"中小企业", "的", "相关", "信息", ",", "加强", "协调", "配合", ",", "保障", "优惠政策", "落实到位", "。"], ["财政部", "税务总局", "科技部"], ["热点推荐", ":", "各省市", "2017", "年", "报名时间", "、", "入口", "【", "专题", "】"], ["财会", "问题", "请", "扫一扫", "加", "周", "老师", "(", "微信", "号", ":", "c18188611406", ")"], ["老师", "邀", "你", "加入", "实操", "交流", "群", ",", "和", "大", "咖", "一起", "学", "会计", "!"], ["进", "群", "还有", "老师", "免费", "答疑", "、", "免费", "直播", "体验", "课", "!"]], "paragraphs": ["财税〔2017〕34号 最新研究开发费用税前加计扣除比例政策", "日前,税务总局、财政部以及科技部联合公布了,关于研究开发费用的最新通知。对研发费用的计扣除相关问题公布了新的规定,以进一步刺激中小企业加大研发投入。以下是相关通知公告的全文,需要对研究开发费用进行纳税申报等行为的企业,可详细了解该通知内容,以便更好处理企业相关税务问题。", "财政部 税务总局 科技部", "关于提高科技型中小企业研究开发费用税前加计扣除比例的通知", "财税〔2017〕34号", "各省、自治区、直辖市、计划单列市财政厅(局)、国家税务局、地方税务局、科技厅(局),新疆生产建设兵团财务局、科技局:", "为进一步激励中小企业加大研发投入,支持科技创新,现就提高科技型中小企业研究开发费用(以下简称研发费用)税前加计扣除比例有关问题通知如下:", "一、科技型中小企业开展研发活动中实际发生的研发费用,未形成无形资产计入当期损益的,在按规定据实扣除的基础上,在2017年1月1日至2019年12月31日期间,再按照实际发生额的75%在税前加计扣除;形成无形资产的,在上述期间按照无形资产成本的175%在税前摊销。  二、科技型中小企业享受研发费用税前加计扣除政策的其他政策口径按照《财政部 国家税务总局 科技部关于完善研究开发费用税前加计扣除政策的通知》(财税〔2015〕119号)规定执行。  三、科技型中小企业条件和管理办法由科技部、财政部和国家税务总局另行发布。科技、财政和税务部门应建立信息共享机制,及时共享科技型中小企业的相关信息,加强协调配合,保障优惠政策落实到位。", "财政部 税务总局 科技部", "热点推荐:各省市2017年报名时间、入口【专题】", "财会问题请扫一扫加周老师(微信号:c18188611406)", "老师邀你加入实操交流群,和大咖一起学会计!", "进群还有老师免费答疑、免费直播体验课!"], "bs_rank_pos": 2}, {"is_selected": true, "title": "政策落地!研发费加计扣除比例提高到75%_东奥会计在线", "most_related_para": 0, "segmented_title": ["政策", "落地", "!", "研发", "费", "加计扣除", "比例", "提高", "到", "75", "%", "_", "东奥", "会计", "在线"], "segmented_paragraphs": [["【", "东奥", "会计", "在线", "—", "—", "中级会计", "职称", "频道", "推荐", "】", "根据", "《", "关于", "提高", "科技型", "中小企业", "研究", "开发", "费用", "税前", "加计扣除", "比例", "的", "通知", "》", "的", "规定", ",", "研发", "费", "加计扣除", "比例", "提高", "到", "75", "%", "。"], ["财政部", "、", "国家税务总局", "、", "科技部", "发布", "《", "关于", "提高", "科技型", "中小企业", "研究", "开发", "费用", "税前", "加计扣除", "比例", "的", "通知", "》", "。"], ["通知", "称", ",", "为", "进一步", "激励", "中小企业", "加大", "研发", "投入", ",", "支持", "科技创新", ",", "就", "提高", "科技型", "中小企业", "研究", "开发", "费用", "(", "以下简称", "研发", "费用", ")", "税前", "加计扣除", "比例", "有关", "问题", "发布", "通知", "。"], ["通知", "明确", ",", "科技型", "中小企业", "开展", "研发", "活动", "中", "实际", "发生", "的", "研发", "费用", ",", "未", "形成", "无形资产", "计入当期损益", "的", ",", "在", "按", "规定", "据", "实", "扣除", "的", "基础", "上", ",", "在", "2017", "年", "1", "月", "1", "日", "至", "2019", "年", "12", "月", "31", "日", "期间", ",", "再", "按照", "实际", "发生额", "的", "75", "%", "在", "税前", "加计扣除", ";", "形成", "无形资产", "的", ",", "在", "上述", "期间", "按照", "无形资产", "成本", "的", "175", "%", "在", "税前", "摊销", "。"], ["科技型", "中小企业", "享受", "研发", "费用", "税前", "加计扣除", "政策", "的", "其他", "政策", "口径", "按照", "《", "财政部", "国家税务总局", "科技部", "关于", "完善", "研究", "开发", "费用", "税前", "加计扣除", "政策", "的", "通知", "》", "(", "财税", "〔", "2015", "〕", "119", "号", ")", "规定", "执行", "。"], ["科技型", "中小企业", "条件", "和", "管理办法", "由", "科技部", "、", "财政部", "和", "国家税务", "总局", "另行", "发布", "。", "科技", "、", "财政", "和", "税务部门", "应", "建立", "信息共享", "机制", ",", "及时", "共享", "科技型", "中小企业", "的", "相关", "信息", ",", "加强", "协调", "配合", ",", "保障", "优惠政策", "落实到位", "。"], ["(", "转载", "自", ":", "中国", "会计", "视野", ")"], ["东奥", "会计", "在线", "微信", "东奥", "中级会计", "职称", "微信"], ["上", "一篇", "文章", ":", "关于", "2016", "年度", "企业", "研究", "开发", "费用", "税前", "加计扣除", "政策", "企业所得税", "纳税申报", "问题", "的", "公告", "下一篇", "文章", ":", "关于", "提高", "科技型", "中小企业", "研究", "开发", "费用", "税前", "加计扣除", "比例", "的", "通知"]], "paragraphs": ["【东奥会计在线——中级会计职称频道推荐】根据《关于提高科技型中小企业研究开发费用税前加计扣除比例的通知》的规定,研发费加计扣除比例提高到75%。", "财政部、国家税务总局、科技部发布《关于提高科技型中小企业研究开发费用税前加计扣除比例的通知》。", "通知称,为进一步激励中小企业加大研发投入,支持科技创新,就提高科技型中小企业研究开发费用(以下简称研发费用)税前加计扣除比例有关问题发布通知。", "通知明确,科技型中小企业开展研发活动中实际发生的研发费用,未形成无形资产计入当期损益的,在按规定据实扣除的基础上,在2017年1月1日至2019年12月31日期间,再按照实际发生额的75%在税前加计扣除;形成无形资产的,在上述期间按照无形资产成本的175%在税前摊销。", "科技型中小企业享受研发费用税前加计扣除政策的其他政策口径按照《财政部国家税务总局科技部关于完善研究开发费用税前加计扣除政策的通知》(财税〔2015〕119号)规定执行。", "科技型中小企业条件和管理办法由科技部、财政部和国家税务总局另行发布。科技、财政和税务部门应建立信息共享机制,及时共享科技型中小企业的相关信息,加强协调配合,保障优惠政策落实到位。", "(转载自:中国会计视野)", "东奥会计在线微信 东奥中级会计职称微信", "上一篇文章:关于2016年度企业研究开发费用税前加计扣除政策企业所得税纳税申报问题的公告 下一篇文章:关于提高科技型中小企业研究开发费用税前加计扣除比例的通知"], "bs_rank_pos": 3}, {"is_selected": false, "title": "税务总局:进一步提高科技型中小企业研发费用加计扣除比例", "most_related_para": 6, "segmented_title": ["税务总局", ":", "进一步提高", "科技型", "中小企业", "研发", "费用", "加计扣除", "比例"], "segmented_paragraphs": [["国家税务总局", "所得税", "司", "副司长", "刘宝柱", "做客", "《", "今天", "我", "值班", "》", "回答", "热点", "问题"], ["央", "广", "网", "北京", "4", "月", "12", "日", "消息", "(", "记者", "刘祎辰", ")", "据", "中国", "之", "声", "《", "央广新闻", "》", "报道", ",", "当前", ",", "“", "大众创业", "、", "万众", "创新", "”", "的", "热潮", "正在", "我", "国", "蓬勃", "兴起", "。", "税收", "优惠政策", "在", "支持", "“", "大众创业", "、", "万众", "创新", "”", "方面", "是", "如何", "发挥", "作用", "的", "呢", "?", "国家税务总局", "所得税", "司", "副司长", "刘宝柱", "做客", "《", "今天", "我", "值班", "》", "直播间", ",", "就", "相关", "热点", "问题", "与", "央", "广", "主持人", "进行", "深入", "交流", "。"], ["关于", "研发", "费用", "加计扣除", "政策", ",", "一些", "科技型", "企业", ",", "尤其", "是", "双创", "的", "小微", "企业", ",", "谈到", "这个", "政策", "特别", "有", "热情", "。"], ["刘宝柱", "表示", ",", "研发", "费用", "加计扣除", "企业所得税", "优惠政策", ",", "是", "助推", "大众", "创业", "、", 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"2016年,执行新修订的研发费用加计扣除政策,和原政策相比,一是政策面扩大,产业导向更加清晰。新修订的政策采取反列举的方式,规定除了烟草制造业等6个行业外,其余行业均能享受,较之原政策的正列举方式,明显扩大了享受政策的行业范围。二是靶向性突出,服务国家创新战略。从修订的内容看,咬定“研发”不放松,在原来允许扣除的范围基础上,考虑企业生产经营及研发活动实际,将外聘人员劳务费、试制产品检验费、专家咨询费、高新科技研发保险,以及与研发直接相关的差旅费、会议费等,也纳入加计扣除范围。三是放管服深化,帮助企业享受政策。税务机关落实政策时,原则上只核实当年情况,对以前年度有涉税问题的,不得影响当年的享受。四是跨部门协调,完善争议解决机制。发生税企争议时,税务机关请有关部门出具意见,有关部门也明确责任,负责解释和复核工作, 免去企业在政府部门间来回跑文的辛苦。", "目前,税务总局正密切配合财政部等牵头部门落实2017年《政府工作报告》的重点工作任务,也就是要进一步提高科技型中小企业的研发费用加计扣除比例,将科技型中小企业开发新技术、新产品、新工艺实际发生的研发费用税前加计扣除比例由50%提高到75%。", "本文系转载,不代表参考消息网的观点。参考消息网对其文字、图片与其他内容的真实性、及时性、完整性和准确性以及其权利属性均不作任何保证和承诺,请读者和相关方自行核实。"], "bs_rank_pos": 4}], "answer_spans": [[29, 37]], "answer_docs": [3], "fake_answers": ["研发费加计扣除比例提高到75%。"], "question": "研发费用加计扣除比例", "segmented_answers": [["第", "一", "种", "：", "加计扣除", "前", "，", "根据", "公示", "：", "A", "=", "利润", "*", "税率", "，", "2", "第", "二", "种", "：", "加计扣除", "后", "因", "研发", "费", "加计扣除", "占", "比例", "的", "50", "%", "，", "推出", "公示", "：", "B", "=", "（", "利润", "-", "研发", "费用", "*", "50", "%", 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"环境", "皮肤性病", "营养保健", "其他", "科室", "保健养生"], ["下载", "APP", ",", "免费", "快速", "问", "医生"]], "paragraphs": ["健康咨询描述: 前几天我用早孕试条测出怀孕,去医院检查也确认是怀孕了,但经常会感觉头晕想吐,浑身不舒服,吃什么吐什么,有时候吃的很少,但感觉胃里满满的好像消化不了的样子,特别难受,今天早上去厕所后又发现内裤上有褐色的东西。", "想得到怎样的帮助:怀孕初期有褐色分泌物正常吗?", "1、专家说,当怀孕初期有褐色分泌物时,只需过一段时间就会好,需注意不要操劳,多休息。褐色分泌物颜色很淡是没什么问题,如果颜色比较深最好去医院栓查一下。 2、另外,要做血HCG检查以明确怀孕。若确为怀孕且孕早期有阴道出血情况,需去医院做阴道B超检查以明确是否宫内孕,若为宫内孕还需做血HCG、孕酮检查以明确激素水平及胎儿发育情况,若为宫外孕则最好采取电子腹腔镜手术以最大限度的保留器官及功能完整性。需去医院检查以明确诊断,对症治疗。 3、一般来讲,阴道分泌物中有褐色的物质,有可能是宫颈糜烂,子宫内膜不规则的少量脱落等,至于是否先兆性流产还不能确立。建议你到妇幼保健院定期产前检查,以便保证胎儿的正常。 4、要是总会断断续续出现褐色分泌物,要暂时避免性交,否则会有流产危险。妊娠早期受精卵(胚胎)在子宫内着床不就,处于发育初期,胎盘尚未发育完全,胎盘和子宫壁连接还不稳固。如果这时性交,子宫会受到震动,很容易使胎盘脱落,造成流产。早期进行性生活,就很可能由于精液中所含的前列腺素使到子宫收缩,子宫宫颈松弛,加上性交时的刺激,能够导致流产。在头10个星期内仍应禁止发生性行为。", "病情分析: 你好,身体的情况考虑与怀孕期间,有出血意外,有先兆流产征兆影响也有关的 指导意见: 怀孕期间的情况,不是很正常的,建议及时到医院看看,更安全的,祝身体健康", "病情分析: 在怀孕早期出现这种分泌物,是不正常的,不排除有导致先兆流产的可能 指导意见: 最好是抽血做个HCG血检与孕酮检查,看是否存在值过低,是否有必要用药来保胎的,祝好孕 以上是对“怀孕初期有褐色分泌物正常吗?”这个问题的建议,希望对您有帮助,祝您健康!", "病情分析: 你好,像你这个症状属于先兆流产,对胎儿发育也是有一定影响的。 指导意见: 我建议你最好再次进行检查来明确孕囊发育的情况,必要时进行药物保胎,期间注意卧床休息,避免剧烈运动,减少吃辛辣刺激的食物,可以少吃多餐,另外定期复查,祝身体健康!", "病情分析: 你好,怀孕早期有褐色的分泌物,这个考虑为先兆流产的可能性,出现这样的症状需要引起重视的 指导意见: 你的情况要及时去医院进行检查,看看孕酮等是否正常,另外就是要注意多卧床休息,禁止性生活,注意营养,及时就医", "怀孕后有褐色分泌物说明有出血的情况,这时一定要提高警惕,建议及时到医院检查,及时处理,千万不要延误病情。无论是怀孕初期还是怀孕任何时期,有任何出血症状都需要告诉医生,通常轻微出血不会被认为是严重问题。但是见红或流鲜血,尤其伴有腹部疼痛或绞痛时,最好能马上就诊,因为这可能是流产或宫外孕的征兆。在怀孕后期,这样的出血症状也可能是胎盘出现了问题。怀孕后有褐色分泌物的几种情况 1、先兆流产,主要表现是在怀孕早期出现怀孕出血和腹痛。一般在先兆流产阶段,阴道出血较少,腹痛轻。如果没有胚胎发育异常,经过保胎治疗,出血就会停止,可以继续妊娠。 2、异位妊娠,异位妊娠也称宫外孕。发生异位妊娠时,一般在停经40天左右出现阴道流血,多伴有下腹部的隐痛、胀痛、坠痛等。此时进行尿妊娠检查可为阳性,而超声检查宫腔内见不到妊娠的胚囊,往往在输卵管的部位可以发现异常的肿物。 3、孕妇宫颈疾病:孕妇怀孕前患有宫颈糜烂、宫颈息肉等疾病也可出现有褐色分泌物等可能。", "病情分析: 怀孕以后的分泌物会发生明显的变化,由于怀孕以后卵巢的黄体分泌大量雌激素和孕激素,因此会出现白带量增多。但不应该出现褐色分泌物,怀孕后有褐色分泌物说明有出血的情况,这时一定要提高警惕,建议及时到医院检查,及时处理,千万不要延误病情。 指导意见: 怀孕后有褐色分泌物的几种情况: 1、宫外孕:宫外孕一般在停经40天左右出现阴道流血,多伴有下腹部的隐痛、胀痛、坠痛等。此时进行尿妊娠检查可为阳性,而超声检查宫腔内见不到妊娠的胚囊,往往在输卵管的部位可以发现异常的肿物。 2、阴道滴虫感染:这种感染出易造成白带增多,还会伴有恶臭,同时还会发生阴部的瘙痒。普遍的认为这种感染是性病的一种,很容易在性伴侣之间相互感染,治疗时必须同时治疗性伴侣才能杜绝交叉感染而彻底治愈。 3、先兆流产:主要表现是在怀孕早期出现怀孕出血和腹痛。一般在先兆流产阶段,阴道出血较少,腹痛轻。如果没有胚胎发育异常,经过保胎治疗,出血就会停止,可以继续妊娠。 4、有可能是怀孕后少量月经的表现,应卧床多休息,不要做剧烈的活动,除了上厕所最好就不要下床,一般这种现象在2天左右就会消失。此外,为了防止孕期阴道出血是妇科疾病引起的,建议去医院了解一下原因; 以上是对“怀孕初期有褐色分泌物正常吗?”这个问题的建议,希望对您有帮助,祝您健康!", "保妇康栓 本品用于湿热瘀滞所致的带下病。...[说明书] 参考价格:￥28", "云南白药胶囊 本品用于跌打损伤,跖骨、趾骨骨折,瘀血肿痛,吐血...[说明书] 参考价格:￥17.5", "用什么能杀死阴虱 引产过程需要几天 阴虱会影响睡眠吗 阴虱感染多久发病 引产会造成不孕吗 引产需要几天时间 阴虱病会不会复发 引产手术需要几天 阴虱对女性有害吗 阴道痒是什么原因", "医生在线免费咨询", "看病 男科 妇科 癫痫 性病 养生 新闻 白癫风 牛皮癣", "就医 疾病信息 健康经验 症状信息 手术项目 检查项目 健康百科 求医问药", "用药 药品库 疾病用药 药品心得 用药安全 药品资讯 用药方案 品牌药企 明星药品", "整形 整形项目 整形案例 整形资讯 整形医院 减肥塑形 眼部整形 鼻部整形 面部整形", "有问必答 内科 外科 儿科 药品 遗传 美容 检查 减肥 中医科 五官科 传染科 体检科 妇产科 肿瘤科 康复科 子女教育 心理健康 整形美容 家居环境 皮肤性病 营养保健 其他科室 保健养生", "下载APP,免费快速问医生"], "bs_rank_pos": 1}, {"is_selected": true, "title": "怀孕初期流褐色分泌物的宝妈们看看这个。 - 2016年10月妈妈帮 - ", "most_related_para": 0, "segmented_title": ["怀孕", "初期", "流", "褐色", "分泌物", "的", "宝", "妈", "们", "看看这个", "。", "-", "2016", "年", "10", "月", "妈妈", "帮", "-"], "segmented_paragraphs": [["惊", "!", "!", "准妈妈", "出现", 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"!"]], "paragraphs": ["惊!!准妈妈出现褐色分泌物!! 什么是褐色分泌物? 出现褐色分泌物的孕妇不在少数,一般都发生在孕前三个月。所谓的褐色分泌物,其实是前宫腔出的血,经过一段时间血液里面的铁元素发生氧化,并随着分泌物排出,因此颜色发褐或者发黑。如果是当时的出血,那么颜色应该是粉色,红色,而不应该是褐色。 造成了褐色分泌物的原因 1、占比最大的,也是自己首先要想到的就是着床血的延迟排出。胎儿在着床的时候,因为绒毛穿透子宫内膜,内膜会出现少量出血。因为血量少,当时不会流出,随着孕期推进,受内分泌影响,孕妇分泌物增加而排出体外。时间会发生在孕30-40天左右。 2、子宫壁是有褶皱的,随着孕期的推进,子宫变大,褶皱慢慢舒展,以前残存的经血也会随着分泌物排出,颜色会比褐色更深一点。这是宝宝在打扫卫生了,宝宝需要一个干净的环境。 3、宫颈炎、盆腔炎、阴道炎这些生殖系统的炎症的存在,导致部分皮肤破溃出血,血量不多。表现为局部瘙痒,分泌物异味以及褐色分泌物不多但是时有时无,持续时间长。 4、孕囊和子宫内膜接触部位,因为不稳固而出现少量子宫内膜脱落的出血,B超的时候会看见积液。如果脱落过多,胚胎可能缺乏营养而造成胎停,可以结合HCG检查进行排除。这属于先兆流产迹象,孕酮一般也会比较低。 5、宫外孕或者葡萄胎这种特殊妊娠,胎儿无法正常发育,会出现流产的迹象,但是一般都会在褐色分泌物后出现粉色,红色的血迹,容易辨别。 出现褐色分泌物了怎么办? 1、观察分泌物,如果是随着白带一起,量少,不持续,无腹痛,三天内就没有了,不必担心,属于着床血或者褶皱血。 2、看孕期反应,如果反应还在,说明孩子没事,以前的出血并没有对孩子造成影响,不必担心。 3、验血,通过HCG是否增长,孕酮是否在正常值(相关数据请在订阅号回复HCG、孕酮查询)来判断胎儿和子宫环境,如果都正常就不必担心。 4、以上问题都排除了,褐色分泌物还是时有时无,检查分泌物,如果有炎症,是可以进行治疗的,生殖系统的严重炎症会上行感染子宫造成胎儿畸形或者早产,如果症状轻微可以不治疗。 5、如果炎症也没有,褐色分泌物不止,去B超看看宫内情况,假如有宫内出血是可以看出来的。确定胎儿大小,如果胎儿大小符合孕周,确定不是宫外孕和葡萄胎,可以进行常规保胎治疗。 以上都是针对没有过不良孕史的MM,如果曾经有过流产经历,子宫内膜比较敏感,那么少量的刺激也可能引起宫缩,也就是我们所说的习惯性流产。这类MM遇到褐色分泌物,请立刻就医。", "本文由妈妈帮用户发布。未经许可,禁止转载。", "想第一时间看到最新的孕育文章,与妈妈们交流育儿经验", "请关注妈妈帮微信服务号:mamabang2017", "只有注册用户才能发表回复。请 登录 或者立即注册", "🐒宝宝一切正常 1楼2016-03-18 09:42", "我是吃药打针了,打了第四天白带正常了,我觉得没事了,就走路比较多,玩上又有一点了,第五天有一点,今天暂时还没有", "只想宝宝健健康康 3楼2016-03-18 10:11", "我是孕酮低,褐色分泌物,去医院医生说是先兆流产,让打七天黄体酮,要是孕酮还没升上去就要持续打针到第十周,要多多卧床休息。", "还有一种可能就是宫缩形成的间隔 12周之后长胎盘了会自动被吸收", "母婴血型不合是引起习惯性流产的一种原因。初次怀孕对胎儿影响不大,分娩次数越多,胎儿、新生儿发病的可能性越大,因此九州家圆的人说常在第二胎以后发病。此类血型不合的病情重,可常见流产、死产、严重的新生儿溶血性黄疸等。", "使用妈妈帮APP或微信号", "免费领取妈妈礼包", "妈妈帮微信号", "领取粉丝专享福利", "妈妈帮百科热门内容", "孩子太磨蹭拖拉,真急人,怎么破?", "治愈妇科炎症,中药更有效!"], "bs_rank_pos": 2}, {"is_selected": false, "title": "孕初期有褐色分泌物,现一切正常、献给有同样经历的宝妈们_孕早期", "most_related_para": 2, "segmented_title": ["孕", "初期", "有", "褐色", "分泌物", ",", "现", "一切正常", "、", "献给", "有", "同样", "经历", "的", "宝", "妈", "们", "_", "孕早期"], "segmented_paragraphs": [["辣妈", "帮", "首页", ">", "孕早期", "辣妈", "帮", ">", "孕", "初期", "有", "褐色", "分泌物", ",", "现", "一切正常", "、", "献给", "有", 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分钟的休息监考官不准大家 离开考场, 如果需要上厕所的话一次只能去 1 男 1 女,且一旦超时直接占用自己 的写作时间。所以要上厕所的请一定要在第二次中场休息时过去。 10、写作考试 2 分钟过去后,就开始了写作考试。 写作考试开始后,监考官会另行下发作文试题。作文试题是一张 A3 的折叠 起来了的硬卡纸, 在所有考生都拿到作文了之后,考官会要求大家在背面填写个 人信息,然后就开始答题。 写作一共是 50 分钟,要求在规定时间内对给出的一篇文章分析他是如何完 成它的目的的。 新 SAT 写作比老 SAT 写作长太多了, 大家一定要带好多好多好多 铅笔,强烈建议大家准备 20 支铅笔,至少也不要低于 5 支铅笔。 11、考试结束 写作考试结束后,时间接近 2 点了。 在考试结束后, 收上去的手机会在考试结束后发下来。 所有考生请务必注意, 在考试接受后拿到自己的手机后不要在考场内打开自己的手机。 另外,在监考官叫停笔后,包括考试结束开始发手机之后,请大家不要再写 任何东西。"], "bs_rank_pos": 0}, {"is_selected": true, "title": "2017年下半年SAT考试出分时间汇总_SAT_新东方在线", "most_related_para": 8, "segmented_title": ["2017", "年", "下半年", "SAT", "考试", "出", "分", "时间", "汇总", "_", "SAT", "_", "新东方", "在线"], "segmented_paragraphs": [["新东方", "在线", "SAT", "频道", "为", "大家", "带来", "2017", "年", "下半年", "SAT", "考试", "出", "分", "时间", "汇总", "一", "文", ",", "希望", "对", "大家", "SAT", "备考", "有所帮助", "。", "更多精彩", "尽", "请", "关注", "新东方", "在线", "SAT", "频道", "!"], ["2017", "年", "下半年", "SAT", "考试", "出", "分", "时间", "分", "国际", "考区", "和", "北美", "考区", ",", "北美", "考区", 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"版权", "等", "问题", ",", "请", "作者", "见稿", "后", "在", "两", "周", "内", "与", "新东方", "在线", "联系", "。"], ["美国", "TOP", "100", "名校", "新旧", "SAT", "及", "托福", "要求", "新", "SAT", "考试", "时间表", "及", "早", "/", "常规", "申请", "安排", "【", "测", "一", "测", "】", "SAT", "与", "ACT", "到底", "考", "哪个", "?", "新", "SAT", "与", "ACT", "分数", "换算表"], ["2017", "年", "SAT", "考试", "时间表", "(", "全年", ")", "新", "SAT", "考试", "1540", "分", "是", "如何", "修炼", "的", "?", "新", "SAT", "样卷", "下载", "及", "新旧", "SAT", "考试", "对比", "新东方", "在线", "全程", "陪跑", "新", "SAT", "备战", "之路"], ["为", "新", "SAT", "而来", "直", "面", "变革", "始终", "巅峰", "超值", "大", "礼包", "—", "—", "SAT", "备考", ":", "词汇", "、", "写作", "、", "阅", ".", ".", ".", "【", "新东方", "在线", "内部", "讲义", "】", "AP", "讲义", "(", "微积分", ",", ".", ".", "."], ["w", "06", "分", "18", "秒", "1", "新东方", "在线", "曾循", ":", "SAT", "写作", "观点", "的", "不同", "写法", "2", "新东方", "在线", "贺", "灿", ":", "SAT", "文法", "中", "的", "平行结构", "3", "新东方", "在线", "焦", "亚", "新", ":", "SAT", "写作", "Reasoning", "的", "写法", "4", "新东方", "在线", "侯仲红", ":", "SAT", "阅读", "中", "的", "三种", "转折", "类型", "5", "新东方", "在线", "徐", "男", ":", "如何", "掌握", "SAT", "阅读", "中", "句子", "的", "逻辑"]], "paragraphs": ["新东方在线SAT频道为大家带来2017年下半年SAT考试出分时间汇总一文,希望对大家SAT备考有所帮助。更多精彩尽请关注新东方在线SAT频道!", "2017年下半年SAT考试出分时间分国际考区和北美考区,北美考区考试场次较多。以下分别介绍2017年下半年SAT北美考区出分时间和2017年下半年SAT国际考区出分时间。", "以上就是新东方在线SAT频道为你带来的2017年下半年SAT考试出分时间汇总,更多精彩敬请关注新东方在线SAT频道。", "相关推荐:", "【考试时间】2017年SAT考试时间表 【SAT改革】 直面新SAT改革 为1600而冲分 【深度解析】2016年SAT考试改革新动向", "SAT数学备考策略 如何拯救SAT数学 SAT需要多少词汇量 SAT词汇词根大全 新SAT写作评分标准 SAT文法100个介词", "新SAT写作满分范文 SAT Essay作文怎么写 SAT文法出错率最高的100个固定搭配 SAT语法备考重点 SAT填空模拟练习题", "版权及免责声明", "1,\"新东方在线\"上的内容,包括文章、资料、资讯等, 本网注明\"稿件来源:新东方在线\"的,其版权 均为\"新东方在线\"或北京新东方迅程网络科技有限公司所有 ,任何公司、媒体、网站或个人未经授权不得转载、链接、转贴或以其他方式使用。已经得到 \"新东方在线\"许可 的媒体、网站,在使用时必须注明\"稿件来源:新东方\",违者本网站将依法追究责任。  2, \"新东方在线\" 未注明\"稿件来源:新东方\"的 文章、资料、资讯等 均为转载稿,本网站转载出于传递更多信息之目的,并不意味着赞同其观点或证实其内容的真实性。如其他媒体、网站或个人从本网站下载使用,必须保留本网站注明的\"稿件来源\",并自负版权等法律责任。如擅自篡改为 \" 稿件来源:新东方 \" ,本网站将依法追究其法律责任。  3,如本网转载稿涉及版权等问题,请作者见稿后在两周内与新东方在线联系。", "美国TOP100名校新旧SAT及托福要求 新SAT考试时间表及早/常规申请安排 【测一测】SAT与ACT到底考哪个? 新SAT与ACT分数换算表", "2017年SAT考试时间表(全年) 新SAT考试1540分是如何修炼的? 新SAT样卷下载及新旧SAT考试对比 新东方在线全程陪跑新SAT备战之路", "为新SAT而来 直面变革 始终巅峰 超值大礼包——SAT备考:词汇、写作、阅... 【新东方在线内部讲义】AP讲义(微积分,...", "w 06分18秒  1 新东方在线曾循:SAT写作观点的不同写法  2 新东方在线贺灿:SAT文法中的平行结构  3 新东方在线焦亚新:SAT写作Reasoning的写法  4 新东方在线侯仲红:SAT阅读中的三种转折类型  5 新东方在线徐男:如何掌握SAT阅读中句子的逻辑"], "bs_rank_pos": 1}, {"is_selected": true, "title": "★SAT考试时间:2017年SAT考试时间-SAT考试时间安排", "most_related_para": 0, "segmented_title": ["★", "SAT", "考试时间", ":", "2017", "年", "SAT", "考试时间", "-", "SAT", "考试时间安排"], "segmented_paragraphs": [["SAT", "考试时间", "网站", "免费提供", "SAT", "考试时间", ":", "2017", "年", "SAT", "考试时间", ",", "SAT", "考试时间安排", "等", "最新", "信息", ",", "欢迎访问", "SAT", "考试", "栏目", "获取", "更多", "SAT", "考试时间", "信息", "。", "SAT", "考试", "时间", "更新时间", ":", "2017", "-", "7", "-", "27"]], "paragraphs": ["SAT考试时间网站免费提供SAT考试时间:2017年SAT考试时间,SAT考试时间安排等最新信息,欢迎访问SAT考试栏目获取更多SAT考试时间信息。SAT考试时间更新时间:2017-7-27"], "bs_rank_pos": 2}, {"is_selected": true, 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4、我们查询其监管号是501320,也可以返回上一步在监管号中输入501320,对于有监管号的平台我们就可以如此查询。点击图中红色链接Basic details可以查看监管的详细信息,如下图所示,可以看到监管地址,联系电话,网址,是否允许持有客户资金等情况。", "【原创声明】凡注明“来源:希财新金融”的文章,系本站原创,任何单位或个人未经本站书面授权不得转载、链接、转贴或以其他方式复制发表。否则,本站将依法追究其法律责任。", "当天能撸钱的手机贷款 小额贷款10分钟下款 快速借到10万 揭秘房价不会暴跌的原因 如何提升借款成功率 马上到账的贷款口子", "相关阅读", "暂时还没有评论哦,来说两句吧!", "怎样正确投资能让你的10万迅速变100万? 易通贷:5年运营放心老平台 进入P2P超市", "风险提示:广告信息均来自平台方,不代表平台安全性,不构成投资建议!红包、体验金、理财金、大礼包、加息券、满减券、优惠券等都不直接等同于现金。参考收益说明不是收益承诺,不代表最终真实收益。理财有风险,投资需谨慎!"], "bs_rank_pos": 2}, {"is_selected": false, "title": "怎么查交易平台的监管账号_百度知道", "most_related_para": 0, "segmented_title": ["怎么", "查", "交易平台", "的", "监管", "账号", "_", "百度", "知道"], "segmented_paragraphs": [["进入", "监管", "的", "网站", ",", "输入", "监管", "号", "查询", "监管", "网站", "和", "监管", "号", ",", "向", "你", "的", "代理人", "索取", ",", "不会", "的", "话", ",", "可以", "让", "他", "教", "你", "(", "一", "个", "有", "实力", "的", "代理", "公司", "不会", "连", "基本", "的", "监管", "查询", "也", "不会", "的", ",", "真", "不会", "的", "话", "那", "就要", "考虑", "清楚", ",", "到底", "什么", "原因", ")", 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"国外的很难查是否真实!还是做香港的吧!外汇选择在银行交易就好!黄金选择香港的!这样才能查到你的单!", "美国监管NFA英国监管FSA百度搜索"], "bs_rank_pos": 3}, {"is_selected": false, "title": "澳大利亚ASIC监管号最新查询方法 - 汇友网", "most_related_para": 7, "segmented_title": ["澳大利亚", "ASIC", "监管", "号", "最新", "查询", "方法", "-", "汇友", "网"], "segmented_paragraphs": [["ASIC", "澳大利亚", "证券", "和", "投资", "委员会成立", "于", "2001", "年", ",", "是", "澳大利亚", "金融服务", "和", "市场", "的", "法定", "监管", "机构", ",", "也是", "一", "个", "独立", "的", "联邦政府", "机构", ",", "依法", "对", "全", "澳大利亚", "金融体系", "、", "各", "金融机构", "和", "专业", "从业人员", "行使", "金融监管", "的", "职能", "。", "致力于", "确保", "澳大利亚", "金融市场", "公平", "与", "透明", ",", "以此", "来", "保障", "澳大利亚", "经济", "的", "名誉", "及", "健康", "。"], ["监管", "范围", ":"], ["牌照", ":"], ["AFSL", "、", "ABN", "(", "澳大利亚", "商业", "注册", "代码", ")", "、", "ACN", "(", "澳大利亚", "公司", "代码", ")"], ["特别提示", ":", "金融公司", "如", "从事", "金融", "衍生品", "交易", "服务", "需要", "三", "个", "号码", "皆", "具备", ",", "仅", "有", "后", "两种", "的", "公司", "是", "没有资格", "开展", "相关", "业务", "的", "。"], ["查询", "澳大利亚", "ASIC", "监管", 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"2、在官网首页的右侧位置选择:Professional registers", "3、在下面的页面中,在输入框中输入相关信息,点击“search”即可(我们这里以Aetos的监管号查询为例)", "4、在查询结果中就会出现该平台的相关信息了。", "本外汇网站所有文章仅代表作者立场及观点,与汇友网本身无关。如果觉得文章符合你的交易观点,可以收藏一下;如果与你的交易观点背驰,也请不要谩骂,每个人的交易理念都是有差别的!"], "bs_rank_pos": 4}], "answer_spans": [[24, 111]], "fake_answers": ["点击网页中间Firms(机构),下面的子栏目FinancialServicesRegister(金融服务注册)。2、点击上方图片红色按钮,将进入搜索页面,点击searchtheregister将跳转到FCA监管页面。页面网址为http://www.fsa.gov.uk/register/home.do。3、点击FinancialServicesFirmSearch,将能够进入监管信息查询页面,可以通过查询监管ID和监管名称进行查询"], "question": "如何查监管号", "segmented_answers": [["1", "、", "打开", "FCA", "官方网站", "，", "点击", "网页", "中间", "Firms", "（", "机构", "）", "，", "下面", "的", "子栏目", "Financial", "Services", "Register", "（", "金融服务", "注册", "）", "。", "2", "、", "点击", "上方", "红色", "按钮", "，", "将", "进入", "搜索", "页面", "，", "点击", "search", "the", "register", "将", "跳转", "到", "FCA", "监管", "页面", "。", "3", "、", "点击", "Financial", "Services", "Firm", "Search", "，", "将", "能够", "进入", "监管", "信息查询", "页面", "，", "可以通过", "查询", "监管", "ID", "和", "监管", 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另外一种方法,是通过图章工具来对水印进行修补的方法,首先同样是从Photoshop打开需要去水印的图片,并且对其图层做拷贝备份。 步骤阅读 2 选择图章工具,按住Alt键点击与水印位置较为相似的位置,松开按钮,在水印位置进行涂抹填充。 步骤阅读 步骤阅读 3 涂抹完后,也一样产生出修补的效果。 步骤阅读 END", "百度经验:jingyan.baidu.com", "1 还有一种方法可能很多人没有用过,就是利用新版Photoshop CS6/CC当中的内容感知移动工具,首先打开图片,备份图层。 步骤阅读 2 选择内容感知移动工具,框选图片上的水印,把框选区域拖拽到图片的边缘外。 步骤阅读 3 效果一下子产生了,很方便是吧。 步骤阅读 END", "百度经验:jingyan.baidu.com", "修补工具修补范围尽量贴近色差较小的位置,否则会产生出修补不协调的效果 图章修补工具的原理是把别处相似的位置复制到修补位置进行视觉上的修补,对于一些细节较多的图片修补很容易会发生位置不搭 内容感知移动工具是CS6版本开始有的一个功能,强大而又方便,适用于大范围修补,不过细节上却抓得不是很紧 各工具应用范围因图而异,可能这个工具能修补这幅图,却不能修补这幅图,这个工具能修补这幅图却不能修补那幅图,需要自己的学习和发觉 本教程适用于Photoshop新手的学习", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "如何利用PS简单处理破旧图片 如何泡好一杯美味香浓的速溶咖啡"], "bs_rank_pos": 0}, {"is_selected": false, "title": "ps怎么去水印?ps去水印的三种方法_脚本之家", "most_related_para": 2, "segmented_title": ["ps", "怎么", "去", "水印", "?", "ps", "去", "水印", "的", "三种", "方法", "_", "脚本", "之", "家"], "segmented_paragraphs": [["在", "网上", "看到", "了", "很多", "漂亮", "的", "图片", ",", "想", "下载", "下来", "当作", "桌面", "壁纸", "或者", 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时长:13:25来自:窝风尚品", "百度经验:jingyan.baidu.com", "对于很多人来说去水印是比较头疼的事情,这里教你六招去水印的方法!", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "1 使用仿制图章工具去除使用仿制图章工具去除文字这是比较常用的方法,具体的操作是,选取仿制图章工具,按住Alt键,在无文字区域点击相似的色彩名图案采样,然后在文字区域拖动鼠标复制以覆盖文字。要注意的是,采样点即为复制的起始点。选择不同的笔刷直径会影响绘制的范围,而不同的笔刷硬度会影响绘制区域的边缘融合效果。 步骤阅读 2 使用修补工具去除文字如果图片的背景色彩或图案比较一致,使用修补工具就比较方便具体操作是:选取修补工具,在公共栏中选择修补项为“源”,关闭“透明”选项。然后用修补工具框选文字,拖动到无文字区域中色彩或图案相似的位置,松开鼠标就完成复制。修补工具具有自动匹配颜色的功能,复制的效果与周围的色彩较为融合,这是仿制图章工具所不具备的。 步骤阅读 3 使用修复画笔工具去除文字操作的方法与仿制图章工具相似。按住Alt键,在无文字区域点击相似的色彩或图案采样,然后在文字区域拖动鼠标复制以覆盖文字,只是修复画笔工具与修补工具一样,也具有自动匹配颜色的功能,可根据需要进行选用。 步骤阅读", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "bs_rank_pos": 2}, {"is_selected": false, "title": "PS如何去除照片水印_百度经验", "most_related_para": 3, "segmented_title": ["PS", "如何", "去除", "照片", "水印", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["水印", "大", "多", "是", "作者", "为了", "保护", "劳动", "果实", "而", "在", "果子", "上", "打的", "标签", ",", "但是", "为了", "满足", "人们", "的", 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比较难处理的是两种颜色相接的区域,这里建议先用纯色覆盖,再进行修补。如图:选择一个贴近花瓣边缘的选框,选择“矩形选框工具”,点击右键,选择“通过拷贝的图层”。会重新新建一个选区的图层,将其移动到花瓣边缘(注意,这里要先将花瓣上的文字处理以后,再将图层合并,用修补工具做进一步调整,因为这里颜色不很一样)。 步骤阅读 步骤阅读 步骤阅读 步骤阅读 4 最后对花瓣上的文字进行处理,这里比较复杂一些,需要有足够的耐心,当然也要细心。 首先要将图片放大,放到足够大,然后再用修补工具小范围的修补,重复操作。最终效果 步骤阅读 步骤阅读 END", "百度经验:jingyan.baidu.com", "1 使用这种方法一般是水印所覆盖的地方比较规则平整,或者有规律,我们可以复制某一部分选取来覆盖水印。 首先要准备工作,选择“矩形选择工具”,调整工具的羽化值,可根据图的大小调整,这里调整为10 步骤阅读 步骤阅读 步骤阅读 2 然后选择和水印大小差不多的选取,在选取内点击右键,在弹出的选框中选择“通过拷贝的图层”,这里要注意的是,一定要是在选择“矩形选择工具”的状态下,才可以操作。 步骤阅读 步骤阅读 3 最后一步:调整。选择拷贝出的图层,调整图层的位置。 步骤阅读 步骤阅读 END", "百度经验:jingyan.baidu.com", "1 这种方法适于有透视的场景,而且背景有规则整齐,像地板,瓷砖之类。首先点选滤镜——消失点,弹出消失点编辑页面 步骤阅读 步骤阅读 步骤阅读 2 沿着地板的缝隙点选出一个梯形的网格,如图 步骤阅读 3 选择 第三个 矩形选框工具,参数如图,可根据需要自由调节。按着alt键,在蓝色方框选取内,画出梯形选取选框,意图在于将选框的内容覆盖在水印上。 步骤阅读 4 将鼠标向水印引动,按住shift键可以使选取按照透视方向平稳移动。最终效果如图。 步骤阅读 5 如果需要特殊的形状,也可以用第四个—图章工具,或第五个—画笔工具,可以选择画笔工具的形状,调出自己想要的效果。 END", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:一个会点设计的环保人"], "bs_rank_pos": 3}, {"is_selected": false, "title": 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"paragraphs": ["很多素材图片上都被加上了各种各样的水印,而我们偏偏非常需要这张图片,那么ps怎么去复杂水印?下面小编就为大家介绍PS快速去除水印方法,一起来看看吧", "作为设计师,相信大家都有收藏佳作的习惯,但像某些图片中包含了非常繁琐的水印时常会影响画面美感,那么ps怎么去复杂水印?下面小编就为大家介绍PS快速去除水印方法,一起来看看吧!", "第一步、打开PS,将准备好要处理的图片打开,然后按快捷键M 切换到选择工具 第二步、按着鼠标,拖动选择我们要去掉的水印,并且按快捷键 Ctrl+J 把刚才我们所选择的水印建立一个新图层。如下图所示 第三步、移动一下刚才新建的图层可见水印图层。如下图所示: 第四步、把图层的混保模式设置为颜色减淡", "第五步、按快捷键 Ctrl+i ,这个快捷键的作用是把图层改为反相状态。如下图所示", "第六步、移动水印图层并且将其对准底图上的水印就处理好了这个局部的效果。如下图所示。", "第七步、继续复制水印图层复盖底图的水印,这样就可以去掉图片中所有的水印效果。如下图所示,一张我们希望看到的光鲜画面效果就呈现出来了。", "注意事项", "软件是死的,人是活的", "教程是用来学习的,不是用来局限人的思维的", "以上就是PS快速去除水印方法介绍,希望能对大家有所帮助!"], "bs_rank_pos": 4}], "answer_spans": [[16, 80]], "fake_answers": ["首先同样是从Photoshop打开需要去水印的图片,并且对其图层做拷贝备份。步骤阅读2选择图章工具,按住Alt键点击与水印位置较为相似的位置,松开按钮,在水印位置进行涂抹填充。步骤阅读步骤阅读3涂抹完后,也一样产生出修补的效果。"], "question": "ps怎么去除水印", "segmented_answers": [["Photoshop", "任意", "版本", "软件", "一款", "；", "修补", "工具", "去", "水印", "：", "首先", "我们", "选择", "好", "那", "张", "带有", "水印", "的", "图片", "拖入", "Photoshop", "中", "打开", "，", "并且", "对", "图片", "图层", "进行", "拷贝", 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自己比以前胖了大概有15斤左右了,由于比较喜欢吃红薯,所以大家都说我是吃过了红薯引起的发胖,请问吃红薯会胖吗,请有经验的人帮忙解答一下,谢谢", "病情分析: 红薯不会发胖,吃红薯对人身体是有好处的,适当的吃可以,不可以吃的太多,不管任何食物超量了之后都会引起发胖的。 指导意见: 我建议平常适当的吃一些,不可以暴饮暴食,吃多了容易胃胀,减肥可以通过多运动,保持很好的身材,减少身体的负担。", "病情分析: 你好,吃什么东西多都会发胖的,吃红薯也一样,你找找发胖的其他原因,是不是运动少了。 指导意见: 你好,红薯是个好东西,可以通便,可以美容,还可以降脂,但什么东西也的适可而止,不能吃太多。", "病情分析: 你好,不会。红薯是一种常见的果蔬,其含有丰富的淀粉,糖含量相对较高,故大多数人认为吃红薯会发胖,但其实,红薯还含有膳食纤维、胡萝卜素、各种维生素以及数十种微量元素,营养价值很高,这些膳食纤维和微量元素可以保持血管弹性,对老年人较好。 指导意见: 而且,膳食纤维在肠道内无法被消化吸收,能刺激肠道增强其蠕动,从而达到通便排毒的功效。所以说,红薯其实是一种健康食品,但最好是也别多吃了,易导致积食。 以上是对“吃红薯会胖吗”这个问题的建议,希望对您有帮助,祝您健康!", "病情分析: 除了正常的三餐外,还吃太多红薯会发胖的.含淀粉和糖份高.如果把红薯当成正餐,没有超量就不会.红薯含有丰富的淀粉、膳食纤维、胡萝卜素、维生素A、B、C、E以及钾、铁、铜、硒、钙等10余种微量元素和亚油酸等,营养价值很高,被营养学家们称为营养最均衡的保健食品。 指导意见: 吃红薯不仅不会发胖,相反能够减肥、健美、防止亚健康、通便排毒。每100克鲜红薯仅含0.2克脂肪,产生99千卡热能,大概为大米1/3,是很好的低脂肪、低热能食品,同时又能有效地阻止糖类变为脂肪,有利于减肥、健美。红薯含有大量膳食纤维,在肠道内无法被消化吸收,能刺激肠道,增强蠕动,通便排毒,尤其对老年性便秘有较好的疗效。吃红薯时要注意一定要蒸熟煮透。食用红薯不宜过量,中医诊断中的湿阻脾胃、气滞食积者应慎食。", "病情分析: 不会,一般人认为红薯里面的主要成分是淀粉,所以吃多了会导致肥胖,其实不是。红薯里面富含丰富的蛋白质,纤维素、果胶等具有阻止糖分转化为脂肪的功能。当然吃什么东西都要适量 指导意见: 吃胖的是薯条,薯片还有其他油炸食品。这些就千万不要吃了啊! 最后提醒你,吃绿豆沙可以帮助排毒,而且吃胡萝卜汁和黄瓜汁对皮肤好哦!希望你越来越漂!", "如果除了正常的三餐外,还吃太多红薯会发胖的.含淀粉和糖份高.如果把红薯当成正餐,没有超量就不会.红薯含有丰富的淀粉、膳食纤维、胡萝卜素、维生素A、B、C、E以及钾、铁、铜、硒、钙等10余种微量元素和亚油酸等,营养价值很高,被营养学家们称为营养最均衡的保健食品。这些物质能保持血管弹性,对防治老年习惯性便秘十分有效。遗憾的是,人们大都以为吃红薯会使人发胖而不敢食用。其实恰恰相反,吃红薯不仅不会发胖,相反能够减肥、健美、防止亚健康、通便排毒。每100克鲜红薯仅含0.2克脂肪,产生99千卡热能,大概为大米1/3,是很好的低脂肪、低热能食品,同时又能有效地阻止糖类变为脂肪,有利于减肥、健美。红薯含有大量膳食纤维,在肠道内无法被消化吸收,能刺激肠道,增强蠕动,通便排毒,尤其对老年性便秘有较好的疗效。 吃红薯时要注意一定要蒸熟煮透。食用红薯不宜过量,中医诊断中的湿阻脾胃、气滞食积者应慎食。", "平均费用:1661元 肥胖一直是长期困扰年轻人,尤其是年轻女性的热点问题,目前世界上无论是发达国家还是发展中国家,肥胖率正以惊人的速度在增长,肥胖不仅影响形体美,...全部>> 手术项目: 奥利司他| 中药减肥| 针灸拔罐减肥| 减肥食谱| 按摩减肥| 刮痧减肥| 西布曲明| 芬氟拉明", "减肥胶囊  参考价格:0 查看说明书", "降脂减肥片  参考价格:24.6 查看说明书", "降脂减肥片  参考价格:90 查看说明书", "轻身减肥片 轻身减肥,益气健脾,活血化淤,宽胸去积。用于单纯...[说明书] 参考价格:￥46", "轻身减肥片 轻身减肥,益气健脾,活血化淤,宽胸去积。用于单纯...[说明书] 参考价格:￥46", "内分泌失调喝什么中药汤? 治疗二型糖尿病的药 十二指肠大乳头腺瘤可以保守治疗吗 妊娠期糖尿病能吃米粉吗 妊娠糖尿病能吃核桃吗 哪些食物不会引起高血糖 明星减肥方法 2型糖尿病病人饮食 妊娠糖尿病吃什么降血糖 中医经络减肥价格", "专家在线免费咨询", "看病 男科 妇科 癫痫 性病 养生 新闻 白癫风 牛皮癣", "就医 疾病信息 健康经验 症状信息 手术项目 检查项目 健康百科 求医问药", "用药 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"。", "。"], ["把", "红薯", "当", "主食", ",", "不", "吃", "米饭", ",", "就", "不会", "胖", ",", "不能", "多", "吃", "哦"]], "paragraphs": ["吃红薯不会使人发胖: 　　根据科学研究,吃红薯是不会使人发胖的,相反红薯还是一种理想的减肥食品。它的含热量非常低,比一般米饭低得多,所以吃了之后不必担心会发胖,反而可起到减肥作用。红薯中还含有一种类似雌性激素的物质,对保护人体皮肤,延缓衰老有一定的作用。因此,国外许多女性把红薯当作驻颜美容食品。红薯生食脆甜,可代替水果;熟食甘软,吃在嘴里,甜在心头。它既可作主食,又可当蔬菜。蒸、煮、煎、炸,吃法众多,一经巧手烹饪,也能成为席上佳肴。 　　红薯的功效与作用 　　1.红薯有益于心脏 　　红薯富含钾、β-胡萝卜素、叶酸、维生素C和维生素B6,这5种成分均有助于预防心血管疾病。钾有助于人体细胞液体和电解质平衡,维持正常血压和心脏功能。β-胡萝卜素和维生素C有抗脂质氧化、预防动脉粥样硬化的作用。补", "吃红薯不会使人发胖。 　　根据科学研究,吃红薯是不会使人发胖的,相反红薯还是一种理想的减肥食品。它的含热量非常低,比一般米饭低得多,所以吃了之后不必担心会发胖,反而可起到减肥作用。红薯中还含有一种类似雌性激素的物质,对保护人体皮肤,延缓衰老有一定的作用。因此,国外许多女性把红薯当作驻颜美容食品。红薯生食脆甜,可代替水果;熟食甘软,吃在嘴里,甜在心头。它既可作主食,又可当蔬菜。蒸、煮、煎、炸,吃法众多,一经巧手烹饪,也能成为席上佳肴。 　　红薯的功效与作用 　　1.红薯有益于心脏 　　红薯富含钾、β-胡萝卜素、叶酸、维生素C和维生素B6,这5种成分均有助于预防心血管疾病。钾有助于人体细胞液体和电解质平衡,维持正常血压和心脏功能。β-胡萝卜素和维生素C有抗脂质氧化、预防动脉粥样硬化的作用。补", "吃红薯不会使人发胖: 　　根据科学研究,吃红薯是不会使人发胖的,相反红薯还是一种理想的减肥食品。它的含热量非常低,比一般米饭低得多,所以吃了之后不必担心会发胖,反而可起到减肥作用。红薯中还含有一种类似雌性激素的物质,对保护人体皮肤,延缓衰老有一定的作用。因此,国外许多女性把红薯当作驻颜美容食品。红薯生食脆甜,可代替水果;熟食甘软,吃在嘴里,甜在心头。它既可作主食,又可当蔬菜。蒸、煮、煎、炸,吃法众多,一经巧手烹饪,也能成为席上佳肴。 　　红薯的功效与作用 　　1.红薯有益于心脏 　　红薯富含钾、β-胡萝卜素、叶酸、维生素C和维生素B6,这5种成分均有助于预防心血管疾病。钾有助于人体细胞液体和电解质平衡,维持正常血压和心脏功能。β-胡萝卜素和维生素C有抗脂质氧化、预防动脉粥样硬化的作用。补", "每百克红薯的热量是119.00千卡。 每百克米饭的热量是116.00千卡(蒸饭) 因此,红薯的热量比米饭的热量高。 红薯并没有减肥的作用, 但是具有促进肠胃蠕动和通便的作用。因为,红薯经过蒸煮后,部分淀粉发生变化,可以起到食物纤维的作用,能有效刺激肠道的蠕动,促进排便。 此外,我们在切红薯时看见的红薯皮下会渗出有一种白色液体,它含有紫茉莉甙,还可以治疗习惯性便秘。 不会长胖。", "每天都吃肯定不好,凡事都有个度,长不长胖影响不大,主要看你的主食吃什么以及运动情况", "不会啊,这个属于粗粮。有利于消化,而且红薯还有好多吃法。。", "把红薯当主食,不吃米饭,就不会胖,不能多吃哦"], "bs_rank_pos": 1}, {"is_selected": false, "title": "天天吃红薯会发胖吗?_百度知道", "most_related_para": 0, "segmented_title": ["天天", "吃", "红薯", "会", "发胖", "吗", "?", "_", "百度", "知道"], "segmented_paragraphs": [["如果", "经常食用", "会", "的", ",", "因为", "红薯", "中", "含有", "淀粉", ",", "淀粉", "在", "人体", "内", "会", "转变", "为", "麦芽糖", ",", "麦芽糖", "是", "人体", "能量", "的", "提供者", ",", "如果", "摄入", 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"特征", "英雄", "分析", "介绍"]], "paragraphs": ["首页 → 安卓游戏资讯→ 安卓游戏攻略→ 王者荣耀怎么开启荣耀战区 荣耀战区开启设置方法", "王者荣耀腾讯版V1.17.1.15 安卓最新版", "王者荣耀怎么开启荣耀战区,荣耀战区怎么设置,王者荣耀新版本即将开启荣耀战区玩法,荣耀战区根据玩家排位赛的战姬和荣耀战力值来计算,选择属于自己的王者荣耀战区,可以获得XX区第一XX的称号,下面带来王者荣耀荣耀战区开启设置方法。", "荣耀战区开启设置方法", "1、如何进入和开启“荣耀战区” 只要开启排位赛即可默认激活”荣耀玩法”,排位赛入口增加荣耀战区入口。 排位赛结算根据单场表现和历史战绩获得英雄荣耀战力值 详细分数和评分因子在结算/数据切页(荣耀战力将是未来最硬的硬通货,此处策划大大一定要老亚瑟敲黑板打着重号) 2、荣耀战区进入和设置 1)我们将在个人资料“交友名片”的入口设置荣耀战区的选择,您可以在这里选择自己专属的战区,开启XX区第一XX的无敌之旅。 2)当然,进入荣耀战区后在地图上方也可以设置专属的荣耀战区。 以上就是王者荣耀怎么开启荣耀战区,荣耀战区开启设置方法,希望对大家有所帮助。(42)王者荣耀  腾讯王者荣耀西西专区为您提供该游戏的体验服电脑版苹果版等相关下载助你在游戏中能够玩的更加的愉快快来下载游戏体验吧王者荣耀介绍腾讯全球首款英雄公平对战手游王者荣耀为喜欢的玩家开启了一个掌上竞技时代。相比端开黑,王者荣耀别出心裁的独创了掌上秒开团的时代,在游戏里里,玩家不仅可以体验到在与好友协同做战的刺激体验,还可以在地图竞技中,体验到端上大杀四方互动性和颇具智慧性的策略战术。王者荣耀攻略王者联盟获胜...更多>>", "(17)大型手机游戏  西西大型手游下载专区为你带来手机上最好玩的游戏,包括最流行的移动大型手游排行榜,以及好玩的单机网游的大型手机游戏下载,帮助你找到最好玩的大型手机游戏,只要你的手机配置够高,还担心没玩的吗精选游戏推荐王者荣耀王者荣耀是全球首款英雄公平对战手游,腾讯最新手游大作作为一款类游戏,王者荣耀特色多多,在同类的游戏中可谓是一枝独秀,艳压全场。王者峡谷深渊大乱斗以及等多样模式一键体验,热血竞技尽享快感海量英雄随...更多>>", "(73)手机装机必备  一般我们在新买了手机后总要装一些必备的软件,比如微信微博之类的,这些软件慢慢的一个个去找会非常麻烦,因此西西在这里为大家整理了一些比较常见的手机装机必备软件。这里包含了用户们经常会使用到的一些手机应用,你可以在西西一次安装所有你需要的手机软件,不用再到处找这些软件,非常节省时间。...更多>>", "12-22王者荣耀总冠军打野刺客英雄有哪些 仙阁打野刺客英雄汇总一览 12-22王者荣耀王胖子特征英雄是谁 王者荣耀王胖子指定英雄分析 12-22王者荣耀shirley杨英雄是谁 王者荣耀shirley杨特征英雄是谁 12-22王者荣耀黄忠新皮肤黑帮大佬怎么获得 黄忠新皮肤黑帮大佬多少钱 12-22王者荣耀王胖子特征英雄是谁 胖子指定英雄是哪两个 12-22王者荣耀鬼吹灯活动shirley杨是谁 寻找shirley杨英雄活动怎么完成 12-22王者荣耀shirley杨是哪两个英雄 shirley杨指定英雄是谁 12-22王者荣耀王胖子是谁 王胖子特征英雄分析介绍 12-22王者荣耀王胖子特征英雄是谁 鬼吹灯活动王胖子英雄分析 12-22王者荣耀shirley杨是谁 shirley杨特征英雄分析介绍"], "bs_rank_pos": 2}, {"is_selected": false, "title": "王者荣耀怎么设置基友_百度经验", "most_related_para": 4, "segmented_title": ["王者", "荣耀", "怎么", "设置", "基友", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["王者", "荣耀", "里面", "关系", "很多", ",", "一", "个", "好", "的", "关系", "有助于", "更好", "的", "打", "排位"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["1", "打开", "王者", "荣耀", "步骤", "阅读", "2", 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"施法方式也就是操作方式，总共有2种选择。自动施法，非常推荐给新手玩家，轻松简单，在释放技能时系统能帮助你自动选择方向释放技能，当然了最优先选择的目标还是玩家锁定的单位;辅助轮盘施法，熟练的玩家必选的操作方式，可以在第一时间内释放所有技能将伤害打在目标身上。选择了辅助轮盘施法，我们还能选择灵敏度和轮盘位置，达到一种随心所欲的效果，极度方便。攻击方式有2种选择，大家也很好了解。一种是优先最近单位的攻击方式，玩家如果没有选定目标就发出了进攻的命令，那么英雄就会自动攻击离他最近的目标;另外一种是优先攻击血量最少的单位，就是在一定范围内，英雄会自动锁定目前血量低的单位进行攻击。在操作设置中，设置成优先血量最少。", "我们将在个人资料交友名片的入口设置荣耀战区的选择，可以在这里选择自己专属的战区，开启某区第一的无敌之旅。当然，进入荣耀战区后在地图上方也可以设置专户的荣耀战区。"], "answer_docs": [2], "segmented_question": ["王者", "荣耀", "怎么", "设置"], "question_type": "DESCRIPTION", "question_id": 181662, "fact_or_opinion": "OPINION", "match_scores": [0.8461538461538463]}
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"日本", "AV", "面试官", "怎样", "挑选", "女优", ",", "需要", "当场", "真枪实弹", "干", "一", "场", "?", "2015", "-", "07", "-", "24", "14", "-", "25", "温佩琪", "父亲", "是", "谁", "?", "家庭背景", "资料", "曝光", "2016", "-", "04", "-", "08", "20", "-", "46"]], "paragraphs": ["旭日阳刚近况?为什么在媒体上比较少看到他们的声影,作为凭空出世的草根歌手,旭日阳刚以质朴的声音赢得了大众追求生活的心,在经历了春晚之后的爆红后,旭日阳刚频频爆出负面报道:", "旭日阳刚成员开豪车持械殴打", "近日,又有网友爆料旭日阳刚吉他手刘刚在丰台区某小区,开着奥迪车,因小区内有老人行动缓慢挡车道而不满,不停按喇叭,见按喇叭不奏效,奥迪车的司机就拐弯停车,下车到后备箱取出一根尖利的铁棍,期间司机还与路人发生口角。这时,该网友称自己也看清了司机的全部体征认出他就是旭日阳刚的刘刚!据称,幸好有保安上前阻拦,否则在一旁鸣不平的路人恐怕也会遭遇不测。该网友透露,刘刚看到自己拍照,还凶巴巴地上前要求删掉照片,更现场恐吓“小心点!”据称,事发时刘刚的车里还坐着一位女性。", "微博爆料原文:", "吉他手刘刚持械斗殴伤人:下午在丰台区某小区,因唱春天里而走红的旭日阳刚组合成员刘刚,开着黑色奥迪,在小区内因有老人行动迟缓挡车道而狂摁喇叭不止,发生口角后又下车从后备箱拿出铁棍动武,光天化日太吓人了。虽说是农民工出身,但有了点钱,也仍然改不了骨子里那股不入流的东西呦。", "此刘刚也在当晚12点左右在腾讯微博连发三条微博表明态度,澄清此事:对于今天下午发生的突然事件,本人向各位关注此事件的各位媒体,网友朋友做出如下回应:1:今天下午16:00左右,我驾车外出,遇到多位行人在道中间行走,我鸣笛提示,并引发之后言语冲突为事实。2:在整个事件中我与对方并未发生肢体上的冲突,更未出现有博友声称的持械殴斗情况。3:在整个事件发生过程中,我与争执方均未出现伤人或更为打人的事情。做为公众人物及艺人,我深刻的反思了自己的行为及做法,就像很多网友提醒我,小区内不应鸣笛,更不该和他人产生激烈的言语冲突。对于此次事件,我向所有对我提出批评的网友,以及支持我的歌迷与媒体朋友们表示歉意。刘刚表示,“今后我会更加注意自己的言行,希望各位朋友监督。”", "加微信公众号\"糗事百科橙人\"或\"qsbkcr\"。福利视频图片尽情看", "北京涉黄高端会所背后的权贵身影  2016-12-26 08-46  夏庆丰进京任国资委副主任? 2016-12-18 10-59  九江某官员床上不雅照被拍? 2016-12-17 02-59  2016年蒙面唱将真实身份汇总 阿凡达妹妹等明星尽揭面  2016-11-30 10-49  2016年喜剧总动员排名 蒋欣获得总冠军  2016-11-28 16-29", "足浴店小姐自述不戴套 姐妹大多有特服经历  2015-07-17 14-10  和少妇约炮微信露骨聊天记录  2015-07-03 18-27  赵本山弟子宋晓峰老婆是谁?杨晓茹个人资料照片曝光  2016-03-17 13-15  神秘的日本AV面试官怎样挑选女优,需要当场真枪实弹干一场? 2015-07-24 14-25  温佩琪父亲是谁?家庭背景资料曝光  2016-04-08 20-46"], "bs_rank_pos": 1}, {"is_selected": false, "title": "旭日阳刚现状和近况_加加网", "most_related_para": 4, "segmented_title": ["旭日", "阳刚", "现状", "和", "近况", "_", "加", "加", "网"], "segmented_paragraphs": [["2010", "年", "旭日", "阳刚", "凭借", "一", "首", "《", 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"号", "上映", "的", "《", "衰", "鬼", "记", "》", "中", "饰演", "流浪歌手", "。"], ["王旭", "于", "1966", "年", "出生", "在", "商丘", "地区", "国", "营", "民权", "农场", "二", "队", "。", "高中毕业", ",", "花", "45", "元", "买", "了", "自己", "的", "第", "一", "把", "吉他", ",", "唱", "费翔", "的", "歌", "。", "20", "世纪", "80", "年代", "末", "90", "年代", "初", ",", "王旭", "和", "志同道合", "的", "一", "帮", "人", "组建", "了", "歌舞团", "四处", "走穴", "。", "2000", "年", ",", "王旭", "到", "北京", "烧", "锅炉", "。", "而后", "10", "年", ",", "他", "在", "北京", "和", "开封", "之间", "游走", ",", "卖", "过", "水果", "、", "水煎包", ",", "唱", "过", "酒吧", "。"], ["刘刚", "家", "在", "黑龙江省", "牡丹江", "地区", "穆棱市", "河西乡", "三兴村", "。", "早年", "当", "过", "兵", "退伍之后", "前往", "北京", ",", "2003", "年", "开始", "“", "北漂", "”", "。"]], "paragraphs": ["2010年旭日阳刚凭借一首《春天里》在网络走红,视频中两人在工地宿舍那些吉他用沙哑的声音唱着这首《春天里》。歌曲唱的真的挺好听,视频一到网上两人就火了!", "他们两个人,王旭来自河南,16岁以后就开始闯荡社会,曾经组建过歌舞团,烧过锅炉,摆过水果摊,刘刚是黑龙江人,曾经当过兵,2003年开始到北京谋生。", "视频火了之后两个人登上了2011年春晚和元宵晚会分别唱了《春天里》和《我的未来不是梦》。从此旭日阳刚组合被全国观众知晓。", "但是两人的事业也不是一帆风顺,汪峰作为《春天里》这首歌的版权所有者,在2011年春晚之后要求旭日阳刚不能再唱《春天里》,汪峰作为一个歌手音乐行业的前辈,对于后辈旭日阳刚所作所为却是有点苛刻。", "旭日阳刚现在多方面发展,商演出厂价为20万元,两人抽的香烟也是中华牌的,开的车价值也60万左右。有网友曾经见过他们出入在北京东三环国贸附近的某知名高档小区。", "草根歌手旭日阳刚发现之路还是比较顺利,希望他们能带来更多的好作品。", "旭日阳刚,是两位流浪歌手组成的音乐组合,主唱王旭,吉他手刘刚。2010年8月网络拍客将旭日阳刚唱歌的视频\"农民工\"版《春天里》上传到网上,使旭日阳刚一下子火了起来,受到不少网友和音乐人的追捧,从而摇身变为了网络红人。他们以其质朴无华的演唱风格红遍了各大视频网站以及各个网站的微博里,目前在人人网、优酷等各大网站论坛上疯传其视频作品。刘刚于2016年2月26号上映的《衰鬼记》中饰演流浪歌手。", "王旭于1966年出生在商丘地区国营民权农场二队。高中毕业,花45元买了自己的第一把吉他,唱费翔的歌。20世纪80年代末90年代初,王旭和志同道合的一帮人组建了歌舞团四处走穴。2000年,王旭到北京烧锅炉。而后10年,他在北京和开封之间游走,卖过水果、水煎包,唱过酒吧 。", "刘刚家在黑龙江省牡丹江地区穆棱市河西乡三兴村。早年当过兵退伍之后前往北京,2003年开始“北漂”。"], "bs_rank_pos": 2}, {"is_selected": true, "title": "旭日阳刚现状_旭日阳刚最新消息_旭日阳刚近况2015_旭日阳刚为什么被", "most_related_para": 4, "segmented_title": ["旭日", "阳刚", "现状", "_", "旭日", "阳刚", "最新消息", "_", "旭日", "阳刚", "近况", "2015", "_", "旭日", "阳刚", "为什么", "被"], "segmented_paragraphs": [["旭日", "阳刚", "近况", "2015"], ["1", "旭日", "阳刚", "两人", "现状", "2", "旭日", "阳刚", "历史", "消息"], ["旭日", "阳刚", "现状", "是", "接", "商演", 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"paragraphs": ["旭日阳刚近况2015", "1旭日阳刚两人现状 2旭日阳刚历史消息", "旭日阳刚现状是接商演,目前出场价为20万元,两人生活条件比以前好了,抽烟的档次一般是中华,两人开着60万左右的高档轿车。不过据网友曝料,旭日阳刚组合其实的生活除了音乐,两人并没有太多交集,对外称兄道弟只是为了维护旭日阳刚这个牌子。", "网曝旭日阳刚生活现状", "据一位北京网友琪妞妞在论坛爆出《我证明:旭日阳刚现在真的暴富了》的帖子引起网友热议,据该网友透露:她亲眼看到旭日阳刚成员刘刚乘坐高档轿跑车,出入在北京东三环国贸附近的某知名高档小区。据该网友介绍,该高档小区位处CBD商圈高级公寓,附近二手房单价都在4万以上,而当天刘刚乘坐的是一辆白色某进口品牌的高档轿跑车,目前的市场均价都在60万左右,车牌号属于黑龙江牌照。很多网友看完帖子都惊叹旭日阳刚的吸金能力,针对旭日阳刚的暴富一说,记者也从圈内人士那里得以证明,据资深演出商罗先生透露:目前旭日阳刚对外的商演价格都在20万左右,春晚之后他们档期都非常满,每个月至少有十场演出,所以按这么算下来每个成员月收入过百万绝对不是问题。", "走红网络", "2010年8月,名不见经传的旭日阳刚在破旧出租房里演唱汪峰的《春天里》,恰巧被一位网络拍客拍到,并将他们演唱的农民工版《春天里》视频传到了网上,随后,走红网络。", "登上春晚", "2011年2月2日,旭日阳刚成功登上了央视春晚,歌唱《春天里》,使演艺生涯攀上了一个新的高峰。", "不过真正让旭日阳刚命运发生大改写的还是在2011年他们走上春晚后。几年过去,他们演出多了,还拍电影,成了闻名全国的明星。", "遭汪峰封杀", "2011年2月11日,旭日阳刚的经纪人黎冬收到了《春天里》原唱者汪峰方面打来的电话,被明确告知旭日阳刚今后不能以任何形式演唱《春天里》。对于这样的结果,黎冬表示尊重他们的意见,但同时也希望事件能有所转机。", "汪峰回应说,自己从一开始的确是本着帮助旭日阳刚的想法,允许他们演唱自己的歌。包括授权其在央视春晚演唱。但后来发现,自己的善意被利用,无奈之下决定停止授权旭日阳刚在任何大型演出、商业活动以及音像制品中演唱自己的作品。", "旭日阳刚签约浙江卫视", "2011年2月21日,在兔年春晚一炮走红的农民工组合旭日阳刚签约浙江卫视,成为《我爱记歌词》节目的领唱,并参加该节目录制。浙江卫视节目中心副主任陈伟透露,正积极为旭日阳刚寻找适合的歌曲,不排除重金买歌的可能。当家主持朱丹更是在节目中以栏目组的名义宣布:在三个月内打造出旭日阳刚的首支单曲。", "回应甩大牌", "2011月8月,因央视春晚而走红的农民工组合旭日阳刚来川演出,演出前,组合成员之一的王旭接受访问时,当提及耍大牌、旭日阳刚要解散等传闻时王旭一一给予回应,王旭还说起旭日阳刚的现状,不太忙,接一些演出,录制一些电视节目。他表示,旭日阳刚现在是一个松散的组合,应该不会解散……我们也没有忘本。", "旭日阳刚解散", "2011年3月4日,有人在微博爆料,称旭日阳刚已于当天下午解体,王旭和李刚各自发展。旭日阳刚组合,今日下午正式解体了,各自发展了。这条4日15时25分发出的一条微博,短短几分钟内就被转发了几百次,引起许多网友的惊讶,不少网友都纷纷询问消息真假。但很快,发布这条微博的博主就将这条爆料微博删除了,随后又发了条微博称:一定要删除!恐怕是假消息要吃官司!但北京某报记走说的不知道是祖话还是假话!怕怕怕……几分钟就122人转载。但这条微博很快也被博主删除了。", "当天21时16分,旭日阳刚的成员李刚在其个人微博贴出了他与王旭的合影,是两人行将上台参加一场电视节目,在候场时所拍。一分钟后,王旭也在自己的微博连发两张二人候场时拍摄的照片。旭日阳刚在微博贴出的照片,让解散谣言不攻自破。", "回应解散传闻", "2011年8月,旭日阳刚各自推出了自己的单曲,随后便传出该组合解散的传闻。当天,王旭直言:我们两个人只有四只手,别人那么多嘴他们说什么我们管不住,旭日阳刚应该不会解散,还和以前一样,有合适的歌就一起唱,不适合就单个唱,旭日阳刚现在是一个松散的组合。有意思的是,当天,旭日阳刚成员王旭和刘刚并没有乘坐同一航班抵达成都,王旭解释说,北京那么大,我和刚子住的地方距离有点远,他没赶上我这个航班,所以我就提前来了。", "旭日阳刚刘刚被爆持械伤人", "2012年9月10日,一位微博认证为《公益时报》记者的网友芳芳是坏蛋微博爆料,旭日阳刚组合的吉他手刘刚,在北京丰台区某小区持铁棍打人。该消息爆出后,引来骂声一片,不少网友在刘刚微博和网上留言骂其忘本没有素质。", "旭日阳刚分手", "2012年12月27日,北京某音乐人向记者爆料:旭日阳刚近日分手单飞了。几天前,王旭和刘刚分别签了不同的经纪公司。王旭签在了北京著名经纪人赵玉的旗下,而刘刚则签约到北京艾音嘉华文化公司负责人万宇旗下。而旭日阳刚两位之前的亲密经纪人,都已下课。", "陷假唱风波", "2013年2月3日,在江苏省盐城市滨海县体育馆,旭日阳刚组合在为某房产商助兴造势演出时,竟然遭到场地方面两次停电骚扰。事发时旭日阳刚依然从容淡定,却被在场观众和当地网友指出:他们是用假唱来忽悠观众。这是该组合继去年9月因超速驾驶行为身陷负面报道之后,又一次被网友和媒体推向了风口浪尖。", "旭日阳刚王旭发新专辑", "2013年6月29日,旭日阳刚主唱王旭携个人新专辑《北漂鱼》在梅地亚中心举行发布会。新专辑中,王旭参与多首歌曲的创作,转型唱作歌手。", "专辑主打歌曲《北漂鱼》,同时也是暑期上映的电影《北漂鱼》主题曲,王旭用他沧桑的嗓音诠释了歌曲主题。在《北漂鱼》这张专辑中,王旭参与了《爱的力量》、《你在哪里》、《故乡》、《我是你的一首歌》、《关于生命》、《麻雀》六首歌的创作。", "旭日阳刚王旭为儿子操办婚事", "2014年9月29日,旭日阳刚组合主唱王旭在老家河南为儿子操办大婚,并邀请了众多圈中好友前来助阵。", "旭日阳刚出场费10万", "2014年9月13日,在德阳市体育中心上演了一幕群星璀璨的演唱会,汪峰、蔡依林、旭日阳刚等明星献唱。令人关注的是,旭日阳刚和汪峰自2010年上海演唱会后再次同台演出。当晚演出结束后,华西都市报记者来到演出明星所住的酒店面对面地采访了旭日阳刚。", "在谈到4年后与汪峰同台献歌的感受时,旭日阳刚说:可以说,汪峰大哥是我们的恩人。正因为我们演唱了汪峰的《春天里》,命运才发生了春天般地变化,出场费涨到10万,又上了央视春晚。如今,全国请我们去演出的公司络绎不绝。"], "bs_rank_pos": 3}, {"is_selected": true, "title": "【图】旭日阳刚现状遭曝光 疑两人忘恩负义竟独自过奢靡的生活_娱乐", "most_related_para": 6, "segmented_title": ["【", "图", "】", "旭日", "阳刚", "现状", "遭", "曝光", "疑", "两人", 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["去年的今天在做平板卧推的时候,热身不足拉伤了,去找中医推拿也没用,后来一直养到现在,今天去医院看了一下,说恢复了70%到80%了。。医生说最短一年半才能好。。唉,今天跑了两家医院,中心医院的医生居然说是肩周炎。。直接无语了我,还给我打印了一张小人图让我照着拉伸。。。现在的医生真没医德,也不检查看看,直接就问我,我说什么他写什么,气得够呛,虽然那我不喜欢吐槽,但是我实在忍不住了。。前后诊疗不到一分钟给我确诊肩周炎。。然后哥换了家医院。。这家医院超级大但是一个人没有,果断不靠谱的样子,挂了个专家,这可真清闲,也不用排队。。直接进去了,诺大个候诊室就我一个。。这个医生比较靠谱,听我说了大概情况,一摸就摸到我的病患处,然后说我是肩部损伤,我说是肩袖损伤吧,医生说你还知道肩袖呀~。。然后说我是上网查的。。。话说哥从受伤就开始查相关资料了。。这个医生还是比较靠谱,也没忽悠俺去做什么磁共振。。让俺继续养着吧。。这医生才有医德嘛,至少没瞎忽悠,哥还得恢复一年估计才差不多。。唉,悲催啊,大家一定要注意呀,以前启鑫发过肩袖的帖子我研究了很久,自己通过恢复动作也恢复了七七八八,这里我自己也有一些恢复训练的动作资料跟大家分享一下。二楼上图~", "盂肱关节是人体活动度最大的关节,稳定性低,需要关节盂唇、韧带和肩袖以及周围的肌肉提供静态和动态的稳定性,肩袖最容易反映肩关节功能状态:肩袖的压痛往往是最早出现的体征之一;针对肩袖的力量训练可以非常有效的提高肩关节功能", "网速真慢 肩关节有肱骨、肩胛骨、锁骨和其附属结构组成,包括盂肱关节、肩锁关节和胸锁关节三个解剖关节,肩胸和肩峰下两个关节样结构,喙锁间的韧带样连接", "公司网速太慢了,回家再发。。。", "一句话带护具,别小看薄薄一层弹力布,我肩锁关节有旧伤,带护肩训练没太大问题。", "我也悲剧 我是训练后 脱衣服时候伤的 衣服太紧了,脱不下来把肩膀拉伤了几个月了", "肩周炎的诊断很简单,楼主是排队排久了憋的吧。拍张片骨头没事就回去休养可以了。如果变粘连肩再考虑其他治疗。", "伤了几个月了 不练肩膀也一个月了 今天刚想练 你一说 不敢了 楼主是否和老夫一个症状 一活动啪啪响 锻炼胸肌或者二头时 3角也火辣辣得 感觉不是骨头响 是筋", "肩伤好痛苦 卧推从210磅5个掉到160磅8个了 好惨···", 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"新闻", "白癫风", "牛皮癣"], ["就医", "疾病", "信息", "健康", "经验", "症状", "信息", "手术", "项目", "检查", "项目", "健康", "百科", "求医问药"], ["用药", "药品", "库", "疾病", "用药", "药品", "心得", "用药", "安全", "药品", "资讯", "用药", "方案", "品牌", "药企", "明星", "药品"], ["整形", "整形", "项目", "整形", "案例", "整形", "资讯", "整形医院", "减肥", "塑形", "眼部", "整形", "鼻部", "整形", "面部", "整形"], ["有问必答", "内科", "外科", "儿科", "药品", "遗传", "美容", "检查", "减肥", "中医科", "五官科", "传染科", "体检", "科", "妇产科", "肿瘤科", "康复科", "子女教育", "心理健康", "整形美容", "家居", "环境", "皮肤性病", "营养保健", "其他", "科室", "保健养生"], ["下载", "APP", ",", "免费", "快速", "问", "医生"]], "paragraphs": ["健康咨询描述: 肩膀疼痛一年了,医生的诊断不一致,有的说是肩袖损伤,有的说是肩周炎,疼痛越来越严重,手臂无力。", "病情分析: 你的情况一般考虑是皮肤血液循环不良引起肩周炎症的情况 指导意见: 建议口服云南白药胶囊以及三七片活血化淤同时毛巾热敷疼痛部位,避免剧烈运动", "病情分析: 从病人的症状看应该是肩关节反复的一个动作导致肩关节周围的韧带和筋膜劳损引起疼痛的,如果工作的时候长时间一个姿势的话就会导致肩周炎 指导意见: 是需要去医院骨科检查肩关节的x光片看看如果没有问题在让医生检查肩关节的活动角度如果有韧带粘连的情况就是肩周炎可以进行功能锻练在使用金不换膏外敷治疗服用强筋松和双氯灭痛就会好转", "病情分析: 你好朋友,肩周炎是可以治愈的,但是需要锻炼,不然的话。时间长了肩周炎,就很难活动了。 指导意见: 肩周炎是自愈性疾病一般的一年,自行恢复。但是一年内需要多锻炼,可以,小燕子飞,还有爬墙。锻炼,肩周炎。 以上是对“肩袖损伤和肩周炎的区别,两者都可以自愈吗”这个问题的建议,希望对您有帮助,祝您健康!", "病情分析: 可能是颈椎病和肩周炎导致的症状啊。注意防止受寒和热敷试试。另外牵引,理疗,红外线,推拿按摩治疗。 指导意见: 一定要避免长期保持一个姿势工作和学习,尤其是低头工作学习,每1小时左右要休息10分钟左右。", "病情分析: 你好,根据你对病情的描述,很有可能是肩周炎或者是颈椎病, 指导意见: 建议你做个颈椎的CT或者MRI,检查一下排除颈椎间盘病变,祝你健康快乐,早日康复!", "病情分析: 肩袖是覆盖于肩关节前、上、后方之肩胛下肌、冈上肌、冈下肌、小圆肌等肌腱组织的总称。位于肩峰和三角肌下方,与关节囊紧密相连。肩袖的功能是上臂外展过程中使肱骨头向关节盂方向拉近,维持肱骨头与关节盂的正常支点关节。肩袖损伤将减弱甚至丧失这一功能,严重影响上肢外展功能。 指导意见: 肩周炎也被称之为五十肩,是指以肩部逐渐产生疼痛,夜间为甚,逐渐加重,肩关节活动功能受限而且日益加重,致一定某种程度后逐渐缓解,直至最后完全复原为主要表现的肩关节囊及其周围韧带、肌腱和滑囊的慢性特异性炎症。女性发病率高于男性,多见于体力劳动者。肩周炎在治疗过程中以及以后的康复治疗中,一般主张治疗与功能锻炼同时进行,避免愈后并发症,配合正确的功能活动,康复后的保健至关重要,", "病情分析: 你这样应和查一下B超,查一下ct看看骨科诊断一下比较好的,。应注意合理的休息保养气血很重要 指导意见: 可以针灸按摩一下。注意保暖合理的休息,调养生活的习惯,可以适当的多喝汤忌辛辣的食物,可以用舒筋活血片,寄生独活汤等 以上是对“肩袖损伤和肩周炎的区别,两者都可以自愈吗”这个问题的建议,希望对您有帮助,祝您健康!", "肩周炎又称肩关节周围炎,俗称凝肩、五十肩,冻肩,五十肩,冻结肩,漏肩风,肩痹,肩凝。以肩部逐渐产生疼痛,夜间为甚,逐渐加重,肩关节活动功能受限而且日益加重,达到某种程度后逐渐缓解,直至最后完全复原为主... 更多>>", "金不换膏  参考价格:29 查看说明书", "舒筋活血片  参考价格:8 查看说明书", "云南白药胶囊 化瘀止血,活血止痛,解毒消肿。本品用于跌打损伤,...[说明书] 参考价格:￥36", "三七片 清热,凉血。本品用于由血热引起的疮疖;由肝热引起...[说明书] 参考价格:￥10", "桑拿按摩全套有哪些 针灸治疗扁桃体发炎 成都正规按摩 针灸治疗闭经 针灸治疗胃酸过多 埋线穴位减肥方法 针灸治疗肝火胃火 宫颈糜烂针灸治疗 针灸能治闭经吗 针灸治疗胃痛", "专家在线免费咨询", "看病 男科 妇科 癫痫 性病 养生 新闻 白癫风 牛皮癣", "就医 疾病信息 健康经验 症状信息 手术项目 检查项目 健康百科 求医问药", "用药 药品库 疾病用药 药品心得 用药安全 药品资讯 用药方案 品牌药企 明星药品", "整形 整形项目 整形案例 整形资讯 整形医院 减肥塑形 眼部整形 鼻部整形 面部整形", "有问必答 内科 外科 儿科 药品 遗传 美容 检查 减肥 中医科 五官科 传染科 体检科 妇产科 肿瘤科 康复科 子女教育 心理健康 整形美容 家居环境 皮肤性病 营养保健 其他科室 保健养生", "下载APP,免费快速问医生"], "bs_rank_pos": 1}, {"is_selected": false, "title": "【肩袖损伤能自愈吗】【图】肩袖损伤能自愈吗? 了解其病因和治疗_伊", "most_related_para": 1, "segmented_title": ["【", "肩袖损伤", "能", "自愈", "吗", "】", "【", "图", "】", "肩袖损伤", "能", "自愈", "吗", "?", "了解", "其", "病因", "和", "治疗", "_", "伊"], "segmented_paragraphs": [["肩", "袖", "是", "覆盖", "于", "肩关节", "前", "、", "上", "、", "后方", "之", "肩胛", "下", "肌", "、", "冈", "上", "肌", "、", "冈", "下", "肌", "、", "小圆肌", "等", "肌腱", "组织", "的", "总称", "。", "位于", "肩峰", "和", "三角肌", "下方", ",", "与", "关节囊", "紧密相连", "。", "肩", "袖", "的", "功能", "是", "上臂", "外", "展", "过程", "中", ".", ".", "."], ["肩", "袖", "是", "覆盖", "于", "肩关节", "前", 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"肩袖是覆盖于肩关节前、上、后方之肩胛下肌、冈上肌、冈下肌、小圆肌等肌腱组织的总称。位于肩峰和三角肌下方,与关节囊紧密相连。肩袖的功能是上臂外展过程中使肱骨头向关节盂方向拉近,维持肱骨头与关节盂的正常支点关节。肩袖损伤将减弱甚至丧失这一功能,严重影响上肢外展功能。本病常发生在需要肩关节极度外展的反复运动中(如棒球,自由泳、仰泳和蝶泳,举重,球拍运动)。", "是年轻人肩袖损伤的主要原因,当跌倒时手外展着地或手持重物,肩关节突然外展上举或扭伤而引起。", "引起肩袖组织退行性变。当肱骨内旋或外旋中立位时,肩袖的这个危险区最易受到肱骨头的压迫、挤压血管而使该区相对缺血,使肌腱发生退行性变。临床上肩袖完全断裂大多发生在这一区域。", "中老年患者其肩袖组织因长期遭受肩峰下撞击、磨损而发生退变。本病常发生在需要肩关节极度外展的反复运动中(如棒球、仰泳和蝶泳,举重,球拍运动)。当上肢前伸时,肱骨头向前撞击肩峰与喙肩韧带,引起冈上肌肌腱损伤。慢性刺激可以引起肩峰下滑囊炎、无菌性炎症和肌腱侵袭。急性的暴力损伤可以导致旋转带断裂。", "本病多见于40岁以上患者,特别是重体力劳动者。伤前肩部无症状,伤后肩部有一时性疼痛,隔日疼痛加剧,持续4～7天。患者不能自动使用患肩,当上臂伸直肩关节内旋、外展时,大结节与肩峰间压痛明显。肩袖完全断裂时,因丧失其对肱骨头的稳定作用,将严重影响肩关节外展功能。肩袖部分撕裂时,患者仍能外展上臂,但有60°～120°疼痛弧。", "对肩峰形态的判断及肩关节骨性结构的改变有帮助。部分肩袖损伤患者肩峰前外侧缘及大结节处有明显骨质增生。", "可帮助确定肌腱损伤的损伤部位和严重程度,尤其是磁共振造影检查(MRA)可以清晰的显示肩袖的部分撕裂,对诊断具有较高的价值。", "根据临床表现及相关检查可做出诊断。", "上一篇文章: 【图】脊髓损伤的治疗方法有哪些 如何预防手术后的畸形 下一篇文章: 【图】米醋泡黑豆的功效是什么? 专家为你解答疑惑"], "bs_rank_pos": 3}, {"is_selected": false, "title": "肩袖损伤能自愈吗_百度知道", "most_related_para": 0, "segmented_title": ["肩袖损伤", "能", "自愈", "吗", "_", "百度", "知道"], "segmented_paragraphs": [["伤筋动骨", "一百天", ",", "就是说", "回复", "的", "时间", "要", "比较", "长", ",", "即使", "是", "好", "了", "你", "也", "不能", "马上", "剧烈", "的", "运动", ",", "毕竟", "伤", "过", "了", "就", "不会", "跟", "之", "前", "一样", "的", "。", "避免", "剧烈运动", ",", "好", "好", "养", "一段时间", "会", "没事", "的", "。"]], "paragraphs": ["伤筋动骨一百天,就是说回复的时间要比较长,即使是好了你也不能马上剧烈的运动,毕竟伤过了就不会跟之前一样的。避免剧烈运动,好好养一段时间会没事的。"], "bs_rank_pos": 5}, {"is_selected": true, "title": "肩袖损伤和肩周炎的区别,两者都可以自愈吗_百度知道", "most_related_para": 1, "segmented_title": ["肩袖损伤", "和", "肩周炎", "的", "区别", ",", "两者", "都", "可以", "自愈", "吗", "_", "百度", "知道"], "segmented_paragraphs": [["有", "区别", ",", "肩周炎", "早期症状", "都", "有", "那些", "呢", "?", "肩周炎", "是", "中老年人", "常见", "的", "一", "种", "疾病", ",", "主要", "表现", "为", "肩关节", "疼痛", "和", "活动", "不便", ",", "严重影响", "患者", "的", "正常", "生活", "和", "工作", ",", "患者", "应", "及", "早", "的", "接受治疗", ",", "那", "肩周炎", "早期症状", "都", "有", "那些", "呢", "?", "我们", "一起来", "了解一下", "。", "　　", "首先", ",", "肩", "痛", ":", "早期", "时", "肩部", "呈", "阵", "发", "性", "疼痛", ",", "多数", "为", "慢性", "发作", ",", "以后", "疼痛", "逐渐", "加剧", "或", "顿", "痛", ",", "且", "呈", "持续", "性", ",", "气候变化", "或", "劳累", "后", ",", "常", "使", "疼痛", "加重", ",", "疼痛", "可", "向", "颈项", "及", "上肢", "(", "特别", "是", "肘部", ")", "扩散", ",", "当", "肩部", "偶然", "受到", "碰撞", "或", "牵拉", "时", ",", "常", "可", "引起", "撕裂", "样", "剧痛", ",", "肩", "痛", "昼", "轻", "夜", "重", "为", "本", "病", "一大", "特点", ",", "多数", "患者", "常", "诉说", "后", "半夜", "痛", "醒", ",", "不能", "成", "寐", ",", "尤其", "不能", "向", "患", "侧", "侧卧", ",", "此种情况", "因", "血虚", "而", "致", "者", "更为", "明显", ";", "若", "因", "受寒", "而", "致", "痛", "者", ",", "则", "对", "气候变化", "特别敏感", "。", "　　", "其次", ",", "肩关节", "活动", "受限", ":", "肩关节", "向", "各", "方向", "活动", "均", "可", "受限", ",", "以", "外", "展", "、", "上", "举", "、", "内外", "旋", "更为", "明显", "。", "随着", "病情", "进展", ",", "由于", "长期", "废", "用", "引起", "关节囊", "及"], ["不可以", ",", "但是", "可以", "治愈", "的", ",", "肩周炎", "和", "肩袖损伤", "多久", "了", "。"]], "paragraphs": ["有区别, 肩周炎早期症状都有那些呢?肩周炎是中老年人常见的一种疾病,主要表现为肩关节疼痛和活动不便,严重影响患者的正常生活和工作,患者应及早的接受治疗,那肩周炎早期症状都有那些呢?我们一起来了解一下。 　　首先, 肩痛:早期时肩部呈阵发性疼痛,多数为慢性发作,以后疼痛逐渐加剧或顿痛,且呈持续性,气候变化或劳累后,常使疼痛加重,疼痛可向颈项及上肢(特别是肘部)扩散,当肩部偶然受到碰撞或牵拉时,常可引起撕裂样剧痛 ,肩痛昼轻夜重为本病一大特点,多数患者常诉说后半夜痛醒,不能成寐,尤其不能向患侧侧卧,此种情况因血虚而致者更为明显;若因受寒而致痛者,则对气候变化特别敏感。 　　其次,肩关节活动受限:肩关节向各方向活动均可受限,以外展、上举、内外旋更为明显。随着病情进展,由于长期废用引起关节囊及", "不可以, 但是可以治愈的,肩周炎和肩袖损伤多久了。"], "bs_rank_pos": 6}], "answer_spans": [[2, 12]], "yesno_answers": ["Yes", "No"], "fake_answers": ["但是可以治愈的,肩周炎和肩袖损伤多久了。"], "question": "肩袖损伤能自愈吗", "segmented_answers": [["肩周炎", "是", "可以", "治愈", "的", "，", "但是", "需要", "锻炼", "，", "不然的话", "。", "时间长", "了", "肩周炎", "，", "就", "很难", "活动", "了", "。"], ["一般", "是", "不可以", "治愈", "的", "，", "需要", "外", "用", "膏药", "，", "结合", "活血化瘀", "的", "药品", "治疗"]], "answers": ["肩周炎是可以治愈的，但是需要锻炼，不然的话。时间长了肩周炎，就很难活动了。", "一般是不可以治愈的，需要外用膏药，结合活血化瘀的药品治疗"], "yesno_type": "OPINION", "answer_docs": [4], "segmented_question": ["肩袖损伤", "能", "自愈", "吗"], "question_type": "YES_NO", "question_id": 181664, "fact_or_opinion": "OPINION", "match_scores": [0.43749999999999994]}
+{"documents": [{"is_selected": true, "title": "【直播】《笑傲江湖》哪个版本好看?【电影票房吧】_百度贴吧", "most_related_para": 4, "segmented_title": ["【", "直播", "】", "《", "笑傲江湖", "》", "哪个", "版本", "好看", "?", "【", "电影", "票房", "吧", "】", "_", "百度贴吧"], "segmented_paragraphs": [["不", "然", ",", "所有", "的", "笑傲江湖", "里", "我", "认为", "李亚鹏", "演", "的", "最", "有味道", ",", "最", "能够", "表现", "这部小说", "的", "主题", "。", "还有", "岳不群", "和", "任我行", "的", "扮演", "也", "很", "恰当", "。"], ["喜欢", "这个时候", "的", "李亚鹏", "和", "许晴", ",", "真的好", "有", "武侠", "的", "感觉"], ["作者", "金庸", "亲", "口", "说", "的", "李连杰", "版", "最", "符合", "原著", "他", "的", "描写"], ["笑傲", "几", "部", "都", "拍", "的", "挺好", "的", ",", "个人", "最喜欢", "tvb", "的", "那", "版"], ["看", "的", "第一版", "笑傲江湖", "就是", "李亚鹏", "版", "的", ",", "挺", "喜欢", "后来", "于", "妈", "版", "实在", "不忍", "吐槽", "TVB", "不喜欢", "色调", "就", "没看过"], ["《", "笑傲江湖", "》", "只有", "一", "个", "真正", "的", "版本", ",", "就是", "徐克", "版", ",", "其它", "都是", "什么", "《", "瞎熬浆糊", "》", "、", "《", "哮嗷江湖", "》", "或", "《", "笑", "凹", "江湖", "》", "和", "什么", "《", "笑", "拗", "江湖", "》", "之类", "的", "。", "当然", "TVB", "或", "其它", "电视剧", "的", "是", "以", "老人", "孩子", "和", "家庭", "妇女", "为", "目标", "观众", "的", ",", "就", "不必", "跟", "它们", "有", "什么", "高", "要求", "了", "。"], ["不管", "哪", "版", "的", "令狐冲", "都", "完爆", "霍建华", "十", "条", "街", "上联", ":", "忆", "昨日", ",", "大妈", "败", "姨", "紫", "婶", "统", "统", "表", "脸", "倒贴", "下联", ":", "见", "今朝", ",", "心", "如", "乔恩", "唐嫣", "谁", "来", "救", "哥", "都", "行", "横批", ":", "霍", "白莲", "宇宙", "无敌", "-", "-", "来自", "最爱", "霍霍", "的", "华", "建", "霍", "吧"], ["明显", "李连杰", ",", "这", "还", "用", "比", "?", "吕颂贤", "的", "也", "还行", ",", "李亚鹏", "的", "看", "了", "几", "集", "尼玛", "那", "能", "看", "?", "内地", "武侠剧", "没", "多少", "江湖", "的", "味道", ",", "有些", "合拍", "的", "还", "凑合"], ["我", "看", "过", "李连杰", "那", "版", ",", "还有", "许冠杰", "版", ",", "我", "喜欢", "看", "香港", "武侠电影", ",", "居然", "还", "看", "过", "周润发", "版", "的", ",", "那个", "小师妹", "好漂亮", ",", "就是", "任盈盈", "太难看", "了"], ["说实话", "目前为止", "没有", "哪", "一", "版", "我", "觉得", "特别", "好看", "当然", "我", "只是", "个人", "意见", "个人", "有个人", "的", "看法"], ["只有", "李亚鹏", "版", "是", "全部", "看", "完", "的", ",", "不过", "那时", "挺", "小", "的", ",", "班上", "很多人", "都", "在", "讨论", ",", "那时", "没看过", "原著", "不知道", "好不好", ",", "只是", "我们", "那个年代", "这", "类", "片", "很多人", "看"], ["当然", "是", "李亚鹏", "诠释", "最", "全面", "许冠杰", "也", "不错", "但是", "是", "电影", "比较", "短"], ["实话实说", ",", "没", "一", "个", "好看", ",", "期待", "有", "新", "的", "精品"], ["许冠杰", "的", "最好", ",", "就是", "年龄", "略", "大"], ["我", "能", "说", "许晴", "版", "的", "任盈盈", "惊艳", "了", "我", "吗", "我", "上学", "时候", "特别", "喜欢", "她"], ["电影版", "必须", "李连杰", "的", ",", "电视剧", "版", "必须", "吕颂贤", "的"]], "paragraphs": ["不然,所有的笑傲江湖里我认为李亚鹏演的最有味道,最能够表现这部小说的主题。还有岳不群和任我行的扮演也很恰当。", "喜欢这个时候的李亚鹏和许晴,真的好有武侠的感觉", "作者金庸亲口说的李连杰版最符合原著他的描写", "笑傲几部都拍的挺好的,个人最喜欢tvb的那版", "看的第一版笑傲江湖就是李亚鹏版的,挺喜欢后来于妈版实在不忍吐槽TVB不喜欢色调就没看过", "《笑傲江湖》只有一个真正的版本,就是徐克版,其它都是什么《瞎熬浆糊》、《哮嗷江湖》或《笑凹江湖》和什么《笑拗江湖》之类的。当然TVB或其它电视剧的是以老人孩子和家庭妇女为目标观众的,就不必跟它们有什么高要求了。", "不管哪版的令狐冲都完爆霍建华十条街 上联: 忆昨日,大妈败姨紫婶统统表脸倒贴 下联: 见今朝,心如乔恩唐嫣谁来救哥都行 横批: 霍白莲宇宙无敌 --来自最爱霍霍的华建霍吧", "明显李连杰,这还用比? 吕颂贤的也还行,李亚鹏的看了几集尼玛那能看? 内地武侠剧没多少江湖的味道,有些合拍的还凑合", "我看过李连杰那版,还有许冠杰版,我喜欢看香港武侠电影,居然还看过周润发版的,那个小师妹好漂亮,就是任盈盈太难看了", "说实话目前为止没有哪一版我觉得特别好看  当然我只是个人意见 个人有个人的看法", "只有李亚鹏版是全部看完的,不过那时挺小的,班上很多人都在讨论,那时没看过原著不知道好不好,只是我们那个年代这类片很多人看", "当然是李亚鹏诠释最全面 许冠杰也不错 但是是电影比较短", "实话实说,没一个好看,期待有新的精品", "许冠杰的最好,就是年龄略大", "我能说许晴版的任盈盈惊艳了我吗 我上学时候特别喜欢她", "电影版必须李连杰的,电视剧版必须吕颂贤的"], "bs_rank_pos": 1}, {"is_selected": true, "title": "第一次看笑傲江湖的话看哪个版本比较好? 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1、《野生动物保护法》第九条将国家重点保护野生动物划分为国家一级保护动物和国家二级保护动物两种,并对其保护措施作出相关规定。 2、根据中国的动物保护法,不同类的猴子保护级别不同: 一级保护动物:金丝猴(所以种)、蜂猴(所有种)、 熊猴 、台湾猴、 豚尾猴 、叶猴(所有种) 、长臂猿(所有种) 。 二级保护动物:短尾猴、 猕猴 、藏酋猴。猴类至少也是国家二级保护动物,而保护动物是不允许私人饲养的。 3、猴科动物都属于国家二级保护动物,甚至是一级保护动物,个人或单位只有因科学研究、驯养繁殖、展览或者其他特殊情况下可准许饲养,且需要办理驯养繁殖许可证,目前尚不允许作为个人宠物饲养。 4、另外,猴子是一种情绪波动很大的动物,如果笼养,就", "猴子是国家级保护动物,养了会触犯野生动物保护法,会受到处罚。", "只要你不是养孙悟空,就不会犯法、"], "bs_rank_pos": 0}, {"is_selected": false, "title": "养猴子犯法吗_百度知道", "most_related_para": 0, "segmented_title": ["养", "猴子", "犯法", "吗", "_", "百度", "知道"], "segmented_paragraphs": [["北京", "动物园", "有关", "人员", "指", "小猴", "属于", "我", "国", "二类", "保护动物", ",", "不允许", "私人", "饲养", "。", "当成", "宠物", "养", "大", "的", "小猴", "即使", "最后", "放归", "自然", "或", "送", "交", "动物园", ",", "小猴", "也", "会", "因为", "可能", "无法", "融入", "野生", "猴群", "而", "受到", "伤害", "。", "即便", "是", "饲养", "野生动物", "的", "动物", "保护", "部门", "或", "动物园", "、", "科研机构", "饲养", "都", "要", "向", "国家", "林业部", "提交", "申请", ",", "严格审核", "合格", "后", "才能", "发放", "许可证", "。"], ["猴子", "是", "国家级", "保护动物", ",", "养", "了", "会", "触犯", "野生动物", "保护法", ",", "会", "受到", "警告", "、", "罚款", "、", "拘留", "等", "处罚", "。"], ["我", "看见", "有人", "养", ",", "但", "不知道", "怎么回事", ",", "他", "也是", "把", "它", "当", "宠物", "养", "的", ",", "我", "帮", "你", "问", "一", "下"], ["你", "要", "么", "15000"]], "paragraphs": ["北京动物园有关人员指小猴属于我国二类保护动物,不允许私人饲养。当成宠物养大的小猴即使最后放归自然或送交动物园,小猴也会因为可能无法融入野生猴群而受到伤害。即便是饲养野生动物的动物保护部门或动物园、科研机构饲养都要向国家林业部提交申请,严格审核合格后才能发放许可证。", "猴子是国家级保护动物,养了会触犯野生动物保护法,会受到警告、罚款、拘留等处罚。", "我看见有人养,但不知道怎么回事,他也是把它当宠物养的,我帮你问一下", "你要么15000"], "bs_rank_pos": 1}, {"is_selected": false, "title": "买只猴子当宠物?个人饲养猴类属于违法行为_社会_温州网", "most_related_para": 4, "segmented_title": ["买", "只", "猴子", "当", "宠物", "?", "个人", "饲养", "猴", "类", "属于", "违法行为", "_", "社会", "_", "温州网"], "segmented_paragraphs": [["温州", "网讯", "“", "哇", ",", "温州", "也有", "人", "卖", "小猴子", ",", "养", "只", "宠物", "猴", "真是", "新", "奇", "又", "拉风", "啊", "。", "”", "近日", ",", "温州", "市民", "林", "小姐", "在", "微信", "朋友圈", "里", "看到", "有人", "发", "了", "“", "出售", "日本", "袖珍", "石猴", "”", "的", "帖子", ",", 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“哇,温州也有人卖小猴子,养只宠物猴真是新奇又拉风啊。”近日,温州市民林小姐在微信朋友圈里看到有人发了“出售日本袖珍石猴”的帖子,有点心动。近年来,这种名为日本袖珍石猴的宠物品种成为新宠,受到不少宠物爱好者的追捧。", "在网上可以搜到不少网友出售、转卖日本袖珍石猴的帖子,卖家都表示石猴性格温顺。不过,市民王女士说,她曾因为被小猴子的小巧可爱“萌翻了”,买过一只,但养了没多久就发现猴子不仅会抢东西吃,有时凶猛起来还会咬人,于是便把石猴转卖给别人了。", "据了解,猴子是国家保护动物,不允许私下买卖,所以宠物市场上并没有出售日本石猴。买卖石猴的人一般都是通过微信、QQ、微博、论坛等网络渠道进行交易。", "据在微信上出售石猴的卖家介绍,去年至今已卖了近20只日本袖珍石猴,所售日本袖珍石猴都是从广州空运来的,是杂交配种第三代,售价根据石猴的年龄大小来定,2~5个月的石猴价格为5800元, 5~8个月的为7500元,8~12个月的为8800元。该卖家说,石猴很好养,平时只用给它喂水果、面包之类的食物就行,石猴最大只能长到矿泉水瓶这么大,平均能活到35岁,相当于人类的七八十岁。至于养猴子是否合法,该卖家表示只要有合法证件就行,并“出示”了石猴的领养证、动物健康免疫证等证件的照片,表示所有石猴出货前都会打疫苗,所以不会有病菌。", "养猴子真的合法吗?据市林业部门介绍,虽然网上所谓的日本袖珍石猴并不在《国家重点保护野生动物名录》里,但不管它属于哪种猴类、是否为野生、是否为国外进口,所有灵长类动物都属国家二级以上保护动物。根据《野生动物保护法》,饲养猴类必须取得省级林业部门的《驯养繁殖许可证》,私自饲养即属违法。申请《驯养繁殖证许可证》必须要有相应的固定场所、设施、资金、技术人员以及饲料来源的证明,且申请此证大多用于科研工作,所以个人申请很难通过。因此,即使卖家提供了猴子的领养证等一系列证件,但没有取得林业部门《驯养繁殖许可证》,个人饲养猴类还是属于违法行为。", "温州动物园兽医侯忠义说,网上卖的日本袖珍石猴大多是假冒的,有些其实就是猕猴的幼仔。在日本,确实有石猴这个品种,但是数量很少。猴类这种动物并不适合放家里养,猴子性格活泼,小的时候会比较乖巧,但长大一点就会很调皮,还会带有很强的攻击性。“之前有人在家养猴子,平时出去都会把猴子关在笼子里,有一天出门忘记把它关在笼子里,结果回家发现家里被猴子闹翻了天。”侯忠义说,猴子很聪明,会与人争宠,所以妒忌心很强,容易产生报复性心理,凶猛起来不仅会咬人还会抓人,饲养在家可能会对老人和小孩带来危险。另外,他认为网上商家所称的石猴只能长到矿泉水这么大是不可能的,这些猴子可能会比一般猴子小,但不至于小到一瓶小矿泉水这么大,而且猴子身上能携带和人互相传染的病菌,所以并不建议家养。", "N编辑: 诸葛之伊|责任编辑: 黄作敏", "网友评论仅供其表达个人看法,并不表明温州网立场。"], "bs_rank_pos": 2}, {"is_selected": false, "title": "养猴子犯法吗,普通人可以养宠物猴吗?_百度知道", "most_related_para": 0, "segmented_title": ["养", "猴子", "犯法", "吗", ",", "普通人", "可以", "养宠物", "猴", "吗", "?", "_", "百度", "知道"], "segmented_paragraphs": [["个人", "也", "不是", "完全", "不能", "养", "猴子", "。", "如果", "你", "到", "当地", "林业", "管理", "部门", "办理", "了", "《", "野生动物", "驯养繁殖许可证", "》", "以后", ",", "就", "可以", "饲养", "国家", "二级", "保护", "以下", "的", "猴子", "种类", "。", "需要", "提醒", "的", "是", ",", "猴子", "是", "一", "种", "情绪波动", "很大", "的", "动物", ",", "如果", "笼养", ",", "就", "失去", "了", "宠物", "的", "意义", ";", "如果", "牵", "着", "养", ",", "则", "需要", "很好", "的", "驯", 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"经验", ":", "jingyan", ".", "baidu", ".", "com"], ["原创", "视频", "制作", "经验", ",", "禁止转载", ",", "转载", "可耻", "!"], ["经验", "内容", "仅供参考", ",", "如果", "您", "需", "解决", "具体", "问题", "(", "尤其", "法律", "、", "医学", "等", "领域", ")", ",", "建议", "您", "详细", "咨询", "相关", "领域", "专业人士", "。"], ["个性签名", ":", "百度", "经验", ",", "为", "生活", "献计献策", "!"]], "paragraphs": ["百度经验:jingyan.baidu.com", "从视频网站下载了视频后,视频的角上会有视频网站的水印,那这个水印该怎么去除呢?其实这个问题难倒了很多的新手,那还是让编导老师给你们分享一下吧。", "百度经验:jingyan.baidu.com", "安装完整的EDIUS 6 软件 网络下载的视频", "百度经验:jingyan.baidu.com", "1 把网络下载的视频导入EDIUS,如果到不进去的话,那就转格式后再导入。 步骤阅读 2 方法一:在PS中制作镂空的上下黑边压屏图片,让黑边的位置刚好压住网站的水印位置。 步骤阅读 3 EDIUS导入图片,调节图片位置,让其完全压住网站水印。 步骤阅读 4 方法二:利用EDIUS区域命令,在水印处画圈,然后添加模糊命令。 步骤阅读 5 调节模糊命令的参数,让网站水印的模糊融合到背景视频里面。 步骤阅读 6 调节完成后,选择合适的输出格式,输出视频。 步骤阅读 END", "百度经验:jingyan.baidu.com", "原创视频制作经验,禁止转载,转载可耻!", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:百度经验,为生活献计献策!"], "bs_rank_pos": 2}, {"is_selected": false, "title": "怎么去除视频字幕清理视频字幕或水印的几种方法 ", "most_related_para": 0, 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"大家", "只要", "继续", "关注", "狸窝", "家园", "就好", "啦", "!"], ["【", "快乐", "笑话", "】", "一", "男", "内急", "欲", "找", "厕所", ",", "路", "遇", "一", "美女", "。", "男", ":", "“", "你好", ",", "你", "知道", "附近", "哪有", "厕所", "吗", "?", "”", "女", ":", "“", "你", "是", "找", "男厕所", "还是", "女厕所", "?", "”", "男", ":", "“", "…", "…", "男厕所", "。", "”", "女", ":", "“", "在", "女厕所", "旁边", "。", "”", "男", ":", "“", "女", "厕所", "在", "哪", "?", "”", "女", ":", "“", "你", "个", "大", "变态", ",", "一", "大老爷们", "找", "女厕所", "…", "…", "”", "男", "尿崩", "而亡", "。"]], "paragraphs": ["相信大家都有看电影的习惯吧,平日里闲暇无事的时候就会在网上下载些电影、电视剧之类的来看看,别说大家,就连我也是酱紫滴!相信大家也都注意到了,网上下载的视频上面的广告字幕和台标水印等很是明显,看的时候就让人郁闷啦,淡定!与其郁闷不如想办法解决,下面就为大家总结出了去除视频字幕或水印的几种方法啦。(分享:剪辑视频片段重新合并 如何制作透明logo) 给视频加水印 控制显示时间 http://www.leawo.cn/space-756442-do-thread-id-37652.html 在开始之前还是先得跟大家说些小知识,这样更有助于大家的学习,添加到视频上的字幕或是水印,一般都会有两种,一种是外挂字幕/水印,它是与视频分开的独立文件,只需将文件删除掉或移至别处即可“去水印”。而另一种就是接下来将要说的,即文字、水印已和视频合成一个完整文件,视频中的水印已经是视频画面的组成部分,是无法消除的。 无法消除的字幕/水印,并不是拿他没办法了,我们还有别的方法解决,只要不影响我们的视觉和看电影的效果,这就OK啦。况且在网上关于去除字幕/水印的方法还真的是比比皆是啊,相信让你看了也没啥心情啦,故此,特地为大家总结出来几种去字幕/水印的方法,狸友们就有福啦啦啦!——! 【方法一:去除外挂字幕】 对于视频与字幕愤慨的外挂字幕/水印,还是很容易就去除掉!不相信的话就跟我一起来见证这个神奇的时刻把,去除外挂字幕软件下载地址:http://www.leawo.cn/ND_upload.php?do=info&id=2296", "安装好狸窝全能转换器之后就运行吧,点击软件左上角的添加视频按钮把需要去除字幕的视频添加进来。添加进来可以在右边的预览窗口看到视频的字幕。", "【方法五:用模糊的方法去字幕/水印】 用模糊的方法去除水印,似乎又是一个很不错的方法,且又不难操作,废话还是少说点啦,先把软件下载下来先吧,下载地址:http://www.leawo.cn/space-722069-do-thread-id-49226.html 打开软件,新建合成之后,把素材导入进来,再把素材拖拽到下方的合成里,选择矩形遮罩工具,把文字水印圈起来,用选择工具双击遮罩还可以调节大小,如下图:", "选择效果 — 模糊与锐化 — 高斯模糊,把模糊量的参数置为15,同时在右边就能看到效果了。", "然后回到项目面板把素材再一次拖拽到合成组里,不过要记得放在第一次素材的下面哦,顺序绝对不能搞反了哦。", "好啦,以上就是我为大家总结的五种去除字幕/水印的方法啦,相信不要让大家看得目不暇接就好啦,别玩可别看眼花啦,为大家整理出来的我也很辛苦滴,可谓是功不可没啊,不过为了狸友们的福利,这点辛苦还是值得的。嘿嘿,大家只要继续关注狸窝家园就好啦!", "【快乐笑话】一男内急欲找厕所,路遇一美女。 男:“你好,你知道附近哪有厕所吗?”女:“你是找男厕所还是女厕所?” 男:“……男厕所。”女:“在女厕所旁边。”男:“女厕所在哪?”女:“你个大变态,一大老爷们找女厕所……”男尿崩而亡。"], "bs_rank_pos": 3}, {"is_selected": false, "title": "有什么办法可以去掉视频中的水印?_百度知道", "most_related_para": 0, "segmented_title": ["有", "什么", "办法", "可以", "去掉", "视频", "中", "的", "水印", "?", "_", "百度", "知道"], "segmented_paragraphs": [["现在", "常见", "的", "视频格式", "是", "没有", "“", "图层", "”", "这个概念", "的", ",", "视频", "在", "被", "加上", "水印", "之后", ",", "被", "水印", "覆盖", "的", "部分", "就", "不会", "再", "存在", "于", "视频", "当", "中", "了", "。", "直接", "地", "说", ",", "没办法", "达到", "真正", "“", "去", "水印", "”", "的", "目的", "。", "当然", "还有", "一", "种", "折", "中", "的", "方法", ",", "就是", "通过", "软件", "算法", "在", "视频", "中将", "水印", "替换", "为", "附近", "的", "图像", ",", "这样的话", "就", "看不到", "这个", "水印", "了", ",", "不过", "这", "部分", "看起来", "依然", "不太", "自然", "。", "@", "刘腾", "远", "所", "介绍", "的", "Video", "Enchancer", "和", "Remove", "Logo", "Now", "应该", "就是", "这", "类", "。", "要", "不", "然", ",", "就", "直接", "打", "个", "马赛克", "吧", "。", "去除", "马赛克", "可以", ",", "因为", "马赛克", "是", "通过", "加密算法", "加密", "了", "视频", "的", "部分", ",", "通过", "软件", "可以", "解密", "。", "最后", "总结一下", ",", "给", "不", "想", "看", "文字", "的", "人", "。", "从", "网络", "上", "找到", "无", "水印", "的", "版本", "。", 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维生素C存在于各种蔬菜、水果中,主要是青菜、菠菜、橙子、红枣、猕猴桃、柑橘、柚子、花菜、葡萄汁、西红柿。据研究,每人每天维生素C的最佳用量应为100―200毫克,最低不少于60微克。 2、含维生素A食物 维生素A也是一种既能促进伤口愈合又有美白作用的营养素。维生素A在细胞分化、上皮成纤维细胞增殖以及胶原蛋白合成中都起重要作用。维生素A缺乏将妨碍手术伤口的愈合。 因此想让伤口愈合快,可多进食含维生素A的食物:动物肝脏、鸡蛋黄、胡萝卜、西红柿、牛奶等。成年男性每天需要摄入700微克维生素A,但注意不能过量,以免对身体健康产生危害。 3、含葡萄糖的食物 糖是人体主要的供能者,供给充足的能量是伤口愈合不可缺少的。在伤口愈合期可多吃含糖丰富的水果,既增加糖分,又能摄取足量的维生素。 为了在一天之内得到充足的葡萄糖,需要注意每顿饭都要摄取富含葡萄糖的食品。最好是吸收速度慢的葡萄糖:通心粉,谷物食品,饼干,黑豆,米饭,木薯粉……", "1、一般戒辛辣、烟酒,以少食油盐、清淡为主。 2、中医讲究忌“发物”,如姜、花椒、胡椒、羊肉、狗肉等;羊肉,牛肉、腊肉等应该不吃。 3、方便面、火腿肠、罐头类食品等要尽量少吃或者不吃。 4、切忌术后补得太多,应根据病人的身体状况及食物特性,选择口感清淡、营养合理、质量适宜的食物。 5、术后一天内,不宜进食牛奶、豆浆等易胀气的食物。能正常进食时,应给予熟烂、嫩、软、少渣以及营养搭配合理的食物。切忌让病人增进食欲,投其所好,进食辛辣、富含脂肪或煎炸食物。", "饮食对于伤口愈合只是辅助,好的换药和伤口护理才是愈合的保障。日常注意护理,对于一些较为严重,长期不能愈合的伤口,除了做好相关的饮食调理之外,还需使用收口方之类的生肌愈口的良药进行治疗。日常做好伤口的护理,保持良好的心态,愿大家健康快乐每一天。", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "剖腹产伤口多久愈合"], "bs_rank_pos": 0}, {"is_selected": true, "title": "伤口愈合吃什么好得快_百度经验", "most_related_para": 3, "segmented_title": ["伤口愈合", "吃什么", "好得快", "_", "百度", "经验"], 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"手术", "后", "伤口", "流水", "怎么办"]], "paragraphs": ["百度经验 > 生活/家居 > 生活常识", "百度经验:jingyan.baidu.com", "国人自古就讲究食疗,好的饮食不但能够帮助人们保养身体,而且好的饮食还能帮助人们治疗疾病。以下就跟大家分享一下常见的能够促进伤口愈合的食物,希望能够对大家有所帮助:", "一、吃一些含锌量丰富的食物能够促进伤口愈合,锌可与维生素C结合,参与体内胶原蛋白的合成,增加抵抗力,促进伤口愈合。含锌的食品有玉米、黄豆、萝卜、蘑菇、坚果、动物肝脏、木耳、海带、蛋、肉类、全谷类、坚果类。 二、吃一些含有葡萄糖丰富的食物能够促进伤口愈合,糖是人体主要的供能者,供给充足的能量是伤口愈合不可缺少的。在伤口愈合期可多吃含糖丰富的水果,既增加糖分,又能摄取足量的维生素。 含有葡萄糖丰富的食物蜂蜜、龙眼肉、红枣、葡萄、西瓜、青木瓜、猕猴桃 、胡萝卜等。 三、吃一些含有蛋白质丰富的食物能够促进伤口愈合,蛋白质能促进伤口愈合,减少感染机会。含蛋白质丰富的食物有各种瘦肉、牛奶、蛋类等。 四、吃一些含有维生素A丰富的食物能够促进伤口愈合,维生素A在细胞分化、上皮成纤维细胞增殖以及胶原蛋白合成中都起重要作用。维生素A缺乏将妨碍手术伤口的愈合。同时,维生素A也是一种抗氧化营养素,能发挥美白皮肤的作用。维生素A含量丰富的食物包括动物肝脏、鸡蛋黄、牛奶等。 五、吃一些含有维生素C丰富的食物能够促进伤口愈合,生素C作为还原剂,还可以减少黑色素的形成,甚至可以将已经生成的黑色素还原成无色物质,具有较好的美白作用。因此,在手术伤口愈合期间可以多吃一些维生素C含量丰富的水果蔬菜,如橙子、猕猴桃、柠檬、大枣等。", "除了以上可以吃的之外,还有一些食物应该尽量避免食用,否则可能影响伤口愈合。一些辛辣刺激性的食物、食用菌类 、海鲜类、牛羊肉之类应该尽量避免,同时避免食用辛辣刺激性的食物。当然伤口愈合过程中除了做好饮食调理之外还应该注意做好相关的护理和治疗工作,长期不愈合的伤口搭配于´氏收口方辅助修复,保持良好的心态,如此才能更好、更快的愈合伤口。", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "痔疮手术后伤口不愈合怎么办 包皮手术后伤口流水怎么办"], 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["向", "医生", "咨询", "相关问题"]], "paragraphs": ["剖宫产,各种手术,还有生活中也会遇到一些意外情况,难免会出现一些伤口,很多人会担心伤口感染,难免有什么方法可以促进伤口的愈合呢?饮食也会影响到伤口愈合的进程,下面就为大家介绍促进伤口愈合的15种食物!", "一、鲈鱼 鲈鱼含有丰富的、易消化的蛋白质、脂肪、维生素B2、尼克酸、钙、磷、钾、铜、铁、硒等。中医认为鲈鱼性温味甘有健脾胃、补肝肾、止咳化痰的作用。喝黑鱼汤对于伤口也很有好处。 二、猪蹄 猪蹄含有丰富的锌、胶原蛋白。缺锌会使纤维细胞功能下降。多补充胶原蛋白能促进伤口愈合的速度。 三、海带 海带所含蛋白质和碳水化合物是菠菜的几倍到几十倍。胡萝卜素、核黄素、硫胺素以及尼克酸等重要维生素的含量也很多。海带中的褐藻酸钠盐,对动脉出血症也有止血效能。 四、木耳 黑木耳富含铁等矿物元素,每日100克黑木耳中含铁高达185毫克,比绿叶蔬菜中含铁量最高的芹菜高出20倍,同时含有丰富的锌。 五、乳鸽 鸽是禽类唯一的无胆动物,肝脏中储存着丰富的胆素,血液中含有丰富的血红蛋白,骨骼中有大量的软骨素。这些特殊的营养成份,对调节人体大脑神经系统,改善睡眠,增进食欲,帮助消化,激活性腺分泌和脑垂体分泌,以至全面平衡人体机能,调理并强壮身体,有着特殊的食疗作用。", "六、西红柿", "西红柿可防止血栓,番茄籽周围的黄色胶状物质可以防止血液中血小板的凝结;还含有丰富的维生素C、番茄红素、胡萝卜素等抗氧化成分,对伤口愈合有利。", "七、蜂蜜", "喝一点蜂蜜对伤口愈合会有较大好处。另外,也可以将蜂蜜直接涂擦在皮肤或伤口上,有消炎、止痛、止血、减轻水肿、促进伤口愈合的作用。", "八、黑豆", "黑豆是各种豆类中蛋白质含量最高的,比猪腿肉多一倍还有余。它含有的脂肪主要是单不饱和脂肪酸和多不饱和脂肪酸,其中人体需要的必需脂肪酸占50%,还有磷脂、大豆黄酮、生物素。中医认为,黑豆性平味甘,有润肠补血的功能,术后体虚血缺患。", "相关问答 相关话题 相关医院 相关医生 相关药品", "向医生咨询相关问题"], "bs_rank_pos": 2}, {"is_selected": false, "title": "促进手术刀口愈合的方法(食物 禁忌等)_百度知道", "most_related_para": 0, 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["10", ".", "黑豆", "。", "黑豆", "是", "各种", "豆类", "中", "蛋白质", "含量", "最高", "的", ",", "比", "猪", "腿", "肉", "多", "一倍", "还有", "余", "。", "它", "含有", "的", "脂肪", "主要", "是", "单", "不饱和脂肪酸", "和", "多", "不饱和脂肪酸", ",", "其中", "人体", "需要", "的", "必需脂肪酸", "占", "50", "%", ",", "还有", "磷脂", "、", "大豆", "黄酮", "、", "生物素", "。", "中医", "认为", ",", "黑豆", "性", "平", "味", "甘", ",", "有", "润肠", "补血", "的", "功能", ",", "食用", "可", "加速", "伤口", "恢复", "。"], ["不利于", "伤口", "食物", ":"], ["牛肉", "、", "狗肉", "、", "羊肉", "、", "海鲜", "、", "酒", "、", "洋葱", "、", "姜", "、", "蒜", "、", "酱油", "、", "榴莲", "、", "荔枝", "、", "芒果", "、", "辛辣", "及", "有", "色素", "(", "菠菜", "、", "紫菜", "、", "巧克力", ")", "色素", "食物", "会", "产生", "色素沉着", "。", "辛辣", "刺激", "的", "食物", "容易", "引发", "感染", "、", "不利于", "伤口愈合", "。", "此外", ",", "吸烟", "、", "喝酒", "、", "心情不好", "、", ",", "信心不足", "都会", "影响", "伤口", "的", "愈合", "。"], ["要", "想", "促进", "伤口愈合", ",", "营养", "一定要", "跟", "上", ",", "而且", "是", "特别", "重要", "。"]], "paragraphs": ["1. 猪蹄。猪蹄含有丰富的锌、胶原蛋白。 缺锌会使纤维细胞功能下降,多补充胶原蛋白能促进伤口愈合的速度。 2. 鲈鱼。鲈鱼含有丰富的、易消化的蛋白质、脂肪、维 生素B2、尼克酸、钙、磷、钾、铜、铁、硒等。中医认为鲈鱼性温味甘有健脾胃、补肝肾、止咳化痰的作用。另外:黑鱼对伤口恢复也特别好。 3. 乳鸽。鸽子是禽类唯一的无胆动物,肝脏中储存着丰富的胆素,血液中含有丰富的血红蛋白,骨骼中有大量的软骨素。这些特殊的营养成份,对调节人体大脑神经系统,改善睡眠,增进食欲,帮助消化,激活性腺分泌和脑垂体分泌,以至全面平衡人体机能,调理并强壮身体,有着特殊的食疗作用。有益于人体能量的储存和利用,维持肌肉和神经系统的正常功能,改善心肌收缩和凝血功能。 4. 虹鳟鱼。原产美国加州的山溪中,喜栖 于清澈无污染的冷水中,以食鱼虾为主,堪称急流勇士。该鱼肉质细嫩、味道鲜美、无小刺、无腥味、高蛋白、低脂肪,在国际上被列为高档绿色食品。虹鳟鱼可做具有异国风味的生鱼片,宜清蒸、清炖,亦可做氽鱼丝、红烧、熏鱼,是极佳的促进伤口愈合的食品。", "5. 海带。 海带所含蛋白质和碳水化合物是菠菜的几倍到几十倍。胡萝卜素、核黄素、硫胺素以及尼克酸等重要维生素的含量也很多。海带中的褐藻酸钠盐,对动脉出血症也有止血效能。", "6.木耳。黑木耳富含铁等矿物元素,每日 100克黑木耳中含铁高达185毫克,比绿叶蔬菜中含铁量最高的芹菜高出20倍,同时含有丰富的锌. 所以它是一种非常好的天然补血食品,能促进术后失血过多患者伤口愈合速度。", "7. 苦瓜。 苦瓜汁含有类似奎宁的蛋白成分,能加强巨噬细胞的吞噬能力, 同时苦瓜中的苦瓜式和苦味素能增进食欲,健脾开胃,有利于伤口恢复。", "8. 西红柿。 西红柿可防止血栓 番茄籽周围的 黄色胶状物质可以防止血液中血小板的凝结; 还含有丰富的维生素C、番茄红素、胡萝卜素等抗氧化成分,对伤口愈合有利。", "9. 蜂蜜。喝一点蜂蜜对伤口愈合会有较大好处。另外,也可以将蜂蜜直接涂擦在皮肤或伤口上,有消炎、止痛、止血、减轻水肿、促进伤口愈合的作用。", "10. 黑豆。黑豆是各种豆类中蛋白质含量最高的,比猪腿肉多一倍还有余。它含有的脂肪主要是单不饱和脂肪酸和多不饱和脂肪酸,其中人体需要的必需脂肪酸占50%,还有磷脂、大豆黄酮、生物素。中医认为,黑豆性平味甘,有润肠补血的功能,食用可加速伤口恢复。", "不利于伤口食物:", "牛肉、狗肉、羊肉、海鲜、酒、洋葱、姜、蒜、酱油、榴莲、荔枝、芒果、辛辣及有色素(菠菜、紫菜、巧克力) 色素食物会产生色素沉着。 辛辣刺激的食物容易引发感染、不利于伤口愈合。此外,吸烟、喝酒、心情不好、,信心不足都会影响伤口的愈合。", "要想促进伤口愈合,营养一定要跟上,而且是特别重要。"], "bs_rank_pos": 4}], "answer_spans": [[0, 323]], "fake_answers": ["一、吃一些含锌量丰富的食物能够促进伤口愈合,锌可与维生素C结合,参与体内胶原蛋白的合成,增加抵抗力,促进伤口愈合。含锌的食品有玉米、黄豆、萝卜、蘑菇、坚果、动物肝脏、木耳、海带、蛋、肉类、全谷类、坚果类。二、吃一些含有葡萄糖丰富的食物能够促进伤口愈合,糖是人体主要的供能者,供给充足的能量是伤口愈合不可缺少的。在伤口愈合期可多吃含糖丰富的水果,既增加糖分,又能摄取足量的维生素。含有葡萄糖丰富的食物蜂蜜、龙眼肉、红枣、葡萄、西瓜、青木瓜、猕猴桃、胡萝卜等。三、吃一些含有蛋白质丰富的食物能够促进伤口愈合,蛋白质能促进伤口愈合,减少感染机会。含蛋白质丰富的食物有各种瘦肉、牛奶、蛋类等。四、吃一些含有维生素A丰富的食物能够促进伤口愈合,维生素A在细胞分化、上皮成纤维细胞增殖以及胶原蛋白合成中都起重要作用。维生素A缺乏将妨碍手术伤口的愈合。同时,维生素A也是一种抗氧化营养素,能发挥美白皮肤的作用。维生素A含量丰富的食物包括动物肝脏、鸡蛋黄、牛奶等。五、吃一些含有维生素C丰富的食物能够促进伤口愈合,生素C作为还原剂,还可以减少黑色素的形成,甚至可以将已经生成的黑色素还原成无色物质,具有较好的美白作用。因此,在手术伤口愈合期间可以多吃一些维生素C含量丰富的水果蔬菜,如橙子、猕猴桃、柠檬、大枣等。"], "question": "促进伤口愈合的食物", "segmented_answers": [["1", "、", "含", "维生素", "C", "的", "食物", "。", "2", "、", "含", "维生素A", "食物", "。", "3", "、", "含", "葡萄糖", "的", "食物", "。", "4", "、", "含", "蛋白质", "丰富", "的", "食物", "。", "5", "、", "含", "锌", "的", "食物", "。"], ["一", "、", "吃", "一些", "含", "锌", "量", "丰富", "的", "食物", "能够", "促进", "伤口愈合", "，", "锌", "可", "与", "维生素", "C", "结合", "，", "参与", "体", "内", "胶原蛋白", "的", "合成", "，", "增加", "抵抗力", "，", "促进", "伤口愈合", "。", "含", "锌", "的", "食品", "有", "玉米", "、", "黄豆", "、", "萝卜", "、", "蘑菇", "、", "坚果", "、", "动物肝脏", "、", "木耳", "、", "海带", "、", "蛋", "、", "肉类", "、", "全", "谷类", "、", "坚果", "类", "。", "二", "、", "吃", "一些", "含有", "葡萄糖", "丰富", "的", "食物", "能够", "促进", "伤口愈合", "，", "糖", "是", "人体", "主要", "的", "供", "能", "者", "，", "供给", "充足", "的", "能量", "是", "伤口愈合", "不可", "缺少", "的", "。", "在", "伤口", "愈合", "期", "可", "多", "吃", "含", "糖", "丰富", "的", "水果", "，", "既", "增加", "糖分", "，", "又", "能", "摄取", "足", "量", "的", "维生素", "。", "含有", "葡萄糖", "丰富", "的", "食物", "蜂蜜", "、", "龙眼肉", "、", "红枣", "、", "葡萄", "、", "西瓜", "、", "青木瓜", "、", "猕猴桃", "、", "胡萝卜", "等", "。", "三", "、", "吃", "一些", "含有", "蛋白质", "丰富", "的", "食物", "能够", "促进", "伤口愈合", "，", "蛋白质", "能", "促进", "伤口愈合", "，", "减少", "感染", "机会", "。", "含", "蛋白质", "丰富", "的", "食物", "有", "各种", "瘦肉", "、", "牛奶", "、", "蛋类", "等", "。", "四", "、", "吃", "一些", "含有", "维生素A", "丰富", "的", "食物", "能够", "促进", "伤口愈合", "，", "维生素A", "在", "细胞", "分化", "、", "上", "皮", "成纤维细胞", "增殖", "以及", "胶原蛋白", "合成", "中", "都", "起重", "要", "作用", "。", "维生素A", "缺乏", "将", "妨碍", "手术", "伤口", "的", "愈合", "。", "同时", "，", "维生素A", "也是", "一", "种", "抗氧化", "营养素", "，", "能", "发挥", "美白", "皮肤", "的", "作用", "。", "维生素A", "含量", "丰富", "的", "食物", "包括", "动物", "肝脏", "、", "鸡蛋", "黄", "、", "牛奶", "等", "。", "五", "、", "吃", "一些", "含有", "维生素", "C", "丰富", "的", "食物", "能够", "促进", "伤口愈合", "，", "生", "素", "C", "作为", "还原剂", "，", "还", "可以", "减少", "黑色素", "的", "形成", "，", "甚至", "可以", "将", "已经", "生成", "的", "黑色素", "还原", "成", "无色", "物质", "，", "具有", "较好", "的", "美白", "作用", "。", "因此", "，", "在", "手术", "伤口", "愈合", "期间", "可以", "多", "吃", "一些", "维生素", "C", "含量", "丰富", "的", "水果蔬菜", "，", "如", "橙子", "、", "猕猴桃", "、", "柠檬", "、", "大枣", "等", "。"], ["一", "、", "鲈鱼", "。", "二", "、", "猪蹄", "。", "三", "、", "海带", "。", "四", "、", "木耳", "。", "五", "、", "乳鸽", "。", "六", "、", "西红柿", "。", "七", "、", "蜂蜜", "。", "八", "、", "黑豆", "。", "九", "、", "苦瓜", "。", "十", "、", "虹鳟鱼", "。", "十一", "、", "锌", "。", "十二", "、", "葡萄糖", "。", "十三", "、", "蛋白质", "。", "十四", "、", "维生素A", "。", "十五", "、", "维生素", "C", "。"]], "answers": ["1、含维生素C的食物。2、含维生素A食物。3、含葡萄糖的食物。4、含蛋白质丰富的食物。5、含锌的食物。", "一、吃一些含锌量丰富的食物能够促进伤口愈合，锌可与维生素C结合，参与体内胶原蛋白的合成，增加抵抗力，促进伤口愈合。含锌的食品有玉米、黄豆、萝卜、蘑菇、坚果、动物肝脏、木耳、海带、蛋、肉类、全谷类、坚果类。二、吃一些含有葡萄糖丰富的食物能够促进伤口愈合，糖是人体主要的供能者，供给充足的能量是伤口愈合不可缺少的。在伤口愈合期可多吃含糖丰富的水果，既增加糖分，又能摄取足量的维生素。 含有葡萄糖丰富的食物蜂蜜、龙眼肉、红枣、葡萄、西瓜、青木瓜、猕猴桃 、胡萝卜等。三、吃一些含有蛋白质丰富的食物能够促进伤口愈合，蛋白质能促进伤口愈合，减少感染机会。含蛋白质丰富的食物有各种瘦肉、牛奶、蛋类等。四、吃一些含有维生素A丰富的食物能够促进伤口愈合，维生素A在细胞分化、上皮成纤维细胞增殖以及胶原蛋白合成中都起重要作用。维生素A缺乏将妨碍手术伤口的愈合。同时，维生素A也是一种抗氧化营养素，能发挥美白皮肤的作用。维生素A含量丰富的食物包括动物肝脏、鸡蛋黄、牛奶等。五、吃一些含有维生素C丰富的食物能够促进伤口愈合，生素C作为还原剂，还可以减少黑色素的形成，甚至可以将已经生成的黑色素还原成无色物质，具有较好的美白作用。因此，在手术伤口愈合期间可以多吃一些维生素C含量丰富的水果蔬菜，如橙子、猕猴桃、柠檬、大枣等。", "一、鲈鱼。二、猪蹄。三、海带。四、木耳。五、乳鸽。六、西红柿。七、蜂蜜。八、黑豆。九、苦瓜。十、虹鳟鱼。十一、锌。十二、葡萄糖。十三、蛋白质。十四、维生素A。十五、维生素C。"], "answer_docs": [1], "segmented_question": ["促进", "伤口愈合", "的", "食物"], "question_type": "DESCRIPTION", "question_id": 181671, "fact_or_opinion": "FACT", "match_scores": [0.9413580246913579]}
diff --git a/PaddleNLP/reading_comprehension/data/demo/testset/search.test.json b/PaddleNLP/reading_comprehension/data/demo/testset/search.test.json
new file mode 100644
index 0000000000000000000000000000000000000000..cee592a05133decb75ec6a8cd64bf4e59f4f7a4c
--- /dev/null
+++ b/PaddleNLP/reading_comprehension/data/demo/testset/search.test.json
@@ -0,0 +1,100 @@
+{"documents": [{"title": "揭秘宋庆龄“第二段婚姻”传言不为人知的真相 - 红色秘史 - 红潮网 ", "segmented_title": ["揭秘", "宋庆龄", "“", "第", "二", "段", "婚姻", "”", "传言", "不为人知", "的", "真相", "-", "红色", "秘史", "-", "红潮", "网"], "segmented_paragraphs": [["宋庆龄", "一生", "没有", "生", "养", "自己", "的", "孩子", ",", "鲜为人知", "的", "是", ",", "花甲之年", "时", ",", "她", "却", "有", "两", "个", "养女", ":", "隋永清", "和", "隋", "永", "洁", "。", "这", "一", "对", "姐妹花", "从", "出生", "不", "久", ",", "就", "伴", "在", "宋庆龄", "身边", ",", "陪", "她", "度过", "了", "人生", "最后", "的", "20", "多", "年", "。"], ["2014", "年", "8", "月", ",", "在", "北京", "西城区", "一", "家", "老式", "茶楼", ",", "环球人物", "杂志", "记者", "见到", "了", "隋", "家", "姐姐", "隋永清", "。", "她", "曾", "是", "一", "位", "电影", "演员", ",", "采访", "当天", "穿着", "简单", "的", "T恤", ",", "言行举止", "无", "不", "透", "着", "大家风范", "。"], ["宋庆龄", "已", "去世", "30", "多", "年", ",", "隋永清", "如今", "也", "年近花甲", "。", "但", "她", "保养", "得", "很好", ",", "皮肤白皙", ",", "声音", "清脆悦耳", "。", "采访", "前", "与", "隋永清", "短信", "联系", "时", ",", "她", "不", "失", "顽皮", ",", "还", "发", "来", "不", 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"的", "父亲", "隋学芳", "是", "东北人", ",", "在", "东北", "参军", ",", "后", "由", "公安部", "从", "部队", "挑选", "考核", "派", "到", "宋庆龄", "身边", ",", "成为", "她", "的", "贴身", "警卫", "秘书", "。", "“", "由于", "工作", "关系", ",", "父亲", "落户", "在", "上海", "。", "结婚后", ",", "因为", "工作", "需要", ",", "我们一家人", "都", "曾", "住在", "宋庆龄", "在", "上海", "住宅", "的", "配", "楼", "里", "。", "”"], ["1957", "年", "年底", ",", "隋学芳", "的", "大女儿", "隋永清", "出生", "。", "知道", "宋庆龄", "喜欢", "小孩", ",", "隋学芳", "就", "把", "襁褓", "中", "的", "女儿", "抱", "到", "宋庆龄", "面前", "。", "跟", "别", "的", "孩子", "不同", ",", "刚", "出生", "的", "隋永清", "一点", "也", "不", "认生", ",", "她", "不哭", "不", "闹", ",", "对着", "宋庆龄", "一直", "笑", "。", "宋庆龄", "正", "高兴", "时", ",", "突然", "觉得", "一", "阵", "温", "热", ",", "原来", "是", "孩子", "撒尿", "了", "。", "周围", "的", "人", "大吃一惊", "。", "大家都知道", ",", "宋庆龄", "是", "特别", "讲卫生", "的", "人", ",", "几", "双手", "同时", "伸", "过来", ",", "要", "从", "宋庆龄", "的", "怀里", "把", "孩子", "抱走", "。", "没想到", ",", "宋庆龄", "坚决", "不", "让", "别人", "插", "手", ",", "连", "声", "说", "道", ":", "“", "别动", "!", "让", "孩子", "尿", "完", ",", "不", "然", "会", "坐下", "病", "的", "。", "”", "大家", "眼睁睁", "地", "看着", "这个", "小家伙", ",", "在", "一辈子", "讲究", "干净", "的", "宋庆龄", "怀里", "放肆", "地", "尿", "了", "个", "痛快", "。"], ["谁", "都", "没料到", ",", "这", "一笑", "、", "一", "尿", "引起", "了", "宋庆龄", "的", "怜爱", "之", "心", ",", "她", "觉得", "同", "这个孩子", "有", "一", "种", "亲密", "的", "缘分", ",", "并", "提出", "希望", "收养", "这个", "女孩", "。", "至今", ",", "隋永清", "回忆", "起来", ",", "都", "说", ":", "“", "我", "觉得", "这种事情", "说", "不清楚", ",", "就是", "冥冥之中", "的", "感觉", "。", "我", "是", "被", "抱", "过去", "众多", "孩子", "中", "的", "一", "个", ",", "但", "我", "是", "最", "幸运", "的", ",", "被", "宋庆龄", "留下", "了", "。", "”"], ["这一年", "宋庆龄", "64", "岁", ",", "按", "年龄", "算", ",", "隋永清", "应是", "宋庆龄", "的", "孙辈", ",", "但", "宋庆龄", "不喜欢", "被", "人", "叫", "成", "阿婆", "、", "奶奶", "。", "隋永清", "叫", "她", "“", "妈妈", "太太", "”", ",", "这个", "称谓", "是", "刚", "学会", "说话", "的", "隋永清", "自己", "创造", "的", "。"], ["“", "她", "对", "我们", "几乎", "都是", "放养", "的", "”"], ["妹妹", "隋", "永", "洁", "出生", "后", ",", "也", "经常", "到", "宋庆龄", "的", "上海", "寓", "所", "玩", ",", "姐妹", "两人", "给", "她", "那", "清幽", "的", "寓", "所", "增添", "了", "生气", "。"], ["1959", "年", ",", "宋庆龄", "来到", "北京", ",", "隋永清", "和", "她", "一起", "随行", ",", "相伴", "左右", "。", "她说", "宋庆龄", "在", "北京", "的", "足迹", "自己", "都", "沿", "路", "跟着", ",", "“", "刚来", "的", "时候", "住在", "北京站", "对面", "的", "方巾巷", ",", "然后", "搬到", "什刹海", "西河沿", ",", "就是", "现在", "的", "郭沫若故居", ",", "1963", "年", "入住", "后海", "北河沿", ",", "如今", "的", "后海", "宋庆龄故居", "。", "这", "是", "宋庆龄", "在", "北京", "最后", "的", "住", "地", "。", "”", "到", "了", "1973", "年", ",", "妹妹", "隋", "永", "洁", "参军", "进京", "也", "住", "进", "了", "后", "海边", "的", "这", "所", "宅子", "。", "比", "起", "妹妹", ",", "隋永清", "在", "宋庆龄", "身边的日子", "更多", "。"], ["这事儿", "过", "了", ",", "保姆", "跟", "宋庆龄", "说", ":", "“", "您", "得", "管", "管", "了", ",", "她", "胆子", "太", "大", "了", ",", "哪里", "都", "敢", "上", ",", "闯祸", "了", "怎么办", "?", "”", "宋庆龄", "答", "道", ":", "“", "现在", "跟", "她说", "这些", "她", "也", "不懂", ",", "小孩子", "这个", "年龄", "就是这样", "。", "她", "爬", "那么", "高", ",", "还", "站", "那儿", "唱歌", ",", "至少", "这", "孩子", "勇敢", "、", "不怕", "高", "。", "”", "跟", "环球人物", "杂志", "记者", "说", "完", "这", "段", "故事", ",", "隋永清", "自己", "也", "乐", "了", "。"], ["宋庆龄", "常", "挂", "在", "嘴边", "的", "一句话", "是", ",", "女孩子", "要", "会", "打扮", "自己", "。", "物资", "紧张", "的", "困难", "岁月", "里", ",", "宋庆龄", "自己", "用", "着", "明显", "发", "旧", "的", "手绢", ",", "穿", "一", "身", "布衣", ",", "但", "对", "隋永清", "、", "隋", "永", "洁", "小时候", "的", "穿着", ",", "她", "下", "足", "了", "功夫", "。", "“", "那个时候", "的", "时髦", "料子", ",", "裙子", "一", "做", "就是", "好", "几", "条", "。", "还有", "冬天", "的", "小羊羔", "皮大衣", ",", "我们", "喜欢", "得", "不得了", "。", "妈妈", "太太", "还", "不许", "我们", "剪", "头发", ",", "要", "留", "得", "长", "长", "的", "。", "每天", "早上", "起床", ",", "她", "帮", "我", "梳头", ",", "要", "我", "自己", "攥", "着", "马尾", ",", "给", "我", "系", "上", "漂亮", "的", "蝴蝶结", "。", "”"], ["姐妹俩", "还", "经常", "跟着", "宋庆龄", "出席", "外事", "活动", ",", "隋永清", "清楚", "地", "记得", ",", "与", "柬埔寨", "西哈努克亲王", "的", "会", "面", ",", "她", "和", "妹妹", "都", "在", "场", "。", "“", "周恩来", "也", "经常", "来", ",", "他", "左右手", "牵", "着", "我们", "两", "个", ",", "带", "我们", "在", "花园里", "散步", "。", "”"]], "paragraphs": ["宋庆龄一生没有生养自己的孩子,鲜为人知的是,花甲之年时,她却有两个养女:隋永清和隋永洁。这一对姐妹花从出生不久,就伴在宋庆龄身边,陪她度过了人生最后的20多年。", "2014年8月,在北京西城区一家老式茶楼,环球人物杂志记者见到了隋家姐姐隋永清。她曾是一位电影演员,采访当天穿着简单的T恤,言行举止无不透着大家风范。", "宋庆龄已去世30多年,隋永清如今也年近花甲。但她保养得很好,皮肤白皙,声音清脆悦耳。采访前与隋永清短信联系时,她不失顽皮,还发来不少搞怪图片,让人很难想象这是57岁的老人。她说自己从小就“被宋庆龄宠腻坏了”。而在记者采访的几个小时里,隋永清口中的宋庆龄,也不是大家熟知的形象,更多的是一个母亲的柔软。", "一尿成了宋庆龄的女儿", "1915年秋天,宋庆龄不顾家人反对,奔赴日本与大自己27岁的孙中山结婚。追随孙中山的10年间,她曾孕育过一个生命,但在军阀陈炯明叛乱的突围中流产,这对宋庆龄是一个重大的打击。更加不幸的是,仅仅两年后,孙中山也匆匆告别人世。", "因为人生中的遗憾,宋庆龄特别喜欢孩子。周围哪家婴儿刚出生,都会找机会抱来给她看看。她还总叮嘱登门的客人“下次一定要带着孩子一起来”。", "隋永清的父亲隋学芳是东北人,在东北参军,后由公安部从部队挑选考核派到宋庆龄身边,成为她的贴身警卫秘书。“由于工作关系,父亲落户在上海。结婚后,因为工作需要,我们一家人都曾住在宋庆龄在上海住宅的配楼里。”", "1957年年底,隋学芳的大女儿隋永清出生。知道宋庆龄喜欢小孩,隋学芳就把襁褓中的女儿抱到宋庆龄面前。跟别的孩子不同,刚出生的隋永清一点也不认生,她不哭不闹,对着宋庆龄一直笑。宋庆龄正高兴时,突然觉得一阵温热,原来是孩子撒尿了。周围的人大吃一惊。大家都知道,宋庆龄是特别讲卫生的人,几双手同时伸过来,要从宋庆龄的怀里把孩子抱走。没想到,宋庆龄坚决不让别人插手,连声说道:“别动!让孩子尿完,不然会坐下病的。”大家眼睁睁地看着这个小家伙,在一辈子讲究干净的宋庆龄怀里放肆地尿了个痛快。", "谁都没料到,这一笑、一尿引起了宋庆龄的怜爱之心,她觉得同这个孩子有一种亲密的缘分,并提出希望收养这个女孩。至今,隋永清回忆起来,都说:“我觉得这种事情说不清楚,就是冥冥之中的感觉。我是被抱过去众多孩子中的一个,但我是最幸运的,被宋庆龄留下了。”", "这一年宋庆龄64岁,按年龄算,隋永清应是宋庆龄的孙辈,但宋庆龄不喜欢被人叫成阿婆、奶奶。隋永清叫她“妈妈太太”,这个称谓是刚学会说话的隋永清自己创造的。", "“她对我们几乎都是放养的”", "妹妹隋永洁出生后,也经常到宋庆龄的上海寓所玩,姐妹两人给她那清幽的寓所增添了生气。", "1959年,宋庆龄来到北京,隋永清和她一起随行,相伴左右。她说宋庆龄在北京的足迹自己都沿路跟着,“刚来的时候住在北京站对面的方巾巷,然后搬到什刹海西河沿,就是现在的郭沫若故居,1963年入住后海北河沿,如今的后海宋庆龄故居。这是宋庆龄在北京最后的住地。”到了1973年,妹妹隋永洁参军进京也住进了后海边的这所宅子。比起妹妹,隋永清在宋庆龄身边的日子更多。", "这事儿过了,保姆跟宋庆龄说:“您得管管了,她胆子太大了,哪里都敢上,闯祸了怎么办?”宋庆龄答道:“现在跟她说这些她也不懂,小孩子这个年龄就是这样。她爬那么高,还站那儿唱歌,至少这孩子勇敢、不怕高。”跟环球人物杂志记者说完这段故事,隋永清自己也乐了。", "宋庆龄常挂在嘴边的一句话是,女孩子要会打扮自己。物资紧张的困难岁月里,宋庆龄自己用着明显发旧的手绢,穿一身布衣,但对隋永清、隋永洁小时候的穿着,她下足了功夫。“那个时候的时髦料子,裙子一做就是好几条。还有冬天的小羊羔皮大衣,我们喜欢得不得了。妈妈太太还不许我们剪头发,要留得长长的。每天早上起床,她帮我梳头,要我自己攥着马尾,给我系上漂亮的蝴蝶结。”", "姐妹俩还经常跟着宋庆龄出席外事活动,隋永清清楚地记得,与柬埔寨西哈努克亲王的会面,她和妹妹都在场。“周恩来也经常来,他左右手牵着我们两个,带我们在花园里散步。”"], "bs_rank_pos": 0}, {"title": "宋庆龄第二任丈夫是谁_百度知道", "segmented_title": ["宋庆龄", "第", "二", "任", "丈夫", "是", "谁", "_", "百度", "知道"], "segmented_paragraphs": [["一", "、", "名义", "上", ":", "宋庆龄", "只有", "一", "个", "丈夫", ",", "就是", "孙中山", "。", "宋庆龄", "在", "孙中山", "死后", ",", "作为", "国", "人", "敬仰", "的", "“", "国母", "”", ",", "不仅", "竭力", "维护", "丈夫", "孙中山", "的", "光辉", "形象", ",", "而且", "为", "中国", "革命", "事业", "不懈努力", "着", "。", "一直", "到", "她", "逝世", ",", "她", "都", "在", "为", "人民", "着", "想", "。", "二", "、", "实际上", "虽然", "她", "没有", "结婚", ",", "我", "想", "应该", "有情人", ",", "比如说", "侍从", "、", "工作", "人员", "、", "年轻", "秘书", ",", "一个女人", ",", "那么", "多", "年", ",", "你", "懂", "的", "。"]], "paragraphs": ["一、名义上:宋庆龄只有一个丈夫,就是孙中山。宋庆龄在孙中山死后,作为国人敬仰的“国母”,不仅竭力维护丈夫孙中山的光辉形象,而且为中国革命事业不懈努力着。 一直到她逝世,她都在为人民着想。 二、实际上虽然她没有结婚,我想应该有情人,比如说侍从、工作人员、年轻秘书,一个女人,那么多年,你懂的。"], "bs_rank_pos": 1}, {"title": "宋庆龄第二任丈夫是谁?_百度知道", "segmented_title": ["宋庆龄", "第", "二", "任", "丈夫", "是", "谁", "?", "_", "百度", "知道"], "segmented_paragraphs": [["宋庆龄", "只有", "一", "个", "丈夫", ",", "就是", "孙中山", "。", "　　", "宋庆龄", "在", "孙中山", "死后", ",", "作为", "国", "人", "敬仰", "的", "“", "国母", "”", ",", "不仅", "竭力", "维护", "丈夫", "孙中山", "的", "光辉", "形象", 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"刚看到解禁的文件,宋在上世纪三四十年代已经改嫁了,其丈夫是宋庆龄的秘书,这是一段婚姻已经被公布,宋曾向组织申请结婚,但是组织考虑其政治影响不同意结婚,但是可以同居,并在组织内公开以夫妻身份生活,这个相关文件能查到,这段婚姻是可查的。另外还有几段恋情盛传的,其中和其警卫员的恋情大众的认可比较高。"], "bs_rank_pos": 2}, {"title": "宋庆龄第几任妻子_百度知道", "segmented_title": ["宋庆龄", "第", "几", "任", "妻子", "_", "百度", "知道"], "segmented_paragraphs": [["孙中山", "是", "有", "个", "儿子", "的", "叫", "孙科", ",", "当", "过", "行政", "院长", "宋庆龄", "至少", "是", "第", "二", "任", "了"]], "paragraphs": ["孙中山是有个儿子的 叫孙科,当过行政院长 宋庆龄至少是第二任了"], "bs_rank_pos": 3}, {"title": "宋庆龄的第二丈夫是谁_百度知道", "segmented_title": ["宋庆龄", "的", "第", "二", "丈夫", "是", "谁", "_", "百度", "知道"], "segmented_paragraphs": [["宋庆龄", "只有", "一", "个", "丈夫", ",", "就是", "孙中山", "。", "宋庆龄", "在", "孙中山", "死后", ",", "作为", "国", "人", "敬仰", "的", "“", "国母", "”", ",", "不仅", "竭力", "维护", "丈夫", "孙中山", "的", "光辉", "形象", ",", "而且", "为", "中国", "革命", "事业", "不懈努力", "着", "。", "一直", "到", "她", "逝世", ",", "她", "都", "在", "为", "人民", "着", "想", "。"], ["宋庆龄", "的", "生活", "秘书", "是", "个", "很", "能", "抓住", "女人", "心理", "的", "男子", ",", "年轻", "而", "健谈", 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"”", "后", ",", "宋庆龄", "勇敢", "地", "递", "上", "了", "结婚", "申请", "。", "一"]], "paragraphs": ["宋庆龄只有一个丈夫,就是孙中山。 宋庆龄在孙中山死后,作为国人敬仰的“国母”,不仅竭力维护丈夫孙中山的光辉形象,而且为中国革命事业不懈努力着。 一直到她逝世,她都在为人民着想。", "宋庆龄的生活秘书是个很能抓住女人心理的男子,年轻而健谈,对宋庆龄更是无微不至的照顾,宋庆龄也非常喜欢这个年纪可以做自己儿子的秘书。只是宋庆龄心无芥蒂,而生活秘书或许是因为宋庆龄的人格魅力,或许是因为真心爱这个老人,或许是为了所谓的遗产和地位,勇敢地向宋庆龄提出了结婚的要求,并且承诺要用自己全部的爱来照顾宋庆龄的晚年。 当时的宋庆龄也经历了很大的思想斗争,一来生活秘书跟自己年龄差距过大,会是真心对自己吗?二来自己和离婚的生活秘书结婚,会带来怎么样的轩然大波呢?最重要的是自己所处的政治地位,再婚会有阻力吗?生活秘书对宋庆龄说,结婚是两个人的事情,两个人心里有对方,其他都不是困难。当生活秘书做通两个女儿的工作,两个女儿改口叫宋庆龄“妈妈”后,宋庆龄勇敢地递上了结婚申请。 一"], "bs_rank_pos": 4}], "question": "宋庆龄第二任丈夫是谁", "segmented_question": ["宋庆龄", "第", "二", "任", "丈夫", "是", "谁"], "question_type": "ENTITY", "fact_or_opinion": "FACT", "question_id": 221574}
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积友之家"}, {"bs_rank_pos": 9, "paragraphs": ["美国航空编辑 锁定 ", "美国航空(American Airlines)作为寰宇一家的创始成员之一,是世界最大的航空公司。联合旗下附属美鹰航空和美国连接,美国航空遍布260余个通航城市——包括美国本土150个城市及40个国家的城市。", " 美国航空致力提供卓越的全球飞行体验,公司共飞往50多个国家和地区的260多个城市。美国航空的机队由近900架飞机组成,每日从芝加哥、达拉斯、沃斯堡、洛杉矶、迈阿密和纽约五大枢纽起飞的航班数量超过3,500个班次。", "公司名称 美国航空  外文名称 American Airlines  总部地点 美国  成立时间 1930年  经营范围 航空服务 ", " 公司性质 航空运输  公司口号 The New American Is Arriving  员工数 38.03万  CEO 吉拉尔德·阿佩  每日航班数 3,500班  航空联盟 寰宇一家 ", " 1 机上服务  ▪ 头等舱  ▪ 商务舱  ▪ 经济舱  2 主要基地  3 发展历程  4 资源整合  5 中国业务  6 机队 ", " 7 意外事件  ▪ 再爆丑闻  8 合作经营  9 美航新形象 ", "这里是高空中宁静的港湾,您可以享受前所未有的私人空间,周到体贴的服务,宽敞的工作、放松和休息的环境。豪华水准被提升至全新的境界。Flagship Suite®旗舰套间内的旋转椅是众多让我们引以为豪的创新项目之一,这使您与同事或客户间面对面的会谈成为现实,", " 每个座椅都配备大型电视屏幕可收看10个频道多达20部电影作品,这确保您在旅途中得到彻底的放松。旗舰套间的座椅还可伸展成6'6\"平躺式床铺,为您提供舒适的睡眠环境。", "这一工作,放松及休息的精英专用区还提供以下便利:", "我们为顾客打造的商务舱,拥有更多私人空间。商务舱宽敞怡人,办公、娱乐、休闲设施一应俱全。", " 乘坐美国航空提供的全新宽敞的波音777商务舱航班,您的飞行,您的时间,完全由您做主。我们将为您提供办公所需的一切工具,提高工作效率,完全不受干扰;使您得到放松、休息,安心睡眠,或尽情娱乐。看一部电影、或玩有挑战性的电子游戏、或追踪世界新闻。我们提供的空间、特色服务以及娱乐设施都将帮助打造一个属于您的私人空间。在您进入全新升级的商务舱的那一刻,您就能体会到我们为顾客所做的一切努力和改变。", "飞行旅途中,您可享受随心所欲的服务。乘坐全新商务舱的航班,自由支配您的时间。[1] ", "便携式旅行套装  个人便携式电脑电源插座  6个方向可调节的皮质座位头枕  60英寸倾斜度  人性化座位控制  腿部活动的额外空间  先进的座椅腰部支撑  完全的小腿下支撑  舒适的皮革及棉布家具装饰  个人卫星接收电话  个人视频系统,可收看10个电视频道的节目(波音777)  BoseQuietComfort®有源消噪耳机 ", " 搭乘美国航空的商务舱航班,集办公、休闲及娱乐为一体。无论从哪一方面考虑,您都可享受舒适的个人空间,使时间过得更有意义。", "我们还提供一系列具有竞争力的商务舱机票价格,满足您的商旅要求。", "无论您需要什么,商务舱都可满足。", "拥有美国引以为傲的飞行体验 —", " 优质的服务,世界级的娱乐设施,以及有求必应的服务态度。这也是美国航空的独到之处。提供更多超值服务,使每个人都能够享受,我们将经济舱的舒适度提高至一个全新的水平。", "倚靠在宽敞的座椅上,享受机上至尊的娱乐体验。接通电源,了解经济动态,收听CD,观看DVD,或通过便携式电脑上网,或为手机充电。", "搭乘我们的经济舱航班,您会发现您的选择的确物有所值。", "奥黑尔国际机场", "达拉斯-沃思堡国际机场", "洛杉矶国际机场", "迈阿密国际机场", "纽约肯尼迪国际机场", "全美航空公司成立于1939年,隶属于全美航空集团,总部位于美国亚利桑那州坦佩。全美航空曾是美国的第五大航空公司,航线包含北美洲、中美洲、加勒比海与欧洲等地区全球240多个目的地。", "2005年5月,曾宣布以15亿美元的代价与美西航空母公司合幷。", "2013年2月,全美航空集团与美国航空原母公司AMR公司宣布合幷,联手组建全球最大的航空公司。", "2013年2月14日,美国航空原母公司AMR公司与全美航空原母公司全美航空集团宣布合幷,幷获得双方董事局批准,联手组建全球航空业巨无霸。美国航空母公司AMR的债权人将会持有新公司的72%股权,而全美航空的股东将持有其余的28%股权。", "2013年11月27日,合幷案获得美国司法部批准。", "2013年12月9日,美国航空母公司AMR和全美航空集团正式宣布,两家公司之间的合幷交易已经完成,从而创造出全球最大的航空运营商。", "2015年10月16日上午10:36分,全美航空公司1939号航班从费城起飞,经停夏洛特市、凤凰城飞往旧金山,这是有着76年历史的全美航空公司运营的最后一架航班。当晚21:55分,该航班将从旧金山起飞幷于17日晨返回费城,全美航空公司也将随之宣告退出历史舞台。", "从2015年10月17日开始,全美航空公司(US Airways Group)与美国航空公司(American Airlines)联手打造的全球最大的航空公司将正式运营。当天,美国一些国际机场的航站楼也将全美航空公司的名字从列表中删除,取而代之的是美国航空公司。", "未来一段时间,带有全美航空公司标识的飞机还将继续运营。据称,直到2016年底,全美航空公司的飞机才能全部“改头换面”,换成美国航空公司的标识。[2] ", "美国老鹰航空(American Eagel Airlines)是美国航空的营运伙伴,基地也设于德州沃斯堡。美国老鹰航空也是AMR公司下属的航空公司。", " 寰宇一家 - 美国航空 ", "美国航空公司是由一个大约82家小航空公司组合通过一系列整合和改革发展而来的。最初,许多飞机都可以自由使用American Airways这个名字作为共有品牌。1934年,美国航空公司陷入财政危机,在E.L. Cord的领导下,将公司更名为“American Airlines”。早期的时候,公司的总部是位于伊利诺斯州的芝加哥中途机场。在这段时期美航的一个创新就是在飞机上使用了空乘人员。", "直到20世纪50年代后期美国航空公司的主要航线还是从纽约和芝加哥经由达拉斯到洛杉矶。早期美国航空公司总裁之一赛勒斯·罗利特·史密斯与唐纳德·道格拉斯密切合作后开发了道格拉斯DC—3飞机,美国航空公司在1936年开始将这种机型投入飞行。对于道格拉斯DC—3,美航给它取了一个航海用的名称,称这种飞机为“旗舰” (Flagships),并成立了“海军将官俱乐部” (Admirals Club),这便是早期的贵宾俱乐部,后来成为世界上第一个航空公司贵宾室。当道格拉斯DC—3飞机停留在机场时,会有一面带有4颗星的“旗舰三角旗” (admiral's pennant) 飘扬在驾驶舱窗外,在那时这就是美利坚航空公司最著名的图像之一。", "美航是研制DC-3飞机的主要推动者。在美航机队中DC-3也被称作“旗舰”。", "体验未来旅行体验,当提升舰队的新飞机上给人们带来一个更现代的,舒适的和连接的经验。", "美国航空很高兴地宣布,它们在更新现有的飞机,美国航空与波音767-300型和波音777-200改造计划开始于2014年。目前正在为您提供767-300更新,增强功能,包括28个新的完全在于所有的走廊通道以及25客舱座位平面的商务舱座位。[1] ", "当我们在改变我们的舰队,我们正在努力提高您的旅行体验。", "作为历史上最大的飞机订单的一部分,我们以一个新飞机一周的平均交货,来更新我们现有的机队,给你带来一个更现代的,舒适的和连接的经验。", "美国航空目前开通由芝加哥奥黑尔德国际机场直飞", "中国北京首都国际机场和上海浦东国际机场的两条直飞航线,以及洛杉矶国际机场直飞上海浦东国际机场的航线。", "航班号分别为:"], "segmented_title": ["美国", "航空", "_", "百度百科"], "segmented_paragraphs": [["美国", "航空", "编辑", "锁定"], ["美国", "航空", "(", "American", "Airlines", ")", "作为", "寰宇一家", "的", "创始", "成员", "之", "一", 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", " 先把QQ的密码改好,然后再设置微信的独立密码。 如果还不行的话,找一下QQ客服吧。 (如果该回答对您有帮助的话,请记得点击“有用”哦,谢谢!)", " 这个不是通用的。微信可以单独设置,是你注册时的密码 ", "上网帮助相关知识 电脑网络技术 电脑网络", "QQ登陆后桌面右下角不见企鹅图标  什么是ID号码?怎么注册啊? 开一般户需要什么资料  符号“~”应该在什么情况下使用  怎么查看QQ的密码? 18P2P怎么进步去了? 以前微博都是按照时间先后顺序显示的,现在... 微博怎么莫名其妙自动关注一些用户啊? 如何注销微博账号? 办理港澳通行证需要多长时间?", "办理港澳通行证需要多长时间? 邮件中的抄送是什么意思? ‘宽带连接’错误678,能优化吗? xp如何查看wifi密码 没有无线网络配... wifi密码没热点可以破解吗 wifi密... 联通的光猫单连能上网连上路由器就不能上网... 出境wifi租赁哪家好? 我用的完美星空源动力盒子,怎么查询IP地... 家里用wifi无线网对宝宝有害吗  满月了可以经常上网吗  我家装无线Wlam网络,我们经常连接使用... 联想一体机怎样连WIFI ", "确定举报此问题", "报告,这不是个问题", " 这不是个问题  这个问题分类似乎错了  这个不是我熟悉的地区 "], "segmented_title": ["【", "qq", "密码", "大全", "】", "微信", "密码", "和", "qq", "密码", "是", "通用", "的", "么", "为什么", "我", "改", "的", "微信", "密码"], "segmented_paragraphs": [["怎么样", "让", "心静", "下来", "?", "求解", "?"], ["先", "把", "QQ", "的", "密码", "改", "好", ",", "然后", "再", "设置", "微信", "的", "独立", "密码", "。", "如果", "还", "不行", "的", "话", ",", "找", "一", "下", "QQ", "客服", "吧", "。", "(", "如果", "该", "回答", "对", "您", "有", "帮助", "的", "话", ",", "请", 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"question_type": "DESCRIPTION", "fact_or_opinion": "FACT", "question_id": 221579}
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"减速", "、", "停车", "、", "避让", "行", "人", "的", ";", "(", "八", ")", "驾驶", "机动车", "违反", "禁令", "标志", "、", "禁止", "标线", "指示", "的", ";"]], "paragraphs": ["机动车违反禁止标线指示的处罚是扣驾驶证3分,罚款20-200元。具体规定如下:根据《道路交通违法记分分值》规定机动车驾驶人有下列违法行为之一,一次记3分:(一)驾驶营运客车(不包括公共汽车)、校车以外的载客汽车载人超过核定人数未达20%的;(二)驾驶中型以上载客载货汽车、危险物品运输车辆在高速公路、城市快速路以外的道路上行驶或者驾驶其他机动车行驶超过规定时速未达20%的;(三)驾驶货车载物超过核定载质量未达30%的;(四)驾驶机动车在高速公路上行驶低于规定最低时速的;(五)驾驶禁止驶入高速公路的机动车驶入高速公路的;(六)驾驶机动车在高速公路或者城市快速路上不按规定车道行驶的;(七)驾驶机动车行经人行横道,不按规定减速、停车、避让行人的;(八)驾驶机动车违反", "一般是两百。 这个根据每个区域处罚额度有差异,不过一般都是两百。 另外附上相关道路交通安全法:第九十条 机动车驾驶人违反道路交通安全法律、法规关于道路通行规定的,处警告或者二十元以上二百元以下罚款。本法另有规定的,依照规定处罚。 希望对你有帮助,谢谢~", "违反禁令标志、标线,当场处罚一百元记三分,如果是电子警察拍的,在验车时处罚不记分。", "机动车违反禁止标线指示的处罚是扣驾驶证3分,罚款20-200元。 具体规定如下: 根据《道路交通违法记分分值》规定 机动车驾驶人有下列违法行为之一,一次记3分: (一)驾驶营运客车(不包括公共汽车)、校车以外的载客汽车载人超过核定人数未达20%的; (二)驾驶中型以上载客载货汽车、危险物品运输车辆在高速公路、城市快速路以外的道路上行驶或者驾驶其他机动车行驶超过规定时速未达20%的; (三)驾驶货车载物超过核定载质量未达30%的; (四)驾驶机动车在高速公路上行驶低于规定最低时速的; (五)驾驶禁止驶入高速公路的机动车驶入高速公路的; (六)驾驶机动车在高速公路或者城市快速路上不按规定车道行驶的; (七)驾驶机动车行经人行横道,不按规定减速、停车、避让行人的; (八)驾驶机动车违反禁令标志、禁止 标线指示的;"], "bs_rank_pos": 0}, {"title": "机动车违反禁止标线指示要扣几分和罚款多少_百度知道", "segmented_title": ["机动车", "违反", "禁止", "标线", "指示", "要", "扣", "几", "分", "和", "罚款", "多少", "_", "百度", "知道"], "segmented_paragraphs": [["驾驶", "机动车", "违反", "禁止", "标线", "的", ";", "扣", "3", "分", ",", "罚款", "200", "元", "。", "根据", "《", "机动车驾驶证", "申领", "和", "使用", "规定", "》", "(", "公安部", "令", "第", "123", "号", ")", "规定", ":", "机动车", "违反", "禁止", "标线", "指示", "的", "有", "以下", "几种情况", "。", "1", "、", "行驶", "过程", "中", "压", "线", "、", "越", "线", "、", "单", "、", "双黄", "实线", "的", "。", "2", "、", "在", "禁止", "停车", "路段", "、", "临时", "或", "长时间", "停车", "的", "。", "3", "、", "在", "不", "允许", "调头", "的", "路段", "掉头", ",", "禁止", "左转弯", "的", "路口", "左转", ",", "都", "属于", "违反", "禁止", "标线", "指示", "。", "禁止", "标线", "即", "地上", "的", "黄", "实线", "和", "禁止", "停车", "标示", ",", "压", "上去", "即", "违反", "禁止", "标线", "指示", "。", "4", "、", "交叉路口", "不", "按", "路面", "导向", "标线", "行驶", "在", "相应", "的", "车道", ",", "同样", "属于", "违反", "禁止", "标线", "指示", "。", "《", "道路交通安全法", "》", "第", "九", "十", "条", "机动车", "驾驶", "人", "违反", "道路交通安全", "法律", "、", "法规", "关于", "道路", "通行", "规定", "的", ",", "处", "警告", "或者", "二", "十", "元", "以上", "二", "百", "元", "以下", "罚款", "。", "本法", "另", "有", "规定", "的", ",", "依照规定", "处罚", "。"], ["一般", "都是", "扣", "3", "分", "罚", "200", "元", "。", "有用", "记得", "采纳", "。"]], "paragraphs": ["驾驶机动车违反禁止标线的;扣3分,罚款200元。 根据《机动车驾驶证申领和使用规定》(公安部令第123号)规定: 机动车违反禁止标线指示的有以下几种情况。 1、行驶过程中压线、越线、单、双黄实线的。 2、在禁止停车路段、临时或长时间停车的。 3、在不允许调头的路段掉头,禁止左转弯的路口左转,都属于违反禁止标线指示。禁止标线即地上的黄实线和禁止停车标示,压上去即违反禁止标线指示。 4、交叉路口不按路面导向标线行驶在相应的车道,同样属于违反禁止标线指示。 《道路交通安全法》 第九十条机动车驾驶人违反道路交通安全法律、法规关于道路通行规定的,处警告或者二十元以上二百元以下罚款。本法另有规定的,依照规定处罚。", "一般都是扣3分罚200元。 有用记得采纳。"], "bs_rank_pos": 1}, {"title": "2014年的机动车违反禁止标线指示的扣几分_百度知道", "segmented_title": ["2014", "年", "的", "机动车", "违反", "禁止", "标线", "指示", "的", "扣", "几", "分", "_", "百度", "知道"], "segmented_paragraphs": [["违法行为", ":", "违反", "禁令", "标志", "、", "禁止", "标线", "指示", "的", "。", "　　", "2013", "年", "新", "处罚", "标准", ":", "罚款", "100", "元", ",", "扣", "3", "分", "。", "　　", "原", "处罚", "标准", ":", "罚款", "100", "元", ",", "扣", "2", "分", "。"]], "paragraphs": ["违法行为:违反禁令标志、禁止标线指示的。 　　2013年新处罚标准:罚款100元,扣3分。 　　原处罚标准:罚款100元,扣2分。"], "bs_rank_pos": 2}, {"title": "机动车违反禁止标线指示,要扣分和罚钱吗?是多少的_百度知道", "segmented_title": ["机动车", "违反", "禁止", "标线", "指示", ",", "要", "扣分", "和", "罚", "钱", "吗", "?", "是", "多少", "的", "_", "百度", "知道"], "segmented_paragraphs": [["违法", "记分", ":", "3", "分", "　　", "罚款", "金额", ":", "￥", "200", "　　", "违法", "暂扣", "主要", "表现", "有", ":", "　　", "1", "、", "单", "、", "双黄", "实线", "都", "属于", "禁止", "标线", ",", "行驶", "过程", "中", "压", "线", "、", "越", "线", "都", "属于", "违法", "。", "　　", "2", "、", "交叉路口", "不", "按", "导向", "标线", "行驶", "在", "相应", "的", "车道", ",", "同样", "属于", "上述", "条款", "的", "内容", "。", "　　", "3", "、", "违反", "临时", "或", "长期", "停车", "。", "　　", "4", "、", "不允许", "调头", "的", "路段", "掉头", ",", "禁止", "左转弯", "的", ",", "都", "属于", "违反", "禁止", "标线", "指示", "。", "禁止", "标线", "即", "地上", "的", "黄", "实线", "(", "路", "中间", "分隔", "带", "禁止", "越过", ")", "和", "黄色", "大", "叉", "(", 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"　　", "2", "、", "交叉路口", "不", "按", "导向", "标线", "行驶", "在", "相应", "的", "车道", ",", "同样", "属于", "上述", "条款", "的", "内容", "。", "　　", "3", "、", "违反", "临时", "或", "长期", "停车", "。", "　　", "4", "、", "不允许", "调头", "的", "路段", "掉头", ",", "禁止", "左转弯", "的", ",", "都", "属于", "违反", "禁止", "标线", "指示", "。", "禁止", "标线", "即", "地上", "的", "黄", "实线", "(", "路", "中间", "分隔", "带", "禁止", "越过", ")", "和", "黄色", "大", "叉", "(", "代表", "禁止", "停车", ")", ",", "压", "上去", "即", "违反", "禁止", "标线", "指示", "。"]], "paragraphs": ["违法记分:3分 　　罚款金额:￥200 　　违法暂扣主要表现有: 　　1、单、双黄实线都属于禁止标线,行驶过程中压线、越线都属于违法。 　　2、交叉路口不按导向标线行驶在相应的车道,同样属于上述条款的内容。 　　3、违反临时或长期停车。 　　4、不允许调头的路段掉头,禁止左转弯的,都属于违反禁止标线指示。禁止标线即地上的黄实线(路中间分隔带禁止越过)和黄色大叉(代表禁止停车),压上去即违反禁止标线指示。", "主要是禁止跨越对向车道分界线(包括双黄实线,黄色虚实线,黄色单实线)和网状线。1、双黄实线,黄色虚实线,黄色单实线用于分割对向行驶交通流,禁止双方向或一个方向车辆越线或压线行驶,很多车主在红绿灯交叉路口转弯角度过小,压到了黄线。2、网状线,禁止任何原因车辆在该区域停车。如果有车主在该区域停车,不管任何原因都要被处罚。 机动车违反禁止标线指示的都要罚款100元的。 希望对你能有所帮助。", "违法记分:3分 　　罚款金额:￥200 　　违法暂扣主要表现有: 　　1、单、双黄实线都属于禁止标线,行驶过程中压线、越线都属于违法。 　　2、交叉路口不按导向标线行驶在相应的车道,同样属于上述条款的内容。 　　3、违反临时或长期停车。 　　4、不允许调头的路段掉头,禁止左转弯的,都属于违反禁止标线指示。禁止标线即地上的黄实线(路中间分隔带禁止越过)和黄色大叉(代表禁止停车),压上去即违反禁止标线指示。"], "bs_rank_pos": 3}], "question": "机动车违反禁止标线指示的扣几分", "segmented_question": ["机动车", "违反", "禁止", "标线", "指示", "的", "扣", "几", "分"], "question_type": "ENTITY", "fact_or_opinion": "FACT", "question_id": 221580}
+{"documents": [{"bs_rank_pos": 0, "paragraphs": [" 凯乐石怎么样?探路者与凯乐石这两个户外品牌,对于常年混迹于户外的朋友们来说都不陌生。探路者和凯乐石哪个好,所谓仁者见仁,智者见智,探路者凯乐石两个品牌都有自己的品牌特色,但是想要了解两个品牌究竟哪个比较好,那么我们就要通过对比。以下内容将会为大家介绍凯乐石和探路者哪个好、凯乐石是哪个国家的品牌以及凯乐石官网等相关信息。 ", " 冈仁波齐神山(KAILASH)海拔21837英尺,坐落于中国西藏境内,是延绵千里的冈底斯山脉的主峰,山峰终年冰雪覆盖。他是一座精神和信仰之山,是不少于四种宗教(藏传佛教、印度教、苯教以及古耆那教)教义里的世界中心,也是亚洲四条大河(布拉马普特拉河, 印度河, 卡纳利河, 萨特莱杰河)的发源地。KAILAS(凯乐石)的品牌名称即源于此。古往今来,每年都有许多的朝圣者来到这里转山。不过转山的旅途非常恶劣,需要经过崎岖的道路和面对高海拔与恶劣天气的挑战。尽管如此,这些障碍并没有令朝圣者却步,因为总有一股强大的动力像一只无形的手在推着他们前进,那只手就是人类最原始的本能-追求精神的自由,对神圣的敬仰和对生命终极意义的探索。 ", " KAILAS作为专业户外品牌,怀着同样的理念和激情,去帮助登山及探险人士完成心中的梦想。我们全力以赴去做最优秀的产品,让户外人士能够充分实现对自我极限的发现和挑战。我们对攀登产品的理解源自于我们自己本身就是这项运动的专业人士,我们对产品的专注不仅仅为了给专业人士提供必要的保护,也包括一切热爱户外的、拥有追求自由和探索未知世界梦想的人士 。 ", " 目前凯乐石的产品线主要有:攀岩、登山、救援用器械;8000米用高山帐及一般探险露营用帐篷;重型徒步背包、专业攀登背包、及日常户外用背包等专业户外运动装备。这一切都只是为了一个目标,那就是协助您到达您要去的地方和找到您心中的那座金字塔。 ", " 在KAILAS,每一个人都怀着一种心态:“努力工作,拼命玩,活得痛快”,这种心态影响了以前、现在和未来的KAILAS人。KAILAS产品的专业度首屈一指,归功于我们大力投入于产品的设计和研发以及不断地得到世界最顶尖的供应商的协助,以此生产出创新、高质量的产品给攀岩、登山及探险人士使用。 ", " KAILAS成为国际攀岩联合会(IFSC)唯一官方器材供应商、美国攀岩协会及攀岩国家队(USA CLIMBING)唯一官方器材供应商、澳洲攀岩国家队和青少年攀岩国家队(SCA)官方合作伙伴,一举奠定了KAILAS品牌在国际上的地位。我们产品的专业度不单得到了国内外一些官方机构与专业人士的认可,而且斩获多项产品专利,荣膺许多世界级的户外行业重要奖项,这一切都全归功于每一位KAILAS员工付出的辛勤汗水。 ", " 除产品之外,KAILAS一直致力于推动民间登山与探险运动的发展,实施了包括“未登峰计划”“寻岩中国”“KAILAS户外教练成长计划”、“KAILAS登山探险学校”等公益项目;又与中国登山协会合作开展“攀岩希望之星”项目,推动了青少年攀岩运动发展,为中国登山及户外运动培养重要的后备人才。而且KAILAS还赞助了包括登山、攀岩、攀冰、越野挑战赛等多项户外专业赛事,为登山及户外运动在中国的普及与发展提供有利的支持。 ", " KAILAS是一个带着梦想去成长的品牌,所以我们全力以赴,务必做到最好。在探索未知世界的道路上,与您同行,以求极致。 ", " 2 凯乐石和探路者哪个好 ", " 创立时间:1999年 ", " 发源地:北京市 ", " 认缴注册资本:51346万元 ", " 企业名称:探路者控股集团股份有限公司 ", " 品牌介绍:专注于户外用品市场,大型户外装备提供商,上市公司,国内户外用品业规模较大/创新能力强的企业。 ", " 产品科技:探路者衣服的的电子恒温加热系统做的非常出色。目前公司已拥有自主的研发中心,占地150平米的户外产品测试实验室也在同期落成。2008年成为“北京2008年奥运会特许供应商”、2009年签约成为“中国南(北)极考察队独家专用产品”并在2013年与中国极地研究中心签署战略协议,双方将进一步加强针对极地环境的户外装备科技研发。 ", " 创立时间:2007年 ", " 发源地:广东省广州市 ", " 认缴注册资本:3000万元 ", " 企业名称:湛江市玛雅旅游用品有限公司 ", " 品牌介绍:凯乐石KAILAS,专业户外品牌,美国国家攀岩队官方安全器材供应商,集户外产品设计开发、生产、销售于一体的专业化大型企业。 ", " 产品科技:凯乐石一直坚持“轻量”这个重点。2016年1月24日,德国慕尼黑ISPO大奖(ISPOAWARD)揭晓。攀登领导品牌凯乐石选送的五款专业产品凭借其创新设计获得国际专业评审们的一致肯定,摘取五项德国ISPO产品大奖!其中,\"EDGE(刃脊)35L技术攀登背包\"荣膺亚洲年度最佳产品。 ", " 十大户外用品-户外装备-野营用品品牌榜中榜,户外用品什么牌子好(2017)", " 创立于1938年,户外服装品牌的先驱,以生产雨衣/雨帽起家,全球顶级的户外用品生产制造商,哥伦比亚运动服装公司", " 创于1966年美国,全球家喻户晓的户外用品品牌,著名的专业登山和徒步装备的制造商,威富服饰(中国)有限公司", " 成立于1999年,致力于为户外活动提供安全舒适的户外装备,国内户外用品业规模较大/创新能力强的公司,探路者控股集团股份有限公司", " 创立于1989年,北美乃至全球领导型的户外品牌,加拿大的顶级户外品牌,亚玛芬体育用品贸易(上海)有限公司)", " 创立于1984年德国,欧洲著名户外用品品牌,世界顶级户外品牌,中国总代理:上海联亚商业有限公司", " 于1973年奥地利,全球知名专业户外运动品牌,奥地利山地救援服装提供商,冲锋衣十大品牌,南京边城体育用品股份有限公司", " 始于1971年美国,世界领先的户外用品品牌,集运动/高科技于一身的革命性户外运动服装装备品牌,中国代理:北京锐极泷峰国际贸易有限公司", " 著名户外运动休闲品牌,舒适感与时尚感的结合,集研发设计/生产制造/营销及出口为一体的服饰企业,广东骆驼服饰有限公司", " 集户外产品设计开发/生产/销售于一体的专业化大型企业,知名户外品牌,湛江市玛雅旅游用品有限公司", " 国际露营联合会中国区官方合作品牌,国内专业户外装备品牌,休闲户外运动发展先锋品牌,浙江牧高笛户外用品有限公司", " 知名(著名)户外用品品牌,知名户外装备品牌,户外用品什么牌子好(2017)", " 以上数据名单由几十项数据统计计算系统自动生成,排序不分先后,仅提供给您作参考。", " 如有侵犯您的版权,请联系我们,我们将及时删除。 ", "行业精彩推荐 月关注排行榜 最新加入文章 历史关注排行"], "segmented_title": ["【", "凯乐石", "】", "凯乐石", "和", "探路者", 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["柠檬"], ["蓝莓"], ["雪莲果"], ["荸荠"], ["无花果"], ["甘蔗"], ["梨"], ["柚子"], ["桑葚", "汁"], ["枣", "(", "干", ")"], ["葡萄"], ["苹果"], ["木瓜"], ["猕猴桃"], ["香蕉"], ["桃"], ["杨桃"], ["椰汁"], ["樱桃"], ["藕"], ["洋葱"], ["西葫芦"], ["丝瓜"], ["淮山药"], ["柿子椒"], ["西红柿"], ["甜玉米"], ["胡萝卜"], ["冬瓜"], ["茄子"], ["南瓜"], ["百合"], ["白萝卜"], ["土豆"], ["黄秋葵"], ["红薯"], ["藕带"], ["石花菜"], ["珊瑚", "藻"], ["裙带菜"], ["紫晶藻"], ["黄瓜"], ["海带"], ["芹菜"], ["空心菜"], ["银耳"], ["西兰花"], ["青稞", "面"], ["黄", "米粉"], ["糜子", "面"], ["黍子", "面"], ["黑", "小米"], ["麦仁"]], "paragraphs": ["不能吃富含嘌呤的食物有:各种动物内脏、肉类汤汁、各种肉食、骨髓、海鱼(特别是风尾鱼、沙丁鱼等)、虾、蟹、海菜;各种豆类(特别是豌豆)、花生米、菠菜、菜花、蘑菇、糙米、粗面粉、全麦片等均应禁止食用。还禁止吃动物油。 1多吃蔬菜,多吃高钾质食物,如香蕉、西兰花、西芹 钾质可减少尿酸沉淀,有助将尿酸排出体外。 2多吃碳水化合物有益于尿酸的排出,痛风病人可食用富含碳水化合物的米饭、馒头、面食等等食物。 3 蛋白质可以根据体重,按照比例来摄取,1公斤体重应摄取0、8克至1克的蛋白质,并以牛奶、鸡蛋为主。如果是瘦肉、鸡鸭肉等,应该煮沸后去汤食用,避免吃炖肉或卤肉。 4 少吃脂肪,因为脂肪可减少尿酸排出。痛风并发高脂血症者,脂肪摄取应控制在总热量的20%至25%以内。 5 适量饮水,每天应喝2000ml至3000ml的水,有益于促", "来自网上医生的答案,请参考: 　　少吃动物内脏(肝,肾,骨髓)及沙丁鱼,蟹,虾,菠菜,大肠,等; 　　多吃蔬菜,海带等 　　要低脂饮食,少饮白酒,忌饮啤酒,多饮水", "适宜食材", "1、多喝水2、多吃含高蛋白的食物3、多吃含铁、硒、锌等矿物质的食物4、适量补充维生素A、C、E", "雪梨", "杏", "西瓜皮", "人参果", "橙", "草莓", "火龙果", "苹果(", "血橙", "西瓜", "柠檬", "蓝莓", "雪莲果", "荸荠", "无花果", "甘蔗", "梨", "柚子", "桑葚汁", "枣(干)", "葡萄", "苹果", "木瓜", "猕猴桃", "香蕉", "桃", "杨桃", "椰汁", "樱桃", "藕", "洋葱", "西葫芦", "丝瓜", "淮山药", "柿子椒", "西红柿", "甜玉米", "胡萝卜", "冬瓜", "茄子", "南瓜", "百合", "白萝卜", "土豆", "黄秋葵", "红薯", "藕带", "石花菜", "珊瑚藻", "裙带菜", "紫晶藻", "黄瓜", "海带", "芹菜", "空心菜", "银耳", "西兰花", "青稞面", "黄米粉", "糜子面", "黍子面", "黑小米", "麦仁"], "bs_rank_pos": 0}, {"title": "痛风应该吃什么水果蔬菜?_百度知道", "segmented_title": ["痛风", "应该", "吃什么", "水果蔬菜", "?", "_", "百度", "知道"], "segmented_paragraphs": [["痛风", "的", "注意事项", "痛风", "病人", "除", "在", "医生", "指导", "下", "应用", "适当", "药物", "外", ",", "在", "日常", "生活", "中", "还", "应", "注意", "以下几点", ":", "(", "1", ")", "饮食", "方面", ":", "①", "猪", "、", "牛", "、", "羊肉", "、", "火腿", "、", "香肠", "、", "鸡", "、", "鸭", "、", "鹅", "、", "兔", "以及", "各种", "动物", "内脏", "(", "肝", "、", "肾", "、", "心", "、", "脑", ")", "、", "骨髓", "等", "含", "嘌呤", "量", "高", ",", "应", "尽量", "不", "吃", ";", "鱼虾", "类", "、", "菠菜", "、", "豆类", "、", "蘑菇", "、", "香菇", "、", "香蕈", "、", "花生", "等", "也有", "一定", "量", "嘌呤", ",", "要", "少", "吃", ";", "大多数", "蔬菜", "、", "各种", "水果", "、", "牛奶", "和", "奶制品", "、", "鸡蛋", "、", "米饭", "、", "糖", "等", "可以", "吃", "。", "②", "多", "饮水", ",", "要", "使", "每日", "尿", "量", "保持", "在", "2000", "ml", "以上", ",", "因", "尿路结石", "的", "发生", "和", "小便", "尿酸", "浓度", "及", "小便", "的", "酸碱度", "有关", ",", "必要时", "可", "服用", "碱性", "药物", ",", "以", "预防", "尿路结石", "的", "发生", "。", "③", "避免", "暴饮暴食", "或", "饥饿", "。", "④", "节制", "烟酒", ",", "尤其", "不能", "酗酒", "。", "⑤", "不喝", "浓茶", "、", "咖啡", "等", "饮料", "。", "(", "2", ")", "妥善处理", "诱发因素", ",", "禁用", "或", "少", "用", "影响", "尿酸", "排泄", "的", "药物", ":", "如", "青霉素", "、", "四环素", "、", "大", "剂量", "噻", "嗪", "类", "及", "氨苯喋啶", "等", "利尿剂", "、", "维生素B1", "和", "B2", "、", "胰岛素", "及", "小", "剂量", "阿司匹林", "(", "每天", "小于", "2g", ")", "等"]], "paragraphs": ["痛风的注意事项 痛风病人除在医生指导下应用适当药物外,在日常生活中还应注意以下几点: (1)饮食方面:①猪、牛、羊肉、火腿、香肠、鸡、鸭、鹅、兔以及各种动物内脏(肝、肾、心、脑)、骨髓等含嘌呤量高,应尽量不吃;鱼虾类、菠菜、豆类、蘑菇、香菇、香蕈、花生等也有一定量嘌呤,要少吃;大多数蔬菜、各种水果、牛奶和奶制品、鸡蛋、米饭、糖等可以吃。②多饮水,要使每日尿量保持在2000ml以上,因尿路结石的发生和小便尿酸浓度及小便的酸碱度有关,必要时可服用碱性药物,以预防尿路结石的发生。③避免暴饮暴食或饥饿。④节制烟酒,尤其不能酗酒。⑤不喝浓茶、咖啡等饮料。 (2)妥善处理诱发因素,禁用或少用影响尿酸排泄的药物:如青霉素、四环素、大剂量噻嗪类及氨苯喋啶等利尿剂、维生素B1和B2、胰岛素及小剂量阿司匹林(每天小于2g)等"], "bs_rank_pos": 1}, {"title": "痛风不能吃什么食物表_百度知道", "segmented_title": ["痛风", "不能", "吃什么", "食物", "表", "_", "百度", "知道"], "segmented_paragraphs": [["(", "1", ")", "无", "嘌呤", "食物", "嘌呤", "含量", "很少", "(", "小于", "20mg", "/", "100", "g", ")", "或", "不含", "嘌呤", "的", "食物", "。", "谷类", ":", "大米", "、", "精", "白米", "、", "小米", "、", "玉米", "、", "精", "白", "面", "、", "富强粉", "、", "精", "白面包", "、", "馒头", "、", "通心粉", "、", "面条", "、", "细", "挂面", "、", "苏打饼干", "。", "蔬菜", "类", ":", "卷心菜", "、", "胡萝卜", "、", "芹菜", "、", "大白菜", "、", "茄子", "、", "苣荬菜", "、", "甘蓝", "、", "莴苣", "、", "刀豆", "、", "南瓜", "、", "黄瓜", "、", "西葫芦", "、", "西红柿", "、", "芜菁", "、", "山芋", "、", "山药", "、", "马铃薯", "、", "泡菜", "、", "咸菜", "、", "紫菜", "、", "海带", "、", "洋葱", "、", "青葱", "。", "蛋类", "、", "乳", "类", ":", "蛋类", "有", "鸡蛋", "、", "鸭蛋", "等", ";", "乳", "类", "有", "各种", "鲜奶", "、", "炼乳", "、", "奶酪", "、", "酸奶", "、", "麦乳精", "、", "奶粉", "、", "蜂蜜", "。", "其他", ":", "各种", "水果", "及", "干果", "类", "(", "苹果", "、", "核桃", "、", "杏", "、", "梨", "、", "橙", "、", "葡萄", "、", "粟", "等", ")", ",", "糖", "及", "糖果", ",", "各种", "饮料", "包括", "汽水", "、", "茶", "、", "巧克力", "、", "咖啡", "、", "可可", "等", ",", "各类", "油脂", ",", "花生酱", "、", "洋", "菜", "冻", "、", "果酱", "、", "藕粉", "。", "(", "2", ")", "低", "嘌呤", "食物", "每", "100", "g", "食物", "中", "嘌呤", "含量", "<", "75", "mg", "。", "蔬菜", "类", ":", "芦笋", "、", "花菜", "、", "四季豆", "、", "豌豆", "、", "青豆", "、", "荷兰豆", "、", "菜豆", "、", "大豆", "、", "花生"]], "paragraphs": ["( 1 )无嘌呤食物 嘌呤含量很少(小于 20mg/ 100g )或不含嘌呤的食物。 谷类:大米、精白米、小米、玉米、精白面、富强粉、精白面包、馒头、通心粉、面条、细挂面、苏打饼干。 蔬菜类:卷心菜、胡萝卜、芹菜、大白菜、茄子、苣荬菜、甘蓝、莴苣、刀豆、南瓜、黄瓜、西葫芦、西红柿、芜菁、山芋、山药、马铃薯、泡菜、咸菜、紫菜、海带、洋葱、青葱。 蛋类、乳类:蛋类有鸡蛋、鸭蛋等;乳类有各种鲜奶、炼乳、奶酪、酸奶、麦乳精、奶粉、蜂蜜。 其他:各种水果及干果类(苹果、核桃、杏、梨、橙、葡萄、粟等),糖及糖果,各种饮料包括汽水、茶、巧克力、咖啡、可可等,各类油脂,花生酱、洋菜冻、果酱、藕粉。 ( 2 )低嘌呤食物 每 100g 食物中嘌呤含量 < 75mg 。 蔬菜类:芦笋、花菜、四季豆、豌豆、青豆、荷兰豆、菜豆、大豆、花生"], "bs_rank_pos": 3}, {"title": "痛风能吃什么菜啊_39健康问答_39健康网", "segmented_title": ["痛风", "能", "吃", "什么", "菜", "啊", "_", "39", "健康", "问答", "_", "39", "健康网"], "segmented_paragraphs": [["痛风", "能", "吃", "什么", "菜", "啊"], ["精选", "回答", "(", "1", ")"], ["擅长", ":", "痛风", "、", "三叉神经痛", "、", "偏头痛", "、", "坐骨神经痛", "、", "椎间盘突出", "、", "肩周炎", "、", "颈椎病", "、", "坐骨神经痛", "、", "雷诺病", "、", "癌痛", "等", "常见病", "、", "多发病", "及", "疑难病", "。"], ["痛风", "患者", "不能", "喝酒", "、", "不能", "吃海鲜", ",", "可以", "多", "吃", "些", "芹菜", "、", "黄瓜", "、", "青菜", "、", "茄子", "、", "卷心菜", "、", "马铃薯", "、", "甘薯", "、", "南瓜", "、", "冬瓜", "、", "赤小豆", "、", "梨子", "、", "苹果", "、", "葡萄", "、", "玉米", "、", "芦根", "这些", "食物", "对于", "痛风", "患者", "缓解", "疼痛", "都", "有", "一定", "的", "帮助", ",", "平时", "的", "时候", "可以", "尝试", "使用", "不用", "的", "烹饪", "方法", "进行", "食用", "。", "像", "一些", "酸性", "比较", "大", "的", "食物", ",", "痛风", "患者", "要", "少", "吃", "或者", "尽量", "不", "吃", "。"], ["2014", "-", "05", "-", "30", "18", ":", "05", "投诉"], ["痛风", "能", "吃", "什么", "?", "痛风", "不能", "吃", "什么", "痛风", "能", "吃", "什么", "油", "痛风", "都", "能", "吃", "什么", "痛风", "不能", "吃", "什么", "痛风", "能", "吃", "什么", "?", "痛风", "不能", "吃", "什么", "痛风", "能", "吃", "什么", "?", "痛风", "能", "吃", "什么", ".", ".", "痛风", "不能", "吃", "什么"], ["痛风", "脚肿", "怎么", "消肿"], ["痛风", ",", "是", "中老年", "年", "比较", "经常出现", "的", "问题", "。", "痛风", "的", "病因", "也有", "很多", "种", ",", "很多人", "也", "为", "怎么", "消肿", "痛风", "脚肿", "而", "烦恼", "。", "现在", "我", "告诉", "大家", "一些", "消肿", "的", "好", "方"], ["痛风", "的", "初期", "症状", "痛风", "最好", "的", "方法", "有", "什么", "痛风", "的", "饮食", "禁忌", "是什么", "痛风", "有", "什么", "食疗方法"], ["乙肝", "(", "viral", "hepatitis", "type", "B", ",", "又", "称", "乙型", "病毒性", "肝炎", ")", "系", "由", "乙肝", "病毒", "(", "HBV", ")", "引起", ",", "临床表现", "为", ":", "乏力", "、", "食欲减退", "、", "恶心", "、", "呕吐", "、", "厌油", "、", "肝", "大", "及", "肝功能", "异常", "。", "发展中国家", "发病率", "高", ",", "多数", "无", "症状", ",", "其中", "1", "/", "3", "出现", "肝", "损害", "的", "临床表现", "。", "据统计", ",", "全世界", "无", "症状", "乙肝病毒携带者", "(", "HBsAg", "携带者", ")", "超过", "2.8", "亿", ",", "我", "国", "约", "占", "9300", "万", "。", "乙肝", "包括", "下面", "类型", ":", "急性", "乙肝", "(", "急性", "黄疸", "型", "肝炎", "、", "急性", "无", "黄疸", "型", "肝炎", ")", "、", "慢性乙肝", "、", "重型", "乙肝", "、", "淤", "胆", "型", "肝炎", "、", "肝炎", "后", "肝硬化", "、", "小儿", "乙型肝炎", "病毒", "相关", "肾炎", "、", "新生儿", "乙型肝炎", "、", "乙型肝炎", "病毒性关节炎", "等", "。"], ["多发", "人群", ":", "所有", "人群", ",", "主要", "见于", "青少年", ",", "绝大多数", "为", "10", "~", "30", "岁", "典型症状", ":", "转氨酶", "增高", "肝肿大", "乙肝", "表面抗原", "(", "HBsAg", ")", "阳性", "乙肝", "e", "抗原", "(", "HBeAg", ")", "阳性", "肝功能", "异常", "临床", "检查", ":", "乙肝病毒", "学", "指标", "凝血酶原时间", "乙肝", "两对半", "血清", "白蛋白", "与", "球蛋白", "比值", "血清", "HBsAg", "免疫", "复合", "物", "血清", "前", "S1", "蛋白", "治疗", "费用", ":", "市", "三甲医院", "约", "(", "3000", "—", "—", "8000", "元", ")"], ["全国", "三甲医院", ",", "主任", "级", "名医", "在线", "坐诊", "已有", "124", "家", "三甲医院", ",", "828", "位", "主任医师", "在线答疑"], ["东营", "骨科医生", "东营", "骨科医院", "贺州市", "人民医院", "网上预约", "中山大学", "附属", "口腔医院", "网上预约", "东营", "骨折", "医生", "东营", "骨折", "医院"]], "paragraphs": ["痛风能吃什么菜啊", "精选回答(1)", "擅长:痛风、三叉神经痛、偏头痛、坐骨神经痛、椎间盘突出、肩周炎、颈椎病、坐骨神经痛、雷诺病、癌痛等常见病、多发病及疑难病。", "痛风患者不能喝酒、不能吃海鲜,可以多吃些芹菜、黄瓜、青菜、茄子、卷心菜、马铃薯、甘薯、南瓜、冬瓜、赤小豆、梨子、苹果、葡萄、玉米、芦根这些食物对于痛风患者缓解疼痛都有一定的帮助,平时的时候可以尝试使用不用的烹饪方法进行食用。像一些酸性比较大的食物,痛风患者要少吃或者尽量不吃。", "2014-05-30 18:05 投诉", "痛风能吃什么? 痛风不能吃什么  痛风能吃什么油  痛风都能吃什么  痛风不能吃什么  痛风能吃什么? 痛风不能吃什么  痛风能吃什么? 痛风能吃什么.. 痛风不能吃什么", "痛风脚肿怎么消肿", "痛风,是中老年年比较经常出现的问题。痛风的病因也有很多种,很多人也为怎么消肿痛风脚肿而烦恼。现在我告诉大家一些消肿的好方", "痛风的初期症状 痛风最好的方法有什么 痛风的饮食禁忌是什么 痛风有什么食疗方法", "乙肝(viral hepatitis type B,又称乙型病毒性肝炎)系由乙肝病毒(HBV)引起,临床表现为:乏力、食欲减退、恶心、呕吐、厌油、肝大及肝功能异常。发展中国家发病率高,多数无症状,其中1/3出现肝损害的临床表现。据统计,全世界无症状乙肝病毒携带者(HBsAg携带者)超过2.8亿,我国约占9300万。 乙肝包括下面类型:急性乙肝(急性黄疸型肝炎、急性无黄疸型肝炎)、慢性乙肝、重型乙肝、淤胆型肝炎、肝炎后肝硬化、小儿乙型肝炎病毒相关肾炎、新生儿乙型肝炎、乙型肝炎病毒性关节炎等。", "多发人群:所有人群,主要见于青少年,绝大多数为10~30岁  典型症状:转氨酶增高 肝肿大 乙肝表面抗原(HBsAg)阳性 乙肝e抗原(HBeAg)阳性 肝功能异常  临床检查:乙肝病毒学指标 凝血酶原时间 乙肝两对半 血清白蛋白与球蛋白比值 血清HBsAg免疫复合物 血清前S1蛋白  治疗费用:市三甲医院约(3000 —— 8000元)", "全国三甲医院,主任级名医在线坐诊已有124家三甲医院,828位主任医师在线答疑", "东营骨科医生 东营骨科医院 贺州市人民医院网上预约 中山大学附属口腔医院网上预约 东营骨折医生 东营骨折医院"], "bs_rank_pos": 4}, {"title": "痛风病人不能吃什么水果呢_有问必答_快速问医生", "segmented_title": ["痛风", "病人", "不能", "吃什么", "水果", "呢", "_", "有问必答", "_", "快速", "问", "医生"], "segmented_paragraphs": [["健康", "咨询", "描述", ":", "我", "大伯", "前段时间", "双腿", "疼痛难忍", ",", "去", "医院", ",", "被", "诊断", "为", "痛风", ",", "现在", "在家里", "养", "着", ",", "我", "准备", "想", "去", "看望", "下", "他", ",", "想", "买", "点", "水果", "去", ",", "但是", "不知道", "痛风", "病人", "饮食", "有没有", "什么", "禁忌", ",", "不知道", "痛风", "患者", "哪些", "水果", "不能吃", "啊", "。"], ["想得到", "怎样", "的", "帮助", ":", "我", "想", "了解", "下", "痛风", "病人", "哪些", "水果", "不能吃", "呢", "?"], ["痛风", "病人", "应该", "吃", "多", "低", "嘌呤", "的", "食物", ",", "少", "吃", "中", "嘌呤", "的", "食物", ",", "不", "吃", "高", "嘌呤", "的", "食物", ",", "还要", "注意", "不要", "喝酒", "。", "低", "嘌呤", "食物", ":", "五谷杂粮", "、", "蛋类", "、", "奶类", "、", "水果", "、", "蔬菜", ";", "中", "嘌呤", "食物", ":", "肉类", "、", "豆类", "、", 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",", "西瓜", ",", "苹果", ",", "芒果", ",", "香蕉", ",", "哈密瓜", ",", "椰子", ",", "菠萝", ",", "山楂", ",", "桑葚", ",", "石榴", ",", "桂圆", ",", "橙子", ",", "梨", ",", "杏", ",", "柿子", ",", "柚子", ",", "橘子", ",", "柠檬", ",", "杨梅", ",", "榴莲", ",", "火龙果", ",", "枇杷", ",", "无花果", "。", "对于", "吃水果", "的", "禁忌", ",", "是", "没有", "什么", "决", "对", "的", "就", "不能", "吃", "什么", "。", "因为", "绝大多数", "水果", "里面", "的", "嘌呤", "含量", "都", "很少", "的", "。", "但是", "吃水果", "也", "不要", "过", "多", "。", "每天", "吃水果", "应该", "在", "150", "~", "200", "克", "为", "宜", ",", "最好", "饭前", "半小时", "吃水果", "。"], ["痛风", "是", "一", "种", "由于", "嘌呤", "生物", "合成", "代谢", "增加", ",", "尿酸", "产生", "过", "多", "或", "因", "尿酸", "排泄", "不良", "而", "致", "血", "中", "尿酸", "升高", ",", "尿酸", "盐", "结晶", "沉积", "在", "关节", "滑膜", "、", "滑", "囊", "、", "软骨", "及", "其他", "组织", "中", "引起", "的", "反复发作", "性", "炎", "性", "疾病", "。", "本", "病", "以", "关节", "液", "和", "痛风", "石", "中", "可", "找到", "有", "双", "折光", "性", "的", "单", ".", ".", ".", "更多", ">", ">"], ["萘普生", "钠", "胶", "参考价格", ":", "5", "查看", "说明书"], ["盐酸", "托", "莫西", "参考价格", ":", "265", "查看", "说明书"], ["二", "十", "五", "味", "驴", "参考价格", ":", "70", "查看", "说明书"]], "paragraphs": ["健康咨询描述: 我大伯前段时间双腿疼痛难忍,去医院,被诊断为痛风,现在在家里养着,我准备想去看望下他,想买点水果去,但是不知道痛风病人饮食有没有什么禁忌,不知道痛风患者哪些水果不能吃啊。", "想得到怎样的帮助:我想了解下痛风病人哪些水果不能吃呢?", "痛风病人应该吃多低嘌呤的食物,少吃中嘌呤的食物,不吃高嘌呤的食物,还要注意不要喝酒。低嘌呤食物:五谷杂粮、蛋类、奶类、水果、蔬菜;中嘌呤食物:肉类、豆类、海鲜;高嘌呤食物:豆苗、黄豆芽、芦笋、香菇、紫菜、动物内脏、鱼类。痛风常并发肥胖、糖尿病、高血压及高脂血症,患者应遵守饮食原则如下: 1.保持理想体重,超重或肥胖就应该减轻体重。不过,减轻体重应循序渐进,否则容易导致酮症或痛风急性发作。 2.碳水化合物可促进尿酸排出,患者可食用富含碳水化合物的米饭、馒头、面食等。 3.蛋白质可根据体重,按照比例来摄取,1公斤体重应摄取0.8克至1克的蛋白质,并以牛奶、鸡蛋为主。如果是瘦肉、鸡鸭肉等,应该煮沸后去汤食用,避免吃炖肉或卤肉。 4.少吃脂肪,因脂肪可减少尿酸排出。痛风并发高脂血症者,脂肪摄取应控制在总热量的20%至25%以内。 5.大量喝水,每日应该喝水2000ml至3000ml,促进尿酸排除。 6.少吃盐,每天应该限制在2克至5克以内。 7.禁酒!酒精容易使体内乳酸堆积,对尿酸排出有抑制作用,易诱发痛风。 8.少用强烈刺激的调味品或香料。 9.限制嘌呤摄入。嘌呤是细胞核中的一种成分,只要含有细胞的食物就含有嘌呤,动物性食品中嘌呤含量较多。患者禁食内脏、骨髓、海味、发酵食物、豆类等。 控制饮食后,正常人血尿酸可降35.7ηmol/L(O.6mg/dl)左右,痛风患者也可降低59.5μmol/L(1.Omg/dl)左右。饮食因素是如何影响血尿酸水平的,目前已有一些合理的解释,包括: 1.过度饮酒会引起痛风发病,以啤酒尤其明显。乙醇影响血尿酸水平的可能机制是: (1)饮酒常伴食含丰富嘌呤的食物; (2)乙醇代谢可以使血乳酸浓度升高,乳酸可以抑制肾脏对尿酸的排泄; (3)过度饮酒造成血酮症,抑制肾脏对尿酸的排泄; (4)乙醇能促进腺嘌呤核苷酸转化而使尿酸增多; (5)有人认为饮用像威士忌类含铅的酒,使痛风发病的危险性增加了3倍。 二、预防痛风饮食 1、戒吃高嘌呤的食物:动物内脏(肝、肠、肾、脑)、海产(鲍鱼、蟹、龙虾、叁文鱼、沙甸鱼、吞拿鱼、鲤鱼、鲈鱼、鳟鱼、鳕鱼)、贝壳食物、肉类(牛、羊、鸭、鹅、鸽)、黄豆食物、扁豆、菠菜、椰菜花、芦笋、蘑菇、浓汤、麦皮。 2、戒吃高胆固醇的食物:动物内脏(肝、肠、肾、脑)、肥肉、鱿鱼、鱼、墨鱼 3、戒酒:不可饮酒、尤其是啤酒、绍兴酒。 一旦血中酒精浓度高达200mg/dl,血中乳酸会随着乙醇的氧化过程而增加,令肾脏的尿酸排泄受阻,结果使血中尿酸增加。 4、戒吃酸性食物,如咖啡、煎炸食物、高脂食物。酸硷不平衡,会影响身体机能,加重肝肾负担。 三、宜吃的食物 1、多吃高钾质食物,如香蕉、西兰花、西芹等。钾质可减少尿酸沉淀,有助将尿酸排出体外。多摄取充足的碱性食物,如海带、白菜、芹菜、黄瓜、苹果、番茄等疏果。 2、多吃固肾的食物。中医学认为,固肾的食物有助排泄尿酸,平日可按「六味地黄」(熟地、山茱萸、山药、泽泻、丹皮、茯苓)配方煎水饮用,以收滋阴补肾功效。 3、亦适宜多吃行气活血、舒筋活络的食物。例如可用桑寄生(一人份量为五钱)煲糖水,但不要放鸡蛋,可加莲子。 4、苹果醋加蜜糖。 这是西方传统的治疗方法,经多项临床测试证明有效。苹果醋含有果胶、维他命、矿物质(磷和钾)及酵素。苹果醋的酸性成份具杀菌功效,有助排除关节、血管及器官的毒素。经常饮用,能调节血压、通血管、降胆固醇、亦有助治疗关节炎及痛风症。饭後可将一茶匙苹果醋及一茶匙蜜糖加入半杯温水内,调匀饮用。 5、痛风患者的食疗方。 薏仁粥:取适量的薏仁和白米,两者的比例约为三比一,薏仁先用水浸泡四、五个钟头,白米浸泡三十分钟,然后两者混合,加水一起熬煮成粥。 冬瓜汤:取冬瓜三百克(不连皮),红枣五、六颗,姜丝少许。先用油将姜丝爆香,然后连同冬瓜切片和红枣一起放入锅中,加水及适量的调味料煮成汤。 6、平常每天多喝水,多上厕所,千万不可忍尿。", "病情分析: 痛风是人体内嘌呤的物质的新陈代谢发生紊乱,引起组织异物炎性反应,发作部位会出现剧烈疼痛,多见于下肢。 指导意见: 根据你的情况,建议多饮水、少吃高嘌呤食物,多运动,少吃有强烈刺激性气味的调味品,多吃碱性食物等,最好到医院做详细检查。", "病情分析: 叔叔前段时间双腿疼痛难忍,去医院,被诊断为痛风,现在在家里养着,我准备想去看望下他,想买点水果去,但是不知道痛风病人饮食有没有什么禁忌,不知道痛风患者哪些水果不能吃啊。 指导意见: 为了预防和治疗痛风,饮食上应做到三多三少:l)多饮水,少喝汤。血尿酸偏高者和痛风患者要多喝白开水,少喝肉汤、鱼汤、鸡汤、火锅汤等。白开水的渗透压最有利于溶解体内各种有害物质。多饮白开水可以稀释尿酸,加速排泄,使尿酸水平下降。汤中含有大量嘌呤成分,饮后不但不能稀释尿酸,反而导致尿酸增高。2)多吃碱性食物,少吃酸性食物。痛风患者本身有嘌呤代谢紊乱,尿酸异常,如果过多吃酸性食品,会加重病情,不利于康复。 以上是对“痛风病人不能吃什么水果呢”这个问题的建议,希望对您有帮助,祝您健康!", "病情分析: 这类病多因是寒凉和劳累引发经络淤塞、气血亏损,风邪外袭,阴寒凝滞而成,脉络流通受阻,引发动脉硬化,气血不能流通关节,筋骨得不到滋养,导致关节屈伸不利,引起大部或全身关节疼痛,由于动脉是对称排列的,所以症状相对也是对称的。 指导意见: 1)、戒酒限烟:酒尤其是烈性酒、啤酒,会加速血液的运行速度,这样会加大血流对血管和心脏的压力,更易促使心脑血管病的发生几率,对于心脑血管病的高危人,有时2~3两酒就可以致命。", "你好,痛风病人可以吃猕猴桃,杨桃,樱桃,桃子,葡萄,西瓜,苹果,芒果,香蕉,哈密瓜,椰子,菠萝,山楂,桑葚,石榴,桂圆,橙子,梨,杏,柿子,柚子,橘子,柠檬,杨梅,榴莲,火龙果,枇杷,无花果。对于吃水果的禁忌,是没有什么决对的就不能吃什么。因为绝大多数水果里面的嘌呤含量都很少的。但是吃水果也不要过多。每天吃水果应该在150~200克为宜,最好饭前半小时吃水果。", "痛风是一种由于嘌呤生物合成代谢增加,尿酸产生过多或因尿酸排泄不良而致血中尿酸升高,尿酸盐结晶沉积在关节滑膜、滑囊、软骨及其他组织中引起的反复发作性炎性疾病。本病以关节液和痛风石中可找到有双折光性的单... 更多>>", "萘普生钠胶  参考价格:5 查看说明书", "盐酸托莫西  参考价格:265 查看说明书", "二十五味驴  参考价格:70 查看说明书"], "bs_rank_pos": 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如果薛之谦喜欢雪芙,他不可能这样表现。薛之谦曾毫无忌讳地曝出自己初恋的细节,还对校花初恋的长相给出了详细的描述,长发、国字脸、五官很集中像萌喵,如此美好的画面却招来田源无情的调侃:你的初恋就是高晓松!。求薛之谦内心的阴影面积!", " 这还没完,薛之谦还自爆被脚踩两只船,丢脸地被情敌拖到草丛暴打,往自己的伤口上撒盐。薛之谦还在节目中爆料了一段斗无良卖家的经历。在他当淘宝卖家的时候,也曾被无良的买家敲诈,这位买家给他六个差评只为了想不花钱得到衣服。而他则向这位贪心买家喊话:小心我neng死你!此话一出,就引起了不少卖家的共鸣:说出了我们卖家的心声。路转粉!说得太痛快了!薛之谦也有了薛正义的番号。", " 钱枫真的对雪芙表白之前,薛不停的在给雪芙鼓劲,又是很好的证明,薛对雪芙根本没感觉。对于郭雪芙和薛之谦的关系,网上也是众说纷纭,有人说郭雪芙喜欢薛之谦,但是也有人说薛之谦对郭雪芙没有感觉,不过呼喊两人在一起的声音总是有的,可惜现实太残酷,两人只不过是朋友关系而已。", " 郭雪芙喜欢薛之谦吗?《火星情报局》的开播火了郭雪芙,更火了薛之谦。不过有细心的网友拔出郭雪芙喜欢薛之谦的证据。其中郭雪芙看薛之谦的眼神,充满爱意。真的是好般配。对于郭雪芙和薛之谦的关系,网上也是众说纷纭,有人说郭雪芙喜欢薛之谦,但是也有人说薛之谦对郭雪芙没有感觉 ,不过呼喊两人在一起的声音总是有的,可惜现实太残酷,两人只不过是朋友关系而已。(图片来源:视觉中国,严禁转载)", "(责任编辑:yanghaiting_JZ) ", " 张艺兴被起诉真实原因曝光  SJ宣布回归他们出道都经历了什么  周杰伦跪地半小时原来是因为她  刘涛现身市场买菜想不到私底下这样  李雨桐公布录音反击 薛之谦反击晒证据陷P图丑闻 ", " 一波不小心泄露的床照 解锁白色主调的N种时尚穿法 男友风以及北美N1服装搭配 给小黑裙加一道“玫瑰味”", " 周杰伦赞林俊杰行走CD 联手隔空呛萧敬腾  周杰伦(周董)《地表最强》世界巡演,28日...... 【转载来源:其他  何洁为乱停车致交通堵塞致歉  近日,有爆料称何洁开豪车接一双儿女放学...... 【转载来源:其他  刘畊宏列3大女婿条件 警告不要乱亲小泡芙  刘畊宏近来带着女儿小泡芙,一起参加《爸...... 【转载来源:其他  孙俪邓超香港逛超市 分工明确娘娘变大力  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+{"documents": [{"bs_rank_pos": 0, "paragraphs": ["因为柠檬水有美容养颜的功效所以很多女性朋友都非常喜欢喝,但喝柠檬水也有些事项需要大家重视起来,柠檬水的功效除了美容养颜外还有什么?柠檬水如何泡才更好喝?今天小编带大家一起来了解下。", " 每天喝柠檬水好吗", " 由于柠檬过酸,不适于鲜食,人们往往用柠檬制成饮品饮用来摄取其中的营养。比如用鲜柠檬片或干柠檬片泡水,或者直接将柠檬榨汁。专家指出,柠檬中的维生素C可以使人体内的组织细胞间质保持正常状态,防止细胞组织变脆,失去抵抗力。所以柠檬能够一定程度上帮助人们预防感冒、抵抗癌症。正常人可以每天饮用柠檬水,但是为了防止柠檬中的酸类物质伤及胃肠,柠檬水的日摄入量最好控制在1000ml以内。胃酸过多或者胃溃疡的人群最好不要畅饮柠檬水。", " 柠檬泡水一定要淡,一片带皮柠檬泡一扎水,能倒3～4杯。这样的柠檬水没有很浓的酸味,不加糖或蜂蜜即可饮用,这样所含能量更低。柠檬一定要带皮,切片一定要薄,因为皮的部分含类黄酮更高,可能比柠檬果肉更值得泡,柠檬精油也主要在皮里面,薄切则柠檬皮中的香气成分容易泡出来。柠檬皮,包括其他柑橘皮,都含有一些苦味的类黄酮物质,比如橙皮甙、柚皮甙之类,但它们也都是有益成分。有一点淡淡的苦味,和酸味配合,天热时喝了之后更有解渴的感觉。", " 有人说柠檬不能用热水泡,怕维生素C损失。实际上,泡柠檬的水不能太凉,否则香味泡不出来。由于柠檬的酸性较强,维生素C在酸性条件下耐热性较好,没有想象中那么容易损失,泡柠檬片的水温度高于60度完全没问题。再说,喝柠檬水也不是仅仅为了那点维生素C。温度升高的时候,食物的酸味会更浓一些,所以热柠檬水也会显得更酸。解决这个问题很简单,只要把柠檬水稍微晾凉再喝就可以了。", " 有人说柠檬水不能和富含钙的食物一起吃,钙和柠檬酸会结合成沉淀,甚至在体内生成结石,这可就是谣言了。柠檬酸钙微溶于水,我查了一下,四水柠檬酸钙的溶解度是0.02克/100克水,似乎不高,但柠檬酸钙却是制作补钙产品的良好材料,因为它不需要胃酸帮忙就能被人体吸收。实际上,柠檬酸不会像草酸那样促进肾结石,相反,柠檬酸等有机酸是有利于食物中钙、镁、铁、锌等多种矿物质吸收的,而研究证明柠檬酸对肾结石的预防也是有利的,甚至已经用于肾结石的治疗当中。", " 有人说胃病患者不能喝柠檬水,因为酸性太强会刺激胃,胃酸过度的更不可用。但实际上用一片柠檬来泡一大瓶水,泡出来的柠檬水味道很淡,根本没有可乐之类甜饮料那么酸,也不至于会造成胃溃疡。又不是喝纯柠檬汁(纯柠檬汁pH2.5左右,和可乐相当)。由于柠檬酸有利于多种矿物质吸收,西方人喜欢把柠檬汁挤在鱼、肉、蛋上,认为能帮助消化。对于消化不良者来说,在柠檬水中加薄薄一片姜,就餐时饮用,对促进消化液分泌会有好处。", " 有人听说柠檬水能美白,但也有人听说柠檬是一种“感光食品”,担心白天喝柠檬水之后皮肤会起斑变黑。但这个说法仅限于网上传言,尚未看到相关研究证据。甚至还有网上信息说维生素C是感光物质,如果真是这样,所有水果蔬菜都含维生素C,看来人们白天什么蔬果都不能吃了——显然是十分荒谬的。从科学角度来说,食物中的主要光敏物质包括叶绿素、核黄素、血红素。要说吃了光敏物质就长斑,大概首先红肉就不能吃了(血红素多),奶类也不能吃了(核黄素多),绿叶菜和猕猴桃都不能吃(有叶绿素),看来白天只能吃粮食了!", "相关阅读推荐:", "肾气不足怎么调理 相信大家都知道肾是人体重要的一种器官,它是人体的...", " 高血压不能吃的食物 如今高血压是非常常见的一种疾病,中老年人是高血压...", " 八大食物降血压效果好 高血压是常见的一种心血管疾病,它的危害不容小觑,...", " 1雨润牌卤汁豆腐干竟有虫子 2九江麦当劳竟买到发霉汉堡 ", " 男人吃这些食物可以补肾脏 男性这样洗澡方式都是错的  男人按摩这五个穴位更健康 “十男九虚”到底是真是假", " 女性经期吃这些身体会更好 教你三招预防更年期综合症 女性脾虚吃这些食物可补脾 女性这样做让身体不再怕冷"], "segmented_title": ["每天", "喝", "柠檬水", "好", "吗", "喝", "柠檬水", "8", "错误", "不能", "犯", "-", "三九", "养生堂"], "segmented_paragraphs": [["因为", "柠檬水", "有", "美容养颜", "的", "功效", "所以", "很多", "女性", "朋友", "都", "非常喜欢", "喝", ",", "但", "喝", "柠檬水", "也", "有些", "事项", "需要", "大家", "重视", "起来", 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"title": "每天喝一杯柠檬水有什么好处_百度知道"}, {"bs_rank_pos": 3, "paragraphs": [" 字体: ", " 边距: ", " 背景: ", " 还原: ", "我喝的是一个柠檬榨出的汁,加一点蜂蜜和水,一口喝下去,不是一片柠檬慢慢泡的那种,因为我想就是喝里面的vc嘛,每天晚上喝,几个月了还没有白,有没有筒子喝柠檬白了的 我喝的是一个柠檬榨出的汁,加一点蜂蜜和水,一口喝下去,不是一片柠檬慢慢泡的那种,因为我想就是喝里面的vc嘛,每天晚上喝,几个月了还没有白,有没有筒子喝柠檬白了的 我喝的是一个柠檬榨出的汁,加一点蜂蜜和水,一口喝下去,不是一片柠檬慢慢泡的那种,因为我想就是喝里面的vc嘛,每天晚上喝,几个月了还没有白,有没有筒子喝柠檬白了的 ", " 楼主发言:12次 发图:0张 | 更多 举报 | 分享 | 楼主 | 点赞 ", " 不酸啊?哎哟喂 举报 | 2楼 | 打赏 | 回复 | 评论 | 赞 ", " 直接买维c 泡腾片不行吗 ", " 没坚持喝,不知效果。不过我的是柠檬泡蜂蜜,做成一罐,想喝倒点出来喝就成。", " 我试过每天半个柠檬,晚上挤汁喝,喝了一个月没白,还把胃搞坏了,放弃了", " 我喝了三个月了,也没觉得白一点点,但没像你用一个那么多。 ", " 不酸啊?哎哟喂 ----------------------------- 加蜂蜜啊 ", " 注意防晒啊还要。。。呃,反正我是变白了 ", " 注意防晒啊还要。。。呃,反正我是变白了 ----------------------------- 喝多久。怎么喝。差多少 ", " 貌似果壳网看的 柠檬水不美白 柠檬面膜更不可能美白 还有可能变黑 ", " 貌似果壳网看的 柠檬水不美白 柠檬面膜更不可能美白 还有可能变黑 ----------------------------- 啊啊啊啊啊啊啊啊啊啊啊。。。。。。真的???? 我每天都在喝柠檬水啊~~~55555555555 ", " 没觉得变白,但是皮肤好像变细了。 ", " 我也没喝白。。。不过可能也是没坚持的关系吧,酸的东西受不了 ", " 我喝那个两天就上火长溃疡、、话说每年夏天都晒黑,以前还努力下现在都直接放弃了,黑就黑呗 ", "在都直接放弃了,黑就黑呗  喝了", " 喝了一个夏天,每天两片泡起来,捏着鼻子就喝下去了,白了很多,痘痘也没了…", "琢撕芏痘痘也没了… 喝了一个夏天,", " 喝了一个夏天,每天两片泡起来,捏着鼻子就喝下去了,白了很多,痘痘也没了…  ----------------------------- 真的啊??晚上喝? ", " vc有一定的美白作用,但是柠檬皮里面的柠檬精油是光敏性的,喝这个可以但白天必须得防晒,不然你就等着变黑吧。 ", "伞 楼主你没上", " 楼主你没上火吗 ", "鹇 泡过柠檬蜂蜜,但是没坚", " 泡过柠檬蜂蜜,但是没坚持……", "帧喝了快大半年了。脸白的很明显啊。而且豆印淡了。 柠檬用白糖腌。天天泡着喝?  柠檬水感光的。。。喝完了", " 柠檬水感光的。。。喝完了晒太阳容易变黑的。。最好晚上喝。。 ", " 我就是把柠檬汁滴到杯子里刷牙,就滴两三滴,几天就酸牙了。楼主真牛,榨汁。。。 ", " 楼主你没上火吗  ----------------------------- 这玩意上火? 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", " 还玩传奇 就孙红雷 那啥 比造型 看这都没兴趣玩哦 ", " 这本来就是诈骗你还充钱?天上不会掉馅饼的!", " 玩个破壁游戏爱充钱,活该!", " 下载贴吧APP看高清直播、视频!", "贴吧页面意见反馈 违规贴吧举报反馈通道 贴吧违规信息处理公示"], "segmented_title": ["贪玩", "你们", "这样", "真的好", "嘛", "?", "-", "蓝月传奇", "吧", "-", "贪玩", "蓝月传奇", "-", "百度贴吧"], "segmented_paragraphs": [["贪玩", "你们", "这样", "真的好", "嘛", "?", "充", "完", "钱", "玩", "了", "一个礼拜", ",", "莫名其妙", "的", "被", "封", ",", "好", ",", "我", "不说话", "!", "今天", "重新注册", "账号", ",", "跟", "朋友", "玩", "1880", "区", ",", "差不多", "一小时", "才", "充", "钱", ",", "没", "充", "钱", "游戏", "玩", "的", "好", "好", "的", ",", "一", "充", "钱", "又", "强制", "被", "踢", "下线", ",", "然后", "禁止", "登陆", "!", "是不是", "封号", "都是", "你们", "爱", "怎么", "封", "怎么", "封", "?", "客服", "说", "我", "拉", "人", "了", ",", "行", ",", "我自己", "创建", "的", "帮会", "没", "人", ",", "我", "世界", "喊", "一句", ":", "帮会", "收人", ",", "稳定", "常", "在", "的", "一起", "打", "boss", "!", "这样", "也有", "错", "?", "第一个", "号", "封", "了", ",", "我", "不说话", ".", "那个", "号", "上", "的", "人", "多", ".", "我", "也", "不", "纠结", ",", "但是", "我", "今天", "刚", "注册", "的", "账号", ",", "刚", "充", "完", "钱", "你", "就", "给", "我", "封", "了", "!", "!", "贪玩", "真", "的", "是", "很", "6", "!"], ["还", "玩", "传奇", "就", "孙红雷", "那", "啥", "比", "造型", "看", "这", "都", "没", "兴趣", "玩", "哦"], ["这", "本来就是", "诈骗", "你", "还", "充", "钱", "?", "天上", "不会", "掉", "馅饼", "的", "!"], ["玩", "个", "破壁", "游戏", "爱", "充", "钱", ",", "活该", "!"], ["下载", "贴吧", "APP", "看", "高清", "直播", "、", "视频", "!"], ["贴吧", "页面", "意见反馈", "违规", "贴吧", "举报", "反馈", "通道", "贴吧", "违规", "信息处理", "公示"]], "title": "贪玩你们这样真的好嘛?-蓝月传奇吧-贪玩蓝月传奇-百度贴吧"}, {"bs_rank_pos": 2, "paragraphs": [" 贪玩蓝月,各种广告,还请了不少大牌明星代言。非常想知道为什么还有人在玩,好玩在哪里? ", " 这是一个很low,但的确有很多人玩并且乐于充钱的神奇游戏。", " 这种游戏的盈利模式很简单,用铺天盖地的广告收取眼球,用明星代言收取认同感,用情怀牌收取玩家粘性,一个典型的割草游戏。", " 首先说受众,这款游戏针对的主要是三类人:一是年过三十,无工作无技能无爱好的三无富二代,这群人除了钱一无所有,又没什么朋友,很难获得认同感。另一类是在一个无聊或冷门的岗位一直呆了很多年,又没本事跳槽或晋升的白领。第三类就是在广大边远地区,缺乏高配电脑的适龄青年。他们有一个共同点就是视野相对封闭,认知水平相对比较低,又有一定的经济基础或者没别的地方用钱。视野封闭决定了他们容易受到蓝月一类无孔不入的广告轰炸而无法关注到其他游戏,认知水平低决定了他们无法去甄别这到底是不是一个靠谱的游戏,有一定经济基础或者没别的地方用钱,加上他们自身的特性,决定了他们几乎只能靠游戏来获得认同感和快感(他们甚至想不到,用氪金的钱,都可以买一台高配电脑走入真正的游戏殿堂,获得更加高质量的享受)。讽刺的是,在当前全国城镇化仅有57.35%的情况下,这三类人的数量还是很庞大的。", " 其次说说营销,这款游戏的营销特点就是两个:信息轰炸和明星代言。有一次公交车上反复循环掏粪男孩的《青春修炼手册》,几站下车后,我竟然情不自禁哼上左手右手一个慢动作。信息轰炸就是这个目的,你很讨厌他,但你就是记住他了。蓝月的明星代言比较特别,他有好几类,一个是“90后老板征服XXX代言”(比如孙红雷、古天乐),一个是明星穿那一套羞耻的戏服现场拍摄(比如林子聪、张卫健和那个韩国大波妹)或者告诉你这游戏怎样怎样(比如刘涛告诉你弓箭手有多牛,有的甚至戏服都懒得给你穿,比如泰森和韩国大波妹那个广告)的小视频,一个是你们经常看到的明星穿戏服的PS照成品(以上提到的明星除泰森都有)。正常人会觉得,这什么吉跋玩意儿?好羞耻啊,刘涛我求求你不要说那些话啊!。但上面提到那三类人会觉得,卧槽,这些人都在玩,叼。说白了,这个游戏的营销一开始就走的是低端路线。", " 最后,扯一下游戏环境。这也是个重要的原因。现在国家在大力提文创,所以有了专项扶持资金,然而,正因为如此,专心开发提高游戏质量,宣传画面宣传游戏性远不如随便弄个项目骗一笔扶持资金来得快。所以现在的游戏环境反而不如九几年的时候自由竞争来得好。", " 1楼说的方向不错,玩这些的要满足几个条件:", " 1追求社会认同感/需求更高层次的社会地位 追求社会认同并不见得在实际生活中是个足不出户的死宅loser,但总之在现实生活中,自己获得的认同或者社会地位仍不满足自身心理预期,需要得到虚拟世界的进一步填补。当然所有热衷于多人在线,玩家互动和带有竞技或者挑战内容游戏的人都有这种心理。 2审美特殊或者单纯审美能力较低 为啥丑到爆土到爆还有人去玩?为啥明星拉低身价去说尴尬的广告词?非常简单的原因就是,至少国内大多数人的审美水平都是较低的,审美水平越高的人越是少数。土的掉渣恰恰就表示了其受众极为广泛。 3对游戏综合素质不具备鉴别能力 审美层次低,基本上也意味着对游戏质量的要求同样也低,不会要求多良好的体验,什么方便的功能,合理的情节内容,人性化的操作方式,公平合理的竞争系统,这一类统统不深究。而这样对于游戏质量不挑剔的人群,比起那些对游戏极端挑剔的玩家,更容易得到满意的体验,也更容易为游戏买单。", " 说实话,中国人有钱人很多,但是有钱不代表受过高等教育,受过高等教育不代表有良好的审美基础和对游戏鉴别能力。换句话说,大量具备经济实力的消费者都是分辨不了游戏品质的,而这一类玩家的钱却好赚得多。", " 5信息获取渠道缺失", " 为啥电视购物那么假,网上到处都在说是骗人的产品还有人上当?因为这些上当的人只看电视不会上网。同理铺天盖地的网络广告,针对的就是那些不会去专业游戏论坛或者游戏资讯平台,没有渠道去近距离接触高品质游戏的人群。只能在低端产品里选择一个看起来最有保障(例如大明星代言)的。而这类人群往往又具备2,3点的特性。所以这种营销方式是非常成功地面向目标人群的。", " 就像玩蓝月传奇的人也想不明白我们这些loser 为什么爱玩王者荣耀,呵呵。都是不同年代的人喜欢不同的东西而已。开心就好。不过确实是有高低之分,不是秀优越感。蓝月传奇跟lol,刀塔2,王者荣耀这些dota 类游戏比就是垃圾。就像以后的会有玩vr网络游戏的新一代,笑我们这些老古董居然会玩dota 这些傻比游戏。", " 反对楼上一些不懂装懂想当然瞎定义的,我本人也玩类似页游,我是开建材店的,上下游基本成熟稳定,所以比较轻松,基本上每天呆在店里看着就行,有空就玩玩这样的游戏,而在这游戏里结识的其他玩家,大多数并不是像楼上总结的那样职场loser或边远山区没见过世面的人,而是各色人员都有,我就列举我现在玩的这个区的这个行会的几个玩家,A,北京人,40岁左右,银监会工作,B,60岁,北京人,退休副厅级,全国100策划家,C,30岁,深圳某数码产品公司二代,已接手公司一般管理职能,D,30岁,大连某小二贷款公司老板,E,35岁,链家地产某部门总监,F,30岁,武汉人,职业游戏商人,有自己工作室,去年最好的时候月入超过5万,现在也基本2万左右…游戏玩到中后期,基本都是类似的玩家,我不觉得他们就是楼上说的傻逼,或者职场loser,他们并不会比知乎键盘侠差…游戏只是一种休闲娱乐方式而已,跟看电视,唱歌,旅游,养小三,赌博,在知乎秀逼格一样,花钱花时间得到了自己想要的快感就可以…", " 哈哈 我就玩蓝月传奇 玩了个道士 小扇子一拿还挺美 这个分人 我玩这个就喜欢能挂机 当个单机玩 一共了花了230块钱(并且不准备再花了) 比好多花3,4千的都玩的好 不用去黑玩这个游戏的人 其中总有比你聪明比你有钱的 人都有选择放松自己方式的自由 没有高端或者低端之分 人家愿意你管的着吗", " 好玩在哪里其实觉得还是玩过才知道吧,女票看男票玩游戏也不理解游戏比我好看在哪里,摊手,大概一个愿打一个愿挨?传奇网页版蓝月传奇好玩吗 传奇网页版哪个游戏好玩"], "segmented_title": ["为什么", "还有", "人", "玩", "蓝月传奇", "?", "-", "知乎"], "segmented_paragraphs": [["贪玩", "蓝月", ",", "各种", "广告", ",", "还", "请", "了", "不少", "大牌", "明星", "代言", "。", "非常", "想", "知道", "为什么", "还有", "人", "在", "玩", ",", "好玩", "在", "哪里", "?"], ["这", "是", "一", "个", "很", "low", ",", "但", "的", "确", "有", "很多人", "玩", "并且", "乐于", "充", 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"保险公司的偿付能力是一种支付保险金的能力,表现为实际资产减去实际负债后的数额。保险公司的偿付能力是影响公司经营的最重要因素。具备足够的偿付能力,保险公司就可以保证在发生保险事故的情况下,有足够的资金向被保险人支付保险金,保证保险公司的正常经营。影响保险公司偿付能力的因素主要有以下3个: ", "(1)资本金,准备金和公积金:保险公司的资本金、准备金和公积金的数额多少直接体现了保险公司的偿付能力大小。(2)业务规模:保险公司的业务规模是指保险公司的业务范围和业务总量。", "(3)保险费率:保险费率是保险的价格,也是保险公司收取保险费的依据。 以上3个因素是影响保险公司偿付能力的主要因素。除此之外,保险资金的运用、再保险业务等情况也对偿付能力有影响。", "虽然保险合同的基本事项原则是相同的,但不同的保险公司其合同条款还是有很大差异。因此,投保人必须非常明确你所购买的保险是否能够满足你的需要。比如:该合同承保哪些风险?被保的财产是什么?被保的责任有哪些?该合同的保险责任(保险收益)有多大?保险期限多长?6个月,还是1年,或更长的时间?损失发生时使用什么样的补偿方法?等等。 ", "理赔实践是投保人了解保险公司的又一重要方面。在购买保险之前,通常从以下几个渠道获取有关公司理赔实践的信息: ", "(1)向保险公司的管理部门咨询该公司受消费者投诉的情况。(2)从相关的报刊杂志上收集各公司有关理赔实践的文章和报导。(3)从保险代理人和经纪人那里获取保险公司过去的理赔情况。(4)从朋友那里打听,他们的保险公司是怎样对待他们的。", "虽然在大多数情况下,只有符合一定条件的投保人才能被保险公司接受其为消费者,这个过程就是业务承保的过程。对保险公司来说,面临如下三种抉择:接受投保;拒绝投保;接受投保,但要作出一些变动。所以投保人投保时最好能了解保险公司的承保能力有多大。", "投保人所需要的服务大致有四个方面,即确定保险需求方面的帮助,选择保险项目方面的帮助,预防损失方面的帮助,和索赔方面的帮助。选择保险公司时,要从两个方面注意其服务质量和数量。一是从保险公司代理人那里所能获得的服务,一是从保险公司那里所能获得的服务。", " 如有侵犯您的版权,请联系我们,我们将及时删除。 ", "行业精彩推荐 月关注排行榜 最新加入文章 历史关注排行", "按行业查看: 保险 理财-网贷 银行 股票 证券 基金 基金托管机构 融资担保 投资-风投 第三方支付 信用卡 律师事务所 会计师事务所 税务师事务所 商标事务所 管理咨询公司 认证机构 展会展览 拍卖行 公关服务 传媒公司 在线视频 电视传媒 ", " 按条件查看: 热门精选专题 按人群专题 生活服务专题 什么牌子好 ", " 热卖网店: 中国人寿人身保险旗舰店 中国平安旗舰店  人保寿险旗舰店 新华保险旗舰店  太平洋保险-寿险 安邦瑞和保险旗舰店  泰康人寿旗舰店 太平人寿保险旗舰店  阳光人寿旗舰店 中国大地保险旗舰店 "], "segmented_title": ["哪家", "保险公司", "好", "十大", "保险公司", "推荐", "_", "买", "购", "网"], "segmented_paragraphs": [["摘要", ":", "保险", "是", "一", "个", "长期", "的", "计划", ",", "您", "所", "选择", "的", "保险公司", "的", "未来", "发展前景", "和", "资产", "管理", "能力", "关系", "到", "您", "的", "一生", ",", "所以", "消费者", "们", "一定要", "慎重", "再", "慎重", "。", "以下", "是", "买", "购", "网", "为您", "提供", "的", "保险公司", "品牌", "榜单", ",", "提供", "给", "您", "参考", "。"], ["随着", "我", "国", "金融业", "的", "发展", ",", "各种", "保险公司", "如", "雨后春笋", "般", "现身", "市场", ",", "其中", "既", "有", "国", "有", "保险公司", ",", "又", "有", "股份制", "保险公司", "和", "外资", "保险公司", "。", "不过", "按照", "行业标准", "来", "说", ",", "目前我国", "的", "十大", "保险公司", "主要有", ":"], ["别", "小", "看", "了", "这个", "榜单", ",", "这", "可是", "十大", "品牌", "网", "maigoo", "根据", "相关", 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"精选专题", "按", "人群", "专题", "生活服务", "专题", "什么", "牌子", "好"], ["热卖", "网店", ":", "中国人寿", "人身保险", "旗舰店", "中国平安", "旗舰店", "人保", "寿险", "旗舰店", "新华", "保险", "旗舰店", "太平洋保险", "-", "寿险", "安邦瑞", "和", "保险", "旗舰店", "泰康人寿", "旗舰店", "太平人寿", "保险", "旗舰店", "阳光", "人寿", "旗舰店", "中国", "大地", "保险", "旗舰店"]], "title": "哪家保险公司好 十大保险公司推荐_买购网"}, {"bs_rank_pos": 2, "paragraphs": [" 我想做保险工作 哪家保险公司比较好?", " 哪家保险公司的基本制度比较好一些?", " 先说结论,目前看来,明亚。", " 各家保险公司的基本制度(就是惯称的“基本法”)大同小异,所有的考核目标都是为了多出单。为了扩大市场就多招代理人,由于从业门槛很低,保险公司在培训时往往采取简单粗暴的培(xi)训(nao)方式,甚至用上很多传销的手段来激发人性当中的恶。各家代理人多数都是“我们最牛逼,别人都是渣”的思维,很难树立正确的保险观念。在这点上,经纪公司要胜出保险公司一筹。", " 当然即便在保险经纪公司,还是要看个人的努力程度。保险销售团队是一种松散的组织结构,同一公司的不同团队是竞争关系,同一团队的不同人员也是竞争关系,所谓的“合作”其实是很有限的。压箱底的功夫不会有人教给你的,最终还是要靠个人去“悟”。", " 最后还是提醒,绝大多数代理人都不是在保险公司“工作”,签的是代理合同,而不是劳动合同,不受劳动法保护。", " 同意蔡师傅和晓东的说法,我也来个现身讲解,选择明亚是正确道路。", " 1、曾经也在太平保险工作,被所谓高大上的表面吸引,其实当你进入这个队伍,才能看清里面的问题,和传销的洗脑风格类似,鸡血早会,营销话术背诵,产品单一,以销售为导向,各种产说会,也就是变相营销。卖产品是一方面,主要精力花费在增员上,保险公司想持续发展,产品无竞争优势,唯独增员,如果你觉得可以,就请削尖脑向塔尖儿奋斗。 这种体制下,很难从客户利益角度出发,毕竟产品局限性摆在这里。 2、 回归保险真谛,保险是一种风险规划的手段,不是零售产品,它看不到也摸不着。保险是一种合同约束,里面涉及法律、医学、经济学等知识,专业知识的学习,理性辨别能力,以及站在客户需求角度选择适合的产品,单一公司产品是无法做到完全透明公正,而能做到几十家公司产品的学习筛选,理性分析,为客户量身定制符合切身利益产品,保险经纪则相对灵活,自主,更有底气。 ", " 现在最为人们熟知的三大保险公司,保险的产品整个行业都差别不是太大。但基本法有很大的区别,保险公司想要长期留存肯定是和晋升制度密切相关。例如一个主任下面几个正式人力可以晋升每个公司之前相差很大的。具体了解可以私信问。个人经历了三家保险公司,对基本法了解的比较多", " 现身说法吧,同前面的回答一样,明亚。", " 我以前曾在平安做过,当时被所谓“优才计划”所“吸引”,总想着保险代理人队伍要鸟枪换炮了,平安还有个被称为业界黄埔的金融学院,但入职之后才发现还是换汤不换药,鸡汤、洗脑加话术,让每个人都觉得公司产品是最牛的,公司的服务是最棒的。这自然不是我辈所钟爱的工作氛围,于是果然挥手告别,最后比较了半天到了明亚。", " 当然,这里无意吐槽老东家,更无意吐槽保险公司的代理人,他们当中优秀的也大有人在。只是屁股决定脑袋,面对一家公司的产品和面对几十家公司的产品时立场和心态是不一样的,前者要应付某家公司的导向性考核(公司力推某一产品就会以各种方式要求代理人推销某种产品),后者可以从容挑选;前者在面对客户的个性化要求时往往捉襟见肘,后者可以真正站在客户的立场灵活配置。", " 在日常工作中,明亚的员工有五险一金,对职业的归属感更强,人员素质也更高些,学习氛围更浓厚些,重视客户的真实需求,熟悉各公司产品差异,所以相对而言,提供的保障规划更加科学,后续服务更为贴心。不过事在人为,关键还是要有一定的学习力和执行力,如果三心二意甚至三观不正的话,在哪都会掉链子的。", " 虽然他号称自己是保险经纪公司,站在客户的立场为客户选择市面上大大小小几十种保险产品,实质上他真正在卖的就那一两样,不管客户有什么需求,不管你要的是退休养老/理财规划/子女教育金/财产传承等等～他最终都会绕到这个一产品上,公司内部几乎没有专业知识的培训,最最重点的重点就是练话术,所以你问他家所谓的经纪人哪家保险产品好,他最终会通过每天练习的话术把你引导到公司指定的那一款上,而且这是一家台湾公司,里面的台独氛围很重,虽然对待大陆员工表面功夫做的比别的台企好(名为保险本质是直销公司要构建团队氛围),但台干还是时不时的流露出台独金句以及蔑视大陆人的心态,真心实意的跟客户说一句,任何无视通货膨胀还侃侃而谈的所谓理财规划都是耍流氓!也真心跟里面的大陆经纪人说一句,在台企凡事多留个心眼,不要被人卖了还乐呵呵的替人数钱,恰巧我知道的比你们稍微多一点,不要被台湾人的表面功夫所蒙骗,他们现阶段在扩张正需要大陆人替他们开拓市场～～一旦成功了希望你们懂的,我们是同胞,不忍心大家被骗! 大家百度可搜索以下关键字 永达理 台独,永达理 央视,再附赠一个彩蛋～关键字,永达理 茶叶蛋教授,恩～他们请了享誉海内外的茶叶蛋教授高志斌作为专业人士来大陆给同胞们上课。", " 首先:提的问题是想做保险工作,保险主要是财险和寿险,工作职能划分主要是,内勤,销售,评估,精算,您想从事什么险种的什么职位?想去保险公司还是保险经纪公司?想解决就业,积累经验,还是想把保险当事业做成年薪百万?", " 建议先细化问题再有针对性的提问。", " 至于基本制度的优劣,请相信大多数MDRT的选择。", " 另外:几家保险经纪公司的确是各有所长,别家我不了解,就说说永达理吧:", " 永达理的优势:只要按照要求保证保证出勤,完成训练和保证访量 就能实现个人能力和收入的快速成长;", " 劣势:如果你想混日子的话,没有五险一金;另外就是即使达成了MDRT和百万年薪,在MDRT合照里面也很难找到自己 。", " 前几天美国MDRT大会的合影为证,我都找不到我爱人在哪。", " 欢迎另外的保险经纪公司和保险公司也来晒图", " 主要是你在那个城市?", " 每家公司的人都会跟你说他们公司的基本制度比较好,所以不多言语。我倒是更希望你考虑下,你希望的工作状态是什么样的,你是希望做一个花言巧语的推销者,还是做一个专业的保险销售人员。", " 我是保险从业人员,可以私信我", " 太平,如果在深圳就来我们团队", " 市场营销教师、养花发烧友、保险发烧友,烹饪发烧友,所有复杂事物发烧友", " 想做保险业,从品质看三大老牌保险公司都差不多,我觉得除了考虑人们常说的产品和基本法之外,你还要考虑:公司离家的远近,因为你几乎每天都要去公司参加早会,路上省点时间,你就有多点时间去拜访客户;你的身边朋友熟人是哪种类型,如果是一线城市白富美,那你最好进入外资公司,如果是中产以上殷实家庭,那你就去平安,因为他的产品很多都是万能险一站购齐,保费要高一点;如果你的销售人群是普通工薪家庭,那就是太平洋保险,因为他的产品都是分开的,需要哪项就买哪项,一次交的保费少;你还要考虑差异化的问题,比如说平安的销售员数量是最多的,可能会和你看上同一个客户,你怎样向客户说明,卖你的产品而不是其他平安销售员的,这就是你的功夫了", " 北大法硕,公众号:何止理赔,不会做手术的律师不是一个好的保险经纪人!", " 同意蔡师傅的观点,明亚保险经纪公司! 我本人就是从保险核赔转经纪的,当时也是对几个市场排名前的经纪公司和代理公司分析对比后,选择明亚的。欢迎加入经纪人行列!"], "segmented_title": ["我", "想", "做", "保险", "工作", "哪家", "保险公司", "比较好", "?", "-", "知乎"], "segmented_paragraphs": [["我", "想", "做", "保险", "工作", "哪家", "保险公司", "比较好", "?"], ["哪家", "保险公司", "的", "基本", "制度", "比较好", "一些", "?"], ["先", "说", "结论", ",", "目前看来", ",", "明亚", "。"], ["各", "家", "保险公司", "的", "基本", "制度", "(", "就是", "惯", "称", "的", "“", "基本", "法", "”", ")", "大同小异", ",", "所有", "的", "考核", "目标", "都是", "为了", 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"大家感觉哪个保险公司比较好! - 爱问知识人"}, {"bs_rank_pos": 4, "paragraphs": [" 本人想从事保险行业,哪家保险公司比较好?", " 本人29岁,大学毕业后在国企工作了5年,了解到保险行业未来宏观前景较好,想从事保险行业。对薪资要求不高(小康水平即可),希望作为长远事业发展(说作为终身事业有点夸大),希望能让对的人买到对的保险。", " 确实考虑清楚换行业了吗?换工作,穷三月;换行业穷三年。慎重!!!我以前是干设计/画图的,现在转行做保险经纪。如果确定了要换,而且去保险行业,那么建议看看保险法中经纪人与代理人的区别?然后再做决定,毕竟咱们都是有理科人,了解完再做决定回好点。PS:我差点去平安了,现在在保险经纪公司", "著作权归作者所有。商业转载请联系作者获得授权,非商业转载请注明出处。", " 谢邀根据个人经历给你一些建议:个人2011年毕业后进入保险行业,在中国太平洋保险做了4年代理人,而且做得还算不错,但4年后,放弃所有职级和续期收入,转型做了保险经纪人。", " 看题主最后一句:“希望能让对的人买到对的保险”,这句话是价值连城的,因为保险销售的从业人员如果想做长久,这句话是核心态度,但是在任何一家保险公司做保险代理人都无法完全做到这一点,因为你只能销售本公司的产品,即使有些许缺陷,或者保费昂贵,甚至不适合客户,你只有两个选择,一个是想办法销售给客户,另一个是放弃客户。我想放弃一两个客户还是有可能的,连续放弃,就不是一般的从业者都能做到的了,因为要有业绩和收入才能在行业中留存下来。", "经纪人的优势在于,可以销售几乎各家公司的产品,当然有个别公司的一些产品并未对经代渠道开放,但是开放是趋势,而且近百家公司,有几家公司并未开放,也不妨碍给客户最适合及性价比最高的产品,因此建议题主如果想做保险销售类的工作,建议做保险经纪人。", " 1,可以去经纪公司,不建议去任何保险公司去做代理人。2,新从事一个行业需要从头学起的,需要很多积累的。3,建议看看知乎上精算师狄升的文章,关于保险,一篇胜读十年书。", " 北京时间7月20日晚,2016年度《财富》世界500强排行榜正式揭晓,国内领先的“A+H”股上市综合性保险集团——中国太平洋保险凭借稳健的经营发展、良好的业绩表现连续六年上榜,位列第251位,比2015年跃升77位!首次跻身前300强。", " 《财富》杂志每年评选及发布的世界500强排行榜,以全球大型公司年度收入和利润为主要评定依据,为国际上衡量商业企业影响力、规模和实力最主要的参照指标,被誉为“终极榜单”。", " 自2011年首次上榜《财富》世界500强以来,中国太平洋保险凭借“专注保险主业,价值持续增长”的发展战略,不断推动“以客户需求为导向”的战略转型,内生动力持续增强,整体价值持续提升。在转型发展的第六个年头,中国太平洋保险以2015年营业收入393.358亿美元、利润28.209 亿美元的良好经营业绩连续第六年登上《财富》世界500强榜单,标志着公司转型发展迈上新台阶。", " 中宏保险,值得你的加入"], "segmented_title": ["本人", "想", "从事", "保险", "行业", ",", "哪家", "保险公司", "比较好", "?", "-", "知乎"], "segmented_paragraphs": [["本人", "想", "从事", "保险", "行业", ",", "哪家", "保险公司", "比较好", "?"], ["本人", "29", "岁", ",", "大学", "毕业后", "在", "国企", "工作", "了", "5", "年", ",", "了解", "到", "保险", "行业", "未来", "宏观", "前景", "较好", ",", "想", "从事", "保险", "行业", "。", "对", "薪资", "要求", "不", "高", "(", "小康", "水平", "即可", ")", ",", "希望", "作为", "长远", "事业", "发展", "(", "说", "作为", "终身", "事业", "有点", "夸大", ")", ",", "希望", "能", "让", "对的人", "买到", "对", "的", "保险", "。"], ["确实", "考虑", "清楚", "换", "行业", "了", "吗", "?", "换", "工作", ",", 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+{"documents": [{"bs_rank_pos": 0, "paragraphs": ["市场上大多数的净水器说废水率1:1,其实都有1:3甚至更高了,旺夫泉这个牌子的净水器是不产生废水的额,特别环保。"], "segmented_title": ["市场", "上", "的", "那些", "净水器", "都", "说", "废水", "率", "才", "1", ":", "1", ",", "请问", "是", "这样", "的", "吗", "?", "_", "百度", "知道"], "segmented_paragraphs": [["市场", "上", "大多数", "的", "净水器", "说", "废水", "率", "1", ":", "1", ",", "其实", "都", "有", "1", ":", "3", "甚至", "更高", "了", ",", "旺夫", "泉", "这个牌子", "的", "净水器", "是", "不", "产生", "废水", "的", "额", ",", "特别", "环保", "。"]], "title": "市场上的那些净水器都说废水率才1:1,请问是这样的吗?_百度知道"}, {"bs_rank_pos": 6, "paragraphs": ["原创2017-08-15Gfk(中国)电器微刊电器微刊", " 行业|深度|视角", " 净水器作为目前最有效的保障饮用水安全问题的解决途径,近几年在国内发展势头迅猛。捷孚凯(GfK中国)零售推总数据显示,2017年1~5月,净水市场零售额超过48亿元,零售量超过310万台,同比增长均超过10%。其中,线上市场占市场整体零售额的28%,占零售量的61%。就近几年的市场表现来看,净水线下市场在2013年经历飞跃式的增长,线上市场则在2014年开始呈现两位数的高速增长。2016年,净水市场规模增速明显放缓,行业进入调整期,消费者趋于理性(详见图1)。", " 品类百花齐放", " 目前,市场在售的净水器品类五花八门,主要有厨下式净水器、龙头净水器、台上式净水器、壁挂式净水器以及净水壶/桶。随着消费者对水污染的关注度提升,厨下式净水器基于多重过滤,过滤精度高,受到消费者的欢迎。捷孚凯(GfK中国)零售监测数据显示,厨下式净水器在整体净水市场中占据超过80%的份额。厨下式净水器近几年的发展趋势主要体现在外观上的改变和便利性的提升。过去,厨下式产品由于隐藏在橱柜内侧,产品往往不注重外观设计,但随着消费者对美观的需求不断提升,厨下式净水器的外观正变得越来越简洁大方,同时水路的设计更加合理,避免二次污染。另外, 过去厨下式净水器因需要售后人员上门安装和更换滤芯而被消费者诟病,增加了很多不必要的成本。随着产品不断优化,目前一部分厨下式净水器可以实现自助安装和更换滤芯,很大程度上提升了产品使用的便利性。", " 捷孚凯(GfK中国)零售监测数据显示,厨下式净水器的平均单价为净水器品类中最高的产品,但线上市场和线下市场的平均单价差距较大,线上市场平均单价超过1000元,线下市场则要超过3000元。除了厨下式净水器外,其他品类净水器也有比较显著的发展,不同品类净水器有着不同的应用。例如,壁挂式净水器很大程度上取代了桶装水的方式,通过过滤自来水进行直接饮用,节省了桶装水的经济投入,更加便捷和卫生。捷孚凯(GfK中国)零售监测数据显示, 壁挂式产品平均单价仅次于厨下式净水器,接近2000元。另外,滤水壶及龙头式产品因便利性及单价较低的优势,也受到消费者的青睐。该类产品的单次投入成本较低。以滤水壶为例,一壶一芯的市场均价低于300元,这对于城市流动人群或学生都极具吸引力。", " 除了常规净水器品类外,近几年市场上还出现了一些新兴的净水产品以满足不同消费者的需求,例如前置过滤器、软水机及中央净水器。前置过滤器是全屋用水的第一道粗过滤设备,可以过滤自来水中的泥沙、铁锈等大颗粒物质。由于其无需更换滤芯,使用成本低,受到消费者的青睐。另外,中央净水器这类适用于全屋净水的产品也深受高端消费者的欢迎。这几类净水器产品虽然在整体净水器行业中占比较小,但极具潜力,2016年的零售额均实现三位数的同比增长。", " 技术型产品带动市场发展", " 在净水市场中,多种净水技术并存,有超滤膜技术、RO反渗透过滤膜技术、活性炭吸附技术和PP棉物理过滤技术等。反渗透膜技术和超滤膜技术是最主要的两种技术。捷孚凯(GfK中国)零售监测数据显示,2017年1~5月,在线下市场,采用反渗透膜技术的产品零售额占比为68%,超滤膜技术产品占比为22%;在线上市场,反渗透膜技术产品占比为57%,超滤膜技术产品占比为32%(详见图2)。反渗透膜技术是在高于溶液渗透压的作用下,依据其他物质不能透过半透膜而将这些物质和水分离开来。反渗透净水器能够去除水中各种杂质,出水口感好,能降低水的硬度,但出水量小,用水量大。同时,该产品拥有水泵,需要通电,可能存在漏电风险,结构复杂,价格成本也较高。超滤技术是通过膜表面的微孔结构对物质进行选择性分离。超滤膜净水器能够去除水中各种有害杂质,不需要用泵,不插电,没有电气安全问题,故障率较低,而且结构简单,价格便宜,不过对供水特发事件的效果一般,适合水质较好的城市使用。", " 在反渗透净水器中,大通量无桶设计可以让净水器不储水,饮水者也无需等待便可以", " 喝到现滤现饮的净水,这类产品称为即滤型净水器。一般来说,市面上的净水器以50G(加仑)的反渗透膜居多,但这样的产品饮用时需要一定的等待时间。大通量反渗透净水器是指400G以上的净水器,流量为50G机器流量的8倍,通过泵的频繁运作来达到即开即饮的目的。无桶设计是在水源污染、自然水管道老化、以及净水器安装空间不大等问题下最好的解决方案。捷孚凯(GfK中国)零售监测数据显示,在反渗透净水器中,大通量无桶设计产品约占六成的市场规模。目前,市场上有一些品牌推出了600G和800G的大通量无桶净水器。未来,大通量无桶设计的净水器是发展趋势所在,这一指标会是消费者购买时所考量的因素之一。大通量无桶设计的反渗透净水器引领净水市场高端化。捷孚凯(GfK中国)零售监测数据显示,2017年1~5月,在线下市场,4000元以上的反渗透净水器约占整体反渗透净水器零售额的60%,这一数值相比2013年增长了1倍;在线上市场,反渗透净水器零售额规模上升较为明显的是2000~3000元的中端价格段,2017年1~5月的市场占比达到约40%。", " 反渗透净水器用水量大是其重要弊端,但如今市场上各品牌对净水器的废水比有所提升。目前,最为常见的是1:1的低废水比,即过滤出一杯纯水需要消耗一杯废水。另外,也有一些企业推出了纯水和废水比例为2:1和3:1的净水器。", " 线下市场大有可为", " 捷孚凯(GfK中国)线下大中城市零售监测数据显示,从城市级别上看,特大型城市净水器零售额最大,同比涨幅最高;从区域上看,华东、华北地区销售情况最好。这与消费者受教育程度以及当地水质有关。大型城市对净水器的接受程度高,净水器的普及率超过10%。", " 捷孚凯(GfK中国)零售监测数据显示,2017年1~5月,家电连锁渠道零售额占线下整体市场的59%,独立店面渠道占比为26%。家电连锁渠道是净水器目前最主要的线下销售渠道,相对于其他渠道,家电连锁具有品牌齐全,专业安装、售后服务等优势。不同于其他小家电,净水器更多和家庭装潢联系在一起,属于装修配套产品之一,因此净水器在独立店面渠道(诸如建材市场,家装商城)的市场规模占比较高。独立店面渠道主要销售厨下式净水机、软水机和中央净水机。究其原因,是因为此类净水机需要在装修时安装。但与家电连锁渠道相比,独立店面渠道目前可选择的品牌少,领导品牌不多。随着未来更多品牌的入驻,独立店面渠道会有更好的发展,在线下市场的占比会更大。", " 市场未来发展与展望", " 净水市场从最初的品牌、技术良莠不齐到现如今的市场规模成形,不仅得益于其本身从品质型产品往刚需型产品的转型,也是企业在核心技术上不断革新下所带动的成长。国家统计局数据显示,2016年,全国居民人均可支配收入同比增长8.4%,具有着受过良好教育、有较高的品牌意识和国际化意识的“80后”“90后”正逐渐成为消费主力军,这类消费者在价格敏感度方面相对较低。"], "segmented_title": ["数据", "|", "2017", "年", "1", "~", "5", "月", "中国", "净水器", "市场", "解析"], "segmented_paragraphs": [["原创", "2017", "-", "08", "-", "15", "Gfk", "(", "中国", ")", "电器", "微刊", "电器", "微刊"], ["行业", "|", "深度", "|", "视角"], ["净水器", 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",", "但", "如今", "市场", "上", "各", "品牌", "对", "净水器", "的", "废水", "比", "有所", "提升", "。", "目前", ",", "最为", "常见", "的", "是", "1", ":", "1", "的", "低", "废水", "比", ",", "即", "过滤", "出", "一", "杯", "纯水", "需要", "消耗", "一", "杯", "废水", "。", "另外", ",", "也有", "一些", "企业", "推出", "了", "纯水", "和", "废水", "比例", "为", "2", ":", "1", "和", "3", ":", "1", "的", "净水器", "。"], ["线", "下", "市场", "大有可为"], ["捷孚凯", "(", "GfK", "中国", ")", "线", "下", "大中城市", "零售", "监测", "数据", "显示", ",", "从", "城市", "级别", "上", "看", ",", "特", "大型", "城市", "净水器", "零售额", "最大", ",", "同比", "涨幅", "最高", ";", "从", "区域", "上", "看", ",", "华东", "、", "华北地区", "销售", "情况", "最好", "。", "这", "与", "消费者", "受", "教育", "程度", "以及", "当地", "水质", "有关", "。", "大型", "城市", "对", "净水器", "的", "接受", "程度", "高", ",", "净水器", "的", "普及率", "超过", "10", "%", "。"], ["捷孚凯", "(", "GfK", "中国", ")", "零售", "监测", "数据", "显示", ",", "2017", "年", "1", "~", "5", "月", ",", "家电连锁", "渠道", "零售额", "占", "线", "下", "整体", "市场", "的", "59", "%", ",", "独立", "店面", "渠道", "占", "比", "为", "26", "%", "。", "家电连锁", "渠道", "是", "净水器", "目前", "最", "主要", "的", "线", "下", "销售渠道", ",", "相对", "于", "其他", "渠道", ",", "家电连锁", "具有", "品牌", "齐全", ",", "专业", "安装", "、", "售后服务", "等", "优势", "。", "不同于", "其他", "小家电", ",", "净水器", "更多", "和", "家庭装潢", "联系", "在一起", ",", "属于", "装修", "配套产品", "之", "一", ",", "因此", "净水器", "在", "独立", "店面", "渠道", "(", "诸如", "建材市场", ",", "家装", "商城", ")", "的", "市场规模", "占", "比较", "高", "。", "独立", "店面", "渠道", "主要", "销售", "厨", "下", "式", "净水机", "、", "软水机", "和", "中央", "净水机", "。", "究", "其", "原因", ",", "是因为", "此类", "净水机", "需要", "在", "装修", "时", "安装", "。", "但", "与", "家电连锁", "渠道", "相比", ",", "独立", "店面", "渠道", "目前", "可", "选择", "的", "品牌", "少", ",", "领导", "品牌", "不", "多", "。", "随着", "未来", "更多", "品牌", "的", "入驻", ",", "独立", "店面", "渠道", "会", "有", "更好", "的", "发展", ",", "在线", "下", "市场", "的", "占", "比", "会", "更大", "。"], ["市场", "未来", "发展", "与", "展望"], ["净水", "市场", "从", "最初", "的", "品牌", "、", "技术", "良莠不齐", "到", "现如今", "的", "市场规模", "成形", ",", "不仅", "得益于", "其", "本身", "从", "品质", "型", "产品", "往", "刚需", "型", "产品", "的", "转型", ",", "也是", "企业", "在", "核心技术", "上", "不断", "革新", "下", "所", "带动", "的", "成长", "。", "国家统计局", "数据", "显示", ",", "2016", "年", ",", "全国", "居民", "人均可支配收入", "同比增长", "8.4", "%", ",", "具有", "着", "受过", "良好", "教育", "、", "有", "较", "高", "的", "品牌意识", "和", "国际化", "意识", "的", "“", "8", "0", "后", "”", "“", "90", "后", "”", "正", "逐渐", "成为", "消费", "主力军", ",", "这", "类", "消费者", "在", "价格", "敏感", "度", "方面", "相对", "较", "低", "。"]], "title": "数据|2017年1~5月中国净水器市场解析"}, {"bs_rank_pos": 7, "paragraphs": [" 如何选择净水器?", " 对每个人来说,干净的水和洁净的空气同等重要,二者缺一不可。然而如今这两样东西似乎都成了奢望,空气污染已严重影响我们的健康,如今水质的恶化同样让人非常头疼:究竟需不需要安装净水器呢?这么多净水产品,都有什么作用?怎么选才最合适呢?本文将就这些困扰大多数人的问题,做一个常识的普及。", " 针对不同水质,各种净水设备能净什么? 不同的设备能够解决哪些痛点? 具体情况如何选择?", " 常见的5种过滤方式:", " 上图的过滤方式是按照过滤精度排列的,它们各自的物质过滤能力是向左边包含的。", " 换成表格的形式:", " 所以针对不同的水质问题,应选用对应的净水设备。", " 近几年的研究表明我国地表水的水质在逐年改善,但局部地区的水质情况仍然令人感到担忧。", " 以我国典型村庄农村环境质量监测结果来看,净化饮用水有很大的必要性。", " ▲此图为全国典型农村村庄环境质量监测图,选取的案例有一定代表性,但不具备普适性。", " 除了自然水质问题之外,住宅区的储水方式、劣质的水管灯,也都有可能导致水里铅、有机物超标。", " 安装前置过滤,可以将管道和水箱中的防锈铁皮、昆虫、浮游生物、藻类、铁锈、泥沙等杂质过滤掉,使水恢复到一定标准!", " 优点:保护后续设备,无需耗材,成本低,清洁方便,使用寿命长(一般可达30年)。", " 缺点:过滤能力有限。", " 用法:产品配有观察虑瓶,使用一段时间后如有杂质,可以打开排污阀进行冲洗。", " 主要用途:通过超滤膜的高精度净化工艺,对水质进行有效的净化,去除水中可能含有的泥沙、铁锈、细菌、悬浮物、藻类、大分子有机物等多种有害物质,制造适合于全屋食用水的净水。", " 优点:能彻底滤除水中的细菌、铁锈、胶体等有害物质,保留水中对人体有益的微量元素和矿物质,且抗污染能力强。", " 缺点:不能降低水的硬度,煮水容器依然存在结垢。", " 用法:一般来说PP滤芯3个月需要更换,活性炭滤芯6个月需要更换。", " 主要用途:利用离子交换原理,去除水中易生成沉淀的钙镁离子,达到软化水质避免结垢的处理效果。", " 化学反应机理如下:", " Ca2+ + 2NaR——CaR2 + 2Na+", " Mg2+ + 2NaR——MgR2 + 2Na+", " 优点:安装软水的好处非常多", " 1、软水机能够有效防止水管道、热水器、马桶易积水垢、热效率低的现象。 2、用来洗菜,能有效清楚农药成分,延长蔬菜保鲜期;煮饭则可使饭粒松软光润,口感上升。 3、衣服在软水中洗涤比在硬水中寿命更长,晾干后不易泛黄。 4、用于洗浴,保证皮肤光线嫩滑,起到美容作用。", " 缺点:不能有效的去除水中的细菌、余氯等有害物质,是不能直接饮用的,必须搭配其他净水机或者直饮水机使用。", " 注:GPG为水硬度单位, 1GPG表示1 加仑水中硬度离子( 钙镁离子) 含量为1 格令。", " 结合我国水质硬度情况以及软水机的优点,笔者认为软水机能够提升日常生活质量。", " 反渗透净水机(RO机)", " 主要用途:一般来说都有五层滤芯,用途如下图", " 优点:比超滤机过滤的更加洁净。", " 缺点:RO膜纯水机要4吨自来水才能制备1吨纯净水,千家万户大量使用这种纯水机,宝贵的水资源会被大量浪费。不提倡北方缺水地区使用。", " 位置:净水系统的终端设备。", " 用途:它好比我们家庭中央空调里的分机,它不能独立使用,必须连接净水主机。", " 1、彻底解决“千滚水”、“阴阳水”的问题。 2、即用即热,省电且利用率(相比普通烧水方式)。 3、3-5秒沸腾,立即享用持续沸水,且杀菌彻底。", " 缺点:目前还没有绝对的。", " 各种机器的实际用途示意图:", " 为了使不同的生活场景能够获得相应的水源,那该如何组合这些产品?", " 厨房用水/饮用水路:需要最彻底的过滤 沐浴/洗涤水路:需要使用软水 日常清洁水路:粗过滤即可", " 这样就可以得到一个软水及全屋水处理的整体方案。", " A:安装全屋净水机、软水机,需要具备,地漏、管道、电源设备放置空间等条件才能安装。", " A:两款产品功能不一样,中央净水机用来去除余氯、异色异味、细菌、重金属灯,软水机则是用来软化水质,解决生活用水问题。", " A:中央净水和软水主要解决洗澡、洗涤等生活用水,不能达到直饮水标准,而净水器是进一步进化水质,解决厨房做饭、饮用水等问题。前置过滤器则主要是去除杂质,减轻后面过滤负担。", " A:喝纯水的确会带走身体部分的矿物质,但是人体吸收微量元素的途径众多,水只是其中之一,且占比不大;人体吸收水的途径也不少,比如饭菜、饮料等。", " [1] 刘允、孙宗光,2001—2012 年全国水环境质量趋势分析,环境化学,2014 [2] 马广文、王晓斐,我国典型村庄农村环境质量监测与评价,中国环境监测,2016", " UF超滤纳滤膜净水器,有多级前置过滤和超滤膜组成,能截留绝大部分有细菌、有害物质,是市场上最常用的分离、过滤效果最为普通的直饮水净水装置", " 净水器如何选择确实经常困扰大家,我作为一名水质监测人员就说说自己的看法。", " 1、每个地方的水质情况都不完全相同,大家要根据情况选择,比如自己这个地方的水质铅含量超标,就需要选择除铅能力比较强的净水器。 2、我们普通人怎么判断净水器的去除这些污染物的实际能力是什么样呢,我觉得还是要借助国际上的权威认证,其实我们搞监测的也很多地方是用的国际上的通用标准,而且现在新建的自来处理厂的设备都是选择的国际上有权威认证的设备。因为现在产品品类太多,包括老外他们也判断不出真实的好坏,所以国际上一般都会认定一个最权威的认证中立机构对主流的净水器或污水处理设备做检测,通过得出的数据判断出真实的设备好坏。我觉得大家也可以借鉴这个办法,毕竟水处理设备是国外发明的,国际标准也是人家制定的,他们会比较专业和权威一些,我们普通人对照这些也可以节省时间和精力。 3、关于主要的两种技术反渗透和超滤的选择问题,大家始终要记住,没有哪个是能100%去除全部污染物的,如果真的可以的话,估计所有的饮料厂和水制品厂都可以开全自动设备自动生产,并取消品控人员了。电视上经常曝光的食品安全问题也不会出,大家都开心了。所以,这都是不现实的事情,还是那句话,参考国际权威认证对设备去除污染物能力给出的报告,普通人也可以看的一清二楚到底去除了多少种污染物!", " 我总结了下,大家对于净水器的症结大致有以下五种:品牌选择困难症,技术脑盲症,价格眼盲症,效果茫然症,买不买纠结症。根据这五大症结,码出这篇攻略,助你砍柴前先磨磨刀。知己知彼,百战不殆,这是互联网时代对消费者的基本要求。", " 大家都知道净水器的关键在滤芯,所以按滤芯可以分为微滤、超滤、纳滤、反渗透,各种技术的特点分别如下。。。。。。。这个我相信大家都不想看了,自己查下就好,现在市面上主要是超滤和反渗透两种,两种的主要区别就是超滤是保留矿物质的,而反渗透是纯净水,什么都不保留的。", " 好吧,看了前面的回答,各路大神对技术方面已经解释的很详细了,不过我相信大多数人看完答案后会更加云里雾里,被各种所谓的技术搞的身心俱疲,还是不知道那种净水器好,该怎么选择。怎么办呢?让我来试试能不能帮到你们。", " 首先,总结下各路大神的答案中我认为存在的几点分歧:"], "segmented_title": ["如何", "选择", "净水器", "?", "-", "知乎"], "segmented_paragraphs": [["如何", "选择", "净水器", "?"], ["对", "每个", "人", "来", "说", ",", "干净", "的", "水", 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"区别", "就是", "超滤", "是", "保留", "矿物质", "的", ",", "而", "反渗透", "是", "纯净水", ",", "什么都不", "保留", "的", "。"], ["好", "吧", ",", "看", "了", "前面", "的", "回答", ",", "各", "路", "大神", "对", "技术", "方面", "已经", "解释", "的", "很", "详细", "了", ",", "不过", "我相信", "大多数", "人", "看", "完", "答案", "后", "会", "更加", "云里雾里", ",", "被", "各种", "所谓", "的", "技术", "搞", "的", "身心", "俱", "疲", ",", "还是", "不知道", "那种", "净水器", "好", ",", "该", "怎么", "选择", "。", "怎么办", "呢", "?", "让", "我", "来", "试", "试", "能不能", "帮", "到", "你们", "。"], ["首先", ",", "总结", "下", "各", "路", "大神", "的", "答案", "中", "我", "认为", "存在", "的", "几点", "分歧", ":"]], "title": "如何选择净水器? - 知乎"}, {"bs_rank_pos": 8, "paragraphs": ["新闻中心 >", " 2016年净水器市场回顾:增速放缓 洗牌初现", "近几年,中国经济关键数字GDP进入了L型增长。2016年前三季度,国内生产总值529971亿元,按可比价格计算,同比增长6.7%。宏观经济的发展也同样影响着净水器行业的发展。", " 在所有家电品类中,空净一直作为净水的参照物而存在。但从2016年来看,空净行业受日益严重的雾霾而获得了又一波高速的增长。而反观净水行业,2016年几乎没有大型水污染事件出现,且消费者对于水污染事件的防御和处理大多是通过购买瓶装水或桶装水,而非通过购买净水设备。由此,2016年净水行业的增长主要依托消费者日益提升的健康饮水意识和较低的市场拥有量。目前,城镇家庭净水设备每百户拥有量不足10台,而洗衣机、冰箱、空调等拥有量均为百余台。据中怡康调查数据显示,消费者在健康类产品的投资上,包括净水器在内的净化类产品仅排第四,排在有机食品、保健品、身体检查之后。", " 2016上海水展", " 洗牌初现 末位淘汰", " 2016年,净水行业的品牌格局大致保持了2015年的状态。但在行业增速放缓的过程中,行业洗牌的现象已经开始显现,一批靠资本力量进入想赚快钱的企业以及一些三无(无实体企业、无资本实力、无售后)企业逐渐退出市场,一些净水业务发展不利的企业则转型厨卫、五金卫浴等领域。", " 第一阵营的三大品牌的年出货额为9亿元以上。并依旧由2015年的三甲:美的、沁园(加联合利华)、A.O.史密斯组成。 第二阵营的年出货额在1亿到5亿元之间。而在5亿元到8亿元区间依旧和2015年一样保持真空,2016年依旧没有一个品牌进入这一区间。第二阵营主要由安吉尔、飞利浦、怡口、四季沐歌、太阳雨、海尔、开能、浩泽、碧水源、3M、滨特尔 (加爱惠浦)等品牌组成。", " 第三阵营的门槛在5000万元到1亿元之间,5000万也可以称为国内净水企业规模化的分水岭。第三阵营主要由格力、TCL、云米(小米)、斯隆、沁尔康、爱尼克斯等品牌组成。", " 在新品牌进入方面,和2015年大举进军净水行业有所不同的是,2016年仅有少数厨卫企业进入净水行业,且大多以一两款产品辅助厨卫产品销售。在明星代言上,2016年也没有企业聘请全新的代言人,大多是原有的代言合同继续履行。", " 主流品牌2016年在营销战略布局和营销手段的革新上仍旧动作频频。", " 美的净水器在2016年最大的事件是在8月重磅推出了旗下高端新品比佛利系列。与此同时,配合比弗利系列与加多宝凉茶展开了跨界组合营销。", " 沁园2016年的工作重心和2015年一样依旧放在常规销售上。以销售为根本目的的净水科普活动贯穿全年,关爱留守儿童的饮水健康公益活动穿插其中。", " A.O.史密斯净水在2014年和2015年取得了飞速发展,但在2016年也受到了行业整体发展放缓的影响。2016年,A.O.史密斯开辟了空净领域实施空净、净水两条腿走路。2016年3月,位于南京的A.O.史密斯水处理项目基地奠基,占地面积约330亩,总投资1.5亿美元,2016年计划投资4800万美金,主要建设水处理和空气净化产品生产基地。项目建成后,约年产净水机300万台、空气净化器100万台、软水机80万台、其他相关产品20万台。2016年9月,A.O.斯密斯花费6亿元人民币收购了美国进口净水器品牌Aquasana(阿克萨纳),进一步扩大在净水业务的布局。除此以外,A.O.斯密斯净水的市场活动也主要以常规销售为主。", " 经销商询问飞利浦智能台式直饮机", " 飞利浦净水在2016年最大动作是将生产与运营基地从上海松江搬迁到了浙江嘉兴全新的基地,并成立飞利浦(嘉兴)健康科技有限公司。在2015年成功将净水产品从单一的水龙头、净水壶扩充至全品类净水后,2016年在渠道上也从原有的线上向线下不断延伸,并在全国范围内扩充线下销售网点。", " 整体而言,在市场活动上,整个2016年的国内净水品牌以夯实市场基础,全力推进渠道建设为主。", " 会销势弱 消费理性", " 2016年的净水器销售渠道和2015年相比,在形式上变化并不大,依旧是通过传统零售渠道、KA卖场、专卖店、电商、建材市场、装修公司及会销、展销等等多种形式。但和2015年会销一篇火热,净水器企业摇摆不定的情况有所不同的是。会销这一独特的销售形式在2016年出现了拐点。", " 2016年期间,广东、湖南、山东、四川、河北、江西、湖北、辽宁等省超过100名消费者在多个投诉渠道投诉多家知名净水器品牌的代理商以“客户答谢会”名义通过欺骗式营销手法推销反渗透净水器,投诉人要求品牌方退款、致歉。", " 根据多名投诉人的描述,代理商的营销模式与2014年-2015年间在全国各乡镇地区肆虐的“净水器骗局”雷同。通过正常的电解水试验,可以使自来水中出现红色、黑色、绿色杂质,而售卖净水器人员却使用该试验冒充“水质测试”蒙骗消费者,故意夸大水质问题,诱导消费者购买产品。投诉事情发生后,几家净水器其企业立即对经销商进行了整改。而这一信号也在净水器行业激起了旋流,会销被大多数净水器企业嗤之以鼻,消费者也不再轻易地相信。整体而言,2016年净水设备的营销模式还是延续了2015年的活动营销的方式,经销商大多以村镇为单位进行地毯式地摸底销售。", " 在电商渠道,接二连三的天猫聚划算、京东众筹等促销活动层出不穷。在电子商务热潮的推动下,线上对净水市场影响力与日俱增。据中怡康公布,2012年线上仅能够占到整体市场销售额的3.9%, 2016年全年线上市场已占到净水设备市场35%的零售额份额。", " 另一方面,厂商对专卖店开设及门店控制的要求变严格以及建材市场、装修公司等小众渠道出货量极少的特点使得小众渠道已逐渐淡出净水器主流销售渠道。目前,厂商所掌握的净水器零售渠道主要以专卖店为主。", " 2016年净水器线上线上销售情况", " 本站支持键盘左右键(←→)翻页", "相关新闻 ", "导购评测 更多 "], "segmented_title": ["2016", "年", "净水器", "市场", "回顾", ":", "增速", "放缓", "洗牌", "初", "现", "-", "艾肯", "家电", "网"], "segmented_paragraphs": [["新闻中心", ">"], ["2016", "年", "净水器", "市场", "回顾", ":", "增速", "放缓", "洗牌", "初", "现"], ["近", "几", "年", ",", "中国", "经济", "关键", "数字", "GDP", "进入", "了", "L", "型", "增长", "。", "2016", "年", "前", "三", "季度", ",", "国内生产总值", "529971", "亿", "元", ",", "按", "可比", "价格", "计算", ",", "同比增长", "6", ".", "7", "%", "。", "宏观经济", "的", 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第二、规划投资预算现在做任何生意,都是需要投资的,净水器行业也不例外。投资首先是从经济和时间两个方面考虑的,要有足够的流动资金和时间。如果没有资金,没有时间,是很难做好净水器的。入行净水器需要全身心的投入,这样才能在净水器行业中转到自己想要的财富。 第三、考察当地市场 在一个地方做生意必须要做的就是考察地方区域市场,因为每个地方的市场环境都是很复杂的,这个需要投资者深入的调查了解。可以通过身边的朋友对净水器是否感兴趣,打听当地净水器专卖店的生意如何,当地人对于净水器品牌有哪些喜好,价格大概多少可以接受,喜欢什么样子的款,当地水质污染情况如何等分析,这样做好前期准备工作能为后期的运营提高很大的效益。 第四、创新营销策略 如何创新营销策略,我之前遇到很多做过净水器的朋友,刚开始做净水器就是开个店采用“守株待兔”的方式。然而效果却是很不理想,在很多地区,很多人对净水器都是比较陌生的,绝大多数消费者都是刚开始了解,那么作为投资者需主动出击,寻找多种营销渠道,比如可以主动联系亲戚朋友,主动去小区推销,主动联系卖场入驻,多跑跑办公写字楼、以及到农村多做宣传活动等。 第五、厂家扶持政策", " 作为厂家的扶持政策对于净水器加盟代理商来说也是很重要的一环,许多厂家对代理商只是给产品,这样是远远不能达到代理商期望的。一定要本着“合作共赢,共创辉煌”的原则。对净水器代理大力支持,解决代理商所需才能更好更长足地发展。首先对净水器代理商会有一个详细的提货方案,提供有效的宣传资料以及其他的物资物料、人员培训、营销模式的支持、以及帮助代理商在前期打好市场基础、这样就能很快帮助一些新手在净水器行业站住脚、赚到钱。"], "segmented_title": ["一", "个", "“", "新手", "”", "该", "如何", "操作", "净水器", "市场", "呢", "-", "健康频道", "-", "手机", "搜狐"], "segmented_paragraphs": [["近年来", "的", "净水器", "行业", "快速发展", ",", "行业", "潜力无限", ",", "也", "受到", "了", "诸多", "投资者", "的", "关注", ",", "那么", "对于", "很多", "没有", "接触", "过", "净水器", "行业", "的", 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Crowne Plaza Hailing Island Hotel  yonni_yy “酒店配套一般,但是海滩非常天然。酒店人员很热情但感觉缺乏培训,早餐一般。酒店的位置离市区非常近,非常适合自驾游,总的来说是一次...”  88个点评 2一呆公寓·阳江海陵岛保利银滩(原阳江海陵岛保利银滩伊思德度假公寓)  lisa “订了房才看到一些不好的评论,因为酒店不能退,只能抱着侥幸的心理过去啦。结果如下,订的海景房,被告知是4_6层,5.6层都没房,那就这...” ", " 416个点评 1阳江海陵岛  一池春水..  “海陵岛很大,但能玩的地方有限,保利银滩那边听说淹死过人,所以封起来了。很多地方禁止下海...”  373个点评 2十里银滩  蚂蜂窝用户 “在离深圳、巽寮、双月湾不远的地方有个盐洲岛,人少景美。驴友称为"天空之境”的玄...” ", " 13个点评 1冰室云吞  yuyu “感觉不是很适合贵州四川人的口味,吃的人很多,但是个人觉得很一般,云吞汤和馅味精味很重,吃完口很渴,猪肝粉没有什么味道,味道主要...”  78个点评 2新城渔邨  dtex8532 “很棒 和爸妈一起来的 点了一大桌海鲜大餐 吃得很满足 不明白为什么评论上面有差评 大概萝卜青菜各有所爱 五年前来过一次海陵岛 当...” ", " 兰兰Jacinthe 打算7.4-5号去阳江海陵岛玩～东莞出发～因为穷游就坐大巴去～求各路大神帮我安排下轻松点的行程～谢谢  南巷少年 最佳答案  这位同学请放心,就算不穷游的同学也是要坐大巴去的,因为去海陵岛-也就是闸坡的话首先: 1:先从你本地的客运汽车站买来阳江客运站的票,到了阳江下车后,直接跑到", " 韩H_X曦 跪求阳江海陵岛详细游玩攻略… 南巷少年 最佳答案  想必您交通状况都计划好了吧?我还是再给您策划一下吧 你先乘搭阳江汽车总站的客运汽车去闸坡,单程票价大概是13-16,双程20左右到达闸坡后直接下车(下车后摩的司", "阳江游记 ", " 收起筛选指南 目前暂不支持“按条件筛选”和“主题推荐”的双重条件筛选。:)例如:“按出发时间”筛选之后,再进行主题推荐的筛选,系统会默认取消“按时间筛选”筛选的操作,只显示主题推荐的内容。反之亦然。", "APP 2017国庆:广州出发-阳江海陵岛-恩平鳌峰公园-开平雕楼-鹤山七星坑-回广州 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",", "回味", "起来", ",", "却", "有", "久久", "不", ".", ".", "."]], "title": "2017阳江旅游攻略,阳江自由行攻略,蚂蜂窝阳江出游攻略游记 - 蚂蜂窝"}, {"bs_rank_pos": 2, "paragraphs": ["阳江市地处广东省西南沿海,紧邻珠三角,扼粤西要冲。海岸线长341.5公里,主要岛屿有30个,岛岸线长49.3公里。辖阳东县、阳西县、江城区、岗侨区、海陵岛经济开发试验区,代管阳春市(县级市)。 特色景点: 1、东湖星岛 东湖水库是阳江八景之一。该库区水面星星点点分布着108个岛,故名曰“东湖星岛”。它位于阳东县那龙镇东南5公里。 2、南鹏岛 一个桃源--南鹏岛　　 3、东水山景区 东水山景区阳西西北山区,距县城约40公里,主峰鹅凰障高达1337米,是广东省第四高山,山顶可直望南海。 4、大澳渔村 大澳渔村为一古渔港,位于东平镇东南,史称“六澳之首”。中国古代南“海上丝绸之路”的必经港口。与广州“十三行”相列,史称“十三行尾”。"], "segmented_title": ["首先", ",", "1", ".", "阳江", "有", "哪里", "比较", "好玩", "的", "地方", "?", "而且", "不要", "太", "贵", "的", "。", "2", ".", "去", "沙扒湾", "旅游"], "segmented_paragraphs": [["阳江市", "地", "处", "广东省", "西南", "沿海", ",", "紧邻", "珠三角", ",", "扼", "粤西", "要", "冲", "。", "海岸线", "长", "341", ".", "5", "公里", ",", "主要", "岛屿", "有", "30", "个", ",", "岛", "岸", "线", "长", "49", ".", "3", "公里", "。", "辖", "阳东县", "、", "阳西县", "、", "江城区", "、", "岗", "侨", "区", "、", "海陵岛", "经济", "开发", "试验区", ",", "代", "管", "阳春市", "(", "县级市", ")", "。", "特色", "景点", ":", "1", 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阳江月亮湾度假村", " 阳江月亮湾度假村位于广东省阳江市阳西县,所在的月亮湾海滨海岸线弯长达5公里,极像新月弧形,故名。其坡缓水清沙细,依山傍水,宛如一弯新月。", " 月亮湾度假中心内分咸温泉浴疗区、海滨活动区、综合服务区三大区域。", " 大角湾风景区", " 大角湾是中国AAAA级著名的旅游景点,位于广东省阳江市海陵岛闸坡镇东南,背倚青山翠岭,独得大自然垂青,以阳光、沙滩、海浪、海鲜驰名于世,是海陵岛知名度最高的景点。该“大角湾”景点与阳西沙扒月亮湾并称为“姐妹湾”。", " 美丽的大角湾滩长45公里,宽100米,螺线形湾似巨大的牛角,故名大角湾。大角湾三面群峰拱护,面向浩瀚南海,两边大角山与望寮岭拱卫,湾内风和浪软,峰顶时有云雾缭绕,正所谓山无水不秀,峰无云不媚,景观层次丰富。", " 春湾风景区内群峰扰聚,千姿百态,名胜奇多,有龙宫岩、春湾石林、通天蜡烛、慈云岩、通真岩等景点。春湾湖畔的“通天蜡烛”,山峰奇秀,湖山相映,垂柳轻飘,景色迷人,有小桂林之称。龙宫岩属于长廊式溶洞,长1000米,岩内石钟乳星辰的景物绚丽多彩,精巧夺目,极象海底龙宫之物。 龙宫岩按照洞内所形成的景致,分迎宾廊、龙王殿、聚宝库、龙母阁四段来游览。这里美如诗画,备受影视界青睐,成为影视外景拍摄基地。通真岩建于隋代,原为铜石岩,是省级文物保护单位。通真岩又名“刘三妹歌台”,相传唐景隆年间,广西贵县刘三姐与祖父自广西传歌至此,得道升仙,故此地又叫“通真岩”,岩内至今仍有当年传歌台和刘三姐祖父的陵墓。", " 1992年6月18日,经广东省人民政府批准设立海陵岛经济试验区,辖海陵、闸坡两镇.海陵岛还有丰富的人文和历史景观,如太傅庙、灵谷庙古炮台、镇海亭、观音阁等10多处,处处都有着一段悲壮动人的故事。十里银滩中,银海城内还有一座面积6万平方米,中国最大的仿宋建筑――宋城,城内有53座仿宋筑,无论远观近瞻,都百态千姿,尉为壮观,更让人亲身体会宋王朝的气派雄伟。外\"东方夏威夷\"之美称,被誉为一块未经雕琢的翡翠。四面环海,以水碧、沙净和游海水、住海边、食海鲜、买海味的特色驰名中外。海陵岛全年日照时间长,年平均气温28度,年晴天数310天,四季分明,气候宜人,是旅游度假的理想地方。海陵岛海鲜可谓物美价廉,渔民特制的\"蛋家菜\"不可错过。虾和烧烤海味也别有风味.海陵岛位于广东阳江市西南端,是广东第四大岛。该岛交通便利,经广湛高速公路至广州仅2小时,经沿海高速公路至珠海仅5小时,是粤西、粤东、粤中的海上中转站。全岛陆地总面积108平方公里,有可供开发的土地58平方公里,耕地8万亩,浅海滩涂17万亩,岛岸线总长147公里,人口6万人。1992年6月18日,经广东省人民政府批准设立海陵岛经济开发试验区,辖海陵、闸坡两镇.海陵岛还有丰富的人文和历史景观,如太傅庙、灵谷庙、古炮台、镇海亭、观音阁等10多处,处处都有着一段悲壮动人的故事。", " 沙扒湾位于阳江地区阳西县沙扒镇,有中国的“马尔代夫”之称。它头枕风光秀丽的北仔岭,面向浩瀚的南海。沙扒湾包括沙扒海滨、月亮湾、青洲岛和白额岭等景点。这里,湾似虹,沙如玉,岸边绿林含翠,海面碧波粼粼,是一个理想的天然浴场。沙扒湾海岸线长2公里,由于住宿接待酒店少,规模较小,外来旅游人数有限,因此这里没有其他海滩的喧哗,2公里长的海岸线上可能只留下你一个人长长的脚印。岸边马尾松林带7万多平方米,绿树成荫。", " 青州岛周围生态保存得非常好,海水清澈、海产丰富。在这里可以畅游清澈的海水,抓海胆、海参,还可以捡五彩的鹅卵石。傍晚坐在沙滩边上的餐厅,吹着海风,看着日落,品着真正的海鲜。晚饭后漫步往古朴的渔村,看着坐在家门织网的渔家妇女,感受粤西渔村的祥和生活。", " 东方银滩坐落在阳江市海陵岛,位于列入世界吉尼斯记录大全的“十里银滩”边,与十里银滩连为一体,由阳江市东方银滩欢乐广场服务有限公司投资开发,2004年底开始修建,于2005年8月1号正式投入市场向游客开放。它位于闸坡镇海滨大道东,地理位置得天独厚,北隅大角山,南面浩瀚的南海,西临鹰嘴角,东面是一望无际的“十里银滩”,项目占地面积12万平方米,已办理手续为8万平方米。海岸线1100平方米,海滨浴场沙滩长700平方米。可同时容纳两万多游客游乐,水清沙洁,环境优美。景区风格独具特色,是一个集大型生态海滨浴场、观海听涛、宾馆住宿、海鲜餐饮、茶艺厅、演艺吧等于一体的多功能旅游风景区。交通便捷,距离广州只需三个小时车程,距珠海、香港等地仅需一个半小时车程。有别于海陵岛其它的海滩景区,南海之窗――东方银滩保留了更原始的自然气息,而又汇聚了人性化构思,更是绽放出吸引极强的魅力。它位于海陵岛之东,是旭日初升的南海所在,风光美不胜收,亚热带季风海洋气候,常年气温大约28度,是盛夏度假胜地,此为天时。环绕整个东方银滩是葱葱郁郁的树林,而且三面环山一面向海,风平浪静,是游泳的最佳浴场,此为地利。早在东方银滩未开发之前,来自五湖四海的游客只能在简陋的望海亭上遥观海景,欣赏这片美丽的海滩,见识到这片处女地并为它的迤俪而折倒,此为人和。", " 玉溪三洞景区", " 玉溪三洞景区位于阳春市西北部,距市区约70公里,省道S369线绕景区而过。该景区既是省级风景名胜区,又是国家地质公园的重要组成部分。 玉溪三洞景区由漠阳江上游的甘婪河水穿透三座石山溶蚀而成,全长3公里多,均可坐船游览,由二洞乘船往上游。第二洞长约330米,河水清澈流璧,水中多生长鲤鱼。洞中景物丰富,尤以“三变石”为趣, 诗人张永枚曰:“远似渔翁钓清流,近如狮子滚绣球,忽变犀牛望明月,一石三景迷轻舟”。第一洞顶的“皇冠”石,酷似皇冠,游人在船上随即可拍摄一幅头戴皇冠的照片。第一洞长400米,上面有一层旱洞,水面距洞顶48米,该洞以“金盏叠泉”最为壮观――一个个金色石盏满载清澈的泉水逐层下溢。诗人韦丘赞道:“串玉溪穿三洞天,柔情碧水石能言,声声燕语留归客,举桨犹疑落桨难。”在玉溪二洞下游的1公里处,还有玉溪第三洞,洞长约500米,既可坐船游玩,又可登岩赏景,洞中景物千姿百态,栩栩如生。坐船慢游其中,如穿梭在山水画廊,颇有桃源之趣。"], 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["还", "可以", "输入", "140", "字"], ["还", "没有人", "评论", "哦", ",", "赶紧", "抢", "一", "个", "沙发", "吧", "!"]], "title": "【孩子多大能办身份证】_新生儿能办身份证吗_孩子多大能领身份证_"}, {"bs_rank_pos": 2, "paragraphs": [" 1.办理入户手续后,系统内即产生相应的身份证号码。故可办理身份证 2.因小孩年龄太小,不方便照相,建议等年纪大点再去办理. 公安部对居民身份证办理年龄规定第二条 居住在中华人民共和国境内的年满十六周岁的中国公民,应当依照本法的规定申请领取居民身份证;未满十六周岁的中国公民,可以依照本法的规定申请领取居民身份证。第五条 十六周岁以上公民的居民身份证的有效期为十年、二十年、长期。十六周岁至二十五周岁的,发给有效期十年的居民身份证;二十六周岁至四十五周岁的,发给有效期二十年的居民身份证;四十六周岁以上的,发给长期有效的居民身份证。未满十六周岁的公民,自愿申请领取居民身份证的,发给有效期五年的居民身份证。 ", " 旋转马戏团 2013-04-05 16:52 ", " 宝宝知道提示您:回答为网友贡献,仅供参考。", " 中国公民年满16周岁,可以到户籍所在地公安部门申领身份证!,这里有个条件是年满16周岁!", " hyr0829 2013-04-05 16:51 ", " 小孩子可以上户口,身份证那么小的孩子还没有的", " 嘎个猪头 2013-04-05 16:51 ", " 不能,太小了 扁予甘雨皿0428 2013-04-05 16:57 ", " 刚出生的儿童就可办证 5年后重新换证", " lumingz11111 2013-04-05 17:04 "], "segmented_title": ["出生", "一个月", "的", "婴儿", "可以", "办", "身份证", "吗", "?", "_", "百度", "宝宝", "知道"], "segmented_paragraphs": [["1", ".", "办理", "入户", "手续", "后", ",", "系统", "内", "即", "产生", "相应", "的", "身份证号码", "。", "故", "可", "办理", 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在大少数人的观念里觉得,孩子只要满16周岁才可以操持身份证。但是如今政策改了,孩子出生后,只需上了户口,有了身份证号码就可以操持身份证了。不过由于孩子发育快,变化比拟大,所以16周岁以下的孩子操持的身份证无效期是5年。也就是假如孩子是3岁操持的身份证,那麼8岁的时分就要去换新的,次要是身份证上的照片需求改换。", " 给孩子操持身份证的流程是什麼呢?", " 孩子操持身份证的时分需求父母的陪同,而且要穿深色的衣服。另外还要带上父母的身份证、孩子的户口本、孩子的出生证明、学籍证明(没有的话可以不带),而且都得是原件。", " 需求留意的是,拍摄照片的时分是需求孩子可以单独坐正的,所以太小的孩子不合适操持身份证。", " 孩子操持身份证的益处有哪些呢?", " 可以获取一定的平安保证,特别是在遇到风险的时分,警方可以疾速确实认孩子身份,与父母联络。还可以留作留念,毕竟小孩子的光阴过来就不复返了。另外,出出境方便,操持业务也比拟方便。返回搜狐,查看更多", "声明:本文由入驻搜狐号的作者撰写,除搜狐官方账号外,观点仅代表作者本人,不代表搜狐立场。"], "segmented_title": ["小宝宝", "可以", "办", "身份证", "了", ",", "你不知道", "吧", "?", "好处", "还", "很多", "。", "_", "搜狐", "母婴", "_", "搜狐网"], "segmented_paragraphs": [["文章", "总", "阅读"], ["黄", "女士", "家境", "比拟", "殷实", ",", "女儿", "如今", "1", "岁", "多", ",", "黄", "女士", "想着", "在", "女儿", "小", "的", "时", "分", "就", "给", "她", "存", "一", "笔", "存款", ",", "当前", "万一", "有", "什", "麼", "变故", "女儿", "也", "不", "至于", "跟着", "享福", "。", "于是", "黄", "女士", "就", "以", "女儿", "的", "名义", "去", "银行开户", ",", "但是", "银行", "方面", "说", "想要", "开户", "得", "女儿", "的", "身份证", 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新注册一个单项业务的商标,公司设计做了个简单的logo,挺简洁好看的,就是用到了方正和汉仪的几个字,会不会涉及字体版权侵权?听说字体库,是把每个字按照一个单独的美术作品列入版权的,会不会有问题?该如何规避法务上的风险?谢谢显示全部", " 有问题. 没法规避.", " 谢邀～看楼上回答有问题,我比较忙就简单说一下了。字体属于著作权法保护的范畴,你将他人享有著作权的作品作为商标注册并使用属于商业使用行为。故你的使用是侵权的。by知识产权石磊", " 《商标法》规定,申请商标注册不得损害他人现有的在先权利。该“在先权利”包括他人在先的著作权。于是,便产生了一个疑问,商标中使用的文字字体,是否可能侵犯他人在先的著作权呢?", " 商标指的是任何能够将自然人、法人或者其他组织的商品与他人的商品相区别开的标志,包括文字、图形、字母、数字、三维标志、颜色组合和声音等。文字,正是商标构成的主要要素之一。", " 汉字,不同于其他语言的书写文字,其经过几千年的演变,形成了丰富多样的字体,不同的字体风格都具有一定的审美价值,也因此诞生了书法这样一门表现艺术。正因汉字字体具有审美价值,我国法律便将其纳入美术作品行列,享受著作权法的保护。", " 商标中使用他人设计的字体是否构成侵权,不能简单的从一个角度来看,而且各地法院的判例也有差异,不足以论必然。", " 首先,商标中使用的文字字数有限,往往不过三两个,因此对于单字是否构成美术作品,享有著作权的保护,是争议的焦点。", " 一者认为有独特的设计,单字也是美术作品,理应受到保护。 【方正诉百味林案】,二审法院南京中级人民法院认为,字库整体艺术风格一致的基础是每个单字之间的艺术风格一致,不能因为字库整体艺术风格一致的独创性而否定单字的独创性,涉案争议的百味林所使用的方正喵呜体中“苏、式、话、梅、盐、津、橄、榄、半、李”十个字具有鲜明的艺术风格,每个单字都凝聚着设计者的智慧和创造性劳动,具有独特的艺术效果和审美意义,达到著作权法意义上的美术作品独创性的要求。因此,百味林公司对方正喵呜体字库中具有独创性单字的使用行为构成侵权。 另者认为单字不构成美术作品,字体库才构成美术作品。 【方正诉宝洁案】,一审法院北京市海淀区人民法院认为从社会对于汉字使用的效果来讲,如果认定字库中的每一个单字构成美术作品,使用的单字与某个稍有特点的字库中的单字相近,就可能因为实质性相似构成侵权,必然影响汉字作为语言符号的功能性,使社会公众无从选择,难以判断和承受自己行为的后果,也对汉字这一文化符号的正常使用和发展构成障碍,不符合著作权法保护作品独创性的初衷。二审北京市第一中级人民法院也予以支持。其次,商标中使用的文字字体使用是否侵权,还要追根溯源。 比如A委托B设计文字商标,B购买了某正版的字库产品进行设计,并将最终的文字商标出售给A进行后续的复制、发行,那么A对该文字商标的使用是合法的。因为B购买正版字库产品时,即同时取得了以合理期待的方式行使该知识产权的默示许可,不需在购买行为之外另行获得许可。 但是,若字库权利人对于购买者有明确、合理且有效的限制,且购买者已接受这一限制,则应认定相应后续使用行为不属于购买者合理期待的使用行为。比如字库权利人限制购买者B使用字库字体设计商标,B在认同该限制条件的情况下依然使用该字库字体设计商标,那么A从B处购买的文字商标就可能涉及到侵权。 对于商标中的文字字体是否涉及到侵权,目前尚存在争议,侵权与否的判断没有形成统一的标准。期待最高人民法院出台相关解释,或者公布相关指导案例,以正视听。 商业发布使用字体,最好追溯版权,取得授权或更换字体,规避法律风险。 要么买字体版权 要么重新设计字体 需要得到方正公司的授权,不然后面找到你跟麻烦,搞不好还要诉讼你们,我在方正工作过 中细软创新科技服务云平台 公众号:gbicom 有可能会涉及侵权,尽量还是不要用的好。方正字体和汉仪字体都是有版权的,还是避免产生不必要的麻烦。如果您非要使用,建议您购买版权后再进行商业使用,这样才有备无患。 想问楼主 后来怎么解决 我们现在也是logo里用了他们的字体 我们想继续使用 汉仪开价好高 是会侵权的,需要购买版权才可以 建议方正字体不要用,现在很多企业收到方正公司的律师函,要求付费使用他们的字体,其他字体所属公司目前很少采取这种行为,不过不排除,还是谨慎为好 谢谢大家,我已经找专业设计公司重新设计字体了。前期花点小钱,好过以后打官司 不要用他们的字体,最近我们公司因为网站的Logo用了汉仪的柏青体,被对方找来说我们侵权 记得老师教过,为了规避设计中的字体版权问题,可以对字体进行二次设计啊,通过修改部分字形比例。对于你说的LOGO设计中的少数出现的字体来说,工作量应该不大,我觉得可行 建议您看一下针对“飘柔”使用倩体,方正诉保洁的判决书。法院认为使用单个字不算侵权。本院认为,方正倩体字库字体具有一定的独创性,符合我国著作权法规定的美术作品的要求,可以进行整体性保护;但对于字库中的单字,不能作为美术作品给予权利保护。方正公司以侵犯倩体字库中“飘柔”二字的美术作品著作权为由,要求认定最终用户宝洁公司的使用行为侵权,没有法律依据,其以此为基础,对宝洁公司和家乐福公司提出的全部诉讼请求,本院不予支持。 在注册商标时,使用普通字体可获得最大的保护,在实际运用中是可以用其他字体的。 商标是商品的生产者、经营者在其生产、制造、加工、拣选或者经销的商品上或者服务的提供者在其提供的服务上采用的,用于区别商品或服务来源的,由文字、图形、字母、数字、三维标志、声音、颜色组合,或上述要素的组合,具有显著特征的标志,是现代经济的产物。根据定义,只是借用字体,不会涉及侵权的问题。"], "segmented_title": ["我", "要", "注册商标", ",", "用", "到", "汉仪", "和", "方正", "公司", "的", "字体", ",", "是否", "涉及", "侵权", "?", "-", "知乎"], "segmented_paragraphs": [["新注册", "一", "个", "单项", "业务", "的", "商标", ",", "公司", "设计", "做", "了", "个", "简单", "的", "logo", ",", "挺", "简洁", "好看", "的", ",", "就是", "用", "到", "了", "方正", "和", "汉仪", "的", "几个", "字", ",", "会不会", "涉及", "字体", "版权", "侵权", "?", "听说", "字体库", ",", "是", "把", "每个", "字", "按照", "一", "个", "单独", "的", "美术作品", "列入", "版权", "的", ",", "会不会", "有问题", "?", "该", "如何", "规避", "法务", "上", "的", 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"其他", "字体", "的", "。", "商标", "是", "商品", "的", "生产者", "、", "经营者", "在", "其", "生产", "、", "制造", "、", "加工", "、", "拣选", "或者", "经销", "的", "商品", "上", "或者", "服务", "的", "提供者", "在", "其", "提供", "的", "服务", "上", "采用", "的", ",", "用于", "区别", "商品", "或", "服务", "来源", "的", ",", "由", "文字", "、", "图形", "、", "字母", "、", "数字", "、", "三维", "标志", "、", "声音", "、", "颜色", "组合", ",", "或", "上述", "要素", "的", "组合", ",", "具有", "显著", "特征", "的", "标志", ",", "是", "现代", "经济", "的", "产物", "。", "根据", "定义", ",", "只是", "借用", "字体", ",", "不会", "涉及", "侵权", "的", "问题", "。"]], "title": "我要注册商标,用到汉仪和方正公司的字体,是否涉及侵权? - 知乎"}, {"bs_rank_pos": 6, "paragraphs": [" 有这么几种字体: 使用网上流传的方正兰亭黑使用正版Windows系统自带的微软雅黑使用正版Windows系统自带中易宋体应用于这么几个场景: 做一个ppt用于公司普通产品展示在威客类网站接单为客户做平面广告(普通文本字体)在威客类网站接单为客户做LOGO这样的条件下,哪些情况不构成侵权?", " 你预设的几种情景都属于同一类型,并不属于著作权法规定的合理使用或法定许可的范畴。关于字体的性质我倾向于认为字库是属于美术作品。那么除了权利竭尽(指系统自带字库,事实上该费用已包含在终端用户购买正版系统的费用中了)、权利人免费授权使用等情形外,一般未获字库的著作权人授权而使用字库字体的行为均有可能发生侵权。", "对于本文设定的几种情形,在字体使用上属于同一类情形,除了系统自带字库外,使用其他非系统字库的情形均会发生被认定为侵权的可能。", " 看到你这个问题:不得不回答下。因为在服务的客户中,有不少收到过方正字体发出的《函》,其内容不过是,未经许可使用了他们的字库,如果要继续使用,花钱把。 我会根据客户的具体经营范围和应用环境,提出不同的解决方案:(1)一个做食品生产的企业客户。因为在其与包装、设计公司签署相关协议的时候,我在审查合同的时候,就提示客户选择使用正版软件和字库包装、设计公司;其次,在合同增加相应的保证和赔偿条款。这样,即使客户遭到起诉,我们也可以来源合法为由予以抗辩,即使抗辩不被法院接受,也能通过当初与包装设计公司之间的协议进行追偿损失。 (2)一个做设计的客户;就要求其购买正版的软件,或通过其它合法途径取得授权; (3)对于规模较小,使用不频繁的企业,就用微软、wps软件自带字库吧,或者网上下载相关的开源字体使用,也是一个不错的选择。 简单总结:(1)不要随便从网上下载字体来使用,特别是商业用途的时候,如果要下载,请选择开源字体(文泉驿)。(2)请尽可能的将重要的设计,转交给正规的设计单位;", " 没有人提及最高院方正诉暴雪的判决,太有误导性了,尽管方正对此判决愤恨不平,但很可能,之后关于字库的案子都会是自此思路。具体而言,最高院判决陈述如下:1. 字库中不是所有字都是构成美术作品。因为很多字是偏旁部首自动组合,而非创作,并且很多字独创性不够。2. 即便构成美术作品,游戏中的使用是“在传递信息意义上的使用”, 构成合理使用,不构成侵权。3. 字库构成计算机软件作品,如果直接使用字库,即复制了其中的代码,构成计算机软件作品侵权。因此,对题主的问题,如果重新编写了软件实现他人的字库,即便字体近似,也不会构成侵权。", " 关于字体字库我写过三篇文章,最后一篇“三析汉字字库著作权”三析汉字字库著作权 除了“额汗原则”为过时引用,其他观点依然保持——即对有独创性的汉字字库(当然不仅限于汉字了,下同),同时享有单字的美术著作权,又享有字库软件著作权。", " 字体的侵权问题在中国法律界一直有争议,争议焦点就在于字体是不是具有法律意义上的著作权。现在主流观点和一些判例的结论是单个字体不具有著作权,但字库是具有的。", "你提到的2和3是正版Windows自带的,因此属于权利用尽,也就是说已经在购买Windows的同时支付了字体的版权费用,后续无论如何使用都无需再向版权人支付费用了。", "至于1,如果使用的是单字,那么按照主流观点也是不侵权的,因为单字没有著作权。不过如果是使用的盗版字库,那就有侵犯字库著作权的问题,因此还是建议购买正版字库,一劳永逸。", " 说实话,看完大家的回复,真不知道怎么实用字体了,包括我现在打字,是不是也要付版权?一个被微软放弃的XP系统,在电脑修理店重装系统(当然这类店里的系统不可能是正版的)之后,再用这非正班的系统来正规的知乎回答问题,我是不是也要付N次的费用?写论文发表了,是不是也要付费?一系列生活中难免用到文档和各种广告等等的事例,要为每一个字或者样子付费?那大家都语音行吗?在这个问题上,会不会把版权的事情过度的扩大了?那是不是把中国上下五千年的文字都自己设计一下形状粗细,然后申请版权之后,就可以看谁用了我的字体形状就发函要求付费否则上告,坐收渔翁之利了?我也是不解,文字是祖先传下来的瑰宝,起到传情达意的作用,字体版权公司拿来设计字体的时候该向哪位祖先交版权?说实话,什么事情过度的理解了,都不太好个人见解而已,仅供讨论。。", " 看样子,题主是个自由设计师,你这些使用场景都算是商用的,都有侵权嫌疑的。要是你每年固定接活儿,客户素质都还比较高,我推荐你用这个Yestone邑石网_云字库_ifontcloud_文鼎字体战略合作伙伴. 一年2000多块,所有字都能随便商用了。我接单的时候会和客户说,用正版的字体的话加500,免去他被告的风险,客户都还愿意的,一年5个单就轻松回来了。", " 我司收到了方正的侵权信 其中就包括这一字体!", " 作为刚入行的设计师对字体已经凌乱。", " 感觉什么字都不能用了,等字体公司把所有的字形全部设计一遍,搞设计的不花钱不用玩了。", " 前几年去参加方正代理商会议,期间谈到过方正字体侵权的问题,方正认为处理字体侵权最麻烦的地方是取证,当时方正想了很多手段去收集侵权证据,也给代理商提供了一条取证后销售字体的道路,不过好像没什么响应。", " 感觉他们是不是 大面积撒网~给每家公司都寄! 能捉住一个是一个~反正寄个律师函也要不了多少钱~万一你用了,又做贼心虚~对字体侵权不了解!唬住一个就能搞不少钱!现在这些字体公司都已经没有艺人的气概了!个个都是满脑子的铜臭!大家维护知识产权的意识固然无可厚非!但是目前这些字体公司的做法确实有点不要脸的意思!!", " 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"paragraphs": ["谁看过这篇博文 加载中…", "《念佛如何得到一心不乱》第01节要与药师经合并参究", "这番道理我们了解了,现在再回转来谈净土宗的念佛法门。念佛法门,几千年来在中国各阶层的民间社会,流传非常普遍,所谓三根普被,指上等、中等、下等三中不同根器的人,皆适合修习。这是以聪明才智的高下所作的一种权宜分类。我们中国佛教的净土行者,不论三根之别,皆着重持名念佛的修行方式。持名念佛所依据的经典,我们大家都晓得是净土三经中的小本阿弥陀经,行者要念南无阿弥陀佛,阿弥陀佛的阿,最好别念成uo音。a是开口音,和uo的发音差别很大,念时阿——弥——陀——佛,各字音节若稍微拉长一点更佳。这一个要点,我曾经再三讲过,希望大家不要忽略。", "我们所念阿弥陀佛的名号,是梵文的直接翻音。这四字主要的含义是“无量光寿”,光明无量,寿命无疆,永远不生不死。所以,念阿弥陀佛,不只是与死人之事有关,也影响到生的一面。其实,阿弥陀佛也称长寿佛,与东方表法的长寿佛“药师琉璃光如来”,彼此合一,一东一西,互通声气。东方西方本无差别,地球是圆的,法界是圆融无碍的,根本没有东西南北之分,有分别是凡夫众生的心量所建立的相对观念。在法界一心佛的境界里,圆融无碍,超越一切时空的限制。因此,我常建议诸位,将阿弥陀经和药师经二者合并参究一下,会有更深一层的体会。念阿弥陀佛也可以使人再创生命的生机,使你去恶为善,消灾延寿,克服苦难。万一时候到了,一口气不来,还可一念往生西方极乐世界阿弥陀佛国土。 那么,念佛法门的念,到底是怎么个念法呢?!这严重了,必须认识清楚。我们现在一提到念佛,大部分人一定马上想到阿弥陀佛,同时便张开嘴巴念出声来,好象没有念出声,不算念佛,这是一种错误的观念。 我们念南无阿弥陀佛,有时发出声来,因此在中文“念”字旁边,加上一个口成“念”,表明是靠口的作用发音,用嘴巴来念佛。但是我们应该晓得,真正念佛的这个念,其实只要在心里想,便是“念”了。想念想念,“想”和“念”二字是同义语,二者合成一个辞。譬如我们中国人讲话,遇到一个久不见面的朋友,往往说,哎呀,我好想念你喔。这个想念,同初步念佛的“念”,是同一回事。现在社会许多家庭,不管有钱没钱,儿女长大出国留学的很多,剩下两个老人家,坐在电视机前,孤零零打发日子,很可怜。这种对儿女的深深思念,在我们学佛的人来讲,我想恐怕比念阿弥陀佛的念,还来得刻骨铭心,还来得真切。念佛要如思念自己心爱的亲生儿女一样,朝思暮想,时时刻刻挂在心头,如此才容易上路。否则,光是嘴巴表示念佛,心不在焉,那是口说无凭的。", "《念佛如何得到一心不乱》第02节念佛是全心全意的想念", "再进一步说,凡是我们的思想念头,以及开口讲话、动作行为等等,在佛学上皆属生灭法。有生就有灭,生生灭灭。当我们念阿弥陀佛,阿弥陀佛的念头一下已经过去了,消逝得无影无踪。或者嘴巴一字一字将阿弥陀佛念出声来,一个声音一个念头,南——无——阿——弥——陀——佛,由南字开始,字字随起随灭,念头和声音此起彼落,乍生乍灭,了不可得。这等于静坐数息观、练气功一样,一呼一吸,一来一往,全是生灭法的现象,有人竟以为这就是道,认错门路,在那里玩弄自己,糟糕透了。", "佛说:“诸行无常,是生灭法。”“行”不只指外在的动作行为,内在的思想念头也是行。思想是没有表达出来的行为,行为是表达了的思想,思想就是行为。“诸行无常”,一切行为语言动作,一切内在思想情绪起伏,所有宇宙有情无情的存在现象,皆是变动不居,难以长久的。因此,“是生灭法”,有生便有死,有来便有去,同样有死也会有生,有去也会有来。所以佛又说:“生灭灭已,寂灭为乐。”“寂灭为乐”便是极乐世界的境界。不生不灭,念佛念到念头不生起;念头不生起,当然也就没有所谓的消灭,彻底的清净,什么牵挂都没有,清明自在,坦然无住。这种境界最乐,故名极乐世界。", "我们念佛一定要认清这个念,是全心全意的想念,不只是口宣佛号便成。几十年来,我看过许多人念佛,有一次在一个地方主持一项活动,有位念佛的老太太念佛已达四十年之久,这种毅力真了不起。她在我面前走过,一直阿弥陀佛、阿弥陀佛的念个不停,恰好旁边有个年轻的女同学,不大注意礼节,坐在那里两脚分开,不成规矩,很难看。这位老太太每次转到她跟前时,阿弥陀佛、阿弥陀佛的念声变了调,意思是提醒这位小姐将脚摆正一点,但是又表示自己没有中断念佛,而且也不是在骂人。你看看,这种念佛能得一心不乱吗?!但到别人咳嗽,心里讨厌,口中就念阿弥陀佛;看到别人不合己意,也不喜欢地念阿弥陀佛;这算念个什么佛呢?你干脆骂人好了。", "所以我常说,我们中国人的念佛很有意思。有时候在街上看到一个人不小心跌倒了,旁人哎呀、阿弥陀佛那一声,意思是说摔得漂亮,你说这不是用阿弥陀佛在咒人吗?这样子的念佛有没有用?有用。但不是我们念佛为求一心不乱,为求往生西方极乐世界,所应效法的。", "《念佛如何得到一心不乱》第03节举一则念佛的笑话", "有个笑话说到一位老太太在家里念佛,阿弥陀佛、阿弥陀佛,整天念个不停。她的儿子年轻,并不大信佛,虽不反对妈妈念佛,却又因房子不大,自己又要读书做功课,常常被吵得无法专心用功。后来,这个儿子实在按捺不住,便想了个办法。当他妈妈念佛念得一心不乱的样子时,就在旁边连叫“妈、妈、妈、妈、妈……”,老太太听了,便停下来问,干什么?儿子这时故意闷声不响,继续做功课。老太太看儿子没有动静,又自己念起佛来。然后,这个儿子又等她念得很专一时,重新又“妈、妈、妈……”连声的喊起来。老母亲一听,赶快回过头来说:干嘛、干嘛!这下儿子又默不作声了。如此,老太太很不高兴,又开始念佛,儿子隔了一会又叫“妈、妈、妈”。最后,老太太实在火大了,发了大脾气,你好讨厌,有什么事要讲就讲,我正在好好念佛,你吵个什么呀?!儿子这时才说:", "“妈妈,我还是您亲生的儿子,只不过这么叫了您几声,您就发脾气,受不了。如果这样,那您每天从早到晚阿弥陀佛念个不停,那个阿弥陀佛不要给您气死了吗?”这个故事看似反对念佛,其实却可作为我们念佛的好教材,了解怎样才是真正的念佛。"], "bs_rank_pos": 4}], "question": "念佛如何得到一心不乱", "segmented_question": ["念佛", 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取100克面粉,将其打成稀面糊,然后趁热刷在纱窗上,待10分钟左右后,用刷子反复地刷洗几遍,再用清水冲洗干净,即可去除油污。", " 4、用茶叶洗窗帘", " 若窗帘的一部分被太阳晒黄了,可将它取下来,放入茶水里泡1晚,则可将晒黄的地方洗净。", " 5、用牛奶洗纱窗帘", " 在洗纱窗帘的时候,在洗衣粉溶液中加入适量的牛奶,这样能把纱窗帘洗得跟新的一样。", " 6、用小苏打洗窗帘", " 首先将浮灰去掉,然后放进加有洗涤剂的温水里轻轻地揉动,待洗完后,再用清水漂洗几遍,然后在清水中加入500克小苏打,把纱窗帘浸入水中漂洗,这样能把纱窗帘洗得非常洁白。", " 一、不同类型厨房纱窗的清洗 1、普通纱窗的清洁 如果是可以拿下来的纱窗可以拿下来用喷壶慢慢冲洗。如果使用的是隐形纱窗,就不必取下来了。隐形纱窗如果取下来就会变成一团,反而洗不干净。这里不妨用水拽着隐形纱窗,拽多少洗多少。比较干净。 2、比较脏的纱窗的清洁 吸尘器法。在纱窗下垫上报纸,怕报纸飞走可以用玻璃压好,做不到的干脆用胶带纸贴着报纸的四个角。报纸要厚些。然后用吸尘器吸纱窗上面的灰尘。如果吸尘器功率大,纱窗很快就干净了。 3、灰尘为主体额纱窗的清洁 转移法。还是利用旧报纸,先把报纸浸湿,然后贴在纱窗上等待风干,能转移一些灰尘。我是这么做的,在报纸上抹上洗剂,比如纱窗油多我就抹上洗涤剂,然后油都溶解在报纸上了,后揭开报纸纱窗就干净了。 4、特污区纱窗的清洁 一般来说,厨房纱窗难搞定。方法是先把纱窗拆下在地上铺上报纸,用钢丝球等刷,洗涤剂什么都用上,等到油渍去掉大半的时候,用湿海绵擦洗。后再用水冲。注意不能一开始就用水,水并不能融油,这样只会把家里弄的更脏。 二、厨房纱窗清洗的方法 1、面糊综合护理法 可以去超市买些面粉,勾兑厚点变成面糊,直接刷在纱窗上然后等待干燥,彻底干了之后直接用自来水冲走,纱窗上面的脏东西就可以跟着面糊冲走了。 2、先将纱窗取下来,然后用沾有洗洁精的抹布用力的擦拭,或者将其平放在卫生间的地上用刷子刷洗,等把纱窗都挨个擦拭了一遍之后,我们就用花洒或者活动水龙头进行冲洗,知道你认为干净了为止,后将其放在通风处晾干即可。 3、如果你家有吸尘器的朋友你就完全没有必要进行上面的清洗方法了,你只需要将纱窗放在地上,或者用手扶着纱窗,然后用吸尘器一点一点的洗掉灰尘即可。 4、可以将废旧的报纸打湿,然后紧紧的覆盖在纱窗表面,报纸干后就换上一张新的报纸重复刚才的操作,这样反复几次,也有不错的清洗效果哦。 5、还有一种用时比较短,操作很简单的方法,那就是用海绵擦拭,当然,这海绵必须得稍微浸湿之后才来擦拭纱窗,这种方法效果的明显,并且还能节约不少的时间呢。 三、厨房纱窗清洗的注意事项 1、隐形纱窗不要取下来洗 2、厨房纱窗切忌直接用水冲洗,并且注意定期上油。 3、再好的纱窗也需要保养,请勿用力拽扯,保持风干。本文仅代表作者观点,不代表百度立场。系作者授权百家号发表,未经许可不得转载。 简介: 书写文字,传播文化,致力于文化传播 "], "segmented_title": ["家里", "纱窗", "怎么", "也", "清理", "不", "干净", "?", "学会", "几个", "小技巧", ",", "纱窗", "焕然一新"], "segmented_paragraphs": [["纱窗", ",", "算是", "夏天", "非常重要", "的", "东西", "了", ",", "因为", "夏天", "蚊虫", "非常", "多", ",", "所以", "纱窗", "的", "重要性", "完全", "不亚于", "蚊香", "。", "而且", "纱窗", "还能", "帮", "我们", "隔绝", "一些", "灰尘", "、", "柳絮", "等等", ",", "让", "室内", "的", "空气", "环境", "更", "清新", ",", "作用", "可以说是", "相当", "大", "了", "。", "不过", "家里", "的", "纱窗", "使用", "的", "时间长", "了", "但是", "又", "没有", "清洗", "的", "话", ",", "纱窗", "上", "就", "很", "容易", "累积", "一", "层", "厚厚", "的", "污垢", ",", "尤其", "是", "厨房", "里", "的", "纱窗", ",", "上面", "还有很多", "油污", ",", "更", "是", "难以", "清理", "。", "所以", "小", "编", "接下来", "介绍", "几个", "清洗", "纱窗", "的", "小", "方法", 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"注意", "定期", "上", "油", "。", "3", "、", "再", "好", "的", "纱窗", "也", "需要", "保养", ",", "请勿", "用力", "拽", "扯", ",", "保持", "风干", "。", "本", "文", "仅", "代表", "作者", "观点", ",", "不", "代表", "百度", "立场", "。", "系", "作者", "授权", "百家", "号", "发表", ",", "未经许可", "不得转载", "。", "简介", ":", "书写", "文字", ",", "传播", "文化", ",", "致力于", "文化", "传播"]], "title": "家里纱窗怎么也清理不干净?学会几个小技巧,纱窗焕然一新"}, {"bs_rank_pos": 8, "paragraphs": [" 看看这些家庭清洁实用小妙招,很多方法可能连家庭专职主妇都不一定知道哦!", " 1、用牙膏去除白色家具表面的污迹 家中的白色家具很容易弄脏,只用抹布难以擦去污痕,不妨将牙膏挤在干净的抹布上,只需轻轻一擦,家具上的污痕便会去除。但注意用力不要太大力,以免伤到漆面。 2、地板或木质家具出现裂缝,自己DIY填补 可将旧报纸剪碎,加入适量明矾,用清水或米汤煮成糊状,用小刀将其嵌入裂缝中,并抹平,干后会非常牢固,再涂以同种颜色的油漆,家具或地板就能恢复本来面目。 3、房间角落或墙壁接缝处的霉垢,牙刷+牙膏就能清洁 接缝是最难打扫的死角,非常容易产生霉垢,可试着用旧牙刷清理刷净。如果遇到比较顽强的污垢,则可用牙膏洗涤剂刷除,再用水擦拭干净,保持干燥即可。 4、纱窗的清洁,用这张“纸”搞定 纱窗上落满灰尘,大多数我们打扫的时候,是拆下纱窗,再用水清洗。这里小编告诉你一个不用拆下纱窗,就能将纱窗打扫得很干净的好办法:将废旧报纸用抹布打湿,再将打湿后的报纸粘在纱窗的背面,五分钟后,将纱窗上的报纸取下,你会发现潮湿的报纸上粘满了纱窗上的灰尘污渍。此种方法打扫纱窗,省时又省力。", " 5、黏贴式挂钩,用醋可以无痕去除", " 挂钩虽然相当便利,可是一旦要拆除时,却得花上好多时间。此时,只要将沾醋的棉花铺在挂钩四周,使醋水渗入紧粘的缝隙中,几分钟之后,便可用扁头螺丝起子轻易拆除挂钩。残留的粘着剂也可用醋擦拭,清除干净。", " 6、茶叶水有助于除尘,擦地板、家具都好用", " 由于茶叶渣可以吸附灰尘,清扫房间灰尘时,可先撒上一些冲泡过的茶叶,再用扫帚干净,这样会更轻松省事。记住,湿茶叶可比干的效果好。", " 7、擦门窗玻璃时,可加洋葱", " 可先把洋葱去皮切成两半,用其切口摩擦玻璃,趁洋葱的汁液还未干时,再迅速用干布擦拭,这样擦后的玻璃既干净、又明亮。另一种作法是,再用干净的湿布沾一点白酒,稍用力在玻璃上擦一遍,也能让你的玻璃即干净又明亮。", " 8、抽油烟机是厨房中最难清洁的厨具", " 先将油盒里的油污倒掉,然后将油盒浸泡在肥皂水,或者中性清洁剂兑成的温水中20分钟左右,如果油污顽固,可浸泡40分钟。油网如果有少量油污,可以直接用温水浸泡后洗净、擦干,如果油污较为严重,可以与油盒一起浸泡。", " 9、瓦斯炉表面的油垢,用小苏打水泡过再擦!", " 瓦斯炉一般在做完饭后清洗,趁瓦斯炉还有余温时,用湿布加肥皂水或苏打水擦拭,就可以轻易去除了。如果等完全冷却后再擦拭,油污、煮粥溅出的米汤等就很难擦净;再把清洁布在啤酒中浸泡一会,然后擦拭有顽渍的厨台厨具,即可光亮如新!", " 10、洗衣机的内桶与外桶,藏了超多脏东西!", " 你每天都在洗衣服,但是有“洗”洗衣机吗?因为藏在内桶和外桶之间的污垢我们平时是看不到的,可根据洗衣机的容量,将半瓶至一瓶食用醋倒入洗衣机内桶,加温水到3/4桶高,浸泡2～3小时,然后开动洗衣机10～20分钟后,放掉脏水后再注入清水,冲洗10～15分钟就大功告成。", " 来源:央视财经", " 责任编辑:邱彩红", " 正文已结束,您可以按alt+4进行评论 ", "[责任编辑:jainali]", " 王语嫣:段誉又出去了 在线找队友>>", " 唐嫣邀请你一起游戏,开启仙侠之旅>>", " 新服开服第一天!网吧团战媳妇喂饭"], "segmented_title": ["洗", "脏", "纱窗", "一", "张", "纸", "就能", "瞬间", "搞定", "_", "新闻", "_", "腾讯网"], 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"哦,这个不能这么简单的说的。因为病情不同,还有你适应症的选择有不同,以及支架植入针对冠心病的治疗是否规范等都有影响的。比如世界上第一例PTCA(现在一般改称PCI),是1977年瑞士苏黎世大学医院由Grüntzig完成的,当时也就仅仅是针对一个38岁的男性患者左前降支一个孤立的狭窄,用球囊扩张一下,并没有进一步用支架(当时还没有支架),以后随访了26年(我看到这本书2004年出版的,以后情况还不知道),没有复发。但是Grüntzig医生本人在1985年,早早因为飞机掉地上摔死了。以后发现单纯球囊扩张,那么血管会有回弹,那么很容易再狭窄,所以现在几乎大多都是应用支架支撑,阻止回弹这样效果确实大大提高了,21世纪开始药物洗脱支架开始应用,因为普通支架还是异物,容易造成覆盖上去的血管内膜增生,那么容易支架内再狭窄,那么一种方法就是药物涂层,抑制内膜增生,但是2006年时又有争论,至今还不好下定论一定就优于普通金属裸支架。至少药物洗脱支架,必须更延长氯吡格雷的使用时间,否则反而比普通支架效果未必就好甚至有的长期效果还不如普通支架。再回过头来看你父亲,放入了5个药物洗脱支架,我就觉得这本身不是介入手术植入支架的最适病例,因为一般说来如果是多支血管病变或者虽然单支血管但是病变比较漫长的,都是建议心外科冠状动脉旁路移植(心脏搭桥)的,你这个不是最适合介入的。本身就先天存在缺陷。不是介入治疗的适应症,硬要介入治疗置放多个支架,这本身就是对预后是不利的。我这个人说话比较直率的,如果是医生要求这样做,那么可能就有些不合适了,当然也可能当地医院没有条件心脏搭桥,或者不是医生建议,而是病人和家属坚决要求介入手术,那就另当别论了。还有我提醒您,不是冠心病作了介入手术植入支架,或者外科冠状动脉旁路移植(搭桥)以后,就万事大吉了,因为你基础病变还在,只是针对了你最严重的地方采取了措施,你别的地方难道就不会再发生问题,另外你支架内也可以再狭窄,或者搭的桥血管也可以发生严重的粥样硬化而狭窄的。因此你必须加强基础的治疗。比如血糖血脂血压都必须严格控制好,必要的抗血小板治疗,那么也必须坚持的。这都是影响预后的很重要的因素。", "你说的这种病情心脏支架手术后一般是需要根据患者的心脏功能情况决定其寿命的,没有严格的说法,建议你可以术后给予口服波利维等抗凝药物治疗,同时需要你定期到医院给予复查", "姜树锋 医师 | 2014-11-4 14:29", "做好以下两方面,就不可能再发病,更不会影响寿命。 1、预防血栓形成的药服够10个月; 2、把脱落到血液中的斑块清除干净。", "得了冠心病先不要着急,用西医的办法治标不治本,反反复复,严重的能活几年就不错了,中医解决反复的问题,只是名医不可求。桂林有位中医大师特别擅长用手法加中药调理治疗心脏病,我的7年冠心病就是他治的,到现在都没有复发,不过他说,做过搭桥手术的治不了。希望我的分享能减少大家的痛苦,他的∨.信:jux1973,方便的话可以去桂林的诊所找他。", "心脏支架手术后寿命一般10-20年不等。"], "bs_rank_pos": 0}, {"title": "心脏支架的寿命只有十年吗_新浪健康_新浪网", "segmented_title": ["心脏", "支架", "的", "寿命", "只有", "十", "年", "吗", "_", "新浪", "健康", "_", "新浪网"], "segmented_paragraphs": [["马长生", ",", "中国医师协会", "心血管", "内科", "医师", "分", "会", "会长", ",", "首都医科大学", "附属", "北京", "安贞医院", "心脏", "内科", "中心主任"], ["乔岩", ",", "医学博士", ",", "首都医科大学", "附属", "北京", "安贞医院", "心脏", "中心", "副主任医师"], ["贺立群", ",", "医学博士", ",", "副主任医师", ",", "武汉市", "第一医院", "心内科", "副主任"], ["●", "选择", "支架", "还是", "搭桥"], ["湖南", "读者", "王先生", "问", ":", "我", "今年", "78", "岁", ",", "半个月", "前", "因", "胸痛", "到", "医院", "检查", ",", "显示", "冠状动脉", "多", "支", "病变", "且", "病变", "段", "较", "弥漫", "。", "医生", "说", "如果", "放", "支架", "至少", "得", "3", "个", ",", "费用", "较", "高", ",", "而且", 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"控制", "好", "血压", ",", "以", "延缓", "冠状动脉", "粥样硬化", "的", "进", "展", "。", "如果", "再次出现", "胸闷", "胸痛", ",", "应", "进一步明确", "是", "支架", "出现", "了", "再", "狭窄", "还是", "其他", "血管", "出现", "了", "新", "的", "病变", "。"], ["●", "术", "后", "嗓子", "疼", "怎么回事"]], "paragraphs": ["马长生,中国医师协会心血管内科医师分会会长,首都医科大学附属北京安贞医院心脏内科中心主任", "乔岩,医学博士,首都医科大学附属北京安贞医院心脏中心副主任医师", "贺立群,医学博士,副主任医师,武汉市第一医院心内科副主任", "●选择支架还是搭桥", "湖南读者王先生问:我今年78岁,半个月前因胸痛到医院检查,显示冠状动脉多支病变且病变段较弥漫。医生说如果放支架至少得3个,费用较高,而且有可能出现不良反应。鉴于我偏高龄,搭桥手术也有一定风险。请问我究竟应该选择哪种治疗方法?", "解答:支架手术优点是风险相对较低、创伤小、恢复快、住院时间短、具有可重复性,缺点是并不适合所有的严重复杂病变。搭桥手术优点是可以处理复杂的、不适 合支架手术的病变,对于糖尿病合并多支病变可能会改善预后。搭桥手术需要开胸,相对创伤较大,手术风险相对高,可重复性差,很难二次开胸搭桥。桥血管也有 闭塞的问题,动脉桥血管10年畅通率可达到90%左右,但是静脉桥血管畅通率约为31%～71%,部分中青年患者10年后不得不面对桥血管闭塞,症状复发 的问题。选择搭桥还是支架手术,主要根据冠脉造影病变的特点以及病人的临床情况综合判断。", "●支架的寿命是十年吗", "天津读者高女士问:听说支架的使用寿命通常为10年,10年后支架咋处理?要二次手术吗?", "解答:目前冠状动脉内植入的支架均为金属支架,在支架植入时通过球囊高压扩张,使其与血管内膜紧密结合,然后通过血管内皮细胞对损伤的血管内膜进行修复, 这一过程就是所谓的“再内皮化”过程。“再内皮化”后的支架与患者自身的血管融为一体,因此,并没有所谓的“支架使用寿命”这一说法。", "支架植入后需服用常规的冠心病二级预防药物,若出现再狭窄并伴有心绞痛,应再次进行冠状动脉造影检查,可在原来的支架植入部位再次植入一个新的支架。某些特殊部位的支架再狭窄有可能需要心血管外科进行冠状动脉搭桥治疗。", "●术后如何控制运动强度", "北京读者崔先生问:我今年85岁,有冠心病和高血压,服用氯沙坦钾片和依姆多(单硝酸异山梨酯缓释片)。2014年5月放了两个支架,术后坚持服用阿司匹林和氯吡格雷。我以前每周打四五次太极拳,但术后每次打拳都感到特别疲劳,请问我该如何控制运动强度?", "解答:快走、慢跑、游泳、打太极拳等有氧运动对冠心病有治疗作用,可改善冠状动脉弹性和血管内皮功能,促进侧支循环建立,稳定斑块。建议每次运动 20～40分钟,每周3～5次。比较方便的运动强度评价方法有目标心率法和自我感知劳累程度分级法。目标心率法是指停止运动心率在静息心率的基础上增加 20～30次/分钟。也可通过自我感觉运动时用力的程度来判断运动强度,控制在“轻度用力”或“有点用力”或“用力”的程度均可。此患者已是85岁高龄, 术后运动需量力而行,每周逐渐增加运动量和运动时间,以不感觉过度疲劳为宜。", "安徽读者韩先生问:我今年72岁,患糖尿病、高血压已多年,一直坚持用药控制。今年以来多次出现胸痛,稍一活动就症状加重,歇一歇就缓解了。去看过医生,说是不稳定型心绞痛,建议放支架。请问我这种情况能否不放支架?有什么药物可以代替手术?", "解答:对于不稳定型心绞痛患者,早期支架介入治疗可改善不稳定型心绞痛的预后,减少远期心血管事件的发生;对于稳定型心绞痛患者,支架手术可明显减少心绞 痛的发作次数和频率。因此,对于症状明显的心绞痛患者,如果规范药物治疗效果不理想,造影发现冠状动脉明显狭窄,则应该考虑支架手术。建议您做冠脉造影, 根据血管狭窄情况来进一步判断是否需要支架手术。药物治疗可以部分缓解心绞痛患者的症状,但是绝大部分患者需要通过支架手术来改善心肌缺血。", "●进口支架更好吗", "山东读者周女士问:我大哥今年69岁,有冠心病,医生建议放支架。据说三甲医院基本都用进口支架,费用比国产的贵一倍,为什么进口和国产价格差那么多?国产支架的质量有保障吗?手术可以医保报销吗?", "解答:从全国范围来看,目前国产支架在我国冠心病患者中的应用比例明显高于进口支架,国家未强制要求三甲医院必须使用进口支架。国产支架与进口支架的临床 疗效已不相上下,是否使用进口药物支架需综合考虑患者的经济条件、病变因素以及是否合并其他疾病等。进口支架与国产支架均属医保报销范围,但在报销比例上 各个地区可能略有差别。", "●术后用药何时可以减量", "新疆读者雷先生问:我今年81岁,1997年和2009年分别放了两个支架,第二次放支架时发现1997年的支架,其中一个出现60%的狭窄,另一个是40%的狭窄。我一直在吃氯吡格雷和阿司匹林,请问药量是否可以减少?国产药的效果比进口药差很多吗?", "解答:1997年植入的支架应该是普通的金属裸支架,早期的冠状动脉支架再狭窄率比较高,如果合并糖尿病,再狭窄率会更高。新一代冠状动脉支架的再狭窄率 已大幅降低,如果没有糖尿病,再狭窄率仅为4%～5%。阿司匹林与氯吡格雷是两种抗血小板药物,主要用于预防冠状动脉支架植入术后的血栓形成。目前的指南 建议,植入药物洗脱支架后需规律服用阿司匹林与氯吡格雷至少一年,一年后可停用氯吡格雷,但需终生服用阿司匹林。", "从2009年植入支架至今已6年,应该可以停用氯吡格雷。但除了继续服用阿司匹林外,还需服用他汀类药物。高龄患者合并多种疾病,应根据患者的肝肾功能检查结果,综合评估药物的安全性。目前国产药物的临床疗效已经得到了很好的验证。", "●术后服用他汀注意什么", "安徽读者王先生问:我今年78岁,2012年放了3个支架,2013年出现新的血管堵塞就又放了一个。我一直在吃阿司匹林和他汀,听说辅酶Q10能够减轻他汀的副作用,是真的吗?哪些药不能和这两种药同时使用?食物有忌口吗?", "解答:辅酶Q10是重要的抗氧化剂以及非特异性的免疫增强剂,主要用于病毒性肝炎、亚急性肝坏死以及慢性活动性肝炎的辅助治疗,他汀类药物确实有肝酶异常 的副作用,但是肝功能异常并不是应用他汀类药物的禁忌症,因为他汀类药物导致的严重肝损害非常罕见。应用辅酶Q10来预防他汀的副作用不仅没有必要,而且 徒增经济负担。", "下列药物或饮食可能会加重他汀类药物的副作用或影响其疗效:贝特类(尤其是吉非贝齐)、烟酸类(罕见)、环孢霉素、吡咯抗真菌药、红霉素和克拉霉素、HIV蛋白酶抑制剂、维拉帕米、胺碘酮、大量西柚汁及酗酒等。", "长期服用阿司匹林可能会导致出血,所有可能导致消化道黏膜损伤的药物与阿司匹林联用均可增加出血的风险,如常用的糖皮质激素等。另外,饮酒也会加重胃黏膜的损伤,并可能导致消化道出血。", "●放支架后要终生服药吗", "吉林读者毛先生问:我今年70岁,糖尿病已10年,每天打一次胰岛素,口服二甲双胍。两年前突发心绞痛,放了3个支架,术后服用氯吡格雷、阿司匹林和他 汀,坚持一年多后逐渐没再吃药了。前段时间又出现胸闷胸痛的感觉,赶紧又恢复了用药。难道放了支架后就要终生服药吗?", "解答:糖尿病与冠心病是“姐妹病”,几乎所有的糖尿病患者最终都会发生不同程度的冠状动脉粥样硬化,因此糖尿病合并冠心病患者无论是否植入支架都需要长 期、规范化的药物治疗。糖尿病患者植入支架后除了更为严格的血糖控制外,还需终生服用阿司匹林、他汀类药物,并控制好血压,以延缓冠状动脉粥样硬化的进 展。如果再次出现胸闷胸痛,应进一步明确是支架出现了再狭窄还是其他血管出现了新的病变。", "●术后嗓子疼怎么回事"], "bs_rank_pos": 1}, {"title": "心脏支架术后患者寿命大概有多久?_有问必答_快速问医生", "segmented_title": ["心脏", "支架", "术", "后", "患者", "寿命", "大概", "有", "多久", "?", "_", "有问必答", "_", "快速", "问", "医生"], "segmented_paragraphs": [["健康", "咨询", "描述", ":", "爷爷", "身体", "一直", "都", "不太", "好", ",", "尤其", "是", "心脏", ",", "患", "有", "心脏病", "等", "疾病", ",", "前不久", "刚", "因为", "心梗", "做", "了", "心脏", "支架", "手术", ",", "但是", "医生", "说", "支架", "手术", "之后", "复发率", "也是", "很高", "的", ",", "不能放松", "对", "病情", "的", "控制", "。"], ["想得到", "怎样", "的", "帮助", ":", "心脏", "支架", "术", "后", "患者", "寿命", "大概", "有", "多久", "?"], 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"高血糖", "喝", "什么", "茶", "好", "心衰", "喝", "凉水", "好", "喝", "什么", "茶", "对", "高血压", "有", "好处", "吃", "了", "降压药", "反而", "头疼", "高血压", "尿酸", "高", "吃什么", "药", "心脏病", "患者", "日常", "的", "饮食", "风湿性心脏病", "能", "吃", "鱼", "吗"], ["专家", "在线", "免费咨询"], ["看病", "男科", "妇科", "癫痫", "性病", "养生", "新闻", "白癫风", "牛皮癣"], ["就医", "疾病", "信息", "健康", "经验", "症状", "信息", "手术", "项目", "检查", "项目", "健康", "百科", "求医问药"], ["用药", "药品", "库", "疾病", "用药", "药品", "心得", "用药", "安全", "药品", "资讯", "用药", "方案", "品牌", "药企", "明星", "药品"], ["整形", "整形", "项目", "整形", "案例", "整形", "资讯", "整形医院", "减肥", "塑形", "眼部", "整形", "鼻部", "整形", "面部", "整形"], ["有问必答", "内科", "外科", "儿科", "药品", "遗传", "美容", "检查", "减肥", "中医科", "五官科", "传染科", "体检", "科", "妇产科", "肿瘤科", "康复科", "子女教育", "心理健康", "整形美容", "家居", "环境", "皮肤性病", "营养保健", "其他", "科室", "保健养生"], ["下载", "APP", ",", "免费", "快速", "问", "医生"]], "paragraphs": ["健康咨询描述: 爷爷身体一直都不太好,尤其是心脏,患有心脏病等疾病,前不久刚因为心梗做了心脏支架手术,但是医生说支架手术之后复发率也是很高的,不能放松对病情的控制。", "想得到怎样的帮助:心脏支架术后患者寿命大概有多久?", "因为每个人的身体状况不同,心态不同,求生意识也不尽相同,而且还要注意术后的调养,和日常的保养,要饮食健康,不抽烟不喝酒,切记不能激动,要时常陪老人聊天,说笑,让老人心界变宽,乐观积极的生活,老人的寿命是会延长的。其实患者是很排斥做心脏支架手术的,至于他们排斥做心脏支架手术的原因:且不说心脏支架手术不能真正治愈冠心病,就是术后经常出现一些并发症就经常使患者望而止步。再者心脏支架手术的寿命期短,最多可保十年乃至更短的时间,支架内就会再次形成血栓,血管再次堵塞,到那时治疗起来就会很棘手。有些人在复发时甚至来不及送医院就“人走茶凉”,就是因为曾经做过心脏支架手术的缘故。因此心脏支架手术有时候对冠心病人来说,不是救命良策而是催命符。", "这还是需要根据具体的病情程度、治疗的效果来确定的。如果还并没有导致严重的心脏功能的问题,及时作支架手术,术后坚持抗凝预防血栓形成,应该不会对寿命产生太大的影响的。因为这要看因为患者们本身的情况,还有适应症的选择有不同,以及支架植入针对冠心病的治疗是否规范等都有影响。普通裸支架的再狭窄率为20-30%,药物涂层支架再狭窄率为10%左右,但严重的需要处理的再狭窄并没有那么多.若半年不发生再狭窄,此后植入支架的部位一般也就不会发生再狭窄了,当然也就不需要再处理了.所以没有“过了使用期,还要再做手术换支架”或“心脏支架有寿命”的说法.但患者血管的其它部位可能还会出现新的病变,这是另外的问题.", "心脏支架手术后,只要按时服药,定期复查,可以长寿。指导意见:介入手术安置支架后,都必须常规使用联合抗血小板治疗。因为血管植入支架的部分,在缺乏有效抗血小板药物的保护下非常易于形成血栓,其后果无异于再次心肌梗塞。由于这类药物可能会影响血小板和白细胞,因此在服用期间,尤其是开始阶段(1-2周左右)需到门诊复查血常规。若出现皮疹、皮肤出血点和淤斑,以及其他出血情况,应及时到医院就诊。 控制好冠心病的易患因素,如降血脂、戒烟,控制好血压、血糖,维持正常体重等。所有植入支架的患者都应常规服用他汀类药物。事实上,冠心病患者使用他汀不仅仅为了降脂,而是因为它是目前唯一一类可以延缓动脉粥样化进展、稳定其斑块的药物。, 改变生活方式,作息有规律,根据自己的实际情况进行锻炼,如散步,做广播体操等。注意饮食清淡,多吃富含维生素C的水果和绿色蔬菜。 以上是对“心脏支架术后患者寿命大概有多久?”这个问题的建议,希望对您有帮助,祝您健康!", "心脏支架只能说针对原发病有治愈或者改善的效果,但是对于能否增加病人的寿命或者改善病人的生活治疗,这一点是不能够预测的,对于支架的病人,术后的调护对于今后的生活质量的改善有重要的作用,按时服药,注意药物的不良发应,同时定期门诊复查,注意术后心血管的变化,只有良好的调护才对于病人的预后有积极的作用。支架术后护理适度合理、循序渐进地运动,可增进身心健康,提高心肌和运动肌肉的效率,减少心肌耗氧量,促进冠状动脉侧支循环形成。运动量以不引起气喘、心悸、头晕等为指标。因支架的结构和组成的原因,所以术后要进行抗凝治疗。同时定期的复查,了解治疗的效果和恢复情况。", "这个不能这么简单的说的。因为病情不同,还有你适应症的选择有不同,以及支架植入针对冠心病的治疗是否规范等都有影响的。不是冠心病作了介入手术植入支架,或者外科冠状动脉旁路移植(搭桥)以后,就万事大吉了,因为你基础病变还在,只是针对了你最严重的地方采取了措施,你别的地方难道就不会再发生问题,另外你支架内也可以再狭窄,或者搭的桥血管也可以发生严重的粥样硬化而狭窄的。因此你必须加强基础的治疗。比如血糖血脂血压都必须严格控制好,必要的抗血小板治疗,那么也必须坚持的。这都是影响预后的很重要的因素。", "心脏支架只能说针对原发病有治愈或者改善的效果,但是对于能否增加病人的寿命或者改善病人的生活治疗,这一点是不能够预测的,对于支架的病人,术后的调护对于今后的生活质量的改善有重要的作用,按时服药,注意药物的不良发应,同时定期门诊复查,注意术后心血管的变化,只有良好的调护才对于病人的预后有积极的作用。药物支架半年内的再狭窄率在10%左右,非药物支架的再狭窄率可高达30-40%,如果你应用的是裸支架(非药物支架),就要注意半年后复查冠脉血管情况,以便及时处理再狭窄。", "心脏病是一类比较常见的循环系统疾病。循环系统由心脏、血管和调节血液循环的神经体液组织构成,循环系统疾病也称为心血管病,包括上述所有组织器官的疾病,在内科疾病中属于常见病,其中以心脏病最为多见,能显著地... 更多>>", "琥乙红霉素  参考价格:9 查看说明书", "地高辛片  参考价格:4 查看说明书", "地高辛片  参考价格:5 查看说明书", "地高辛片 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"糖尿病", ",", "一定要", "控制", "好", "血糖", "水平", ",", "以", "减缓", "冠心病", "的", "进展", "。"], ["6", ".", "多", "运动", "运动", "不仅可以", "维持", "体重", ",", "还", "可", "控制", "血糖", "、", "血脂", "和", "血压", "。", "在", "医生", "允许", "的", "情况", "下", ",", "可", "每天", "锻炼", "30", "~", "60", "分钟", "。"], ["7", ".", "吃", "健康", "的", "食物", "对", "心脏", "健康", "的", "食物", "包括", ":", "水果", "、", "蔬菜", "、", "谷物", "。", "另外", ",", "应", "尽量减少", "含", "饱和脂肪", "、", "胆固醇", "和", "钠", "多", "的", "食物", "。", "建议", "一", "周", "吃", "1", "~", "2", "次", "的", "鱼", "会", "对", "心脏", "很不错", "哦", "。"], ["8", ".", "学会", "管理", "压力", "尽可能", "减少", "压力", "。", "学会", "一些", "健康", "的", "减压", "技巧", ",", "如", "肌肉", "放松", "、", "深呼吸", "。"]], "paragraphs": ["您当前的位置: 首页 > 健康讲座 > 科普文章", "作者: 来源: 时间:2016-09-05 08:54:51 浏览次数:6927 次", "冠状动脉支架手术(PCI)作为冠心病主要的治疗方法,不仅能够迅速解除血管堵塞,恢复血运,而且使患者术后的生活质量也有了明显提高。但冠状动脉支架术是有创手术,术后有可能会出现严重的并发症,医生,患者和家属迫切想了解它的预后情况及影响预后的危险因素。", "对西北地区冠状动脉支架术后患者的生存质量情况调查显示,影响患者预后的影响因素有:喜欢肉类食物,单支病变,多支病变,再住院,再次血运重建,生存质量各个方面(生理、身体疼痛、社会、情感、精神、活动)。 —来源于《冠状动脉支架术预后影响因素分析及生存质量对比研究》(第四军医大学)", "上面的研究也进一步证实了,冠脉支架手术并不是一劳永逸的,因为植入的支架并不能阻止动脉粥样硬化及冠心病的进展;要维持良好的手术效果,最佳的方法是建立对心脏有益的健康生活方式。", "专家观点:关于心脏支架术后患者寿命的问题,如果能严格按照医生的要求做好药物和生活的护理,养成良好的生活习惯,合理膳食,并注意情绪的控制,病情是可以得到有效控制的,术后的生存时间不会受到很大影响,和正常人一样活到预期的寿命。", "下面这些方法是您完全可以做到的!!!", "1. 保持适当的体重减少卡路里的摄入,加强锻炼。 2. 戒烟尼古丁会收缩血管,加快心跳,而一氧化碳会降低血液中的氧气含量,损伤血管壁。如果你吸烟的话,戒烟是减少患心脏病风险的最佳方式。 3. 控制血压定期测量血压。如果有高血压的话,需遵医嘱服药。 4. 控制胆固醇定期检查胆固醇。少吃含高胆固醇的食物,遵医嘱服用降脂药物。", "5. 控制好糖尿病如果你有糖尿病,一定要控制好血糖水平,以减缓冠心病的进展。", "6. 多运动运动不仅可以维持体重,还可控制血糖、血脂和血压。在医生允许的情况下,可每天锻炼30~60分钟。", "7. 吃健康的食物对心脏健康的食物包括:水果、蔬菜、谷物。另外,应尽量减少含饱和脂肪、胆固醇和钠多的食物。建议一周吃1~2次的鱼会对心脏很不错哦。", "8. 学会管理压力尽可能减少压力。学会一些健康的减压技巧,如肌肉放松、深呼吸。"], "bs_rank_pos": 3}, {"title": "心脏支架手术之后寿命会有影响吗_最真实的经验分享_健康经验_快速问", "segmented_title": ["心脏", "支架", "手术", "之后", "寿命", "会", "有", "影响", "吗", "_", "最", "真实", "的", "经验分享", "_", "健康", "经验", "_", "快速", "问"], "segmented_paragraphs": [["专家", "在线", "免费咨询"], ["其实", "这个", "手术", "做完", "以后", "很多", "的", "患者", "觉得", "这样", "我们", "身上", "的", "疾病", "就", "彻底", "的", 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"]", "心血管", "内科"], ["[", "费用", "一般", "多少", "]", "根据", "不同", "医院", ",", "收费标准", "不", "一致", ",", "市", "三甲医院", "约", "(", "5000", "—", "—", "10000", "元", ")"]], "paragraphs": ["专家在线免费咨询", "其实这个手术做完以后很多的患者觉得这样我们身上的疾病就彻底的好了,这个疾病是很容易复发的,更多的患者愿意接受支架介入治疗,如果平时的生活不去注意的话,以后也就不注意了,我们也会获得一个长期健康的身体,也是要继续的长期的用药来进行调理的,慢慢的经过多年的不懈努力,其实这样是不对的,目前随着支架介入手术的普遍开展,血液会很干净血管斑块会很稳定,", "1甚至比以前更严重,有的病人手术后效果很好,首先一个不能否认现在做支架术的很多,一个是病情的问题,这个效果不好的原因一个是医生的水平问题,很有可能是诊断不对或者是利益的趋势造成的,有的人效果不好, 2心脏支架手术之后寿命不会有影响,日常生活不太受到限制,在支架早期比较容易出问题,正常工作、生活都不受到影响,往往病人心脏症状绝大多数都会解除,支架可能会坏或者怎么样,冠状动脉支架放完以后,一旦半年以后,放了冠状动脉支架之后,不是随着时间延长,再狭窄的几率反而会减少, 3所以支架术后一定要注意长期预防,因为除支架本身以外,需要预防冠脉其它部位的狭窄出新问题,还有就是我们做了支架以后一定要在一年之内把血栓的概率降到最低,需要服用阿司匹林和波立维一年,当然这个要看医生怎么说了,", "支架术后没有认真服药治疗等等,总之支架术和搭桥术不能一劳永逸解决冠心病心绞痛的问题,最后我们要说的是病人的问题就可能是病情复杂,还要药物继续治疗,", "温馨提示:以上经验分享由网友原创,仅供参考,未经许可禁止转载,违者必究", "冠心病 心绞痛", "[看病挂什么科]心血管内科", "[费用一般多少]根据不同医院,收费标准不一致,市三甲医院约(5000 —— 10000元)"], "bs_rank_pos": 4}], "question": "心脏支架手术后寿命", "segmented_question": ["心脏", "支架", "手术", "后", "寿命"], "question_type": "ENTITY", "fact_or_opinion": "OPINION", "question_id": 221605}
+{"documents": [{"bs_rank_pos": 0, "paragraphs": ["您所在的位置:首页>热点追踪", "经纪代理服务业营改增主要政策简介", "发布者:admin 来源:官方来源 发布时间:2016-11-29", " 2016年3月23日,财政部、国家税务总局正式颁布《关于全面推开营业税改征增值税试点的通知》(财税〔2016〕36号),自2016年5月1日起,全面推开营改增,将试点范围扩大到建筑业、房地产业、金融业、生活服务业。为便于经纪代理服务业纳税人了解此次营改增的主要内容,广州市国家税务局现对经纪代理服务业的主要政策整理如下,供社会各界参阅。", " 一、增值税纳税人  在中华人民共和国境内提供经纪代理服务的单位和个人,为增值税纳税人。 单位,是指企业、行政单位、事业单位、军事单位、社会团体及其他单位。 个人,是指个体工商户和其他个人。 二、征税范围  根据《销售服务、无形资产、不动产注释》规定,经纪代理服务,是指各类经纪、中介、代理服务。包括金融代理、知识产权代理、货物运输代理、代理报关、法律代理、房地产中介、职业中介、婚姻中介、代理记账、拍卖等。 货物运输代理服务,是指接受货物收货人、发货人、船舶所有人、船舶承租人或者船舶经营人的委托,以委托人的名义,为委托人办理货物运输、装卸、仓储和船舶进出港口、引航、靠泊等相关手续的业务活动。 代理报关服务,是指接受进出口货物的收、发货人委托,代为办理报关手续的业务活动。 三、增值税纳税人分类  (一)增值税一般纳税人:适用增值税税率,其进项税额可以抵扣。 1.年应税销售额超过500万元的纳税人应登记为一般纳税人。其中:(1)年应税销售额超过500万元的其他个人不属于一般纳税人。 (2)年应税销售额超过500万元但不经常提供应税服务的单位和个体工商户选择按照小规模纳税人纳税。 2.年应税销售额未超过500万元的纳税人,会计核算健全,能够提供准确税务资料的,可以向主管税务机关办理一般纳税人资格登记,成为一般纳税人。 (二)增值税小规模纳税人:适用增值税征收率,其进项税额不可以抵扣。 (三)增值税扣缴义务人:中华人民共和国境外单位或者个人在境内发生应税行为,在境内未设有经营机构的,以购买方为增值税扣缴义务人。 四、税率和征收率  五、增值税计税方法  (一)一般计税方法  一般纳税人发生应税行为适用一般计税方法计税,按以下公式计算:  应纳税额=当期销项税额-当期进项税额  销项税额=销售额×税率  销售额=含税销售额÷(1+税率)  (二)简易计税方法  小规模纳税人发生应税行为适用简易计税方法计税。简易计税方法的应纳税额,是指按照销售额和增值税征收率计算的增值税额,不得抵扣进项税额。应纳税额计算公式:  应纳税额=销售额×征收率  简易计税方法的销售额不包括其应纳税额,纳税人采用销售额和应纳税额合并定价方法的,按照下列公式计算销售额: ", " (一)一般计税方法  一般纳税人发生应税行为适用一般计税方法计税,按以下公式计算:  应纳税额=当期销项税额-当期进项税额  销项税额=销售额×税率  销售额=含税销售额÷(1+税率)  (二)简易计税方法  小规模纳税人发生应税行为适用简易计税方法计税。简易计税方法的应纳税额,是指按照销售额和增值税征收率计算的增值税额,不得抵扣进项税额。应纳税额计算公式:  应纳税额=销售额×征收率  简易计税方法的销售额不包括其应纳税额,纳税人采用销售额和应纳税额合并定价方法的,按照下列公式计算销售额:  销售额=含税销售额÷(1+征收率)  (三)扣缴计税方法  扣缴义务人适用扣缴计税方法计税。 应扣缴税额=接受方支付的价款÷(1+税率)×税率  六、销售额的计算  (一)一般规定  销售额,是指纳税人发生应税行为取得的全部价款和价外费用,财政部和国家税务总局另有规定的除外。价外费用,是指价外收取的各种性质的收费,但不包括以下项目: ", " (一)一般规定  销售额,是指纳税人发生应税行为取得的全部价款和价外费用,财政部和国家税务总局另有规定的除外。价外费用,是指价外收取的各种性质的收费,但不包括以下项目:  1.代为收取并同时满足以下条件的政府性基金或者行政事业性收费。 (1)由国务院或者财政部批准设立的政府性基金,由国务院或者省级人民政府及其财政、价格主管部门批准设立的行政事业性收费;  (2)收取时开具省级以上(含省级)财政部门监(印)制的财政票据;  (3)所收款项全额上缴财政。 2.以委托方名义开具发票代委托方收取的款项。 (二)特殊规定  1.经纪代理服务,以取得的全部价款和价外费用,扣除向委托方收取并代为支付的政府性基金或者行政事业性收费后的余额为销售额。向委托方收取的政府性基金或者行政事业性收费,不得开具增值税专用发票。 3.试点纳税人按照上述的规定从全部价款和价外费用中扣除的价款,应当取得符合法律、行政法规和国家税务总局规定的有效凭证。否则,不得扣除。 上述凭证是指:  (1)扣除的政府性基金、行政事业性收费,以省级以上(含省级)财政部门监(印)制的财政票据为合法有效凭证。 (2)人力资源外包服务可凭劳动合同、工资单等证明材料,作为差额扣除凭证。", " 纳税人取得的上述凭证属于增值税扣税凭证的,其进项税额不得从销项税额中抵扣。", " 七、增值税进项税额抵扣 ", " (一)允许抵扣进项税额  增值税扣税凭证包括:  1.从销售方取得的增值税专用发票(含税控机动车销售统一发票)上注明的增值税额。 2.从海关取得的海关进口增值税专用缴款书上注明的增值税额。 进项税额=买价×扣除率  买价,是指纳税人购进农产品在农产品收购发票或者销售发票上注明的价款和按照规定缴纳的烟叶税。 购进农产品,按照《农产品增值税进项税额核定扣除试点实施办法》抵扣进项税额的除外。 4.从境外单位或者个人购进服务、无形资产或者不动产,自税务机关或者扣缴义务人取得的解缴税款的完税凭证上注明的增值税额。 (二)不得抵扣项目  1.用于简易计税方法计税项目、免征增值税项目、集体福利或者个人消费的购进货物、加工修理修配劳务、服务、无形资产和不动产。其中涉及的固定资产、无形资产、不动产,仅指专用于上述项目的固定资产、无形资产(不包括其他权益性无形资产)、不动产。 纳税人的交际应酬消费属于个人消费。 2.非正常损失的购进货物,以及相关的加工修理修配劳务和交通运输服务。 3.非正常损失的在产品、产成品所耗用的购进货物(不包括固定资产)、加工修理修配劳务和交通运输服务。 4.非正常损失的不动产,以及该不动产所耗用的购进货物、设计服务和建筑服务。", " 1.用于简易计税方法计税项目、免征增值税项目、集体福利或者个人消费的购进货物、加工修理修配劳务、服务、无形资产和不动产。其中涉及的固定资产、无形资产、不动产,仅指专用于上述项目的固定资产、无形资产(不包括其他权益性无形资产)、不动产。 纳税人的交际应酬消费属于个人消费。 2.非正常损失的购进货物,以及相关的加工修理修配劳务和交通运输服务。 3.非正常损失的在产品、产成品所耗用的购进货物(不包括固定资产)、加工修理修配劳务和交通运输服务。 4.非正常损失的不动产,以及该不动产所耗用的购进货物、设计服务和建筑服务。 5.非正常损失的不动产在建工程所耗用的购进货物、设计服务和建筑服务。 纳税人新建、改建、扩建、修缮、装饰不动产,均属于不动产在建工程。", " 6.购进的旅客运输服务、贷款服务、餐饮服务、居民日常服务和娱乐服务。", " 接受贷款服务向贷款方支付与该笔贷款直接相关的投融资顾问费、手续费、咨询费等费用。", " 八、纳税地点 ", " (一)固定业户应当向其机构所在地或者居住地主管税务机关申报纳税。总机构和分支机构不在同一县(市)的,应当分别向各自所在地的主管税务机关申报纳税;经财政部和国家税务总局或者其授权的财政和税务机关批准,可以由总机构汇总向总机构所在地的主管税务机关申报纳税。 (二)非固定业户应当向应税行为发生地主管税务机关申报纳税;未申报纳税的,由其机构所在地或者居住地主管税务机关补征税款。 (三)扣缴义务人应当向其机构所在地或者居住地主管税务机关申报缴纳扣缴的税款。", " 九、纳税义务时间 ", " (一)发生应税行为并收讫销售款项或者取得索取销售款项凭据的当天;先开具发票的,为开具发票的当天。 (二)视同销售服务,为服务完成的当天。"], "segmented_title": ["经纪", "代理服务业", "营改增", "主要", "政策", "简介"], "segmented_paragraphs": [["您", "所在", "的", "位置", ":", "首页", ">", "热点追踪"], ["经纪", "代理服务业", "营改增", "主要", "政策", "简介"], ["发布者", ":", "admin", "来源", ":", "官方", "来源", "发布时间", ":", "2016", "-", "11", "-", "29"], ["2016", "年", "3", "月", "23", "日", ",", "财政部", "、", "国家税务总局", "正式颁布", "《", "关于", "全面推开", "营业税", "改", "征", "增值税", "试点", "的", 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"扣除", "向", "委托", "方", "收取", "并", "代为", "支付", "的", "政府", "性", "基金", "或者", "行政", "事业", "性", "收费", "后", "的", "余额", "为", "销售额", "。", "向", "委托", "方", "收取", "的", "政府", "性", "基金", "或者", "行政", "事业", "性", "收费", ",", "不得", "开具", "增值税专用发票", "。"]], "title": "营改增后,纳税人提供代理服务,是否还有差额征税的规定?_百度知道"}, {"bs_rank_pos": 4, "paragraphs": ["2016年3月23日,财政部、国家税务总局正式颁布《关于全面推开营业税改征增值税试点的通知》(财税〔2016〕36号),自2016年5月1日起,全面推开营改增,将试点范围扩大到建筑业、房地产业、金融业、生活服务业。为便于经纪代理服务业纳税人了解此次营改增的主要内容,广州市国家税务局现对经纪代理服务业的主要政策整理如下,供社会各界参阅。 ", " 一、增值税纳税人  在中华人民共和国境内提供经纪代理服务的单位和个人,为增值税纳税人。  单位,是指企业、行政单位、事业单位、军事单位、社会团体及其他单位。  个人,是指个体工商户和其他个人。  二、征税范围  根据《销售服务、无形资产、不动产注释》规定,经纪代理服务,是指各类经纪、中介、代理服务。包括金融代理、知识产权代理、货物运输代理、代理报关、法律代理、房地产中介、职业中介、婚姻中介、代理记账、拍卖等。  货物运输代理服务,是指接受货物收货人、发货人、船舶所有人、船舶承租人或者船舶经营人的委托,以委托人的名义,为委托人办理货物运输、装卸、仓储和船舶进出港口、引航、靠泊等相关手续的业务活动。  代理报关服务,是指接受进出口货物的收、发货人委托,代为办理报关手续的业务活动。  三、增值税纳税人分类  (一)增值税一般纳税人:适用增值税税率,其进项税额可以抵扣。  1.年应税销售额超过500万元的纳税人应登记为一般纳税人。其中:(1)年应税销售额超过500万元的其他个人不属于一般纳税人。  (2)年应税销售额超过500万元但不经常提供应税服务的单位和个体工商户选择按照小规模纳税人纳税。  2.年应税销售额未超过500万元的纳税人,会计核算健全,能够提供准确税务资料的,可以向主管税务机关办理一般纳税人资格登记,成为一般纳税人。  (二)增值税小规模纳税人:适用增值税征收率,其进项税额不可以抵扣。  (三)增值税扣缴义务人:中华人民共和国境外单位或者个人在境内发生应税行为,在境内未设有经营机构的,以购买方为增值税扣缴义务人。  四、税率和征收率  五、增值税计税方法  (一)一般计税方法  一般纳税人发生应税行为适用一般计税方法计税,按以下公式计算:  应纳税额=当期销项税额-当期进项税额  销项税额=销售额×税率  销售额=含税销售额÷(1+税率)  (二)简易计税方法  小规模纳税人发生应税行为适用简易计税方法计税。简易计税方法的应纳税额,是指按照销售额和增值税征收率计算的增值税额,不得抵扣进项税额。应纳税额计算公式:  应纳税额=销售额×征收率  简易计税方法的销售额不包括其应纳税额,纳税人采用销售额和应纳税额合并定价方法的,按照下列公式计算销售额: ", " (一)一般计税方法  一般纳税人发生应税行为适用一般计税方法计税,按以下公式计算:  应纳税额=当期销项税额-当期进项税额  销项税额=销售额×税率  销售额=含税销售额÷(1+税率)  (二)简易计税方法  小规模纳税人发生应税行为适用简易计税方法计税。简易计税方法的应纳税额,是指按照销售额和增值税征收率计算的增值税额,不得抵扣进项税额。应纳税额计算公式:  应纳税额=销售额×征收率  简易计税方法的销售额不包括其应纳税额,纳税人采用销售额和应纳税额合并定价方法的,按照下列公式计算销售额:  销售额=含税销售额÷(1+征收率)  (三)扣缴计税方法  扣缴义务人适用扣缴计税方法计税。  应扣缴税额=接受方支付的价款÷(1+税率)×税率  六、销售额的计算  (一)一般规定  销售额,是指纳税人发生应税行为取得的全部价款和价外费用,财政部和国家税务总局另有规定的除外。价外费用,是指价外收取的各种性质的收费,但不包括以下项目: ", " (一)一般规定  销售额,是指纳税人发生应税行为取得的全部价款和价外费用,财政部和国家税务总局另有规定的除外。价外费用,是指价外收取的各种性质的收费,但不包括以下项目:  1.代为收取并同时满足以下条件的政府性基金或者行政事业性收费。  (1)由国务院或者财政部批准设立的政府性基金,由国务院或者省级人民政府及其财政、价格主管部门批准设立的行政事业性收费;  (2)收取时开具省级以上(含省级)财政部门监(印)制的财政票据;  (3)所收款项全额上缴财政。  2.以委托方名义开具发票代委托方收取的款项。  (二)特殊规定  1.经纪代理服务,以取得的全部价款和价外费用,扣除向委托方收取并代为支付的政府性基金或者行政事业性收费后的余额为销售额。向委托方收取的政府性基金或者行政事业性收费,不得开具增值税专用发票。  3.试点纳税人按照上述的规定从全部价款和价外费用中扣除的价款,应当取得符合法律、行政法规和国家税务总局规定的有效凭证。否则,不得扣除。  上述凭证是指:  (1)扣除的政府性基金、行政事业性收费,以省级以上(含省级)财政部门监(印)制的财政票据为合法有效凭证。  (2)人力资源外包服务可凭劳动合同、工资单等证明材料,作为差额扣除凭证。 ", " 纳税人取得的上述凭证属于增值税扣税凭证的,其进项税额不得从销项税额中抵扣。 ", " 七、增值税进项税额抵扣 ", " (一)允许抵扣进项税额  增值税扣税凭证包括:  1.从销售方取得的增值税专用发票(含税控机动车销售统一发票)上注明的增值税额。  2.从海关取得的海关进口增值税专用缴款书上注明的增值税额。  进项税额=买价×扣除率  买价,是指纳税人购进农产品在农产品收购发票或者销售发票上注明的价款和按照规定缴纳的烟叶税。  购进农产品,按照《农产品增值税进项税额核定扣除试点实施办法》抵扣进项税额的除外。  4.从境外单位或者个人购进服务、无形资产或者不动产,自税务机关或者扣缴义务人取得的解缴税款的完税凭证上注明的增值税额。  (二)不得抵扣项目  1.用于简易计税方法计税项目、免征增值税项目、集体福利或者个人消费的购进货物、加工修理修配劳务、服务、无形资产和不动产。其中涉及的固定资产、无形资产、不动产,仅指专用于上述项目的固定资产、无形资产(不包括其他权益性无形资产)、不动产。  纳税人的交际应酬消费属于个人消费。  2.非正常损失的购进货物,以及相关的加工修理修配劳务和交通运输服务。  3.非正常损失的在产品、产成品所耗用的购进货物(不包括固定资产)、加工修理修配劳务和交通运输服务。  4.非正常损失的不动产,以及该不动产所耗用的购进货物、设计服务和建筑服务。 ", " 1.用于简易计税方法计税项目、免征增值税项目、集体福利或者个人消费的购进货物、加工修理修配劳务、服务、无形资产和不动产。其中涉及的固定资产、无形资产、不动产,仅指专用于上述项目的固定资产、无形资产(不包括其他权益性无形资产)、不动产。  纳税人的交际应酬消费属于个人消费。  2.非正常损失的购进货物,以及相关的加工修理修配劳务和交通运输服务。  3.非正常损失的在产品、产成品所耗用的购进货物(不包括固定资产)、加工修理修配劳务和交通运输服务。  4.非正常损失的不动产,以及该不动产所耗用的购进货物、设计服务和建筑服务。  5.非正常损失的不动产在建工程所耗用的购进货物、设计服务和建筑服务。  纳税人新建、改建、扩建、修缮、装饰不动产,均属于不动产在建工程。 ", " 6.购进的旅客运输服务、贷款服务、餐饮服务、居民日常服务和娱乐服务。 ", " 接受贷款服务向贷款方支付与该笔贷款直接相关的投融资顾问费、手续费、咨询费等费用。 ", " 八、纳税地点 ", " (一)固定业户应当向其机构所在地或者居住地主管税务机关申报纳税。总机构和分支机构不在同一县(市)的,应当分别向各自所在地的主管税务机关申报纳税;经财政部和国家税务总局或者其授权的财政和税务机关批准,可以由总机构汇总向总机构所在地的主管税务机关申报纳税。  (二)非固定业户应当向应税行为发生地主管税务机关申报纳税;未申报纳税的,由其机构所在地或者居住地主管税务机关补征税款。  (三)扣缴义务人应当向其机构所在地或者居住地主管税务机关申报缴纳扣缴的税款。", " 九、纳税义务时间 ", " (一)发生应税行为并收讫销售款项或者取得索取销售款项凭据的当天;先开具发票的,为开具发票的当天。  (二)视同销售服务,为服务完成的当天。  (三)扣缴义务发生时间为纳税人增值税纳税义务发生的当天。 ", " 十、税收优惠 ", " (一)起征点 "], "segmented_title": ["广州", "国税", ":", "经纪", "代理服务业", "营改增", "主要", "政策", "简介"], "segmented_paragraphs": [["2016", "年", "3", "月", "23", "日", ",", 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平台版本,这是目前全世界最具有想象力的一款游戏。虽然我的世界Mac版V1.8.1所呈现的世界并不是华丽的画面与特效,而是注重在游戏性上面。整个游戏没有剧情,玩家在游戏中自由建设和破坏,透过像乐高一样的积木来组合与拼凑,轻而易举的就能制作出小木屋、城堡甚至城市,但是若再加上玩家的想象力,天空之城、地底都市都一样能够实现。玩家不仅可以创造房屋建筑,甚至可以创造属于自己的都市和世界,玩家可以通过自己创造的作品来体验上帝一般的感觉。在这款游戏里,不仅可以单人娱乐,还可以多人联机。  基本简介 在开始和大家讲怎么使用之前,我们得郑重其事的感谢正直无私切勇于奉献网友 @Dust 同学的辛勤工作,在他几个昼夜的努力下我的世界Mac版V1.8.1免费版终于成功问世了,虽然使用过程有点繁琐,但是总算是成功问世了,不需要注册,不需要付费!有疑问的亲们,可以加 432429883 群,详细咨询。  好了!言归正传,我们先来讲 我的世界Mac版 V1.8.1试用版的,使用方法,你需要先更新至V1.8.1试用版,在使用启动器之后才能进入我的世界V 1.8.1游戏界面。  然后,使用启动器,免费进入正式版游戏界面没有游戏时间限制(我的世界1.8.1 Mac版免费教程),然后你还可以联机玩(我的世界Mac版联机教程)。  首先,在本页面百度网盘连接当中,下载“我的世界Mac版V1.8.1”DMG文件,打开之后双击我的世界Logo启动游戏,这个时候你会进入如下图所示界面:  到了此处,你需要耐心的等待程序加载完毕,此窗口类似 dows 界面会不断加载代码,一直等他自动走完跳转到下图所示界面。如果你发现,上图所示界面不在加载代码,并提示报错,请关掉上图所示界面,重新打开,重新加载即可。  按照编辑亲自测试的经验来看,大概尝试两道三次之后就可以走到下一步。  如下图示所示界面,点下图当中的“Play Offline”,进入下一步。  点击“Play Offline”进入到下一步,当然这一步也需要一点点时间,这个时间不长可能几十秒或者一分钟都有可能,就会出现如下图所示界面,进入试用版登陆界面。  到这里,你会发现我的世界官方要求你先注册之后才可以试用哦!这时候你可以点上图当中的“Register”,登陆我的 Minecraft 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"]"]], "title": "我的世界1.8.1免费版for mac|我的世界1.8.1免费mac版 V1.8.1免费版 "}, {"bs_rank_pos": 3, "paragraphs": ["当前位置:首页 → MAC软件→ Mac游戏→ 我的世界2Mac版 V2.1正式版", " 我的世界2Mac版是一款经典的沙盒模拟游戏,也是世界上第一款沙盒游戏,玩家可以在一个自由的界面环境中行动,用三维世界中的各种方块来建造你心中的梦想,游戏中没有剧情,所以玩法由你而定,只要你愿意,建造巴比伦通天塔都无人可以限制你。", " Minecraft for mac直译为《我的世界》,又译《麦块》《我的作品》《当个创世神》)是一款Mac平台上的沙盒建造游戏,我的世界mac版也是世界上第一款沙盒游戏,玩家可以在一个三维世界里用各种方块建造建筑物。最初由马库斯“诺奇”椠尔松(Markus Notch Persson)单独开发,现已成立麻将规范公司(Mojang)来负责开发此游戏。", " 第一步,自然是下载安装包,打开我的世界,界面如下:", " 第二步、随便键入一个用户名(不输也没事),点击——Enter Game进入游戏,这个时候肯定有童鞋问为啥不是中文呢?因为要进了游戏之后才能设置语言。看下图就知道。", " 第三步、你会发现,需要更新,而且进度条读的很慢,小编开始的时候竟然只有 0.22KB/S ,最后没办法,小飞机上场,开启独立服务器,再然后速度就给力了,一分钟不到就更新完了。当然这也不绝对,可能是网速的问题,小编换了台迷你盒子之后不开小飞机速度照样给力,也是一分钟搞定。", " 第四步、更新完毕之后进入如下图所示界面,当然是英文的界面,但是童鞋们要按照下图所示的操作进入设置中文简体或者繁体的语言,如下图所示进入设置选项。。", " 第五步、如下图所示的,同样的位置有一个——Language,选项,点击进入选中——简体中文或者繁体中文即可。", " 最后,接下来自然是创建一个新游戏开玩啦!步骤很简单,小编不再细说,反正有中文,大家自个看。", " 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"从铁路部门了解到,注册12306账号的用户可以将账号注销,但是,网上和客服是无法办理注销的,用户只能拿着自己的二代居民身份证到火车站售票窗口办理注销手续。也可以按12306网上订票常见问题里的方法向铁路客服中心提交资料申请。以电子邮件形式发送到12306yhhd@rails.com.cn邮箱,12306.cn网站接收后,处理结果将回复至邮箱。", "通过介绍,大家知道了12306账号怎么注销如何注销了吧。下面,我们一起来学习一下12306其它问题。", "12306账号怎么修改", "用户登录自己的账号后,点击查看个人信息,然后选择编辑,将姓名和身份证号进行修改后点击保存即可。", "如果用户帐号无法激活或未收到激活邮件怎么办", "如果您收到激活邮件但无法激活用户帐号或者未收到激活邮件,请您尝试通过以下方法解决:", "(1)检查您电子邮件信箱中的垃圾箱或广告箱,邮件有可能被误认为垃圾或广告邮件。 (2)使用用户名和密码重新登录12306.cn网站后,网站将提示您“您的用户尚未激活”,请点击“重发激活邮件”,网站将立即发送激活邮件,请重新收取激活邮件,并按提示激活用户账号。 (3)使用用户名和密码重新登录12306.cn网站,修改“个人资料”中的“电子邮件”,更换一个有效电子邮件信箱。", "免责声明:火车网文章信息来源于网络以及网友投稿,本站只负责对文章进行整理、排版、编辑,是出于传递更多信息之目的,并不意味着赞同其观点或证实其内容的真实性。", "深圳到广州汽车票价 广州到深圳汽车票价 杭州到上海汽车票价 杭州到宁波汽车票价 杭州到苏州汽车票价 北京到哈尔滨汽车票价 哈尔滨到佳木斯汽车票价 北京到西安汽车票价 青岛到烟台汽车票价 上海到南通汽车票价 厦门到广州汽车票价 南京到扬州汽车票价 杭州到黄山汽车票价 杭州到西安汽车票价 南京到泰州汽车票价 广州到厦门汽车票价 成都到重庆汽车票价 上海到青岛汽车票价", "北京到上海特价机票 广州到重庆特价机票 广州到成都特价机票 上海到西安特价机票 上海到郑州特价机票 广州到西安特价机票 广州到海口特价机票 厦门到广州特价机票 深圳到重庆特价机票 上海到武汉特价机票 广州到南宁特价机票 广州到厦门特价机票 上海到成都特价机票 广州到南京特价机票 杭州到西安特价机票 广州到郑州特价机票 广州到上海特价机票 上海到重庆特价机票", "从北京到上海多少公里 从广州到重庆多少公里 从成都到上海多少公里 从北京到厦门多少公里 从厦门到上海多少公里 从北京到郑州多少公里 从上海到天津多少公里 从郑州到上海多少公里 从福州到北京多少公里 从成都到北京多少公里 从北京到拉萨多少公里 从厦门到三亚多少公里 从深圳到厦门多少公里 从拉萨到郑州多少公里 从深圳到北京多少公里", "普快火车时刻表 空调普慢火车时刻表 普慢火车时刻表 直达特快火车时刻表 空调普快火车时刻表 空调快速火车时刻表 空调特快火车时刻表 快速火车时刻表 动车组火车时刻表 城际高速火车时刻表", "湖北多地发布雷电黄色预警 2017年浙江出梅时间 浙江17年什么时候出梅 钱塘江流域洪水最新消息 2017年6.26四川宜宾市地... 2017年6月26日四川宜宾市... 2017.6.26四川宜宾地震... 2017年6月26日四川地震最新消息 钱塘江流域暴发洪水 深圳高温补贴2017标准"], "bs_rank_pos": 0}, {"title": "如何注销12306账号_百度经验", "segmented_title": ["如何", "注销", "12306", "账号", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["12306", "账号", "暂时", "没有", "注销", "的", "功能", "。", "不过", ",", "您", "如果", "想", "注销", "账号", ",", "无非", "就是", "怕", "泄露", "信息", ",", "那么", ",", "如果", "把", "12306", "账号", "的", "信息", "修改", "了", ",", "也", "就", "起", "到", "了", "注销", "的", "效果", "了", "。", "下面", ",", "小", "编", "就", "分享", "一", "下", "如何", "“", "注销", "”", "12306", "账号", "。"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["1", "打开", "12306", "网站", ",", "登录", "您", "的", "账号", "。", "步骤", "阅读", "2", "点击查看", "个人", "信息", "。", "步骤", "阅读", "3", "选择", "编辑", "。", "步骤", "阅读", "4", "将", "姓名", "和", "身份证号码", "修改", ",", "然后", "输入", "登录", "密码", "。", "点击", "保存", "。", "步骤", "阅读", "5", "成功", "修改", ",", "点击", "确认", "。", "步骤", "阅读", "6", "同样", "的", "方法", ",", "将", "下面", "信息", "继续", "修改", "即可", "。", "步骤", "阅读", "END"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["修改", "身份证号码", "时", ",", "可能会", "出现", "身份证号码", "不", "正确", "的", "情况", ",", "多", "换", "几个", "试一试", "即可", "。"], ["经验", "内容", "仅供参考", ",", "如果", "您", "需", "解决", "具体", "问题", "(", "尤其", "法律", "、", "医学", "等", "领域", ")", ",", "建议", "您", "详细", "咨询", "相关", "领域", "专业人士", "。"], ["个性签名", ":", "经验", ",", "奔跑吧", ".", ".", ".", "."]], "paragraphs": ["百度经验:jingyan.baidu.com", "12306账号暂时没有注销的功能。不过,您如果想注销账号,无非就是怕泄露信息,那么,如果把12306账号的信息修改了,也就起到了注销的效果了。下面,小编就分享一下如何“注销”12306账号。", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "1 打开12306网站,登录您的账号。 步骤阅读 2 点击查看个人信息。 步骤阅读 3 选择编辑。 步骤阅读 4 将姓名和身份证号码修改,然后输入登录密码。点击保存。 步骤阅读 5 成功修改,点击确认。 步骤阅读 6 同样的方法,将下面信息继续修改即可。 步骤阅读 END", "百度经验:jingyan.baidu.com", "修改身份证号码时,可能会出现身份证号码不正确的情况,多换几个试一试即可。", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:经验,奔跑吧...."], "bs_rank_pos": 1}, {"title": "12306账号注册过的账号怎么注销掉_百度知道", "segmented_title": ["12306", "账号", "注册", "过", "的", "账号", "怎么", "注销", "掉", "_", "百度", "知道"], "segmented_paragraphs": [["(", "1", ")", "注册", "12306", "账号", "的", "用户", "可以", "将", "账号", "注销", ",", "但是", ",", "网上", "和", "客服", "是", "无法", "办理", "注销", "的", ",", "用户", "只能", "拿着", "自己", "的", "二代", "居民", "身份证", "到", "火车站", "售票", "窗口", "办理", "注销", "手续", "。", "(", "2", ")", "有没有", "其他", "简单", "省", "时", "的", "办法", "?", "办理", "注销", "手续", ",", "一", "是", "觉得", "以后", "可能", "用", "不到", "了", ",", "二", "是", "担心", "自己", "的", "身份", "信息", "可能", "泄露", "出去", ",", "因为", ",", "注册时", "填写", "的", "都是", "真实", "信息", ",", "泄露", "出去", "可能", "会", "对", "用户", "的", "生活", "造成", "一定", "的", "影响", "。", "那么", ",", "可不可以", "将", "注册", "的", "信息", "修改", "一", "下", "呢", "?", "这样", "不是", "也", "同样", "起", "到", "了", "注销", "的", "效果", "了", "。", "(", "3", ")", "如何", "修改", "信息", "?", "用户登录", "自己", "的", "账号", "后", ",", "点击查看", "个人", "信息", ",", "然后", "选择", "编辑", ",", "将", "姓名", "和", "身份证号", "进行", "修改后", "点击", "保存", "即可", "。"], ["(", "1", ")", "注册", "12306", "账号", "的", "用户", "可以", "将", "账号", "注销", ",", "但是", ",", "网上", "和", "客服", "是", "无法", "办理", "注销", "的", ",", "用户", "只能", "拿着", "自己", "的", "二代", "居民", "身份证", "到", "火车站", "售票", "窗口", "办理", "注销", "手续", "。", "(", "2", ")", "有没有", "其他", "简单", "省", "时", "的", "办法", "?", "办理", "注销", "手续", ",", "一", "是", "觉得", "以后", "可能", "用", "不到", "了", ",", "二", "是", "担心", "自己", "的", "身份", "信息", "可能", "泄露", "出去", ",", "因为", ",", "注册时", "填写", "的", "都是", "真实", "信息", ",", "泄露", "出去", "可能", "会", "对", "用户", "的", "生活", "造成", "一定", "的", "影响", "。", "那么", ",", "可不可以", "将", "注册", "的", "信息", "修改", "一", "下", "呢", "?", "这样", "不是", "也", "同样", "起", "到", "了", "注销", "的", "效果", "了", "。", "(", "3", ")", "如何", "修改", "信息", "?", "用户登录", "自己", "的", "账号", "后", ",", "点击查看", "个人", "信息", ",", "然后", "选择", "编辑", ",", "将", "姓名", "和", "身份证号", "进行", "修改后", "点击", "保存", "即可", "。"], ["不能", "注", "晓", "的"]], "paragraphs": ["(1)注册12306账号的用户可以将账号注销,但是,网上和客服是无法办理注销的,用户只能拿着自己的二代居民身份证到火车站售票窗口办理注销手续。 (2)有没有其他简单省时的办法?办理注销手续,一是觉得以后可能用不到了,二是担心自己的身份信息可能泄露出去,因为,注册时填写的都是真实信息,泄露出去可能会对用户的生活造成一定的影响。那么,可不可以将注册的信息修改一下呢?这样不是也同样起到了注销的效果了。 (3)如何修改信息?用户登录自己的账号后,点击查看个人信息,然后选择编辑,将姓名和身份证号进行修改后点击保存即可。", "(1)注册12306账号的用户可以将账号注销,但是,网上和客服是无法办理注销的,用户只能拿着自己的二代居民身份证到火车站售票窗口办理注销手续。 (2)有没有其他简单省时的办法?办理注销手续,一是觉得以后可能用不到了,二是担心自己的身份信息可能泄露出去,因为,注册时填写的都是真实信息,泄露出去可能会对用户的生活造成一定的影响。那么,可不可以将注册的信息修改一下呢?这样不是也同样起到了注销的效果了。 (3)如何修改信息?用户登录自己的账号后,点击查看个人信息,然后选择编辑,将姓名和身份证号进行修改后点击保存即可。", "不能注晓的"], "bs_rank_pos": 2}, {"title": "12306网怎么注销账号?_百度知道", "segmented_title": ["12306", "网", "怎么", "注销", "账号", "?", "_", "百度", "知道"], "segmented_paragraphs": [["12306", "账号", "一", "经", "注册", "便", "无法", "注销", "。", "可以", "进入", "12306", "账号", "将", "所有", "的", "有关", "个人", "的", "信息", "全部", "删除", "掉", ",", "但是", "账号", "还是", "存在", "的", ",", "可以", "不", "使用", ",", "也", "没有", "必要", "注销", "。", "12306", "是", "铁道部", "火车", "票", "网上订票", "唯一", "官网", "站", "。", "任何", "操作", "都", "要", "去", "官网", ",", "小心", "信息", "泄露", "。"], ["你", "通过", "邮箱", "找回", "就是", "了", ",", "怎么", "那么", "麻烦", ",", "一", "个", "号", "以后", "就", "别", "改", "了", "。", "多", "了", "容易", "乱"]], "paragraphs": ["12306账号一经注册便无法注销。可以进入12306账号将所有的有关个人的信息全部删除掉,但是账号还是存在的,可以不使用,也没有必要注销。 12306是铁道部火车票网上订票唯一官网站。任何操作都要去官网,小心信息泄露。", "你通过邮箱找回就是了,怎么那么麻烦,一个号以后就别改了。 多了容易乱"], "bs_rank_pos": 3}, {"title": "12306账号怎么注销_百度知道", "segmented_title": ["12306", "账号", "怎么", "注销", "_", "百度", "知道"], "segmented_paragraphs": [["12306", "不提供", "账号", "注销", "服务", "。", "。", "。", "您", "目前", "这种情况", ",", "如果", "您", "还记得", "注册", "邮箱", "的", "话", ",", "可以直接", "利用", "注册", "邮箱", "找回", "用户名", "和", "密码", "。", "操作方法", ":", "在", "登陆", "界面", "的", "登录", "按钮", "旁", ",", "有", "一", "个", "“", "忘记", "用户名", "/", "密码", 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"信息", "”", "一栏", "中", ",", "把", "“", "姓名", "”", "随便", "乱", "填", ",", "然后", "证件类型", "改为", "“", "护照", "”", ",", "证"]], "paragraphs": ["12306不提供账号注销服务。。。 您目前这种情况,如果您还记得注册邮箱的话,可以直接利用注册邮箱找回用户名和密码。 操作方法:在登陆界面的登录按钮旁,有一个“忘记用户名/密码”,点击之后,最上面就是“用注册邮箱找回用户名/密码”,您只需输入注册邮箱地址,系统就会自动向这个邮箱发邮件,邮件中包含您的用户名!然后,邮件中有一个连接,点击重置密码即可。 如果您连注册邮箱都不知道的话,那就只能提交申诉,上传身份证扫描件和照片,申请找回了。 如果你想注销手里的某个账号的的话,我给你提供一种方法,但是仅限于你能够登陆这个账号时使用 1 登陆此账号 2 登陆后,在左下角“我的信息”中点击个人资料 3 进入个人资料后,点击最下面的“修改” 4 在“详细信息”一栏中,把“姓名”随便乱填,然后证件类型改为“护照”,证", "12306不提供账号注销服务。。。 您目前这种情况,如果您还记得注册邮箱的话,可以直接利用注册邮箱找回用户名和密码。 操作方法:在登陆界面的登录按钮旁,有一个“忘记用户名/密码”,点击之后,最上面就是“用注册邮箱找回用户名/密码”,您只需输入注册邮箱地址,系统就会自动向这个邮箱发邮件,邮件中包含您的用户名!然后,邮件中有一个连接,点击重置密码即可。 如果您连注册邮箱都不知道的话,那就只能提交申诉,上传身份证扫描件和照片,申请找回了。 如果你想注销手里的某个账号的的话,我给你提供一种方法,但是仅限于你能够登陆这个账号时使用 1 登陆此账号 2 登陆后,在左下角“我的信息”中点击个人资料 3 进入个人资料后,点击最下面的“修改” 4 在“详细信息”一栏中,把“姓名”随便乱填,然后证件类型改为“护照”,证"], "bs_rank_pos": 4}], "question": "12306账号注销", "segmented_question": ["12306", "账号", "注销"], "question_type": "DESCRIPTION", "fact_or_opinion": "FACT", "question_id": 221610}
+{"documents": [{"bs_rank_pos": 0, "paragraphs": ["如果不被重写(原生)的hashCode和equals是什么样的?", "不被重写(原生)的hashCode值是根据内存地址换算出来的一个值。 不被重写(原生)的equals方法是严格判断一个对象是否相等的方法(object1 == object2)。", " 为什么需要重写equals和hashCode方法?", " 在我们的业务系统中判断对象时有时候需要的不是一种严格意义上的相等,而是一种业务上的对象相等。在这种情况下,原生的equals方法就不能满足我们的需求了", " 所以这个时候我们需要重写equals方法,来满足我们的业务系统上的需求。那么为什么在重写equals方法的时候需要重写hashCode方法呢?", " 我们先来看一下Object.hashCode的通用约定(摘自《Effective Java》第45页)", "在一个应用程序执行期间,如果一个对象的equals方法做比较所用到的信息没有被修改的话,那么,对该对象调用hashCode方法多次,它必须始终如一地返回 同一个整数。在同一个应用程序的多次执行过程中,这个整数可以不同,即这个应用程序这次执行返回的整数与下一次执行返回的整数可以不一致。 如果两个对象根据equals(Object)方法是相等的,那么调用这两个对象中任一个对象的hashCode方法必须产生同样的整数结果。 如果两个对象根据equals(Object)方法是不相等的,那么调用这两个对象中任一个对象的hashCode方法,不要求必须产生不同的整数结果。然而,程序员应该意识到这样的事实,对于不相等的对象产生截然不同的整数结果,有可能提高散列表(hash table)的性能。", "HashMap的底层处理机制是以数组的方法保存放入的数据的(Node[] table),其中的关键是数组下标的处理。", "如果该数组位置上已经有放入的值了,且传入的键值相等则不处理,若不相等则覆盖原来的值,如果数组位置没有条目,则插入,并加入到相应的链表中。检查键是否存在也是根据hashCode值来确定的。所以如果不重写hashCode的话,可能导致HashSet、HashMap不能正常的运作、", " 如果我们将某个自定义对象存到HashMap或者HashSet及其类似实现类中的时候,如果该对象的属性参与了hashCode的计算,那么就不能修改该对象参数hashCode计算的属性了。", "有可能会移除不了元素,导致内存泄漏。", " 在重写equals方法的时候,需要遵守下面的通用约定:", " 对于任意的引用值x,x.equals(x)一定为true。", " 对于任意的引用值x和y,当且仅当y.equals(x)返回true时,x.equals(y)也一定返回true。", " 对于任意的引用值x、y和z,如果x.equals(y)返回true,并且y.equals(z)也返回true,那么x.equals(z)也一定返回true。", " 对于任意的引用值x和y,如果用于equals比较的对象没有被修改的话,那么,对此调用x.equals(y)要么一致地返回true,要么一致的返回false。", " 5、对于任意的非空引用值x,x.equals(null)一定返回false。", " 重写hashCode方法的大致方式:", " a、把某个非零常数值,比如说17(最好是素数),保存在一个叫result的int类型的变量中。 b、对于对象中每一个关键域f(值equals方法中考虑的每一个域),完成一些步骤:", " 1、为该域计算int类型的散列吗c: 1)、如果该域是boolean类型,则计算(f?0:1)。 2)、如果该域是byte、char、short或者int类型,则计算(int)f。 3)、如果该域是float类型,则计算Float.floatToIntBits(f)。 4)、如果该域是long类型,则计算(int)(f ^ (f>>>32))。 5)、如果该域是double类型,则计算Double.doubleToLongBits(f)得到一个long类型的值,然后按照步骤4,对该long型值计算散列值。 6)、如果该域是一个对象引用,并且该类的equals方法通过递归调用equals的方式来比较这个域,则同样对这个域递归调用hashCode。如果要求一个更为复杂的比较,则为这个域计算一个“规范表示”,然后针对这个范式表示调用hashCode。如果这个域的值为null,则返回0(或者其他某个常数) 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"由于为了提高程序的效率才实现了hashcode方法,先进行hashcode的比较,如果不同,那没就不必在进行equals的比较了,这样就大大减少了equals比较的次数,这对比需要比较的数量很大的效率提高是很明显的,一个很好的例子就是在集合中的使用;", "我们都知道java中的List集合是有序的,因此是可以重复的,而set集合是无序的,因此是不能重复的,那么怎么能保证不能被放入重复的元素呢,但靠equals方法一样比较的话,如果原来集合中以后又10000个元素了,那么放入10001个元素,难道要将前面的所有元素都进行比较,看看是否有重复,欧码噶的,这个效率可想而知,因此hashcode就应遇而生了,java就采用了hash表,利用哈希算法(也叫散列算法),就是将对象数据根据该对象的特征使用特定的算法将其定义到一个地址上,那么在后面定义进来的数据只要看对应的hashcode地址上是否有值,那么就用equals比较,如果没有则直接插入,只要就大大减少了equals的使用次数,执行效率就大大提高了。", "继续上面的话题,为什么必须要重写hashcode方法,其实简单的说就是为了保证同一个对象,保证在equals相同的情况下hashcode值必定相同,如果重写了equals而未重写hashcode方法,可能就会出现两个没有关系的对象equals相同的(因为equal都是根据对象的特征进行重写的),但hashcode确实不相同的。", "posted on 2016-11-15 11:23 救赎者 阅读(...) 评论(...) 编辑 收藏 ", "Powered by:  博客园 ", " 随笔- 81  文章- 0  评论- 1  引用- 0 "], "segmented_title": ["为什么", "重写", "equals", "时", "必须", "重写", "hash", "Code", "方法", "?", "-", "救赎", "者", "-", "博客园"], "segmented_paragraphs": [["首先", "我们", "先", "来看", "下", "String", "类", "的", "源码", ":", "可以", "发现", "String", "是", "重写", "了", "Object", "类", "的", 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"paragraphs": ["我们都知道Java语言是完全面向对象的,在java中,所有的对象都是继承于Object类。Ojbect类中有两个方法equals、hashCode,这两个方法都是用来比较两个对象是否相等的。", " 在未重写equals方法我们是继承了object的equals方法,那里的 equals是比较两个对象的内存地址,显然我们new了2个对象内存地址肯定不一样", " 对于值对象,==比较的是两个对象的值 对于引用对象,比较的是两个对象的地址", " 默认的equals方法同==,一般来说我们的对象都是引用对象,要重写equals方法。", " 现在有一个学生对象,有属性学号跟姓名,现在我新建了一个学生对象,又从数据里查出一个学生对象,这两个对象的学号跟姓名都一样,那这两个对象是不是相等呢?一般情况下,除非你有特殊需求要处理,这两个对象是相等的,可如果用==去比较,返回的结果是错误的。", " 这时候我们就必须重写equlas方法了。如果学号是主键,在equals方法里,我们认为只要学号相同,就可以返回true。", " hashCode方法也是可以用来比较两个对象是否相等的。但是我们很少使用,应该说是很少直接使用。hashCode方法返回的是一个int值,可以看做是一个对象的唯一编码,如果两个对象的hashCode值相同,我们应该认为这两个对象是同一个对象。", " 一般如果使用java中的Map对象进行存储时,他会自动调用hashCode方法来比较两个对象是否相等。", " 所以如果我们对equals方法进行了重写,建议一定要对hashCode方法重写,以保证相同的对象返回相同的hash值,不同的对象返回不同的hash值。", " 如上面的学生例子,如果学号相同,不管姓名相不相同,返回的hash值一定要是一样的,这时我们的hash值只与学号有关。", " public class Test { public static void main(String[] args) { HashMap hm = new HashMap(); hm.put(new key(1),new value(2)); if(hm.containsKey(new key(1))) System.out.println(hm.get(new key(1))); else System.out.println(\"dont have such a key\"); } } ", "  你每次new 一个新对象出来hashcode肯定不一样,所以你拿不到你要的key。", " class key { int i ; public key(int i) { this.i = i; } @Override public boolean equals(Object obj) { if(obj instanceof key) { if(((key)obj).i == i) return true; } return false; } @Override public int hashCode() { return i; } } ", " 1、重写equals方法时需要重写hashCode方法,主要是针对Map、Set等集合类型的使用; a: Map、Set等集合类型存放的对象必须是唯一的; b: 集合类判断两个对象是否相等,是先判断equals是否相等,如果equals返回TRUE,还要再判断HashCode返回值是否ture,只有两者都返回ture,才认为该两个对象是相等的。 2、由于Object的hashCode返回的是对象的hash值,所以即使equals返回TRUE,集合也可能判定两个对象不等,所以必须重写hashCode方法,以保证当equals返回TRUE时,hashCode也返回Ture,这样才能使得集合中存放的对象唯一。 Android知识库 37778 关注| 3162 收录  React知识库 3597 关注| 393 收录  人工智能基础知识库 16685 关注| 212 收录 ", " Java 知识库 35146 关注| 3748 收录 ", " 下说法有误!如果两个对象的hashCode值相同,我们应该认为这两个对象是同一个对象。正解如下:hashCode主要为散列表(如HashMap)提供支持。如果一个对象的equals没有被改写,那么在执行时无论调用多少次hashCode()总是返回相同的值(对象的内部地址转化为该数值)。如果两个对象相等(equals相等),那么对应的hashCode也相等。两个对象不相等(equals不相等),其对应的hashCode也不相等,这种规范不是必须的(亦即两个对象不相等,其对应的hashCode可能相等)。延伸理解,hashCode相等的两个对象不一定相等(equals相等)。但是建议开发者遵守为两个不同对象返回不同的散列码的规范(比如重写时,但是Object.hashCode()确实为不同的对象返回不同的hashCode), 这种可以提升hash tables的性能。特殊情况:并不一定每次执行hashCode()方法都返回完全一样的值。(参考JavaDoc)尽管不常见,但是确实有些Java库实际上在不同执行的过程中返回不同的hashCode值,如 Google’s Protocol Buffers[size=medium][/size]"], "segmented_title": ["为什么", "在", "重写", "了", "equals", "(", ")", "方法", "之后", "也", "必须", "重写", "hash", "Code", "(", ")", "方法", "-", "-"], "segmented_paragraphs": [["我们", "都", "知道", "Java", "语言", "是", "完全", "面向对象", "的", ",", "在", "java", "中", ",", "所有", "的", "对象", "都是", "继承", "于", "Object", "类", "。", "Ojb", "ect", "类", "中", "有", "两", "个", "方法", "equals", "、", "hash", "Code", ",", "这", "两", "个", "方法", "都是", "用来", "比较", "两", "个", "对象", "是否", "相等", "的", "。"], ["在", "未", 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A、雪莉酒 B、贵腐酒 C、白兰地 D、冰酒 2、下列哪一组完全属于酿造红葡萄酒的品种? A、霞多丽、赤霞珠、梅洛、品丽珠 B、赤霞珠、品丽珠、梅洛、黑比诺 C、雷司令、龙眼、天帕尼洛、圣祖维赛 D、品丽珠、蛇龙珠、长相思、赤霞珠 3、下面哪一项完全属于新世界葡萄酒国家? A、新西兰、奥地利、美国、中国 B、西班牙、葡萄牙、匈牙利、牙买加 C、新西兰、阿根廷、澳大利亚、法国 D、中国、智利、美国、新西兰 4、波尔多著名的 1855 年分级中,以下位列当时一级庄的酒庄?", "A、拉菲庄园、拉图庄园、木桐嘉棣、玛歌庄园 B、木桐庄园、拉菲庄园、玛歌庄园、奥颂庄园 C、拉图庄园、玛歌庄园、拉菲庄园、奥比昂庄园 D、白马庄园、帕图斯庄园、玛歌庄园、拉菲庄园 5、法定波尔多左岸葡萄品种的?", "A、赤霞珠、加本力苏维翁、梅洛、品丽珠 B、梅洛、霞多丽、巴贝拉、赤霞珠 C、赤霞珠、梅洛、品丽珠、小维度 D、赤霞珠、黑皮诺、卡门、色拉子 6、以下正确的意大利葡萄酒分级制度?", "A、VDP、VDT、AOP、AOC B、VDM、VDLA、DO、DOC C、VDT、IGT、DOC、DOCG D、VDT、VDP、VDQS、AOC 7、普罗旺斯以哪种葡萄酒闻名?", "8、A、香槟 B、桃红葡萄酒 C、贵腐甜酒 D、博若莱新酒 8、 只有哪个葡萄品种不能用来酿制世界上最著名的起泡酒——香槟?", "A、黑皮诺 B、皮诺莫尼耶 C、霞多丽 D、佳美 10、下面哪一个系列产自澳大利亚?", "A、傲娃系列 B、干露红魔鬼系列 C、杰卡斯系列 D、拉菲系列 11.全世界大部分的葡萄酒种植区域主要位于以下哪里?", "12.A.南北纬 30-50 度 B.赤道附近 C.南北纬 10-20 度 D.南北极地区 12.下列哪个葡萄酒等级评估制度属于法国葡萄酒评级体系? A.DOCG B.DO C.AOC D.DOC 13.葡萄酒挂杯的现象说明了下列哪种情况? A.糖分含量高 B.酒精含量高 C.单宁含量高 D.矿物质含量高 14.法国波尔多 5 大顶级名庄分别是指下列哪五个庄园? 15.A.宝嘉龙庄园;力士金庄园;雄狮庄园;里鹏庄园;柏图斯庄园 B.拉菲庄园;拉图庄园;玛歌庄园;木桐庄园;奥比良庄园 C.法拉利庄园;龙船庄园;罗曼尼康帝;佳德美庄园;大宝庄园 D.西施佳雅;靓茨伯庄园;玫瑰山庄;泰来斯红酒王;圣卡罗红酒王 17.赛拉图(雅斯提)属于以下哪种葡萄酒? A.红葡萄酒 B.白葡萄酒 C.香槟 D.起泡酒 20.木桐酒庄 2000 年份选用了下列哪种动物作为酒标?", "A.孔雀 B.白色老虎 C.金羊 D.棕熊 22.贵腐葡萄酒最著名的产地苏玳区属于下面哪个国家?(B) A.意大利 B.法国 C.德国 D.匈牙利 23.号称红葡萄皇后的葡萄品种是?(A) A.Pinot Noir B.Merlot C.Shiraz D.Cabernet Sauvignon 24.饮用葡萄酒的室温通常指的是什么?(B) A.中国的室内温度 B.原产地的温度 C.美国的室内温度 D.法国的室内温度 26.适合新酒或者丰厚浓郁型的葡萄酒的醒酒器是下列哪种类型?(C) A.中肚长颈型 B.短肚短颈带盖型 C.大肚长颈型 D.小肚短颈型 27.处于已成熟期的红葡萄酒一般呈现的是什么颜色?", "12.葡萄酒通常有比较特殊的适饮温度,白葡萄酒应为 7-10 ℃,红葡萄酒应为 10-13 ℃。 13.在葡萄酒世界里,旧世界指的是 法国、意大利、德国、葡萄牙、西班牙、 匈牙利 等,新世界指的是 澳洲、美国、新西兰、智利、南非、阿根廷等。 14.法国葡萄酒的评级由高到低分别是 AOC VDQS VDP VDT 15.红葡萄酒的颜色来自于 紫黑 色的葡萄皮,葡萄皮对葡萄酒的 品质 影响非 常大,葡萄皮中含有大量的 单宁 是构成葡萄酒结构的主要因素,用于酿造白 葡萄酒的葡萄一般都是 白葡萄,但少数红葡萄品种例如 灰贝露 酿造白葡萄酒 的时候是榨汁发酵的,因为 去皮 发酵,所以白葡萄酒是不含有 单宁 的。", "17.请分别写出下列葡萄酒英文名词的相对应中文: Red wine 红葡萄酒、 Pink wine 粉红葡萄酒、Blanc wine 白葡萄酒、Ice wine 冰酒、Champagne 香槟、Sparkling wine 起泡葡萄酒 20.葡萄酒的酿造过程可以用个简单的公式来表达:酵母 =酒精+二氧化碳+热量。", "果糖 氧气 21.葡萄酒的质量主要处决于:产地 树龄 年份 采摘 产量 工艺。", "38. 法国五大名庄之首的指的是 拉菲 庄园。 39. 动物型香气包括了皮革,皮毛,生肉,麝香 等类型。", "40. 用来酿酒的葡萄品种很多,和日常食用的葡萄有较大区别。酿制葡萄酒的葡 萄皮厚,果肉少,汁多,颗粒小 ,基本不适合日常食用。 43. 从 2011 年开始,从法国灌装的葡萄酒开始启用新的评级标准,AOC 修改为 AOP ;VDP 修改为 IGP ;VDQS 取消,取而代之的是不标注 葡萄品种 和 年份 的 Vin de France 葡萄酒,以便于消费者认知,最低级别统称为法国制造。 44. 法文中的 chateau 指的是城堡的意思。 45. 香槟地区主要使用三个葡萄品种,分别是 雪当利 诺。 、黑皮诺 和 莫尼耶皮 47."], "bs_rank_pos": 1}, {"title": "葡萄酒试题_百度文库", "segmented_title": ["葡萄酒", "试题", "_", "百度", "文库"], "segmented_paragraphs": [["暂无", "评价", "|", "0", "人", "阅读", "|", "0", "次", "下载", "|", "举报", "文档"], ["笑话大全", "爆笑", "版", "幽默", "笑话大全", "全球", "冷笑话", "精选", "999", "感冒", "灵", "市场", "营销", "方案"], ["999", "感冒", "灵", "市场", "营销", "方案", "汽车", "品牌", "的", "足球", "世界杯", "营销", "网络营销", "部", "电商", "运营", "工作计划"]], "paragraphs": ["暂无评价|0人阅读|0次下载|举报文档", "笑话大全爆笑版 幽默笑话大全 全球冷笑话精选 999感冒灵市场营销方案", "999感冒灵市场营销方案 汽车品牌的足球世界杯营销 网络营销部电商运营工作计划"], "bs_rank_pos": 2}, {"title": "《开瓶温度》常说红葡萄酒室温下饮用,但室温一般是指15到18度,? - ", "segmented_title": ["《", "开", "瓶", "温度", "》", "常", "说", "红葡萄酒", "室温", "下", "饮用", ",", "但", "室温", "一般", "是", "指", "15", "到", "18", "度", ",", "?", "-"], "segmented_paragraphs": [["红葡萄酒", "放入", "冰箱", "后", ",", "经过", "1", "小时", ",", "酒", "温", "便", "会", "下降", "10C", ",", "一般", "只要", "将", "红葡萄酒", "放入", "冰箱", "1", "小时", ",", "酒", "温", "就会", "降", "至", "20C", "以下", "。", "假如", "忘记", "了", "红葡萄酒", "在", "电", "冰箱"], ["放", "了", "多久", ",", "也", "不用", "担心", ",", "只要", "将", "红葡萄酒", "取出", ",", "开启", "并", "倒入", "杯", "中", ",", "隔", "30", "分钟", "左右", ",", "该", "红", "葡萄酒", "的", "温度", "便", "约", "为", "20C", ",", "即", "我们", "所", "指", "的", "“", "室温", "”", "。"], ["室温", "要", "高", "一些", ",", "18", "-", "22", "摄氏度", "!", "对于", "年轻", "或", "差", "一些", "的", "红酒", "可以", "考虑", "稍", "作", "冰镇", ",", "越好", "的", "酒", "则", "饮用", "温度", "越", "高", "!"], ["《", "开", "瓶", "温度", "》", "常", "说", "红葡萄酒", "室温", "下", "饮用", ",", "但", "室", ".", ".", ".", "帝", "可", "古堡", "长相思", "干白葡萄酒", "多少", "钱"], ["一般", "十分钟", "即可", ",", "不过", "不", "建议", "进", "冰箱", ",", "使用", "冰桶", "更好", "控制", "。"], ["生活常识", "相关知识"], ["确定", "举报", "此", "问题"], ["报告", ",", "这", "不是", "个", "问题"], ["这", "不是", "个", "问题", "这个问题", "分类", "似乎", "错", "了", "这个", "不是", "我", "熟悉", "的", "地区"], ["祁门红茶", "的", "贮存", "有", "什么", "技巧", "?", "应", "怎样", "进行", "祁门红茶", "的", "鉴赏", "?", "祁门红茶", "有", "哪些", "保健功效", "?", "应", "怎样", "进", "行政", "和", "工夫茶", "的", "鉴赏", "?", "我", "国", "黄茶", "主要", "有", "什么", "样", "的", "特质", "?", "政和", "工夫茶", "有", "哪些", "保健功效", "?", "君山银针", "茶", "有", "什么", "特点", "?", "祁门红茶", "有", "什么", "特征", "?", "政和", "工夫", "茶", "有", "什么", "特点", "?", "政和", "工夫", "茶", "的", "贮存", "方法", "是", "什么", "?", "白", "咖啡", "的", "好处", "辅食", "胡萝卜", "汁", "喝咖啡", "后", "运动", "苏打水", "加", "柠檬", "开", "奶茶", "的", "作用"]], "paragraphs": ["红葡萄酒放入冰箱后,经过1小时,酒温便会下降10 C,一般只要将红葡萄酒放入冰箱1小时,酒温就会降至20 C以下。假如忘记了红葡萄酒在电冰箱", "放了多久,也不用担心,只要将红葡萄酒取出,开启并倒入杯中,隔30分钟左右,该红葡萄酒的温度便约为20 C,即我们所指的“室温”。", "室温要高一些,18-22摄氏度!对于年轻或差一些的红酒可以考虑稍作冰镇,越好的酒则饮用温度越高!", "《开瓶温度》常说红葡萄酒室温下饮用,但室... 帝可古堡长相思干白葡萄酒多少钱", "一般十分钟即可,不过不建议进冰箱,使用冰桶更好控制。", "生活常识相关知识", "确定举报此问题", "报告,这不是个问题", "这不是个问题  这个问题分类似乎错了  这个不是我熟悉的地区", "祁门红茶的贮存有什么技巧? 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", " 您需要登录后才可以回帖 登录 | 注册 "], "segmented_title": ["还有", "人", "关注", "时光", "徽章", "吗", "?", "怎么", "好多", "天", "了", "都", "没", "见到", "价格", "低于", "7", "W", "了", "-", "魔兽", "热点"], "segmented_paragraphs": [["如果", "酒", "能", "消愁", ",", "那", "我", "每次都会", "一醉方休", "。"], ["UID", "14106563", "帖子", "23564", "威望", "1", "多玩", "草", "1236", "草"], ["不知道", ",", "现在", "我", "看", "一直", "在", "6.5W", "-", "-", "-", "-", "7", "w", "之间"], ["打", "个", "雷劈", "死", "我", "吧", "!"], ["UID", "42782696", "帖子", "5025", "威望", "0", "多玩", "草", "0", "草"], ["从", "阴影", "中", "降临"], ["UID", "23798982", "帖子", "21972", "威望", "11", "多玩", "草", "1238", "草"], ["再", "等等", "看"], ["mei", "人", "关注", "了", "把"], ["现在", "都", "在", "70000", "-", "74000", "直接", "徘徊"], ["UID", "54178968", "帖子", "84199", "威望", "15", "多玩", "草", "392", "草"], ["前几天", "那么", "火", "换", "了", "那么多", "游戏时间", "总", "要", "慢慢", "玩", "一段时间", "再说吧"], ["UID", "58184970", "帖子", "5410", "威望", "0", "多玩", "草", "100", "草"], ["连续", "几天", "7", "W", "了", ",", "说明", "了", "打钱", "依然", "很", 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+{"documents": [{"bs_rank_pos": 0, "paragraphs": [" 什么是窦性心动过缓?", "窦性心律过缓编辑 锁定 ", "窦性心律过缓指如果心电图是窦性,低于60次/分为心律过缓,而正常的窦性心律是60-100次/分。窦性心律过缓本身一般不需处理,主要是针对病因进行治疗。", " 窦性心律慢于每分钟60次称为窦性心律过缓。可见于健康的成人,尤其是运动员、老年人和睡眠时。在器质性心脏病中,窦性心律过缓可见于冠心病、急性心肌梗塞、心肌炎、心肌病和病窦综合征。", "西医学名 窦性心律过缓  发病部位 心脏", " 传染性 无传染性  传播途径 无", " 1 临床表现  2 诊断依据  3 治疗原则  4 用药原则  5 辅助检查  6 疗效评价 ", " ▪ 窦性心律  ▪ 检查  ▪ 分类 ", "1.窦性心律过缓如心律不低于每分钟50次,一般无症状。2.如心律低于每分钟40次时常可引起心绞痛、心功能不全或晕厥等症状。", "心电图显示窦性P波,P波速率低于每分钟60次,PR间期大于0.12秒。", "1.窦性心律过缓如心律不低于每分钟50次,无症状者,无需治疗。2.如心律低于每分钟40次,且出现症状者可用提高心律药物(如阿托品、麻黄素或异丙肾上腺素。3.显著窦性心律过缓伴窦性停搏且出现晕厥者可考虑安装人工心脏起搏器。4.原发病治疗。", "5.对症、支持治疗。", "1.大部分病人在消除病因或诱因后,症状可消失。2.有明确的原发性疾病时应积极治疗。", "1.窦性心律过缓心律不低于每分钟50次。2.窦性心律过缓心律低于每分钟40次。", "1.治愈:心律提高到每分钟60次以上,自觉症状消失。2.好转:心律提高到每分钟50次以上,自觉症状好转。3.无效:治疗前后心律无变化,症状无缓解。", "人体右心房上有一个特殊的小结节,由特殊的细胞构成,叫做窦房结。它可以自动地、有节律地产生电流,电流按传导组织的顺序传送到心脏的各个部位,从而引起心肌细胞的收缩和舒张。人体正常的心跳就是从这里发出的,这就是“心脏起搏点”。窦房结每发生1次冲动,心脏就跳动1次,在医学上称为“窦性心律”。所以,心脏正常的跳动就应该是窦性心律。", "窦房结是心脏搏动的最高“司令部”,那么,正常的心脏必须有正常的窦房结,正常的窦房结具有强大的自律性。凡是由窦房结发出激动所形成的心律总称为窦性心律。窦房结的频率每分钟60～100次,但有25%的青年人心率为50～60次/分,6岁以前的儿童可超出100次/分,初生婴儿则可达100～150次/分。", "许多人,特别是年轻人,在体检检查心电图时,常常会得到“窦性心律不齐”的诊断。多数人都知道心跳应该是规律整齐的,所以一听说“心律不齐”,不少人便认为这是不正常的现象。虽然没有任何症状,但也要四处求医,找专家,想赶快治好“病”。但医生往往又说:“不用治。”这些人心里又打鼓了,是不是什么疑难杂症,治不好了。", "其实,“窦性心律不齐”是一种正常的生理现象,不是病。“窦性心律不齐”以儿童、青少年最常见,成年人也不少见。", "一般情况下,心跳节律是规律整齐的,如果心脏跳动不整齐,我们称其为心律失常。从这个角度上来说,“窦性心律不齐”是最常见的一种心律失常,是由于来自窦房结的信号并不完全规整所致。但是,这种“心律失常”大多数属于“呼吸性窦性心律不齐”,这是一种正常生理现象,它的特点是随呼吸的变化而变换,吸气时心率可增加数跳,呼气时又可减慢数跳,其快慢周期恰好等于一个呼吸周期,屏气时心律转为规则。这种随呼吸变化的“窦性心律不齐”是完全正常的,不必担心,也不用治疗。", "因此,被诊断为“窦性心律不齐”的人,可以注意一下自己心率的加速和减速跟呼吸之间有没有关系。只要没有任何其他症状,就不需要治疗,完全可以从事正常的学习和生活。", "现在不少医生也注意到“窦性心律不齐”给人带来的恐慌,于是在诊断时会写作“窦性心律”。", "此外,精神紧张等因素也可能造成“窦性心律不齐”,这种情况也不需要治疗。", "窦性心律的心电图必须符合下列两个条件:(1)P波在Ⅰ、Ⅱ、aVF、V5导联直立,aVR倒置。(2)P-R间期大于0.12秒。凡是由于窦房结自律性改变而引起的心律失常称为窦性心律失常。影响窦房结自律性改变的有神经因素和体液因素及窦房结自身的因素,但主要的因素是神经因素和体液因素两种,只有少数情况下窦房结自律性的改变是由于窦房结本身的器质性损害引起。在神经因素中,主要是迷走神经的影响,其次是受交感神经的影响。因此,在情绪激动、体力活动、餐后及发热时均可引起心动过速。同样,一些体液因素也能影响窦房结的自律性而引起窦性心律失常。", "窦房结发出的激动不规则,心动周期显著快慢不均,称为窦性心律不齐。临床上,窦性心律不齐分以下几种类型:", "(1)呼吸性窦性心律不齐:呼吸性窦性心律不齐是窦性心律不齐中最常见的一种。多发生于儿童、青年及老年人,中年人较少见。呼吸性窦性心律不齐发生机理是由于在呼吸过程中,体内迷走神经与交感神经的张力发生变化,使窦房结自律性也因之发生周期性、规律性改变。吸气时交感神经张力增高,心率增快,呼气时迷走神经张力增高,心率变慢。心率快慢变化的周期恰等于一个呼吸周期,停止呼吸时心律转为规整。其心电图特点如下:", "①P波为窦房结发出的“窦性P波”(Ⅰ、Ⅱ导联中正向,aVR导联中负向),其形态恒定。②心率的快慢随呼吸变化而变化,吸气时心率增快,呼气时心率减慢,其快慢变化的周期恰等于一个呼吸周期。③在同一个导联中,R-R间距或P-P间距差异达0.12秒以上。④P?R间期大于0.12秒。", "非呼吸性窦性心律不齐较少见,具体原因还不十分清楚,有人认为与生气、情绪不稳定、或使用某些药物(如洋地黄,吗啡等)有关。其心电图特点如下:", "①P波为窦房结发出的“窦性P波”(Ⅰ、Ⅱ导联中正向,aVR导联中负向),其形态较恒定。②P波频率变化与呼吸无关,心率有时突然增快。③在同一导联中,P-P间距或R?R间距差异达0.12秒以上。④P?R间期大于0.12秒。", "(3)窦房结内游走性节律:", "激动的发生点在窦房结内移动,因此,心电图上的P波形态、大小与方向逐渐发生变化,其心电图特点如下:", "①P波是窦性P波(Ⅰ,Ⅱ导联中正向,aVR导联中负向);②P波形态、大小变化不一致;③P?R间期发生长短变化,但都已超过0.12秒。", "(4)与心室收缩排血有关的窦性心律不齐:", "与心室收缩排血有关的窦性心律不齐,是由于心室收缩排血异常致窦房结血液供应不均匀,从而造成窦房结的自律性发生改变。", "(5)异位心律诱发的窦性心律不齐:异位激动,尤其是发自心房的异位激动,有时可使窦房结的激动提早发生,继之窦房结受抑制,因而发生一过性异位激动所诱发的窦性心律不齐。"], "segmented_title": ["窦性心律", "过", "缓", "_", "百度百科"], "segmented_paragraphs": [["什么", "是", "窦性心动过缓", "?"], ["窦性心律", "过", "缓", "编辑", "锁定"], ["窦性心律", "过", "缓", "指", "如果", "心电图", "是", "窦", "性", ",", "低于", "60", "次", "/", "分为", "心律", "过", "缓", ",", "而", "正常", "的", "窦性心律", "是", "60", "-", "100", "次", "/", "分", "。", "窦性心律", "过", "缓", "本身", "一般", "不需", "处理", ",", "主要", "是", "针对", "病因", "进行", "治疗", "。"], ["窦性心律", "慢", "于", "每", "分钟", "60", "次", "称为", "窦性心律", "过", "缓", "。", "可", "见于", "健康", "的", "成人", ",", "尤其", "是", "运动员", "、", "老年", "人和", "睡眠", "时", "。", "在", "器", "质", "性", "心脏病", "中", ",", "窦性心律", "过", "缓", "可", "见于", "冠心病", "、", "急性心肌梗塞", "、", "心肌炎", "、", "心肌病", "和", "病", "窦", "综合征", "。"], ["西医", "学", "名", "窦性心律", "过", "缓", "发病", "部位", "心脏"], ["传染性", "无", "传染性", "传播途径", "无"], ["1", "临床表现", "2", "诊断", "依据", "3", "治疗原则", "4", 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", " 窦性心律过缓不是什么大问题,但是一种病,必须重视。心跳过缓其实挺好(前提是不影响身体的各项功能)这样的话身体的新陈代谢就会变慢,就会比一般人的生命长,综合功能也强。好是好,但必须去看看,毕竟是一种病。祝你健康。", " 正常的心率是60-100次/分,低于60次/分,且为窦性心律者为窦性心动过缓.心率59次/分,如果是窦性的,并且身体没什么不适,可以不用担心.对身体没什么影响", " 窦性心律过缓,中医的观点来看,多是属于气血的毛病,因为气虚、血虚所造成的血液运行不畅、血液量不足。 除了增加营养,适当运动,药物调节外,可以试试这两招,会受益的!!!气能行血,气能摄血,气能生血,血能载气,气行血则行,气滞血则凝。调节呼吸,气血通畅,经络通畅了,一切就好了。冬季养生早卧晚起,以待日光。春季养生,早卧早起,锻炼身体。中午少睡一会足够。子时(晚上11~1点)和午时(中午11~1点)都是阴阳交接的时辰,晚上子时阴盛至极,开始阳升阴降,中午午时阳盛至极,开始阳降阴升。中医养生注重睡好子午觉,可以健康长寿。教你一招,调节呼吸法——也叫呼吸吐纳法。呼吸的方法有自然呼吸和正呼吸两种:第一自然呼吸,也叫做腹式呼吸,因为在呼吸时,一呼一吸,必须都能达到下腹部。在吸气时,空气入肺,充满周身,肺底舒张,抑压横膈膜,使空气下降,这时胸部空松,腹部外凸,在呼气时,腹部收缩,横膈膜被推而上,上抵肺部,使肺底浊气,外散无余。可见呼吸作用,虽与肺叶有密切关系,但它的伸缩,常须依靠下腹和横膈膜的运动,方能合乎自然大法,使血液循环流畅。我们不但在静坐时,须用此法,实际上,不论行、住、坐、卧,应该随时使用。关于自然呼吸法的调和方法如下:(a)、呼息时,脐下腹部收缩,横膈膜向上,胸部紧窄,肺底浊气可以挤出。"], "segmented_title": ["窦性心律", "过", "缓", "是", "怎么", "一", "回事", "?", "能", "医治", "吗", "?", "_", "百度", "拇指", "医生"], "segmented_paragraphs": [["?", "窦性心律", "过", "缓", "是", "怎么", "一", "回事", "?", "能", "医治", "吗", "?"], ["拇指", "医生", "提醒", "您", ":", "该", "问题", "下", "为", "网友", "贡献", ",", "仅供参考", "。"], ["我", "心跳", "大概", "是", "每", "分钟", "59", ",", "这样", "对", "身体", "有", "什么", "影响", "?"], ["窦性心律", "过", "缓", "不是", "什么", "大", "问题", ",", "但是", "一", "种", "病", ",", "必须重视", "。", "心跳", "过", "缓", "其实", "挺好", "(", "前提", "是", "不", "影响", "身体", "的", "各项", "功能", ")", "这样的话", "身体", "的", "新陈代谢", "就会", "变", "慢", ",", "就会", "比", "一般人", "的", "生命", "长", ",", "综合", "功能", "也", "强", "。", "好", "是", "好", ",", "但", "必须", "去", "看看", ",", "毕竟", "是", "一", "种", "病", "。", "祝你健康", "。"], ["正常", "的", "心率", "是", "60", "-", "100", "次", "/", "分", ",", "低于", "60", "次", "/", "分", ",", "且", "为", "窦性心律", "者", "为", "窦性心动过缓", ".", "心率", "59", "次", "/", "分", ",", "如果", "是", "窦", "性", "的", ",", "并且", "身体", "没什么", "不适", ",", "可以", "不用担心", ".", "对", "身体", "没什么", "影响"], ["窦性心律", "过", "缓", ",", "中医", "的", "观点", "来看", ",", "多", "是", "属于", "气血", "的", "毛病", ",", "因为", "气虚", "、", "血虚", "所", "造成", "的", "血液", "运行", "不畅", "、", "血液", "量", "不足", "。", "除了", "增加", "营养", ",", "适当", "运动", ",", "药物", "调节", "外", ",", "可以", "试", "试", "这", "两", "招", ",", "会", "受益", "的", "!", "!", "!", "气", "能", "行", "血", ",", "气", "能", "摄", "血", ",", "气", "能", "生", "血", ",", "血", "能", "载", "气", ",", "气", "行", "血", "则", "行", ",", "气滞", "血", "则", "凝", "。", "调节", "呼吸", ",", "气血", "通畅", ",", "经络", "通畅", "了", ",", "一切", "就好", "了", "。", "冬季", "养生", "早", "卧", "晚", "起", ",", "以", "待", "日光", "。", "春季养生", ",", "早", "卧", "早起", ",", "锻炼身体", "。", "中午", "少", "睡", "一会", "足够", "。", "子时", "(", "晚上", "11", "~", "1", "点", ")", "和", "午时", "(", "中午", "11", "~", "1", "点", ")", "都是", "阴阳", "交接", "的", "时辰", ",", "晚上", "子时", "阴", "盛", "至", "极", ",", "开始", "阳", "升", "阴", "降", ",", "中午", "午时", "阳", "盛", "至", "极", ",", "开始", "阳", "降", "阴", "升", "。", "中医养生", "注重", "睡", "好", "子", "午觉", ",", "可以", "健康长寿", "。", "教", "你", "一", "招", ",", "调节", "呼吸法", "—", "—", "也", "叫", "呼吸", "吐纳", "法", "。", "呼吸", "的", "方法", "有", "自然", "呼吸", "和", "正", "呼吸", "两种", ":", "第", "一", "自然", "呼吸", ",", "也", "叫做", "腹式呼吸", ",", "因为", "在", "呼吸", "时", ",", "一", "呼", "一", "吸", ",", "必须", "都", "能", "达到", "下腹部", "。", "在", "吸气", "时", ",", "空气", "入", "肺", ",", "充满", "周", "身", ",", "肺", "底", "舒张", ",", "抑", "压", "横膈膜", ",", "使", "空气", "下降", ",", "这时", "胸部", "空", "松", ",", "腹部", "外", "凸", ",", "在", "呼气", "时", ",", "腹部", "收缩", ",", "横膈膜", "被", "推", "而", "上", ",", "上", "抵", "肺部", ",", "使", "肺", "底", "浊气", ",", "外", "散", "无余", "。", "可见", "呼吸作用", ",", "虽", "与", "肺叶", "有", "密切", "关系", ",", "但", "它", "的", "伸缩", ",", "常", "须", "依靠", "下腹", "和", "横膈膜", "的", "运动", ",", "方", "能", "合乎", "自然", "大", "法", ",", "使", "血液循环", "流畅", "。", "我们", "不", "但", "在", "静坐", "时", ",", "须", "用", "此", "法", ",", "实际上", ",", "不", "论", "行", "、", "住", "、", "坐", "、", "卧", ",", "应该", "随时", "使用", "。", "关于", "自然", "呼吸法", "的", "调和", "方法", "如", "下", ":", "(", "a", ")", "、", "呼息", "时", ",", "脐", "下腹部", "收缩", ",", "横膈膜", "向上", ",", "胸部", "紧", "窄", ",", "肺", "底", "浊气", "可以", "挤出", "。"]], "title": "窦性心律过缓是怎么一回事?能医治吗?_百度拇指医生"}, {"bs_rank_pos": 3, "paragraphs": [" 请问心电图检查有1.窦性心律过缓53次/分 ", "请问心电图检查有1.窦性心律过缓53次/分 2.完全性右束支阻滞.是不是很严重?可以治愈吗?谢谢! 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"加盟东方爱婴早教中心总部会给加盟商提供充分的营运支持,从开业督导到不定期现场督导、不定期大区会议;从每月快报到及时资讯交流,从每季资讯整合报告到每年年会的高峰交流,东方爱婴都会悉心组织。东方爱婴还利用网络科技手段建立了快捷的沟通渠道,从盟商在线沟通到企业电子邮局,再到网络数据库管理,让加盟商即便与总部相隔万里依然能够得到总部的全面支持。", "东方爱婴早教中心的发展模式符合中国的国情和早期教育市场的发展趋势,具有广阔的发展前景,并具有极强的竞争优势。早期教育行业是一项固定资产投资较小、现金流量较大、社会效益极佳的一个投资项目,并且增长率高并具有市场潜力的行业。东方爱婴早教中心投资费用大概是多少?万元投资好项目。", "如果您的钱包里有这么多的加盟费用,也符合东方爱婴的加盟条件,那您不妨尝试一下加盟哦,面对这么具有发展前景的行业,面对这么有名的早教品牌,难道您还不心动吗?心动不如行动吧!点击留言板就可以给我们留言了哦,还可以获得更多东方爱婴加盟最低费用的资料。", "责任编辑:蕾欧娜", "企业名称:沈阳东方爱婴咨询公司", "企业地址:铁西万达中心:兴华南街58-24号A2-6门(中国邮政储蓄对面)", "官网地址:http://qiye.3158.cn/30458/"], "bs_rank_pos": 3}, {"title": "东方爱婴早教中心价格是多少—中国连锁网", "segmented_title": ["东方爱婴", "早教中心", "价格", "是", "多少", "—", "中国", "连锁", "网"], "segmented_paragraphs": [["孩子", "和", "妈妈", "的", "需求", "是", "很多", "样", "的", ",", "尤其", "是", "刚", "出生", "的", "孩子", "和", "生产", "完", "的", "妈妈", "需要", "更", "是", "专业", "的", "呵护", ",", "母婴", "用品", "店", "中", "的", "东方爱婴", "早教中心", "是", "一", "个", "非常棒", "的", "项目", ",", "这个", "项目", "的", "实力", "和", "水准", "是", "很", "棒", "的", ",", "不仅", 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"对于很多做母婴项目的人来说,一个适合自己的项目是非常重要的,母婴用品店中的东方爱婴早教中心是一个非常典型的代表,这个项目的实力和水准,名气和声誉都是最好的,您想要加盟现在就是一个绝好的机会,东方爱婴早教中心真诚的欢迎您的到来!", "山道健身会所", "小资生活", "向博士包点", "一半一伴咖啡", "看过本文的人还看过", "全国地区加盟站", "项目推荐 项目报道 十大品牌", "餐饮加盟十大品牌 服装加盟十大品牌 家纺加盟十大品牌 教育加盟十大品牌 干洗加盟十大品牌 美容加盟十大品牌 珠宝加盟十大品牌 饰品加盟十大品牌 婴童加盟十大品牌 汽车加盟十大品牌 家居加盟十大品牌 建材加盟十大品牌 金融加盟十大品牌 酒店加盟十大品牌 环保加盟十大品牌 特色加盟十大品牌", "免责声明:本网站为开放性注册平台,以上所有展示信息均由会员自行提供,内容的真实性、准确性和合法性均由发布会员负责。连锁网对此不承担任何法律责任。", "友情提醒:为规避投资风险,建议您在加盟前务必多咨询、多考察,降低投资风险。", "投诉电话:010-69005529 转 800", "投诉邮箱:tousu@liansuo.com"], "bs_rank_pos": 4}], "question": "东方爱婴收费价格多少", "segmented_question": ["东方爱婴", "收费", "价格", "多少"], "question_type": "ENTITY", "fact_or_opinion": "OPINION", "question_id": 221623}
+{"documents": [{"bs_rank_pos": 0, "paragraphs": ["欢迎关注“创事记”的微信订阅号:sinachuangshiji", " 今日(9月15日),一张“北京地区虚拟货币交易场所清理整治工作要求”文件截图流出,要求各交易所在20日前,制定详细的“无风险清理清退工作方案”。 ", " 持续一周的交易所关停风波,终于尘埃落定。", " 确定消息后,比特币价格更是跳水腰斩,最低跌至16906元。", " 自9月4日,央行等七部委联合发布关于防范代币发行融资风险的公告后,从项目众筹平台,到虚拟货币交易平台,从各种“创新币”,到比特币等“主流币种”,步步沦陷。", " 这场暴富神话和韭菜游戏,戛然而止……", " 01 关停", " 币圈资深玩家罗心雨像往常一样,起床后第一件事,就是查看币价。结果让他大跌眼镜。比特币居然跌破2万大关,最低价到了1.8万。", " 谁都没有料到,一轮疯狂的ICO,让一切化为云烟,甚至殃及了比特币。", " 持续一年的癫狂戏码,在灰烬中缓缓落幕。", " 一切早有预兆。", " 年初,央行先后两次约谈国内主要比特币交易平台负责人,除了要求平台自查,又组成联合检查组开展现场调查。", " 比特币的价格在几次约谈消息放出后,出现大跌,却几经波折,又冲上了3万高峰。", " 而在今年5月份,涌出了一批新的虚拟货币交易平台,他们加速了ICO市场的疯狂与混乱,让币圈内外的人,都开始意识到,监管到来的脚步加速了。", " 据自媒体“互金咖”报道,对于是否取缔比特币交易平台,监管部门内部分歧一直存在,各方力量也在博弈之中。", " 没有料到,ICO的疯狂,成了压死“骆驼”的最后一根“稻草”。", " 9月4日,央行等七部委发布关于防范代币发行融资风险的公告,称代币发行本质上是一种未经批准非法公开融资的行为,涉嫌非法发售代币票券、非法发行证券以及非法集资、金融诈骗、传销等违法犯罪活动。", " 本以为这次监管只针对ICO,万万不会波及到核心的几大币种,但监管风暴却愈演愈烈。", " “不可能关,你想吧,比特币在国际上都得到认可了,这是个国际化的产品”,罗心雨称。", " 9月8日,财新发表《虚拟货币交易所时代结束》文章:监管当局决定关闭中国境内虚拟货币的交易所,这涉及“OKCoin”“火币网”和“比特币中国”等为代表的,所有虚拟货币与法币之间的交易所。", " 被点名的三家交易所纷纷发表声明,表示“未接到通知”。", " 其中,比特币中国团队回应称,“没有接到监管机构的通知,也无法证实该新闻的真实性”。但他同时表示,“如果该报道属实,比特币中国将停止目前的比特币对人民币的交易,转型为数字资产点对点交易的信息平台”。", " 三方的回答,都无比圆润而妥帖,留下无尽想象。", " 此后的一周内,交易所是否一刀切关停,成为悬在币圈人头顶的达摩克利斯之剑。", " 有些投资人认为是ICO团队故意放出消息,币价大跌后,他们方便按照政策要求“退币”,激动的情绪甚至上升到对记者的人身攻击上。", " “不可能,肯定是有人故意放假消息,试图操纵币价”,直到此时,罗心雨还是执拗地拒绝相信。", " 财经专栏作家、财经评论员肖磊,在接受一本财经专访时表示,一些投资人会把各种政策解读成“庄家”“幕后”,按照自己的想法来,财富刺激下,根本听不进去任何劝解。", " 利益面前,不要妄谈任何理性。", " 直到昨日,第一财经报道称,监管已对国内比特币交易平台下定论:“全部关停,并于近期退出市场。”", " 随后,有媒体跟进,上海已下口头通知,关停比特币交易平台,时间结点在9月底。", " 而傍晚比特币中国的公告也验证了关停消息:", " 1.比特币中国数字资产交易平台今日(9月14日)起停止新用户注册;", " 2.2017年9月30日数字资产交易平台将停止所有交易业务。", " 今日(9月15日),一张“北京地区虚拟货币交易场所清理整治工作要求”文件截图流出,要求各交易所在今日24:00点前发布“明确停止所有虚拟货币交易的最终时间”的公告,在20日前制定详细的“无风险清理清退工作方案”。", " 罗心雨最后一刻的幻想泡沫被击破,一切尘埃落定。", " 02 洗钱案", " 为什么说停就停,如此决绝?业内思考猜测不断。", " 但一个不容忽视的细节是,昨日央行天津分行发布了《洗钱案例——比特币交易平台人间蒸发》:某比特币交易平台,以10倍杠杆高额回报为诱饵吸引4000多名会员加入,平台管理者打着赚取手续费的名义,实际上,将会员的钱通过多张银行卡转账取现,购买房产、店面、并用于消费、赌博等。", " 这起洗钱大案,可能是行业的最后致命一箭。", " 虽然比特币已在跨国支付、线下商场消费,亚马逊、steam等网站上大展拳脚,但因为其匿名特性,依然与洗钱等非法活动接近。", " 相比在阳光下的交易,这种黑暗属性,更让监管警惕。", " 尤其是在近期,完善反洗钱机制,被监管多次提及。", " “比特币等所谓‘虚拟货币’日益成为洗钱、贩毒、走私、非法集资等违法犯罪活动的工具。”中国互联网金融协会发布风险提示称,各类所谓“币”的交易平台在我国并无合法设立的依据。", " 国际上,对于比特币等虚拟货币的监管也不同。", " 比特币,中国、俄罗斯政策严苛,日本、瑞士、新加坡等国家态度相对开放。", " 尤其是在日本,今年承认了比特币作为虚拟货币支付手段合法,免除数字货币交易税,等等利好政策,促使日本取代中国,成为比特币交易第一大国。", " 还有一个有意思的原因,“因为比特币之父‘中本聪’的发音像是一个日本名字,所以他们有一种自豪感”,业内人士表示。", " 不过,在出台一系列利好政策的同时,日本政府也要求比特币交易所实施更加严格的KYC(Know Your Customer)政策,交易所必须开始核查开户用户的身份,保管交易记录,向监管机构报告可疑交易,预防反洗钱等非法行为。", " 目前来看,国内虚拟货币交易所的KYC技术并没有达到监管要求。", " 在今年1月18日晚,央行公布调查的结果显示,几大交易平台都不同程度存在违规行为。", " 三大比特币交易平台均未按规定建立相关反洗钱内控制度,其中比特币中国存在超范围经营、违规开展配资业务、投资者资金未实行第三方存管等问题;“币行”(OKCoin)、“火币网”违规开展融资融券业务,导致市场异常波动。", " 03 未来?", " 比特币中国发布停止交易公告后,命运已定。", " 而火币网、OKCoin未来会如何?", " 火币网回复一本财经,表示“没有收到明确的文件或通知”;OKCoin币行表示,“目前币行正常运营,一切以我们官网最新公告为准”。", " 两家均表示,会遵守监管政策,同时也会保护投资人的权益。但不得不提的细节是,某交易平台已将一个媒体公关群解散。", " 比特中国的公告中也曾提到,“监管机构并没有宣布比特币本身非法,也没有禁止用户和用户之间的点对点交易”。", " 目前,投资人已在各大群里开始讨论,如何自建比特币钱包,如何场外点对点交易,甚至如何上国外交易所继续炒币。", " “交易所的币能不能提,还要看交易所的后续政策。”业内人士向一本财经表示,“否则只能提现,或者卖掉了”。", " “现在不止ICO项目要去国外,连交易所也要开在国外了。”有投资人这样感慨。", " 而比特币OTC,又是一个更混乱的江湖。抄底还是离开?投资人分为两派。", " 一派认为,已到了抛售的时候。", " “我昨天离场的,这段时间不打算做了。”一位投资人对一本财经表示,他认可比特币的收藏价值,但是对于使用价值,“要看从哪个角度出发了”。", " 时至今日,关于“比特币是不是泡沫”的观点还在争论不休。"], "segmented_title": ["比特币", "价格", "跌破", "17000", ",", "撤退", "大", "潮", "与", "疯狂", "抄底", "交集", "_", "创事记", "_", "新浪科技", "_", "新浪"], "segmented_paragraphs": [["欢迎", "关注", "“", "创事记", "”", "的", "微信", "订阅号", ":", "sina", "chuang", "shiji"], ["今日", "(", "9", "月", "15", "日", ")", ",", "一", "张", "“", "北京地区", "虚拟货币", "交易", "场所", "清理整治", "工作", "要求", "”", "文件", "截图", "流出", ",", "要求", "各", "交易所", "在", "20", "日前", ",", "制定", "详细", "的", "“", "无风险", "清理", "清退", "工作", "方案", "”", "。"], ["持续", "一", "周", "的", "交易所", "关停", "风波", ",", "终于", "尘埃落定", "。"], ["确定", "消息", "后", ",", "比特币", 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tags:一个,暂时,出局,故事,我先,下我,14年,投资,比特,虚拟  4. BITUSD(BTU)是如何取代美元的? ...币并不真正代表任何实物资产的时代。(写到这里大家可能会发现原理其实和比特币有点类似,比特币不也是一个概念吗?和纸币一样也不代表任何资产)牙买加浮动货币体系看似美好,但从一开始就种下了祸根。 由于美元占据全球交易规模的一半以上,美元的弱势资本开始流向新兴市场导致了新... tags:BITUSD,美元  5. 朱立:详解闪电网络、雷电网络和CORDA  比特币闪电网络即将正式发布,闪电网络将带来极致的交易处理能力和近乎瞬时的交易确认,远超目前的VISA系统。以太坊上的类似项目雷电网络也预计将在几个月后发布。本文剖析了它们背后的原理和技术细节,并据此对R3 Corda 的原理作出一番揣测。 ", "朱立 上交所技术有限责任公...", " tags:闪电网络,雷电网络,CORDA  6. 一次说服朋友接受比特币的愉快经历  ...干练。席间,她随口问起了近况。 ", "春节后,我和我的团队一直在做比特币相关的项目。 ", "比特币?,她惊呼着打断了我的话,扫过来的眼神就像是看到了个安利贩子。 ", "你听说过比特币? ", "我当然听说过,不就是那个叫中本聪的发明的电子货币吗?好多人挖矿,炒的很高,政府一... tags:接受比特币  7. Zcash挖矿大全--0.1 版本  ... 在矿池转账页面查看转账记录。", "矿池采用自动转账模式,默认余额满0.1个币自动转给矿工钱包地址。你可以在设置中修改自动转账数量,并填写矿机公网IP进行身份验证。【玩币族(http://www.wanbizu.com)专业币圈媒体,重点关注炒币本身。】 在星火矿池挖矿不需要注册账户,根据下面的介绍...", " tags:Zcash挖矿  8. [公告] KeyID/DNS DAC正式创世区块链v 0.1  版本号 0.1 完整列表请看这里 http://pastebin.com/6WJLZhXD (译注:官方提供链接可能被墙,可从Snail提供的贡献链接查看http://mybts.qiniudn.com/6WJLZhXD.txt ) 其中有两个栏位, KeyID 以及要获取KeyID的公钥 我还需要作的/再次确认的事情: 再次确认这个输出 (档案似乎有...", "2014年7月12日,一个名为VPAL(V宝)的虚拟货币横空出世,首日上线开盘价为331元/个。上线一个月... tags:V宝,传销,骗局  10. 比特币科普:如何给60岁的人讲明白比特币  对于同龄人讲解比特币时,不喜欢任何比喻,正确的做法是让他们直接去挖矿,买币,交易,然后在这个过程中看论文和看几篇经典的基础教程文章,一番下来,大约就知道比特币是怎么回事了。然而,面对我的父亲,一位50后,一辈子面朝黄土,不会使用电脑,甚至手机编写短信都颇为吃力,... tags:比特币 "], "segmented_title": ["0", ".", "1", "比特币", "价值", "多少", "-", "玩", "币", "族"], "segmented_paragraphs": [["1", ".", "比特币", "算", "力", "增长", "的", "新", "计算", "方法", ",", "就是这么简单", "最近", "比特币", "算", "力", "暴涨", "了", "几百个", "P", ",", "刷新", "了", "矿工", "的", "三观", ",", "这个", "比", "币", "价", "跌", "到", "2000", "更", "有", "杀伤力", ",", "都", "很奇怪", "哪里", "冒", "出", "这么多", "算", "力", ",", "现在", "就", "谈谈", "计算", "比特币", "算", "力", "的", "新", "方法", "。", "关于", "挖矿", "算", "力", "增长", "这个事情", ",", "其实", "算法", "很简单", ",", "以前", "我们", "都是", "拍脑袋", "算", "的", ",", "比如", 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"比特币", "是", "怎么回事", "了", "。", "然而", ",", "面对", "我的父亲", ",", "一", "位", "50", "后", ",", "一辈子", "面", "朝", "黄土", ",", "不会", "使用", "电脑", ",", "甚至", "手机", "编写短信", "都", "颇", "为", "吃力", ",", ".", ".", ".", "tags", ":", "比特币"]], "title": "0.1比特币价值多少-玩币族"}, {"bs_rank_pos": 3, "paragraphs": [" 每一次比特币价格的大涨都难以持续很长一段时间,投资者已经看惯了币价的大起大落。对于此次价格的上涨保持谨慎态度将是一种明智的做法。 ", " 4月28日讯 据最新数据显示,昨天(4月27日)比特币价格开市后短暂冲高便遇阻回落,虽然调整了很长时间但是调整幅度相当有限,跌幅仅仅120点左右,随后一路向上攀升,最高涨至8015一线高点。截止今日发稿前,比特币价格依然维持在高位运行,最新交投于7860附近。", " 比特币价格日线图", " 技术分析:从比特币行情1小时图来看,比特币早盘突破了昨天的盘整区,再次上涨,其上涨的支撑是图中黄色均线MA30,均线系统仍显多头排列格局,不过技术指标MACD有走弱迹象,虽然后市依旧看涨,然而短期上涨遇阻,此时不建议追高,上方压力8015和8220,下方支撑7830和7678。", " 比特币的价格在今日已创下了历史新高,截至本文发稿时,多家海外比特币交易所的比特币价格大约为1330美元(约合9200元人民币),交易所Bitfinex最高时达到了1449.7美元(9995元人民币),距离万元人民币大关仅剩一步之遥。", " 本月内,比特币的表现可谓是非常抢眼,在4月1日时,其在各平台上的平均价格要低于1100美元,而在短短几周内,其价格上涨的幅度已超过了20%。", " 对于近期比特币价格的攀升,有业内人士表示,在美国证券交易委员会(SEC)宣布批准Bats BZX 证券交易所提交的比特币ETF重审请愿之后,市场对比特币表现出了明显的乐观情绪。", " ETF提案的推动:推动比特币价格近期上涨的主要原因是美国证券交易委员会(SEC)决定重新审查Winklevoss兄弟提出的比特币ETF事项。", " 上个月,SEC拒绝了Winklevoss兄弟在Bats证券交易所上市比特币ETF的提案。此次价格的上涨也是寄希望于此次审核会一举通过。", " 由于市场开始对比特币ETF通过美国监管机构的审批重新燃起希望,比特币的价格在昨日一路飙升至1343美元。据统计,比特币的价格 在本周已经上涨了大约4.5%。尽管一夜之间,比特币的价格又回落到了1300美元左右,低于3月10日创下的历史高点,但仍不能否认比特币近期正迎来一波强劲的上涨趋势。", " 当然,除了比特币ETF带来的影响以外,其他因素也在推动比特币的发展,例如,日本最近决定将比特币认定为法定货币。", " 日元交易目前主宰了比特币市场,占全球市场的35%左右。日本在本月初开始认可了比特币支付方式,导致日本的一些商家开始尝试数字货币。日前,日本弘前市首次尝试以官方形式接受比特币捐款,用于为樱花节筹集部分资金。此外,日本最大的电子产品连锁企业Bic Camera也在4月初开始接受比特币,成为了该国首家接受比特币的主要零售商。", " 随着日本探索出更多与比特币相关的应用领域,该数字货币在全世界的认可度会进一步提高,这在一方面推动了比特币价格的上涨。然而,随着价格的上涨,一些比特币的投资者却越来越感到担忧。", " 然而,从目前来看,无论年底的3000美元是否可期,过去的经验告诉我们,每一次比特币价格的大涨都难以持续很长一段时间,投资者已经看惯了币价的大起大落。对于此次价格的上涨保持谨慎态度将是一种明智的做法。"], "segmented_title": ["揭秘", "比特币", "价格", "二", "次", "暴涨", "的", "原因", "后", "市", "比特币", "价格行情", "还会", "涨", "?", "_", "第", "一", "黄金"], "segmented_paragraphs": [["每一次", "比特币", "价格", "的", "大涨", "都", "难以", "持续", "很", "长", "一段时间", ",", "投资者", "已经", "看", "惯", "了", "币", "价", "的", "大起大落", "。", 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"的", "做法", "。"]], "title": "揭秘比特币价格二次暴涨的原因 后市比特币价格行情还会涨?_第一黄金"}, {"bs_rank_pos": 5, "paragraphs": ["自从6月登顶3000美元之后,比特币价格一直盘旋下跌,7月16日单日下跌近11%,而7月13日到16日4天之间,币价最高值17160元人民币,最低值12900元人民币,波动达到了25%。7月17日,又出现了5%的价格回升。", " 比特币世界出现了冲击波——扩容采用软分叉还是硬分叉,对币价产生了持续性的影响。", " 自从6月登顶3000美元之后,比特币价格一直盘旋下跌,7月16日单日下跌近11%,而7月13日到16日4天之间,币价最高值17160元人民币,最低值12900元人民币,波动达到了25%。7月17日,又出现了5%的价格回升。", " 由于比特币是整个数字货币界的“风向标”,近日其他数字货币也开始下跌,其中最为“惨烈”的就是以太币,7月以来跌幅已达54%,目前已经跌破1000元。", " 为何数字货币反复动荡,这牵涉到一个比特币扩容的问题,彭博社将其称之为“比特币的南北战争”。", " 由于比特币这个投机市场用户日益增多,发生了网络堵塞现象——比特币网络每秒只允许发生约7笔交易,然而比特币之父中本聪在原先设计比特币网络时,并不知道后期会需要处理大量交易,远远超过目前的运载能力。", " 于是比特币基金会的首席科学家加文·安德烈森建议,为了加快比特币交易处理速度,增加比特币网络的交易能力,应该对该网络实现“硬分叉”,简而言之,就是比特币分裂成两条链。", " 此处需要解释的是什么是“硬分叉”,OKCoin币行研究院指出,比特币的“硬分叉”是两个比特币改进提议和两个软件版本的争斗,比特币领域发布的另一个软件版本在软件领域被称为“分叉”。类比看的话,分叉在软件领域是一个司空见惯的现象——在谷歌创建了安卓系统之后,亚马逊、诺基亚和其他设备制造商也创建了他们自己的版本,为了使自己的产品区别于其他产品,谷歌允许开发者对安卓系统进行一些修改,但不允许特别巨大的改动,他们会阻止与其不兼容的设备使用自己的应用商城。这种情况下,安卓系统就是半开源性质的。", " “硬分叉”就意味着,之前比特币的区块链变成一旧一新两条链,需要在某个时间点所有节点的全部同意分叉升级,不同意的将会进入原旧链。", " 但是“硬分叉”可能导致一个状况出现:两个比特币所存在的区块链形成竞争关系,甚至可能分化为两种比特币,这种内耗会导致价格急剧下降。", " 于是“软分叉”的理念应运而生,这是一种较为温和的过渡方式,指比特币交易的数据结构(这就是被广泛流传的“共识”)发生改变时,未升级的节点可以验证已经升级的节点生产出的区块,而且已经升级的节点也可以验证未升级的节点生产出的区块。", " 如果上面的解释过于复杂,可以理解为,家里的房子漏水,硬分叉就是一劳永逸地重新购置新房,软分叉就是找装修队修修补补,新旧共存,比特币的区块链上没有升级的节点照旧,已经升级的按照扩容后的新规则计算,不会有新链产生,也不会有新的币种产生。", " 至于比特币采用哪种方式进行扩容,目前币圈在5月23日达成了一项纽约共识,同意将软分叉激活门槛设为80%全网算力支持。需要说明的是,这意味着,代表全网算力80%的“矿工”(用自己计算机的算力解答比特币网络中的数学题来获取记录全网交易的权利的人)同意的话,就可以实行软分叉,反之则大概率硬分叉。这次投票从7月21日开始,31日截止,但比特币价格的波动可能持续时间更久。", "[责任编辑:wemoney PF055]", " 责任编辑:wemoney PF055", "免责声明:本文仅代表作者个人观点,与凤凰网无关。其原创性以及文中陈述文字和内容未经本站证实,对本文以及其中全部或者部分内容、文字的真实性、完整性、及时性本站不作任何保证或承诺,请读者仅作参考,并请自行核实相关内容。", "14岁孕妇被公婆烧死 其父与丈夫表妹私奔 40岁女博士生四个娃 身材赛过18岁少女", "凤凰财经官方微信", " 1我们能做什么来帮助孩子预防近视?  2 德国红点设计至尊奖 超薄智能键盘  3源自美国 Fitkid菲宝智能护眼台灯  4 凤凰卫视独家定制礼品  5景德镇设计便捷旅行茶具 悦旅 "], "segmented_title": ["比特币", "价格", "4", "天", "波动", "25", "%", "背后", ":", "扩容", "分歧", "引发", "一", "场", "内战", "_", "凤凰财经"], 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"最近几年古力娜扎是娱乐圈的活跃份子,本身就是少数民族的她可谓参演过不少影视剧,在各种社会活动中、颁奖仪式中基本上都会有古力娜扎的身影。我们都知道身为少数民族,每个民族都有自己的信仰与生活方式,那么,在现实生活中,古力娜扎是否是穆斯林呢?", "古力娜扎是一名九零后内地姑娘,据说她未走上表演道路时,她的主要职业就是模特与舞蹈,2011年古力娜扎考入北京电影学院后,她的演员梦想才卓一实现。古力娜扎出生在新疆乌鲁木齐,她是回教徒、信仰《古兰经》,并且他们也有很严格的教规。和古力娜扎接吻后,在一星期之内是不能吃猪肉的,最近几年和古力娜扎合作过的影视男星也不在少数,但属最惨的莫过于张翰。2015年张翰和古力娜扎的“地下恋情”被公布,不久他们的接吻照也随即公布,现在的古力娜扎是否已经汉化,这并不被我们所知,而我们知道的是他们的这份恋情并不被外界所看好。", "少数民族与汉族有一定的样貌差距,随着古力娜扎的荧屏活跃,曾经有不少网友认为她是整过容的,古力娜扎为了证明自己的清白,她不惜在微博中晒出自己小时候的照片,通过这些照片做对比,其实,古力娜扎和小时候的样貌并没有多大改变,而唯一有改变的地方、也许就是时间与岁月的沉淀,现在我们也来看看古力娜扎幼时的萌照。", "古力娜扎是哪个少数民族的?", "古力娜扎1992年出生于新疆乌鲁木齐,是个维吾尔族姑娘;而维吾尔族有这么一个传统,就是不食猪肉!他们主要以馕、抓饭、烤包子、拌面为主食。", "维吾尔族人禁吃猪肉、驴肉、食肉动物,禁食一切动物的血和自亡的、非伊斯兰教徒宰杀的牲畜。只吃穆斯林(伊斯兰教徒)宰杀的牛、羊、骆驼、马肉入鸡、鸭、鱼肉。平时以白面、大米为日常主食。饮食花样丰富多彩,独具特色。", "她们主要有抓饭、拉面、肉粥、薄皮包子、烤包子、油塔子、曲曲(形似小饺)、烤羊肉、牛羊肉汤及大小厚薄不同形状的烤馕。蔬菜主要有皮芽子(洋葱)、卡马古尔(蔓菁)、胡萝卜、辣椒、豇豆、西红柿等。调味除盐醋外,喜用极椒、孜然(安息茴香)。早饭一般喝茶吃馕,不多炒菜;午、晚两餐常吃抓饭、包子、拌面、汤面、有时吃手抓羊肉。在吃抓饭和手抓羊肉前都要净手,不用筷子,直接用手抓食。"], "bs_rank_pos": 0}, {"title": "古丽娜扎是穆斯林吗?_百度知道", "segmented_title": ["古丽", "娜扎", "是", "穆斯林", "吗", "?", "_", "百度", "知道"], "segmented_paragraphs": [["谁", "说", "了", "都", "不", "算", "。", "《", "古兰经", "》", "和", "《", "圣训", "》", "是", "穆斯林", "的", "生活指南", ",", "是", "真假", "善恶", "的", "试金石", "。", "一", "个", "自称", "穆斯林", "的", "人", ",", "如果", "他", "的", "思想", "行为", "不符合", "古兰", "圣训", "的", "精神", "和", "教导", ",", "那么", "他", "就", "不", "算是", "一", "个", "穆斯林", "。"]], "paragraphs": ["谁说了都不算。《古兰经》和《圣训》是穆斯林的生活指南,是真假善恶的试金石。一个自称穆斯林的人,如果他的思想行为不符合古兰圣训的精神和教导,那么他就不算是一个穆斯林。"], "bs_rank_pos": 1}, {"title": "请问古力娜扎、帕丽扎提、迪丽热巴的穿着打扮和言行举止是否符合", "segmented_title": ["请问", "古力", "娜扎", "、", "帕", "丽", "扎提", "、", "迪", "丽", "热", "巴", "的", "穿着打扮", "和", "言行举止", "是否", "符合"], "segmented_paragraphs": [["古力", "娜扎", ":", "迪", "丽", "热", "巴", ":", "帕", "丽", "扎提", ":", "显示全部"], ["前", "一", "份", "工作", ",", "接触", "了", "公司", "的", "穆斯林", "财务", ",", "很", "开朗", "很", "热情", "的", "大姐", "。", "有", "次", "吃饭时", "别人", "问", "她", "为啥", "不用", "戴", "头巾", "她", "就", "讲", "他", "的", "姑姑", "劝", "他", "戴", "回族", "女士", "的", "头巾", ",", "她", "啥", "都", 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"?", "维族", "不全", "是", "穆斯林", ",", "正", "像", "汉族", "也有", "穆斯林", "一样", "。"]], "paragraphs": ["古力娜扎: 迪丽热巴: 帕丽扎提: 显示全部", "前一份工作,接触了公司的穆斯林财务,很开朗很热情的大姐。有次吃饭时别人问她为啥不用戴头巾她就讲他的姑姑劝他戴回族女士的头巾,她啥都没说,她奶奶就开始训她姑姑了:管好自己的事儿,你让人家戴头巾你给她发工资吗?人家每个月能拿五千,你要发五千工资我监督着她戴。一天到晚的想这些东西,咋不想咋挣钱把自己儿子丫头好好培养,整的跟盲道一样。这句话送给所有闲的蛋疼的人", "谢邀,别挑事。我宁愿像她们这样生活的穆斯林女生越来越多。", "这是好事,要多鼓励,人要做自己,不做教法的奴隶", "题主我和你说,我从小到大,在新疆待了20年", "见到穿热裤出门的维吾尔族妹子多了去了", "夏天30多度,汉族妹子穿什么衣服,维族、哈族、还有其他信仰伊斯兰教的妹子也穿类似的衣服", "你问这种问题,可能是电视看多了。", "我刚好有个维吾尔族朋友,女性,前段时间伊斯坦布尔念书,确认是信仰伊斯兰教的。", "她是JK制服爱好者(就是水手服)", "她拍过死库水的个人写真(就是日本学生泳装)", "大学有一个同班同学,还有一个比我大一级的其他专业的学姐,一个维吾尔族,一个哈萨克族。确认是信仰伊斯兰教的。", "日常的穿衣在我看来和汉族人并无明显区别。", "那个同班同学,参加多次院级校级晚会跳舞,穿热裤或者其他裸露程度比较多的衣服跳舞,特别劲爆的舞蹈。", "找到一个详细介绍瓦哈比和沙特的文章", "地球知识局 - 全球传教,沙特王国的传教大业 | 地球知识局(分享自知乎网)https://zhuanlan.zhihu.com/p/26759299?utm_source=com.meizu.notepaper&utm_medium=social", "~~~~~~~~~~分割线~~~~~~~~~~~~~~", "何为传统,传统的定义是什么,标准是什么?请问一个汉族女孩,一个韩国女孩,日本女孩这么穿有没有问题?为什么没有人说她们不传传统服饰。因为这是个现代社会,世俗化的社会。另外,给你们这些小白科普一下,什么是维族传统服饰", "这才是维族传统服饰。维族不是阿拉伯人,维族不是阿拉伯人。维族不是阿拉伯人。重要的事情说三遍。", "头巾也是宗教服饰不是民族更不是传统。土耳其,哈斯克斯坦等国家女性就是不戴头巾的。", "下图是土耳其街头,是不是感觉很现代,世俗,没头巾,没黑袍", "即便是现在非常保守极端的阿富汗,伊朗,那些黑袍也不是传统,更没有多久的历史。", "下图为上世纪苏联入侵阿富汗前时,阿富汗的女学生,距离现在三十多年年,看看那是的穿着", "这是1979年伊斯兰革命前的伊朗,距离现在也还是只过了三十多年", "那么这些中东地区历史上是怎样呢,你所谓的传统是啥呢?", "这是阿富汗哈扎拉人的传统服饰,是不是很漂亮", "这是13世纪到16世纪阿拉伯半岛上妇女的传统穿着,图一为吉达妇女,图二为贝都因妇女", "那么为何历史上,传统上,民族上,近代都没有极端宗教服饰,现在的中东,阿富汗,伊朗,沙特却成了这样", "因为极端宗教,因为原教旨主义抬头。而这其中,沙滩瓦哈比派绝对是罪魁祸首。", "这个伊斯兰中末流的小派别,创立一百多年一点气候都没有,根本没人鸟的节奏,一百年后阿拉伯半岛,瓦哈比与沙特家族结合,1801年瓦哈比派攻占伊拉克卡尔巴拉,拆毁侯赛因陵墓。1803～1804年相继攻克麦加和麦地那,捣毁先知穆罕默德陵墓,迫使麦加谢里夫臣服,并从土耳其人手中夺取了整个希贾兹,后又吞并了哈萨。1805年袭击了奥斯曼统治的叙利亚和伊拉克。1811年建立了第一个沙特家族统治的瓦哈比派王,但很快被奥斯曼击溃,沙滩王室逃亡科威特,瓦哈比信徒逃亡沙漠。有一百年终于有了起色,奥斯曼帝国解体,瓦哈比派卷土重来1921～1925年,先征服了拉希德家族的领地,后进攻希贾兹国王侯赛因的属地,先后占领塔伊夫、麦加、麦地那、吉达,后又兼并了阿西,建立沙特阿拉伯,立瓦哈比派教义为国教。", "楼主要是有这样的精神对待宪法就好了。", "自从我回答了伊斯兰教的一些问题后,时不时有这种人提一些明显恶意的问题来邀请我回答,要的就是你们互相咬,然后人家看猴戏,当然,也能挣x量,所以我已经很久不答这方面的东西了,我建议大家以后看见这种问题就举报,最起码别理它。", "这里是中国……维族回族不一定就得信教啊,送她去艺术院校的还是她的母亲呢。", "题主,您是塔利班遗老么? 为何比宗教警察还宗教警察?宗教信仰是有自由的。 民族出身并不能决定一个人的信仰。 维族可以不信伊斯兰教。 汉族也可以皈依任何教。 别拿人家穿什么来说事。 想黑穆斯林可以, 但你要选择正确的姿势。 不然可就贻笑大方了。", "符合人家的职业啊亲!而且古力娜扎迪丽热巴都是北疆的,相对来说开放一些。我家是喀什的,这么穿的成年维族姑娘很少…我几乎没见过。十岁左右的小姑娘有穿短裤的,但不是热裤。讲真,我五月份回喀什,我舅舅都不让我穿热裤,但是我坚持穿,确实少数民族同志会用或好奇或厌恶或好笑的眼光看你,但我觉得没关系啊,我穿什么是我的自由。在喀什依旧能看到戴大头巾的维族大妈。但这也是人家的习惯,无可厚非。女孩穿什么是她的个人意愿,和宗教没关系。", "你的生活符合共产主义么?", "先知啊!你应当对你的妻子、你的女儿和信士们的妇女说:她们应当用外衣遮住自己的身体。", "你对信女们说,叫她们降低视线,遮蔽下身,莫露出首饰,除非自然露出的,叫她们用面纱遮住胸膛,末露出首饰,除非对她们的丈夫,或她们的父亲,或她们的丈夫的父亲,或她们的儿子,或她们的丈夫的儿子,或她们的兄弟,或她们弟兄的儿子,或她们姐妹的儿子,或她们的女仆,或她们的奴婢,或无性欲的男仆,或不懂妇女之事的儿童。", "据阿布杜拉传述:先知(愿真主祝福他,并使他平安)说:“妇女全身都是羞体,她一出门,恶魔就欺负她。”《提尔密济圣训集》(1173)", "据阿伊莎(愿真主喜悦她)传述:“我们曾和使者(愿真主祝福他,并使他平安)一起受的戒,当有商队经过我们时,他们很接近我们时,我们就将长衫垂下来,遮住脸,他们走后,我们再露出脸。”《艾布达吾德圣训集》(1833)/《艾哈迈德圣训集》(24067)", "严格来说,这些维族女士的穿着都不符合穆斯林的基本法,但你一定要问我,资兹不资兹这种穿着打扮,她们现在放福利,我们怎么能不资兹福利?", "题主牛逼,你这一句话,我对这三个女明星瞬间路人转粉。", "那电视上全是黑粽子你就开心了?自古以来,黑色罩袍,黑色面纱就不是畏乌尔传统服装。人家穿什么随意,貌似题主管的太宽。", "这都是好姑娘!", "她们为什么要守伊斯兰教法?她们是我飞天面条教教徒,必须每周吃一次面条。", "首先得信教,然后才能谈教法好吗?维族不全是穆斯林,正像汉族也有穆斯林一样。"], "bs_rank_pos": 2}, {"title": 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"新疆", "住", "了", "二十多年", "还", "没", "见过", "黑纱", "蒙面", "的", "。", "那些", "维族", "哈族", "妹子", "一", "个", "个", "时尚", "的", "一", "逼", "。"], ["当然", ",", "南疆", "某些", "穷", "乡", "辟", "壤", "这种", "要", "蛾子", "比较", "多", ",", "但是", "那些人", "估计", "连", "地球", "是", "圆", "的", "可能", "都", "不知道", ",", "你", "也", "不能", "要求", "太多", "。"], ["维族", "传统", "服装", "都是", "五颜六色", "的", ",", "话说", "伊斯兰", "好多", "派别", ",", "只有", "最", "保守", "那", "派", "才是", "黑沙", "蒙面", "。"], ["但是", "肯定", "不能", "跟", "吃", "猪肉", "的", "汉族", "人", "结婚", ",", "不", "然", "就", "是", "叛", "教"]], "paragraphs": ["他们的族人怎么看待她们?毕竟当明星不能戴黑纱吧", "都什么年代了,谁说一定要带黑纱,我们班有一个哈密维族姑娘,从没带过什么黑纱,她说都没听说过要带黑纱 -- And I'll tell you all about it When I see you again We've come a long way From where we began", "愚昧到家,还黑纱,乌鲁木齐都出法律禁止黑纱", "新疆的克孜恰兹还是很漂亮的嘛", "之前有人截过图,说ins上这几个维族女星的关注都很不正常", "外国msl不知道 反正我在乌鲁木齐久没见年轻的维族姑娘戴头巾的", "老子在新疆住了二十多年还没见过黑纱蒙面的。那些维族哈族妹子一个个时尚的一逼。", "当然,南疆某些穷乡辟壤这种要蛾子比较多,但是那些人估计连地球是圆的可能都不知道,你也不能要求太多。", "维族传统服装都是五颜六色的,话说伊斯兰好多派别,只有最保守那派才是黑沙蒙面。", "但是肯定不能跟吃猪肉的汉族人结婚,不然就是叛教"], "bs_rank_pos": 3}, {"title": "古力娜扎是维吾尔族!那她是不是穆斯林?查一下_百度知道", "segmented_title": ["古力", "娜扎", "是", "维吾尔族", "!", "那", "她", "是不是", "穆斯林", "?", "查", "一", "下", "_", "百度", "知道"], "segmented_paragraphs": [["人生", "于", "哪", "一", "种", "信仰", "的", "家庭", ",", "属于", "哪个", "民族", ",", "属于", "哪个", "国家", "从来", "都", "不是", "判断", "一个人", "是否", "是", "穆斯林", "的", "标准", "。", "一个人", "是不是", "穆斯林", ",", "乃是", "真", "神", "安", "拉", "判断", "的", ",", "也", "非", "某", "一", "个", "旁", "人", "说了算", "的", "。"], ["她", "完完全全", "是", "个", "穆斯林", ",", ",", ",", ",", "他", "父母", "都是", "穆斯林", ",", "虽然", "现在", "维吾尔族", "的", "的", "样子", "很随意", "的", "呢", "种", ",", "比如说", "不", "带", "头巾", "等等", ",", ",", "但", "他们", "心中", "时刻", "保留", "着", "对", "安", "拉", "的", "忠诚", "和", "感恩", "。", "伊斯兰", "在", "维吾尔族", "心里", "的", "地位", "不是", "那么", "容易", "动摇", "的", ",", "维吾尔族", "的", "现状", "最", "接近", "于", "土耳其人", "。", "你们", "不知道", "就", "别", "乱", "说", ","], ["她", "是", "维族", ",", "但", "不是", "穆斯林", ",", "她", "做", "了", "很多", "穆斯林", "不能", "做", "的", "东西", ",", "比如", "饮食", ",", "习性", ",", "礼仪", ",", "着装", ",", "等等", "。"], ["古力", "娜扎", "出生于", "维吾尔族", "家庭", ",", "然而", "维吾尔族", "和", "穆斯林", "不是", "同等", "概念", ",", "并且", "他", "的", "所作所为", "也", "不符合", "穆斯林", "行为", ",", "也", "不符合", "维吾尔族", "女人", "的", "行为", "。", "但", "她", "是不是", "穆斯林", "由", "伟大", "的", "subhana", "ta", "allah", "决定", "的", "。"], ["是不是", "穆斯林", "无关", "你们", "信仰", ",", "你", "自己", "吃饭", "管", "不住", "别人", "拉屎", ",", "除非你", "是", "极端", "教徒", "。"], ["如果", "是", "穆斯林", ",", "还是", "让", "她", "自行", "了", "断", "见", "真主", "吧", ",", "以", "免", "贻害", "人类"]], "paragraphs": ["人生于哪一种信仰的家庭,属于哪个民族,属于哪个国家从来都不是判断一个人是否是穆斯林的标准。一个人是不是穆斯林,乃是真神安拉判断的,也非某一个旁人说了算的。", "她完完全全是个穆斯林,,,,他父母都是穆斯林,虽然现在维吾尔族的的样子很随意的呢种,比如说不带头巾等等,,但他们心中时刻保留着对安拉的忠诚和感恩。 伊斯兰在维吾尔族心里的地位不是那么容易动摇的,维吾尔族的现状最接近于土耳其人。你们不知道就别乱说,", "她是维族,但不是穆斯林,她做了很多穆斯林不能做的东西,比如饮食,习性,礼仪,着装,等等。", "古力娜扎出生于维吾尔族家庭,然而维吾尔族和穆斯林不是同等概念,并且他的所作所为也不符合穆斯林行为,也不符合维吾尔族女人的行为。但她是不是穆斯林由伟大的subhana ta allah决定的。", "是不是穆斯林无关你们信仰,你自己吃饭管不住别人拉屎,除非你是极端教徒。", "如果是穆斯林,还是让她自行了断见真主吧,以免贻害人类"], "bs_rank_pos": 4}], 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Music:因为音乐,生活才变得更加美丽! 蓝色城堡:蓝色城堡,命运的开始! 花儿朵朵:花儿朵朵,永远绽放! 雪白精灵:精灵!精灵!永远开心! 开心果:天天开心,开心到底! 天使小组:天使天使,永远聪明 学习城堡:学习城堡,欢乐无限 银河星:闪亮的星云,飘在太空中 雄鹰小组:让雄鹰把梦想带往天空。 阳光小组:我们阳光,我们快乐 斯莱特林:50%的努力加50%的勤奋等于成功 彩色风铃:一片彩色是童年,叮叮当当是未来 SUOPER STUDENGT:SUPER STUDENT勇往直前 七彩小组:七彩阳光 七色生活 卓越小组:超越超越 卓尔不凡 快乐小组:我们快乐、团结、勤奋、齐心合力、勇争第一! 火箭小组:我参与 我喜欢 小春蕾:挑战自我,团结第一 团结胜利组:团结胜利,勇争第一! 欢乐城堡:我们快乐,"], "segmented_title": ["最好", "的", "小组", "该", "取", "什么", "名字", "呢", "?", "_", "百度", "知道"], "segmented_paragraphs": [["小组", "名", "及", "口号", "紫", "海马", ":", "海马", "海马", ",", "永远", "神奇", "!", "Music", ":", "因为", "音乐", ",", "生活", "才", "变得", "更加", "美丽", "!", "蓝色", "城堡", ":", "蓝色", "城堡", ",", "命运", "的", "开始", "!", "花儿朵朵", ":", "花儿朵朵", ",", "永远", "绽放", "!", "雪白", "精灵", ":", "精灵", "!", "精灵", "!", "永远", "开心", "!", "开心果", ":", "天天开心", ",", "开心", "到底", "!", "天使", "小组", ":", "天使", "天使", ",", "永远", "聪明", "学习", "城堡", ":", "学习", "城堡", ",", "欢乐无限", "银河", "星", ":", "闪亮", "的", "星云", ",", "飘", "在", "太空", "中", "雄鹰", "小组", ":", "让", "雄鹰", "把", "梦想", "带", "往", 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"paragraphs": ["日期:2016-11-11 www.azg168.cn 祥安阁风水网 编辑:azg16802 ", "在学习的过程中,老师会让同学们几个人组成一个小组,那么就得需要一个组名了,如何起一个霸气的名字?就来看看下面的相关文章吧!", " 霸气学习小组组名大全", " 霸气一点的团队名称", " 敢死队KingBack(王者归来)", " 最好的朋友永远的first", " 学习小组名字很霸气", " 组名:进击的学霸", " “我们这个小组目标是做学霸,所以取了这个名字,要让每个同学积极向上,发奋图强”。组长杨雯语气坚定。", " 下午第三节课后,一群组长围在教室黑板前,有的在默算着自己小组的分数,有的在看自己小组加了多少星,还有的在看哪些小组分数在前面。个个脸上都是激动、兴奋,生怕自己小组落后。", " 组名:Dream Team(梦之队)", " “由于各种巧合,我们这组的名字是由语文老师命名的”。说起自己的小组,组长徐岳嫣然很自豪。", " 组名:皇族(皇族这个名字十分霸气,同学们认为他们是最强的,最具有统治地位的小组)", " 组名:女·汉纸(因为这组有四个女生,一个男生,所以叫女·汉纸)", " 组名:Super god (我们已经超越神了!)", " 组名:冲上云霄(组员取的,从组长名字中取一个字组成一个词语)", " 组名:战狼(根据\"战狼传说\"得名)", " 组名:To be No.1(有奋发向上的精神中不失霸气!)", " 学习小组组名个性霸气的", "看过这篇文章的网友还看了以下名字相关文章: ", " 住宅小区楼盘风水有何讲究  打麻将风水的注意事项与秘笈  你知道风水礼品送什么最好吗?", " 明年是什么生肖年2018  2017年最有福的生肖是哪个  十二生肖财运排名 2017年财运最旺的生肖 ", " 十二生肖春夏秋冬生肖  十二生肖寓意详解  十二生肖顺序顺口溜口诀 ", " 在线八字算命婚姻幸福吗? 如何免费算命婚姻配对  怎么算命八字秘诀大公开 ", " 拍马屁的来源 拍马屁的由来  历史上的妲己真的是狐狸变的吗  最惨烈的起义太平天国死了多少人 ", " 段姓宝宝取名大全  胡姓男宝宝洋气的名字  鸡年魏姓男宝宝取名大全 ", " 如何看面相算命  胎记的位置与命运图  民间传统的看相算命顺口溜 ", " 霸气学习小组组名大全 小女孩什么名字好听 好听的学习小组组名大全 给女儿取个好听的名字 女孩起什么名好听 初中学习小组组名大全 个性好听的游戏情侣名 好听的销售团队名有哪些 好听有诗意的情侣名大全 好听的韩国名字男孩大全 盘点好听的情侣备注名 好听的英文情侣名大全 霸气好听的cf战队名大全 小男孩什么名字好听 好听的衣服网店名大全 好听女儿名字 好听的淘宝旺旺名大全 好听的后宫群名称大全 好听的男装网店名大全 好听的两字情侣名大全 男孩起什么名好听 英雄联盟好听游戏名字 好听的精品店名大全 起个好听的店铺名字 好听的韩国名字有哪些 好听纯英文情侣名字带翻译 女孩子最好听名字 给男儿取个好听的名字 男生最好听名字 好听的微信情侣名 好听的饰品店名大全 好听的淘宝账户名有哪些 有哪些好听的家具店名 盘点各地好听的粥店名字 两个字好听的游戏名 好听的礼品店名大全", ">>相关阅读文章:", " 好听有诗意的情侣名大全 霸气学习小组组名大全 好听的英文情侣名大全 好听的情侣网名有哪些 好听烧烤店名 两个字好听的游戏名 最好听的qq名大全"], "segmented_title": ["霸气", "学习", "小组", "组", "名", "大全", "_", "祥安阁", "好听", "名"], "segmented_paragraphs": [["日期", ":", "2016", "-", "11", "-", "11", "www", ".", "azg168", ".", "cn", "祥安阁", "风水", "网", "编辑", ":", "azg16802"], ["在", "学习", "的", "过程", "中", ",", "老师", "会", "让", "同学们", "几个", "人", "组成", "一", "个", "小组", ",", "那么", "就", "得", "需要", "一", "个", "组", "名", "了", ",", "如何", "起", "一", "个", "霸气", "的", "名字", "?", "就", "来", "看看", "下面", "的", "相关文章", "吧", "!"], ["霸气", "学习", "小组", "组", "名", "大全"], ["霸气", "一点", "的", "团队", 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"2010年国际贸易出现恢复性快速反弹。2011年以来,世界经济低速增长及欧债危机深化,抑制了全球进口需求,国际贸易增长转趋疲弱。2013年国际贸易状况仍不乐观,特别是发达国家进口萎缩,拖累了全球贸易增长。世界贸易组织(WTO)最新预测,2013年全球货物贸易量将增长2.5%,其中发达经济体出口增长1.5%,进口萎缩0.1%,发展中经济体和独联体出口增长3.6%,进口增长5.8%。在全球经济脆弱复苏、国际贸易总体低迷的背景下,一些国家为了缓解就业压力和企业困难,大力推行贸易保护主义,在一定程度上阻碍了世界贸易复苏进程[18,19]。 ", " (5)国际大宗商品价格稳中有降,市场需求尚未强势回升 ", " 2013年,受世界经济低迷的影响,国际大宗商品价格基本稳定,多数品种有所下降。全年IMF初级产品价格指数仅上涨1%,不包括能源价格的美国商品研究局(CRB)大宗商品价格指数下降5.8%。从供需基本面看,当前多数大宗商品供应较为充足,而过去多年来作为大宗商品需求增量主要来源的新兴经济体经济增速放缓,抑制大宗商品需求,使大宗商品价格面临较大下行压力。从资金面看,随着发达国家经济复苏、股市回报回升,一些投资基金开始从商品市场撤出,大宗商品呈现出一定程度的“去金融化”[20]。 (二)国内环境 ", " 从国内看,中国经济开局平稳,但仍存下行压力。一季度,中国国内生产总值增长7.4%,处于预期合理区间之内。城镇新增就业人数超过300万,外出务工劳动力增加了288万人,增长1.7%,就业形势稳中向好。与此同时,中国经济结构调整和转型升级取得新的进展,经济发展质量和效益稳步提升,内生动力不断增强。但当前中国正处于经济增速换挡期、结构调整阵痛期和前期刺激政策消化期“三期”叠加阶段,工业产能严重过剩等矛盾没有根本缓解,部分区域房地产市场又出现了新的波动,加上外部环境依然复杂严峻,经济增长仍面临下行压力。 (三)我国对外贸易发展面临的突出问题 ", " 经济全球化的今天,任何国家都势必会受到国际经济贸易形势变化所带来的影响,中国也不例外。综合考虑国际国内环境,2014年中国外贸发展面临的环境略好于上年,但形势仍然不容乐观,还存在不少挑战和压力,新的经济环境下,中国出口贸易面临的问题如下: ", " (1)资源环境约束日益强化  我国对外贸易发展仍主要依靠资源、能源、土地和环境等有形要素投入,资源和环境压力较大。在矿产资源方面,石油、天然气人均储量不足世界平均水平的1/10,即使是比较丰富的煤炭资源,人均储量也不到世界平均水平的40%。同时,我国能源资源消耗大,单位产品能耗高。2009年,我国消耗的钢材占全球的46%,煤炭占45%,水泥占48%,我国单位GDP能耗是美国的2.9倍、日本的4.9倍、欧盟的4.3倍,矿产资源总回收率比世界先进水平约低20%。我国已成为全球第二大能源消费国,其中石油、铁矿石、铝土矿、铜矿等重要能源资源消费对进口的依存度都超过50%。预计今后一段时期,我国外贸发展的资源和环境约束将更趋强化。  (2)贸易自由化和保护主义的斗争  随着时代的发展,各国之间国际贸易往来日渐频繁。经济的衡量已不再局限在一国之内,经济全球化发展有目共睹且势不可挡。随着世界各国之间经济贸易往来的加速增长,其规模亦不断扩大中,贸易自由化随之而来。一方面,贸易自由化促进了世界经济贸易的发展,另一方面,世界经济贸易发展的同时贸易摩擦也随之而出。全球各个区域经济发展的不同,贸易利益分配的不同等导致贸易保护主义的产生。  在全球经济低速增长、失业率总体偏高的情况下,一些国家为了维护本国产业的市 场份额,仍在实行各种形式的贸易保护主义。不仅发达国家层层设置贸易壁垒,一些发展中国家也频频出台新的贸易限制措施,中国是近年来全球贸易保护主义的最大受害国。2013年,中国共遭遇19个国家和地区发起的92起贸易救济调查,比2012年增长了18%,其中新兴经济体和发展中国家发起的案件约占2/3。2014年一季度,中国又遭遇11个国家和地区发起的27起贸易救济调查,同比增长23%,其中美国对中国多个产品发起反倾销反补贴调查。从产业领域看,钢铁产品是中国遭遇国外贸易救济调查的“重灾区”,发达国家对中国战略性新兴产业出口设限的势头没有根本缓解,新兴经济体对中国劳动密集型产品的摩擦时有发生。在国际竞争日趋激烈的环境下,贸易摩擦已经成为影响中国出口稳定增长的重要因素。  (3)外部需求存在不确定性  随着全球经济贸易复苏步伐加快,国际市场对中国出口商品的需求总体呈回升之势,但风险依然较多。当前最大的市场风险来自新兴经济体,新兴大国普遍经济增长乏力、进口需求疲弱。为走出经济困境,一些新兴经济体推行结构性改革,加大财政整固力度,采取措施恢复国际收支平衡,将进一步抑制进口需求。据世贸组织统计,以美元计价,一季度印度、巴西进口额分别下降12.6%和0.6%,前2个月俄罗斯、南非进口额分别下降7.5%和7.2%。金融危机以来,新兴市场在中国出口增量中占比超过40%,其进口需求持续降温将对中国出口造成一定冲击。与此同时,发达国家积极推进“再工业化”,在保持高端制造业竞争优势的同时,也在一定程度上恢复了传统制造业的竞争力,其经济复苏与进口需求增长明显不同步。据世贸组织统计,2013年美国、欧盟进口额分别下降0.2%和3.4%,2014年前2个月分别下降0.1%和0.6%。  (4)是中国出口竞争力面临多重挑战  中国劳动力、土地等要素成本仍处于持续上涨期,传统劳动密集型产业竞争力不断削弱。沿海地区出口企业用工成本在连续多年上涨后,2014年以来又上涨了10-15%,目前已相当于越南、印度、柬埔寨等周边国家的2-3倍甚至更高。一季度全国105个主要监测城市工业地价同比上涨5.3%,2010年以来累计涨幅达到17%。受成本上涨影响,中国传统劳动密集型产业出口订单和产能向周边国家转移趋势明显。2013年,中国7大类劳动密集型产品占美国、欧盟、日本市场份额比2010年高峰时期下降2.8个百分点。近年来,中国机械装备等高端制造业技术实力和出口竞争力明显提升,成为出口稳定增长和转型升级的重要支撑,但在国际营销、售后服务、融资支持等方面与发达国家仍存在不小差距。随着发达国家加大对先进制造业的重视和投入,中国相关出口产业面临的国际竞争日趋激烈,扩大市场份额的难度增大[19]。  (5)人民币汇率将进一步市场化  汇率是一国货币兑换另一国货币的比率。汇率的高低会直接影响进出口贸易中产品的价格,从而会影响到各国进出口贸易的数量以及发展程度。一般来说,本币汇率降低,能起到利于出口,抑制进口作用;本币汇率上升,则起到利于进口,抑制出口作用。2014年以来,人民币兑美元小幅贬值,改变了长期以来单边升值的态势,近期人民币汇率浮动幅度扩大,有利于进一步推动汇率市场化进程。但2010年至今人民币兑美元累计升值超过10%,而同期周边国家货币总体呈贬值态势。人民币相对竞争对手货币大幅升值,后续叠加影响持续发酵,对劳动密集型产品和收汇周期长的大型成套设备出口都带来了负面影响[18]。 ", " (6)对外贸易发展存在不平衡现象 "], "segmented_title": ["论", "我", "国", "对外贸易", "如何", "应对", "国际", "经济", "形势", "变化", "25", "-", "第", "3", "页"], "segmented_paragraphs": [["2010", "年", "国际贸易", "出现", "恢复", "性", "快速", "反弹", ";", "(", "5", ")", "国际", "大宗商品", "价格", "稳中有降", ",", "市场需求", "尚未", "强势", ";", "2013", "年", ",", "受", "世界经济低迷", "的", "影响", ",", "国际", "大宗商品", "价", ";", "从", "国内", "看", ",", "中国经济", "开局", "平稳", ",", "但", "仍", "存", "下行", "压力", ";", "经济全球化", "的", "今天", ",", "任何", "国家", "都", "势", "必", "会", "受到", "国际", "经济", "贸", ";", "(", "1", ")", "资源", "环境", "约束", "日益", "强化", ";", "我", "国", "对外贸易", "发展", "仍", "主要", "依靠", "资源", "、", "能源", "、", "土地", "和", "环境", ";", "-", "10", "-", ";", "石", "、", "铝土矿", "、", "铜矿", "等", "重要", 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"浮动幅度", "扩大", ",", "有利于", "进一步", "推动", "汇率", "市场化", "进程", "。", "但", "2010", "年", "至今", "人民币", "兑美元", "累计", "升值", "超过", "10", "%", ",", "而", "同期", "周边国家", "货币", "总体", "呈", "贬值", "态势", "。", "人民币", "相对", "竞争对手", "货币", "大幅", "升值", ",", "后续", "叠加", "影响", "持续", "发酵", ",", "对", "劳动密集型", "产品", "和", "收汇", "周期", "长", "的", "大型", "成套", "设备", "出口", "都", "带来", "了", "负面影响", "[", "18", "]", "。"], ["(", "6", ")", "对外贸易", "发展", "存在", "不平衡", "现象"]], "title": "论我国对外贸易如何应对国际经济形势变化25-第3页"}, {"bs_rank_pos": 5, "paragraphs": ["当前所在位置:中国论文网 > 经济法律论文发表 > 浅析我国对外贸易如何应对国际经济形势变化 ", "好消息:杂志之家论文发表、写作服务和杂志订阅支持对公帐户付款!安全又可靠!欢迎在线咨询>> ", "【摘要】我国对外贸易需要面临的国际风险日益增多,外部环境更加恶劣和复杂,国际经济形势变化要求我国对外贸易调整增长方式,又好又快地推动我国对外贸易发展。本文结合笔者的工作经验,就我国对外贸易如何应对国际经济形势变化进行了深入的探讨,提出了自己的看法和建议,具有一定的参考价值。中国论文网 http://www.xzbu.com/2/view-3237375.htm 【关键词】对外贸易;国际;经济形势;变化 一、 前言 我国的改革开放目前已经经过三十多年的岁月,我国对外贸易与全球经济的互动性、同一性趋势日益增强,参与经济全球化的程度日益加深,但是机遇与风险总是并肩而来,我国对外贸易需要面临的国际风险日益增多,外部环境更加恶劣和复杂,国际经济形势变化要求我国对外贸易调整增长方式,又好又快地推动我国对外贸易发展。 二、加快建立促进外贸发展方式转变的服务平台和政策体系。 政府有关部门应该完善和建立起可预测、透明、稳定的保险、外汇、信贷、财税等政策体系,完善公平竞争的市场环境,健全相关的法规、法律,减少重组成本和资源流动,明确政府对企业参与到全球化竞争的投资风险保障、税收减免优惠、融资支持等鼓励政策,有效地提高资源配置效率。政府有关部门应该努力帮助各类进出口企业有效规避经营风险和政治风险,解决它们在国外发展所面临到的困难,及早建立高效率、全方位的企业服务机构,尽量避免企业承受不必要的负面影响和损失。 三、尽快实现跨境贸易人民币结算 从日元、欧元、美元等货币的国际化历程表明,货币国际化的必经之路和最初表现就是将其作为结算货币在跨境贸易中应用。我们应该将其看作是一个复杂的渐进过程,必须采取渐进的方式来将其实现。目前我国人民币已初步具备国际化,已经基本实现资本项目部分可兑换、经常项目可自由兑换的基本条件。2010年12月15日,人民币在俄罗斯实现首次境外直接挂牌交易。同期,尼日利亚央行把人民币列入法定外汇交易货币,2011年1月起可在外汇市场进行公开交易。作为发展跨境贸易人民币结算的方式之一,银联卡支付范围也继续扩大,截至2010年11月,境外业务开通国家和地区已达98个,进一步促进了贸易、投资和居民消费便利化。中国人民银行应尽快研究制定相关的政策来有效顺通人民币结算渠道,合理规划国家支付体系,为我国人民币支付体系的科学发展提供政策的指导。继续依法加强支付体系监督管理,推动非现金支付工具的创新和普及,推动我国证券登记结算系统和银行业金融机构行内支付系统的完善和发展。按期建成运行电子商业汇票系统,对金融基础设施。同时,针对审核出口试点企业资格的环节过多这个问题,笔者建议中国人民银行可以制定表格式审核流程及管理办法,明确规定出政府各有关机关在接到出口试点企业的申请之后几个工作日必须回复,制定表格式审核流程,提高出口试点企业审核效率。 四、完善调整外资项目和加工贸易 应该严格限制甚至取消那些高污染、高物耗、高能耗的外资项目和加工贸易,发挥加工贸易的产业带动和技术溢出效应,努力提升加工贸易的发展档次和技术水平。同时,还应该根据国家经济结构调整和产业发展政策的要求,增强对外贸易企业的自主创新能力,鼓励提高产业竞争力,大力提高和发展产业配套能力。 五、实施出口品牌战略,提高出口产品和对外贸易企业的国际竞争力。 目前我国的对外贸易还大多处于初级加工的水平,在国际竞争上还没有形成品牌意识。笔者建议应该实施出口品牌战略,大力提高商品附加值,制定相关的政策支持体系来帮助企业实现国际品牌发展战略,除此之外,还应该为对外贸易企业提供开拓海外市场的合作、采购和信息平台。 总之,面对后危机时期复杂多变的国际经贸形势及国内外的竞争压力,加快推进对外贸易发展的战略转型,实现从规模速度到质量效益转型,从外生动力到内生动力转型,从市场广度到市场深度转型,从低端要素到高端要素转型的战略跨越。 参考文献 [1]王来健,张琼.徐州市发展循环经济的对策研究[J].中国资源综合利用.2006(5) [2]黄攸立.基于可持续发展的生态建设问题与对策研究[J].生态经济.2006(1) [3]刘展新,张军,刘洁清.农业环境污染综合治理与可持续发展对策研究[J].水利科技与经济.2008(9) [4]傅宁.关于我国家族企业可持续发展问题的几点思考[J].企业导报.2009(6) [5]汪华林.家族企业可持续发展对策研究[J].中国高新技术企业.2008(24) ", " 转载请注明来源。原文地址:http://www.xzbu.com/2/view-3237375.htm "], "segmented_title": ["浅析", "我", "国", "对外贸易", "如何", "应对", "国际", "经济", "形势", "变化"], "segmented_paragraphs": [["当前", "所在", "位置", ":", "中国", "论文", "网", ">", "经济", "法律", "论文", "发表", ">", "浅析", "我", "国", "对外贸易", "如何", "应对", "国际", "经济", "形势", "变化"], ["好消息", ":", "杂志", "之", "家", "论文", "发表", "、", "写作", "服务", "和", "杂志订阅", "支持", "对公帐户", "付款", "!", "安全", "又", "可靠", "!", "欢迎", "在线咨询", ">", ">"], ["【", "摘要", "】", "我", "国", "对外贸易", "需要", "面临", "的", "国际", "风险", "日益增多", ",", "外部环境", 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"玩久了LOL,想试试手机游戏,微信游戏里正好有一款很相像,但是手机操作又特别不爽,有没有办法用电脑玩呢?而且方法要简单?我来教你吧!!!", "百度经验:jingyan.baidu.com", "1 下载腾讯的游戏平台: 可能很多玩英雄联盟的朋友,对这个并不陌生,这个和盒子差不多,有很多辅助功能,你可别以为这个只能用来玩英雄联盟,玩其他游戏,甚至手机游戏,都是可以的。 步骤阅读 2 账号进行登录: 下载好了之后,进行登录,这边直接用QQ号码来登录就可以了,如果你电脑上是有QQ登录着的情况下,是可以直接用账号快速登录的(另外如果在这个程序里启动一些游戏程序的话,可以直接跳过登录的地方哦) 步骤阅读 3 找到手游电脑版看: 进入到游戏平台里面之后,我们不用去看其他的不相关的信息,直接在最上方的导航栏,找手机游戏就是了(这个平台不仅仅是手机游戏,还有很多腾讯旗下的电脑游戏) 步骤阅读 4 点击游戏进行下载: 找到我们要玩的游戏了,这里直接进行下载就可以(移动到图标里之后)点启动游戏,会自动去下载的,不用搞什么模拟器,一键操作,比较的简单,比较方便。 步骤阅读 5 直接在平台中点击试玩: 游戏下载之前会先解压,所以时间会久一点,安装的时候,估计你电脑会卡一段时间,不过没什么关系,电脑下嘛。。比手机肯定要快很多,下载完就可以直接玩了。 步骤阅读 步骤阅读", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:丰富自己,帮助他人;欢迎关注"], "bs_rank_pos": 1}, {"title": "腾讯TGP为何下架王者荣耀 英魂之刃?-无线频道-手机搜狐", "segmented_title": ["腾讯", "TGP", "为何", "下架", "王者", "荣耀", "英魂", "之", "刃", "?", "-", "无线", "频道", "-", "手机", "搜狐"], "segmented_paragraphs": [["近日", ",", "小", "编", "发现", "腾讯", "TGP", "平台", "突然", "有", "了", "变化", ",", "TGP", "平台", "上", "的", "网页", "游戏", "和", "手机游戏", "入口", "均", "已", 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"喜欢", "在", "STEAM", "平台", "上", "购买", "游戏", "。", "而", "在", "未来", "阶段", ",", "TGP", "在", "单机游戏", "领域", "将会", "有", "何", "大动作", "?", "这一切", "值得", "期待", "。"], ["作为", "普通", "玩家", ",", "今后", "不能", "在", "TGP", "上", "耍耍", "王者", "荣耀", "和", "英魂", "之", "刃", "了", ",", "使用", "习惯", "方面", "的", "问题", "还是", "有", "一些", "的", "。", "对此", ",", "你", "怎么看", "呢", "?"]], "paragraphs": ["近日,小编发现腾讯TGP平台突然有了变化,TGP平台上的网页游戏和手机游戏入口均已消失,所以我们经常玩的TGP版《王者荣耀》、《英魂之刃》的首页入口也一同消失了。TGP作为腾讯旗下的著名游戏平台,被定位为“囊括腾讯全平台游戏”,这其中就包含了单机游戏、网络游戏、网页游戏以及手机游戏。本次TGP把网页及手游两大入口取消下架,不知道将会在业务方面有何变化?", "在TGP平台官网方面则有一条公告消息:“TGP手游业务迁移至腾讯手游助手、TGP网页游戏业务迁移至QQ游戏大厅,计划将于2017年3月开始迁移,预计持续1个月时间之后TGP内手游与页游将会下架,具体时间待后续通知。”(官网原文)不难看出,本次TGP下架两大游戏业务后,将只剩下网络端游和单机游戏两大业务领域,相信这与去年动作频繁的单机游戏业务有着一定关系,而在未来的业务领域方面,腾讯TGP官方尚未给出正式的消息。", "一元开房就是这么任性 广告", "根据TGP官网公告的内容,腾讯TGP手游业务将准备开启全新的品牌运营平台,而网页游戏业务将陆续整合到QQ游戏大厅之中。不过随着小编的深入了解,发现到目前为止,TGP虽然正式下架了两大业务版块,但也仅仅是取消了首页入口,在TGP上方的搜索栏目中仍然可以搜索到王者荣耀、英魂之刃、拳皇98OL、街头篮球等游戏,并且可以下载安装,安装后可正常进入游戏。账号数据方面也尚未发现变化。", "随着本次页游级手游业务的正式下架,TGP将仅剩下网络端游和单机游戏两大业务内容。", "就TGP现状而言,网络端游为TGP的老牌业务,腾讯旗下众多人气游戏均汇聚于此,TGP不仅整合了游戏下载及社区等游戏盒子功能,还在官方辅助领域一块做出了不错的成绩,成为了玩家最常用的优秀整合平台。而在单机游戏方面,单机游戏是2016年TGP开展的全新业务版块,由国产游戏《符石守护者》开始,陆续上架了《男孩与泡泡怪》、《饥荒》、《都市天际线》、《这是我的战争》、《圣女之歌-零》、《侠客风云传前传》等知名单机游戏,在TGP玩家群体间获得了一定的销量与口碑。", "根据小编分析,未来TGP在做好网络端游的基础上,将加大单机游戏的运营力度,或许腾讯将有计划把TGP单机游戏部分做大做强,整合成国内最大的单机游戏销售平台趋势。当然,目前在单机游戏部分,TGP上架游戏数量较少,仅有7款游戏。其中国产游戏3款,将近半数。在单机游戏玩家方面,对此有多少能够买账,尚未可知。但腾讯经营游戏领域多年,在运营游戏方面有着得天独厚的运营优势,以《饥荒》为例,分别推出了单机版与联机版,其中联机版更是多次版本迭代,推出了不少游戏活动和节日限定产品,添加了很多原版没有的全新内容,值得褒奖。", "但目前国内单机领域,除了盗版玩家外,正版单机玩家以STEAM平台用户占绝对主流,TGP若想开展单机业务,仍有一定压力。就目前的大趋势来看,STEAM平台已上架游戏万余款,折扣力度更大,绝大多数游戏也没有内购元素,在单机游戏玩家间具有不错的口碑。大多数人也还是喜欢在STEAM平台上购买游戏。而在未来阶段,TGP在单机游戏领域将会有何大动作?这一切值得期待。", "作为普通玩家,今后不能在TGP上耍耍王者荣耀和英魂之刃了,使用习惯方面的问题还是有一些的。对此,你怎么看呢?"], "bs_rank_pos": 2}, {"title": "如何在电脑玩王者荣耀_百度经验", "segmented_title": ["如何", "在", "电脑", "玩", "王者", "荣耀", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["好多", "小伙伴", "苦恼", "手机", "电量", "不够", ",", "上", "分", "困难", "。"], ["今天", "我", "给", "大家", "介绍", "一", "个", "在", "电脑", "上", "玩", "王者", "荣耀", "的", "办法", "。"], ["或许", "很多", "小伙伴", "知道", "使用", "tgp", "来玩", "王者", "荣耀", ",", "但是", "tgp", "时常", "出现", "卡顿", "的", "状况", "。"], ["今天", "我", "就", "教", "大家", "如何", "电脑", "玩", "王者", "荣耀", "不", "卡", "。"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["1", "首先", "我们", "先", "找到", "王者", "荣耀", "的", "官网", ",", "点击", "进去", "。", "2", "找到", "下载", "游戏", ",", "点击", "pc", "模拟器", "下载", "。", "步骤", "阅读", "步骤", "阅读", "3", "下载", "完成", "打开", ",", "并", "找到", "王者", "荣耀", "安装", "。", "步骤", "阅读", "步骤", "阅读", "4", "安装", "完毕", "后", ",", "选择", "qq", "还是", "微信", "进入游戏", "。", "步骤", "阅读", "5", "进入", "游戏", "后", ",", "不要着急", "开始游戏", ",", "这", "时候", "我们", "需要", "调整", "一", "下", "键位", ",", "在", "游戏", "的", "右边", "有", "状态栏", ",", "选择", "键位", ",", "这里", "我", "已经", "帮", "大家", "调试", "好", "了", ",", "大家", "照搬", "就好", "。", "步骤", "阅读", "6", "建议", "先", "打", "两把", "匹配", "适应", "一", "下", ",", "然后", "才", "开始", "自己", "的", "排位", "超神", "之", "旅", "。", "步骤", "阅读", "END"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["这个方法", "不是", "肯定", "不", "卡", ",", "但是", "经过", "试验", ",", "大", "部分", "电脑", "都", "能", "完美", "兼容", "小", "玩", "怡情", ",", "大", "玩", "伤身", ",", "注意休息", "眼睛"], ["经验", "内容", "仅供参考", ",", "如果", "您", "需", "解决", "具体", "问题", "(", "尤其", "法律", "、", "医学", "等", "领域", ")", ",", "建议", "您", "详细", "咨询", "相关", "领域", "专业人士", "。"], ["个性签名", ":", "健身", "达人", ",", "自创", "家庭", "健身", "动作"]], "paragraphs": ["百度经验:jingyan.baidu.com", "好多小伙伴苦恼手机电量不够,上分困难。", "今天我给大家介绍一个在电脑上玩王者荣耀的办法。", "或许很多小伙伴知道使用tgp来玩王者荣耀,但是tgp时常出现卡顿的状况。", "今天我就教大家如何电脑玩王者荣耀不卡。", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "1 首先我们先找到王者荣耀的官网,点击进去。 2 找到下载游戏,点击pc模拟器下载。 步骤阅读 步骤阅读 3 下载完成打开,并找到王者荣耀安装。 步骤阅读 步骤阅读 4 安装完毕后,选择qq还是微信进入游戏。 步骤阅读 5 进入游戏后,不要着急开始游戏,这时候我们需要调整一下键位,在游戏的右边有状态栏,选择键位,这里我已经帮大家调试好了,大家照搬就好。 步骤阅读 6 建议先打两把匹配适应一下,然后才开始自己的排位超神之旅。 步骤阅读 END", "百度经验:jingyan.baidu.com", "这个方法不是肯定不卡,但是经过试验,大部分电脑都能完美兼容 小玩怡情,大玩伤身,注意休息眼睛", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:健身达人,自创家庭健身动作"], "bs_rank_pos": 3}, {"title": "腾讯游戏平台tgp王者荣耀怎么不见了_百度知道", "segmented_title": ["腾讯", "游戏", "平台", "tgp", "王者", "荣耀", "怎么", "不见", "了", "_", "百度", "知道"], "segmented_paragraphs": [["因为", "TGP", "和", "王者", "荣耀", "有", "一点", "不兼容", ",", "还是", "一些", "什么", "问题", ",", "导致", "游戏", "下架", "了", ",", "所以", "现在", "小伙伴", "们", "很", "着急", ",", "请", "大家", "不要着急", "。", "相信", "没", "多久", ",", "腾讯", "就", "可以", "把", "这个问题", "给", "解决", "了", "。", "我们", "的", "小伙伴", "只要", "耐心等待", "一段时间", "就", "可以", "了", "。", "你", "可以", "暂时", "先", "用", "别", "的", "模拟器", "先", "玩", ",", "如果", "喜欢", "就", "可以", "一直", "用", ",", "如果", "你", "是", "腾讯", "的", "重视", "粉丝", "的", "话", ",", "你可以", "多", "去", "腾讯", "的", "官网", "看看", ",", "有", "什么", "消息", "他们", "都是", "第一时间", "发布", "的", "。"], ["最近", "没", "使用", "TGP", ",", "不过", "应该", "里面", "有", "的", "啊", ",", "手游", "专区", "。", "或者", "不用", "TGP", "登陆", "。"]], "paragraphs": ["因为TGP和王者荣耀有一点不兼容,还是一些什么问题,导致游戏下架了,所以现在小伙伴们很着急,请大家不要着急。 相信没多久,腾讯就可以把这个问题给解决了。我们的小伙伴只要耐心等待一段时间就可以了。 你可以暂时先用别的模拟器先玩,如果喜欢就可以一直用,如果你是腾讯的重视粉丝的话,你可以多去腾讯的官网看看,有什么消息他们都是第一时间发布的。", "最近没使用TGP,不过应该里面有的啊,手游专区。 或者不用TGP登陆。"], "bs_rank_pos": 4}], "question": "tgp王者荣耀在哪", "segmented_question": ["tgp", "王者", "荣耀", "在", "哪"], "question_type": "ENTITY", "fact_or_opinion": "FACT", "question_id": 221632}
+{"documents": [{"bs_rank_pos": 0, "paragraphs": ["就是参战参试部队的军人.。参战意即参加过为抵御外来侵略,完成祖国统一,捍卫国家领土和主权完整,保卫国家安全而进行武力打击和抗击敌方的军事行动的军人,包括参加过中越边境自卫还击作战、珍宝岛自卫反击作战、援越抗美作战、援老抗美作战、西沙群岛自卫反击作战、南沙群岛自卫反击战的军队退役士兵,解放一江山岛作战、川甘青藏滇地区平叛作战、炮击金门作战、中缅边境勘界警卫作战、“八•六”海战、崇武以东海战;参试意即原8023部队及代管的参加过核试验的和参加保障任务的人员。"], "segmented_title": ["什么", "是", "两", "参", "人员", "_", "百度", "知道"], "segmented_paragraphs": [["就是", "参战", "参", "试", "部队", "的", "军人", ".", "。", "参战", "意", "即", "参加", "过", "为", "抵御", "外来", "侵略", ",", "完成", "祖国", "统一", ",", "捍卫", "国家", "领土", "和", "主权", "完整", ",", "保卫", "国家安全", "而", "进行", "武力", "打击", "和", "抗击", "敌方", "的", "军事", "行动", "的", "军人", ",", "包括", "参加", "过", "中越边境", "自卫", "还击", "作战", "、", "珍宝岛", "自卫", "反击", "作战", "、", "援越", "抗美", "作战", "、", "援", "老", "抗美", "作战", "、", "西沙群岛", "自卫", "反击", "作战", "、", "南沙群岛", "自卫", "反击战", "的", "军队", "退役士兵", ",", "解放", "一江山岛", "作战", "、", "川", "甘", "青藏", "滇", "地区", "平叛", "作战", "、", "炮击金门", "作战", "、", "中缅", "边境", "勘界", "警卫", "作战", "、", "“", "八", "&", "#", "8226", ";", "六", "”", 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只要在个人档案中,如记载有“在自卫反击战中(以来)…”,或“在作战中…”、“在战斗中…”、“在(到)前线…”,或“履行了国际主义义务”等文字表述的,可认定为参战人员。但对于只有请战表述,没有参战结论的,或只有“战备、备战、训练、留守、国防施工”等表述的,不予认定。 (3)对个人持有参战纪念章、纪念物品的认定。 对个人持有的参战纪念章、毛巾、茶缸等纪念物品,不作为参战身份认定的依据。主要考虑这类纪念品数量众多,纪念品上又没有本人姓名,容易相互转借,如以此认定参战身份容易造成混乱。 (4)对于个人档案中无参战记载或找不到档案,同时又没有战友证明的人员,属人数相对较多且反映强烈的,由县级民政部门派专人前往他们的原部队查阅有关原始资料,只要部队有原始参战记载,其原部队番号与个人档案或退伍证中部队番号一致的,可做为认定的依据。 2、原8023部队和其他参加核试验军队退人员的界定。 8023部队是国防科工委21试验基地20世纪六七十年代的代号,从1946年至1996年,多次执行核试验任务。 其他参加核试验军队退役人员:包括参加过核试验效应试验的退役人员、执行过核试验保障任务的退役人员、参加过核爆炸条件下军事演习的退役人员、与原8023部队驻同一区域部队的退役人员、曾在核导弹、核潜艇部队涉核岗位服役的退役人员等。"], "segmented_title": ["两", "参", "人员", "是", "如何", "认定", "的", "(", "建议", "收藏", ")"], "segmented_paragraphs": [["1", "、", "参战", "退役", "人员", "的", "界定", "。", "参战", "退役", "人员", "是", "指", "曾", "在", "军队", "服役", "并", "参加", "过", "作战", "的", "退役", "人员", "。", "作战", "是", "指", "武装力量", "打击", "或", "抗击", "敌方", "的", "军事", "行动", ",", "具体", "界定", "为", ":", "我", "军", "为", "抵", "卸", "外来", "侵略", ",", "完成", "祖国", "统一", ",", "为", "捍卫", "国家", "领土", "和", "主权", 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", " 社会的两类社会社会矛盾学说 (三)简答题 1.《论十大关系》的主要内容是什么?...改革不合理的规章制度,工人群众、 领导干部和技术人员三结合,即“两参一改三... ", " ? 两参一改三结合:干部参加劳动,工人参加管 理,...合理的规章制度,工人群众、领导 干部和技术人员三...工作者体现出 来的两弹一星精神的具 体内涵是什么... ", " 下一步我们将做好两参人员、三 属人员、病退人员等档案归档工作,使民政档案的工作提高到一个更高的水平。 三、做好带病还乡人员的审批工作 切实做好带病还乡... ", " 来,我们扎实做好农民就业工作,加强农民就业培训工作,不断 提高农民群众素质,促进农村剩余劳动力就业;扎实抓好“三类 人员” 、下岗职工、下放知青及“两参人员”... ", " 仁民街社区走访两参人员记录表_人力资源管理_经管营销_专业资料。仁民街社区走访两参人员(动员购买养老保险政策宣传)记录表两参人员 姓名 性别 出生年月 现居住... ", " 瓦店二中参培人员_数学_小学教育_教育专区。瓦店二中参培人员 杨振华、石美红、杨丽霞、葛翠晓、庞英、张合英、孙文 英 瓦店二中参培人员 杨振华、石美红、... ", " 二、每年春节和八一建军节,各区民政局按照属地管理的原则对本辖区关系在厦门市企业离退休人员社会化管理中心的“两参人员”发给慰问金,标准调整为:春节每人300元,... ", " 什么是两参人员?其生活补助标准是多少? 13. 什么是两参人员?其生活补助标准是多少? 曾在军队服役并参加过作战的退役人员、原 8023 部队和 其他参加核试验军队... ", " 比如退休两参人员的优抚待遇问题,一直都是 不稳定因素的焦点问题,经常会有人因此到处上访,给我们的稳定工 作造成了很大压力;我们也经常向上级主管部门反映此问题,... 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经过:淘宝常客浏览闲鱼网站,一则低价转让佳能单反相机的页面闪现,点击进入附图并声情并茂的描述了转让二手相机的原因——\"和男朋友分手了,不想看见伤心过往的\"遗物\"。价格动心、故事动人的情况下决定出手,根据妹妹留下的qq(876250852)与其谈妥交易价钱,点击确定。 这时,就在这时屏幕提示\"亲,已有人拍下此宝贝但还未付款,你还有机会哦\",遂与妹妹联系告其状况,妹妹得知淘宝常客能立即拍下付款购买,她把“产品重新上架”后发个链接过来(淘宝网,淘我喜欢!),淘宝常客点击进入,像往常淘宝天猫购物卖家改价格情况类似,输入支付宝帐号、密码、确认产品信息后拍下付款,等待妹妹发货? 数小时后,淘宝常客发现没有任何物流信息和交易记录,立即联系支付宝客服查询结果,显示4600元以魔游游电脑充值的方式被划入北京传美科技有限公司帐下。", " 内心:疑惑遭遇钓鱼链接、自嘲贪便宜吃大亏、悔恨山闲鱼买相机、愤怒骗子无处不在、失望无人管无处诉!", " 续集:逗逗闷子联系妹妹何时发货,无应答。 联系淘宝闲鱼在线客服电话,打不通。 致电北京市传美魔游游公司,无人接。 报告公安诉案情4600嫌太少,不立案。 请律师写诉状直接法院起诉,成本高、周期长、执行难!", " 试问:淘宝闲鱼系统存漏洞,监管缺失,是否应该承担主要责任赔偿本人经济损失? 闲鱼商品交易页面是否应该出现“亲,已有人拍下此宝贝但还未付款,你还有机会哦”这样的页面提示语?这可是的的确确在闲鱼页面直接出现的!这个页面不能进行拍下商品,才给了骗子卖家通过qq发来产品重新上架后的链接,支付宝客服提示说,由于不是通过闲鱼平台也未通过阿里旺旺聊天记录,而是通过qq发送的链接导致的交易损失,也认同本人或许遭遇了非法钓鱼链接,深表同情,至于对方支付宝账户和北京传美科技公司账户对于此笔交易的信息也只能警方查询和调取证据,对于受害的消费者本人损失不能提供赔偿也不能进行任何处理。 好吧,得到这样的解释只能去找警察叔叔了,到了北京通州某派出所做了笔录,其实就是记录了一个“平常故事”而已,诈骗金额低于5000人民币不能立案侦查,只能做个笔录让其沉睡吧,后悔,怎么就没多给他转几百块呢,哪怕是5001元呢,看来这个骗子还是一个非常熟悉法律常识的高手。 淘宝闲鱼,你能不能有点担当?能不能别推卸责任?能不能别让原本十分信任你的消费者丧失信心?能不能别让本淘宝常客也认为“淘宝卖假货、闲鱼养骗子”这个平台都是来欺诈百姓的呢?能不能别光顾着自己赚钱视而不见别人受罪?能不能天天别老吹牛逼自己的10000精英团队,每人3个亿的业绩,能不能别老鄙视竞争对手京东人员庞大?能不能多派些人手花一些心思在商户入驻资质上、商户的诚信上?能不能出现欺诈事件后给消费者一个满意的答复?敢不敢像百度推广一样承诺因此受到欺诈全额赔偿?能不能别光在页面上承诺“卖家实名认证、支付宝担保交易、专业团队支持”瞎忽悠消费者?读完本文后淘宝人能不能扪心自问一下,问题出在哪里?别让我们受害者认为你们就是十足的骗子帮凶!好吗?4600元虽不至于倾家荡产,也许在你坐着迈巴赫的马云看来只是“洒洒水啦”或许只是一次洗车的消费而已,但是也足足够俺儿子两个月的奶粉钱啦!各位都尽点责、出点力、用点心帮俺追回这损失,本人代表俺全家先谢谢你们啦,希望幸运降临,期待奇迹发生,欢迎各大新闻网络媒体骚扰——等待热线:13269066022 王先生", " 曝光骗子信息: 闲鱼账号:tommy17173 作案QQ:876250852 关联公司:北京传美科技有限公司 作案手段:魔游游游戏电脑在线充值", " 时间去哪了?上网购物了!金钱去哪了?骗子骗走了!骗子去哪了?入驻闲鱼了!索赔去哪了?投诉无门了!证据去哪了?永远珍藏了!法律去哪了?新法难产了!警察去哪了?去干大事了!爸爸去哪了?办理移民了!", " 你们遇上了职业的恶意买家,很多的现在。", "小到100来块钱的东西,大到几千上万的,都不放过。小打小闹的就是想让你退点款,大的可能是以假当真给你退货,恶意制造瑕疵给你退回来等等。", "首先尽量察言观色避免遇到这种人,再有主动留证,包括聊天记录(只认旺旺和闲鱼)、宝贝细节照、发货照片视频等。", "群众评审一般不靠谱,尽量让小二解决,不要嫌麻烦让对方得逞。", " 现在突然觉得。我要赶紧开电脑把我卖的手机写上仅限同城面交!!!太恐怖了。", " 简直都不想说太恶心了。。买家讲价成三十块包邮的一双百丽鞋,到手后觉得太旧了不想要了。。。然后使劲地在旺旺闲鱼上骂我诈骗穷疯了30都骗人渣,还给我打骚扰电话。。。老子图片摆在那两百字的说明已经够详细了,总有些极品又想便宜又想是正品又想东西新,花三十块钱装三百块的逼,真的感觉惹了一只疯狗没办法给退款了。。闲鱼里想好好卖闲置的卖家也是弱势群体", " 你们可以看看他们有没有卖东西,用个小号买回来。。。", " 我也是受害者,请加群67170920,联合我们的力量打倒骗子!", " 才玩闲鱼没几天 卖出去一裙子 我花110买的 穿两次 之后我标价40 6块的运费 还是遭到砍价 20包邮 我不同意 之后就在那磨最后32包邮 她又问各种细节 真害怕收到货之后又这又那 后悔了 不如在那放着了 我的妈呀。", " 我在闲鱼上被卖家给骗了,700。现在闲鱼改版了,不能申请退款了。这他们的就是坑啊"], "segmented_title": ["淘宝", "闲鱼", "卖家", "被骗", "!", "?", "-", "知乎"], "segmented_paragraphs": [["淘宝", "闲鱼", "卖家", "被骗", "!", "?"], ["我", "在", "闲鱼", "上", "卖", "了", "一", "个", "神器", ",", "这个", "卖家", "当时", "是", "直接", "拍", "下", "付款", "的", ",", "我以为", "遇到", "一", "个", "爽快", "的", "人", "了", ",", "货", "发出", "前", "我", "还", "特", "地", "问", "她", "要不要", "给", "你", "拍", "段", "视频", "看", "下", "货", ",", "结果", "她说", "不用", ",", "相信我", "。", "第二天", "神器", "就", "寄", "到", "她", "手", "里", "了", ",", "然后", "她", "就", "在", "阿里", "上", "问", "我", "闪光灯", "怎么", "坏", "了", ",", "拍", "视频", "有问题", "等", "种", "种", "原因", "。", "我自己", "的", "机子", 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我是老实人 可不是傻子事实证明 作为老实人 卖家一点都不傻 可是作为骗子 就没见过这么傻的还记得上面的长截图吗 我第一时间拍了照 发给了卖家 卖家说的话吗卖家当时是这么表示的 我是老实人 我发的货都是保质保量的 国行原包的cpu虽然包装没了 但是我会补给你一个杠杠的散热器的 玄冰300 不错吧 单买99呢 于是我想到查重量 一个玄冰300 净重470克 而我的快递只有280克我又打电话找到我这单快递的发货网点让他拍下了内网的重量记录0.28克 没错吧 这下没话说了吧其中最夸张的 我这边刚被骗 那边他又在14号卖出了一颗i7 4790k后来16号撤了一天 该列的都列了 包括包装的尺寸 怎么都不可能塞得下一个玄冰300 加 i7 4790k 独立包装 终于淘宝判定我成功 让我根据后续收到的地址退货 但是 老实人发飙了 表示 我要报警 你们欺负人并表示 你7号收的货 中间都10天了 你对我的快递做了见不得人的事 好吧我太污了并合情合理的复原了案件的过程 其中 一句都不提他自己说的 玄冰300 在哪里 怎么塞进一个质量为280克 高不过14cm的盒子里 还是独立包装的并表示会委托公安 继续跟进案情最后表示 别欺负我 我警察局里有人 警察叔叔可是我邻居 以此警示广大闲鱼买家 这次是我碰上个傻骗子 前言不搭后语 行骗 语言毫无逻辑 最后才让我找到漏洞 各位记得闲鱼交易 贵重物品 千万 千万 千万 当面拆包 特别是cpu之类小又贵重的物品最后曝光 骗子的 名字 id姓名:张勇职业:骗子闲鱼id:煎熬的岁月551居住地:陕西省 渭南市 大荔县 城关镇南西二巷12号电话17802935902快递单照片个人主页截图芝麻信用590 近期退款6笔 再次告诫各位 遇到这些红名 千万小心再小心 就像我说的骗子太多了 傻子明显不够用 总有一天会骗到你头上 小心无大错 小东西 被骗了 给个差评 就够了 不值钱的东西 永远没自己的信用值钱 贵重物品千万记得当面 签收 实在不行的 让朋友拿着手机 拍好包装 并录好开苞视频 有空可以帮我呼死他谢谢各位", " 快递网店的重量照片", " 并表示要死皮赖脸不签收赖账了", " 没看到近期内6笔退款吗～一般卖u直接给贴吧收u党了,不会没事挂闲鱼的,不过你放心,有这快递重量记录你不可能输的,最简单的玄冰300怎么可能那么轻～最后,远离闲鱼,珍爱生命～", " 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"安排", "2017", "年", "放假", "时间表", "2017", "全年", "日历", "农历表", "二十四节气", "养生", "大全", "二十四节气", "古诗", "歌", "谚语", "2017", "年", "二十四节气", "表", "2017", "年", "节日", "大全", "2017", "年", "财神", "方位", "查询", "2017", "黄道吉日", "查询", "表"]], "paragraphs": ["喜欢周杰伦的《发如雪》,尤其喜欢那一句:“铜镜映无邪扎马尾你若撒野今生我把酒奉陪”。流年偷换青丝为白雪,铜镜若不再映无邪,你可否仍愿低头为她画眉?纵使许多夫妻相处些许时日便分道扬镳,但,更多的饮食男女还是从一而终,消去了爱与恨,平静地过婚姻生活直到生命终结。 那么,当结婚十九年是什么婚呢?就让本期的“结婚纪念日”告诉你答案吧。", "结婚十九年印花婚 其实,除了浪漫的法国,没有国家给结婚十九年纪念日命名。印花,不是枝头上娇艳欲滴的鲜花,而是一种以特殊的方式永远被保存下来的花,永不凋谢,宛如与自己相伴十九年的那个伴,爱意不甚当初那么毕露无遗,却以一种细水长流的方式融到彼此的血肉中。", "下面,让我们来分享一篇文章吧。 《走过印花婚》 4月18日是我与妻子结婚19周年纪念日,西方人称之为“印花婚”。", "或许是早已习惯平平淡淡的家庭生活,如果不静下心来回望过去的19年,我真的并未留意大把幸福时光正悄然从身边溜过。我与妻子携手同行19年,一路走来并非坦途。我们曾经恣意挥霍初婚的甜蜜,也曾经遭遇磨合中幸福的烦恼,甚至还有过彼此间任性恶毒的伤害。但正是在这种曲折反复中,伴随着锅碗", "瓢盆的交响和个人事业的起伏,我们与孩子一起成长,一天天将爱情修炼成亲情。如今我们视家庭为倦鸟栖息的避风港,无论春夏秋冬,夫妻俩总会一起做饭洗衣,一起打扫房间,晚饭后一起散步,一起打羽毛球,或者一起看大片看偶像剧,尽享二人世界的甜蜜。", "妻子是一个理性的女人。凡事她都会三思而行,不像我跟女儿,多血质,太感性,很容易意气用事。每当我和女儿为一出电视剧感动得泪眼汪汪时,妻总会安慰说那是做戏,不必那么投入。遇到大是大非的问题,她比谁都冷静有主见,总要分析前因后果、利害得失,然后做出决定,而且总能让人心悦诚服。", "妻子是一个独立的女人。她对我从不依赖,工作之外,教子之余,要么进健身房跑步,要么去练习瑜珈,生活过得相当充实。反倒是我和女儿,买衣购物总离不开她的陪伴,没有她的“参谋”我们就会六神无主。", "妻子是一个知足的女人。她对我没有过高的要求,只愿我平平安安,一帆风顺。生日一份小礼物,情人节一束红玫瑰,甚至大年三十夜仅仅是象征意义的“压岁钱”,也能使她满心欢喜。她从不抱怨我挣钱不多,穿衣打扮,她只讲究得体而非华贵,一袭普通衣饰在她的精心打理下,同样能穿戴出时尚的韵味。", "妻子与我相识时才19岁,正是一朵怒放的鲜花。当初红娘对我“矮个子”形象的描述,一度让她在“见与不见”之间徘徊,是我后来的岳父大人一句“小平同志个子就矮”,鼓足了她的勇气,坚定了她的选择,也使我们今生终究没有擦肩而过。见面两年后,这朵鲜花心甘情愿地插到了我这堆“牛粪”上。我曾经给她阳光,也曾给她阴霾;曾经给她雨露,也曾给她泪水。就这样日复一日,年复一年,19年过去了,如今,不敢说我这堆“牛粪”有多肥沃,也不敢说给了妻子怎样的滋养,但在不少人眼中,年届不惑的妻子比起她的同龄人,更靓丽、更精神、更显年轻,真可谓冬去春来,“花”开不败! 19年婚姻经历,让我明白一对陌生男女之所以能够从相识相知到相濡以沫,除了感谢上苍赐予我们这段缘分,更需要我们珍惜拥有,用心经营,让平淡的日子弥漫甜蜜的芳香。 执子之手,与子携老。走过印花婚,期待我们还会有银婚、金婚、钻石婚…… 2017年端午放假安排 2017年放假时间表 2017全年日历农历表 二十四节气养生大全 二十四节气古诗歌谚语 2017年二十四节气表 2017年节日大全 2017年财神方位查询 2017黄道吉日查询表"], "bs_rank_pos": 0}, {"title": "结婚19年是什么婚_百度知道", "segmented_title": ["结婚", "19", "年", "是", "什么", "婚", "_", "百度", "知道"], "segmented_paragraphs": [["第", "1", "年", "§", "纸婚", "、", "第", "2", "年", "§", "棉", "婚", "、", "第", "3", "年", "§", "皮革婚", "、", "第", "4", "年", "§", "水果", "婚", 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24年一萨丁婚; 25年一银婚; 26年一玉婚; 27年一桃花心木婚; 28年一镍婚; 29年一绒婚; 30年一珍珠婚; 31年一羊皮婚; 32年一紫铜婚; 33年一斑岩婚; 34年一琥珀婚; 36年一梅斯林婚; 37年一纸婚; 38年一水银婚;39年一绉纱婚; 40年一祖母绿婚; 41年一铁婚; 42年一珠质婚; 43年一法兰绒婚; 44年一黄玉婚; 45年一朱红婚; 46年一薰衣草婚 47年一开斯米婚;48年一紫晶婚;", "可称为金婚了,努力向💎婚迈进吧"], "bs_rank_pos": 2}, {"title": "2015结婚19周年是什么婚_百度知道", "segmented_title": ["2015", "结婚", "19", "周年", "是", "什么", "婚", "_", "百度", "知道"], "segmented_paragraphs": [["一周年", ":", "纸婚", "Paper", "Wedding", "二", "周年", ":", "棉", "婚", "Cotton", "Wedding", "三周年", ":", "皮", "婚", "、", "皮革婚", "Leather", "Wedding", "四", "周年", ":", "毅", "婚", "、", "花果婚", "Silk", "Wedding", "五", "周年", ":", "木婚", "Wood", "Wedding", "六周年", ":", "铁", "婚", "、", "糖", "婚", "Iron", "Wedding", "七", "周年", ":", "铜婚", "、", "毛", "婚", "Copper", "Wedding", "八", "周年", ":", "陶器", "婚", "、", "铜婚", "Appliance", "Wedding", "九", "周年", ":", "柳", "婚", "、", "陶", "婚", "Pottery", "Wedding", "十", "周年", ":", "锡", "婚", "Tin", "Wedding", "十一", "周年", ":", "钢", "婚", "Steel", "Wedding", "十二", "周年", ":", "绕仁婚", "、", "丝", 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Crystal Wedding 二十周年:搪瓷婚、磁婚 China Wedding 二十五周年:银婚 Silver Wedding 三十周年:珠婚、珍珠婚Pearl Wedding 三十五周年:珊瑚婚 (碧玉婚) Coral(Jade)Wedding 四十周年 红宝石婚 Rudy Wedding 四十五周年 蓝宝石婚 Sapphiye Wedding 五十周年 金 婚 Golden Wedding 五十五周年 翠玉婚 Emerald Wedding"], "bs_rank_pos": 3}, {"title": "结婚19年是什么婚 结婚19年送什么礼物_百度知道", "segmented_title": ["结婚", "19", "年", "是", "什么", "婚", "结婚", "19", "年", "送", "什么", "礼物", "_", "百度", "知道"], "segmented_paragraphs": [["1", "年", "一", "纸婚", ";", "2", "年", "一", "布", "婚", ";", "3", "年", "一", "皮", "婚", ";", "4", "年", "一", "丝", "婚", ";", "5", "年", "一", "木婚", ";", "6", "年", "一", "铁", "婚", ";", "7", "年", "一", "铜婚", ";", "8", "年", "一", "电", "(", "器", ")", "婚", ";", "9", "年", "一", "陶", "(", "器", ")", "婚", ";", "10", "年", "一", "锡", "婚", ";", "11", "年", "一", "钢", "婚", ";", "12", "年", "一", "(", "亚", ")", "麻婚", ";", "13", "年", "一", "花边", "婚", ";", "14", "年", "一", "象牙婚", ";", "15", "年", "一", "水晶婚", ";", "20", "年", "一", "瓷婚", ";", "25", "年", "一", "银婚", ";", "30", "年", "一", "珍珠", "婚", ";", "35", "年", "一", 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"fact_or_opinion": "FACT", "question_id": 221638}
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"你", "还是", "直接", "食用", "干果", ",", "而", "不", "去", "烹饪", "吃", "。"], ["夏威夷果", "怎么", "开"]], "paragraphs": ["功效调节血脂和益智作用,非常适合老年人或血脂不好人的滋补食品。", "禁忌人群消化力弱的人少吃。", "适宜人群一般人群均可食用。", "夏威夷果是一种原产于澳洲的树生坚果,也称澳洲坚果或火山豆,味道香甜可口。食用部分是果仁。", "夏威夷果(澳洲坚果)果仁香酥滑嫩可口,有独特的奶油香味,是世界上品质最佳的食用用果,有“干果皇后”,“世界坚果之王”之美称,风味和口感都远比腰果好。夏威夷果(澳洲坚果)除了制作干果外,还可制作糕点、巧克力、食用油、化妆品等。", "其功效主要为:调节血脂和益智作用,非常适合老年人或血脂不好人的滋补食品。", "很多人可能对于这种坚果都很陌生,但实际上它已经被引入了我国,在很多地方也是可以买到的。实际上很多坚果都有着益智养生的良好作用,而夏威夷果更是如此,如果有条件的话,推荐大家可以在平时适当食用一些,即美味,又健康。", "夏威夷果的营养价值", "夏威夷果(澳洲坚果)果仁营养丰富(呈奶白色),其外果皮青绿色,内果皮坚硬,呈褐色,单果重15—16克。夏威夷果营养成分极其丰富,其含油量高达60%-80%,还含有丰富的钙、磷、铁、维生素B1、B2和氨基酸。", "夏威夷果油是最好的保健油,其所含各种营养成分的人体消化吸收率极高,是胃肠道最容易吸收的油类。夏威夷果油具有减少胃酸,阻止胃炎及十二指肠溃疡的功能。", "夏威夷果油中富含的单不饱和脂肪酸能降低血压、调节和控制血糖水平、改善糖尿病患者的脂质代谢,是糖尿病患者最好的脂肪补充来源;此外,澳洲坚果油中富含的抗氧化剂,可以限制糖尿病患者体内的过氧化过程。美国著名的Jean Carper医学博士说:“油酸能降低血压,平衡血脂、血糖,使之正常”。难以经常就医的糖尿病患者,坚持饮食疗法是比较容易做到的事。", "夏威夷果油中富含的维生素E能起到一个“有益的类似抑制物的作用”。人体摄入饱和脂肪酸后,它们分解产生促进炎症的荷尔蒙;而当人体摄入夏威夷果油中的不饱和脂肪酸,它们将分解产生抑制炎症的荷尔蒙。因此长期食用夏威夷果油可以预防风湿性关节炎。", "在夏威夷果油的众多成分中,胡萝卜素和叶绿素赋予澳洲坚果油金黄透绿的独特色泽,而叶绿素起新陈代谢作用,促进细胞生长,加速伤口愈合,还有助于营养和美化皮肤,减少皱纹的产生。", "肠癌在另一项研究的研究人员证实,B一谷甾醇是存在于夏威夷果油中的一种植物脂肪,能阻止前列腺癌细胞的生长。最近的一项研究则表明:澳洲坚果油能预防肠癌,夏威夷果油同胃酸反应后能防止肠癌与直肠癌的攻击。", "夏威夷果补脑益智脑细胞由60%的不饱和脂肪酸和35%的蛋白质构成。因此,对于大脑的发育来说,需要的第一营养成分是不饱和脂肪酸。夏威夷果(澳洲坚果)中含有大量的不饱和脂肪酸,还含有15%——20%的优质蛋白质和十几种重要的氨基酸,这些氨基酸都是构成脑神经细胞的主要成分。坚果中对大脑神经细胞有益的维生素b1、b2、b6,维生素e及钙、磷、铁、锌等的含量也较高。因此,吃坚果对改善脑部营养很有益处,特别适合孕妇和儿童食用。", "夏威夷果的功效与作用", "补充脑营养。", "吃夏威夷果对改善脑部营养很有益处,特别适合孕妇和儿童食用。", "降低血压。", "经医学证明,夏威夷果油能很有效地降低血压。国外于1996年进行的一项研究中,16位血压偏高的妇女同意在一个月的时间里改吃夏威夷果油。结果,她们的血压平均从161/94降到了151/85,舒张压与收缩压几乎都降低了10毫米汞柱。", "缓解便秘。", "夏威夷果油还能够增加肠蠕动,使肠道畅通,并且具有温和轻泻剂的功效,可消除慢性便秘。早晚空腹服用两汤匙夏威夷果油对缓解慢性便秘具有不错的功效。", "预防血栓形成。", "长期食用夏威夷果油,人的血脂会控制在一个健康的状态下。由于澳洲坚果油对血脂的双相调节作用,能有效地降低血液粘稠度,从而防止动脉粥样硬化,可以有效地保护心脑血管系统。最新研究认为夏威夷果油有预防血栓形成的作用。", "治疗皮肤疾病。", "夏威夷果油是一种极好的治疗皮肤疾病的天然药物。根据皮肤科外用药治疗原则,用夏威夷果油制成的3种剂型对5类15种渗出性皮炎、溃疡疗效显著。", "防辐射。", "由于夏威夷果油含有多酚和脂多糖成分,所以夏威夷果油还有防辐射的功能,因此夏威夷果油常被用来制作宇航员的食品。经常使用电脑者更视其为保健佳品。", "防病、提高骨密度。", "夏威夷果油在预防这种疾病方面有非常积极的效果。摄取的夏威夷果油越多,骨骼的矿化就越好。夏威夷果油中所含的丰富油酸能促进机体对钙、磷、锌和其他矿物质的吸收,提高骨密度。", "调节血脂。", "吃夏威夷果调节血脂的作用也很好,非常适合老年人或血脂不好人的滋补食品。", "促进胆囊收缩。", "夏威夷果油能促进胆囊收缩,提高胰酶的活力,使油脂降解,被肠粘膜吸收。夏威夷果油对胆囊驰缓具有极佳的疗效。夏威夷果油与医生所开的药物和其他具有类似功效的食物相比,具有更快速的和长时间的疗效。", "抗癌。", "研究发现,澳洲坚果油能减少致癌物的酮衍生物在肠道形成肿瘤的速率。夏威夷果油的保护作用与其自身富含单不饱和脂肪酸、抗氧化剂以及其他微量元素有关。它们彼此协同作用,增强了保护效应。在防肿瘤食物中夏威夷果油的摄入(伴随蔬菜水果)特别重要。在经常食用夏威夷果油的人群里,各种癌症的发病率很低,特别是乳腺癌和胃肠系统的癌症的发病率明显低于其他人群。地中海地区由于食用夏威夷果油,肺癌的发病率比美国低50%。", "夏威夷果的热量", "夏威夷果热量:719卡路里/100克。", "广澳夏果笑口夏威夷果热量:736卡路里/100克。", "澳大利亚夏威夷果仁巧克力热量:590卡路里/100克。", "夏威夷果仁热量:718卡路里/100克。", "夏威夷果仁(干烤,添加盐)热量:716卡路里/100克。", "夏威夷果仁(干烤,无添加盐)热量:718卡路里/100克。", "夏威夷果仁(朱古力面层)热量:676卡路里/100克。", "719大卡热量相当于做以下运动所消耗掉的热量:", "走路182分钟。", "跑步91分钟。", "跳绳72分钟。", "健美操 155分钟。", "评价:", "夏威夷果是脂肪含量最高的坚果,有将近80%的脂肪。", "夏威夷果富含热量,热量摄取过多,多余的热量会以脂肪的形式储存起来,导致肥胖。所以夏威夷果不适合减肥的人使用。", "夏威夷果好吃吗", "澳洲的坚果夏威夷果在坚果里最为有名,它比核桃的营养还高,而且比核桃的外壳还硬,可是夏威夷果好吃吗?有什么样的特点呢?都说吃这个夏威夷果可以补脑,而且还能降脂,老少皆宜的一种食品,可是在味道口感上怎么样呢?夏威夷果好吃吗?", "夏威夷果营养丰富,味道甘美,香脆可口、风味独特,被誉为世界最高级的食用坚果。同时被广泛采用,制西饼及蛋糕。亦可用以佐膳及送酒。", "夏威夷果的食用特点", "原味:刚入口有一股油腥味,很清淡,口感清脆,非常香,比较符合南方人的口味,清香脆。", "盐焗味:经过调味的果仁口感更好,更加香脆,淡淡的咸味,更符合北方人的口味,咸香。", "奶油味:清香奶油口味,入口爽滑,回味无穷。比较受中老年人的青睐。", "注意:", "正因为夏威夷果富含单不饱和脂肪酸,所以它不仅有调节血脂血糖作用,可有效降低血浆中血清总胆固醇和低密度脂蛋白胆固醇的含量。但是由于单不饱和脂肪酸油脂往往有易氧化、产生过氧化物、安全性欠佳、易产生负面后果等缺点,所以建议你还是直接食用干果,而不去烹饪吃。", "夏威夷果怎么开"], "bs_rank_pos": 1}, {"title": "吃夏威夷果的好处和坏处 夏威夷果的食用禁忌_腾牛健康网", "segmented_title": ["吃", "夏威夷果", "的", "好处", "和", "坏处", "夏威夷果", "的", "食用", "禁忌", "_", "腾", "牛", "健康网"], "segmented_paragraphs": [["导读", ":", "在", "日常", "生活", "中", ",", "很多人", "都会", "喜欢", "吃", "夏威夷果", ",", "夏威夷果", "是", "很", "受欢迎", "的", "一", "种", "坚果", "食品", ",", "它", "含有", "丰富", "的", "营养", ",", "具有", "很好", "的", "食用", "价值", "。", "那么", "吃", "夏威夷果", "的", "好处", "和", "坏处", "有", "哪些", "呢", "?"], ["夏威夷果", "含有", "大量", "的", "不饱和脂肪酸", ",", "并", "含有", "15", "%", "-", "20", "%", "的", "优质", "蛋白质", "以及", "十", "多", "种", "重要", "的", "氨基酸", ",", "经常食用", "夏威夷果", "可以", "健脑", "益智", ",", "调节血脂", "血糖", 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"妇女如果每天有一餐食用夏威夷果油,能减少25%的癌症危险,如果用夏威夷果油代替其他食用油可减低50%的癌症。研究表明:单不饱合脂肪酸摄取较多的妇女患乳腺癌的比率大幅降低。B一谷甾醇是存在于夏威夷果油中的一种植物脂肪,能阻止前列腺癌细胞的生长,对预防肠癌很有效。", "夏威夷果中富含胡萝卜素喝叶绿素,叶绿素起促进新陈代谢的作用,能促进细胞生长,加速伤口愈合,还有助于营养和美化皮肤,减少皱纹的产生。夏威夷果油制成的3种剂型外涂,对5类15种渗出性皮炎、溃疡疗效显着。", "夏威夷果油有防辐射的作用,常被用来制作宇航员的食品,经常对着电脑的人也可以多吃夏威夷果抗辐射。", "夏威夷果油具有温和轻泻剂的功效,能够增加肠蠕动,使肠道畅通,帮助消除慢性便秘。早晚空腹服用两汤匙夏威夷果油对缓解慢性便秘具有不错的功效。", "适当吃夏威夷果没有坏处,但是过量吃会对健康产生以下坏处。", "夏威夷果是坚果炒货,果仁经烘香或油泡后,可提升口感,但却偏向燥热,多吃容易上火,出现长痘痘或者咽喉肿痛的情况。", "由于果粒坚硬,且含油脂、蛋白质丰富,不太容易消化,消化力弱人士吃太多会觉得梗胃,导致消化不良。", "1、澳洲坚果果仁香酥滑嫩可口,有独特的奶油香味,是世界上品质较佳的食用坚果,素有“干果皇后”“世界坚果之王”之美称,风味和口感都远比腰果好。 2、澳洲坚果营养丰富,含油量70%-79%,尤其以富含不饱和脂肪酸为特点;以油酸和棕榈酸为主,光壳种澳洲坚果种仁的不饱和脂肪酸与饱和脂肪酸的比值为6.2,粗壳种为4.8;蛋白质含量有9%,还含有丰富的钙,磷,铁,维生素B1、B2和人体必需的8种氨基酸。", "1、由于夏威夷果果粒坚硬,消化力弱多吃会觉梗胃,宜少吃。 2、嚼碎吞咽时,容易呛喉,而果屑藏存喉间摺壁内,会胀大发臭,很难清理。 3、果仁经烘香或油泡后,可提升口感,但却偏向燥热,引发豆豆、喉痛和痔疮。 4、夏威夷果富含单不饱和脂肪酸,所以它不仅有调节血脂血糖作用,可有效降低血浆中血清总胆固醇和低密度脂蛋白胆固醇的含量。但是由于单不饱和脂肪酸油脂往往有易氧化、产生过氧化物、安全性欠佳、易产生负面后果等缺点,所以建议你还是直接食用干果,而不去烹饪吃。", "夏威夷果果仁营养丰富,其外果皮青绿色,内果皮坚硬,呈褐色,单果重15—16克,含油量70%左右,蛋白质9%,含有人体必需的8种氨基酸,还富含矿物质和维生素。其具有调节血脂和益智作用,非常适合老年人或血脂不好人的滋补食用。", "温馨提示:以上内容整理于网络,仅供参考,如果对您有帮助,留下您的阅读感言吧!", "夏威夷果怎么吃 吃夏威夷果的注意事项 夏威夷果吃多了会上火吗 夏威夷果吃多了会怎么样", "这8种水果晚上不宜吃 最佳减肥水果排行榜前十名20172017-03-22 黑糖姜茶能空腹喝吗 早上空腹喝黑糖姜茶好吗2017-03-22"], "bs_rank_pos": 2}, {"title": "夏威夷果的营养价值 常吃夏威夷果的好处 - 三九养生堂", "segmented_title": ["夏威夷果", "的", "营养价值", "常吃", "夏威夷果", "的", "好处", "-", "三九", "养生堂"], "segmented_paragraphs": [["夏威夷果", "在", "我们", "的", "日常", "生活", "中", "应该", "是", "很少", "见到", "了", ",", "它", "是", "一", "种", "原", "产", "于", "澳洲", "的", "树", "生", "坚果", ",", "也称", "澳洲坚果", "或", "火山豆", ",", "味道", "香甜可口", "。", "夏威夷果", "的", "营养价值", "很高", ",", "含有", "丰富", "的", "钙", "、", "磷", "、", "铁", "、", "维生素B1", "、", "B2", "和", "氨基酸", ",", "有", "“", "干果", "皇后", "”", "的", "美味", "食品", ",", "那么", "你", "知道", "夏威夷果", "的", "食用方法", "有", "哪些", "吗", "?", "常吃", "夏威夷果", "有", "哪些", "好处", "呢", "?", "今天", "小", "编", "就", "来", "告诉你", "夏威夷果", "的", "一些", "相关知识", ",", "一起来", "看看", "吧", "。"], ["目录"], 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"(", "呈", "奶", "白色", ")", ",", "其", "外果皮", "青绿色", ",", "内果皮", "坚硬", ",", "呈", "褐色", ",", "单", "果", "重", "15", "—", "16", "克", "。", "夏威夷果", "营养成分", "极其", "丰富", ",", "其", "含油", "量", "高达", "60", "%", "-", "80", "%", ",", "还", "含有", "丰富", "的", "钙", "、", "磷", "、", "铁", "、", "维生素B1", "、", "B2", "和", "氨基酸", "。"], ["相关", "阅读", "推荐", ":"], ["鸡内金", "的", "食疗方法", "有", "哪些", "相信", "很多", "朋友", "都", "知道", "鸡内金", "这种", "食物", ",", "事实上", "它", "不仅", "是", ".", ".", "."], ["注射", "乙肝疫苗", "的", "注意事项", "很多", "的", "新生儿", "和", "大人", "们", "都会", "选择", "注射", "乙肝疫苗", "来", "预防", "乙", ".", ".", "."], ["得", "了", "胃癌", "会出现", "哪些", "症状", "什么", "是", "胃癌", "?", "很多人", "患", "上", "胃癌", "后", "就", "觉得", "自己", "已经", "没有", "多", ".", ".", "."], ["1", "饿", "了", "吗", "点", "的", "饭", "拿到", "手", "是", "臭", "的", "2", "保质期", "内", "的", "好丽友", "薯片", "生", "虫"], ["26岁", "小伙", "这里", "太", "臭", "竟", "招人", "嫌弃", "男人", "尿毒症", "换肾", "后", "还能", "活", "多久", "男人", "不可", "百日", "无", "姜", "竟是", "因为", "这", "一分钱", "都", "不", "花", "的", "中医", "补肾", "秘方"], ["多", "吃", "这种", "水果", "让", "你", "白皙", "如", "少女", "月经期间", "吃什么", "好", "排毒", "排污", "血", "女子", "天天", "喝", "这东西", "肾", "竟然", "坏", "了", "耳朵", "里", "长", "这", "物", "竟", "让", "她", "头晕", "不", "已"]], "paragraphs": ["夏威夷果在我们的日常生活中应该是很少见到了,它是一种原产于澳洲的树生坚果,也称澳洲坚果或火山豆,味道香甜可口。夏威夷果的营养价值很高,含有丰富的钙、磷、铁、维生素B1、B2和氨基酸,有“干果皇后”的美味食品,那么你知道夏威夷果的食用方法有哪些吗?常吃夏威夷果有哪些好处呢?今天小编就来告诉你夏威夷果的一些相关知识,一起来看看吧。", "目录", "夏威夷果是什么", "夏威夷果是什么呢,很显然夏威夷果肯定是来自于西方国家而不是我们国家的特产,夏威夷果不是一种水果,而是一种干果,我们将夏威夷果叫做是干果之王,可见夏威夷果的地位,夏威夷果我们也叫做是澳洲胡桃或者叫昆士兰果,夏威夷果的营养价值和经济价值是非常高的。", "夏威夷果本名叫澳洲坚果(拉丁学名:Macadamia ternifolia F. Muell.)。别名:昆士兰栗、澳洲胡桃、夏威夷果、昆士兰果,是一种原产于澳洲的树生坚果。澳洲坚果属常绿乔木,双子叶植物。树冠高大,叶3～4片轮生,披针形、革质,光滑,边缘有刺状锯齿。总状花序腋生,花米黄色,果圆球形,果皮革质,内果皮坚硬,种仁米黄色至浅棕色。适合生长在温和、湿润、风力小的地区。", "山龙眼科、澳洲坚果属植物在世界上众多的干果之中,澳洲坚果的经济价值最高,素来享有“干果之王”的誉称。分布区域:澳大利亚东部、新喀里多尼亚、印度尼西亚苏拉威西岛。此外澳洲坚果还具有很高的营养价值和药用价值。", "夏威夷果的营养价值", "澳洲坚果果仁澳洲坚果被认为是世界上最好的桌上坚果之一。他们含有高量的油,因此非常育肥。", "澳洲坚果营养丰富,含油量 70% -79%, 尤其以富含不饱和脂肪酸为特点,以油酸和棕榈酸为主,光壳种澳洲坚果种仁的不饱和脂肪酸与饱和脂肪酸的比值为6.2,粗壳种为4.8;蛋白质9% ,还含有丰富的钙,磷 ,铁,维生素B1、B2和人体必需的8种氨基酸。", "夏威夷果(澳洲坚果)果仁营养丰富(呈奶白色),其外果皮青绿色,内果皮坚硬,呈褐色,单果重15—16克。夏威夷果营养成分极其丰富,其含油量高达60%-80%,还含有丰富的钙、磷、铁、维生素B1、B2和氨基酸。", "相关阅读推荐:", "鸡内金的食疗方法有哪些 相信很多朋友都知道鸡内金这种食物,事实上它不仅是...", "注射乙肝疫苗的注意事项 很多的新生儿和大人们都会选择注射乙肝疫苗来预防乙...", "得了胃癌会出现哪些症状 什么是胃癌?很多人患上胃癌后就觉得自己已经没有多...", "1饿了吗点的饭拿到手是臭的 2保质期内的好丽友薯片生虫", "26岁小伙这里太臭竟招人嫌弃 男人尿毒症换肾后还能活多久 男人不可百日无姜竟是因为这 一分钱都不花的中医补肾秘方", "多吃这种水果让你白皙如少女 月经期间吃什么好排毒排污血 女子天天喝这东西肾竟然坏了 耳朵里长这物竟让她头晕不已"], "bs_rank_pos": 3}, {"title": "夏威夷果的营养价值及功效_百度经验", "segmented_title": ["夏威夷果", "的", "营养价值", "及", "功效", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ">", "健康", "/", "养生", ">", "保健养生"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["夏威夷果", "属于", "南方", "盛产", "的", "一", "种", "坚果", "类", "的", "营养", "果实", "。", "医生", "认为", "多", "吃", "夏威夷果", ",", "可以", "提高", "人体", "的", "免疫力", ",", "对于", "MM", "们", "常吃", "这个", "也是", "美颜", "的", "作用", "哦", "。"], ["夏威夷果", "的", "营养价值"], ["夏威夷果", "里", "富含", "丰富", "的", "营养物质", ",", "我们", "吃", "这个", "坚果", "的", "时候", "会", "发现", "这个", "果肉", "会", "非常", "的", "油", ",", "那", "是因为", "它", "里面", "的", "含油", "量", "将近", "有", "70", "%", "左右", ",", "而", "蛋白质", "含", "9", "%", ",", "还", "包含", "一些", "钙", "、", "磷", "、", "铁", "、", "维生素B1", "、", "B2", "和", "氨基酸", "等", "营养成分", ",", "对于", "孕妇", "这个", "还是", "少", "吃", ",", "不宜", "多", "吃", "。"], ["夏威夷果", "的", "功效"], ["长期", "的", "使用", "夏威夷果", "可以", "健脑", "益智", ",", "因为", "人体", "的", "的", "脑细胞", "是", "由", "60", "%", "的", "不饱和脂肪酸", "和", "35", "%", "的", "蛋白质", "构成", ",", "常吃", "夏威夷果", "对于", "大脑", "的", "发育", "是", "很不错", "的", ",", "因为", "夏威夷果", "里", "含有", "不饱和脂肪酸", ",", "还", "含有", "18", "%", "~", "30", "%", "的", "蛋白质", "和", "氨基酸", ",", "这些", "营养成分", "都是", "脑神经", "细胞", "所", "需要", "的", "主要", "成分", "。"], ["服用", "夏威夷果", "可以", "降血脂", ",", "减少", "血液", "中", "血浆", "的", "血清", ",", "调节", "血液", "的", "血脂", "、", "血糖", "营养价值", ",", "经常食用", "夏威夷果", "具有", "软化", "血管", ",", "降低", "心脏病", "、", "高血压", "发病率", "的", "作用", ",", "对", "人体", "健康", "十分", "有益", "。"], ["每个女人", "都", "想", "吃", "什么", "都", "要", "美颜", ",", "多", "吃", "夏威夷果", ",", "可以", "使", "肌肤", "起", "到", "保湿", "、", "增加", "皮肤", "活力", "的", "作用", ",", "对", "MM", "们", "的", "保健", "美容", "有", "显著", "功效", "。"], ["经验", "内容", "仅供参考", ",", "如果", "您", "需", "解决", "具体", "问题", "(", "尤其", "法律", "、", "医学", "等", "领域", ")", ",", "建议", "您", "详细", "咨询", "相关", "领域", "专业人士", "。"], ["个性签名", ":", "想要", "幸福", ",", "就要", "自己", "争取", "!", "!"]], "paragraphs": ["百度经验 > 健康/养生 > 保健养生", "百度经验:jingyan.baidu.com", "夏威夷果属于南方盛产的一种坚果类的营养果实。医生认为多吃夏威夷果,可以提高人体的免疫力,对于MM们常吃这个也是美颜的作用哦。", "夏威夷果的营养价值", "夏威夷果里富含丰富的营养物质,我们吃这个坚果的时候会发现这个果肉会非常的油,那是因为它里面的含油量将近有70%左右,而蛋白质含9%,还包含一些钙、磷、铁、维生素B1、B2和氨基酸等营养成分,对于孕妇这个还是少吃,不宜多吃。", "夏威夷果的功效", "长期的使用夏威夷果可以健脑益智,因为人体的的脑细胞是由60%的不饱和脂肪酸和35%的蛋白质构成,常吃夏威夷果对于大脑的发育是很不错的,因为夏威夷果里含有不饱和脂肪酸,还含有18%~30%的蛋白质和氨基酸,这些营养成分都是脑神经细胞所需要的主要成分。", "服用夏威夷果可以降血脂,减少血液中血浆的血清,调节血液的血脂、血糖营养价值,经常食用夏威夷果具有软化血管,降低心脏病、高血压发病率的作用,对人体健康十分有益。", "每个女人都想吃什么都要美颜,多吃夏威夷果,可以使肌肤起到保湿、增加皮肤活力的作用,对MM们的保健美容有显著功效。", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:想要幸福,就要自己争取!!"], "bs_rank_pos": 4}], "question": "夏威夷果的功效与作用", "segmented_question": ["夏威夷果", "的", "功效", "与", "作用"], "question_type": "DESCRIPTION", "fact_or_opinion": "FACT", "question_id": 221639}
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工伤医疗费报销比例是多少 工伤医疗费的内容有哪些?那么,工伤医疗费用报销比例是多少?下面就由律伴网小编 为大家整理的有关工伤医疗费", "律伴网(www.lvban365.net)律伴让法律服务更便捷!", "工伤医疗费报销比例是多少 工伤医疗费的内容有哪些?", "那么,工伤医疗费用报销比例是多少?下面就由律伴网小编 为大家整理的有关工伤医疗费的内容工伤医疗费用报销比例的相关内容,以供大家阅 读,希望对大家有所帮助。 工伤医疗费的内容 工伤医疗费包括医疗费用明细清单(包括:药物、检查、治疗、手术、化验等每项的名 称、用量、次数,单价,每项总价)、有效报销单据(有财政部门监制章或税务部门监制 章和就诊医院收费专用章)。工伤认定《工伤保险条例》规定:职工发生事故伤害,所 在单位应当自事故伤害发生之日起三十日内,向劳动保障部门提出工伤认定申请。 工伤医疗费用报销比例是多少 因工伤发生的下列费用,按照国家规定从工伤保险基金中支付: (一)治疗工伤的医疗费用和康复费用; (二)住院伙食补助费; (三)到统筹地区以外就医的交通食宿费; (四)安装配置伤残辅助器具所需费用; (五)生活不能自理的,经劳动能力鉴定委员会确认的生活护理费; (六)一次性伤残补助金和一至四级伤残职工按月领取的伤残津贴; (七)终止或者解除劳动合同时,应当享受的一次性医疗补助金; (八)因工死亡的,其遗属领取的丧葬补助金、供养亲属抚恤金和因工死亡补助金; (九)劳动能力鉴定费。 其中,工伤医疗康复类待遇相关规定如下: (1)治疗工伤的医疗费用和康复费用,包括治疗工伤所需的挂号费、医疗费、药费、住 院费等费用和进行康复性治疗的费用。律伴网(www.lvban365.net)律伴让法律服务更便捷! (2)住院伙食补助费和异地就医的交通食宿费。职工治疗工伤需要住院的,由工伤保险 基金按照规定发给住院伙食补助费;经医疗机构出具证明,报经办机构同意,工伤职工 到统筹地区以外就医的,所需交通、食宿费由工伤保险基金负担。 (3)护理费。生活不能自理的,经劳动能力鉴定委员会确认的生活护理费,由工伤保险 基金负担。生活护理费按照生活完全不能自理、生活大部分不能自理或者生活部分不 能自理三个不同等级支付,其标准分别为统筹地区上年度职工月平均工资的 50%、 40%和 30%。 (4)劳动能力鉴定费。劳动能力鉴定是职工配置辅助器具、享受生活护理费、延长停工 留薪期、享受伤残待遇等的重要前提和必经程序,因此产生的劳动能力鉴定费也由工 伤保险基金负担。《工伤保险条例》没有明确规定劳动能力鉴定费的负担问题,各省 规定也不尽相同。 如发生治疗工伤的医疗费用和康复费用、住院伙食补助费等法律规定的情形,则从工 伤保险基金中支付。以上就是律伴网小编为大家整理的有关工伤医疗费的内容工伤医 疗费用报销比例的相关内容。如还有其他疑问,欢迎在线咨询。", "文章来源:律伴网 http://www.lvban365.net/ 律伴让法律服务更便捷!"], "bs_rank_pos": 0}, {"title": "工伤保险报销比例是多少 工伤保险报销比例是多少?", "segmented_title": ["工伤保险", "报销", "比例", "是", "多少", "工伤保险", "报销", "比例", "是", "多少", "?"], "segmented_paragraphs": [["工伤保险", "报销", "比例", "是", "多少", "2017", "-", "06", "-", "12", "来自", ":", "网络"], ["工伤保险", "报销", "比例", "是", "多少"], ["由", "国务院", "劳动", "保障", "行政部门", "会同", "国务院", "卫生行政部门", "参保", "职工", "一旦", "认定为", "工伤", "。", "治疗", "工伤", "所需", "费用", "符合", "工伤保险", "诊疗", "项目目录", ",", "但是", "依然", "属于", "该", "条例", "中", "的", "劳动者", "、", 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"由国务院劳动保障行政部门会同国务院卫生行政部门参保职工一旦认定为工伤。治疗工伤所需费用符合工伤保险诊疗项目目录,但是依然属于该条例中的劳动者、工伤保险住院服务标准的,依法受该条例调整,是百分之百全额支付的。市内的定点医疗机构都可以进行直接结算、工伤保险药品目录。《中华人民共和国工伤保险条例》第六十一条规定,从工伤保险基金支付:“本条例所称职工。”这就是说,是指与用人单位存在劳动关系(包括事实劳动关系)的各种用工形式。工伤保险诊疗项目目录、工伤保险住院服务标准、各种用工期限的劳动者,你虽然是在试用期:“职工因工作遭受事故伤害或者患职业病进行治疗。该条例第二十九条规定。职工治疗工伤应当在签订服务协议的医疗机构就医,享受工伤医疗待遇,情况紧急时可以先到就近的医疗机构急救、工伤保险药品目录、药品监督管理部门等部门规定"], "bs_rank_pos": 1}, {"title": "工伤保险怎么报销、报销比例_工伤保险怎么交、缴费比例-华律网", "segmented_title": ["工伤保险", "怎么", "报销", "、", "报销", "比例", "_", "工伤保险", "怎么", "交", "、", "缴费比例", "-", "华", "律", "网"], "segmented_paragraphs": [["更新时间", ":", "2017", ".", "06", ".", "08", "10", ":", "57", ":", "24"], ["您", "了解", "工伤", "险", "吗", "?", "您", "知道", "工伤", "险", "的", "存在", "对于", "我", "国", "劳务", "工作", "人员", "而", "讲", "具有", "怎样", "的", "价值", "吗", "?", "工伤", "险", "是", "我", "国", "社会保险", "中", "至关重要", "的", "一类", "保险", "项目", ",", "它", "保障", "了", "我", "国", "劳务", "工作者", "的", "切身利益", "。", "当", "我们", "在", "工作", "中", "受到", "伤害", "时", ",", "它", "的", "存在", "能够", "很大", "程度", 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"工伤职工", "的", "民事赔偿", "请求权", "。"], ["受伤", "多久", "才能", "申请", "工伤保险", "呢", "?", "你好", ",", "从", "受伤", "日", "算", "起", "一年内", "都", "可以", "申请", "工伤认定", "。"]], "paragraphs": ["更新时间:2017.06.08 10:57:24", "您了解工伤险吗?您知道工伤险的存在对于我国劳务工作人员而讲具有怎样的价值吗?工伤险是我国社会保险中至关重要的一类保险项目,它保障了我国劳务工作者的切身利益。当我们在工作中受到伤害时,它的存在能够很大程度上缓解了这项伤害带给我们的经济压力,是对我们基本生活的一个保障。现在购买工伤险的人越来越多,也表现了我国社会保障体系的完善。所以,了解工伤险对于我们来说是很有必要的。本专题收集了工伤保险缴费和报销的一系列知识,希望对您有所帮助。", "1工伤保险怎么交 2工伤保险缴费比例 3工伤保险如何赔偿 4工伤保险赔偿标准 5工伤保险待遇及业务办理流程 6工伤保险怎么报销", "7工伤保险报销比例怎么算 8工伤保险报销范围 9单位拖延申报工伤保险怎么办", "工伤保险费如何缴纳 ?", "工伤保险费由用人单位缴纳,职工个人不缴纳。用人单位以本单位全部职工工资总额为缴费基数,本市工伤保险费缴费率按行业风险程度分为0.6%、1.2%、2.4%三个档次。缴费费率根据企业工伤保险费的使用、工伤发生率等情况定期进行调整。", "用人单位缴费基数如何确定?", "用人单位以上一年度全部职工月平均工资总额为缴费基数,职工个人月平均总额按本人上年度月平均工资收入计算。", "很多员工都对工伤保险缴纳的标准有疑惑,工伤保险费由社会保险经办机构按国家和本省有关规定征收。那么,2017年工伤保险多少钱呢?接下来由华律网的小编为大家整理了一些关于2017最新工伤保险缴费标准方面的知识,欢迎大家阅读!", "根据人力资源社会保障部 财政部 关于做好工伤保险费率调整工作进一步加强基金管理的指导意见,各统筹地区社会保险经办机构要严格按照《通知》规定的行业类别划分,根据用人单位的工商登记注册和主要经营生产业务等情况,分别确定其所对应的行业工伤风险类别。对劳务派遣企业,可根据被派遣劳动者实际用工单位所在行业,或根据多数被派遣劳动者实际用工单位所在行业,确定其工伤风险类别。", "工伤保险赔偿流程:", "1.首先用人单位应该在一个月内向劳动局提出工伤认定申请。 2.如果单位不申请的话,自己写份工伤认定申请,向当地劳动局申请工伤认定。提交的资料包括:工伤认定申请表; 与用人单位存在劳动关系(包括事实劳动关系)的证明材料;医疗诊断证明或者职业病诊断证明书。 3.劳动局做出工伤认定决定 4.伤者去有资质的医院做伤残鉴定(骨折的话最低也是10级伤残)", "一般工伤具体赔偿项目、标准", "1、要求:治疗工伤所需费用符合工伤保险诊疗项目目录、工伤保险药品目录、工伤保险住院服务标准。 2、法律依据:《工伤保险条例》第30条第3款。 3、备注:用人单位没有参加工伤保险的,不是必须到签有服务协议的医疗机构治疗。", "(二)住院伙食补助费、交通费、食宿费", "用人单位在员工受了工伤以后公司应该尽快帮员工办理工伤赔偿手续,比如进行工伤认定,接下来由华律网的小编为大家整理了一些关于员工受了工伤公司怎么办理工伤保险方面的知识,欢迎大家阅读!", "职工因工作遭受事故伤害或者患职业病进行治疗,享受以下工伤保险待遇:", "(一)由工伤保险基金支付的费用包括:", "1、治疗工伤的医疗费用和康复费用;2、住院伙食补助费;3、到统筹地区以外就医的交通食宿费;4、安装配置伤残辅助器具所需费用;5、生活不能自理的,经劳动能力鉴定委员会确认的生活护理费;6、一次性伤残补助金;7、一级至四级工伤人员伤残津贴;8、终止或者解除劳动(聘用)合同时应当享受的一次性医疗补助金;9、因工死亡职工的遗属领取的丧葬补助金、供养亲属抚恤金、一次性工亡补助金;10、工伤认定调查和劳动能力鉴定费用;11、工伤预防宣传和培训费用。", "一、发生工伤24小时内要向社会保险经办机构报送工伤报告表。 二、发生工伤单位应当自事故伤害之日或被诊断、鉴定为职业病之日起30日内,向劳动科提出工伤认定申请,用人单位未在规定的时限内提交工伤认定申请,在此期间发生符合工伤保险条例规定的工伤待遇等有关费用由该用人单位负担。 三、工伤保险报销所需材料", "(一)工伤认定决定书(过行政复议期后)。 (二) 发票原件(本人签字)。", "工伤职工受到事故伤害后,所在单位在第一时间电话通知或三日之内以书面形式向工伤经办机构报告,并填写《工伤职工就医诊治申请表》报经办机构批准。", "问:父亲是光明瓷厂的一名退休工人(有矽肺病),近几年经常住院,咨询一下报销比例是多少,光明瓷厂最近改制,我父亲的医药费是不是按100%比例报销;还有一件事咨询一下:厂里这一次为已经退休的职业病工人买了103000元,这是什么钱,还有职业病退休工人病故后,抚恤金和丧葬费是怎么计算的,谢谢,请详细回答。", "以下各种发生在劳动能力鉴定中心(以下简称市劳鉴中心)核准的工伤医疗期、康复期内的医疗费用,属于工伤医疗费用报销范围:", "1、工伤保险参保人在工伤协议医疗机构发生的与工伤相关的门(急)诊、住院的医疗费用。 2、工伤保险参保人发生工伤后遵循就近抢救的原则,在非工伤协议医院发生的与工伤直接相关的门(急)诊、住院的医疗费用。即工伤发生当日的门(急)诊、工伤发生当日起7天内的住院费用。", "发生工伤以后工伤应该对员工的工伤情况进行申报,但是很多用人单位不认定工伤,或者故意拖延工伤认定时间,以期待工伤认定无效的情况,这时候要是单位拖延申报工伤保险怎么办接下来由华律网的小编为大家整理了一些关于这方面的知识,欢迎大家阅读!", "由于用人单位故意拖延申报工伤时间,导致职工超过工伤认定申请时效,无法认定工伤。法院在查清事实的基础上,依据相关法规,判决用人单位赔偿职工应享受的工伤待遇。", "工伤保险请求权和民事赔偿请求权是两个不同的请求权,任何法律法规没有从实体上否定工伤职工的民事赔偿请求权。", "受伤多久才能申请工伤保险呢? 你好,从受伤日算起一年内都可以申请工伤认定。"], "bs_rank_pos": 2}, {"title": "工伤报销比例的计算方法", "segmented_title": ["工伤", "报销", "比例", "的", "计算", "方法"], 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"工伤职工受到事故伤害后,所在单位在第一时间电话通知或三日之内以书面形式向工伤经办机构报告,并填写《工伤职工就医诊治申请表》报经办机构批准。", "问:父亲是光明瓷厂的一名退休工人(有矽肺病),近几年经常住院,咨询一下报销比例是多少,光明瓷厂最近改制,我父亲的医药费是不是按100%比例报销;还有一件事咨询一下:厂里这一次为已经退休的职业病工人买了103000元,这是什么钱,还有职业病退休工人病故后,抚恤金和丧葬费是怎么计算的,谢谢,请详细回答。", "律师回复:", "1、目前正常缴费单位的工伤职工医药费用按景劳社[1997]5号规定:按费比例段报销:分别为95%、90%、80%和70%,差额部分由单位承担。 2、按《工伤保险条例》规定,用工单位一次性向工伤经办机构缴纳1—4级伤残职工的有关工伤保险费用,根据我市退休工残人员平均费用状况,暂定为103000元。 3、职业病退休工作病故后,按《工伤保险条例》享受相关待遇(2004年1月1日以前被鉴定为1—4级工伤的职工和退休人员,按景劳医[2006]4号文件精神执行,为社平工资的30个月)。", "推荐阅读:", "政策栏目编辑推荐", "工伤报销比例的计算 工伤报销比例的计算方法", "工伤保险报销标准", "工伤保险报销的范围 工伤报销比例的计算方法", "政策最新资讯 更多", "政策首页头条推荐"], "bs_rank_pos": 3}, {"title": "工伤保险报销比例", "segmented_title": ["工伤保险", "报销", "比例"], "segmented_paragraphs": [["文章", "阅读"], ["企业", "工伤保险", "缴费比例"], ["新注册", "的", "公司", "根据", "行业类别", "区分", "工伤保险", "缴纳", "的", "比例", ",", "需", "到", "社保", "中心", "核对", ",", "大概", "情况", "如", "下", ":", "1", "、", "企业", "属于", "第", "一", "行业", "类别", "的", ",", "缴费比例", "为", "0", ".", "5", "%", ";", "2", "、", "企业", "属于", "第", "二", "行业类别", "的", 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+{"documents": [{"bs_rank_pos": 0, "paragraphs": ["要根据具体皮肤状态进行选择针对性的护理,而不是专门看哪个牌子"], "segmented_title": ["伊索", "与", "antipodes", "哪个", "好", ",", "个人", "是", "混合", "敏感", "皮", ",", "内", "干", "外", "油", ",", "而且", "现在", "皮肤", "有"], "segmented_paragraphs": [["要", "根据", "具体", "皮肤", "状态", "进行", "选择", "针对性", "的", "护理", ",", "而", "不是", "专门", "看", "哪个", "牌子"]], "title": "伊索与antipodes哪个好,个人是混合敏感皮,内干外油,而且现在皮肤有"}, {"bs_rank_pos": 1, "paragraphs": [" Thanks for sharing the love of Antipodes Scientific Organic Skincare. Keep in touch #lovethelove ", " 有机用的较少,不过看着蛮不错 ", " 这两款哪款洗面奶比较好用 ", " 还挺不错 ", " 您需要登录后才可以回帖 登录 | 立即注册 "], "segmented_title": ["Trilogy", "和", "Antipodes", ",", "我", "的", "有机", "护肤", "时代", "!", "-", "晚", "九", "点", "-", "闺蜜", "网"], "segmented_paragraphs": [["Thanks", "for", "sharing", "the", "love", "of", "Antipodes", "Scientific", "Organic", "Skin", "care", ".", "Keep", "in", "touch", "#", "love", "the", "love"], ["有机", "用", "的", "较", "少", ",", "不过", "看着", "蛮", "不错"], ["这", "两", "款", "哪款", "洗面奶", "比较好", "用"], ["还", "挺不错"], ["您", "需要", "登录", "后", "才", "可以", "回帖", "登录", "|", "立即注册"]], "title": "Trilogy和Antipodes,我的有机护肤时代!-晚九点-闺蜜网 "}, {"bs_rank_pos": 2, "paragraphs": ["最近又想买一些澳洲本土的护肤品,但实在不知道如何下手,有没有人来八一下 natio,trilogy之类的,不是茱莉蔻,伊索那些大牌,感觉那些就是名气大,但效果一般,伊索用一次长一次痘,简直恨死的一个牌子。 本人混油皮,外油内干,有没有人有推荐?", " ty_就这样子吧 2016-09-12 13:51:49 ", " 有没有人听说过antipodes这个牌子,最近刚买也不知道怎么样,据说孕妇可用,有机纯植物", " 仙女小茜 2017-04-13 09:45:40 ", " 我用Natio爽肤水也刺痛,用了两次就没用了,感觉香味太刺激了,像烧香的味道。", " wendywwwww 2015-01-25 17:57:01 ", " 今天去试了一下trilogy的玫瑰水,感觉味道?很刺鼻,有点受不了,有人有其他牌子的玫瑰水推荐吗?味道淡一点,然后方便随身带的就好。", " 茜茜娜娜 2016-09-12 13:56:54 ", " 朱丽蔻只有精油还行。", " 妈望景顿 2017-05-13 12:56:57 ", " wendywwwww 2015-01-25 18:10:11 ", " 没有人理我么", " ohmycaicai 2015-01-25 18:37:32 ", " 我用natio的rehydrating toner 感觉有刺痛感TUT.. 但是我觉得Sukin的晚霜我用得挺顺的。。", " 碎碎念念念念念碎 2015-01-25 18:37:57 ", " trilogy的玫瑰精油特别好用 ", " 碎碎念念念念念碎 2015-01-25 18:39:12 ", " sukin洗面奶挺好用,但是那个水和乳我觉得太干,可能我没用霜吧=_=", " wendywwwww 2015-01-25 18:42:59 ", " 我用natio的rehydrating toner 感觉有刺痛感TUT.. 但是我觉得Sukin的晚霜我用得挺顺的。。 ----------------------------- 水我一直用sk2,没办法,混油皮的亲妈。你是什么肤质?sukin的晚霜会很油吗?", " wendywwwww 2015-01-25 18:38:26 ", " 小白在nz 2015-01-25 18:31:44 ", " 我在新西兰 用的是a开头的牌子也 。难道trilogy最有名的不是玫瑰精油么 natio据说是可以不卸妆过夜的那种", " -----------------------------"], "segmented_title": ["有没有", "人", "来", "八一", "下", "澳洲", "好", "用", "的", "护肤品", "啊", "?", "_", "娱乐八卦", "_", "天涯", "论坛"], "segmented_paragraphs": [["最近", "又", "想", "买", "一些", "澳洲", "本土", "的", "护肤品", ",", "但", "实在", "不知道", "如何", "下", "手", ",", "有没有", 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["我", "用", "natio", "的", "rehydrating", "toner", "感觉", "有", "刺痛", "感", "TUT", ".", ".", "但是", "我", "觉得", "Sukin", "的", "晚霜", "我", "用", "得", "挺", "顺", "的", "。", "。", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "水", "我", "一直", "用", "sk2", ",", "没办法", ",", "混", "油皮", "的", "亲妈", "。", "你", "是", "什么", "肤质", "?", "sukin", "的", "晚霜", "会", "很", "油", "吗", "?"], ["wendyw", "wwww", "2015", "-", "01", "-", "25", "18", ":", "38", ":", "26"], ["小白", "在", "nz", "2015", "-", "01", "-", "25", "18", ":", "31", ":", "44"], ["我", "在", "新西兰", "用", "的", "是", "a", "开头", "的", "牌子", "也", "。", "难道", "trilogy", "最", "有名", "的", "不是", "玫瑰精油", "么", "natio", "据说", "是", "可以", "不", "卸妆", "过夜", "的", "那种"], ["-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-"]], "title": "有没有人来八一下澳洲好用的护肤品啊?_娱乐八卦_天涯论坛"}, {"bs_rank_pos": 3, "paragraphs": ["2016-02-14 20:33:19", "《海外买什么?》系列上一篇反响还不错,于是我就让空姐妹妹再帮我到国外去挖点护肤宝贝回来。前段时间她飞澳洲(澳大利亚)、新西兰比较多,所以下面你看到的东西大部分是来自于那两个地方。这次拍摄的地点是在北京的某甜品店内,因为是她刚好有机会飞到北京过夜,然后和我短聚几小时而已,所以我们只能找了一个碰面地点拍摄。看,妹妹为了分享护肤也是蛮拼的!", " 澳洲和新西兰的护肤品主要还是以纯净、有机、自然为卖点,因为他们的自然环境实在是太可以称颂了,当年《魔戒三部曲》正是因为新西兰的森林植被和地理风貌保存非常好(也是导演家乡),才大量取景此地。 与欧美护肤品喜欢主打科技成分不同,澳洲和新西兰的护肤品以纯植物配方、天然护肤理念还有安全度高、性价比较高为优点,虽然品牌影响力没有欧美的大,但是从内容物上来说也是值得尝试的。 妹妹的肤质是混合偏油性肌肤,有些痘痘和痘印。1、Antipodes安媞珀奇异果籽精华眼霜30ml", "1、Antipodes安媞珀奇异果籽精华眼霜30ml Antipodes是什么品牌?Antipodes品牌于2005年由年轻母亲伊丽莎白?巴巴利奇(Elizabeth Barbalich)创立。有科学研究及理疗方面背景的巴巴利奇在位于新西兰惠灵顿市的自家厨房中开始了这一品牌的生产。曾获具有国际声誉的Agriquality认证的有机产品是该品牌至今最受消费者欢迎的产品。Antipodes所使用的主要成分包括:经认证的有机特级初榨鳄梨油、生长于热带雨林的卡瓦卡瓦(kawakawa,常用于毛利人传统医药的一种本地小型树木)、马马库黑蕨(mamaku)、马努卡蜂蜜以及被称作新西兰圣诞树的波胡图卡瓦(pohutukawa)。 这款眼霜是妹妹极力推荐的,说很滋润很好用不输于大牌,我想这种滋润感应该大部分来源于其中所含的奇异果精华、胡萝卜籽精华以及牛油果。这三样能很好地给肌肤滋养保湿效果,奇异果中的维他命C含量很高,可以美白和修复受损肌肤。 关键它还是眼霜中的“空中客车”(大容量30ml),比起一般的15ml足足多出一倍。价格大约300元左右,性价比还是可以的。 它的包装相对比较简单,连塑封都没有,单独纸外盒包装。淡绿色膏体,成分纯天然,不含香精和防腐剂,开封后的使用期限只有6个月。2、新西兰Antipodes天然麦卢卡蜂蜜面膜75ML 还是Antipodes家的东西,这款天然麦卢卡蜂蜜面膜由麦卢卡", " 还是Antipodes家的东西,这款天然麦卢卡蜂蜜面膜由麦卢卡蜂蜜、100%纯正的鳄梨油,新西兰圣诞树Pohutukawa和香草荚提炼而成。麦卢卡蜂蜜是新西兰毛利人常用于修复肌肤功能用,它还能滋润肌肤,新西兰圣诞树Pohutukawa含有天然的抗氧化剂、圣洁的香草和柑橘的香气,会带给人平和的感觉。", " 75ml的牙膏状包装,乳白色的膏体状,你可以做清洁面膜+保湿面膜使用,痘痘肌肤用起来会比较舒服,一开始会有些清凉的感觉,里面的蜂蜜可以消炎和修复痘痘。", " 这款面膜也是获奖无数,基本都是因为其是用的有机成分和滋润修复的卓越功效。3、新西兰Trilogy天然野玫瑰果油20ml ", " Trilogy品牌——争做低碳环保的护肤品 “给您的肌肤带来最大的效用,给环境留下最小的影响”是新西兰天然护肤品公司Trilogy的口号。 该公司于2003年由萨拉?吉布斯(Sarah Gibbs)和凯瑟琳?德?格鲁特(Catherine de Groot)姐妹俩在惠灵顿市首创,已由最初的精品小店发展成为一个受国际认可与关注的品牌。 该公司的37种产品在大洋洲、亚洲、英国与美国的3500多家门店销售,甚至连远在南极的斯科特基地(Scott Base)都使用该公司的产品。 Trilogy所有产品均为低碳产品,并采用可循环包装,其成分则源自正当交易的产品,并尽可能采用受认证的有机成分。", " 很多人不喜欢用油,觉得太油腻,肌肤触感不好,但实际上现在护肤品品牌都会将油的浓度或是粘稠度通过加入其他成分进行稀释,同时也增加油之外的其他功能成分,让油的使用感受和功效都不断完善变好。", " Trilogy经过认证的有机野玫瑰花果油是经过认证的有机野生玫瑰的花和果实的精华素,它可使用于人的面部和全身的皮肤。", " 它的分子很小,涂抹在肌肤上能很快被肌肤吸收,不留油腻的残留。妹妹买它主要是为了去痘印,因为玫瑰果油具有淡化疤痕,促进细胞新生、修复愈合作用。 玫瑰花果油并不是精油,所以相对植物精油来说,价格比较亲民,200元左右20ml容量。4、新西兰trilogy平衡净颜啫喱泡沫洁面乳", " 这是一款非常适合混合性肌肤偏油性或是油性肌肤的洗面啫喱,因为里面成分芦荟和黄瓜精华混合物,接骨木精华,紫草根,紫锥菊,中国柑橘,苦橙叶、葡萄柚籽精华,柠檬醛等物质都是控油植物成分。可以清洁脸部肌肤多余油脂,再加上维生素E、玫瑰果油、麦卢卡蜂蜜等进行修复和滋润作用,所以balancing gel的称呼是非常准确的。", " 挤出来就行是蜂蜜的金黄色,很好看。完全不含皂质和硫酸盐,以植物凝胶作为产品基底,泡沫量一般,并不是那种高泡型的。洗完后,在南方脸部肌肤不会有紧绷感。", " 5、Aesop/伊索樱草洁净敷面膜/樱草面膜", " Aesop品牌是由Emeis Cosmetics Pty Ltd所研发之系列产品,总部设立于澳洲墨尔本。植物及蔬菜的萃取物是其所有产品的主要成份,为保持产品中植物萃取的活性,Aesop使用天然的植物防腐剂搭配最少量的合法人工保存剂(仅为其他产品的10%)。其鲜明独特的黑色玻璃包装,就是用心良苦防止紫外线破坏商品成分,有助保持产品的新鲜和稳定性!这个品牌也是空姐妹子的新爱。大油皮痘痘脸怎么能缺少清洁面膜?清洁面膜一周用一次即可,除非是长时间在户外工作的,可以适当增加一次。清洁面膜一般都是由黏土作为清洁基础,然后再添加一些护肤成分。", " 这款伊索樱草敷面膜主打的是晚樱草.玫瑰天竺葵.鼠尾草成分,比较消炎镇定肌肤的作用。黏土取自法国,并且蕴含维他命E及多种能柔软肌肤的抗氧化活性植物精华,替皮肤作深层清洁,而不会令皮肤变得干燥。", " 请忽略上图已经用了一半的瓶子,空姐妹子说这个面膜清洁毛孔效果很好,有时候还会有白头浮出来,淡淡黄色膏体,用完后皮肤会变得软软的。6、LUSH Cynthia Sylvia Stout啤啤洗发露100g", " LUSH不是澳洲的也不是新西兰的,而是英国的,每次走进LUSH的店,就觉得充满梦幻的感觉,这些护肤品都像是糖果、食品一样充满诱惑。", " 这款俗称啤啤洗发露也是经过空姐妹子的油头鉴定过的,超过40%爱尔兰黑啤酒,其中还含有白兰地油、柠檬草、酵母菌、甜醋。貌似是为男士研发的,但是油头其实都适用。", " 啤酒能让头发柔顺亮丽,头发会很服帖听话,而白兰地油、柠檬草、甜醋能很好地控油,调理头皮,香气还不错。欢迎订阅微信:himolly00个人微信号:lovemollycm欢迎向我扔来变美的问题。。。", "赞 1", " 深有同感 0", " 抱抱 0", " 呵呵 0", " 沟通无望 0", " 等 1 个幸福客表达了自己的情绪"], "segmented_title": ["海外", "买", "什么", "?", "︱", "空姐", "妹子", "鉴定", "过", "的", "油皮", "救星", "们", "-", "我最爱", "的", "好", "用", "单", "品", "-"], "segmented_paragraphs": [["2016", "-", "02", "-", "14", "20", ":", "33", ":", "19"], ["《", "海外", "买", "什么", "?", "》", "系列", "上", "一篇", "反响", "还不错", ",", "于是", "我", "就让", "空姐", "妹妹", "再", "帮", "我", "到", "国外", "去", "挖", "点", "护肤", "宝贝", "回来", "。", "前段时间", "她", "飞", "澳洲", "(", "澳大利亚", ")", "、", "新西兰", "比较", "多", ",", "所以", "下面", "你", "看到", "的", "东西", "大部分", "是", "来自", "于", "那", "两", "个", "地方", "。", "这次", "拍摄", "的", "地点", "是", "在", "北京", "的", "某", "甜品", "店", "内", ",", "因为", "是", "她", "刚好", "有", "机会", "飞", "到", "北京", "过夜", ",", "然后", "和", "我", "短", "聚", "几", "小时", "而已", ",", "所以", "我们", "只能", "找", "了", "一", "个", "碰面", "地点", "拍摄", "。", "看", ",", "妹妹", "为了", "分享", "护肤", "也是", "蛮", "拼", "的", 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"一般", ",", "并", "不是", "那种", "高", "泡", "型", "的", "。", "洗", "完", "后", ",", "在南方", "脸部", "肌肤", "不会", "有", "紧绷", "感", "。"], ["5", "、", "Aesop", "/", "伊索", "樱草", "洁净", "敷", "面膜", "/", "樱草", "面膜"], ["Aesop", "品牌", "是", "由", "Emeis", "Cosmetics", "Pty", "Ltd", "所", "研发", "之", "系列", "产品", ",", "总部", "设立", "于", "澳洲", "墨尔本", "。", "植物", "及", "蔬菜", "的", "萃取", "物", "是", "其", "所有", "产品", "的", "主要", "成份", ",", "为", "保持", "产品", "中", "植物", "萃取", "的", "活性", ",", "Aesop", "使用", "天然", "的", "植物", "防腐剂", "搭配", "最", "少量", "的", "合法", "人工", "保存", "剂", "(", "仅", "为", "其他产品", "的", "10", "%", ")", "。", "其", "鲜明", "独特", "的", "黑色", "玻璃", "包装", ",", "就是", "用心良苦", "防止", "紫外线", "破坏", "商品", "成分", ",", "有助", "保持", "产品", "的", "新鲜", "和", "稳定性", "!", "这个品牌", "也是", "空姐", "妹子", "的", "新爱", "。", "大", "油皮", "痘痘", "脸", "怎么", "能", "缺少", "清洁", "面膜", "?", "清洁", "面膜", "一", "周", "用", "一次", "即可", ",", "除非", "是", "长时间", "在", "户外", "工作", "的", ",", "可以", "适当", "增加", "一次", "。", "清洁", "面膜", "一般", "都是", "由", "黏土", "作为", "清洁", "基础", ",", "然后", "再", "添加", "一些", "护肤", "成分", "。"], ["这", "款", "伊索", "樱草", "敷", "面膜", "主", "打的", "是", "晚樱草", ".", "玫瑰", "天竺葵", ".", "鼠尾草", "成分", ",", "比较", "消炎", "镇定", "肌肤", "的", "作用", "。", "黏土", "取", "自", "法国", ",", "并且", "蕴含", "维他命", "E", "及", "多", "种", "能", "柔软", "肌肤", "的", "抗氧化", "活性", "植物", "精华", ",", "替", "皮肤", "作", "深层清洁", ",", "而", "不会", "令", "皮肤", "变得", "干燥", "。"], ["请", "忽略", "上", "图", "已经", "用", "了", "一半", "的", "瓶子", ",", "空姐", "妹子", "说", "这个", "面膜", "清洁", "毛孔", "效果很好", ",", "有时候", "还会", "有", "白头", "浮", "出来", ",", "淡淡", "黄色", "膏", "体", ",", "用完后", "皮肤", "会", "变得", "软", "软", "的", "。", "6", "、", "LUSH", "Cynthia", "Sylvia", "Stout", "啤", "啤", "洗发露", "100", "g"], ["LUSH", "不是", "澳洲", "的", "也", "不是", "新西兰", "的", ",", "而是", "英国", "的", ",", "每次", "走进", "LUSH", "的", "店", ",", "就", "觉得", "充满", "梦幻", "的", "感觉", ",", "这些", "护肤品", "都", "像", "是", "糖果", "、", "食品", "一样", "充满诱惑", "。"], ["这", "款", "俗称", "啤", "啤", "洗发露", "也是", "经过", "空姐", "妹子", "的", "油", "头", "鉴定", "过", "的", ",", "超过", "40", "%", "爱尔兰", "黑啤酒", ",", "其中", "还", "含有", "白兰地", "油", "、", "柠檬草", "、", "酵母菌", "、", "甜醋", "。", "貌似", "是", "为", "男士", "研发", "的", ",", "但是", "油", "头", "其实", "都", "适用", "。"], ["啤酒", "能", "让", "头发", "柔顺", "亮丽", ",", "头发", "会", "很", "服帖", "听话", ",", "而", "白兰地", "油", "、", "柠檬草", "、", "甜醋", "能", "很好", "地", "控油", ",", "调理", "头皮", ",", "香气", "还不错", "。", "欢迎订阅", "微信", ":", "himolly", "00", "个人", "微信", "号", ":", "love", "molly", "cm", "欢迎", "向", "我", "扔", "来", "变", "美", "的", "问题", "。", "。", "。"], ["赞", "1"], ["深", "有", "同", "感", "0"], ["抱抱", "0"], ["呵呵", "0"], ["沟通", "无望", "0"], ["等", "1", "个", "幸福", "客", "表达", "了", "自己", "的", "情绪"]], "title": "海外买什么?︱空姐妹子鉴定过的油皮救星们 - 我最爱的好用单品 - "}, {"bs_rank_pos": 4, "paragraphs": [" 最好是性价比比较高的。水,乳液,洗面奶balabala........", " 以前有朋友在澳洲读书,也一起做过澳洲代购,说到澳洲的护肤品,就随意说说好了。", "我没有太多的心得可提供,但是可以给大家一个大致的方向,具体产品心得可能还需要做做功课。", "总体而言,我觉得澳洲的护肤品不是那么出众,不是不能替代,如果生活在澳洲,必定想要多多买买本土的东西,但是不是生活在澳洲的话,可能对澳洲的护肤品兴趣没有那么强烈。", " 这个算是很出名的单品,我自己以前也是用的这个。要提醒的是淘宝上有出售一些杂七杂八的绵羊油,可能并不是澳洲本土大家用的那种。还算挺滋润的,我之前用来抹身体不错,便宜大碗。", "2.Swisse家一些的保健品,这个胶原蛋白液也是挺出名的,我自己喝了一瓶,实在是坚持不了每天服用就放弃了,效果嘛,我没有太大感觉。", "3.羊奶皂,洗澡挺不错的,味道也不错。当时朋友从澳洲给我带的。但是我记得那会包装不是这个样子,不过很多年前了,可能包装改了,我在淘宝搜到的照片基本都是这个样子的。", "4.木瓜万能膏。说实在话味道真不好,很油,使用感不太好,但是我觉得蚊虫叮咬什么的抹了确实不错,其他那些神奇功效没发现。也是朋友给我带的。", "5.茱莉蔻,大名鼎鼎的牌子了。手霜。化妆水喷雾,精华之类的都是一些明星产品。味道也是特别好闻,号称是敏感肌肤,孕妇都可以用。想买澳洲产品的可以去一些论坛或者app上专门搜搜这个牌子的产品,看看大家的使用心得。", "6.伊索,也是非常有名的澳洲品牌,主打一些植物系的纯露精油植物油,我个人对这个品牌很心生向往。香港海港城的连卡佛有个很大的专柜。以前每次去香港的时候都会很想要大采购一番,可惜这个牌子在各种法国护肤品日本护肤品当中还是太小众了。", "7.trilogy 应该也算小众牌子了,他家玫瑰果油算是明星产品。我一直也有兴趣买一瓶,做按摩油也好,混面霜一起用应该也很好。 ", "8.ultraceuticals,更小众的澳洲药妆品牌。", "9.moogoo ,澳洲算比较平价的牌子。", "纯属个人意见,不喜轻拍,若有用词不当之处,敬请告之,谢谢阅读。", "若有什么想要咨询,可加微信 2363480813", " 来到澳洲你会发现,那些大牌护肤品也许并不是首选,那些本土的 护肤品也能成为你的心头好。知道下面这些本土牌子,就不会只在 Jurlique 或者 Aesop 的专柜前徘徊了。在澳洲这样一个无限接近大自然的地方,他们出产的护肤品可谓性价比极高,不需要花费大量的金钱也可以购买到非常好品质的产品!下面是澳洲非常有特色的本土护肤品牌的大集合,快收藏~", " 图片来源:Lush 作者:俗人链接:这些澳洲本土护肤品牌你都知道吗?来源:矮油论坛-矮油网 著作权归作者所有。商业转载请联系作者获得授权,非商业转载请注明出处。 ", " 天然草本:Aesop ", " 图片来源:【猫】说说澳洲本土品牌【Aesop伊索】以及混合皮秋冬该品牌产品的选择 1987年在澳大利亚墨尔本成立,和茱莉蔻一样,致力于天然草本护肤品。伊索从来不做任何宣传和推广,这也是为什么名声没有茱莉蔻大,知道的人也没有知道茱莉蔻的多。但是,相比之下,伊索的性价比比较高。", " 品牌伊索 (Aesop),名字起源于“伊索寓言”。伊索 (Aesop) 在天然植物护肤的基础上率先推行有机理念,并结合内外调理的“理智”生活哲学,是美容界的“偏力”品牌。伊索 (Aesop) 的品牌创办人 Dennis Paphitis 从发型师起家,一贯主张“爱护环境=保护自己”,少用污染性化学产品。", " 推荐Aesop 家的樱草洁面敷面霜 ,这是 Aesop 最火的产品。以及香芹籽抗氧化活肤调理液,被称为 Aesop 家的“梦幻产品”。属于高机能化妆水,不含酒精。官网链接:Home page", " 天然护肤:Jurlique ", " 生物化学家兼自然疗法医生 Dr. Jurgen Klein 和他的妻子与1985年在墨尔本创立了茱莉蔻。澳大利亚天然草本护肤品的代表,知名度也很高。所有产品95%的原料都来自澳大利亚南部阿德莱德的 Mylor 活机农场种植的植物,因此被认为是“世界上最天然纯净的护肤品”。", " Jurlique 的创始人克莱恩博士明白,“不可能制造出纯天然的保养品”,所以他们决定从纯天然原料的栽培开始,确保生产“纯正天然的健康保养品”。 Jurlique 的有机园种植各种芳香疗法的草药,其中最出名的,当属玫瑰、薰衣草、洋甘菊、金盏花。", " 天然平价:Sukin ", " 图片来源:https://twitter.com/nzhealthfoodco Sukin 是澳洲本土自产的天然生态有机护肤品牌,一直以提供最高标准的天然护肤,护发等美容产品为准则。这个品牌从原料的采购开始,就以高标准来要求自己。他们的天然植物浓缩精华,各种植物提取油等都是澳洲本土生产,保证了产品的质量和带来的效果。从原料采购开始的纯澳洲本土产品,平价大碗又好用。", " 更难能可贵的是,Sukin的产品在澳洲本土的价格十分可爱,在药房出售,容易找到也容易上手。针对不同的肌肤,他们也有不同系列来应对,让您可以针对自己的肤质切实地选择适合的产品。", " 他们家值得推荐的产品有:天然有机草本沐浴露,天然有机洁面泡沫,天然有机抗氧化眼霜。", " 有机头牌:Trilogy ", " 图片来源:https://www.trilogyproducts.com/ingredients/ 来自新西兰,经世界认可的有机护肤品牌,野玫瑰果油风靡全球,全功能修复皮肤,连防腐剂都采用全天然配方。", " Trilogy 是新西兰有机化妆品品牌,确切地说应该是\"有机美容产品\"品牌。产品主要市场为澳洲,欧洲,北美,日本。新西兰各大化妆品店及药品店都有销售。Trilogy 所有产品均为100%纯植物制品,不添加任何人工化学成分,即使最敏感肌肤也可使用。", " Trilogy 100%有机野玫瑰果油,Trilogy的明星产品,几乎每个钟爱 Trilogy 的人都是最先使用有机野玫瑰果油而爱上 Trilogy 的。Trilogy 100%有机野玫瑰果油能够深层保湿,促进肌肤光滑美白,使皮肤色素淡化,重现均匀色调,使皮肤平滑。", " 手工浴球:Lush ", " 图片来源:5 Lush Products That Will Change Your Life, Hair and Skin Lush 是澳大利亚知名的新鲜自然环保护肤品牌。Lush其实是英国品牌,崇尚自然环保,在全球很多国家设有加工厂,在澳洲有很多门店。 他们的团队对品牌的贡献已经有逾二十年的历史,但一直都以不断出现的新鲜创意为目标。以护发产品为开始,他们一直以来为全世界的品牌比如 Body Shop 供应新鲜配方的护肤产品设计。最具特色的是手工香氛浴球,放一颗在泡澡缸里,回味好多天。配方天然,香气自然。", " 但他们自身则一直以小规模的手工工厂为基础,因为他们的目的就是提供新鲜手制的产品。随着产品越来越被大众所认识,他们的香水,泡浴产品等经典系列已经获得了广大的好评,是很多澳洲人日常都会选购的必需品。坚持手工艺信念的他们,并不愿用机器生产来扩大产量,而是一直保持产品的高品质和原汁原味。特别是他们的原料,你可能发现全世界的 Lush 产品颜色都不尽相同。因为在不的地区,他们都采用当地最新鲜的原料,确保质量。", " 药妆主打:Dr.Lewinn's ", " 图片来源:澳洲本土四大天王护肤品推荐! -6park.com 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来到拜阳台,远远可以看见丹霞山的标志--阳元石,由于相隔不远处有天造地设的阴阳二物,因此生殖器崇拜在丹霞山理所当然。", " 对生殖器的崇拜是自古以来人类的图腾,丹霞山有如此逼真雄伟的男性生殖器,难免成为生殖崇拜的胜地。", " 一位女子正在祭拜阳元石,来此祭拜宏伟男根的,大多是女子,据说日本女子来到这里,情不自禁跪地祭拜。", " 传说在拜阳台参拜阳元石,拜过之后欲儿得儿,欲女得女。阳元石下的断石村,全村男丁旺盛,据考证全村生男孩的比例占75%以上,生双胞胎的10%以上,又名“多仔村”.", " 广东省政府副秘书长、省人口计生委主任张枫曾建议给丹霞山阳元石“戴套”,宣传性文化和广东旅游,不过,国土部门以“不利于保护地质地貌”的反对理由否决了这个“金点子”.", " 关于阳元石,当地有三句顺口溜:“不可明知故问,不可实物比较,不可自愧不如”,而一般导游的介绍是:“阳元石,女人看了兴奋,男人看了惭愧。”", " 大自然确实鬼斧神工,但从地质科学的角度讲,阳元石只不过是一个普通的石柱而已。", " 除了阳元石、阴元石,可能很多人还不知道,丹霞山居然还有双乳石,只是阳元石和阴元石确实是惟妙惟肖,但双乳石就显得有点牵强了。", " 我们顺着指示牌找了好久,才终于在合适的角度拍到这张照片,如果不是有长焦镜头,一般是拍不出来的。", " 人类的性崇拜主要分为生殖崇拜、性交崇拜和生殖器崇拜,在中国貌似主要以生殖崇拜和生殖器崇拜为主,而丹霞山主要又是生殖器崇拜。丹霞山太神奇了,大自然的造化在这里显得天衣无缝,如此逼真的阴阳二物,我想,在其他任何地方都是难以找到的。", " 鬼斧神工丹霞山阴元石", " 丹霞山阴元石", " 鬼斧神工阴元石", " 鬼斧神工阴元石", " 去年我慕名来到广东丹霞山旅游,现将其主要景观阳元石与阴元石介绍如下: 广东丹霞山位于仁化县城南9公里,离韶关市51公里。丹霞山有着神奇瑰丽的丹霞地貌,逶迤飘逸的锦水飞泉,梦幻迷离的仙山琼阁……是地理学界“丹霞地貌”的命名地。是世界地质地貌景观中的瑰宝,是大自然鬼斧神工的杰作。特别是丹霞山的阳元石与阴元石更加神奇,大自然杰作出奇得让人不敢相信又不能不信。 号称天下第一奇石的阳元石,位于阳元山景区与丹霞山主景区隔江相望。该石高28米,直径7米。由于风化作用,活脱一具男性阴茎直傲苍穹。 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用上网,手机中会保留一个由Wi-Fi连接分配的网络IP地址,它会被应用到当前Wi-Fi中,如果没有出现地址冲突,就不用担心,如果有冲突就得关闭并重启Wi-Fi连接。方法如下: 打开手机菜单,进入“设置”栏,选择“无线和网络设置”,进入“Wi-Fi设置”项目,在新窗口中选择“关闭Wi-Fi”,在完成关闭操作后,重新进入该项目,选择“打开Wi-Fi”,即可重启Wi-Fi连接。 第二招:忽略网络 如果上面的做法没有起作用的话,就应该选择让安卓设备忽略该网络,并重新进行添加操作。 移动到需要忽略网络的覆盖范围之外,选择忽略该网络,返回到覆盖范围之中,等待网络自动显示出来,然后输入网络密码重新登入。这样安卓手机就应该可以获得新的地址,并正常工作了。 第三招:检查Wi-Fi的密码 通常情况下,Wi-Fi的密码应该是定时更改的;但是,当网络进行这样的调整时,手机不会自动更新,需要手动更新Wi-Fi的密码。 移动到出现问题网络...", "上网帮助相关知识", "确定举报此问题", "报告,这不是个问题", "这不是个问题  这个问题分类似乎错了  这个不是我熟悉的地区"], "bs_rank_pos": 0}, {"title": "手机连不上wifi是怎么回事 简单五步解决_百度经验", "segmented_title": ["手机", "连不上", "wifi", "是", "怎么回事", "简单", "五步", "解决", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ">", "生活", "/", "家居", ">", "生活常识"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["手机", "连不上", "wifi", "是", "一", "件", "很郁闷", "的", "事情", ",", "不能", "随意", "地", "畅游", "网络", "真", "的", "让", "人", "很", "恼火", "!", "那么", "手机", "连不上", "wifi", "是", "怎么回事", "?", "稍安勿躁", ",", "接下来", ",", "小", "编", "就", "为", "大家", "带来", "手机", 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这是最普遍存在的问题,有可能只是一时连接陷入死循环,最简单的解决方法就是拔插一下路由器电源接口,让路由器重启,这样能解决90%路由器引起的问题。当然也有可能是路由器硬件故障,这只能送去修理。现在路由器价格在50-200元之前,并不昂贵。 此外还有可能是路由器配置问题,这就要看路由器自身是否能连上网络,假如无法链接网络,就要对路由器重新配置。 END", "百度经验:jingyan.baidu.com", "1 一般出现这种问题都是在工作环境,或者是提供免费wifi的咖啡厅,商场等公共场所才会出现。假如没有更改路由器设置的权限,只能尽量避免在联网高峰期上网,或者是提前连上网络。假如出现了IP冲突的情况,就要在路由器中对各个连接设备进行IP分配,以避免互相造成影响。 此外还有一些个例,比如说中国电信赠送的天翼路由器,是被官方强行锁死了路由功能,因此想要用天翼路由器进行wifi上网,就必须先解锁才行。 END", "百度经验:jingyan.baidu.com", "1 无线路由器的无线信号会受到外界影响而削弱,最直接的就是距离,一般普通的路由器超出十米信号就会变得很差。所以建议在连接wifi的时候尽可能地靠近信号源,以增强wifi信号。此外,如果你跟无线路由器之间有介质阻隔,比如说墙壁,信号也会变得很差。 END", "百度经验:jingyan.baidu.com", "1 手机接收wifi信号都是依靠手机内的无线网卡来实现的。有的手机无线网卡已经快被淘汰或者是严重老化,都会造成wifi信号接收不良,甚至一个电话打进来就会导致无线网断网,这种情况只能拿到维修中心去修理解决。 END", "百度经验:jingyan.baidu.com", "1 基带就是负责通讯方面的模块,往往一部手机有多个基带可供选择。假如你在刷ROM的时候刷了一个不合适的基带,就有可能造成你的wifi连接不正常,甚至是短信发不出去,电话也打不出去。此外如果ROM自身不完善,那么也会存在wifi连接不正常的问题。比如说安卓4.1刚一发布,有的机友就刷了测试版的CM10(基于4.1框架的第三方ROM),结果无法连接上wifi,这就必须重新刷一个比较完善的ROM来解决。 END", "百度经验:jingyan.baidu.com", "1 以上就是小编对手机连不上wifi是怎么回事的解答,相信大家对此已经有所了解了。只要根据你自己遇到的实际情况找到问题所在,所有的问题就可以迎刃而解了。祝大家生活愉快! 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"iCloud", "下载", "在", "哪", "、", "怎么", "用", ",", "iCloud", "(", "Windows", "版", ")", "怎么", "更新", "?", "仿宋", "gb", "2312", "和", "仿宋", "有", "什么", "区别", "?", "电脑", "没有", "安装", "仿宋", "gb", "2312", "字体", "怎么办", "?", "Mac", "迅雷", "下载速度", "为", "0", "解决方法", "mac", "迅雷", "下载", "速度", "慢", "怎么办", "Office", "Home", "and", "Student", "是什么", "软件", ",", "有", "什么", "用", "?", "Office", "Home", "and", "Student", "怎样", "激活", "?"]], "paragraphs": ["首页 → 西西教程→ 其它教程→ 手机连不上wifi是怎么回事 2015手机连不上wifi解决办法", "手机连不上wifi是一个经常会出现的问题,造成原因有多个,那么对于我们的小白朋友来说如何解决手机连不上wifi的问题了,小编为您整理了一些常见问题和解决办法!", "这是最普遍存在的问题,有可能只是一时连接陷入死循环,最简单的解决方法就是拔插一下路由器电源接口,让路由器重启,这样能解决90%路由器引起的问题。当然也有可能是路由器硬件故障,这只能送去修理。现在路由器价格在50-200元之前,并不昂贵。", "此外还有可能是路由器配置问题,这就要看路由器自身是否能连上网络,假如无法链接网络,就要对路由器重新配置。", "多台设备同时连接", "一般出现这种问题都是在工作环境,或者是提供免费wifi的咖啡厅,商场等公共场所才会出现。假如没有更改路由器设置的权限,只能尽量避免在联网高峰期上网,或者是提前连上网络。假如出现了IP冲突的情况,就要在路由器中对各个连接设备进行IP分配,以避免互相造成影响。", "此外还有一些个例,比如说中国电信赠送的天翼路由器,是被官方强行锁死了路由功能,因此想要用天翼路由器进行wifi上网,就必须先解锁才行。", "超出信号覆盖范围", "无线路由器的无线信号会受到外界影响而削弱,最直接的就是距离,一般普通的路由器超出十米信号就会变得很差。所以建议在连接wifi的时候尽可能地靠近信号源,以增强wifi信号。此外,如果你跟无线路由器之间有介质阻隔,比如说墙壁,信号也会变得很差。", "无线网卡故障", "手机接收wifi信号都是依靠手机内的无线网卡来实现的。有的手机无线网卡已经快被淘汰或者是严重老化,都会造成wifi信号接收不良,甚至一个电话打进来就会导致无线网断网,这种情况只能拿到维修中心去修理解决。", "基带或ROM问题", "基带就是负责通讯方面的模块,往往一部手机有多个基带可供选择。假如你在刷ROM的时候刷了一个不合适的基带,就有可能造成你的wifi连接不正常,甚至是短信发不出去,电话也打不出去。此外如果ROM自身不完善,那么也会存在wifi连接不正常的问题。比如说安卓4.1刚一发布,有的机友就刷了测试版的CM10(基于4.1框架的第三方ROM),结果无法连接上wifi,这就必须重新刷一个比较完善的ROM来解决。", "从手机屏幕最上面拉下通知栏,再切换到“开关”:", "可以看到“WLAN”和“数据”都已经打开,再触控灰色的“飞行模式”:", "“飞行模式”马上就会变成高亮了,而“WLAN”和“数据”都已变成灰色的:", "然后,再重新关闭“飞行模式”,再重新打开“WLAN”或“数据”,应该可以上网了。", "如果还不行,则尝试先关闭WIFI,再重新打开。因为手机不网络环境有变的情况下可能是某些网络信息没有同步更新过来:", "还不行,则检查下所用的路由器信道问题,应该将路由器无线信道设置为常用的信道,如6和9较常用。苹果设置对信道要求貌似比较严格:", "如果还不行,则建议将手机和无线路由器都重新启动一下看看:", "(40)wifi热点  西西软件园提供大量免费的热点软件,现在很多厂商都发布了随身,可以给笔记本设置热点,然后供手机等电子设备无线上网,看电视剧电影啥的不用担心卡顿,再也不用担心流量超标或不够用啦。现在猎豹小米等都开发了随身,如果不想花钱买这些产品,那么以下的电脑热点软件就很适合大家啦...更多>>", "01-27平安WiFi 有什么用 支持哪些热点 是不是免费的 01-27ios版平安WIFI用不了 暂无卡可用等常见问题 01-27平安WIFI怎么用 平安wifi免费使用图文教程 01-08WiFi万能钥匙iOS正版安装使用教程 IOS8-8.1越狱版失败解决办法 01-06青青草原wifi热点设置方法 青青草原wifi热点使用教程 12-24Connectify创建WIFI无线热点图文教程 12-11手机qq热聊怎么用 qq wifi热聊加入方法 12-01WiFi万能钥匙安卓手机版怎么用?WiFi万能钥匙使用教程 11-23新路由newifi插件大全 新路由newifi插件推荐 11-10360随身WiFi 4G版怎么用?360 4G版随身WiFi体验评测", "火狐浏览器所有的快捷键大全 Windows版iCloud下载在哪、怎么用,iCloud(Windows版)怎么更新? 仿宋gb2312和仿宋有什么区别?电脑没有安装仿宋gb2312字体怎么办? Mac迅雷下载速度为0解决方法 mac迅雷下载速度慢怎么办 Office Home and Student是什么软件,有什么用?Office Home and Student怎样激活?"], "bs_rank_pos": 6}, {"title": "手机连不上家里的无线网怎么办,前两天还可以的!_百度知道", "segmented_title": ["手机", "连不上", "家里", "的", "无线网", "怎么办", ",", "前两天", "还", "可以", "的", "!", "_", "百度", "知道"], "segmented_paragraphs": [["1", "、", "这种情况", "一般来说", "是", "你", "的", "路由器", "的", "问题", ",", "你", "拔掉", "路由器", "的", "电源", ",", "几秒钟", "后", "再", "插", "回去", ",", "应该", "可以", "解决", "问题", "。", "2", "、", "如果", "还是", "连接", "不", "上", ",", "在", "手机", "里", "点", "你", "WIFI", "的", "名称", ",", "选择", "不保存", ",", "然后", "关闭", "手机", "的", "WLAN", ",", "几秒钟", "后", "再", "开启", "WLAN", "功能", ",", "应该", "可以", "解决", "问题", "。"], ["无线网", "要", "连", "手机", ",", "得", "打开", "wlan", "后", ",", "能", "搜索", "到", "你", "的", "家", "的", "无线网络", ",", "你", "家", "的", "网络", "是", "公开", "的", "吗", "?", "如果", "不是", ",", "连接", "是", "需要你", "设定", "的", "无线网", "密码", ",", "正确", "后", ",", "连接", "成功", "才", "可以", "用", "的"]], "paragraphs": ["1、这种情况一般来说是你的路由器的问题,你拔掉路由器的电源,几秒钟后再插回去,应该可以解决问题。 2、如果还是连接不上,在手机里点你WIFI的名称,选择不保存,然后关闭手机的WLAN,几秒钟后再开启WLAN功能,应该可以解决问题。", "无线网要连手机,得打开wlan后,能搜索到你的家的无线网络,你家的网络是公开的吗?如果不是,连接是需要你设定的无线网密码,正确后,连接成功才可以用的"], "bs_rank_pos": 7}], "question": "手机wifi连不上", "segmented_question": ["手机", "wifi", "连不上"], "question_type": "DESCRIPTION", "fact_or_opinion": "FACT", "question_id": 221647}
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"如果上边的链接失效,请打开网盘: https://pan.baidu.com/s/1o8Tte8i", "记得按一下左上角保存。", "卡西欧的手表大同小异,如果你想找你的机芯说明书,", "可以打开 http://support.casio.com/cn/manual/manuallist.php?cid=009这个网页,输入你后盖的机芯4位号码。", "更多精彩文章,可以访问biaoshu8.com", "声明:本文由入驻搜狐公众平台的作者撰写,除搜狐官方账号外,观点仅代表作者本人,不代表搜狐立场。", "本文相关推荐", "casio+g+shock怎么调时间  g-shock手表怎么调闹钟  卡西欧g-shock怎么设置  白色的g-shock怎么清洗  日本买回的g-shock调时间  g-shock&vans特别款  g-shock如何看真假  g-shock表带更换图解  g-shock电子时间不正确  g-shock香港专门店  g—shock5230  g+shock漆擦掉了", "还没有评论,快来抢沙发吧!"], "bs_rank_pos": 2}, {"title": "卡西欧G一SHOCK手表怎么调时间日期_百度知道", "segmented_title": ["卡西欧", "G", "一", "SHOCK", "手表", "怎么", "调", "时间", "日期", "_", "百度", "知道"], "segmented_paragraphs": [["在", "计时", "模式", "下", ":", "(", "数显", "时间", ")", "1", ".", "按住", "左", "上", "不动", "约", "三秒", ",", "手表", "显示", "TYO", "在", "闪烁", ",", "按", "右上", "三", "下", "是", "BJS", "或", "BKG", "。", "2", ".", "按一下", "左", "下", "键", "夏令时", "DST", "关闭", "状态", ",", "再", "按", "左", "下", "键", "若是", "12", "H", "按", "右", "下", "键", "变成", "24H", "。", "按", "左", "下", "键", "按", 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"否则", ",", "日期", "和", "星期", "将", "不能", "正常", "转换", ",", "或", "导致", "手表", "操作失常", "。"], ["“", "万", "表", "世界", "”", "官方", "微信"]], "paragraphs": ["卡西欧G-SHOCK说明书大全,卡西欧怎么调时间", "防震一直都是被许多喜欢运动的表迷选择手表的一个重要的标准,而卡西欧G-SHOCK就是这样一款拥有超强的防震能力。卡西欧G-SHOCK,手表的品牌之一,具有傲人的强悍耐用、防水等特点。前面的G代表重力gravity。1983年至今,卡西欧G-SHOCK手表以创新,抗震概念和强大功能设计,卡西欧G-SHOCK的抗震传奇已经进入第25年,卡西欧G-SHOCK一如卡西欧一贯以来所倡导的“超越梦想,挑战极限”的品牌精神将不断被沿袭和传播。G-SHOCK被创造出来,源于一个简单的信念:创造一个永远不会摔坏的手表。接下来就让我们看看卡西欧G-SHOCK说明书大全,卡西欧G-SHOCK说明书讲解怎么样?", "卡西欧gshock手表说明书:上发条的使用方法", "1、首先,为gshock手动上弦装置的自动机械表。 2、通常情况下,当gshock手表戴在手上通过手腕的震动能使手表的主发条上弦。以及通过转动转柄来上弦。 3、当gshock手表完全停止走动时,通过甩动手表可使手表走动,但最好请通过手动转动转柄来使发条充分上满弦,以及调整日期和时间。 手动上弦时,把转柄转到0段位置,慢慢向右回转(12点方向)为主发条上弦。转柄向左方向(6点方向)是为空转,不能为发条上弦。另外,发条上满弦后可能会出现打滑的情况,不必担心损害手表的零件情况。 4、gshock手表一段上满弦,可持续走动41小时。 若gshock手表在未上满弦的状态下使用,则手表会出现或慢或快的现象。为避免此现象的发生,最后每天戴10个小时以上,若不将手表戴在手腕上而使用手表时,则务必每天在一个固定的时间为其上满弦。", "若gshock手表在发条未上弦的状态下停下里,即使转动转柄给发条上弦也不可能使手表立刻启动。这是因为机械表独有的特性造成主发条的转矩(动力)在转动初期处于微弱的状态。持续转动主发条后,当转矩达到一定的强度后秒针开始转动。当然,若想使手表快些启动,可甩动使手表强行转动。", "以上这款表,带有显示日期和星期的功能。日期在凌晨0但转换,星期在凌晨4点转换。但,如果上午和下午设定不正确的话,日期在白天的12点,星期在下午的4点转换。", "1、将转柄拉出到1段。 2、转动转柄可设定日期和星期。转柄向右转动设定星期,向左转动设定日期。调整到想设定的日期和星期的前一日出现为止(如:想设定的日期是“6号”,就调整到“5号”)。星期有两国语言表示,请选择自己需要的语言。 3、当秒针走到“12点”的位置时,把转柄拉出到第二段。(秒针停止转动)。转动转柄使向前移动直到日期出现下一天也就是想要设定的日期出现。此时时间被设定到上午时间段。继续移动指针以设定正确的星期和时间。 4、按照点钟报时信号,将转柄推回到正常的0段位置并开始走动。", "当2月(一个月为28天,闰月为29天)和小月(一个月为30天)时,需要进行调整日期。", "例:小月的第二天1日的早晨。调整日期时,显示的是31日而不是1日。请把gshock转柄拉出到第一段,把转柄向左旋转,日期显示到1日,然后推回转柄。", "注意:勿在晚上9点到凌晨4点之间的时间带设定日期和星期。否则,日期和星期将不能正常转换,或导致手表操作失常。", "“万表世界”官方微信"], "bs_rank_pos": 4}], "question": "g-shock 怎么调时间", "segmented_question": ["g", "-", "shock", "怎么", "调", "时间"], "question_type": "DESCRIPTION", "fact_or_opinion": "FACT", "question_id": 221648}
+{"documents": [{"bs_rank_pos": 0, "paragraphs": ["百度经验:jingyan.baidu.com", " 如果驱动程序安装盘是以可执行文件方式提供,则最简单直接运行SETUP.exe就可以按照其安装向导提示一步一步完成。", " 如果只提供了驱动程序文件,则安装相对麻烦。这里以Windows XP系统为例介绍。", " 百度经验:jingyan.baidu.com", " 1 首先打开控制面板,然后双击控制面板中的打印机和传真图标。如下图  步骤阅读 2 接着弹出如下窗口:  步骤阅读 3 这个窗口将显示所有已经安装了的打印机(包括网络打印机)。安装新打印机直接点左边的添加打印机,接着弹出添加打印机向导, 步骤阅读 4 点下一步,出现如下窗口询问是安装本地打印机还是网络打印机,默认是安装本地打印机: 步骤阅读 5 如果安装本地打印机直接点下一步,系统将自动检测打印机类型,如果系统里有该打印机的驱动程序,系统将自动安装。如果没有自动安装则会报一个错,点下一步出现如下窗口: 步骤阅读 6 这里一般应使用默认值,点下一步,弹出询问打印机类型的窗口:  步骤阅读 7 如果能在左右列表中找到对应厂家和型号,则直接选中然后点下一步;如果没有则需要我们提供驱动程序位置,点从磁盘安装,然后在弹出的对话框中选择你驱动程序所在位置,比如软驱,光盘等,找到正确位置后点打开(如果提供位置不正确,点打开后将没有相应,暗示你重新选择),系统将开始安装,然后系统提示你给正在安装的打印机起个名字,并询问是否做为默认打印机(即发出打印命令后,进行相应的那一台),如下图: 步骤阅读", " 经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", " 个性签名:尘世中一个迷途的小书童!!!"], "segmented_title": ["怎样", "安装", "打印机", "驱动程序", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["如果", "驱动程序", "安装盘", "是", "以", "可执行文件", "方式", "提供", ",", "则", 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":", "尘世", "中", "一", "个", "迷途", "的", "小书童", "!", "!", "!"]], "title": "怎样安装打印机驱动程序_百度经验"}, {"bs_rank_pos": 1, "paragraphs": ["经常有网友反应说下载的驱动不知道怎么安装,经分析多为解压后没有setup.exe文件的驱动,通常这种驱动我称它为inf驱动,虽然本站有不少关于打印机驱动安装方面的教程,但还是满足不了各网友的需求,今天就再跟大家啰嗦下inf驱动的安装方法, 首先将打印机与电脑用USB线连接好,将电源打开,并下载好驱动,这里小编以OKI 7700F+为例,向大家讲解 大家可以看下解压后的文件夹内并没有setup.exe文件(如下图:) 下面来说下安装方法:", " 第一种:用微软提供的 DPInst 驱动自动安装卸载程序来安装,点这里下载DPInst 下载后和驱动放在同一文件夹内,并点击右键选择 “解压到当前文件夹”解压后文件夹内会多出来几个文件(如下图:) 运行setup.exe 点击下一步(如下图:) 稍等一下,如出现“Windows 无法验证此驱动程序软件的发布者,点 始终安装此驱动程序软件”等待安装完成后,点击完成(如下图:) 重起一下电脑或把打印机关了再开 第二种:通过添加打印机的方法来安装 依次点击开始菜单——设备和打印机——添加打印机 添加本地打印机(如下图:) 选择打印机端口USB001(USB虚拟打印端口) 这个就是,并点确定(如下图:) 接下来点从磁盘安装(如下图:) 点浏览选择驱动所在目录,并点打开,并点确定(如下图:) 如果该驱动支持多个型号的打印机,则会出现多个型号以供选择(如下图),如果该驱动仅支持该型号则会自动跳到下一步 按下来系统就会自动安装驱动,点击完成即可。 第三种:通过更新设备驱动的安装方法: 这种方法与方法二有点相似,详见:http://www.dyjqd.com/articie/5931.html 第四种:网络打印机的安装方法,点击次数: 更新时间:2015-06-05 【打印此页】 【关闭】 hp order a cartridge 安装惠普打印机出现“新设备 打印任务无法删除怎么办? 打印机自动打印之前的页面的 安装驱动提示错误“文件的哈 HP打印机驱动安装失败的解决 打印机驱动安装方法 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+{"documents": [{"bs_rank_pos": 0, "paragraphs": ["在我们日常的生活中大家对于尿素氮偏高是否有足够的了解呢,那么大家知道尿素氮偏高的原因是什么吗,尿素氮偏高是怎么回事呢,尿素氮偏高的治疗是怎么样的呢,下面就让我们一起来了解一下吧。", " 在测量肾功能的指标中,有两个指标是非常重要的一个是尿素氮还有一个就是血肌酐了,这两个数值的高与低都会影响到我们肾脏的正常功能。", " 尿素氮和血肌酐同是肾功能的两个最重要的指标,尿素氮数值的高与低反映着肾脏功能的好与坏。尿素氮数值高于正常值即是尿素氮偏高。", " 正常值", " 正常成人空腹尿素氮为3.2-7.1mmol/L(9-20mg/d1)。各种肾实质性病变,如肾小球肾炎、间质性肾炎、急慢性肾功能衰竭、肾内占位性和破坏性病变均可使血尿素氮增高。", " 大多数是由于肾脏外的因素导致的血尿素氮的升高的情况,如果大家能够排除肾病之外的因素的话,那么就能很好的诊断疾病的原因了。", " 原因一 肾性偏高见于急性肾炎、慢性肾炎、中毒性肾炎、严重肾盂肾炎、肾结核、肾血管硬化症、先天性多囊肾和肾肿瘤等引起的肾功能障碍。 原因二 肾前性偏高见于充血性心力衰竭、重度烧伤、休克、消化道大出血、脱水、严重感染、糖尿病酸中毒、肾上腺皮质功能减退、肝肾综合征等。 原因三 肾后性偏高见于因尿路梗阻增加肾组织压力。所以,为明确病因,最好跟医生咨询过后再决定治疗手段。 尿素氮偏高的症状 症状一 尿素氮出现偏高的一个症状就是尿路梗阻造成的肾组织压力增加,会使得肾小球的压力出现降低的情况,前列腺增大,肿瘤也是会导致尿素氮增高的。", " 症状一 尿素氮出现偏高的一个症状就是尿路梗阻造成的肾组织压力增加,会使得肾小球的压力出现降低的情况,前列腺增大,肿瘤也是会导致尿素氮增高的。 单纯的尿素氮升高可以因为饮食中摄入过多的蛋白质类食物引起,改变饮食结构,平衡蛋白质、含糖类食物和蔬菜的搭配,很快就可以恢复正常。 症状二 肾前性偏高见于充血性心力衰竭、重度烧伤、休克、消化道大出血、脱水、严重感染、糖尿病酸中毒、肾上腺皮质功能减退、肝肾综合征等。 症状三 肾性偏高见于急性肾炎、慢性肾炎、中毒性肾炎、严重肾盂肾炎、肾结核、肾血管硬化症、先天性多囊肾和肾肿瘤等引起的肾功能障碍。 尤其是对尿毒症的诊断有特殊价值,其增高程度与病情严重性成正比,如氮质血症期BUN超过9mmol/L,至尿毒症期BUN可超过20mmol/L,通过这些数据的有效判断,可以确保确诊的准确性。", "相关阅读推荐:", "盐水洗头去头屑吗 干净清爽的头发给外在形象加了很多分,所有头发如果...", " 早上起来口苦吃什么 日常生活中很多人都会遇到早上起来口苦的现象,很多...", " 皮肤粗糙怎么改善效果好 皮肤粗糙是一种让人十分头疼的事情,很多朋友在遇到...", " 1典典的外婆凉皮馄饨里吃出大虫 2超市购买的鸡蛋吃起来像塑料 ", " 这些长寿第一物你都经常吃吗 每天这样喝水至少多活二十年 这六个健康养生习惯是在找病 晚餐吃这物小心竟会诱发癌症", " 经常吃这五类食物小心会变丑 一粒南瓜子竟能够治疗这些病 喝酒时吃这东西小心肝脏中毒 这篇秋季养生三字经值得收藏", " 男性要如何来自测前列腺炎 男人有这症状是更年期来临 男人吃这些食物身体更强壮 男人肾虚需要常吃这些东西", " 女人吃这物淡化皱纹还瘦身 教你一些小妙招轻松去黑头 女性吃这些食物可滋养子宫 女性月经提前会有什么危害"], "segmented_title": ["尿素氮", "偏高", "的", "原因", "这些", "原因", "导致", "尿素氮", "偏高", "-", "三九", "养生堂"], "segmented_paragraphs": [["在", "我们", "日常", "的", "生活", "中", "大家", "对于", "尿素氮", "偏高", "是否", "有", "足够", "的", "了解", "呢", ",", "那么", "大家知道", "尿素氮", "偏高", "的", "原因", "是", "什么", "吗", ",", "尿素氮", "偏高", "是", "怎么回事", "呢", ",", "尿素氮", "偏高", "的", "治疗", "是", "怎么样", "的", "呢", ",", "下面", "就", "让", "我们", "一起来", "了解一下", "吧", "。"], ["在", "测量", "肾功能", "的", "指标", "中", ",", "有", "两", "个", "指标", "是", "非常重要", "的", "一", "个", 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"滋养", "子宫", "女性", "月经", "提前", "会", "有", "什么", "危害"]], "title": "尿素氮偏高的原因 这些原因导致尿素氮偏高 - 三九养生堂"}, {"bs_rank_pos": 1, "paragraphs": ["尿素氮编辑 锁定 ", "即指“血尿素氮”,血浆中除蛋白质以外的一种含氮化合物,它从肾小球滤过而排出体外。在肾功能不全失代偿时,BUN将升高。所以临床以将其作为判断肾小球滤过功能的指标。", "中文名 尿素氮  外文名 BUN ", " 具体 肾功能主要指标  氮含量 28/60 ", " 1 简述  2 尿素氮的正常值  3 代谢过程及影响因素 ", " 4 尿素氮增高  ▪ 病因  ▪ 治疗  5 尿素氮偏低 ", " ▪ 病因 6 饮食控制 ", "尿素中的氮,尿素浓度可以尿素或尿素氮表示,国内惯用后者。肝脏将氨基酸代谢生成的有毒的氨,水解为无毒的UN,是其解毒功能之一,血清尿素氮(BUN)由肾小球过滤排出。参考值:2.9～7.5mmol/L(8～21mg/dl)。BUN升高,称氮质血症,见于肾功能不全,但在肾小球过滤率(GFR)降低达50%时才可见其升高,敏感性较差。消耗性疾病、消化道出血、脱水等肾外因素和高蛋白饮食也可使BUN升高,故它不是评估GFR的最好指标。常用酶学方法测定,有试剂盒供应。[1] 尿素氮正常值为2.86-7.14 mmol/L,测定值会因饮食的内容而有所变动。[2] ", "尿素氮是人体蛋白质代谢的主要终末产物。氨基酸脱氨基产生NH3和CO2,两者在肝脏中合成尿素,每克蛋白质代谢产生尿素0.3g。尿素中氮含量为28/60,几乎达一半。通常肾脏为排泄尿素的主要器官,尿素从肾小球滤过后在各段小管均可重吸收,但肾小管内尿流速越快重吸收越少,也即达到了最大清除率。和血肌酐一样,在肾功能损害早期,血尿素氮可在正常范围。当肾小球滤过率下降到正常的50%以下时,血尿素氮的浓度才迅速升高。正常情况下,血尿素氮与肌酐之比(BUN/Scr)值约为10.1,高蛋白饮食、高分解代谢状态、缺水、肾缺血、血容量不足及某些急性肾小球肾炎,均可使比值增高,甚至可达20～30;而低蛋白饮食,肝疾病常使比值降低,此时可称为低氮质血症。", "各种肾实质性病变,如肾小球肾炎、间质性肾炎、急慢性肾功能衰竭、肾内占位性和破坏性病变均可使血尿素氮增高。多肾外因素也可引起血尿素氮升高,如能排除肾外因素,BUN),21.4mmol/L(60mg/dL)即为尿毒症诊断指标之一。", "较易受饮食、肾血流量的影响,如有蛋白质分解因素—感染,肠道出血、甲亢等可使尿素氮升高。肾小球滤过率下降至正常的1/2~~1/3时,尿素氮逐步升高,一般情况下血尿素氮与血肌酐的比值是10.1,比值升高的原因有胃肠道出血,溶血,心功能不全和组织分解增强(烧伤、高热、肾上腺皮质激素治疗等),多为肾前因素引起,比值降低见于蛋白质摄入过少,严重肝肾功能不全等。", "3、蛋白质分解或摄入过多 如急性传染病、高热、上消化道大出血、大面积烧伤、严重创伤、大手术后和甲状腺功能亢、高蛋白饮食等,但血肌酐一般不升高。以上情况矫正后,血BUN可以下降。", "血肌酐〈Scr〉和尿素氮〈BuN〉两者分别为含氮的有机物和蛋白质代谢的终末产物,在肾功能正常的情况下,这些小分子物质从肾小球滤出,故可用作肾小球滤过功能的诊断和过筛指标。当肾小球滤过功能减低时,血肌酐和尿素氮 因潴留而增高。", "肾病血肌酐、尿素氮高是以微小循环损伤,肾小球硬化和基底膜损伤为主要病理变化的。解决尿素氮高,就必须从这种病理原因出发,对因治疗才能彻底。", "1、肾功能失调。尿素氮偏低,可能与蛋白质吃得太少、怀孕、肝衰竭有关。2、肝功能衰竭。肝脏是人体重要的代谢器官,肝功能衰竭,造成营养物质不能正常吸收。另一个原因是患者蛋白质摄入不够,再加上因肝功能不正常而大量消耗。", "造成尿素氮偏低的原因有多个,因此,患者如果出现了尿素氮偏低,一定要到正规医院进行检查,不能笼统地把尿素氮偏低的原因理解成肾功能失调,从而使自己过度恐慌。因此,为了防止受到不必要的惊吓,最好先做个肝功能检查,确诊好病因后,再对症下药,避免盲目治疗,从而耽误治疗时间。", "1.肾功能不全失代偿期Ccr20-50ml/min. 蛋白质应控制在45-50g, 蛋白质应以优质蛋白。应尽量少进植物蛋白,因其含非必需氨基酸较多有条件,米、面中所含的植物蛋白质最好设法除去。要应用含有优质的蛋白质,如瘦肉、鱼,鸡蛋等食物。2.保证共给热成分热量,以减少体内蛋白库的消耗。每日宜供应30Kca/Kg体重 的热量,主要由碳水化合物和脂肪供给。饮食不佳的病人可给予中药二陈汤(半夏、茯苓、陈皮、甘草) 并加入谷芽、神曲等消导药。 食物应注意给含B族维生素和维生素C丰富的食物。", "参考资料 1. 罗慰慈.协和医学词典:北京医科大学中国协和医科大学联合出版社,1998 2. 尿素氮肌酐高严重吗,用什么法子可以治? .肾病健康咨询网.2012-05-28[引用日期2012-010-4]", " 词条统计 浏览次数:次 编辑次数:42次历史版本 最近更新: 创建者:kazezero"], "segmented_title": ["尿素氮", "_", "百度百科"], "segmented_paragraphs": [["尿素氮", "编辑", "锁定"], ["即", "指", "“", "血", "尿素氮", "”", ",", "血浆", "中", "除", "蛋白质", "以", "外", "的", "一", "种", "含", "氮化合物", ",", "它", "从", "肾小球滤过", "而", "排出体外", "。", "在", "肾功能不全", "失", "代偿", "时", ",", "BUN", "将", "升高", "。", "所以", "临床", "以", "将", "其", "作为", "判断", "肾小球滤过", "功能", "的", "指标", "。"], ["中文名", "尿素氮", "外文", "名", "BUN"], ["具体", "肾功能", "主要指标", "氮", "含量", "28", "/", "60"], ["1", "简述", "2", "尿素氮", "的", "正常值", "3", "代谢", "过程", "及", "影响因素"], ["4", "尿素氮", "增高", "病因", "治疗", "5", "尿素氮", "偏低"], ["病因", "6", "饮食", "控制"], ["尿素", "中", "的", "氮", ",", "尿素", "浓度", "可以", "尿素", "或", "尿素氮", "表示", ",", "国内", "惯用", "后者", "。", "肝脏", "将", "氨基酸", "代谢", "生成", "的", "有毒", "的", "氨", ",", "水解", "为", "无毒", "的", 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盲僧有一种操作被称为光速摸眼回旋踢,其要义就是在第二段Q造成伤害之前就按出插眼W这两个动作。须知,盲僧第二段Q冲向目标也就不到1秒的时间,在这1秒时间内完成插眼以及W两个动作,不是听上去那么容易完成的。一般来讲,第二段Q造成伤害后使用W,是常规摸眼回旋踢的做法。 步骤阅读 5 如此操作后,使用R技能,就完成了摸眼回旋踢的操作。至于R闪回旋踢,其要义在于使用R的瞬间按出闪现,这样,目标就会被踢向与你闪现位置指向目标方向一致。盲僧的R技能可以让踢飞的目标击中敌方英雄造成AOE伤害,所以闪现的位置亦可以根据局势进行选择。 步骤阅读 END", "百度经验:jingyan.baidu.com", " 回旋踢此招,勤加练习才能更为熟练的掌握。 摸眼的位置以及闪现的位置需要根据局势来进行选择。", " 经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "segmented_title": ["LOL", "盲僧", "回旋踢", "摸", "眼", "R", "如何", "操作", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["盲僧", "是", "LOL", "最", "受欢迎", "的", "英雄", "之", "一", ",", "下面", "我", "来", "介绍一下", "盲僧", "的", "招牌", "套路", "回旋踢", ",", "究竟", "是", "怎样", "进行", "操作", "的", "。"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["1", "盲僧", "回旋踢", "一般", "有", "摸", "眼", "回旋踢", "和", "R", "闪", "回旋踢", "两种", ",", "我", "个人", "主张", "有", "闪现", "的", "时候", "尽量", "使用", "R", "闪", "回旋踢", ",", "很多人", "总是", "喜欢", "保留", "闪现", ",", "尤其", "是", "低端局", "玩家", "。", 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"夫妻感情", "确", "已", "破裂", "。", "此时", "是因为", "一方", "过错", "而", "导致", "离婚", "的", ",", "那么", "在", "具体", "进行", "财产分割", "的", "时候", ",", "该", "如何", "分", "呢", "?", "下面", ",", "我们", "一起来", "看看", "相关", "介绍", "吧", ".", "阅读全文", "夫妻", "离婚", "财产分割", "夫妻", "是", "因为", "爱情", "选择", "彼此", "结合", ",", "通过", "法律", "的", "形式", "而", "成为", "的", "。"], ["400", "-", "676", "-", "8333", "(", "周一", "至", "周五", "8", ":", "30", "-", "18", ":", "00", ")"], ["微信", "扫一扫", ",", "关注", "找", "法网"]], "title": "夫妻共同财产一方死亡时如何分割_找法网(Findlaw.cn)"}, {"bs_rank_pos": 5, "paragraphs": ["今日律师风向标:", "婚后一方去世,其婚前的个人财产的一半会变为夫妻共同财产,另一半变为遗产按顺序继承。如果希望保护婚前财产归其父母,是否可以在婚前写遗嘱,一旦婚后去世,婚前财产还是归一方父母,遗嘱需要公证吗?", "wl1542irnc 北京- 北京 2016-10-24 15:38 ", " 专业擅长: 离婚、债权债务、合同法 服务热线: 18310609527 ", " 继承: 根据《继承法》第五条 继承开始后,按照法定继承办理;有遗嘱的,按照遗嘱继承或者遗赠办理;有遗赠扶养协议的,按照协议办理。如果没有遗嘱那么应当按照法定继承办理继承事宜。根据《继承法》第十条 遗产按照下列顺序继承:第一顺序:配偶、子女、父母。第二顺序:兄弟姐妹、祖父母、外祖父母。继承开始后,由第一顺序继承人继承,第二顺序继承人不继承。没有第一顺序继承人继承的,由第二顺序继承人继承。根据《继承法》第十三条规定,同一顺序继承人继承遗产的份额,一般应当均等。对生活有特殊困难的缺乏劳动能力的继承人,分配遗产时,应当予以照顾。对被继承人尽了主要扶养义务或者与被继承人共同生活的继承人,分配遗产时,可以多分。有扶养能力和有扶养条件的继承人,不尽扶养义务的,分配遗产时,应当不分或者少分。继承人协商同意的,也可以不均等。", " 2016-10-24 16:35 0 ", " 专业擅长: 刑事辩护、婚姻家庭、房地产 服务热线: 13910491366 ", " 婚前财产不会变为夫妻共同财产。", " 2016-10-24 16:35 0 ", " 专业擅长: 征地补偿、拆迁 服务热线: 13910447988 ", " 您好,可以写遗嘱,公正遗嘱的效力最强,建议公正遗嘱,也可以其他方式立遗嘱,如有需要请来电咨询。", " 2016-10-24 16:51 0 ", " 专业擅长: 离婚、合同法、公司法 服务热线: 18301622545 ", " 你好,可以的 2016-10-24 17:01 0 ", " 专业擅长: 离婚、债权债务、房地产 服务热线: 15330148095 ", " 您好,建议公证也建议不公正 2016-10-24 17:11 0 ", " 专业擅长: 继承、债权债务、损害赔偿 服务热线: 13801026893 ", " 不公证也是有效的 2016-10-24 17:30 0 ", " 专业擅长: 继承、离婚、公司法 服务热线: 18810671972 ", " 你好,可以公证 2016-10-24 17:31 0 ", " 专业擅长: 继承、离婚、公司法 服务热线: 15911082185 ", " 你好,需要的 2016-10-24 18:18 0 ", " 专业擅长: 继承、离婚、债权债务 服务热线: 13811188981 ", " 继承: 根据《继承法》第五条 继承开始后,按照法定继承办理;有遗嘱的,按照遗嘱继承或者遗赠办理;有遗赠扶养协议的,按照协议办理。如果没有遗嘱那么应当按照法定继承办理继承事宜。", " 2016-10-24 19:37 0 ", " 专业擅长: 离婚、常年法律顾问、刑事辩护 服务热线: 18811161*** ", " 你好,可以的。。。", " 2016-10-24 16:27 0 ", " 专业擅长: 劳动工伤、劳动争议、公司法 服务热线: 15902097*** ", " 您好,可以订立遗嘱,可以公证也可以不公正 2016-10-24 16:47 0 ", " 专业擅长: 继承、离婚、损害赔偿 服务热线: 13811712*** ", " 可以公证的006 2016-10-25 15:58 0 ", "以上回复,不符合你的实际情况?马上咨询在线律师!", "相关法律咨询 ", "标签:夫妻共同财产 ", "相关文章推荐 ", "最新法律咨询 ", "相关律师推荐 北京律师 北京离婚律师 离婚律师 ", " 找律师 看文章 搜标签"], "segmented_title": ["婚后", "一方", "去世", ",", "其", "婚前", "的", "个人财产", "的", "一半", "会", "变为", "夫妻共同财产", ",", "另一半", "变"], "segmented_paragraphs": [["今日", "律师", "风向标", ":"], ["婚后", "一方", "去世", ",", "其", "婚前", "的", "个人财产", "的", "一半", "会", "变为", "夫妻共同财产", ",", "另一半", "变为", "遗产", "按", "顺序", "继承", "。", "如果", "希望", "保护", "婚前财产", "归", "其", "父母", ",", "是否", "可以", "在", "婚前", "写", "遗嘱", ",", "一旦", "婚后", "去世", ",", "婚前财产", "还是", "归", "一方", "父母", ",", "遗嘱", "需要", "公证", "吗", "?"], ["wl", "1542", "irnc", "北京", "-", "北京", "2016", "-", "10", "-", "24", "15", ":", "38"], ["专业", "擅长", ":", "离婚", "、", "债权债务", "、", "合同法", "服务热线", ":", 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", " youanyyou06-02 15:24 等级 12楼 ", " 牛X的公司都用自己的框架…… ", " 哈哈公司让用啥就得用啥。 ", " qw1986010806-02 20:53 等级 15楼 ", " spring虽有自己的MVC框架,但是spring最优秀的地方还是其控制翻转,作为控制层的最为优秀的。而Struts本身其自身的控制层不是很强势。框架组合的时候肯定要取长避短了。 ", " chainhou06-02 22:11 等级 16楼 ", " 呵呵,以后会用的多一些 ", " bao11090806-03 00:48 等级 17楼 ", " Spring + Struts + Hibernate 是人云亦云的结果,现在是不管什么项目一上来就是所谓的“SSH”。 ", " cnhbzl06-07 12:11 等级 18楼 ", " spring最强大的功能是控制翻转,您说得很有道理。 ", " wishd06-07 12:30 等级 19楼 ", " Spring Web Flow ", " mycity2106-07 14:09 等级 20楼 ", " 只要是新的项目,要用到MVC的,基本上都改用spring mvc了。 ", " sammyke04-25 21:23 等级 22楼 ", " 都没学过,我们公司还让用,烦啊。 ", " shbcomeon04-25 22:10 等级 23楼 ", " 好像大多公司有自己封装的框架吧 ", " bao11090804-25 23:36 等级 24楼 ", " 因为你用的是 Annotation 方式的 Spring MVC,如果你用用 XML 方式的你会直接想死! ", " pywepe04-26 08:54 等级 25楼 ", " SpringMVC不是跟容器耦合的吗 ", " 我们公司就是用的 SpringMVC 3.0.5 用annotation配置,个人感觉比struts2.x舒服些。 不知道大家怎么看,反正我喜欢。 ", " linmarklin04-26 10:42 等级 27楼 ", " 公司基本使用自己框架 安全性有保障 ", " yuanxiao2704-26 18:14 等级 28楼 ", " wdz56707-06 11:42 等级 29楼 ", " SPIRNG MVC 感觉三层架构不是非常清晰! 和SPRING的天然集成反而有把人绕晕的感觉! 优点就是开发很方便 而且少导入很多其他莫名其妙的! 而且SPRING MVC感觉用的人不多! 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+{"documents": [{"bs_rank_pos": 1, "paragraphs": [" 您好,精华帖至少要有15张图片,文字不少200个字!并且是原创内容,布局合理。 查看精华帖标准>> ", "宝宝感冒咳嗽也是常见病症了,尤其是在春天,早晚温差大,宝宝感冒咳嗽更是时有发生。一旦发现宝宝咳嗽要及时治疗,注意观察咳嗽症状,如果宝宝咳嗽、发烧,但是精神状态好的话,一般是感冒引起的,这时候服用999小儿止咳糖浆就可以。但宝宝一旦出现发高烧、咳嗽、喘鸣伴呼吸困难时,需要立即送医院紧急处理。 如果宝宝咳嗽厉害的话,除了求医问药,妈妈最好用一些食疗方法进行辅助治疗,这里给大家分享一些宝宝咳嗽厉害小妙招,在宝宝咳嗽的时候做给宝宝吃,能够有效缓解咳嗽症状。 (图片来自于网络) 1.生姜+红糖+大蒜 这是用于治疗风寒咳嗽的,孩子得了风寒感冒,喝温热的生姜红糖水能起到治疗作用,如果同时还伴有咳嗽,那就在生姜红糖水里再加2～3瓣大蒜一起煮,要用小火煮10分钟,把蒜头的辣味煮掉,这样孩子才肯喝。 2.烤橘子 橘子性温,有化痰止咳的作用。这是止咳化痰比较常用的一种方法,宝宝吃了烤橘子之后能有效祛痰,止咳效果比较明显。 3.梨+花椒+冰糖 宝宝咳嗽厉害小妙招里怎么少得了梨子呢!梨子具有生津止渴、益脾止泻、和胃降逆的功效。吃较多梨的人远比不吃或少吃梨的人感冒机率要低。做法也很简单,就是把梨子横断切开挖去中间核后,放入20颗花椒(花椒味不大,宝妈可以多放一些),2 粒冰糖,再把梨对拼好放入碗中,上锅蒸半小时左右即可,一只梨可分两次吃完。 (图片来自于网络) 以上分享的宝宝咳嗽厉害小妙招主要是针对风寒咳嗽的,下面再为大家分享几个治疗风热咳嗽的宝宝咳嗽厉害小妙招。 1.萝卜冰糖汁:白萝卜取汁100～200毫升,加冰糖适量隔水炖化,睡前1次饮完,连用3～5 次。 2.梨、冰糖、川贝一起蒸。制作方法很简单,梨1个洗净,靠柄部横断切开,挖去中间的核后放入冰糖2～3粒、敲碎成末的川贝10粒(小儿5～6粒),然后把梨上部拼对好,也可用牙签插紧放入碗中,上锅蒸30分钟左右即可,小儿分2次吃,有润肺、止咳、化痰的作用。 (图片来自于网络) 3.白萝卜煮水。把白萝卜切成3到5片,放入锅里,加入大半碗水,烧开后文火煮五分钟即可。等白萝卜水不热了,就可以给孩子喝了,一天喝1到2次就可以。 以上分享了几个宝宝咳嗽厉害小妙招,在宝宝咳嗽厉害的时候可以用这些食疗方法辅助治疗,这样宝宝咳嗽才会好得快。 ", " 发表于 2017-05-14 21:20", " 轻微的咳嗽是没什么问题的,如果症状比较严重的话,那就需要去看看医生了,这个期间还是要吃些清淡容易消化的饭菜,像是有机优博这种奶粉还可以喝一些 ", " 发表于 2017-06-23 14:37", " 我觉得宝宝呼吸道感染就容易咳嗽的,这种情况,如果不严重的话,可以多饮食多休息,饮食吃清淡一点,可以吃点容易消化的优博。 ", " 发表于 2017-07-06 11:15", " 咳嗽多喝水,再就是吃的东西要清淡一些,喝奶粉的话可以喝点优-博核苷酸 ", " 发表于 2017-07-24 12:22", " 咳嗽有的是因为感冒了,还有是空气有污染,或者是食物过敏所致,这些都得注意,比如奶粉,尽量选择跟母乳接近的,比如优博. ", " 发表于 2017-08-18 10:37", " 感冒咳嗽是宝宝常见的病症,主要是身体抵抗力弱的关系,要注意补充营养均衡一点,你可以给他喝圣元. 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"999小儿止咳糖浆带来最全宝宝咳嗽厉害小妙招_奇瑞论坛_太平洋汽车网"}, {"bs_rank_pos": 2, "paragraphs": ["年轻人做好这事胜过吃伟哥!每周3次性生活,等于一年跑两次马拉松。关注微信公众号【39健康网】,发送【J64】赶快学起来。", " 宝宝经常咳嗽,这是很多妈妈都会遇到的烦心事儿。小宝宝抵抗力低,容易受到细菌病毒的侵袭,尤其在这个感冒病毒繁衍滋生的季节,孩子最容易感冒咳嗽了。通常的办法就是给孩子吃点止咳药,现在市面上小儿止咳药挺多的,其中999小儿止咳糖浆是最为常见的一种止咳药了,它和999小儿感冒药联合赞助播出的《育儿大作战》深受宝爸宝妈的欢迎。除了用药治疗之外,小编在这里还要给大家分享一些治疗宝宝咳嗽厉害小妙招。", " 给宝宝做按摩,这是治疗宝宝咳嗽厉害小妙招里最常见的一种方式,这里小编给大家介绍两种常见的方式。", " 1)开天门。以拇指指肚自下而上交替直推。用力柔和均匀,推动时要有节律,频率为200到300次/分,推50至100次。 2)按揉天突。胸骨上窝正中。以拇指或掌根在一定部位或穴位上逐渐用力向下按压 称按法。拇指或中指端吸定于穴位上 按顺时针或逆时针方向旋转揉。", " 小编在这里只介绍两种方式,用按摩方法治疗宝宝咳嗽厉害小妙招有很多,家长可以系统学一下,但需要注意的是,给宝宝按摩要注意手法,力度不要过重,最 好找专业按摩师了解下按摩机理,这样才不至于伤到宝宝脆弱的皮肤和筋骨。", " 我们都知道,宝宝咳嗽除了吃药之外,还可以用一些食疗方法辅助治疗的,而针对风寒咳嗽和风热咳嗽,也会有不同的食疗方法,这里小编给大家分享最为常见的两个治疗宝宝咳嗽厉害小妙招的食疗方法,家长在宝宝咳嗽厉害的时候,不妨做这些食物给宝宝吃,能够缓解咳嗽的。", " 1)煮萝卜水 白萝卜洗净,切4~5薄片,放入小锅内,加大半碗水,放火上烧开后,再改用小火煮5分钟即可。等水稍凉后再给宝宝喝,此方治疗风热咳嗽、鼻干咽燥、干咳少痰的效果是不错的,2岁以内的宝宝收到的效果更好。为什么小编要推荐这个方子呢,因为食材容易获得,做法也简单,即便是再不会烹饪的妈妈,也能学得会,而且这个方法治疗小儿风热咳嗽效果还是比较明显的。 2)蒸大蒜水 取大蒜2～3瓣,拍碎,放入碗中,加入半碗水,放入一粒冰糖,把碗加盖放入锅中去蒸,大火烧开后改用小火蒸15分钟即可。当碗里的蒜水温热时喂给孩子喝,大蒜可以不吃。一般一天2～3次,一次小半碗。大蒜性温,入脾胃、肺经,治疗寒性咳嗽、肾虚咳嗽效果非常好。小编选取这个方法,同样也是因为食材易得、操作简单,而且大蒜有止咳化痰的功效,治疗孩子风寒咳嗽还是有一定效果的。", " 以上分享了治疗宝宝咳嗽厉害小妙招,家长可以多掌握一些治疗宝宝咳嗽的按摩方法和食疗方法,这样在宝宝轻度咳嗽的时候,就可以用这些方法调理,如果宝宝咳嗽厉害了,一定要用药物治疗,家中常备999小儿止咳糖浆,用药治疗的同时辅以食疗和按摩方法,相信宝宝咳嗽很快就会好的。", " (免责声明:苏州巨火网络登载此文出于传递更多信息之目的。文章所述内容仅代表文章原作者的个人观点。所写文章的原创性以及文中陈述信息未经证实,对该文所写全部或者部分内容、文字的真实性、有效性不作任何保证或承诺,请读者仅作参考,并请自行仔细核实相关内容。若有疑问请致电:0512-85889237,纠错电话:18120051373)", " 冬季吃冷食能减肥?信了你就上当社会万象 某些阴道细菌增加女性感染HIV风险权威发布 孩子营养够不够 得看符合不符合这五条学术会议 “吃货爹爹”突发胰腺炎险丢命 社会万象 为何癌症儿童深受放化疗毒害?研究发现", " 小腿抽筋怎么办?6招快速解决! 女性月经期间吃什么好呢? 床上干这6件事,一下短命好几年保健 女性补血不易老 不同时期方法不同女性 女人如何远离盆腔炎骚扰妇科 头发没干透就睡觉的五种严重后果保健 手掌颜色暗含大秘密保健", " 精选 饮食 女性 保健 ", " 多吃3类食物年轻不长皱纹美容食谱 7个小技巧 让老公倍感温暖!怎样增进夫妻感情 拉粑粑太用力会有什么严重后果?健康资讯 为什么每天一定要吃香蕉?饮食", " 吃粗粮开胃抗衰老,你适合吃吗?抗衰老饮食 三高人群喝什么茶有助调理心血管健康?三高人群调理 吃哈密瓜竟有这么多好处!哈密瓜功效 上班族晚餐这样吃健康又营养!晚餐食谱 怎样快速做个营养早餐?营养早餐", " 女人月经期一定不能吃7种药经期保健 这两种方式洗澡竟真会致癌?!身体保健 原来这样的内裤不能穿!女性用品 阴道松弛怎么办?3步骤让你紧实身体护理 女人小便后如何擦拭外阴?身体护理", " 对象睡觉老打呼噜?告诉TA这样做疾病预防 这样洗头防头皮老化 头发越洗越美身体护理 秋季吃什么水果能解毒养肝呢?秋季养生 中午趴着睡充电?胃炎找上门疾病预防 女性多久洗一次头最保健最健康呢?女性保健", "查疾病 查药品 好医院 好医生 ", " 热门 痛经 荨麻疹 宫颈糜烂 高血压 登革热 尖锐湿疣", " 妇科 月经不调 阴道炎 子宫肌瘤 ", " 男科 包皮过长 前列腺炎 早泄 ", " 儿科 手足口病 小儿感冒 小儿肺炎 ", " 内科 冠心病 肺结核 支气管炎 ", " 外科 肩周炎 乳腺增生 骨质增生 ", " 皮肤 湿疹 狐臭 脚气 ", " 热门 六味地黄丸 逍遥丸 金匮肾气丸 云南白药 阿胶 片仔癀", " 妇科 保妇康栓 调经丸 妈富隆 ", " 男科 伟哥 前列舒乐胶囊 养血生发胶囊 ", " 儿科 小儿感冒颗粒 小儿止咳糖浆 小儿七星茶颗粒 ", " 心脑血管 厄贝沙坦片 血脂康胶囊 速效救心丸 ", " 糖尿病 美迪康 消渴丸 拜唐苹 ", " 跌打损伤 云南白药气雾剂 云南白药创可贴 跌打万花油 ", " 外科 心胸外科 泌尿外科 乳腺外科 肛肠外科 肝胆外科 血管外科", " 内科 神经内科 消化内科 呼吸内科 内分泌科 肾内科 风湿科", " 其他科室 妇科 耳鼻喉科 整形科 中医科 皮肤科 心理咨询", " 按疾病找 劲椎病 脑梗塞 高血压 ", " 外科 心胸外科 泌尿外科 乳腺外科 ", " 内科 神经内科 消化内科 呼吸内科 ", " 其他科室 妇科专家 产科专家 中医专家 ", " 外科 烧伤专家 骨折专家 肺癌专家 ", " 内科 胃炎专家 高血压专家 冠心病专家 ", " 其他科室 头痛专家 湿疹专家 肾结石专家 "], "segmented_title": ["分享", "宝宝", "咳嗽", "厉害", "小", "妙招", ",", "及时", "服用", "999", "小儿", "止咳糖浆", "_", "39", "健康网"], "segmented_paragraphs": [["年轻人", "做", "好", "这", "事", "胜过", "吃", "伟哥", "!", "每周", "3", "次", "性生活", ",", "等于", "一年", "跑", "两次", "马拉松", "。", 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"耳鼻喉科", "整形", "科", "中医科", "皮肤科", "心理咨询"], ["按", "疾病", "找", "劲", "椎", "病", "脑梗塞", "高血压"], ["外科", "心胸外科", "泌尿外科", "乳腺", "外科"], ["内科", "神经内科", "消化内科", "呼吸内科"], ["其他", "科室", "妇科专家", "产科", "专家", "中医", "专家"], ["外科", "烧伤", "专家", "骨折", "专家", "肺癌", "专家"], ["内科", "胃炎", "专家", "高血压", "专家", "冠心病", "专家"], ["其他", "科室", "头痛", "专家", "湿疹", "专家", "肾结石", "专家"]], "title": "分享宝宝咳嗽厉害小妙招,及时服用999小儿止咳糖浆_39健康网"}, {"bs_rank_pos": 4, "paragraphs": ["春暖花开的季节,家长都喜欢带孩子去踏青旅游,这个时候要注意了,孩子身体小,抵抗力弱,容易受到感冒病毒的侵袭,稍有不慎就容易患上感冒。小孩老是咳嗽怎么办,咳嗽往往是最晚消失的一种感冒症状,有的时候会持续好几周,看着宝宝一直咳嗽,家长难免不着急,这个时候可以给宝宝吃点安全有效的止咳药,像999小儿止咳糖浆这种市面上比较常见的药物就可以。另外,应对小孩老是咳嗽,正确的护理才是最重要的,家长要做好四件事。", " 第1件事:得了感冒,要让宝宝好好休息 小孩老是咳嗽怎么办,如果宝宝患上感冒了,就不建议孩子去幼儿园了,孩子体质弱,容易交叉感染,加重病情。需要等发烧、咳嗽、流涕等症状消失了再去幼儿园。这个时候应该让孩子尽量多休息,但也不需要卧床,只是要保证充足睡眠,家长要给孩子营造一个舒适安静的睡眠环境,更不要在室内吸烟。 第2件事:适当补充维C 维生素C有抗氧化、调节免疫的作用,多给孩子吃含有维生素C的水果或者果汁,比如橙子、草莓、橘子、桂圆等。 第3件事:小孩老是咳嗽怎么办 饮食以清淡为主 孩子感冒期间肠胃消化能力弱,胃口差,这个时候不要强迫孩子吃饭,更不要给她吃甜腻的食物,甜腻的食物易生痰,导致舌苔厚腻,有碍脾胃运化,感冒后的脾胃功能本来就会下降,再吃甜腻食物等于火上浇油,因此孩子感冒期间要饮食清淡。[A1] 可以多给孩子吃一些软烂的米粥、面条。如果咳嗽厉害的话,可以试试川贝冰糖梨这个方法。 第4件事:小孩老是咳嗽怎么办,多给孩子喝温开水 在平时就应该让孩子养成多喝水的习惯,少喝果汁饮料,喝水有助于排毒泻火,还可以降低炎症,尤其是在孩子感冒发烧的时候,更应该让孩子多喝水。 春天是小儿感冒高发季节,所以这个时候家中应该常备一些感冒药,像999小儿止咳糖浆就应该准备一些。另外给宝宝吃感冒药的时候要注意两点,一是不要滥用抗生素,很多感冒都是病毒引起的,用抗生素不但不起作用,反而增加副作用,影响孩子肠胃消化能力,导致孩子胃口更差。给孩子用抗生素一定要遵医嘱。二是药的味道,太苦的药孩子不肯吃,要给孩子选择那种让孩子喜欢或者能接受的口味,这样可以让孩子尽快吃药,不耽误治疗时机。现在市面上一些口服液、糖浆、感冒冲剂之类的比较多,像999小儿止咳糖浆这种就行,口感好,易于服用,给孩子吃药也方便。而且999小儿止咳糖浆是临床上应用比较普遍的一种药,安全性高,很多家长把999小儿止咳糖浆作为家中常备药,对缓解小儿咳嗽效果比较好。"], "segmented_title": ["999", "小儿", "止咳糖浆", "帮", "您", "解决", "小孩", "老是", "咳嗽", "怎么办", "的", "难题", "-", "延展", "阅读", "-"], "segmented_paragraphs": [["春暖花开", "的", "季节", ",", "家长", "都", "喜欢", "带", "孩子", "去", "踏青", "旅游", ",", "这个时候", "要", "注意", "了", ",", "孩子", "身体", "小", ",", "抵抗力", "弱", ",", "容易", "受到", "感冒", "病毒", "的", "侵袭", ",", "稍", "有", "不", "慎", "就", "容易", "患", "上", "感冒", "。", "小孩", "老是", "咳嗽", "怎么办", ",", "咳嗽", "往往", "是", "最晚", "消失", "的", "一", "种", "感冒", "症状", ",", "有的时候", "会", "持续", "好", "几", "周", ",", "看着", "宝宝", "一直", "咳嗽", ",", 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"作为", "家中", "常备", "药", ",", "对", "缓解", "小儿", "咳嗽", "效果", "比较好", "。"]], "title": "999小儿止咳糖浆 帮您解决小孩老是咳嗽怎么办的难题 - 延展阅读 - "}, {"bs_rank_pos": 7, "paragraphs": [" 您好,精华帖至少要有15张图片,文字不少200个字!并且是原创内容,布局合理。 查看精华帖标准>> ", "由于小孩器官敏感,稍不注意就会引起感冒咳嗽,为了孩子健康,家长们应该掌握更多关于儿科的医学常识,咳嗽是小孩的常见病症,诱因有很多,因此家长们应该重视,小孩咳嗽怎么办,诱因不同,家长会采取不同的措施,比如说有家长会给宝宝服用999小儿止咳糖浆,也有家长会将999小儿感冒药和999小儿止咳糖浆结合起来服用,还有一部分家长会通过食疗或其它一些偏方帮助宝宝缓解症状,总之都希望让宝宝早日恢复健康。 (图片来源于网络) 虽然已经进入了夏季,但难免有些宝宝会因为各种原因引起咳嗽,看到宝宝咳嗽后,很多家长会紧张不知所措,向周围朋友询问小孩咳嗽怎么办,其实家长不用这么紧张,只要找到诱因,采用正确的治疗方案及时给宝宝服用999小儿止咳糖浆,最好是能在配点999小儿感冒药,就能慢慢缓解小儿咳嗽症状。 现在大多家庭都是一个宝宝,因此宝宝身体稍微出现不适,家长就会坐立不安,为了帮到大家以下就为各位家长介绍药物治疗和食疗两种治疗方法,希望能帮助更多宝宝恢复健康。 小孩咳嗽怎么办,首先家长可以根据宝宝病情让宝宝服用999小儿止咳糖浆,有医学常识的朋友应该知道小儿咳嗽易发生的主要原因是肺脏娇嫩,常有不足,以及卫表不固,御邪之力薄弱等内在不足。小儿咳嗽首先要弄清情况,如有无痰液等。3岁以下的小儿,呼吸系统发育尚不成熟,如给予较强的止咳药,咳嗽虽暂时停止,但痰液不能顺利排出会造成气管堵塞,引起缺氧、脉搏加快,甚至发生肺不张、心力衰竭等并发症。而999小儿止咳糖浆针对小儿脏腑娇嫩之特点,采用中西结合之法,扶正固本,标本兼治。迅速解除小儿咳痰、喘等症状,有抗菌消炎之功效,对金黄色葡萄球菌有抑制作用,专治小儿咳、痰、喘之顽疾。另外糖浆剂型,安全,携带方便口感好,易服用。在服用999小儿止咳糖浆时,家长可以遵照医嘱再给宝宝服用999小儿感冒药,这样效果会刚好。 (图片来源于网络) 小孩咳嗽怎么办,很多家长不愿意为宝宝服用药物,希望通过食疗的方法缓解孩子症状,这部分家长不妨试试给宝宝熬制川贝梨水喝,止咳效果很不错。要让宝宝坚持喝上3--5天才能见效。若是通过食疗不能缓解宝宝症状时,家长就要小心,是不是需要咨询下医生给宝宝服用999小儿止咳糖浆,要么就是食疗加药疗同时进行。 为了自己或家人健康,特别是家里的小孩,大家应该掌握更多简单疾病的治疗方法,例如小孩咳嗽怎么办,只有这样在孩子出现症状时才能及时的给予治疗,帮助宝宝缓解病情。", " 发表于 2017-06-09 16:47", " 如果孩子太小的话,能不用药尽量不要用药,药物到底还是有副作用。通常小儿咳嗽通过按摩的方式就能治好,就像我们亲戚家的孩子,以前经常咳嗽,在北京大福堂推拿按摩了一段时间后,咳嗽就明显少多了,现在基本好了 "], "segmented_title": ["小孩", "咳嗽", "怎么办", ",", "喝", "999", "小儿", "止咳糖浆", "家长", "放心", "省", "心", "_", "奇瑞", "论坛", "_", "太平洋"], "segmented_paragraphs": [["您好", ",", "精华帖", "至少", "要", "有", "15", "张", "图片", ",", "文字", "不", "少", "200", "个", "字", "!", "并且", "是", "原创", "内容", ",", "布局合理", "。", "查看", "精华帖", "标准", ">", ">"], ["由于", "小孩", "器官", "敏感", ",", "稍", "不", "注意", "就会", "引起", "感冒", "咳嗽", ",", "为了孩子", "健康", ",", "家长们", "应该", "掌握", "更多", "关于", "儿科", "的", "医学常识", ",", "咳嗽", "是", "小孩", "的", "常见", "病症", ",", "诱因", "有很多", ",", "因此", "家长们", "应该", "重视", ",", "小孩", "咳嗽", "怎么办", ",", "诱因", "不同", ",", "家长会", "采取", "不同", "的", "措施", ",", "比如说", "有", 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GT特别版——证明勇气的四星球 TV剧场版01-神龙传说 TV剧场版02-魔神城内的睡美人 TV剧场版03-摩诃不思议大冒险 TV剧场版04-最强之道 Z TV特别版01--悟空父亲的故事 Z TV特别版16-未来篇 Z剧场版01-热血热斗 Z剧场版02-世上最强的人 Z剧场版03-地球争霸战 Z剧场版04-超级赛亚人孙悟空 Z剧场版05-最强对最强 Z剧场版06-一百亿能量战士 Z剧场版07-激战人造人 Z剧场版08-传说中的超级赛亚人 Z剧场版09-银河战士 Z剧场版10-两人面临危机 Z剧场版11-击倒超战士 Z剧场版12-悟空与达尔的合体 Z剧场版13-龙拳爆发 龙珠外传-赛亚人灭绝计划(地球篇) 龙珠外传-赛亚人灭绝计划(宇宙篇)"], "bs_rank_pos": 3}, {"title": "求指点,龙珠的顺序是怎样的?_七龙珠吧_百度贴吧", "segmented_title": ["求", "指点", ",", "龙珠", "的", "顺序", "是", "怎样", "的", "?", "_", "七龙珠", "吧", "_", "百度贴吧"], "segmented_paragraphs": [["最近", "看", "了", "龙珠改", ",", "是", "小时候", "那个", "后面", "发生", "的", "事", ",", "最近", "想", "补", "下", "这个", "动漫", ",", "从", "哪个", "开始", "看", "起", "?"], ["龙珠改", ",", "然后", "是", "布欧", "篇", ",", "龙珠", "超", "。"], ["龙珠", ",", "龙珠", "z", ",", "GTt", "略", "掉", "最后", "是", "龙珠", "超", "中间", "还有", "部分", "的", "剧场版"], ["龙珠", ",", "龙珠Z", "(", "或者", "龙珠改", ")", ",", "龙珠gt", "(", "同人", ",", "可以", "不看", ")", ",", "龙珠", "超", "。", "剧场版", "推荐", ":", "神与神", ",", "复活", "的", "f", "～", "～", "～"], ["龙珠", "tv", ",", "龙珠z", "(", "可以", "看", "龙珠改", ")", ",", "龙珠", "超", "。", "龙珠gt", "是", "东映", "原创", "的", "结局", ",", "没", "出", "超前", "都", "看", "这个"], ["龙珠", "动画", "观看", "顺序", "旧", "版", ":", "龙珠", "—", "—", "龙珠", "Z", "—", "—", "龙珠GT", "新版", ":", "龙珠", "—", "—", "龙珠", "改", "—", "—", "龙珠", "超龙珠", "(", "鸟山明", "创作", "的", "漫画", ")", "又", "名", ":", "七龙珠", ",", "于", "1984", "年", "在", "漫画", "杂志", "《", "少年", "JUMP", "周刊", "》", "连载", "。", "漫画", "(", "全", "519", "话", ")", "连载", "期间", "1984", "年", "-", "1995", "年", "出版", "期间", ":", "1985", "年", "9", "月", "10", "日", "-", "1995", "年", "8", "月", "4", "日", "单行本", "册", "数", ":", "42", "卷", "(", "单行本", ")", "出版社", ":", "集英社", "1986", "年", "动画", "化", "动画制作", ":", "东映", "动画", "株式会社", "龙珠", "TV", "第", "1", "篇", "至", "第", "194", "篇", "集数", ":", "153", "集", "龙珠Z", "第", "195", "篇", "-", "第", "519", "篇", "集数", ":", "291", "集", "龙珠GT", "(", "东映", "原创", "续作", ")", "集数", ":", "64", "集", "龙珠改", "【", "龙珠Z", "重制", "版", "龙珠改", "集数", ":", "98", "集", ",", "龙珠改", "魔人布欧", "篇", "集数", ":", "61", "集", "】", "龙珠Z", ":", "神与神", "剧场版", "鸟山明", "担当", "故事", "及", "角色设定", "。", "2013", ".", "03", ".", "30", "龙珠Z", ":", "复活", "的", "F", "剧场版", "鸟山明", "担当", "故事", "及", "角色设定", "。", "2015", ".", "04", ".", "18", "龙珠", "超", "(", "鸟山明", "担当", "故事", "原案", ",", "Toy", "ble", "作画", "执笔", "的", "漫画", ")", "正统", "续篇", "2015", "年", "6", "月", "20", "日", "在", "集英社", "旗下", "《", "V", "JUMP", "》", ",", "连载中", "TV", "动画版", "2015", "年", "7", "月", "5", "日", "播出", "《", "龙珠", "剧场版", "》", "(", "官方", "剧场版", "动画", "改编", ")", "01", "神龙传说", "1986", ".", "12", ".", "2002", "魔神", "城", "内", "的", "睡美人", "1987", ".", "07", ".", "1803", "摩诃", "不可思议", "大冒险", "1988", ".", "07", ".", "0904", "把", "悟饭", "还给我", "(", "卡利", "克", "二世", "剧场版", ")", "1989", ".", "07", ".", "1505", "史上最强者", "1990", ".", "03", ".", "1506", "地球", "超级", "大决战", "1990", ".", "07", ".", "1707", "超级赛亚人", "孙悟空", "1991", ".", "03", ".", "1908", "超越极限", "的", "最强", "对", "最强", "1991", ".", "07", ".", "2009", "100", "亿", "能量", "战士", "1992", ".", "03", ".", "7", "10", "极限", "之", "战", "!", "三大", "超级赛亚人", "1992", ".", "07", ".", "1111", "燃烧吧", "!", "热战", "!", "烈战", "!", "超激战", "!", "1993", ".", "03", ".", "06", "12", "银河面临危机", "身手不凡", "的", "高手", "1993", ".", "07", ".", "1013", "二人面临危机", "超", "战士", "难以", "成", "眠", "1994", ".", "03", ".", "1214", "击倒超战士", "胜利", "非我莫属", "1994", ".", "07", ".", "0215", "复活", "的", "融合", "!", "悟空", "和", "贝吉塔", "1995", ".", "03", ".", "0416", "龙拳爆发", "!", "悟空", "舍我其谁", "1995", ".", "07", ".", "1517", "迈向", "最强", "之", "道", "1996", ".", "03", ".", "0218", "龙珠Z", ":", "神与神", "2013", ".", "03", ".", "3019", "龙珠Z", ":", "复活", "的", "F", "2015", ".", "04", ".", "18", "【", "OVA", "】", "OVA", "1", "赛亚人灭绝计划", "地球篇", "1993", "年", "OVA", "2", "赛亚人灭绝计划", "宇宙", "篇", "1994", "年", "2010", "年", "被", "重制", "OVA", "3", "归来", "!", "悟空", "和", "他", "的", "朋友们", "2008", "年", "OVA", "4", "巴", "达", "克", "之", "章", "2011", "年", "特别", "篇", "1", "一夫当关", "的", "最后决战", "1995", "年", "2", "遥远的悟空传说", "(", "后", "被", "改编", "成", "游戏", ")", "1996", "年", "3", "勇气", "的", "证明", "!", "4", "星球", "1997", "年", "4", "挑战", "绝望", "!", "仅存", "的", "超", "战士", "悟饭", "和", "特", "南", "克", "斯", "1993", "年", "《", "龙珠", "SD", "》", "(", "官方", "短篇", ")", "《", "龙珠", "超次元乱战", "》", "(", "同人", ")", "《", "龙珠", "零界", "篇", "》", "《", "龙珠", "AF", "》", "《", "新", "龙珠", "AF", "》", "《", "龙珠", "EX", "》", "等", "皆", "为", "同人", "续作"], ["1986", "年", "-", "1989", "年", ":", "DRAGON", "BALL", "(", "153", "话", ")", "1989", "年", "-", "1995", "年", ":", "DRAGON", "BALL", "Z", "(", "291", "话", ")", "1996", "年", "-", "1997", "年", ":", "DRAGON", "BALL", "GT", "(", "64", "话", "+", "番外", ")", "2009", "年", "-", "2010", "年", ":", "DRAGON", "BALL", "改", "(", "98", "话", ")", "2014", "年", "-", "2015", "年", ":", "DRAGON", "BALL", "改", "之", "魔人布欧", "篇", "(", "61", "话", ")", "2015", "年", "-", ":", "DRAGON", "BALL", "超", "(", "更新", "连载中", ")", "1986", "年", "-", "2015", "年", ":", "龙珠", "剧场版", "(", "18", "话", ")"], ["下载", "贴吧", "APP", "看", "高清", "直播", "、", "视频", "!"], ["贴吧", "页面", "意见反馈", "违规", "贴吧", "举报", "反馈", "通道", "贴吧", "违规", "信息处理", "公示"]], "paragraphs": ["最近看了龙珠改,是小时候那个后面发生的事,最近想补下这个动漫,从哪个开始看起?", "龙珠改,然后是布欧篇,龙珠超。", "龙珠,龙珠z,GTt略掉 最后是龙珠超 中间还有部分的剧场版", "龙珠,龙珠Z(或者龙珠改),龙珠gt(同人,可以不看),龙珠超。剧场版推荐:神与神,复活的f～～～", "龙珠tv,龙珠z(可以看龙珠改),龙珠超。龙珠gt是东映原创的结局,没出超前都看这个", "龙珠动画观看顺序旧版:龙珠——龙珠Z——龙珠GT新版:龙珠——龙珠改——龙珠超龙珠(鸟山明创作的漫画)又名:七龙珠,于1984年在漫画杂志《少年JUMP周刊》连载。漫画(全519话)连载期间1984年-1995年出版期间:1985年9月10日-1995年8月4日单行本册数:42卷(单行本)出版社:集英社1986年动画化动画制作:东映动画株式会社龙珠TV第1篇至第194篇 集数:153集龙珠Z第195篇-第519篇 集数:291集龙珠GT(东映原创续作)集数:64集龙珠改【龙珠Z重制版龙珠改 集数:98集,龙珠改魔人布欧篇集数:61集】龙珠Z:神与神 剧场版鸟山明担当故事及角色设定。2013.03.30龙珠Z:复活的F剧场版鸟山明担当故事及角色设定。2015.04.18龙珠超(鸟山明担当故事原案,Toyble作画执笔的漫画)正统续篇2015年6月20日在集英社旗下《V JUMP》,连载中TV动画版2015年7月5日播出《龙珠剧场版》(官方剧场版动画改编)01 神龙传说 1986.12.2002 魔神城内的睡美人 1987.07.1803 摩诃不可思议大冒险 1988.07.0904把悟饭还给我(卡利克二世剧场版)1989.07.1505 史上最强者1990.03.1506 地球超级大决战1990.07.1707 超级赛亚人孙悟空 1991.03.1908 超越极限的最强对最强 1991.07.2009 100亿能量战士 1992.03.710 极限之战!三大超级赛亚人 1992.07.1111 燃烧吧!热战!烈战!超激战!1993.03.06 12 银河面临危机 身手不凡的高手 1993.07.1013 二人面临危机 超战士难以成眠 1994.03.1214 击倒超战士 胜利非我莫属 1994.07.0215 复活的融合!悟空和贝吉塔 1995.03.0416 龙拳爆发!悟空舍我其谁 1995.07.1517 迈向最强之道 1996.03.0218 龙珠Z:神与神2013.03.3019 龙珠Z:复活的F2015.04.18【OVA】OVA1 赛亚人灭绝计划地球篇 1993年OVA2 赛亚人灭绝计划宇宙篇1994年2010年被重制 OVA3 归来!悟空和他的朋友们 2008年OVA4 巴达克之章 2011年特别篇1 一夫当关的最后决战 1995年2 遥远的悟空传说(后被改编成游戏) 1996年3 勇气的证明!4星球 1997年4 挑战绝望!仅存的超战士悟饭和特南克斯 1993年《龙珠SD》(官方短篇)《龙珠超次元乱战》(同人)《龙珠零界篇》《龙珠AF》《新龙珠AF》《龙珠EX》等皆为同人续作", "1986年 - 1989年:DRAGON BALL(153话)1989年 - 1995年:DRAGON BALL Z(291话)1996年 - 1997年:DRAGON BALL GT(64话+番外)2009年 - 2010年:DRAGON BALL 改(98话)2014年 - 2015年:DRAGON BALL 改之魔人布欧篇(61话)2015年 - :DRAGON BALL 超(更新连载中)1986年 - 2015年:龙珠剧场版(18话)", "下载贴吧APP看高清直播、视频!", "贴吧页面意见反馈 违规贴吧举报反馈通道 贴吧违规信息处理公示"], "bs_rank_pos": 4}], "question": "龙珠顺序", "segmented_question": ["龙珠", "顺序"], "question_type": "ENTITY", "fact_or_opinion": "FACT", "question_id": 221657}
+{"documents": [{"bs_rank_pos": 0, "paragraphs": ["近日铑黑多少钱一斤", "产品价格: 9999￥", " 品牌: 富源贵金属回收", " 规格型号: 不限", " 供货总量: 1", " 起订量: 1", "发布公司: 富源贵金属回收公司", "联系电话: 张清海(13621809378) 联系我时,请告知来自中国化工产品网,谢谢!", " 近日铑黑多少钱一斤【报价热线:张先生:13621809378】【询价QQ:2294600298】【微信问价:fuyuanhs999】诚信经营高价回收近日铑黑多少钱一斤首选富源贵金属回收,专业的近日铑黑多少钱一斤公司", " 富源贵金属回收有限公司秉持着价优为基础、公平求生存、信誉作保证、信守承诺、现金支付、安全快捷,对提供业务信息者提供业务佣金的宗旨。面向全国高阶回收一切含有(金、银、铂、铑、钯)贵金属及废料。", " --1.--含金类别:金盐、金水、镀金、金线、金渣、金丝、金牌、边角料、镀金废液等  --2.--含银类别:银浆、导电银浆、银焊条、擦银布、导电银漆、银胶、导电布、银触点、银膏、、银粉、银线、氯化银、导电银浆、氧化银等  --3.--含钯类别:钯碳、废钯炭、氧化钯、钯浆、钯盐、钯水、钯粉、钯触媒、钯碳催化剂、钯黑、钯管、钯、海绵钯、  --4.--含铂类别:铂碳、废铂炭、海绵铂、铂金粉、铂金水、废铂坩埚、铂碳催化剂、铂铑丝、铂金粉、铂金水、铂碳。 ", " --5.--含铑类别:铑水、铑粉、氯化铑、铑碳、等一切含金银铂钯铑的贵金属。", " 近日铑黑多少钱一斤】=其中,天津的gdp增速目标从2016年的9%左右调低到2017年的8%;上海从6.5-7%,调低到6.5%;山西从6%左右调低到5.5%;河南从8%左右调低到7.5%以上;湖北从9%左右调低到8%左右;湖南从8.5%左右,调低到8%左右。21世纪经济报道记者了解到,各地之所以主动调低经济增速,是因为过去的模式已经不可持续。以天津为例,天津2016年前三季度经济增速为9.1%,位于9%左右的预期。但是天津2016年10月规模以上工业增速为4.3%,这是约15年来好低值。一些主要的汽车主机厂还在不断的加码投资建设,我们还要注意到城市出租车的年限强制更新,汽车租赁行业的蓬勃发展等都将带来对汽车用钢的庞大增长需求。所以说汽车行业的爆发与购置税是有一定关系,但更要看到汽车的庞大基数,以及出口等方面的因素影响。关于热轧卷板的产能增长,笔者从行业了解到扬州恒润投产3座1080m高炉(原西城项目)建有热轧卷板和中厚板生产线、安徽铜陵旋力建有2座1080m高炉生产中厚板以及板坯等、河北敬业钢铁增加了热卷的投放量、以及普阳、新金都在出1250mm以下规格卷板主要是c料、山西建龙复产后亦有热卷、板坯等投放计划,整体估算,。全国万元国内生产总值能耗下降5.0%。工业企业吨粗铜综合能耗下降9.45%,吨钢综合能耗下降0.08%,单位烧碱综合能耗下降2.08%,吨水泥综合能耗下降1.81%,每千瓦时火力发电标准煤耗下降0.97%。", " 2017海绵钯回收多少钱我们需要增加劳动力,零利率不正常会带来扭曲,商业投资领域缺乏增长,经济能够承受缓慢、渐进式的加息。2、据周二(1月12日)的一份金泥报告显示,美国12月进口物价指数终于缓和了市场对通胀停滞的担忧情绪,不过表现低于预估,通缩周期的结束有助于提振美联储的加息预期。美国劳工部(DOL)公布的数据显示,美国12月进口物价指数月率上升0.4%,预期上升0.7%,前值为下降0.3%。3、据周四(1月12日)的一份金泥报告称,美国上周初请人数好于预期但逊于前值,潜在的趋势表明当前劳动力市场正在收紧,不过续领失业金人数变化需要关注。美国劳工部(DOL)公布的数据显示,美国1月7日当周季调后初请失业金人数为24.7万人,为连续97周处于30万人门槛以下,预估为25.5万人,前值修正为23.7万人。据证券日报2月6日报道,兴业银行、华福证券首席经济学家鲁政委表示,2016年,金融机构银浆余额同比增长13.5%,2017年房地产销售增速可能低于2016年,进而拖累居民银浆增长,预计2017年银浆余额增幅可能放缓至13.1%,对应全年新增银浆规模约14万亿元,如果2017年银浆投放节奏与2016年相似,2017年1月份新增人民币银浆规模可能达到2.8万亿元,招商证券研究发展中心分析师谢亚轩预计,2017年1月份铂黑规模达到2.7万亿元,他表示,央行严格调控总体银浆规模和居民房贷规模,且去年10月初房地产新政的严格执行导致居民中长期银浆规模趋势性下行,预计今年银浆增速继续下行,“不过年初银行出于抢占优质客户和早投放早收益的原因,放贷规模将维持高位,”。", " 商家:富源贵金属回收公司", " 联系人:张清海 联系固话:86-021-13621809378 传真号码:86-021-13621809378 商家地址:上海市松江区 商家网址:http://www.chemcp.com/shop/145152/", "富源贵金属回收公司", " 超级会员 第 1 年 ", "富源贵金属回收公司相关产品", " 免责声明:以上信息由会员自行提供,内容的真实性、准确性和合法性由发布会员负责,本网对此不承担任何责任。中国化工产品网不涉及用户间因交易而产生的法律关系及法律纠纷,纠纷由您自行协商解决。 防骗警示:为避免产生购买风险,建议您在购买相关产品前务必确认供应商资质及产品质量。过低的价格、夸张的描述、私人银行账户等都有可能是虚假信息,请采购商谨慎对待,谨防欺诈,对于任何付款行为请您慎重抉择! 温馨提示:当您的合法权益受到侵害时,欢迎您致电0371-61553802,我们会在1个工作日内为您处理,感谢您对中国化工产品网的关注与支持!"], "segmented_title": ["近日", "铑", "黑", "多少", "钱", "一斤", "-", "近日", "铑", "黑", "多少", "钱", "一", "-", "近日", "铑", "黑", "多少", "钱", "一"], "segmented_paragraphs": [["近日", "铑", "黑", "多少", "钱", "一斤"], ["产品", "价格", ":", "9999", "￥"], ["品牌", ":", "富源", "贵金属", "回收"], ["规格型号", ":", "不限"], ["供货", "总量", ":", "1"], ["起订量", ":", "1"], ["发布", "公司", ":", "富源", "贵金属", "回收", "公司"], ["联系电话", ":", "张清海", "(", "13621809378", ")", "联系", "我", "时", ",", "请", "告知", "来自", "中国", "化工产品", "网", ",", "谢谢", "!"], ["近日", "铑", "黑", "多少", "钱", "一斤", "【", "报价", "热线", ":", "张先生", ":", "13621809378", "】", "【", "询价", "QQ", ":", "2294600298", "】", "【", "微信", "问", "价", ":", "fuyuan", "hs", "999", "】", "诚信经营", "高价回收", "近日", "铑", "黑", "多少", "钱", "一斤", "首选", "富源", "贵金属", "回收", ",", "专业", "的", "近日", "铑", "黑", "多少", "钱", "一斤", "公司"], ["富源", "贵金属", "回收", "有限公司", "秉持", 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"法", "伤", "结果", "未必", "不如", "法", "伤", "。"]], "paragraphs": ["比如秒1000。法术伤害加100就是1100,法爆了就是2200。法术伤害结果加100就是1100。法爆是2100?", "带法爆吧,不要考虑这两个了", "伤我觉得害跟伤害结果差不多。。。但是法术伤害不波动呀", "法伤通过不同阵法会改变 结果应该是固定的吧 我自己的理解 不对别喷我", "掀起你的盖头来,让我看看你的脸。你的脸儿红又圆,好像那高潮后的样子。", "那到底是法伤好还是法伤结果好啊", "在岁月中跋涉,每个人都有自己的故事,看淡心境才会秀丽,看开心情才会明媚", "每个法术技能是由法术伤害然后通过公式计算造成的伤害的。而法术伤害结果就是固定造成伤害", "就是这个意思~ 不过法伤100 不是1000变1100吧 而且法防可以抵御法伤 法伤结果可以无视法防", "结果是固定的啊。这不难理解", "理解差不多吧,不过法伤能阵法加成", "比如你1级打个175的,假如命中,法伤100,打过去通常是1点,法伤结果100,则是100点。", "法术伤害跟阵、修等有关,比如100的法术伤害,你有可能打出120,或者80,这跟双方的法抗有关系。法术伤害结果的话,是固定伤害,跟你们的修炼,阵,没关系,是多少就是多少,100点就是100点。法爆就不解释了,你懂得", "结果拿来秒175龙就有效果,直接1+法伤结果", "法术结果只对法系更法系有用", "法术的伤害会根据秒出去数量而减少,会先去乘上比例后加成法术结果。秒的多其实差不多", "5L 正解。1点法伤实际多打的血量,由阵卡以及法术本身的伤害系数影响,不能统一下结论,比如秒4的LG法伤结果未必不如法伤。"], "bs_rank_pos": 2}, {"title": "法术伤害和法术伤害结果是啥区别_百度知道", "segmented_title": ["法术", "伤害", "和", "法术", "伤害", "结果", "是", "啥", "区别", "_", "百度", "知道"], "segmented_paragraphs": [["法术", "伤害", "是", "你", "的", "法", "攻", "所", "能", "造成", "的", "伤害", ",", "法术", 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[图文]一般的MOD都没冲突,如果是添加内置修改器有关联文件跟热咖啡冲突导致失效 你得用jj按 我准备先办亚曼达接着是崔西,最后嘛当然是茉莉小姐了 我安装完怎么对着路人不行 我", " [图文]小编这次给大家带来的是GTA5的热咖啡MOD,给游戏增加了更多的福利绅士内容,快来和你喜欢的女性角色来啪啪啪吧! 使用说明 1、下载解压之后复制到游戏目录中 2、进", " [图文]侠盗猎车5GTA5PC版 热咖啡MOD 啪啪啪MOD 嘿咻MOD 侠盗猎车5 GTA5 PC版 热咖啡MOD 啪啪啪MOD 嘿咻MOD 曾几何时多少玩家对“茉莉”魂牵梦绕,又为她悲惨的命", " [图文]更新时间: 补丁专题: GTA5邪恶mod合集 我要评分: 444 人评分 GTA5作为一款自由... 包括各种邪恶的事情。 ( 85 人阅读) 补丁介绍 《侠盗猎车手5》热咖啡MOD 大家对4代", " [图文]《GTA5》热咖啡MOD,使用说明:,1、下载解压之后复制到游戏目录中,2、进入游戏,3、F3键开启功能,按E键就可以做你想做的那些事情了,注:必须有Script hook v", " [图文]当前位置: 首页 →补丁工具 →游戏MOD → 侠盗猎车手5福利向热咖啡绅士MOD 侠盗猎车手5福利向热咖啡绅士MOD 软件大小:97.0 KB 软件语言:英文 软件类型:游戏MOD", " 本帖最后由 kuangjian 于 2015-9-10 20:25 编辑 By:dehan MOD简介:当年4代的热咖啡补丁名噪一时,这次5代也不例外,看截图就不用做太多解释了,你懂得啊!可以随时随地在", " gta5热咖啡mod怎么用", " [图文]《侠盗猎车手:圣安地列斯》热咖啡风波告一段落,Take-Two公司今天正式对媒体宣布,他们已经就“《GTA》事件”与联邦贸易委员会(FTC)达成了调解相关意向,结束了", " [图文]侠盗猎车手5(Grand Theft Auto 5)热咖啡MOD ==>>由51mag提供 侠盗猎车手5 热咖啡MOD ==>>由99DDD提供 玩家还下载了这些补丁 攻略热点 资讯热点 近期补丁 热门图片 游", " [图文]这是一个侠盗猎车手:圣安地列斯热咖啡MOD下载,圣安地列斯热咖啡补丁是侠盗猎车手游戏中非常不错的mod补丁,完全结合游戏,需要做到girlfriend女友任务的时候才可看", " [图文]侠盗猎车手5热咖啡MOD当年4代的热咖啡补丁名噪一时,这次5代也不例外,看截图就不用做太多解释了,你懂得啊!可以随时随地在任何地方和街边的女士做你想做的事情了", " [图文]侠盗猎车手5 热咖啡MOD;当年4代的热咖啡补丁名噪一时,这次5代也不例外,看截... 全站导航 欢迎访问3DMGAME下载站, 这里有最新最全的单机游戏, 电玩资源及补丁下", " [图文]在GTA中热咖啡MOD是一个非常经典的绅士MOD,具体效果就是可以和各种女性角色啪啪啪啦,甚至随时随地都可以干你想干的那些事咯,总之你懂得。小编这次给大家带来", " [图文]侠盗猎车手5 操作系统: WinXP/Win7/Vista/Win8 更新时间: 补丁专题: GTA5邪恶... 热咖啡MODV1.2 当年4代的热咖啡补丁名噪一时,这次5代也不例外看截图就不用做太", " [侠盗飞车4热咖啡补丁]::侠盗飞车4热咖啡视频::gta4秘籍::gta... 第20161220集 星球大战外传:侠盗一号 Career~违规的警察署 BD1280超清中字 BD... 8.0 乱世丽人行 第28集 抗战时期的上海,进步女青年韩疏影被婶婶陷害,被迫嫁给苏北大亨谢...", "发表我的评论 ", "网友评论 ", "BMW 等级:高级贵宾 级别:上市公司执行董事 ", " 文章:13984 积分:27968 ", "金桥论坛友情链接 更多>> "], "segmented_title": ["侠盗猎车手", "热咖啡", "图片", "gta5", "热咖啡", "mod", "怎么", "用", "_", "游戏玩家", "_", "影音娱乐"], "segmented_paragraphs": [["侠盗猎车手", "热咖啡", "图片", "gta5", "热咖啡", "mod", "怎么", "用"], ["本", "帖", "最后", "由", "Dream", "_", "-", "g", "_", "情", "魄", "于", "2015", "-", "7", "-", "3", "19", ":", 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"清除", "远程桌面", "访问", "痕迹", ",", "删除", "远程桌面", "缓存", "记", "[", "重新", "阅读", "]"]], "title": "如何清除远程桌面访问痕迹,删除远程桌面缓存记_互联网_百度经验"}, {"bs_rank_pos": 3, "paragraphs": ["当前位置:系统城 > 系统教程 > Win7系统教程 > win7系统删除远程桌面连接记录的详细步骤(图文)", "win7系统开启远程桌面连接其他电脑后,会留下连接过的主机地址记录,这样很容易泄露隐私安全。如果你的电脑不是一个人使用的话,那么就有可能泄露你的主机IP,这样很容易受到别人攻击。那么win7系统如何删除远程桌面连接记录呢?我们可以通过注册表来删除远程桌面连接记录,而且方法很简单,下面一起来看下win7系统删除远程桌面连接记录的详细步骤。 ", " 具体方法如下: ", " 1、首先我们点击“开始——运行”,接着输入“regedit”命令后单击键盘上的回车按键,进入注册表编辑器;  2、在打开的注册项里面我们按照顺序依次找到HKEY_CURRENT_USER/Software/Microsoft/Terminal Server Client/Default,在该项下我们可以看到我们远程连接过的主机地址;  3、接着大家把不需要的记录删除即可。 ", " 上述内容就是win7系统删除远程桌面连接记录的详细步骤,简单三个步骤,是不是很简单,这样远程桌面连接记录就被删除了。 ", "热门教程推荐", "热门系统专题", "Win7系统推荐", "深度技术SDJS ghost win7 32位纯净标准版V2016.112016-11-12 人气:8931641 风林火山flhs ghost win7 64位一键装机旗舰版V2016.112016-11-01 人气:8746740 技术员联盟ghost win7 X86(32位)精简旗舰版V2016.102016-10-15 人气:6423223 大地系统ghost win7 64位装机旗舰版V2016.102016-10-20 人气:6003369 中关村zgc ghost win7 64旗舰中文纯净版V2016.122016-12-08 人气:5901790"], 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+{"documents": [{"bs_rank_pos": 1, "paragraphs": [" UID 2766977 帖子 2112 威望 0 多玩草 76 草", " 碎片不够换 "], "segmented_title": ["怎么", "兑换", "加百利", "的", "SS", "碎片", "在线", "等", "急", "~", "~", "~", "~", "~", "~", "~", "~", "~", "~", "~", "~", "~", "-", "地下城与勇士", "-", "多玩"], "segmented_paragraphs": [["UID", "2766977", "帖子", "2112", "威望", "0", "多玩", "草", "76", "草"], ["碎片", "不够", "换"]], "title": "怎么兑换加百利的SS碎片 在线等 急~~~~~~~~~~~~~-地下城与勇士-多玩"}, {"bs_rank_pos": 2, "paragraphs": ["DNF史诗碎片兑换是怎么进行的呢?明天就要更新新版本了,我也是为大家简单的介绍一下碎片兑换的进行方法吧,要不然别等加百利好不容易出现一次却错失了兑换SS碎片的机会哦。话不多说,下面让我们开始正题。", " DNFSS碎片兑换机制解析:", " 玩家在击杀全部的深渊派对怪物后会有一定几率遇上NPC加百利(几率大概和出SS差不多),玩家可以将不用的SS装备碎片在加百利处进行兑换。", " 使用50个不需要的SS装备碎片可以兑换5个自己想要的SS装备碎片(兑换比例为10:1)。", " 其中有几点需要注意的是:", " 1、SS碎片仅支持同级别SS装备碎片兑换,例如你想换别云剑(85SS巨剑),那么你就要用其他85SS装备碎片来进行兑换。 2、其中兑换的50个SS碎片分为5种各10个SS碎片(5件装备,每件装备都需要有10个碎片)。如果你无法找到5种均拥有10个碎片的装备。那么本次兑换将无法进行(例如你有30个手搓光碎片+20个阳剑碎片+15个阴剑碎片+40个天从云碎片,然而你其他所有SS装备的碎片均不满10个,那么你将无法进行本次兑换)。 3、兑换SS装备碎片并没有部位要求,从下图可以看到兑换炎龙之天罚碎片时,是有防具碎片的哦。", " DNF史诗碎片兑换商店就为大家简单介绍到这里,也相信大家对SS碎片兑换有了一个较为直观的了解,个人感觉SS碎片兑换还是相当不错的,虽然兑换比例有点纠结,不过总比不能兑换还是好上不少的。", " DNF大转移更多精彩推荐:", "上一篇:DNF3月24日即将删除道具与结束活动一览  下一篇:赚钱攻略全分享 3月24日版本商机囤货解析 ", "DNF《阿拉德:宿命之门》宣传美图  DNF女圣职者原画图片欣赏 [09-19]DNF帕拉丁90级刷图加点 国庆版本刷图加点 [09-19]2017年921国庆版本\"终末之光\"更 [09-19]2017国庆版本将上线!9.21维护更新删除道具 [09-19]怎么用才划算?2017国庆节周商店凭证兑换攻 [09-19]春节五一国庆各类光环排行 谁才是最强顶尖 [09-18]DNF强化多少会碎?国庆版本强化改版必知Tips"], "segmented_title": ["DNFSS", "碎片", "兑换", "_", "DNF", "史诗", "碎片", "怎么", "兑换", "?", "新版本", "必看", "教学", "演示", "_", "52PK", "DNF"], "segmented_paragraphs": [["DNF", "史诗", "碎片", "兑换", "是", "怎么", "进行", "的", "呢", "?", "明天", "就要", "更新", "新版本", "了", ",", "我", "也是", "为", "大家", "简单", "的", "介绍一下", "碎片", "兑换", "的", "进行", "方法", "吧", ",", "要", "不", "然", "别", "等", "加百利", "好不容易", "出现", "一次", "却", "错失", "了", "兑换", "SS", "碎片", "的", "机会", "哦", "。", "话", "不", "多", "说", ",", "下面", "让", "我们", "开始", "正题", "。"], ["DNFSS", "碎片", "兑换", "机制", "解析", ":"], ["玩家", "在", "击", "杀", "全部", "的", "深渊", "派对", "怪物", "后", "会", "有", "一定", "几率", "遇上", "NPC", "加百利", "(", "几率", "大概", "和", "出", "SS", "差不多", ")", ",", "玩家", "可以", "将", "不用", "的", "SS", "装备", "碎片", "在", "加百利", "处", "进行", "兑换", "。"], ["使用", "50", "个", "不需要", "的", "SS", "装备", "碎片", "可以", "兑换", "5", "个", "自己", "想要", "的", "SS", "装备", "碎片", "(", "兑换", "比例", "为", "10", ":", "1", ")", "。"], ["其中", "有", "几点", "需要", "注意", "的", "是", ":"], ["1", "、", "SS", "碎片", "仅", "支持", "同", "级别", "SS", "装备", "碎片", "兑换", ",", "例如", "你", "想", "换", "别云剑", "(", "85SS", "巨剑", ")", ",", "那么", "你", "就要", "用", "其他", "85SS", "装备", "碎片", "来", "进行", "兑换", "。", "2", "、", "其中", "兑换", "的", "50", "个", "SS", "碎片", "分为", "5", "种", "各", "10", "个", "SS", "碎片", "(", "5", "件", "装备", ",", "每", "件", "装备", "都", "需要", "有", "10", "个", "碎片", ")", "。", "如果", "你", "无法找到", "5", "种", "均", "拥有", "10", "个", "碎片", "的", "装备", "。", "那么", "本次", "兑换", "将", "无法", "进行", "(", "例如", "你", "有", "30", "个", "手", "搓", "光", "碎片", "+", "20", "个", "阳", "剑", "碎片", "+", "15", "个", "阴", "剑", "碎片", "+", "40", "个", "天", "从", "云", "碎片", ",", "然而", "你", "其他", "所有", "SS", "装备", "的", "碎片", "均", "不满", "10", "个", ",", "那么", "你", "将", "无法", "进行", "本次", "兑换", ")", "。", "3", "、", "兑换", "SS", "装备", "碎片", "并没有", "部位", "要求", ",", "从", "下图", "可以看到", "兑换", "炎龙", "之", "天罚", "碎片", "时", ",", "是", "有", "防具", "碎片", "的", "哦", "。"], ["DNF", "史诗", "碎片", "兑换", "商店", "就", "为", "大家", "简单", "介绍", "到", "这里", ",", "也", "相信", "大家", "对", "SS", "碎片", "兑换", "有", "了", "一", "个", "较为", "直观", "的", "了解", ",", "个人感觉", "SS", "碎片", "兑换", "还是", "相当不错", "的", ",", "虽然", "兑换", "比例", "有点", "纠结", ",", "不过", "总", "比", "不能", "兑换", "还是", "好", "上", "不", "少", "的", "。"], ["DNF", "大转移", "更多精彩推荐", ":"], ["上", "一篇", ":", "DNF", "3", "月", "24", "日", "即将", "删除", "道具", "与", "结束", "活动", "一览", "下一篇", ":", "赚钱", "攻略", "全", "分享", "3", "月", "24", "日", "版本", "商机", "囤货", "解析"], ["DNF", "《", "阿拉德", ":", "宿命", "之", "门", "》", "宣传", "美", "图", "DNF", "女", "圣职者", "原画", "图片", "欣赏", "[", "09", "-", "19", "]", "DNF", "帕拉丁", "90", "级", "刷图", "加点", "国庆", "版本", "刷图", "加点", "[", "09", "-", "19", "]", "2017", "年", "921", "国庆", "版本", "\"", "终", "末", "之", "光", "\"", "更", "[", "09", "-", "19", "]", "2017", "国庆", "版本", "将", "上线", "!", "9", ".", "21", "维护", "更新", "删除", "道具", "[", "09", "-", "19", "]", "怎么", "用", "才", "划算", "?", "2017", "国庆节", "周", "商店", "凭证", "兑换", "攻", "[", "09", "-", "19", "]", "春节", "五一", "国庆", "各类", "光环", "排行", "谁", "才", "是", "最强", "顶尖", "[", "09", "-", "18", "]", "DNF", "强化", "多少", "会", "碎", "?", "国庆", "版本", "强化", "改版", "必知", "Tips"]], "title": "DNFSS碎片兑换_DNF史诗碎片怎么兑换?新版本必看教学演示_52PKDNF"}, {"bs_rank_pos": 3, "paragraphs": ["表示一晚上搞了几次都是失败,求老司机解答一下,我是真的看不懂什么意思,。。上次跟一个蓝拳PK,他开场直接把武器插到地上,我一个猛龙断龙斩过去捡了就跑,回城分一看,妈的白装! 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微信消息撤回的这条消息更改成别的文字方法:(以下图片如果不动请用网页版的打开就可以动了,用网页打开,在我的提问中找到这个提问,进入)1、打开这个经验认真看完 http:jingyan.baidu.com/article/9113f81b1d0df32b3214c79a.html 2、按照经验进行操作,里面有详细的操作步骤。持续更新中,只要记住这上经验就可以了。3...", " 过程简单跟大家分享下:1:首先打开个人设置,把原本的微信昵称删除(注意,要全部删除)2:然后黏贴这句话:向您踢了一脚。3:从最后往回删除,你会发现删掉的是前面的字,然后你就可以加上你的名字了!4:长按你要撤消的信息 5:之后撤回消息就会是“撤回消息还向您踢了一脚”", " 微信的“后悔药”:可撤回两分钟内发出的信息,微信即将推出5.2新版本,在新版本中将会出现撤销发送的功能,可以撤回两...参考邮箱的信息撤回功能,一般来说,相同系统内(比如网易邮箱、QQ 邮箱)的邮件撤回,只要对方没有打开,发出去的邮件都是可以撤回的,并没有时间限制,用户也能够依此判断发出的信息是否已被对方阅读。", " 05 接下来,如果有朋友跟你私聊或者在微信群中撤回了一条信息的话,你再也不用担心你会错过了,下拉打开通知栏,你就会看到撤回消息的内容了。另外,如果在锁屏状态下,想要看到的话,就要将通知选项里点开“在锁定屏幕上显示”选项。注意: 这个方法只能防患于未然,如果撤销消息之前没有开启上述功能,那么我们是不能看到...", " 1、微信撤回消息功能是在微信的5.3.1中新增的,所以如果你的微信版本过低的话,请先升级微信。2、如果需要撤回微信消息,长按刚刚发出去的消息,会弹出菜单,其中有撤回选项。3、点击撤回后可以看到提示,会撤回刚才发出去的最后一条微信消息。4、点击确定后,你会看到“你撤回了一条消息”的提示,同时刚才的消息也从聊天...", " 1、更新微信,打开对话框,然后发送一条消息右击对话框,点击撤回 2、大家可以看到微信会提示你撤回了一条消息 大家注意哦,微信在发送2分钟之内撤销哦,不然是撤不回来的", " 怎么设置微信消息在状态栏提醒  如何撤回超过两分钟的微信消息  怎样让微信显示撤回消息并带动作  微信消息如何快速进行中英文翻译  苹果ios版微信无法使用语音功能的解决方法图解  微信公众号怎么申请?及微信公众号功能介绍  微信技巧不为人知的功能  微信公众平台帐号信息设置规则  qq消息提醒功能怎么取消?", " Win8如何设置开机自动联网无需手动点击宽带连接  Windows8和Windows8 RT版的区别介绍  在Word2003中为Word文档中添加水印  直接对内存的访问 (DMA)的启用或禁用  通过修改注册表键值开启Win8中的隐藏功能  Windows 2008之AD LDS轻型目录服务解析  win7系统共享xp打印机设置图文教程  如何快速关闭Win8 Modern UI应用  linux批量管理推送工具mussh和pssh介绍  2015我是电影迷4月第二期答案大全 "], "segmented_title": ["微信", "消息", "撤回", "功能", "在哪里", "设置"], "segmented_paragraphs": [["首页", "/", "卡饭", "教程", "/", "微信", "消息", "撤回", "功能", "在哪里", "设置"], ["微信", "撤回", "消息", "自定义", "设置方法", ":", "在", "手机", "浏览器", "里", "打开", ":", "http", ":", "/", "/", "ruguo", "app", ".", "com", "/", "unicode", ".", "html", "?", "from", "=", "group", "message", "&", "isappinstalled", "=", "0", ",", "注意", "不要", "直接", "在", "微信", "中", "打开", "链接", ",", "否则", "会", "访问", "不", "了", "。", "打开", "链接", "后", "进入", "下面", "的", "名字", "生成", "页面", "。", "网页", "里", "有", "很", "清晰", "的", "教程", ",", "第一步", "输入", "你", "的", "名字", ",", "也", "可以", "是", "你", "的", "微信", "名字", ",", "然后", "在", "后缀", "里", "写上", ":", "并", "亲", 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如果需要撤回微信消息,长按刚刚发出去的消息,会弹出菜单,其中有撤回选项。 步骤阅读 3 点击撤回后可以看到提示,会撤回刚才发出去的最后一条微信消息。 步骤阅读 4 点击确定后,你会看到“你撤回了一条消息”的提示,同时刚才的消息也从聊天记录中删除了。 步骤阅读 5 关于升级微信版本,在微信右上角菜单中的设置,可以找到关于微信。 步骤阅读 6 点击关于微信,即可进入查看微信版本,点击检查新版本即可找到最新的微信版本。 步骤阅读 7 一般微信的最新版本首先以内测形式发放,用户可以通过一些应用商场获得微信最新的内测版本。比如应用宝。 步骤阅读 END", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", " 经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "segmented_title": ["微信", "如何", "撤回", "消息", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["小伙伴", "有没有", "遇到", "微信", "信息", "刚", "发出", "就", "想", "微信", "撤回", "消息", ",", "或者", "微信", "撤销", "发送", "呢", "?", "微信", "发出", "的", "消息", "如何", "撤回", ",", "今天", "来", "介绍一下", "微信", "撤回", "信息", "的", "功能", ",", "了解", "微信", "撤回", "功能说不定", "能", "帮", "上", "大忙", "。"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["1", "微信", "撤回", "消息", "功能", "是", "在", "微信", "的", "5", ".", "3", ".", "1", "中", "新增", "的", ",", "所以", 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西安约克空调售后电话:〔1〕029-6187-5227〔2〕029-6187-5227约克专线为您服务到家,西安市约克空调维修情暖千万家,包您满意,西安市约克空调维修,原厂配件,服务人民-----诚实守信------随叫随到---------服务体贴-------质量最好!----------------西安约克空调售后维修电话24小时报修热线〔1〕029-6187-5227〔2〕029-6187-5227约克品牌(西安)专业维修站点--报修热线--(全程上门为您提供最优质服务!空调专业维修师)", " 24小时报修电话:029-6187-5227相信你的选择没有错!好生活从约克空调开始随着我国人民的生活水平日益提高,空调已成为人们日常生活中必不可少的空调,随着用户的增加空调维修故障也随之大量增加,给用户的日常生活带来了很大的不便!西安约克售后服务中心成立于2006年,400-6578-123自成立以来紧紧围绕\"专业化\"的服务标准,以\"我用心,你放心\"的服务精神促进企业发展壮大,以\"诚信务实\"的服务理念赢取市场和回报社会,使企业在竞争异常激烈的家电市场中连续多年稳健发展,取得了良好的经济效益和社会效益。", " 西安约克空调售后维修服务承诺:", " (1)严格按照维修程序及操作规程维修,确保维修质量。 (2)严把配件质量关,杜绝伪劣配件以枷旧配件的使用。 (3)服务热线24小时有人值班,10分钟内做出回应。维修车间及前台接待节日不休息,保用户随到随修建立维修制度及时成立抢修小组,可随时到达现场抢修。 (4)收费方面严格执行市物价局和我公司收费标准,不夸大故障,杜绝乱收费。", " (5)外地顾客远程故障判断、技术故障解答、需要邮递配件迅速办理。外地客户自行送修的我们会加急为您的机器排除故障,力求当天完成维修。", " (6)经我中心(西安约克空调维修)维修的机器一律实行保修,在保修期内如因维修质量或更换配件质量出现问题,我中心负责返修。", " 约克服务目标:0缺陷,客户100%满意(全市)认真接听客户心", " 公司宗旨:经验丰富,质量保证,安全第一,客户,舒心,安心放心", " 一、企业精神:开拓、创新、求实、进取 二、企业方针:科学管理、质量第一;优质服务、信誉第一 三、企业目标:创一流的品牌 四、员工宗旨:做一流的员工", " 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"西安三星冰箱24小时报修热线〔1〕051267369525〔2〕400--8357--008", "随着网络的发展,网民的增加,网络媒体和传统媒体比起来,发挥着独特的优势,主要体现在以下两个方面:是辐射能力强。", "传统的杂志类媒体的发行量会受到诸多条件的限制,例如印刷成本、发行渠道等。而网络媒体则不同,只要用户能够上网,就能够访问到家电联盟网站,这对传统媒体是一个根本性的优势。", "西安三星冰箱统一维修热线 〔1〕051267369525〔2〕051267369525", "2.以法律、法规及有关规定为准则,以事实为依据才", "4. 售后服务由售后服务中心处理,顾客投诉的由投诉处处理", "客户100%满意,只要您拨打公司24小时报修热线本公司将时间竭诚为您服务", "西安三星冰箱售后服务中心成立于2005年,自成立以来紧紧围绕\"专业化\"的服务标准,以\"我用心,你放心\"的服务精神促进企业发展壮大,以\"诚信务实\"的服务理念赢取市场和回报社会,使企业在竞争异常激烈的家电市场中连续多年稳健发展,取得了良好的经验效益和社会效益。维修人员都有国家劳动部颁发的等级,有多年维修积累的工作经验。不但具有丰富的维修经验, 而且还具备良好的职业素质和高度责任感。维修服务中心始终本着“至城服务、以致求信”的宗旨,“锐意进取、不断开拓”的精神,本公司将时间竭诚为您服务。", "..........您.....的.....满.....意.....我.....们.....的.....追.....求..........", "西安三星冰箱售后)……24小时报修热线〔1〕051267369525〔2〕400--8357--008", "在售后服务工作中我们不断总结经验,不断提高服务质量,不断加强工作力度,逐步形成一套优质,完整的售后服务体系,解决了用户的后顾之优,提高了产品质量的可信度。 推进供给侧结构性改革,是适应和引领经济发展新常态的重大创新,是适应国际金融危机发生后综合国力竞争新形势的主动选择,是适应我国经济发展新常态的必然要求。近年来,深改组力求从体制创新上能推进供给侧结构性改革,着力解决制约经济社会发展的体制制问题,推进五大任务取得了稳当前、利长远的显着成效。", "公司宗旨:经验丰富,质量保证,安全,客户,舒心,安心放心", "一、企业精神:开拓、创新、求实、进取二、企业方针:科学管理、质量;优质服务、信誉", "【西安三星冰箱】【指】【定】【维】【修】【中】【心】好生活从西安三星 冰箱开始。", "“西安三星冰箱\"的服务标准,以\"我用心,你放心\"的服务精神促进企业发展壮大,以\"诚信务实\"的服务理念,赢取市场西安三星冰箱回报社会,使企业在竞争异常激烈的家电市场中连续多年稳健发展,取得了良好的经济效益西安三星冰箱社会效益。维修人员都有劳动部颁发的等级证书,有多年维修积累的工作经验。不但具有丰富的维修经验,而且还具备良好的职业素质西安三星冰箱高度责任感。维修服务中心始终本着“至城服务、以致求信”的宗旨,“锐意进取、不断开拓”的精神,本公司将时间竭诚为您服务", "西安三星冰箱售后维修中心许诺:", "noooo?:emoji:┏━┓?:emoji:?oooo", "?\\_:emoji:┗━┛?:emoji:_/", "(1)严格按照修理程序及操作规程修理,保证修理质量。(2)严把配件质量关,根绝伪劣配件以及废旧配件的运用。(3)服务热线24小时有人值勤,20分钟内做出回应。修理车间及前台接待节假西安三星冰箱不歇息,保证用户随到随修;树立修理准则;及时成立抢修小组,可随时抵达现场抢修。不夸张毛病,根绝乱收费。", "(5)外地顾客长途毛病判别、技术毛病回答、需求邮寄配件敏捷处理。外地客户自行送修的咱们会加急为您的机器排除毛病,力求当天完结修理。", "(6)经我中间(西安三星冰箱修理中间)修理的机器一概实施保修,保修期为12个月,在保修期内如因维修质量或更换配件质量出现问题,我中间担任返修", "(7)客户在我中间修理过机器,修理费半价待遇。", "(8)树立回访准则:定时对我公司修理过的机器运用情况以及我公司的效劳质量情,向用户调查满意率、树立用户满意率调查表", "(9)对客户发布咱们的监督电话,期待客户及有关部门监督咱们的效劳作业。", "西安三星冰箱修理中间具有建设部颁布的专业级施工资质证书,家电协会团体会员西安修理专业服务商。", "西安三星冰箱统一维修热线 〔1〕051267369525〔2〕400--8357--008", "只要您拨打公司24小时报修热线,本公司将时间竭诚为您服务。", "公司宗旨:经验丰富,质量保证,安全,客户,舒心,安心,放心", 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"1、“推进国家治理体系和治理能力化”。过去我们讲国家和社会的关系、和人民的关系,最早是国家统治,后来讲国家管理,现在我们又确立了国家治理。统治就是我强你弱,我是统治者,我管着你、管着社会。后来用的“管理”也有管的意思,我管着你,你要听我的。现在用治理,治理是一个新的词汇,现在在全球也是比较流行的。它强调多主体,大家都是主体。国家管理者是主体,人民也是主体,还有各组织、各单位,都是参加管理的主体之一,所以这是从过去的一个主体变为多主体。治理化就是要更加科学、更加民主,同时也要更制度化、规范化、程序化。这都是我们化的含义,不受过去传统的那种生硬的、干巴巴的,或者是非常强硬的方式,向科2、“经济体制改革是全面深化改革的重点,核心是处理好和市场的关系,使市场在资源配置中发挥决定性作用和更好发挥作用”。这是《决定》提出的一个重大理论观点。发挥经济体制改革的牵引作用,推动生产关系同生产力、上层建筑和经济基础相适应,推动经济社会持续快速健康发展,这一鲜明思路,体现了我国长期处于社会主义初级阶段这个最大实际,也契合发展仍是解决我国所有问题的关键这个重大战3、“加强顶层设计和摸着石头过河相结合。改革是复杂的系统工程。当改革打响攻坚战,迫切需要把顶层设计和基层创新结合起来,在摸石过河中把握规律,在统筹规划中推进改革。强调顶层设计,是改革的大局观。尊重基层首创,是改革的群。“摸着石头过河和加强顶层设计是辩证统一的”,十八大以来,上与下的呼应,最大程度聚合着改革正能量,激发了和地方、和社会的积极性创造性。4、“公有制经济和非公有制经济都是社会主义市场经济的重要组成部分”。说公有制经济和非公有制经济都是社会主义市场经济的重要组成部分,放到了同等重要的位置上。由此体现了我们前面所讲的市场机制的决定性作用,因为市场机制是讲公平的,在市场中各个主体的竞争是公平状态,而不能有政策上的歧视。在同一市场中,因为你是国有的,所以,政策上扶持你;然后你亏损了,后面可以不断地有财政补贴。而非公经济我和你竞争,我亏损了自担。这种竞争就不应该了。所以,这回讲要公平。这次如果要讲亮点的话,它的亮点就强调在这两类的公", "5、“科学的财税体制是优化资源配置、维护市场统一、促进社会公平、实现国家长治", "阐明了财税体制的本质和目的,明确了它的政治职能、社会职能、经济职能,相当于", "指明了财税体制改革的目标。", "营改增的全面推进已开启了中国新一轮财税改革,下一步税制改革还应包括优化收入", "结构与税制结构、完成``改革、改革个人所得税、改革和强化房地产税等多个方面。", "观税负,由于经济总量的增加,要稳定宏观税负就需要继续进行结构性减税,两者是", "立事权和支出责任相适应的制度”。", "可以在完善事权的基础上强调与地方支出责任相适应,形成了财税体制非常重要的一", "此处的改革必须和职能转变改革相配套,事权的重新划分不仅包括一部分上收,一部", "分划归地方,还将有一部分交给社会中介组织。我们不能重复过去“一放就乱,一收", "法,要纳入更复杂的规则,也要相配套。", "8、“赋予农民更多财产权利”。", "让农民拥有更多的财产权,就要有这样一种尝试,让宅基地、农民的自留地、农民的", "承包地能够以某种形式在市场上流转,形成这样一种收益,能够保障农民的生活,因", "为土地是农民的生活和生产资料。", "9、“建立更加公平可持续的社会保障制度”。", "可持续是首次放在“社会保障制度”之前,不仅仅是只社会保障``的可持续性,还包", "括了公平以及保障资金效率的可持续性。", "附近\"的服务标准,以\"我用心,你放心\"的服务精神促进企业发展壮大,以\"诚信务实\"", "的服务理念赢取市场回报社会,使企业在竞争异常激烈的家电市场中连续多年稳健发", "经济效益社会效益。维修人员都有国家劳动部颁发的等级证书,有多年维修积累的工", "作经验。不但具有丰富的维修经验,而且还具备良好的职业素质高度责任感。维修服", "至城服务、以致求信”的宗旨,“锐意进取、不断开拓”的精神,本公司将第一时间", "三星冰箱售后维修中心许诺:"], "segmented_title": ["欢迎访问", "$", "西安", "三星", "冰箱", "网站", "】", "全国", "售后服务", "各", "点", "=", "咨询电话", "-", "西安", "便民"], "segmented_paragraphs": [["欢迎访问", "$", "西安", "三星", "冰箱", "网站", "】", "全国", "售后服务", "各", "点", "=", "咨询电话"], ["三星", "冰箱", "以", "用户", "需求", 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+{"documents": [{"bs_rank_pos": 3, "paragraphs": ["成都正规人流医院有哪些?哪家医院做无痛人流好? ", "成都正规人流医院有哪些?哪家医院做无痛人流好? ", "精选回答(3)", " 你好这个情况可以选择市级以上的医院做无痛人流的", " 2011-07-25 19:49 投诉 ", " 建议你及时到正规医院妇科进行相关检查,医生会根据你的身体状况,妊娠天数,选择适合你的终止妊娠的方法", " 2011-07-26 12:01 投诉 ", " 擅长:阴道炎,盆腔炎,不孕不育,月经不调,宫颈炎,子宫肌瘤,宫颈癌,子宫内膜癌等疾病的诊治。", " 你好,你说这种情况的话。建议你可以去当的是人民医院选择做药物进行流产。或者是说着无痛人流也是完全可以的。一般情况的来话的话,建议你多注意休息。注意个人的卫生。预防感染就可以了。建议你们一个月之内是不能选择同房的。容易造成阴道感染。", " 2017-08-23 10:53 投诉 ", "医生回答(4)", " 擅长:妇科炎症、不孕不育、月经不调、乳腺疾病、计划生育以及现代微创技术等", " 人流的方式有药物流产和一般的人流术及无痛的人流术。具体选择哪种方式人流需要看本人的情况看而定,药物流产是用于停经49天之内的孕妇,人流术用于怀孕的前三个月的都可以用。如果你是第一次怀孕,并且在49天之内就建议用药物流产,不过做流产之前需要去医院做B超看看孕囊的大小在做决定。如果是人流术的话需要做传染病的检查后才能做人流术的。无痛人流前准备工作: 1:停止性生活,以免增加感染阴道炎的危险. 2:去医院的当天早上洗个淋浴,着重清洗外阴部,但注意不要让水进入阴道. 3:穿上便于穿脱的内裤和外裤. 4:最好有人陪.穿梭挂号处,门诊,检验科,交费处,药房,两个人比一个人快些. 5:出门前,带几片卫生护垫,如果符合手术标准那么就可以直接做手术,术后会有一定的出血!6:建议最好是早上空腹过来,因为无痛人流术前是需要禁食水4小时左右,吃了东西有可能会耽误手术时间", " 2016-11-30 17:56 投诉 ", " 擅长:擅长领域:秦秋凤主任从事妇科临床近40年,多次前往北京上海等三甲医院进修学习,对妇科疾病的诊治积累了丰富的经验。", " 您好,做人流最好是去正规的妇产专科医院,一场好的人流主要包括了技术设备的先进、医师专业水平的高、手术环境的无菌度高以及对任 何意外情况预计的处理等方面的综合评估。而正规的妇产专科医院在这些方面更能满足人流的要求,专业性强,安全性高 ,另外,这类医院收费标准公开,手术费用也更合理。", " 2016-12-02 15:11 投诉 ", " 擅长:人流,引产,妇科炎症,内分泌,微创手术,不孕不育治疗等", " 建议选择正规的大医院进行人流手术人流后需要休息2周,保持外阴部清洁卫生,少食辛辣刺激性食物,多吃些富有营养的食物,使身体尽快恢复正常。流产后在没有月经来潮前要禁止性生活,禁止坐浴,禁止阴道冲洗和给药。因为这个时候宫口松弛,内膜脱落,局部防御能力下降,容易引起逆行感染,而造成子宫内膜炎、附件炎等盆腔炎症,严重了可以引起不孕症。", " 2017-05-03 14:51 投诉 ", " 你好,你怀孕了想做无痛人流是可以的,但无痛人流是怀孕10周以内比较合适。月份越小越安全,这样出血会越少。此时胎体尚未形成,子宫不太大,子宫壁肌肉也较厚,胚胎容易吸出来,因而手术时间短,出血量少,人流后身体恢复得也较快。意见建议:你应去医院做b超检查确诊怀孕周数,在医生指导下选择合适的无痛人流方法。", " 2017-05-14 14:57 投诉 ", " 女性内生殖器官中,输卵管、卵巢被称为子宫附件。附件炎是指输卵管和卵巢的炎症。一般指急性附件炎、慢性附件炎、输卵管炎、卵巢炎,但输卵管、卵巢炎常常合并有宫旁结缔组织炎、盆腔腹膜炎,且在诊断时也不易区分,这样,盆腔腹膜炎、宫旁结缔组织炎,就也被划入附件炎范围了。在盆腔器官炎症中,以输卵管炎最常见,由于解剖部位相互邻近的关系,往往输卵管炎、卵巢炎、盆腔腹膜炎同时并存且相互影响。 ", " 全国三甲医院,主任级名医在线坐诊已有124家三甲医院,828位主任医师在线答疑", " 白沙妇产科医生 白沙妇产科医院 三原县眼科医院网上预约 三原县大程医院网上预约 白沙附件炎医生 白沙附件炎医院"], "segmented_title": ["成都", "正规", "人流", "医院", "有", "哪些", "?", "哪家", "医院", "做", "无痛人流", "好", "?", "_", "39", "健康", "问答", "_", "39", "健康"], "segmented_paragraphs": [["成都", "正规", "人流", "医院", "有", "哪些", "?", "哪家", "医院", "做", "无痛人流", "好", "?"], 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沈阳建筑大学位于沈阳浑南新区的主校区占地面积1500亩,建筑面积48万平方米。主校区规划设计体现了以人为本、与自然和谐共生的理念,规划布局合理,建筑形式现代、质朴、简练,功能设施齐全。教学区为网格式、具有东方文化底蕴的庭院组合,有利于资源共享和学科交流。", " 创建时间 学校类型 学科类型 隶属单位 1948年 公立 理工 辽宁省人民政府", " 以上沈阳建筑大学是985吗?沈阳建筑大学是211吗?由高三网收集整理,想要了解更多关于211工程大学和985工程大学最新信息请查看高校介绍频道。"], "segmented_title": ["沈阳建筑大学", "是", "985", "吗", "?", "沈阳建筑大学", "是", "211", "吗", "?", "_", "高三", "网"], "segmented_paragraphs": [["世界上", "最", "美丽", "的", "15", "所", "大学", "校园", "盘点", "长春", "文理科", "特色", "类", "优质", "高校", "沈阳建筑大学", "是", "985", "吗", "?", "沈阳建筑大学", "是", "211", "吗", "?"], ["家长们", "一直", "告诉", "自己", "家", "的", "孩子", "要", "考上", "好", "大学", ",", "提到", "好", "大学", "所有人", "第一时间", "内", "想到", "的", "绝对", "会", "是", "985", "、", "211", "大学", "。", "那么", "大家知道", "哪些", "大学", "是", "211", "以及", "哪些", "是", "985", "大学", "?", "沈阳建筑大学", "是", "985", "吗", "?", "沈阳建筑大学", "是", "211", "吗", "?", "是", "广大", "考生", "和", "家长", "朋友们", "十分", "关心", "的", "问题", ",", "以下", "是", "高三", "网", "为", "大家", "整理", "的", "最新", "沈阳建筑大学", "相关", "信息", ":"], ["沈阳建筑大学", "不是", "211", 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+{"documents": [{"bs_rank_pos": 0, "paragraphs": [" 注册时间: 最后登录: ", "飘窗通常一般能够分为两种形状,首先种是台阶式的,另一种则是彻底落地、与地上天衣无缝的的。飘窗不只能够增强房子的采光和通风效能,还能给房子的外立面增加更多的魅力。有很多人傻傻分不清飘窗和凸窗,那么,飘窗与凸窗的区别?", " 飘窗不像以前传统的平窗只要一面是玻璃,飘窗的三面都装有玻璃,窗台的高度比起通常的窗户要低。这么的规划能够使得室内的视觉空间扩展。在卧室配有飘窗,能够享用足够的室内光线,还能够作为很不错的观景台。", " 凸窗为“凸出修建外墙面的窗户”,通常也叫飘窗,落地窗是一般窗的一种,不属于凸窗,有些文章按窗台的凹凸来差异凸窗和落地窗,还有些人以为飘窗即是落地窗,都是过错的。", " 它们的首要差异有以下几点:", " 1.凸(飘)窗有必要是凸出外墙面的窗户,在规划和使用时就有别于地板(楼板)的延伸,也即是说不能把地板延伸出去而仍称之为凸窗。凸窗的窗台仅仅墙面的一部分,且距地上应有必定的高度,而落地窗则不杰出外墙,通常下设300mm宽,150mm高的挡水台,有的落地窗直接坐落地(楼)板面之上。 2.凸(飘)窗属修建构件,不核算面积,而落地窗算全面积。 3.规范请求酷寒和冰冷区域不宜采用对节能晦气的凸(飘)窗,对落地窗无请求。 4.凸(飘)窗顶板和底板按请求需做保温。", " 凸(飘)窗和落地窗按规范请求都应设置安全防护设备。《住所规划规范》(GB50096-1999)和《民用修建规划公例》(GB50352-1995)对此都有规则。", " 《住所规划规范》第3.9.1及注[外窗窗台距楼面、地上的高度低于0.90m时,应有防护设备,窗台的净高度或防护栏杆的高度均应从可踏面起算,确保净高0.90m。][有效的防护高度应确保净高0.90m,间隔楼(地)面0.45m以下的台面、横栏杆等简单造成无意识攀登的可踏面,不该计入窗台净高。]", " 《民用修建规划公例》第6.10.1.4[临空的窗台低于0.80m时,应采纳防护办法,防护高度由楼地上起核算不该低于0.80m;][注:1住所窗台低于0.90m时,应采纳防护办法;2低窗台、凸窗等下部有能上人站立的宽窗台面时,贴窗护栏或固定窗的防护高度应从窗台面起核算。]", " 《民用修建规划公例》第6.6.3条阐明对可踏面界说:“底部有宽度B≥0.22m,且高度h≦0.45m的可踏部位”。", " 上述二本规范对凸窗防护栏杆设置说法不够清晰,新版《住所规划规范》(GB50096-2011)首次对凸窗(bay-window)给出了规范的界说(凸出修建外墙面的窗户),并在第5.8.2条中对凸窗做了具体的规则:", " [当设置凸窗时应契合下列规则:窗台高度低于或等于0.45m时,防护高度从窗台面起算不该低于0.90m;可敞开窗扇窗洞口底距窗台面的净凹凸于0.90m时,窗洞口处应有防护办法。其防护高度从窗台面起算不该低于0.90m;]", " 注册时间: 最后登录: ", " 《修建工程规划技术办法-修建》(2009年版)是由住宅和城乡建设部工程质量安全监管司安排中国修建规范规划研究院等单位编制的用以辅导民用修建工程规划为主的技术文件,在第10.5节中对临空窗台和凸窗也有具体的规则,应当说是现在对低窗护栏规则首先细首先清晰的一本技术办法。", " 注册时间: 最后登录: ", " 上面就是小编给大家提供的飘窗与凸窗的区别的资料,大家在选择的时候要慎重,希望小编的建议可以帮助大家。", "您现在发表的是匿名回复,您可以登录或者免费注册 昵称: (您可在此修改爱称,不超过20个字符或10个汉字) 依据《互联网用户账号名称管理规定》2015年9月1日起未绑定手机号进行真实身份认证的用户将禁止发布内容,请认证。 现在认证 验证码  看不清楚吗?换一张  发表 预览 ", "请输入验证信息 ", "相关标签 装修效果图 功能间 户型 风格 局部 热门标签 cd架 ", "只有认证会员或版主才能发起活动,请您先登录:)", " 只有认证会员或版主才能发起活动,请先去认证吧:)", "房天下手机客户端", " 点击或扫描下载"], "segmented_title": ["飘窗", "与", "凸窗", "的", "区别", "-", "装修", "攻略", "-", "北京", "房", "天下"], "segmented_paragraphs": [["注册时间", ":", "最后登录", ":"], ["飘窗", "通常", "一般", "能够", "分为", "两种", "形状", ",", "首先", "种", "是", "台阶", "式", 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"凸窗在小户型里的作用重要,由于小户型室内空间都相对紧凑,而凸窗则是室内空间的有效延伸,可以承担类似书桌、茶几、躺椅等功能,提高室内空间的使用效率。在调查中也证明,小户型购买客户对凸窗是普遍认同的,对凸窗的进深要求多在1米以上。主要的特点有依稀的几点:", "1、凸窗是室内窗向室外凸起,呈矩形或梯形,扩展视野,使室内更大限度地感受与亲近自然。2、凸窗的窗台较低甚至为落地窗,属于现代简约风格,几何线条修饰、色彩明快跳跃、外立面简洁流畅,以波浪、架廊式挑板或装饰线、带、块等异型屋顶为特征,立面立体层次感较强,合理运用色块色带处理。3、住建部文件规定:净高2.2米以内的凸窗不算面积,也不会算入销售面积,因此业主购买房屋时不需为凸窗面积增加费用,因此普遍受到购买者的喜欢。另因凸窗丰富了建筑立面,凸窗可直可弧,出挑宽度可大可小,在建筑立面产生丰富的阴影变化,体块穿插,虚实对比,因此也是设计师喜欢的设计元素。4、凸窗可以做成娱乐休闲室(如果客厅不大,只要在外飘窗中间摆一个刻有棋盘的小茶几,左右各摆一个软软的团垫,您就可以和朋友一起喝茶、下棋、谈天说地……不但通风采光一流,视野也是一流)、电视新家园(把电视稳稳当当地放在飘窗台上,再把床头斜对或正对着飘窗而放,接下来,不管是档的电影大片,还是午夜场的悲伤韩剧,你都能舒舒服服地“坦然面对”了)、做一套漂亮的柜子(外窗台上靠边三分之一的空间里,魔术般变出一张精巧的梳妆台,收罗全部的美丽武器)。", "以上就是对房屋内凸窗和飘窗的区别的解答,其实两者是一种窗户的两种不同叫法,众多优良的特点得到了广大用户的喜爱。", "看过本文的人还看过", "相关网友问答"], "segmented_title": ["【", "经验", "】", "房屋", "内", "凸窗", "和", "飘窗", "的", "区别", "是", "什么", ",", "有", "哪些", "特点", "呢", "?", "-", "家居", "知识", "-", "房"], "segmented_paragraphs": [["房", "天下", ">", "北京", "装修", ">", "装修", "流程", ">", "房屋", "内", "凸窗", "和", "飘窗", "的", "区别", "是", "什么", ",", "有", "哪些", 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霸!!! 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"闻", "姊", "来", ",", "磨刀霍霍", "向", "猪", "羊", "。", "开我东阁门", ",", "坐我西阁床", ";", "脱", "我", "战", "时", "袍", ",", "著", "我", "旧", "时", "裳", ";", "当", "窗", "理", "云鬓", ",", "对", "镜", "帖", "花", "黄", "。", "出门", "看", "火", "伴", ",", "火", "伴", "皆", "惊", "忙", ":", "同行", "十二", "年", ",", "不知", "木兰", "是", "女郎", "。"], ["雄", "兔", "脚", "扑", "朔", ",", "雌", "兔", "眼", "迷离", ";", "双兔傍地走", ",", "安能", "辨", "我", "是", "雄雌", "?"], ["机杼", "声", "一声", "连着", "一声", ",", "木兰", "正", "对着", "门", "织布", "。", "突然", "听不到", "了", "织布机", "的", "声音", ",", "只", "听见", "木兰", "的", "叹气", "声", "。"], ["母亲", "问", "她", "在", "想什么", ",", "思念", "什么", "。", "木兰", "说", ":", "“", "我", "没", "想什么", ",", "也", "没", "思念", "什么", "。", "只是", "昨天晚上", "看见", "军", "中", "的", "文", "告", ",", "可汗", "在", "大规模", "地", "征兵", ",", "征兵", "的", "名册", "有很多", "卷", ",", "每", "卷", "都", "有", "父亲", "的", "名字", "。", "父亲", "没有", "成年", "的", "儿子", ",", "木兰", "没有", "兄长", "。", "我愿意", "为此", "去", "买", "鞍马", ",", "从此", "替", "父亲", "出征", "。", "”"], ["于是", "木兰", "到", "东市", "、", "西", "市", "、", "南", "市", "、", "北", "市", "买", "了", "俊", "马", "买", "马具", "买", "嚼子", "和", "缰绳", "。", "早晨", "辞别", "爹娘", "离开", ",", "晚上", "住在", "黄河", "边", ",", "听不到", "爹娘", "呼唤", "女儿", "的", "声音", ",", "只", "听到", "黄河", "流水", "“", "溅", "溅", "”", "的", "声音", "。", "早晨", "又", "离开", "黄河", ",", "晚上", "到", "了", "黑山", "头", ",", "听不到", "爹娘", "呼唤", "女儿", "的", "声音", ",", "只", "听到", "燕山", "胡人", "的", "骏马", "发出", "的", "“", "啾啾", "”", "的", "响声", "。"], ["不远万里", ",", "奔赴", "战场", "。", "像飞一样", "地", "跨过", "一", "道", "道", "的", "关", ",", "越过", "一座座", "的", "山", "。", "北方", "的", "寒气", "传送", "着", "打更", "的", "声音", ",", "寒冷", "的", "月光", "照", "着", "身上", "的", "铠甲", "。", "将军", "和", "战士", "身经百战", ",", "转", "战", "数", "载", ",", "有", "的", "为", "国", "而", "死", ",", "有", "的", "胜利", "归来", "。"], ["归来", "后", "拜见", "天子", ",", "天子", "坐", "在", "朝", "堂", "上", "。", "给", "木兰", "记", "最大", "的", "功", ",", "赏赐", "了", "很多", "财物", "。", "可汗", "问", "木兰", "还", "想要", "什么", ",", "木兰", "说", ":", "“", "我不愿", "做", "大", "官", ",", "希望", "骑", "上", "千里马", ",", "赶快", "回故乡", "。", "”", "[", "1", "]"], ["爹娘", "听说", "女儿", "回来", "了", ",", "互相扶持", "着", "走出", "城外", "迎接", ";", "姐姐", "听说", "妹妹", "回来", "了", ",", "对着", "门", "梳妆打扮", ";", "小弟", "听说", "姐姐", "来", "了", ",", "对着", "猪", "羊", "把", "刀", "磨", "得", "“", "霍霍", "”", "响", "。", "木兰", "打开", "房间", "的", "门", ",", "坐", "在", "房间", "的", "床上", ",", "脱掉", "打仗", "时", "的", "战袍", ",", "穿上", "以前", "的", "衣服", ",", "对着", "窗户", "对着镜子", "理", "头发", ",", "贴", "发髻", "上", "的", "饰物", "。", "走出门", "看", "同", "伍", "的", "士兵", "们", ",", "木兰", "的", "战友们", "一", "见", ",", "都", "十分", "惊奇", "地", "说", ":", "“", "哎呀", ",", "同行", "这么多年", ",", "怎么", "不知", "你", "是", "个", "女孩", "。", "”"], ["提", "着", "兔子", "的", "耳朵", "悬", "在", "半", "空", ",", "雄", "兔", "四", "只", "脚", "不停", "地", "动弹", ",", "雌", "兔", "眯着眼", ",", "所以", "容易", "辨认", "。", "但", "如果", "两只", "兔子", "一起", "并排", "跑", ",", "怎能", "分辨", "出", "哪一个", "是", "雄", "兔", "、", "哪个", "是", "雌", "兔", "呢", "?", "[", "2", "]"], ["1", ".", "这件事", "错综复杂", ",", "虽", "经", "多", "方", "核查", ",", "仍", "给", "人", "以", "扑朔迷离", "之", "感", ",", "使", "之", "难明", "真相", "。", "2", ".", "杨至城", "《", "巧", "使", "敌人", "就范", "》", ":", "“", "敌军", "却", "始终", "扑朔迷离", ",", "不知", "我", "军", "主力", "所在", "。", "”"]], "paragraphs": ["[pū shuò mí lí] 扑朔迷离(汉语成语)编辑 锁定", "扑朔:扑腾的意思;原意指把兔子耳朵提起,雄兔扑腾,雌兔眯眼,可是在地上跑的时候就雌雄难辨了。", "后来形容事物错综复杂,不容易看清真相。《木兰诗》:“雄兔脚扑朔,雌兔眼迷离;双兔傍地走,安能辨我是雄雌?”", "中文名 扑朔迷离  解释 形容事物错综复杂不容易看清真相", "【近义】: 眼花缭乱、千头万绪  【反义】: 一清二楚、一目了然", "1 基本释义  2 成语辨析  3 成语故事  4 成语示例", "【释义】扑朔:扑腾的意思;原意指把兔子耳朵提起,雄兔扑腾,雌兔眯眼,可是在地上跑的时候就雌雄难辨了。后来形容事物错综复杂,不容易看清真相。", "【反义】一清二楚、一目了然 、显而易见、不言而喻。", "【用法】现多用来形容事情杂乱;一般作谓语、定语。", "【出处】①清 梁绍壬 《两般秋雨盦随笔·无题诗》:“钩輈格磔浑难语,扑朔迷离两不真。”② 端木蕻良《曹雪芹》第二三章:“只见他们一色戎装,红缨银枪相映生辉,踩着鼓点,操练起来,由慢而快,扑朔迷离,看得人眼花缭乱。”③ 王蒙 《歌神》:“这些传说尽管扑朔迷离,却唤起了我的希望。”④ 李二和《远行》:“远行人孤独的坐在码头上,遥望着烟霞迷漫的远山和扑朔迷离的河面与神秘的星夜出神。酒鬼上来,他腰挂盛酒的葫芦。”", "⑤北朝民歌《木兰辞》(又名木兰诗或木兰歌):“雄兔脚扑朔,雌兔眼迷离;双兔傍(bàng)地走,安能辨我是雄雌?”", "【例句】①侦探小说中扑朔迷离的情节让我一头雾水,完全忘记了如何思考。②这件事真是扑朔迷离。", "【启示】扑朔迷离的意思本来只是难辨真伪,后渐渐演变为对复杂的情况无从判断,难于了解底细。可用于表示对某件事或某个人的不可了解或难于了解。", "【特别注意】关于“雄兔脚扑朔,雌兔眼迷离”的理解存在两种误区。", "一说这是互文手法,说雄兔和雌兔都是脚扑朔、眼迷离。其实不是,原文意思是:脚扑朔是雄兔的特征,眼迷离是雌兔的特征,当双兔在地上跑时,特征不明显,且人们看不清,所以难以分辨。说明的是跑动时难辨雌雄,若是互文的话,在静止时也是难辨雌雄的了,与本来意思也不同。部分教材存在错误,但大家应该用正确的说法。", "另一说是兔子被提起双耳悬空时,雄兔前脚不断动弹,雌兔双眼眯着——这种说法实在荒唐——当兔子被提起双耳悬空时,只有雄兔子知道痛,雌兔子就不知道痛?事实上,不论什么兔子(只要不是死兔子和病危的),只要被提起双耳悬空,都会睁大眼睛,拼命挣扎,连“扑朔”也不会了(扑朔是爬搔的意思),更别说眼迷离了。", "对于该句子,正确的解释应作:脚扑朔是一种动作,表现的是雄兔子好动的一面;眼迷离是一种神态,表现得是雌兔子好静的一面。然而,当雌雄两只兔子同时在地上跑的时候,雌兔子好静的特征就变得不明显了,因而难以分辨雄雌。在《木兰辞》中,有“脱我战时袍,著我旧时裳。当窗理云鬓,对镜帖花黄”等一系列描写,表现木兰作为一个女孩子的特征重新显露出来之后,再出门见伙伴,伙伴才发现木兰是一“女子”。而木兰的战友之所以“同行十二年,不知木兰是女郎”就是因为木兰将自己作为女孩子的特征掩盖,并且和男人一起行军打仗(即双兔傍地走)。", "【辨形】扑:不能写作“仆”。朔(shuò):不能写读作“sù”。", "【辨义】“扑朔迷离”和“眼花缭乱”都有“不容易看清楚”的意思。但“扑朔迷离”偏重指客观事情错综复杂;“眼花缭乱”偏重指主观感受十分复杂,一时分辨不清。", "唧唧复唧唧,木兰当户织。不闻机杼声,惟闻女叹息。", "问女何所思,问女何所忆。女亦无所思,女亦无所忆。昨夜见军帖,可汗大点兵。军书十二卷,卷卷有爷名。 阿爷无大儿,木兰无长兄。愿为市鞍马,从此替爷征。", "东市买骏马,西市买鞍鞯,南市买辔头,北市买长鞭。旦辞爷娘去,暮宿黄河边。不闻爷娘唤女声,但闻黄河流水鸣溅溅。旦辞黄河去,暮至黑山头。不闻爷娘唤女声,但闻燕山胡骑鸣啾啾。", "万里赴戎机,关山度若飞。朔气传金柝,寒光照铁衣。将军百战死,壮士十年归。", "归来见天子,天子坐明堂。策勋十二转,赏赐百千强。可汗问所欲,木兰不用尚书郎,愿驰千里足,送儿还故乡。", "爷娘闻女来,出郭相扶将;阿姊闻妹来,当户理红妆;小弟闻姊来,磨刀霍霍向猪羊。开我东阁门,坐我西阁床;脱我战时袍,著我旧时裳;当窗理云鬓,对镜帖花黄。出门看火伴,火伴皆惊忙:同行十二年,不知木兰是女郎。", "雄兔脚扑朔,雌兔眼迷离;双兔傍地走,安能辨我是雄雌?", "机杼声一声连着一声,木兰正对着门织布。突然听不到了织布机的声音,只听见木兰的叹气声。", "母亲问她在想什么,思念什么。木兰说:“我没想什么,也没思念什么。只是昨天晚上看见军中的文告,可汗在大规模地征兵,征兵的名册有很多卷,每卷都有父亲的名字。父亲没有成年的儿子,木兰没有兄长。我愿意为此去买鞍马,从此替父亲出征。”", "于是木兰到东市、西市、南市、北市买了俊马买马具买嚼子和缰绳。早晨辞别爹娘离开,晚上住在黄河边,听不到爹娘呼唤女儿的声音,只听到黄河流水“溅溅”的声音。早晨又离开黄河,晚上到了黑山头,听不到爹娘呼唤女儿的声音,只听到燕山胡人的骏马发出的“啾啾”的响声。", "不远万里,奔赴战场。像飞一样地跨过一道道的关,越过一座座的山。北方的寒气传送着打更的声音,寒冷的月光照着身上的铠甲。将军和战士身经百战,转战数载,有的为国而死,有的胜利归来。", "归来后拜见天子,天子坐在朝堂上。给木兰记最大的功,赏赐了很多财物。可汗问木兰还想要什么,木兰说:“我不愿做大官,希望骑上千里马,赶快回故乡。”[1]", "爹娘听说女儿回来了,互相扶持着走出城外迎接;姐姐听说妹妹回来了,对着门梳妆打扮;小弟听说姐姐来了,对着猪羊把刀磨得“霍霍”响。木兰打开房间的门,坐在房间的床上,脱掉打仗时的战袍,穿上以前的衣服,对着窗户对着镜子理头发,贴发髻上的饰物。走出门看同伍的士兵们,木兰的战友们一见,都十分惊奇地说:“哎呀,同行这么多年,怎么不知你是个女孩。”", "提着兔子的耳朵悬在半空,雄兔四只脚不停地动弹,雌兔眯着眼,所以容易辨认。但如果两只兔子一起并排跑,怎能分辨出哪一个是雄兔、哪个是雌兔呢?[2]", "1.这件事错综复杂,虽经多方核查,仍给人以扑朔迷离之感,使之难明真相。2.杨至城《巧使敌人就范》:“敌军却始终扑朔迷离,不知我军主力所在。”"], "bs_rank_pos": 3}, {"title": "扑朔迷离_扑朔迷离的意思_扑朔迷离是什么意思_扑朔迷离什么意思_扑", "segmented_title": 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"模糊不清", "、", "难以", "看清", "真相", "。"], ["手机", "查看", "成语", "扑朔迷离", "的", "意思", ",", "微信", "扫一扫", "页面", "右侧", "二维码", ",", "关注", "查询", "大全", "(", "微信", "号", "www", "911", "cha", ")", "后", "回复", "cy", "8695", "或", "扑朔迷离", "?", "即可"], ["成语大全", "为您", "提供", "扑朔迷离", ",", "扑朔迷离", "的", "意思", ",", "扑朔迷离", "是什么", "意思", ",", "扑朔迷离", "什么", "意思", ",", "扑朔迷离", "的", "近义词", ",", "扑朔迷离", "的", "反义词", ",", "扑朔迷离", "的", "拼音", ",", "扑朔迷离", "的", "解释", ",", "扑朔迷离", "的", "同义词"], ["扑朔迷离", "相关", "成语"], ["成语", ":", "附翼攀鳞", "成语", ":", "蜂攒蚁聚", "成语", ":", "淅", "淅", "沥", "沥", "成语", ":", "白昼", "见鬼", "成语", ":", "四分五剖", "成语", ":", "跋前疐后", "成语", ":", "见机行事", "成语", ":", "来鸿去燕", "成语", ":", "扑朔迷离"]], "paragraphs": ["pū shuò mí lí", "成语解释:扑朔:兔脚搔爬;迷离:兔眼半闭。原指难辨兔的雄雌;比喻辨认不清是男是女。后来形容事情错综复杂;不容易看清真相。", "成语出处:南宋 郭茂倩《乐府诗集 横吹曲辞五 木兰诗》:“雄兔脚扑朔,雌兔眼迷离。双兔傍地走,安能辨我是雄雌?” 911cha.com", "成语注音:ㄆㄨㄕㄨㄛˋㄇㄧˊㄌㄧˊ", "成语用法:扑朔迷离联合式;作谓语、定语;形容事情杂乱。", "成语正音:朔,不能读作“shù”。", "成语辨形:扑,不能写作“仆”。", "成语辨析:扑朔迷离和“眼花缭乱”;都有“不容易看清楚”的意思。但扑朔迷离偏重指客观事情错综复杂;“眼花缭乱”偏重指主观感受十分复杂;一时分辨不清。", "成语例子:敌军却始终扑朔迷离,不知我军主力所在。(杨至城《巧使敌人就范》)", "英语翻译:complicated and confusing ", "俄语翻译:неразбираться", "其他翻译:<德>verwickelt ", "成语故事: 古时候,有个女孩叫花木兰,她是个非常勤劳孝顺的姑娘。 木兰织得一手好布,每天,她总是天刚刚亮,就把自己关在织房里精心地纺线织布。 有一天,她却一直留在自己的闺房,坐在窗前叹气。她的母亲觉得很奇怪,就问她有什么心事。 木兰在母亲的一再追问下,不得不说:“没有什么心事,只是昨晚看见国王征兵的文书,那上面有父亲的名字,可是父亲的年纪大了,已经禁不起征战的辛劳,小弟弟年龄又小,不能代替。女儿我就为这件事焦急。” 停了一会儿,木兰又说:“我从小跟父亲练就一身好武功,可以替父亲去从军。” 母亲为难地说:“可你是个女孩,怎么能去从军呢?” 木兰坚定地说:“我有办法。” 征期到了,花木兰女扮男装告别了父母,随大军到了边塞作战。 战争中,木兰表现得很勇敢,立了很多大功。经过十多年的艰苦战斗, 战争终于结束,木兰胜利归来。论功行赏,皇帝要赏给木兰很多钱物,并封她为尚书郎。 但是木兰说:“感谢皇上恩典,但我不想做官,只求皇上允许我回到故乡与父母团聚。”皇上答应了木兰的请求。 木兰回到家,立刻来到自己房里,换上女孩穿的衣服,梳好辫子,戴上花,露出自己原来女孩的模样走出来。送木兰回来的同伴们一见,都十分惊奇地说:“哎呀,同行这么多年,怎么不知你是个女孩。” 后来有人为此写了一首《木兰诗》,诗的最后几句是: 雄兔脚扑朔,雌兔眼迷离。双兔傍地走,安能辨我是雄雌? 意思是:提着兔子的耳朵悬在半空时,雄兔两只前脚时时动弹,雌兔两只眼睛时常眯着,所以容易辨认。如果两只兔子贴着地面并排跑,谁能分辨出哪一个是雄兔、哪个是雌兔呢? 这个成语指事物在人心目中模糊不清、难以看清真相。", "手机查看成语扑朔迷离的意思,微信扫一扫页面右侧二维码,关注查询大全(微信号www911cha)后回复 cy8695 或扑朔迷离? 即可", "成语大全为您提供扑朔迷离,扑朔迷离的意思,扑朔迷离是什么意思,扑朔迷离什么意思,扑朔迷离的近义词,扑朔迷离的反义词,扑朔迷离的拼音,扑朔迷离的解释,扑朔迷离的同义词", "扑朔迷离 相关成语", "成语: 附翼攀鳞 成语: 蜂攒蚁聚 成语: 淅淅沥沥 成语: 白昼见鬼 成语: 四分五剖 成语: 跋前疐后 成语: 见机行事 成语: 来鸿去燕 成语: 扑朔迷离"], "bs_rank_pos": 7}, {"title": "扑朔迷离的意思,扑朔迷离的读音", "segmented_title": ["扑朔迷离", "的", "意思", ",", "扑朔迷离", "的", "读音"], "segmented_paragraphs": [["【", "解释", "】", ":", "指", "难", "辨", "兔", "的", "雌雄", "。", "形容", "事情", "错综复杂", ",", "难以", "辨别", "清楚", "。", "【", "出自", "】", ":", "《", "乐府诗集", "·", "横吹曲", "辞", "五", "·", "木兰诗", "》", ":", "“", "雄", "兔", "脚", "扑", "朔", ",", "雌", "兔", "眼", "迷离", ",", "双兔傍地走", ",", "安能", "辨", "我", "是", "雄雌", "。", "”", "【", "示例", "】", ":", "敌军", "却", "始终", "～", ",", "不知", "我", "军", "主力", "所在", "。", "◎", "杨", "至", "城", "《", "巧", "使", "敌人", "就范", "》", "【", "语法", "】", ":", "联合", "式", ";", "作", "谓语", "、", "定语", ";", "形容", "事情", "杂乱"], ["【", "近义词", "】", ":", "虚无飘渺", "、", "眼花缭乱", "【", "反义词", "】", ":", "一清二楚"], ["基本", "释义", "【", "释义", "】", ":", "扑", "朔", ":", "扑腾", "的", "意思", ";", "原意", "指", "把", 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diff --git a/PaddleNLP/reading_comprehension/data/demo/trainset/search.train.json b/PaddleNLP/reading_comprehension/data/demo/trainset/search.train.json
new file mode 100644
index 0000000000000000000000000000000000000000..b8d151279e64ff3044b6560ca91766ac05007906
--- /dev/null
+++ b/PaddleNLP/reading_comprehension/data/demo/trainset/search.train.json
@@ -0,0 +1,100 @@
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"项目中有个需求,让用户可以从分享到微信的网页中启动自己的APP,如果本机没有安装该应用程序则打开应用商店并打开该程序在商店中的搜索结果页面。在这里跟大家分享一下:", "首先需要和服务端协商好网页中按钮点击事件的字段", "启动应用程序", "启动应用程序", "各个项目含义如下所示:", "scheme:判别启动的App。", "path:传值时必须的key", "query:获取值的Key和Value", "如果只是启动APP而不需要传值的话,一个scheme字段就足够了", "在客户端(android)需要做的就是在AndroidManifest.xml文件中对MainActivity的中增加一个标签", "", "原来很简单嘛,加上之后运行程序,测试一切正常,退出程序回到桌面,哎呦我去,我的APP跑哪去了?!!!", "原来,当我们在原来的中增加标签后,相当于入口activity由一个隐式的intent来启动,应用图标自然就不见了,", "要解决该问题也比较简单,只需要在该activity中再加一个标签,把挪到新的标签中就可以了。", "        ", "实践:从微信分享的网页中启动app,如果本机安装了该应用可以正常启动,如果没有安装该应用,会跳转到应用宝的网页,提示你下载应用", "如果是从其他浏览器启动app的话,除了自带浏览器和谷歌浏览器外,其他第三方浏览器可能会有兼容性问题,也就是说,有可能无法从第三方浏览器启动app。", "相关文章推荐", "猜你在找", "个人资料", "文章搜索", "文章分类", "文章存档", "阅读排行", "评论排行", "推荐文章", "最新评论"], "bs_rank_pos": 3}, {"is_selected": true, "title": "微信浏览器中点击链接怎么打开本地app_百度经验", "most_related_para": 4, "segmented_title": ["微信", "浏览器", "中", "点击", "链接", "怎么", "打开", "本地", "app", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], 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"等", "领域", ")", ",", "建议", "您", "详细", "咨询", "相关", "领域", "专业人士", "。"]], "paragraphs": ["百度经验:jingyan.baidu.com", "微信已经成为现代人生活中必不可少的一部分,下面我就教大家如何在微信浏览器中打开本地APP吧!", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "1 将手机微信打开。 步骤阅读 2 打开微信中的链接。 步骤阅读 3 如图我们打开百度经验的链接,用微信浏览器进入网页以后,点击右上方如图符号。 步骤阅读 4 进入选择页面,点击“在浏览器”打开。 步骤阅读 5 如图会出现手机已安装的本地APP浏览器,我们选择一个自己想用的本地浏览器点击下方“仅一次”或“总是”都可以打开打开本地APP浏览器。 步骤阅读 6 此时,我们就已经在微信浏览器中将本地APP浏览器打开了 步骤阅读 END", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "bs_rank_pos": 4}], "answer_spans": [[0, 110]], "fake_answers": ["1将手机微信打开。步骤阅读2打开微信中的链接。步骤阅读3如图我们打开百度经验的链接,用微信浏览器进入网页以后,点击右上方如图符号。步骤阅读4进入选择页面,点击“在浏览器”打开。步骤阅读5如图会出现手机已安装的本地APP浏览器,我们选择一个自己想用的本地浏览器点击下方“仅一次”或“总是”都可以打开打开本地APP浏览器。步骤阅读6此时,我们就已经在微信浏览器中将本地APP浏览器打开了"], "question": "微信分享链接打开app", "segmented_answers": [["iOS", "里", "，", "把", "一", "个", "页面", "链接", "分享", "给", "微信", "好友", "（", "会话", "）", "，", "好友", "在", "微信", "里", "打开", "这个", "链接", ",", "也", "就是", "打开", "了", "一", "个", "网页", "，", "点击", "网页", "里", "的", "某", 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"有些朋友睡觉时会带着手表、项链、戒指等等的饰品睡觉,而这样的情况其实是最危险的,不提我们睡觉中流出的汗液会将这些饰品腐蚀吸收将重金属通过毛孔吸收到体内这样的状况,就说有些时候我们做噩梦之时可能就是应为这些小饰品带给身体上的感觉导致大脑被欺骗,从而让人“脑死亡”也就是说大脑因为这些小饰品带来的感觉再和深度睡眠之后深入到潜意识之中的噩梦所结合,觉得自己已经死了自动停止了生命活动的特征导致人在睡梦中猝死。", "相关阅读推荐:", "三大著名补肾固元的长寿穴 身体上面有很多的穴位,如果我们能利用好这些养生穴...", "西红柿能防4种癌 吃法有讲究 西红柿含大量番茄红素、维生素、胡萝卜素及微量元素...", "最养女人的7种食物 菜场就有 女人如花,鲜艳绚烂却总有凋零时,因而每个女人都渴...", "1盐城绝味店买的毛豆吃出苍蝇 2天兴隆的沙琪玛里有好多蚂蚁", "26岁小伙这里太臭竟招人嫌弃 男人尿毒症换肾后还能活多久 男人不可百日无姜竟是因为这 一分钱都不花的中医补肾秘方", "有助于女人长寿的11大新发现 女人额头脂肪填充能维持多久 女子天天喝这东西肾竟然坏了 耳朵里长这物竟让她头晕不已"], "bs_rank_pos": 1}, {"is_selected": false, "title": "有什么方法可以在睡眠中死亡?比如在_快速问医生_免费咨询", "most_related_para": 3, "segmented_title": ["有", "什么", "方法", "可以", "在", "睡眠", "中", "死亡", "?", "比如", "在", "_", "快速", "问", "医生", "_", "免费咨询"], "segmented_paragraphs": [["健康", "咨询", "描述", ":", "有", "什么", "方法", "可以", "在", "睡眠", "中", "死亡", "?", "比如", "在", "睡觉", "前", "吃", "了", "或", "喝", "了", "什么", "东西", ",", "然后", "睡着", "后", "慢慢", "的", "死去", ",", "没有", "痛苦", "。"], ["北京", "军颐", "中医", "医院", "擅长", "失眠", "、", "抑郁", 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"16日凌晨五时许,福建医科大学的大三学生小梅在宿舍睡觉时,突然呼吸急促,还伴有尖叫,后被送医院,但医生也没能挽回这条年轻的生命。据了解,小梅是家中的独女,今年才20岁。同学们都说,没有发现小梅在死前有何异常。目前警方已介入调查。", "睡梦中尖叫舍友以为做恶梦", "小梅的身亡,来得有点突然。据小梅的舍友介绍,当日凌晨5时许,她们在睡梦中,突然听到小梅的尖叫声,“声音还不小,把我们都惊醒了。”其中一位舍友看到,小梅的腿在抽筋,身子在晃动,还在急促地喘气。开始以为小梅在做恶梦,舍友们喊了她名字,小梅似乎安静了一会,但随即又开始尖叫和喘气。意识到不妥,舍友们赶紧拨打了120,并通知了辅导员。", "据小梅的同学介绍,小梅被同学们抬下送到校医室时,心跳已经很慢。校医赶紧给她做心脏按压,进行急救。后来120救护车将小梅送往医院抢救,但也没能挽回小梅的生命。", "还原生前一天活动并无异常", "昨日,在福建医科大学的大学城校区,小梅生前所在的宿舍仍然被封锁着,小梅的舍友也转移到了隔壁宿舍住。“小梅没有说过有不舒服,也没有吃药。”许多同学都表示,没有发现小梅有异常。", "记者采访了小梅的多位同学,大体还原了小梅在15日这一天的活动。上午,小梅在教室自习。下午,观看班里男生的篮球赛,在比赛的休息时间,小梅还上场投了两个球,但没有更多的剧烈运动。比赛后,小梅和班里一大群人去附近的一家川菜馆吃饭。吃饭期间,小梅喝了一点酒。“这家店我们经常去,其它人吃了,也没有明显的不舒服。”一位学生告诉记者。", "当晚11时许,小梅上床睡觉,16日凌晨前后上了一趟厕所。谁也没想到小梅这一躺下,就再没醒过来。", "小梅妈妈:家人无先天性疾病", "小梅突然身亡的消息,让小梅父母措手不及。记者昨日见到小梅的妈妈杨女士,发现她已经哭肿了双眼。", "“在电话里,小梅还说自己肥了,要少吃点零食。我还告诉她,没关系,以后工作了,就会瘦下来。”回忆起母女的最后一次通话,杨女士失声痛哭。", "据杨女士介绍,家人都没有先天性疾病或遗传病。", "昨日,校方证实,小梅系福建医科大学2012级五年制医学影像学专业学生。16日11点30分,该生因抢救无效死亡。校方有关人士表示,该生在送医过程中,学校相关部门负责人、学院领导一直陪同协调处理急救事项。当日,该生所在学院领导陪同家属到荷塘派出所报案。", "在各种社会新闻里,素来健康的人突然死亡总会引起观者的各种猜疑,评论中不乏“好好的大活人因为一点小伤小病就死了?这可能吗?”之类的问题。", "理论上来说,这是有可能的。我们的身体很坚韧,许多人可以带着疾病或是生理缺陷长期过着波澜不惊的生活,看上去完全健康;我们的身体也很脆弱,当那些潜藏的疾病或是缺陷突然爆发的时候,身体机能会遭受沉重的打击,迅速地危及生命,也就是说,死亡可能毫无预兆地降临在任何人身上。是的,我们在谈论猝死,一种由于机体潜在疾病或重要器官功能急性功能障碍导致的意外死亡,它没有我们想象的那么罕见,要知道,在自然死亡的人数中,猝死占了大概10%还多。", "猝死的原因非常多样,心、肺、脑等人体各个重要器官和系统的疾病都有可能,而死神最常用的手段和《死亡笔记》一样是心血管疾病,其中“荣登榜首”的病是冠心玻在当下,我们上了年纪的亲戚朋友里总有几个冠心病患者,该病平时可能相当温和,许多患者根本感觉不到它的存在,可当疾病突然", "发作导致患者心肌梗死的时候,死神就向着他们招手了,心脏破裂、心律失常等各种并发症都能迅速地夺走生命。如果说,冠心病这种常见的慢性病导致的死亡突然有余,却不够让人感到意外的话,死神还有诸如Marfan综合征这样隐蔽的“定时炸弹”。这种病是一种从娘胎里就带出来的遗传病,主要影响骨骼、眼睛和心血管,患者的身材会高大四肢会细长,这看上去还不算太糟糕,有些患者还靠这些身体优势成了运动员。可如果心血管也受到了影响,患者的心脏会随着年龄增长被血压撑得越来越大,主动脉甚至会像气球一样膨大成瘤状,这可就大大的不妙了,想象一下当患者激动或剧烈运动血压骤然升高,那个连接着心脏的“气球”受不了压力破掉造成大出血会是个什么后果吧,美国著名女排球运动员海曼、我国著名排球运动员朱刚这些看似健康乃至强壮的运动员都因此倒在了球场上。", "除了潜在的疾病导致的猝死,有些死亡可以说来得有些“无厘头”。比如青壮年猝死综合征,死者身上经常找不到显著的病变,学界仅从众多案例中总结出了以男青年多见、既往健康、多于睡眠中迅速死亡等特点——是的,年轻人“睡觉死”是可能的!再比如抑制死,学界认为有些人的神经天生比一般人敏感,只要一点很平常的刺激,比如挨几下只能造成皮肉伤的打击、受到惊吓或是呛了冷水等等,就可能通过神经反射抑制心脏活动导致死亡——是的,真有喝水呛了一口就撒手人寰的“喝水死”案例!", "实际的案情常常比理论复杂得多,突然发生的死亡虽有自然发生的可能,却也藏着种种人为的可能性。因此不管是面对死因明确的案例,还是来得“全无道理”的死亡,一个法医下猝死的判断时都得十二分谨慎,尤其是后者,在详细的解剖和检验,结合案情、现场情况、死者既往信息等多种资料,排除各种可能性之后才能下结论。"], "bs_rank_pos": 3}, {"is_selected": false, "title": "带七种东西睡觉--死亡危险_百度文库", "most_related_para": 1, "segmented_title": ["带", "七种", "东西", "睡觉", "-", "-", "死亡", "危险", "_", "百度", "文库"], "segmented_paragraphs": [["带", "七种", "东西", "睡觉", "-", "-", "死亡", "危险", "_", "工作总结", "/", "汇报", "_", "总结", "/", "汇报", "_", "实用", "文档", "。", "带", "七种", "东西", "睡觉", "-", "-", "死亡", "危险"], ["人", "的", "睡眠", "是", "最", "完整", "、", "最", "系统", "、", "最有效", "的", "休息", "方法", ",", "也是", "科学", "养生", "的", "重要", "内容", "。", "但", "如果", "忽略", "了", "睡眠", "中", "的", "一些", "细小", "事情", ",", "会", "对", "健康", "不利", "。", "1", "戴", "「", "表", "」", "睡觉", "。", "有", "的", 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清热解毒、凉血;用于温热发热、发斑、风热感冒、咽喉肿烂、流行性乙型脑炎、肝炎、 腮腺炎 。", "【药物功效分类】 清热解毒药 。", "【用法与用量】 开水冲服,一次10克,一日三次,重症加倍;小儿酌减;预防流感、乙脑,一日10克,连服五日。", "【保存方式】 密闭、防潮、避光干燥处保存。", "1.抗菌抗病毒作用:菘蓝根对多种细菌有作用。水浸液对枯草杆菌、金黄色葡萄球菌,八联球菌、大肠杆菌、伤寒杆菌、副伤寒甲杆菌、痢疾杆菌(志贺氏、弗氏)、肠炎杆菌等都有抑制作用;丙酮浸出液也有类似作用,且对溶血性链球菌有效(皆用琼脂小孔平板法)。对A型脑膜炎球菌之抑菌作用与大蒜、金银花相似。板蓝根、大青叶抗菌、抗病毒的有效成分,有人认为是靛甙。实验证明,靛甙在体内外皆无抗菌作用;体外抗病毒作用亦不突出;在体内排泄又快,故认为它无明显生理和抗菌的活性作用。其有效成分尚待研究。2.抗钩端螺旋体作用:1:100以上的板蓝根或大青叶,在试管内均有杀钩端螺旋体的作用。3.解毒作用:据报道,犬用板蓝根、黄连粉与藜芦同服(各2.0g/kg),能解藜芦毒,降低死亡率;若藜芦中毒后再用之,则无效;分别单用板蓝根粉或黄连粉,效果亦不好。4.提高免疫功能:小鼠腹腔注射板蓝根多糖50mg/kg,可显著促进小鼠免疫功能,表现为:能显著增加正常小鼠脾重。白细胞总数及淋巴细胞数,对氢化可的松所致免疫功能抑制小鼠脾指数、白细胞总数和淋巴细胞数的降低有明显对抗作用;显著增强二硝基氯苯所致正常及环磷酰胺所致免疫抑制小鼠的迟发型过敏反应;增强正常小鼠外周血淋巴细胞ANAE阳性百分率,并明显对抗氢化可的松所致的免疫抑制作用;但板蓝根多糖体外实验对刀豆素A诱导的小鼠脾细胞淋转反应无明显增强作用。此外,板蓝根多糖还能明显增强抗体形成细胞功能,增强小鼠静脉注射碳粒廓清速率。", "5.抗肿瘤作用:对大鼠W256实体瘤和小鼠Lewis肺癌注射靛玉红200mg/kg,连续6-7天,结果对大鼠W256实体瘤的抑制率分别为47-50%和50-58%,皮下注射最减少到每日100mg/kg以下时,其抑制作用不明显。靛玉红500mg/kg灌胃,对大鼠W256实体瘤的抑制率为23-33%,对小鼠肉瘤180的抑制率约30%。靛玉红能延长淋巴白血病L7212小鼠的生存时间20%左右。此外,经小鼠精原细胞法试验证明,靛玉红不能特异性地抑制B型精原细胞和精子细胞。", "6.对白血病作用:药理研究初步表明,靛玉红有破坏白血病细胞的作用。从超微结构形态来看,在靛玉红作用下,变性坏死的细胞多呈肿胀、溶解性坏死。实验中发现靛玉红能增强动物的单核巨噬系统的吞噬能力。单核巨噬系统在机体免疫反应中起一定的作用,故靛玉红的抗癌作用可能与提高机体免疫能力有关。", "词条统计 浏览次数:次 编辑次数:12次历史版本 最近更新: 创建者:laoxutongzhi"], "bs_rank_pos": 0}, {"is_selected": true, "title": "长期喝板蓝根颗粒有哪些好处还坏处_百度知道", "most_related_para": 0, "segmented_title": ["长期", "喝", "板蓝根", "颗粒", "有", "哪些", "好处", "还", "坏处", "_", "百度", "知道"], "segmented_paragraphs": [["板蓝根", "对", "感冒", "、", "流感", "、", "流脑", "、", "腮腺炎", "、", "肺炎", "等", "疾病", "都", "有", "良好", "的", "预防", "和", "治疗", "效果", "。", "而", "正", "因为", "其", "功效", "有", "“", "清热解毒", "”", "作用", ",", "所以", "容易", "被", "人", "认为", "是", "非常", "安全", "的", "“", "居家", "良药", "”", "。", "事实上", ",", "据", "南方医院", "中医科", "教授", "陈达理", "介绍", ",", "长时间", "大", "剂量", "使用", "板蓝根", ",", 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"剂量", "服用", "。", "如果", "在", "用药", "中", "发生", "了", "过敏反应", ",", "首先", "应", "立即", "停药", ",", "并", "迅速", "送往", "医院", ";", "如是", "上消化道出血", ",", "除", "立即", "停药", "外", ",", "还", "应", "禁食", ",", "并", "尽快", "送", "医院", "处置", "。"]], "paragraphs": ["养生导读:板蓝根是一种中成药,家中常备的防治感冒的就有板蓝根冲剂。其实板蓝根的用途不仅是治疗感冒,板蓝根的功效与作用多,对多种细菌性、病毒性疾病都有较好的预防与治疗作用。但要注意了,板蓝根也有副作用,不是所有人都能吃板蓝根的!", "板蓝根是大家非常熟悉的一种中成药。板蓝根是十字花科植物菘蓝和草大青的根,性味苦寒,归心、肺经,具有清热解毒,凉血利咽的功效。自古以来传统医学用之治疗温毒发斑、喉痹、大头瘟、丹毒、痈肿等的热毒证。", "对于流感、病毒感染引起的急性扁桃体炎、咽炎、流行性腮腺炎等疾病,服用板蓝根冲剂可起到预防和治疗作用。成人服用剂量可以每次1～2袋,每日3次。幼儿园和学校易出现流感、腮腺炎、水痘、流脑、手足口病等疾病的传播和流行,若服用板蓝根冲剂可起到良好预防作用。儿童用量为半袋至1袋,每日3次。", "总的来说,板蓝根是清热解毒的中药,毒副作用小,使用方便,很受一些家长青睐。它含有多种氨基酸、靛青等,有清热、解毒、凉血等功效,对多种细菌性、病毒性疾病,譬如流感、流脑、肺炎、肝炎等,都有较好的预防与治疗作用。", "病毒性肝炎:取茵陈、板蓝根、贯众各15克,水煎服,每次100毫升,每日2次。", "腮腺炎:取板蓝根30克,贯众15克,甘草3克,水煎服,每日1剂,连用5天为1个疗程,或取板蓝根30克,野菊花5克,金银花10克,水煎代茶饮。", "此外,如果病情严重的,建议马上就医。", "板蓝根是家中常备的治疗感冒的制剂,也成为家长给儿童预防感冒等疾病的首选。专家提醒,经常服用板蓝根预防疾病并不科学,因为常用板蓝根可能造成孩子免疫功能下降,甚至损害消化系统、造血系统。所以小儿感冒不可常用板蓝根。", "板蓝根作为一种药品,也有一定的毒副作用。当人体的肝脏解毒能力下降时,如果仍然长时间大剂量服用板蓝根,就会造成蓄积中毒,伤害人体消化系统与造血系统,容易造成上呼吸道出血、白细胞减少等,这样的病症在幼儿中尤为常见,这是因为幼儿的各种器官尚未发育完全,肝脏功能尚不完善,体内分泌的解毒酶不足。", "板蓝根属于性寒之药,对于预防和治疗实热之证有较好疗效,但是对于那些脾胃虚寒的幼儿来说,经常服用板蓝根,会让他们出现口淡、疲劳等症状,造成体质下降,不仅不能防病,反而容易致病。", "实际上在临床中使用板蓝根冲剂造成小儿过敏反应、消化系统和造血系统损害的病例屡见不鲜。据研究,板蓝根对消化道有刺激作用,有的患者口服板蓝根后有较明显的消化道粘膜刺激症状,表现为胃肠绞痛和消化道出血(《张冰等,中药不良反应概论》)。有的因服用板蓝根而发生急性溶血性反应,出现黄疸、急性肾功能不全,也有服用板蓝根出现药疹的报道。另外,板蓝根注射液的不良反应案例也非常多。", "虽然板蓝根具有清热解毒的效果,但专家提醒:从中医角度来看,板蓝根并不适合所有人群,脾胃虚寒者、幼儿或年老体虚者及过敏体质者并不合适服用板蓝根。", "药监部门还提示,老人、小孩、孕妇、哺乳期妇女以及糖尿病、高血压、心脏病、肾病等慢性病较严重的患者不可随意服用板蓝根,一定要咨询医生后遵医嘱服用。", "服用“板蓝根”等呼吸道疾病药物时,有过敏史者一定要注意不要轻易服用。对于小儿,千万不能用成人剂量,更不能长期大剂量服用。如果在用药中发生了过敏反应,首先应立即停药,并迅速送往医院;如是上消化道出血,除立即停药外,还应禁食,并尽快送医院处置。"], "bs_rank_pos": 2}, {"is_selected": false, "title": "板蓝根冲剂的功效与作用_健康经验_快速问医生_有问必答", "most_related_para": 1, "segmented_title": ["板蓝根", "冲剂", "的", "功效", "与", "作用", "_", "健康", "经验", "_", "快速", "问", "医生", "_", "有问必答"], "segmented_paragraphs": [["夏天", "的", "天气", "比较", "炎热", ",", "很多人", "喜欢", "吹", "空调", 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我小时候喝的板蓝根,是一种颗粒状的冲剂,可以排除自己身体内的热毒,减少上火,我爸妈为了保护我,不让我在流感期间感冒,家里经常会买板蓝根,他们说可以治疗和预防感冒,直到现在,板蓝根是我家必备的药物,用来治疗感冒效果十分显著。服用板蓝根,一是有利于增强抵抗力,避免感冒,二是它可以杀死体内的病毒,防止感冒进一步恶化。 02 大多数人生病感冒都会喝点板蓝根,一是可以排毒体内的毒素,二是可以减少发烧导致身体内的不适,如果是感冒症状十分严重,并且身体发烫,建议先去医院看医生,在医生的帮助下,可以适当服用降体温的药物,这时,病人最好喝粥,不可以吃辣的,咸的食物,不可以喝酒,不可以吃大鱼大肉等油腻的食物,要多喝点水,注意休息。 03 治疗感冒,除了喝板蓝根,还要注意自己的饮食健康。我们要养成良好的饮食习惯,一日三餐要吃,不要暴饮暴食或睡前进食。另外我们要多吃鱼,补充营养,不要吸烟,少喝酒,不要喝冰水,避免烟酒、生冷食物等对身体的损害。上班的人要善于调节自己的工作节奏,保证充足睡眠,以缓解工作压力。放松精神、保持平和心。", "板蓝根是一种寒性的药物,有些人是不适宜服用的。比如有的人经常感到脾胃不和,容易拉肚子,身体怕冷,体质虚寒的人,就不适宜多喝板蓝根颗粒。因为板蓝根具有寒性,会损伤胃,很可能会带来一系列胃肠道反应,如胃痛、胃寒、没有胃口等。在使用板蓝根的过程中,应根据自己的病情,按量服用,不要把板蓝根当作茶来喝。", "胃痛症状百科 胃痛又称胃脘痛,是以胃脘近心窝处常发生疼痛为主的疾患。胃痛是临床上常见的一个症状,多见急慢性胃炎,胃...>>"], "bs_rank_pos": 3}, {"is_selected": false, "title": "板蓝根颗粒的功效与作用是什么_康爱多网上药店", "most_related_para": 3, "segmented_title": ["板蓝根颗粒", "的", "功效", "与", "作用", "是", "什么", "_", "康爱多", "网上", "药店"], "segmented_paragraphs": [["首页", ">", "用药", "问答", ">", "健康", "专区", ">", "板蓝根颗粒", "的", "功效", "与", "作用", "是", "什么"], ["来源", ":", "康爱多", 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"疾病", "并发症", ",", "服用", "板蓝根颗粒", ",", "一定要", "有", "适应症", ",", "不能盲目", "食用", "。"], ["没有", "口臭", "成就", "阳光", "型男", "女性", "情趣", "用具", "找回", "性福", "生活", "三", "疗程", "轻松", "恢复", "血脂", "正常值", "巧", "用", "避孕套", "让", "浪漫", "升级", "女人", "私处", "水", "润", "老公", "的", "最爱"], ["最", "受", "读者", "关注", "的", "资讯"], ["最新", "用药", "资讯"], ["还", "没有任何", "项目", "!"]], "paragraphs": ["首页 > 用药问答> 健康专区 > 板蓝根颗粒的功效与作用是什么", "来源:康爱多网上药店 发布时间:2013年04月06日", "温馨提示: 在禽流感流行的季节,板蓝根的购买又有如当年抢盐抢醋般上演着。不少市民处于恐慌之中。纷纷购买板蓝根,以预防禽流感。尽管当下并没有证实板蓝根对H7N9的治疗作用。那么,板蓝根颗粒的功效与作用是什么?", "在禽流感流行的季节,板蓝根的购买又有如当年抢盐抢醋般上演着。不少市民处于恐慌之中。纷纷购买板蓝根,以预防禽流感。尽管当下并没有证实板蓝根对H7N9的治疗作用。那么,板蓝根颗粒的功效与作用是什么?", "板蓝根颗粒,主要成分为板蓝根,辅料为蔗糖,糊精。为棕色或棕褐色的颗粒;味甜,微苦。主要有含糖型和无糖型两种。感冒期间服用板蓝根,有利于增强免疫,杀灭体内病毒和致病菌,有利于康复;流感期间和病毒性疾病期间服用板蓝根,有利于增强抵抗力,避免传染。", "在临床上板蓝根清热凉血、抗病毒抗菌作用明显,用量大,堪称中成药之最。研究结果表明,板蓝根具有显著的清除体内能引起体温升高的热原作用,并已分离得到这种活性物质。", "由于发现板蓝能根治疗病毒、细菌感染、流感等疾病有效后,使用板蓝根及制剂(如冲剂、口服液)十分普遍。但是板蓝根属寒凉之品,虽无中毒的报道,但长期大量服用亦会有不良反应,尤其是年老体虚者。素体阳虚又过服寒凉,可出现口淡、疲乏等表现。", "总所周知,吃药用药一定要严格在医师的指导下进行,患者不可以擅自停药或者增减药量,否则可能会导致病情的恶化或者引发其他疾病并发症,服用板蓝根颗粒,一定要有适应症,不能盲目食用。", "没有口臭 成就阳光型男 女性情趣用具找回性福生活 三疗程轻松恢复血脂正常值 巧用避孕套 让浪漫升级  女人私处水润 老公的最爱", "最受读者关注的资讯", "最新用药资讯", "还没有任何项目!"], "bs_rank_pos": 4}], "answer_spans": [[5, 28]], "fake_answers": ["清热解毒、凉血;用于温热发热、发斑、风热感冒、咽喉肿烂、流行性乙型脑炎、肝炎、腮腺炎。"], "question": "板蓝根颗粒的功效与作用", "segmented_answers": [["清热解毒", "、", "凉血", "；", "用于", "温", "热", "发热", "、", "发", "斑", "、", "风热感冒", "、", "咽喉", "肿", "烂", "、", "流行性乙型脑炎", "、", "肝炎", "、", "腮腺炎", "。"], ["板蓝根", "的", "用途", "不仅", "是", "治疗", "感冒", "，", "板蓝根", "的", "功效", "与", "作用", "多", "，", "对", "多", "种", "细菌性", "、", "病毒", "性", "疾病", "都", "有", "较好", "的", "预防", "与", "治疗", "作用", "。"], ["对于", "流感", "、", "病毒感染", "引起", "的", "急性扁桃体炎", "、", "咽炎", "、", "流行性腮腺炎", "等", "疾病", "，", "服用", "板蓝根冲剂", "可", "起", "到", "预防", "和", "治疗", "作用", "。", "幼儿园", "和", "学校", "易", "出现", "流感", "、", "腮腺炎", "、", "水痘", "、", "流脑", "、", "手足口病", "等", "疾病", "的", "传播", "和", "流行", "，", "若", "服用", "板蓝根冲剂", "可", "起", "到", "良好", "预防", "作用", "。"]], "answers": ["清热解毒、凉血；用于温热发热、发斑、风热感冒、咽喉肿烂、流行性乙型脑炎、肝炎、 腮腺炎 。", "板蓝根的用途不仅是治疗感冒，板蓝根的功效与作用多，对多种细菌性、病毒性疾病都有较好的预防与治疗作用。", "对于流感、病毒感染引起的急性扁桃体炎、咽炎、流行性腮腺炎等疾病，服用板蓝根冲剂可起到预防和治疗作用。幼儿园和学校易出现流感、腮腺炎、水痘、流脑、手足口病等疾病的传播和流行，若服用板蓝根冲剂可起到良好预防作用。"], "answer_docs": [0], "segmented_question": ["板蓝根颗粒", "的", "功效", "与", "作用"], "question_type": "DESCRIPTION", "question_id": 91161, "fact_or_opinion": "FACT", "match_scores": [0.9583333333333334]}
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"date", "(", "’", "2003", "-", "10", "-", "17", "21", ":", "15", ":", "37", "’", ",", "’", "yyyy", "-", "mm", "-", "dd", "hh24", ":", "mi", ":", "ss", "’", ")", "from", "dual"]], "paragraphs": ["carekee Happy Learning", "1、Oracle的日期格式Oracle缺省的时间格式即时间数据的显示形式,与所使用的字符集有关。一般显示年月日,而不显示时分秒。例如,使用us7ascii字符集(或者是其他的英语字符集)时,缺省的时间格式显示为:28-Jan-2003,使用zhs16gbk字符集(或其他中文字符集)的时间格式缺省显示为:2003-1月-28。向表中插入数据时,如果不使用转换函数,则时间字段的格式必须遵从会话环境的时间格式,否则不能插入。查看当前会话的时间格式,可以使用以下的SQL语句:SQL> select sysdate from dual; 2、修改Oracle日期格式的方法(1)在sql*plus中修改当前会话的日期格式SQL> alter session set nls_date_format = 'yyyy-mm-dd hh24:mi:ss';将当前会话的时间格式修改为这种格式: 2003-01-28 15:23:38,即四位年-两位月-两位日,空格,24小时的制的小时:分钟:秒。这种修改方法,只对当前会话有效。 注意,是对当前会话,而不是当前的sql*plus窗口。即如果你这样修改之后,又使用connect命令以其他用户连接到数据库或者是连接到其他的数据库,则这个日期格式就失效了,又恢复到缺省的日期格式。", "(1)在sql*plus中修改当前会话的日期格式SQL> alter session set nls_date_format = 'yyyy-mm-dd hh24:mi:ss';将当前会话的时间格式修改为这种格式: 2003-01-28 15:23:38,即四位年-两位月-两位日,空格,24小时的制的小时:分钟:秒。这种修改方法,只对当前会话有效。 注意,是对当前会话,而不是当前的sql*plus窗口。即如果你这样修改之后,又使用connect命令以其他用户连接到数据库或者是连接到其他的数据库,则这个日期格式就失效了,又恢复到缺省的日期格式。(2)修改注册表(只对windows系统)在注册表/hkey_local_machine/software/oracle/home0主键中增加一个字串(8i版本),字串名为nls_date_format,字串的值为你希望定义的时间格式,如: yyyy-mm-dd hh24:mi:ss ,然后重新启动sql*plus。这种修改方法,对sql*plus窗口有效,即不论你打开多少个sql*plus窗口,缺省的都是这种时间格式。修改服务器端的注册表无效,只有修改客户端的注册表才有效。(3)Linux下:oracle用户编辑 .bash_profile下 加入以下内容 export NLS_DATE_FORMAT='YYYY-MM-DD HH24:MI:SS ' 重新登录即可生效(4)用sysdba登录然后更新props$这个表里的字段即可update props$ set value = 'YYYY-MM-DD HH24:MI:SS' where parameter = 'NLS_DATE_FORMAT';D", "DATE格式转换:", "一、在使用Oracle的to_date函数来做日期转换时,很多Java程序员也许会直接的采用“yyyy-MM-dd HH:mm:ss”的格式作为格式进行转换,但是在Oracle中会引起错误:“ORA 01810 格式代码出现两次”。 如:select to_date( '2005-01-01 13:14:20 ', 'yyyy-MM-dd HH24:mm:ss ') from dual; 原因是:SQL中不区分大小写,MM和mm被认为是相同的格式代码,所以Oracle的SQL采用了mi代替分钟。 select to_date( '2005-01-01 13:14:20 ', 'yyyy-MM-dd HH24:mi:ss ') from dual; 二、另要以24小时的形式显示出来要用HH24 假设当前时间为:2013-11-25 18:45:32select to_char(sysdate, 'yyyy-MM-dd HH24:mi:ss ') from dual;//mi是分钟 2013-11-25 18:45:32select to_char(sysdate, 'yyyy-MM-dd HH24:mm:ss ') from dual;//mm会显示月份 2013-11-25 18:11:321.日期格式参数及其含义说明 D 一周中的星期几 DAY 天的名字,使用空格填充到9个字符 DD 月中的第几天 DDD 年中的第几天 DY 天的简写名 IW ISO标准的年中的第几周 IYYY ISO标准的四位年份 YYYY 四位年份", "假设当前时间为:2013-11-25 18:45:32select to_char(sysdate, 'yyyy-MM-dd HH24:mi:ss ') from dual;//mi是分钟 2013-11-25 18:45:32select to_char(sysdate, 'yyyy-MM-dd HH24:mm:ss ') from dual;//mm会显示月份 2013-11-25 18:11:321.日期格式参数及其含义说明 D 一周中的星期几 DAY 天的名字,使用空格填充到9个字符 DD 月中的第几天 DDD 年中的第几天 DY 天的简写名 IW ISO标准的年中的第几周 IYYY ISO标准的四位年份 YYYY 四位年份 YYY,YY,Y 年份的最后三位,两位,一位 HH 小时,按12小时计 HH24 小时,按24小时计 MI 分 SS 秒 MM 月 Mon 月份的简写 Month 月份的全名 W 该月的第几个星期WW 年中的第几个星期", "1.日期时间间隔操作 当前时间减去7分钟的时间 select sysdate,sysdate - interval '7' MINUTE FROM dual; 当前时间减去7小时的时间 select sysdate,sysdate - interval '7' HOUR FROM dual; 当前时间减去7天的时间 select sysdate,sysdate - interval '7' DAY FROM dual; 当前时间减去7月的时间 select sysdate,sysdate - interval '7' MONTH FROM dual; 当前时间减去7年的时间 select sysdate,sysdate - interval '7' YEAR FROM dual; 时间间隔乘以一个数字 select sysdate,sysdate - 8*interval '2' HOUR FROM dual; 2.日期到字符操作 select sysdate,to_char(sysdate,’yyyy-mm-dd hh24:mi:ss’) from dual select sysdate,to_char(sysdate,’yyyy-mm-dd hh:mi:ss’) from dual select sysdate,to_char(sysdate,’yyyy-ddd hh:mi:ss’) from dual select sysdate,to_char(sysdate,’yyyy-mm iw-d hh:mi:ss’) from dual 参考oracle的相关关文档(ORACLE901DOC/SERVER.901/A90125/SQL_ELEMENTS4.HTM#48515) 3.字符到日期操作 select to_date(’2003-10-17 21:15:37’,’yyyy-mm-dd hh24:mi:ss’) from dual"], "bs_rank_pos": 1}, {"is_selected": true, "title": "oracle sql查询语句 时间格式_百度知道", "most_related_para": 1, "segmented_title": ["oracle", "sql", "查询", "语句", "时间格式", "_", "百度", "知道"], "segmented_paragraphs": [["可以", "先", "通过", "to", "_", "char", "将", "时间", "转换", "为", "字符串", ",", "之后", "通过", "to", "_", "date", "方式", "统一", "时间", "样式", ",", "这样", "就能", "灵活", "的", "获取", "时间", "的", "格式", "了", "。", "sql", ":", "SELECT", "to", "_", "date", "(", "to", "_", "char", "(", "sys", "date", ",", 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"绑卡几年,手机号也一样,前几个月都可以解封,这两个月莫名其妙也解不开", "登录环境异常已被限制登录,我也是一用的是WIFI,一直用这个", "那里面的理财怎么办?", "看帮助说可以不解封临时登录把钱提出来,但根本找不到这个入口,有理财资产的也乱封太扯蛋了 详情 回复 发表于 2017-1-4 19:10", "一般设置的是到期自动体现到银行卡。不知道封了微信对这个有没有影响 详情 回复 发表于 2017-1-4 19:11", "确实很牛,解封还不行", "打电话投诉客服", "几千块钱都是微信红包?", "误伤", "前不久也是 2W多的活期在里面 自己的家人也解不了 说不符合资格 真是莫名其妙 反正之前没钱的时候从来没遇到过这种提示", "您需要登录后才可以回帖 登录"], "bs_rank_pos": 4}], "answer_spans": [[13, 74]], "fake_answers": ["1:将手机上的客户端删除,随后重新下载官方最新版本微信2:登录微信,依次按照提示对微信账号进行解封3:账号解封成功登陆后,请勿添加好友及修改微信资料等操作4:尽可能将微信在手机上挂2-3天后,在进行其它操作"], "question": "微信登录环境异常被限制登录怎么办", "segmented_answers": [["1", "：", "将", "手机", "上", "的", "客户端", "删除", "，", "随后", "重新", "下载", "官方", "最新版本", "微信", "2", "：", "登录", "微信", "，", "依次", "按照", "提示", "对", "微信", "账号", "进行", "解封", "3", "：", "账号", "解封", "成功", "登陆后", "，", "请勿", "添加", "好友", "及", "修改", "微信", "资料", "等", "操作", "4", "：", "尽可能", "将", "微信", "在", "手机", "上", "挂", "2", "-", "3", "天后", "，", "在", "进行", "其它", "操作", "。"]], "answers": 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"怀孕的喜悦和最难熬的三个月过去后,我就开始帮宝宝准备出生后的东西了,衣服,鞋子,尿布(不是纸尿裤,而是拿以前的纯棉T,给宝宝裁,嘻嘻,环保又省钱)等等。宝宝在肚子里刚过六个月,就在准备着奶瓶水杯这些宝宝稍微长大一点就要用的用品了。看着国内国外各种品牌,各种外形,各种材质的奶瓶,刚开始真的很纠结,问自己的妈妈呢,说自己小时候哪用过这些,刚出生喝母乳,长大一点大人吃什么,随便喂一点给自己就够了,哪像现在的小孩这么金贵的,各种吃的,用的,眼花缭乱的。现在宝宝也3岁多了,马上就要上幼儿园了,偶尔还是会用奶瓶喝点牛奶。看着虎头虎脑的儿子,想想当初的手忙脚乱,这里也给快要和刚当妈妈的一点点自己总结的心得吧。", "百度经验:jingyan.baidu.com", "1 玻璃奶瓶 玻璃奶瓶的优点是安全性佳、耐热性佳,且不易刮伤、不易藏污垢、好清洗,而且价格也不贵,适合需要多次吃奶的新生儿使用。玻璃奶瓶的缺点就是除了强度不够,易碎,因此适合在家里由妈妈拿着喂宝宝。 玻璃奶瓶 玻璃奶瓶是传统认为最安全的奶瓶,不容易刮花,好清洗,温水一泡,轻轻一晃或者用奶瓶刷子刷一下就好了,不过要注意的是玻璃奶瓶和玻璃杯一样,一下子加开水进去很容易爆裂,我就遇到过一次,那时候宝宝刚满月,想着用开水消毒一下后,装点水给宝宝喝,刚烧好的开水倒进去瓶子就裂了,吓了我一跳,宝宝也吓哭了,刚开始还以为是质量问题,结果网上查了一下,玻璃制品都是这样的。后面知道这个了,也小心了很多。玻璃瓶还有个不好的地方就是重又容易碎,宝宝六个月之前都是用的玻璃瓶,不知道摔坏多少,真是又心疼又担心。 步骤阅读", "百度经验:jingyan.baidu.com", "1 总的来说,玻璃奶瓶除了强度不够,易碎之外,其他品质都优于塑料奶瓶。但塑料奶瓶有个最大的优点就在于其轻巧不易碎,可以让宝宝自己拿,可以出门时携带。所以,玻璃奶瓶主要还是适合小婴儿,父母在家亲自喂养时可以用。当宝宝长大些,想自己拿奶瓶时,塑料奶瓶就要开始派上大用场了。 塑料中,PES,PPSU质轻强度高,不易破碎,高度透明,性能都不错,不考虑价格因素的话,是首选。尤其新新材料PPSU,比PC更易洗、耐用,几近于玻璃。 个人觉得婴儿期还是用玻璃的好,大人拿着奶瓶喂。等宝宝可以自己拿奶瓶喝奶喝水了,就换PPSU的吧,不容易摔碎! 玻璃材质的比较好,有防爆型的,不易碎,也可以用不含双酚A的塑料奶瓶。就是比较贵。 我用了两个,最安全的用玻璃,最方便的用塑料,不嫌贵的话用不含双酚A的PPSU塑料。 END", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "bs_rank_pos": 0}, {"is_selected": true, "title": "塑料奶瓶什么材质好?四大常用材质大比拼_39健康网_育儿", "most_related_para": 4, "segmented_title": ["塑料", "奶瓶", "什么", "材质", "好", "?", "四大", "常用", "材质", "大比拼", "_", "39", "健康网", "_", "育儿"], "segmented_paragraphs": [["核心", "提示", ":", "目前", "市面上", "的", "塑料", "奶瓶", "的", "材质", "主要有", "PP", "(", "聚丙烯", ")", "、", "PPSU", "(", "聚苯砜", ")", "和", "PES", "(", "聚醚砜", "树脂", ")", "三种", ",", "也有", "少量", "的", "PA", "(", "聚酰胺", ")", "材质", ",", "究竟", "哪", "一", "种", "材质", "最好", "呢", "?"], ["孩子", "吃", "得", "多", "、", "吃", "得", "好", ",", "就", "一定", "长", "得", "高", "吗", "?", "中山", "大学", "附属", "第", "一", "医院", "儿科", "权威", "告诉你", ",", "答案", "是否", "定", "的", "。", "孩子", "如果", "吃", "了", "过", "多", "这", "三种", "食物", ",", "不", "但", "不", "长", "个", ",", "还会", "伤身", "!", "孩子", "长高", "一定要", "避免", "的", "三种", "食物", "是", "什么", ",", "关注", "微信", "公众", "号", "39", 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"胸部", "摸上去", "会", "痛"], ["想", "生", "男孩", "AA", "体位", "要", "深入", "实拍", ":", "宝宝", "是", "如何", "生", "出来", "的", "看", "每个月", "的", "胎儿", "像", "什么", "水果", "剖腹产", "横切", "和", "竖", "切", "的", "区别", "哪", "种", "爱爱", "频率", "最", "易", "受孕", "震撼", ":", "婴儿", "产道", "露", "头", "淋漓", "瞬间", "看穿", "腹中", "胎", "宝宝", "性别", "的", "12", "招", "宝宝", "为什么", "喜欢", "“", "重复", "”", "怀孕", "初期", "症状", "-", "身体", "的", "16", "个", "信号", "这", "六", "种", "牛奶", "不要", "给", "宝宝", "喝"], ["只要", "填", "资料", "就能", "免费", "领取", "巧虎", "早教", "课程", ".", ".", ".", "详情", ">", ">"], ["宝宝", "需", "额外", "补", "DHA", "?", "专家", "说", "吃", "这些", "就", "够了", ".", ".", ".", ".", "点击", "阅读"], ["如何", "预防", "春季", "咳嗽"], ["扫一扫", "关注", "39", "健康网", "微信"]], "paragraphs": ["核心提示:目前市面上的塑料奶瓶的材质主要有PP(聚丙烯)、PPSU(聚苯砜)和PES(聚醚砜树脂)三种,也有少量的PA(聚酰胺)材质,究竟哪一种材质最好呢?", "孩子吃得多、吃得好,就一定长得高吗?中山大学附属第一医院儿科权威告诉你,答案是否定的。孩子如果吃了过多这三种食物,不但不长个,还会伤身!孩子长高一定要避免的三种食物是什么,关注微信公众号 39健康网 ,发送 长高查询结果。", "塑料奶瓶什么材质好呢?这个问题很多妈妈都非常关心,尤其是面对超市里各种各样的奶瓶的时候。目前市面上的塑料奶瓶的材质主要有PP(聚丙烯)、PPSU(聚苯砜)和PES(聚醚砜树脂)三种,也有少量的PA(聚酰胺)材质,早期也常用PC(聚碳酸酯)制造奶瓶,但由于PC奶瓶含有双酚A已退出市场。", "由于塑料奶瓶共同的特点就是轻巧耐摔,且以上四种材质均安全不含双酚A,要想知道哪种材质好,可以通过透明度、耐磨性、耐高温性能、清洁难易度、更换时间、价格等来比较。", "透明度和耐磨性:PPSU和PA最佳", "相比于玻璃奶瓶,塑料奶瓶的劣势就是透明度差和容易磨损,使用时间久了表面就会雾化,无法清楚观察瓶内的奶液,且不便于清洁。不过随着科技的进步,PPSU和PA奶瓶的透明度和耐磨性已经可以媲美玻璃奶瓶。", "PPSU一般呈现出淡淡的金黄色,但是透明度极高,且不容易磨损。PA为无色透明材质,透明度也非常好。PP材质则是呈现出淡淡的奶白色,通透度要相对差一些。PES的透明度则是介于PP和PPSU之间。", "耐高温性能:均能耐受100℃以上的高温", "由于奶瓶需要高温消毒,因此一般要求有较好的耐高温特性。PP奶瓶一般能耐120℃高温,PPSU奶瓶可耐180℃高温 ,PES奶瓶也可耐受180℃的高温,PA奶瓶可耐220℃高温。", "清洁难易度:材质关系不大,关键看奶瓶形状", "塑料奶瓶一般比玻璃奶瓶难以清洁一些,因此奶瓶刷必不可少。从实际使用来看,这四种材质的奶瓶清洁难易程度都差不多,关键还是看奶瓶的造型,如果奶瓶造型较奇特且死角较多,一般不容易清洁,所以一般建议妈妈购买奶瓶形状较为周正的产品。", "更换时间:越透明和耐磨使用时间越长", "关于塑料奶瓶的更换时间说法不一,有的商家称3个月就可以更换奶瓶,有的则认为半年才更换,但作为消费者来说,3个月更换一次奶瓶未免太奢侈,通常都是等到奶瓶雾化才开始更换。从这一点来看,如果奶瓶的透明度较高,且比较耐磨,一般能否使用较长的时间,也就是说PPSU、PA、PES的使用时间通常会比PP更长一些,当然这也跟个人的习惯有关。", "有些消费者担心不严格遵守商家的3个月一换,会威胁宝宝健康。其实合格的塑料产品也是坚固耐用的,虽说使用时间不定,但也不至于才用了3个月就老化,很多产品摆在货架上都不止3个月。", "产品价格:PP产品最便宜", "从市面上的产品来看,PPSU(如贝亲、bobo、好孩子)、PES(如新安怡、布朗博士)、PA(如NUK)产品的价格较高,一般在100元左右,而PP产品(大多数牌子都有)的价格要低一些,一般在40～80元不等。奶瓶的价格跟品牌关系也比较好,一般进口品牌售价会相对高一些,如果是同一品牌,PP材质的价格会相对低一些。", "婴儿洗衣液评测 性价比 质地/泡沫 温和度 清洁实验", "突然发烧,一会又退了 新生儿一整天没有排便怎么办 百合吃了都有什么功效 她说胸部摸上去会痛", "想生男孩 AA体位要深入 实拍:宝宝是如何生出来的 看每个月的胎儿像什么水果 剖腹产横切和竖切的区别 哪种爱爱频率最易受孕 震撼:婴儿产道露头淋漓瞬间 看穿腹中胎宝宝性别的12招 宝宝为什么喜欢“重复” 怀孕初期症状-身体的16个信号  这六种牛奶不要给宝宝喝", "只要填资料就能免费领取巧虎早教课程... 详情>>", "宝宝需额外补DHA?专家说吃这些就够了....点击阅读", "如何预防春季咳嗽", "扫一扫关注39健康网微信"], "bs_rank_pos": 1}, {"is_selected": false, "title": "奶瓶什么材质好_百度经验", "most_related_para": 9, "segmented_title": ["奶瓶", "什么", "材质", "好", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["目前", "市场", "上", "的", "奶瓶", "玲", "琅", "满目", ",", "各种", "材质", "都", "有", ",", "玻璃", "奶瓶", "、", "塑料", "奶瓶", "、", "硅胶", "奶瓶", "、", "不锈钢", "奶瓶", "还有", "陶瓷", "奶瓶", "。", "我们", "都", "知道", "病从口入", ",", "所以", "在", "给", "宝宝", "选择", "奶瓶", "的", "时候", "特别注意", ",", "有", "的", "材质", "的", "奶瓶", "会", "产生", "有毒物质", ",", 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"当然", "也", "可以", "选择", "硅胶", "奶瓶", ",", "只是", "硅胶", "还是", "有", "一点", "味道", "的", "。", "END"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["经验", "内容", "仅供参考", ",", "如果", "您", "需", "解决", "具体", "问题", "(", "尤其", "法律", "、", "医学", "等", "领域", ")", ",", "建议", "您", "详细", "咨询", "相关", "领域", "专业人士", "。"], ["个性签名", ":", "希望", "我的经历", "能", "带给", "你", "一", "丝", "帮助"]], "paragraphs": ["百度经验:jingyan.baidu.com", "目前市场上的奶瓶玲琅满目,各种材质都有,玻璃奶瓶、塑料奶瓶、硅胶奶瓶、不锈钢奶瓶还有陶瓷奶瓶。我们都知道病从口入,所以在给宝宝选择奶瓶的时候特别注意,有的材质的奶瓶会产生有毒物质,如pc塑料奶瓶含有含有双酚A的成份,对宝宝的身体发育有影响。到底什么样材质的奶瓶好呢?各种材质奶瓶的优缺点是什么呢?", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "1 刚出生的宝宝,建议使用玻璃奶瓶,一般0-6个月的宝宝很少会自己拿着奶瓶吃奶喝水,所以这个时段建议爸爸妈妈给宝宝买玻璃奶瓶。 2 6个月以上或者开始想自己拿奶瓶的话,可以选择ppsu材质的塑料奶瓶,这样轻巧不容易碎,而且也比较安全,当然也可以选择硅胶奶瓶,只是硅胶还是有一点味道的。 END", "百度经验:jingyan.baidu.com", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:希望我的经历能带给你一丝帮助"], 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使用寿命只有十几次,用一个多星期后,就会出现问题,因为PP最高使用温度为107摄氏度,长期蒸煮消毒会是其使用能力下降硅胶奶瓶 安全 但是很难清洗。玻璃的烫 易碎 也比较重所以建议PPSU。", "百度知道用户 2015-08-07 16:32", "宝宝知道提示您:回答为网友贡献,仅供参考。", "看个人需求和宝宝大小再进行选择。 1、玻璃奶瓶:适合新生儿使用 玻璃奶瓶的优点是安全性佳、耐热性佳,且不易刮伤、不易藏污垢、好清洗,而且价格也不贵,适合需要多次吃奶的新生儿使用。玻璃奶瓶的缺点就是除了强度不够,易碎,因此适合在家里由妈妈拿着喂宝宝。 2、塑料奶瓶:适合三个月以上...的宝宝使用 塑料奶瓶轻便,三个月之后可以给宝宝使用塑料奶瓶。塑料奶瓶的三种材质中PP奶瓶最常见,性能也好,是塑料奶瓶的首选。PES与PP一样拥有众多优点,而且更容易清洗耐用,接近玻璃的优点,不过其价格比较贵。如何不考虑价格因素的话,只考虑安全耐用,哪么新材料PPSU最安全,比PC更易洗、耐用,几近于玻璃。但是PPS奶瓶价格比较贵,同时可供选择的品牌也较少。 3、不锈钢奶瓶:1岁以上宝宝使用 不锈钢奶瓶使用寿命长,价格便宜,可以作为1岁以上宝宝的奶瓶选择。不过因为不锈钢奶瓶不透明,没法看到里面奶量还剩多少。 4、硅胶奶瓶:适合所有年龄段宝宝使用 硅胶奶瓶安全,质量轻,适合任何年龄的宝宝的使用。当然,由于硅胶奶瓶质地柔软,小宝宝使用会觉得更加舒适。 挑选婴儿奶瓶的方法 奶瓶的材质:刚刚出生的宝宝建议使用玻璃奶瓶,因为刚刚出生宝宝抵抗力较差,玻璃奶瓶本身材质因为是玻璃,因此化学特性比较稳定,无论采用什么消毒方式,不会分泌任何对宝宝不良的化学物质。 安全性较好:刚刚出生宝宝,基本都是爸爸妈妈喂哺,因此玻璃奶瓶虽然重量较重,但影响不大;对以能够独立饮食的宝宝,建议可以使用塑料奶瓶,因为耐用,实用性强。如相较玻璃奶瓶,不易破裂。 奶瓶的数量:建议不用材质的各购买两个,一个容量大的,方便在家里喂哺,一个容量小的,便于外出携带目前市场上有宽口径和标准口径两类奶瓶,您可以根据自己的实际情况来选择。个人觉得标准口径比较实惠,无论是奶瓶还是以后更换奶嘴,价格都比较低。宽口径,主要的优点是清洗方便。"], "bs_rank_pos": 3}, {"is_selected": false, "title": 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"宝宝选择奶瓶塑料材质的话是PES和PPSU的最好,但是价格也高些。玻璃材质的建议选择高硼硅材质的,最安全的奶瓶就是玻璃奶瓶,主要原因是玻璃奶瓶在高温清洗和消毒的过程中不会产生有毒物质。", "挑选婴儿奶瓶的方法", "奶瓶的材质:刚刚出生的宝宝建议使用玻璃奶瓶,因为刚刚出生宝宝抵抗力较差,玻璃奶瓶本身材质因为是玻璃,因此化学特性比较稳定,无论采用什么消毒方式,不会分泌任何对宝宝不良的化学物质。", "安全性较好:刚刚出生宝宝,基本都是爸爸妈妈喂哺,因此玻璃奶瓶虽然重量较重,但影响不大;对以能够独立饮食的宝宝,建议可以使用塑料奶瓶,因为耐用,实用性强。如相较玻璃奶瓶,不易破裂。", "奶瓶的数量:建议不用材质的各购买两个,一个容量大的,方便在家里喂哺,一个容量小的,便于外出携带目前市场上有宽口径和标准口径两类奶瓶,您可以根据自己的实际情况来选择。个人觉得标准口径比较实惠,无论是奶瓶还是以后更换奶嘴,价格都比较低。宽口径,主要的优点是清洗方便。", "关于什么材质的奶瓶最好,现在的你充分了解到了吗。", "婴儿内衣什么牌子好 怎样选购婴儿内衣2016-12-26 11:36:00 宝宝爬行垫什么牌子好 怎样选购2016-12-26 11:36:25 安全套什么牌子好 怎样选购2016-12-23 10:52:27 婴儿洗衣液什么牌子好 怎样选购2016-12-23 10:52:36 婴儿米粉什么牌子的好 怎样选购婴儿米粉2017-01-10 15:04:35"], "bs_rank_pos": 4}], "answer_spans": [[4, 7]], "answer_docs": [1], "fake_answers": ["PPSU和PA最佳"], "question": "奶瓶什么材质的最好", "segmented_answers": [["最安全", "的", "用", "玻璃", "，", "最方便", "的", "用", "塑料", "，", "不", "嫌", "贵", "的", "话", "用", "不含", "双酚A", "的", "PPSU", "塑料", "。"], ["PPSU", "和", "PA", "最佳"]], "answers": ["最安全的用玻璃，最方便的用塑料，不嫌贵的话用不含双酚A的PPSU塑料。", 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B:期刊文章 [序号]主要责任者.文献题名[J].刊名,年,卷(期):起止页码 [1]何龄修.读南明史[J].中国史研究,1998,(3):167-173. [2]OU J P,SOONG T T,et al.Recent advance in research on applications of passive energy dissipation systems[J].Earthquack Eng,1997,38(3):358-361. C:论文集中的析出文献 [序号]析出文献主要责任者.析出文献题名[A].原文献主要责任者(可选).原文献题名[C].出版地:出版者,出版年.起止页码 [7]钟文发.非线性规划在可燃毒物配置中的应用[A].赵炜.运筹学的理论与应用——中国运筹学会第五届大会论文集[C].西安:西安电子科技大学出版社,1996.468. D:报纸文章 [序号]主要责任者.文献题名[N].报纸名,出版日期(版次) [8]谢希德.创造学习的新思路[N].人民日报,1998-12-25(10). E:电子文献 [文献类型/载体类型标识]:[J/OL]网上期刊、[EB/OL]网上电子公告、 [M/CD]光盘图书、[DB/OL]网上数据库、[DB/MT]磁带数据库 [序号]主要责任者.电子文献题名[电子文献及载体类型标识].电子文献的出版或获得地址,发表更新日期/引用日期", "[12]王明亮.关于中国学术期刊标准化数据库系统工程的进展[EB/OL].", "[8]万锦.中国大学学报文摘(1983-1993).英文版[DB/CD].北京:中国大百科全书出版社,1996.", "按热门参考文献查阅>>", "电子商务专业论文参考文献 艺术专业论文参考文献范文 小学体育论文参考文献 软件开发毕业论文参考文献 测绘工程论文参考文献 论文参考文献的着录格式要求与规 学术论文参考文献范例 建筑工程论文参考文献格式范例 企业战略管理论文参考文献范例 电子信息工程论文参考文献范例 艺术学教育论文参考文献 论文正文和文后参考文献的标注", "发布时间:2017-06-12参考文献著录注意事项列举 参考文献的著录需要统一的标准和格式,以下是小编为大家推荐的参考文献著录注意事项列举,希望能帮到大家。 1、个人作者(包括译者、编者)著录时一律姓在前,名在后,由于各国(或民族)的姓名写法不同,著录时应特别注... 发布时间:2017-06-12毕业论文参考文献规范参考 参考文献也是要有严格的规格要求的,不合格则可能不通过毕不了业。以下是小编为大家推荐的毕业论文参考文献规范参考,希望能帮到大家。 1.期刊类 【格式】[序号]作者.篇名[J].刊名,出版年份,卷号(期号):起止页码.... 发布时间:2017-06-12行政管理的毕业论文参考文献 行政管理专业的论文参考文献很多,其中哪些比较常用?以下是小编为大家推荐的行政管理毕业论文参考文献,希望能帮到大家。 参考文献一: [1]张志强.内蒙古科技与经济.2005 (7). [2]王晓耕.开放区竞争力评价与发展路线... 发布时间:2017-06-10会计论文的参考文献 会计电算化毕业论文 企业会计电算化的现状与完善对策摘要:随着经济的飞速发展和电子商务的迅速兴起,会计电算化在具体应用中存在着认识不足、人才短缺、信息安全风险、没有统一规范的数据接口等一系列问题,本文针... 发布时间:2017-06-10会计核算论文参考文献"], "bs_rank_pos": 0}, {"is_selected": true, "title": "论文参考文献标准格式如何写_百度知道", "most_related_para": 0, "segmented_title": ["论文", "参考文献", "标准", "格式", "如何", "写", "_", "百度", "知道"], "segmented_paragraphs": [["参考文献", "规范", "格式", "　　", "一", "、", "参考文献", "的", "类型", "　　", "参考文献", "(", "即", "引文", "出处", ")", "的", "类型", "以", "单", "字母", "方式", "标识", ",", "具体如下", ":", "　　", "M", "—", "—", "专著", "C", "—", "—", "论文集", "N", "—", "—", "报纸", "文章", "　　", "J", "—", "—", "期刊", "文章", "D", "—", "—", "学位论文", "R", "—", "—", "报告", "　　", "对于", "不", "属于", "上述", "的", "文献", "类型", ",", "采用", "字母", "“", "Z", "”", "标识", "。", "　　", "对于", "英文", "参考文献", ",", "还", "应", "注意", "以下", "两点", ":", "　　", "①", "作者姓名", "采用", "“", "姓", "在", "前", "名", "在", "后", "”", "原则", ",", "具体", "格式", "是", ":", "姓", ",", "名字", "的", "首", "字母", ".", "如", ":", "Malcolm", 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"CD", "〕"]], "paragraphs": ["参考文献书写格式应符合GB7714-1987《文后参考文献著录规则》。常用的参考文献书写格式是怎么样的呢?YJBYS小编为大家介绍如下:", "(1)期刊[序号] 主要作者.文献题名[J].刊名,出版年份,卷号(期号):起止页码.例如: [1] 袁庆龙,候文义.Ni-P 合金镀层组织形貌及显微硬度研究[J].太原理工大学学报,2001,32(1):51-53.(2)专著[序号] 著者.书名[M].出版地:出版者,出版年:起止页码.例如:[2] 刘国钧,王连成.图书馆史研究[M].北京:高等教育出版社,1979:15-18,31.(3)论文集[序号] 著者.文献题名[C].编者.论文集名.出版地:出版者,出版年:起止页码.例如:[3] 孙品一.高校学报编辑工作现代化特征[C].中国高等学校自然科学学报研究会.科技编辑学论文集(2).北京:北京师范大学出版社,1998:10-22.(4)学位论文[序号] 作者.题名[D].保存地:保存单位,年份.如:[4] 张和生.地质力学系统理论[D].太原:太原理工大学,1998.(5)报告[序号] 作者.文献题名[R].报告地:报告会主办单位,年份.例如:[5] 冯西桥.核反应堆压力容器的LBB 分析[R].北京:清华大学核能技术设计研究院,1997.", "(6)专利文献", "[序号] 专利所有者.专利题名[P].专利国别:专利号,发布日期.", "例如:[6] 姜锡洲.一种温热外敷药制备方案[P].中国专利:881056078,1983-08-12.", "(7)国际、国家标准", "[序号] 标准代号,标准名称[S].出版地:出版者,出版年.", "例如:[7] GB/T 16159—1996,汉语拼音正词法基本规则[S].北京:中国标准出版社,1996.", "(8)报纸文章", "[序号] 作者.文献题名[N].报纸名,出版日期(版次).", "例如:[8] 谢希德.创造学习的思路[N].人民日报,1998-12-25(10).", "(9)电子文献", "[序号] 作者.电子文献题名[文献类型/载体类型].电子文献的出版或可获得地址,发表或更新的期/引用日期(任选).", "例如:[9] 王明亮.中国学术期刊标准化数据库系统工程的[EB/OL].", 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作者.书名[M].出版地:出版社,出版年,起～止页码.吴敏金.分形信息导论[M].上海:上海科技出版社,1998,56～82.(3)新闻报纸文献书写示例:文献序号 作者.文献名[N].报刊名,出版日期(版次).李劲松.21世纪的光电子产生[N].科学时报,2001.02.19(2).(4)论文集文献书写示例:文献序号 析出文献的主要责任者.析出文献题名[A].原文献主要责任者。原文献题名[C].出版地:出版者,出版年,析出文献的起止页码.例如:钟文发.非线性规划在可染毒物配置中的应用[A].赵玮.运筹学的理论与应――中国运筹学会第五届大会论文集[C].西安:西安电子科技大学出版社,1996,468～471.", "(5)学位论文文献书写示例:", "大学生论文写作指导 论文的选题方法 毕业论文开题报告格式 毕业论文开题报告注意... 毕业论文格式 毕业论文注意事项", "文献序号 作者.论文名[D]:[××学位论文].授予单位所在地:授予单位,授予年. 例如:李立千.基于Internet的数控系统故障诊断研究[D]:[博士学位论文].北京:北京航空航天大学,2000.", "(6)报告文献书写示例:", "文献序号 作者.报告题名[R].报告单位所在地:报告单位,报告年.", "例如: 冯西桥.高等教育管理的决策分析[R].长沙:中南大学高等教育研究所,2002.", "(7) 专利文献书写示例", "文献序号 申请者.专利名[P].专利国别:专利号,日期. 例如:[5] 杨务滋,吴万荣,刘忠.预节流双向液压锁[P].中国专利:ZL97208080,1998―05―10.", "(8) 技术标准文献示例", "文献序号 发布单位.技术标准代号[S].技术标准名称。出版地:出版者,出版日期.", "例如:[6] 中华人民共和国国家技术监督局.GB3100―3102[s].中华人民共和国国家标准一量与单位.北京:中国标准出版社,1994-11-01.", "(9) 电子文献书写示例", "文献序号 主要责任者.电子文献题名[电子文献及载体类型标识].电子文献的出处或可获得地址,发表或更新日期/引用日期(任选).", "例如:[9] 王明亮.关于中国学术期刊标准化学数据库系统工程的进展[EB/OL].", "例如:[3] Sun Microsystems.The Java TM2 Enterpri se Edit ion Developer’S Guide.", "论文的其他格式要求,请严格按照贵阳学院关于毕业论文(设计)的管理文件规定。", "在毕业论文核查中,凡参考文献体例与上述规定格式严重不符者,将责令重作。请同学们在论文定稿时按以上要求修改,以免影响正常毕业。", "知识扩展:在引用参考文献的时候我们应该注意", "1、尽管直接引用在论文写作中比较少见,但是对于原始资料的改写却很常见。如果使用了他人的思想和成果,就需要在正文里注明出处,并将其列在文章最后的文献列表里。2、参考文献不仅是对他人贡献的一种承认,也可进一步引领读者获取其他感兴趣的文献中的信息。另外,文献还给编辑提供了审稿人信息,并显示了作者对本专业领域的熟悉程度。3、大多数作者会收集超出稿件所需要的文献。从所有文献中立刻筛选出相关文献比较困难。评述文章因包含大量信息,所以含有很多文献,而研究论文则不同,它应当只列出最相关的文献,并且为使读者容易阅读,应尽量将文献数量控制在合适的或期刊要求的规模。4、最相关文献通常是期刊论文、书籍和博士学位论文。会议摘要、会议论文集、个人通信和未出版的数据可以在文中以括号引用,但这些文献一般不列在文后的参考文献里,通常只能用来支持研究结果,而不应用来支持任何重要的结论,不能作为最相关或有效的文献。", "为降低文献数量,引用原始文章并选择最重要的、水平最高的或最近的论文,而不是列出关于某个话题的所有论文。应当引用第一手资料来证实特定的研究结果,此类资料是经过同行评审出版的科学家的原始数据、结果和理论。在主题的概述中,也可使用评述文章,意在提供关于某个主题的全面概述。", "引用别人的作品都必须注明完整的出处——脚注或者尾注都可以。故而,我们在引用参考文献的时候还需要学会正确的标注。", "更多相关文章推荐阅读:", "本科毕业论文参考文献格式相关推荐"], "bs_rank_pos": 4}], "answer_spans": [[200, 218]], "fake_answers": ["【格式】[序号]作者.篇名[J].刊名,出版年份"], "question": "论文参考文献的格式", "segmented_answers": [["【", "格式", "】", "[", "序号", "]", "作者", ".", "篇", 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　　 　　2、独特精美的女生生日礼物,适合心中特别的人。可以挑一个特别的日子,送自己手工DIY的工艺品、独家的收藏品等,满足她喜欢特别的小心思,让她感受到你的用意,让别人投以羡慕的眼光。 　　3、代表自己心意的小玩意儿,适合普通的女同学、小妹妹、比较疏远但想与之交朋", "如果你爱她,在她有男朋友的时候,就不要再去打扰她了,可能你的出现会打扰到她的生活。想送礼物的话,可以去照相馆提前付款,让她照一套写真照,把写真照的票子以及地点给她。十八岁的生日的留念,很有意义!", "十八岁的女生刚好是蜕变的阶段、送一些成熟点的东西、比如说香水、口红、高跟鞋等、一定要好牌子、我建议香水就买Dior、口红选MAC的、去专柜多看看。这些其实不怎么会用到、但是可以满足一个女孩子那种向往成熟的虚荣心。加油噢", "礼物不在贵不贵,你应该送她一些她喜欢的东西,或者她现在缺什么就送什么吧,你对她好,她自然慢慢就会记住你的", "没希望还是算了吧 付出越多对你自己伤害越大"], "bs_rank_pos": 1}], "answer_spans": [[34, 39]], "answer_docs": [0], "fake_answers": ["创意U盘。2、水晶钢琴"], "question": "送女生什么生日礼物比较好", "segmented_answers": [["1", "、", "创意", "U盘", "。", "2", "、", "水晶钢琴", "。", "3", "、", "DIY", "报纸杂志", "。", "4", "、", "饰品", "礼盒", "。"]], "answers": ["1、创意U盘。2、水晶钢琴。3、DIY报纸杂志。4、饰品礼盒。"], "entity_answers": [["创意U盘", "水晶钢琴", "DIY报纸杂志", "饰品礼盒"]], "segmented_question": ["送", "女生", "什么", "生日礼物", "比较好"], "question_type": "ENTITY", "question_id": 91172, "fact_or_opinion": "OPINION", "match_scores": [0.4799999999999999]}
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固定期限劳动合同到期后单位可以不需要任何理由而直接终止合同不续签,只需要按照劳动者2008.1.1后的该单位工龄支付经济补偿金。 而单位想要解除无固定期限劳动合同必须要有法定的理由,否则就属于违法解除,劳动者可以要求单位继续履行合同,不要求单位继续履行合同的,单位应当支付双倍的经济补偿金。而且经济补偿年限是从劳动者入职之日算起的(对于2008.1.1前入职的有利)。单位违法解除劳动合同的成本更高。当然,如果是依法解除劳动合同,那么无固定期限劳动合同的区别不大,最多是依据劳动合同法第四十一条规定解除劳动合同时在同等条件下有优先留用的权力。", "订立无固定期限合同的好处主要在于劳动关系的相对稳定性。 无固定期限劳动合同,是指用人单位与劳动者约定无确定终止时间的劳动合同。无固定期限合同和固定期限在合同订立主体、订立程序和订立形式上均无差别,其最主要的区别即在合同期限上,固定期限合同有确定的终止期限,无固定期限合同没有确定的终止期限。 没有确定终止时间,是指劳动合同没有一个确切的终止时间,劳动合同的期限长短不能确定,但并不是没有终止时间。只要出现法律规定的条件,无固定期限劳动合同也同样能够解除和终止。 订立无固定期限的劳动合同,劳动者可以长期在一个单位或部门工作。这种合同适用于工作保密性强、技术复杂、工作又需要保持人员稳定的岗位。这种合同对于用人单位来说,有利于维护其经济利益,减少频繁更换关键岗位的关键人员而带来的损失", "没有特别的好处,若硬要说好处:①在企业处于一些特殊情况下会先留无固定的人;②省时省力。其他基本没有什么不同。"], "bs_rank_pos": 1}], "answer_spans": [[121, 135]], "fake_answers": ["劳动者不用担心用人单位因为劳动合同到期而与劳动者终止劳动合同的情形出现。"], "question": "无固定期限劳动合同的好处", "segmented_answers": [["劳动者", "不用担心", "用人单位", "因为", "劳动合同", "到期", "而", "与", "劳动者", "终止", "劳动合同", "的", "情形", "出现", "。"], ["无", "固定", "期限", "劳动合同", "，", "是", "指", "用人单位", "与", "劳动者", "约定", 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"现在", ",", "我们", "跑到", "这里", ",", "休整", "一", "下", ",", "又", "可以", "继续前进", "。", "[", "1", "]"], ["参考资料", "1", ".", "揭秘", "中国", "驻", "吉布提", "军事基地", ":", "美国", "都", "要", "给", "腾", "位置", "!", ".", "西陆网", "[", "引用", "日期", "2017", "-", "01", "-", "22", "]", "2", ".", "吉布提", "美军基地", "旁", "中国", "开", "建", "保障", "设施", ".", "光明网", "[", "引用", "日期", "2017", "-", "01", "-", "22", "]"], ["词条", "统计", "浏览次数", ":", "次", "编辑", "次数", ":", "2", "次", "历史", "版本", "最近更新", ":", "创建", "者", ":", "sky", "天天向上", "zxc"]], "paragraphs": ["中国驻吉布提基地编辑 锁定", "吉布提,这个位于东非亚丁湾西岸的神秘小国,它不仅是美国在非洲的唯一军事基地,法国、日本也陈兵于此。如今,中国也将宣布在此建设海外第一个永久军事基地,成为中国国际安全影响力不断扩大的最新标志。[1] 中文名 中国驻吉布提基地  外文名 China base in Djibouti  地点 东非亚丁湾西岸 1 中国驻吉布提基地介绍 2 建设吉布提军事基地的意义 吉布提是一个位于非洲东北部亚丁湾西岸的国家,东临红海进入印度洋的要冲曼德海峡,是世界能源供应的第四大海洋通道,战略位置十分重要。美国媒体近日关注中国的吉布提“军事基地”,描述了“中国军事基地的兴起”。文章称,为了给“中国基地”腾出空间,美军还将部分设置在吉布提港湾北边的设施搬迁到了南部。吉布提美军基地司令官库尔特·桑塔格对中国“军事基地”的存在表现出“难以接受”的反应。[2] 随着中国在非洲吉布提的首个海外“军事基地”建设步入快车道,当地的军事紧张氛围也在增强。吉布提美军基地距离中国“军事基地”仅十几公里,美方为此焦虑不已。中国外交部和国防部新闻发言人先后证实,中国和吉布提已就中方在吉布提建设保障设施达成一致。这是中国在海外建设的首个后勤保障设施。“吉布提欢迎中国”,吉布提外交部长尤素福撰文称:“在我们与恐怖主义和海盗作斗争时,中国是我们重要的战略伙伴。”“中国建立保障设施,不会借此谋求军事干预地区和其他国家事务,也不会借此谋求军事扩张,中国一贯奉行防御性国防政策。”中国海军军事学术研究所研究员张军社介绍,军事基地是指驻扎一定数量的兵力,并以此为依托遂行作战和训练任务的地方。而中国在吉布提的后勤保障设施只用来储存油料、食品、蔬菜水果等,更多的是服务于中国在亚丁湾、索马里海域执行护航任务的中国海军舰艇编队。[2]", "吉布提位于亚丁湾的西岸,它面积只有2.3万平方公里。值得注意的是,该地区同时也是红海的唯一出海口,从这里向北通过苏伊士运河可抵达地中海,向东可直通阿拉伯海及印度洋。而这条航线正是全球最重要的海上贸易通道。", "近些年来,随着中非之间交流的增强,具有独特地理和战略位置的吉布提越来越受到中国企业的重视,他们在吉布提的基础设施建设方面发挥着不可替代的作用。", "到2030年,我国可能会是世界上最大的债权国,我们在海外的投资可能会超过三万亿美元,我们在海外从业人员超过一千万。这样一笔巨大的资产和大量的民众在海外,军事力量要提供保护。此外,我军要参加联合国的维和维稳任务,包括反海盗、反恐等等,以及和其他大国的合作。", "中国海军出外巡航的次数越来越多,难度也越来越大。正是在这种多方面因素下,建设中国自有的海外军事基地就显得异常重要。", "比如亚丁湾巡航舰队,以前出门巡航一次,战士们几个月下不了甲板,连陆地上的陌生人都很少见到。另一方面,海外军事基地的建立还能大大提高我国海军远程续航能力。以前,咱们是跑到这里,就只有打道回府,现在,我们跑到这里,休整一下,又可以继续前进。[1]", "参考资料 1. 揭秘中国驻吉布提军事基地:美国都要给腾位置! .西陆网[引用日期2017-01-22] 2. 吉布提美军基地旁 中国开建保障设施 .光明网[引用日期2017-01-22]", "词条统计 浏览次数:次 编辑次数:2次历史版本 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"我军首个真正意义上的海外基地——吉布提保障基地有望年内竣工,这将为中国军队尤其是海军在非洲之角进行护航、撤侨等行动提供一个大本营,而中国海军又会派出怎么样的一支舰艇编队常驻那里,很受关注也很有想象空间。", "中国海军护航编队官兵在吉布提港防波堤上留下的涂鸦,中国海军将成为这里的常客", "今年春节期间的央视七套《军事报道》节目,一则报道军委领导在春节慰问境外执行任务官兵的新闻,显示我军先遣人员已经进驻“中国人民解放军驻吉布提保障基地联合先遣组值班室”,这显示这一基地目前不仅已经在加速建设,同时我军先遣组人员也已到位,为接收基地做好准备。", "根据俄罗斯媒体和国外媒体披露的基地建设最新进展信息,我军驻吉布提保障基地很有可能会在年内完工。2016年8月外媒发布的卫星图片显示,位于吉布提共和国首都吉布提市西郊的这一港口不仅完成了土地平整,码头、防波堤也已基本建好,营房等建筑也建了不少。", "但这一基地始终定位于小型保障基地,整体规模有限,中国海军未来也很难在此部署大批作战和保障舰艇。外媒报道这一基地仅有90英亩,因此和中国海军一些大型海军基地如青岛小口子军港、三亚亚龙湾军港相比,它不可能具备大型军港完整的功能,如大规模的武器弹药仓库、大型油库以及舰艇消磁站甚至是修船厂等设施。", "这意味着,一支小而精干的舰艇编队会成为我军驻吉布提保障基地的“常客”。2008年持续至今的亚丁湾护航任务,中国海军标准的配置都是2艘水面作战舰艇和1艘综合补给舰,随着海盗袭击事件减少,近年来更多的是以2艘054A护卫舰和1艘综合补给舰这样的组合,而一支类似规模的舰艇编队,也更适合吉布提基地的保障能力。", "吉布提地理位置上处于非洲之角区域,扼守红海、曼德海峡等战略要道,虽然主要是为了打击海盗、为我国途径这一重要航道的商船护航,但我海军护航编队也在过去几年中也参与执行了利比亚撤侨、叙利亚化学武器护航等任务,需要从这一区域千里驰援,因此采用全柴动力、续航力很大的054A型护卫舰极受海军欢迎。", "吉布提基地投入使用后,我军舰艇将有更为便利的常态化驻泊条件,不再需要频繁的往返吉布提、阿曼等港口进行补给和修整,对作战舰艇的自持力要求也无需那么高,一艘自持力较强的054A型护卫舰,搭配一艘更小一些的053H3型(江卫II型)甚至是056型护卫舰,会成为更为经济的选择,将054A这样的主力舰艇更多的留在自家大门,投入南海、东海等热点海区。", "在中东和非洲,我军海外任务更多的还是撤侨、护航等非战争军事任务,有一定对海对空能力的护卫舰作为作战舰艇已经足够,考虑到撤侨任务需要舰艇具备更好的居住条件,072型坦克登陆舰和新型岛礁运输舰,都是5000吨左右,对码头要求比较少,也可部署在此,可以执行运输、撤侨甚至搭载陆战队进行小规模武力打击任务,而1艘综合补给舰也可更好的支持此类行动。", "在海外基地使用上,美国、法国和英国等国家早已是“老司机”,有所侧重的部署军事力量是常态化做法,如法国海军在法属波利尼西亚等海外领地,通常都只有1艘2600吨级的花月级护卫舰常态化部署,一舰守卫一方,对这些相对和平的区域也是更为经济的做法。", "在海外军事力量部署方面,我军还是一位好好学习的好学生,适当借鉴“老司机”的做法少走弯路,没错的。(作者署名:乌龙防务评论)", "本栏目所有文章目的在于传递更多信息,并不代表本网赞同其观点和对其真实性负责。凡本网注明版权所有的作品,版权均属于新浪网,凡署名作者的,版权则属原作者或出版人所有,未经本网或作者授权不得转载、摘编或利用其它方式使用上述作品。", "新浪军事:最多军迷首选的军事门户!"], "bs_rank_pos": 1}, {"is_selected": false, "title": "吉布提人的真实生活图照 世界上最热的国家吉布提,在吉布提中国人有", "most_related_para": 10, "segmented_title": ["吉布提", "人", "的", "真实", "生活", "图", "照", "世界上", "最", "热", "的", "国家", "吉布提", ",", "在", "吉布提", "中国人", "有"], "segmented_paragraphs": [["【", "行政区划", "】", "全国", "共", "分", "1", "个", "市", "和", "5", "个", 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"比亚", "的", "远方", "-", "伊萨", "区", "。"], ["本", "文", "相关新闻", "推荐", ":"], ["其他", "网友", "正在", "看"]], "paragraphs": ["【行政区划】 全国共分1个市和5个地区:吉布提市(Djibouti-ville)、塔朱拉地区(Région de Tadjourah)、奥博克地区(Région d’Obock)、阿里萨比地区(Région d’Ali-Sabieh)、迪基尔地区(Région de Dikhil)和阿尔塔地区(Région d’Arta)。", "非洲之角吉布提将于2016年大选,现任的伊斯梅尔总统在促进自身作为一个安全的避风港的同时,他的政权面临着复苏的反对派力量和怀疑。吉布提能建立一个新的联盟吗? 在柏林墙倒塌25年之后,非洲之角的吉布提站在有风险的外交对抗的大舞台。", "在世界上拥有最强大军队的国家中,中国,法国,日本,俄罗斯和美国,都在对吉布提塔朱拉湾虎视眈眈。 吉布提塔朱拉湾俯瞰红海和亚丁湾,在印度洋是世界上最繁忙的航道的门户,吉布提具有至高无上的地位。", "这是该地区最大的深水港和美国在东非和阿拉伯海湾军事行动主要的后勤基地。 吉布提也有美国在阿富汗发动“捕食者”无人机的主要基地。", "法国,吉布提的前殖民主义国家,维护其在非洲最大的军事基地,布置发射无人机的基地。欧盟的首次联合反海盗任务演习,主要就是在亚特兰大和吉布提。 俄罗斯海军在塔朱拉湾的北侧奥博克有协商的经营权,中国在二月和吉布提签署了军事装备和训练协议之后,正在建设自己的海军基地。", "世界上强大国家的军队都在吉布提有或多或少的影响力,伊斯梅尔总统自从1999年接替他叔叔阿普蒂敦古莱德之后统治吉布提,他现在是“大地主”。", "吉布提打着精明的算盘,伊斯梅尔总统一直利用吉布提的战略地位,筹集资金,并使他的政府的民主和人权免于西方的批评。", "但政治危机正在行程,伊斯梅尔总统已经66岁,2016年总统大选后将走向何方他自己也不知道。 在三月伊斯梅尔总统告诉《青年非洲》:“我累了,我知道我的极限[……]我认为我的使命即将完成。”", "“作为这个区域唯一的法语国家我们显得很孤立。”这句话表明了这个政治强人在闪转腾挪之间无暇他顾。", "吉布提夹在敌对的厄立特里亚和索马里之间,所以为其安全性的担忧加深。 索马里首都各帮派之间的小武器走私来让这个国家胆战心惊。", "埃塞俄比亚政府大力支持伊斯梅尔总统。在2008年厄立特里亚军队越过边境进入吉布提南部时,亚的斯亚贝巴政府派出25000部队保卫那个一度被称为埃塞俄比亚的远方-伊萨区。", "本文相关新闻推荐:", "其他网友正在看"], "bs_rank_pos": 2}, {"is_selected": false, "title": "讨论中国人民解放军吉布提基地的规模和军力配置-海军版-超级大本营", "most_related_para": 33, "segmented_title": ["讨论", "中国人民解放军", "吉布提", "基地", "的", "规模", "和", "军力", "配置", "-", "海军", "版", "-", "超级大本营"], "segmented_paragraphs": [["您", "需要", "登录", "才", "可以", "下载", "或", "查看", ",", "没有", "帐号", "?", "加入", "超大", "军事"], ["最具", "影响力", "军事论坛", "-", "超级大本营", "军事论坛", "欢迎", "你", "!", "超然物外", ",", "有", "容", "乃", "大", "。"], ["最具", "影响力", "军事论坛", "-", "超级大本营", "军事论坛", "欢迎", "你", "!", "超然物外", ",", "有", "容", "乃", "大", "。"], ["成立", "印度洋", "舰队", ",", "167", "舰", "为", "旗舰", ",", "2", "艘", "052", ",", "4", "艘", "053", "H3", ",", "4", "艘", "056", "A", ",", "4", "条", "基洛", ",", "1", "个", "中队", "的", "苏", "30", "。"], ["最具", "影响力", "军事论坛", "-", "超级大本营", "军事论坛", "欢迎", "你", "!", "超然物外", ",", "有", "容", "乃", "大", "。"], 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"最近iPhone7上市,很多之前想购买iPhone的朋友,都会选择在这个时候购买。但是手机行业水深,就连京东、苏宁等一些正品商城都有可能买到翻新机。", "今天就来教大家新机到手后怎么验机:", "一、快递开封 1、首先当着快递员拆开外包装,在未拆手机盒膜的时拿捏手机盒,如果机盒棱角分明,进行下一步看封装。 2、封边在手机盒四个棱边上,长封边工整划一无歪斜、手机膜亮丽光滑贴实无趋纹、膜上气孔排列整齐有规律,进行下一步。 3、看盒上标签,小标签字迹清晰,字母间距符合真标要求,字体颜色符合真标要求,进行下一步。 二、封装验收后操作 1、进入苹果官网,输入标签上的IMEI号或序列号,看机型、激活、保修是否符合,进入下一步。 2、上述均符合,则可拆开膜,拿捏着盒盖处,将整个机盒提到距桌面一定的高度,看内盒是否缓慢的自行滑落(仿机盒内盒是不会自行缓慢的滑落的)。如符合,进入下一步。 3、验证手机配件,如耳机、数据线、充电器符合原装,进入下一步。(具体教程看官网) 4、查看手机上的原贴保护膜上是否有划痕、指纹、污迹、拆趋,如无,进入下一步。 5、查看机身上所贴IMEI号是否与盒上一致,如一致,进入一下步。 三、开机验收 1、插卡、开机、激活,进入界面,打开拨号,输入*井06井,查看弹出的IMEI号是否与手机系统、手机机身标、手机盒标所示是否一致,如一致,进入一下步。 2、连上iTunes,让其自动识别手机,看是否与卖家所述的型号规格及IMEI号一致(如iTunes不能识别、或识别有误,退货维权吧)。 3、再次进入苹果官网,验证激活、保修是否符合。 4、连上91手机助手,查看是否越狱,如无越狱,进入一下步。 5、测试指纹识别功能,输入大拇指指纹,解锁时用所有手指测试解锁,如只能大拇指可解锁则进入下一步。", "6、测试三网4G网速,和平时4G上网网速无差别则可;测试相机慢动作功能,正常即可;测试通话、短信功能,正常即可。", "上述步骤全部测试通过,恭喜你,你的手机是原封未激活全新正品机!", "更多iPhone行业内幕交流,请关注公众号:weishenghuokejifuwu"], "bs_rank_pos": 2}, {"is_selected": false, "title": "iPhone可以不激活验证机子真假吗_iphone7吧_百度贴吧", 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"个人", "微信", "。", "步骤", "阅读", "END"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["经验", "内容", "仅供参考", ",", "如果", "您", "需", "解决", "具体", "问题", "(", "尤其", "法律", "、", "医学", "等", "领域", ")", ",", "建议", "您", "详细", "咨询", "相关", "领域", "专业人士", "。"], ["个性签名", ":", "微信", "公众", "号", ":", "wm", "dsn", "sd"]], "paragraphs": ["该经验图片、文字中可能存在外站链接或电话号码等,请注意识别,谨防上当受骗!", "百度经验 > 健康/养生 > 保健养生", "一分钟告诉你怎样裸睡更健康 标签:裸睡  时长:01:39来自:摇滚歌手", "百度经验:jingyan.baidu.com", "任何事情都是有两面性的,那么对于裸睡一说,我想很多人都是清楚的知道它是有一定的好处的,但是很少却出于羞涩也是很少人提及到或者极少人在进行裸睡的,其实裸睡是有一定的好处的,但是裸睡也是有着一定的坏处的,现在就和大家说说裸睡的好处和坏处。", "百度经验:jingyan.baidu.com", "1 裸睡在睡觉的过程中是有一种舒适的感觉,很让人感受到温暖的,而且对于很多女性朋友们,在裸睡的时候其实对于月经是有一定的好处的,同时,裸睡对于治疗失眠也是有一定疗效的。 步骤阅读 2 裸睡对于健康是有好处的,如果长时间穿着紧身的内裤来睡眠的话,那么时间长了会对健康造成损伤的。 步骤阅读 3 美容效果,其实对于很多女性朋友们来说,裸睡没有衣服之后,可以对于我们身体的新陈代谢有着很好的功效的,对于肌肤是大有好处。 步骤阅读 4 裸睡对于很多的疾病都是有这一定的效果的,在裸睡的夜间中,我们身体中的血液循环会加快,可以对于慢性的便秘有一定的疗效。 步骤阅读 5 裸睡的时候总是可以感觉有一种很好的自由感的,而且我们的肌肤裸露在外面的,也是有利于肌肤的排泄的,而且还是可以对于神经有一定的调节的。增强身体的免疫力。 步骤阅读 END", "百度经验:jingyan.baidu.com", "1 其实裸睡的时候尤其是在冬季时候,裸睡如果没有盖好被子的话,很容易着凉的,对于身体健康来说还是很重要的。 步骤阅读 2 裸睡如果是一般的集体生活,在我看来还是免了,而且喜欢裸睡的朋友们,一定要勤加注意自己的卫生的,经常的去洗澡。 步骤阅读 END", "百度经验:jingyan.baidu.com", "1 经验是分享的,在分享中获得快乐,我是日月之美003,你喜不喜欢我不重要,你关注不关注我也没事,只有经验对于你有所帮助,就ok了。更多消息扫描下面关注我的个人微信。 步骤阅读 END", "百度经验:jingyan.baidu.com", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:微信公众号:wmdsnsd"], "bs_rank_pos": 0}, {"is_selected": false, "title": "女人裸睡的好处和坏处是什么? - 爱问知识人", "most_related_para": 1, "segmented_title": ["女人", "裸睡", "的", "好处", "和", "坏处", "是", "什么", "?", "-", "爱问知识人"], "segmented_paragraphs": [["新手", "提问", "啊", "大家", "进来", "帮帮忙", "了", "本人", "19", "级", "WX", ".", ".", ".", "如何", "选购", "液晶", "品牌电脑", "?", "主要", "用于", "上网", "的", "?"], ["医学", "报告", "指出", ",", "裸睡", "是", "健康", "、", "自在", "、", "卫生", "而且", "性感", "的", ",", "一般", "专家", "们", "认为", ",", "裸睡", "可以", "让", "人", "有无", "拘束", "的", "自由", "快感", "。", "裸睡", ",", "不", "单", "是", "产生", "全身放松", "的", "感觉", ",", "裸睡", "更", "有利于", "血液循环", "、", "增强", "皮", "腺", "和", "汗腺", "的", "分泌", ",", 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"谈", "中药", "护肤", "的", "三大", "好处"]], "paragraphs": ["核心提示:裸睡,听起来很不卫生,也很不干净,更是很不文雅,甚至有的女性朋友觉得裸睡就是一种变态。其实,裸睡还是有不少的好处,对健康是有益的,不过,也有一些坏处,我们可以娶妻精华弃其糟粕嘛。", "喝水时,如果在水中加一样东西,有助排光体内毒素,外表年轻10岁。具体加什么,关注微信公众号 39健康网 ,发送 排毒了解详情。", "裸睡,听起来很不卫生,也很不干净,更是很不文雅,甚至有的女性朋友觉得裸睡就是一种变态。其实,裸睡还是有不少的好处,对健康是有益的,不过,也有一些坏处,我们可以娶妻精华弃其糟粕嘛。", "一、裸睡的好处 1、能保护私处 裸睡不但使人意外感到温暖和舒适,连妇科常见的腰痛及生理性月经痛也得到了减轻;以往因手脚冰凉而久久不能入睡的妇女,采取裸睡方式后,很快就能入睡了。 2、能治失眠 裸睡对失眠的人也会有一定的安抚作用。没有衣服束缚,身体自然放松,血流通畅,能改善某些人手脚冰凉的状况,有助进入深层次睡眠。 3、能减肥 由于人体皮肤能分泌和散发出一些化学物质,若和衣而眠,无疑会妨碍皮肤的正常“呼吸”和汗液的蒸发,衣服对肌肉的压迫和摩擦还会影响血液循环。 4、能美容 没有了衣服的隔绝,裸露的皮肤能够吸收更多养分,促进新陈代谢,加强皮脂腺和汗腺的分泌,有利皮脂排泄和再生,皮肤有一种通透的感觉。 5、能祛痛 裸睡的时候身体自由度很大,肌肉能有效放松,对治疗紧张性疾病的疗效极高,特别是腹部内脏神经系统方面的紧张状态容易得到消除,还能促进血液循环,使慢性便秘、慢性腹泻以及腰痛、头痛等疾病得到较大程度的改善。 二、裸睡的坏处 1、在冬季的时候,裸睡如果没有盖好被子的话,很容易着凉的。 2、如果你处于集体生活的状态,还是应该避免裸睡,一定要勤加注意自己的卫生的,经常洗澡。别阻拦!宝宝“摸打滚爬”好处多 朱玉忠谈中药护肤的三大好处"], "bs_rank_pos": 2}, {"is_selected": true, "title": "女人裸睡的好处和坏处有哪些_保健常识_女性_99健康网", "most_related_para": 2, "segmented_title": ["女人", "裸睡", "的", "好处", "和", "坏处", "有", 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"关注", "我", "的", "热心", "医生", "快速", "免费咨询", "医生", "妇科", "好评", "医院", "男科", "好评", "医院", "儿科", "好评", "医院", "眼科", "好评", "医院", "皮肤科", "好评", "医院", "肿瘤科", "好评", "医院", "肝病", "好评", "医院", "心", "血管", "好评", "医院", "整形", "好评", "医院", "性病", "科", "好评", "医院", "呼吸科", "好评", "医院", "口腔科", "好评", "医院"], ["北京", "三甲医院", "上海", "三甲医院", "广州", "三甲医院", "深圳", "三甲医院", "天津", "三甲医院", "江苏省", "三甲医院", "浙江省", "三甲医院", "福建省", "三甲医院", "山东省", "三甲医院", "湖北省", "三甲医院", "湖南省", "三甲医院", "辽宁省", "三甲医院"], ["北京医院", "排行榜", "上海", "医院", "排行榜", "广州", "医院", "排行榜", "深圳", "医院", "排行榜", "成都", "医院", "排行榜", "杭州", "医院", "排行榜", "南京医院", "排行榜", "天津", "医院", "排行榜", "武汉", "医院", "排行榜", "重庆", "医院", "排行榜", "长沙", "医院", "排行榜", "青岛", "医院", "排行榜"]], "paragraphs": ["对于女性来说,了解一些健康的养生方式很有必要,尤其是一些重视健康生活的MM们,那女人裸睡怎么样?有什么好处?下面就让小编告诉你。", "女人的裸睡对日常保健是有好处的。我们一起来看看裸睡的好处介绍吧!", "一、女人裸睡的好处 1、裸睡有利于皮肤的呼吸通畅 从过现代养生的角度来说,没有了衣服的阻隔,裸露的皮肤可以吸收更多的养分,能够促进新陈代谢。 因为,裸睡可以让皮肤毛孔保持畅通状态,可以确保皮腺和汗腺分泌出来的物质能够顺利地排泄与再生,也可以确保皮肤的吸收、免疫、代谢、气体交换等功能的正常工作,而健康通透的肌肤是一个人身体健康的基本要素,让皮肤能够生活在通畅的环境之中,绝对有利于全身机体功能和免疫能力的健康生长。 3、裸睡有利于改善便秘 裸睡的好处有很多。裸睡还利于改善慢性便秘、慢性腹泻及腰痛、头痛的问题,一些妇女的痛经与颈肩痛等问题也会逐渐减轻。裸睡在日本很受推崇。北海道有个村庄,所有居民都有裸睡的习惯,几乎无人失眠。专家认为这是因为裸睡能减少衣物带来的束缚感,让人从被捆绑一天的感觉中解放出来,利于提高睡眠质量。 4、裸睡有利于减肥 裸睡对于美容瘦身也是有好处的。由于人体皮肤能分泌和散发出一些化学物质,若和衣而眠,无疑会妨碍皮肤的正常“呼吸”和汗液的蒸发,衣服对肌肉的压迫和摩擦还会影响血液循环。 裸睡有种无拘无束的自由快感,让血液得到很好的循环,皮肤充分呼吸,油脂消耗加快,有助于减肥。 5、裸睡好处之一就是有利于美容 没有了衣服的隔绝,裸露的皮肤能够吸收更多养分,促进新陈代谢,加强皮脂腺和汗腺的分泌,有利皮脂排泄和再生,皮肤有一种通透的感觉。 6、裸睡有利于促进血液循环 睡觉时不再有束缚感就不会有血脉不和的紧张感,让全身血畅其流,就不会造成局部缺血的供氧不足,自然也就不会由于血脉不畅而导致的身体疾病。裸睡对治疗紧张性疾病的功效不可忽视,特别是腹部内脏神经系统方面出现的紧张症状特别有效。促进了血液循环,也使得许多患有慢性疾病症状的病症得到了较大程度的改善,从而让自己一觉醒来感到肢体更加的舒适。 7、女性裸睡可防治痛经 女性裸睡时,身体自然放松,血流通畅;另外,女性裸睡的时候身体自由度很大,肌肉能有效放松,对治疗紧张性疾病的疗效极高。私处部位能有充分的通风透气,有利于神经的调节,有利于增强适应和免疫能力,可以有效达到治疗痛经的目的。 二、女人裸睡的坏处 许多女性的妇科炎症都是在不经意间感染,随处可见的细菌就可能让阴道炎不知不觉的找上你。其实这样的“裸”,对家里的卫生状况要求很高。但家里不可能做到无菌,沙发、椅子甚至床上,都难免成为细菌的聚集场所。夏天炎热、多雨潮湿,人身上的汗液、病菌也是最多的,随处倚靠后,沙发套、床单、被套也就成为病菌温床。当你清凉着下半身坐在沙发上,细菌就会趁虚而入引起阴道炎了。如果你或者你老公有脚气、脚癣或其他皮肤病,床单、被套里就会滋生着无数病菌,加上我们的被褥洗换率远远低于内衣裤,于是裸睡时,细菌也会乘机侵袭,就给你带来了各种妇科炎症。 尤其青春期少女,由于年龄小,对生殖卫生知识的贫乏,如果有裸睡的习惯正好致使会阴受不洁床上用品的污染,病菌乘机滋生进犯,引起“少女阴道炎”。对于青春期的女生,正在发育的器官是很脆弱的,如果小小年纪就被感染发病一次,对生殖系统今后正常发育的打击是很严重的。 因此,专家提醒,在湿热的季节里,女性在家千万别图清凉、性感而忽略了穿内裤。如果你要裸睡一定要先治好脚气和其他皮肤病,床单也应勤更换。出门时要注意少穿紧身裤、丁字裤和透气性不好的牛仔裤。另外,正常情况下每天用专盆、清水清洗外阴一次就可以了,不要用任何洗夜,如果发现有哪些不适一定要在医生指导下用药,切不可盲目采取措施。 (责任编辑:王少华)2012-02-08 13:27 女人裸睡有什么好处 5大好处  2013-07-08 09:50 女人手淫的10大好处  2014-01-13 14:04 裸睡好吗 女人裸睡的好处 女人吃薏米的好处有哪些 女人吃阿胶的好处及禁忌 土豆对女人的5大好处 女人喝豆浆的9大好处 女人吃红糖有什么好处 吃花生酱对女人的好处 肾虚测试  孕期体重计算器  亚健康测试小工具  上火测试  痛经程度自测  手淫测试  减肥自测 羊奶变洗发乳 老人误喝洗发乳 金宇彬患鼻咽癌 五个方法预防鼻咽癌 癫痫病 大排档用脚洗菜 如何预防食物中毒 扫一扫关注我的热心医生 快速免费咨询医生  妇科好评医院 男科好评医院 儿科好评医院 眼科好评医院 皮肤科好评医院 肿瘤科好评医院 肝病好评医院 心血管好评医院 整形好评医院 性病科好评医院 呼吸科好评医院 口腔科好评医院", "北京三甲医院 上海三甲医院 广州三甲医院 深圳三甲医院 天津三甲医院 江苏省三甲医院 浙江省三甲医院 福建省三甲医院 山东省三甲医院 湖北省三甲医院 湖南省三甲医院 辽宁省三甲医院", "北京医院排行榜 上海医院排行榜 广州医院排行榜 深圳医院排行榜 成都医院排行榜 杭州医院排行榜 南京医院排行榜 天津医院排行榜 武汉医院排行榜 重庆医院排行榜 长沙医院排行榜 青岛医院排行榜"], "bs_rank_pos": 3}, {"is_selected": false, "title": "为什么有人要裸睡?裸睡有什么好处和坏处呢_百度经验", "most_related_para": 5, "segmented_title": ["为什么", "有人", "要", "裸睡", "?", "裸睡", "有", "什么", "好处", "和", "坏处", "呢", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ">", "生活", "/", "家居", ">", "生活常识"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["高校", "处分", "裸睡", "学生", ",", "在", "这段时间", "闹", "得", "沸沸扬扬", ",", "裸睡", "也", "全面", "进入", "人们", "的", "视线", "。", "那么", "为什么", "要", "禁止", "学生", "裸睡", "呢", "?", "裸睡", "有", "什么", "好处", "和", "坏处", "?", "现在", "小", "编", "就", "来", "盘点", "一", "下", "!"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["1", "舒服", ":", "夏天", "是", "裸睡", "的", "高峰期", ",", "没有", "被子", "束缚", "、", "没有", "小", "内裤", "的", "紧", "箍", ",", "全身", "都是", "自由", "的", "。", "早上醒来", "有如", "忽如一夜春风来", ",", 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"如果", "你", "遇到", "这种", "问题", "不", "妨", "试", "一", "下", "本", "文", "中", "的", "办法", "来", "帮助", "大家", "解决", "问题", "。", "第", "一", ".", ".", "."]], "paragraphs": ["生活 母婴 女人 婚嫁 祝福 美食 旅游", "资料 谜语 影视歌曲 人物资料 娱乐圈 影视", "优酷2001出错怎么办?安下小编胖胖带来了优酷出错代码2001解决办法,很多朋友在使用优酷观看视频时会遇到2001出错代码,相信大家在看优酷视频的时候会频繁遇到优酷视频出错代码2001,无法观看视频,画面显示白屏或者黑屏的情况,如果你遇到这种问题不妨试一下本文中的办法来帮助大家解决问题。", "第一步,安装或升级Flash最新播放器 下载安装后, 须关闭所有浏览器窗口, 重新开启浏览器方可正常观看 第二步,清空浏览器缓存: 打开IE浏览器菜单, ”工具” —> ”Internet选项” —> ”常规” —> ”浏览历史记录” —> 清除浏览器Internet临时文件、缓存、垃圾、Cookie等, 重启浏览器 第三步,在flash播放器视频框上点右键, ”全局设置” —> ”高级” —> ”浏览数据和设置” —> ”所有删除”, 重启浏览器。 第四步,为浏览器开启Java及Active功能。 打开IE浏览器顶部菜单的\"工具\" —> \"Internet选项\", 切换到\"安全\"选项卡, 点击\"默认级别\"按钮, 确定。", "如出现警告提示框, 请选择\"是\"。(见下图)", "或者在第四步基础上, 选择左边按钮\"自定义级别\", 拖动滚动条, 找到关于脚本的描述, 点选启用。", "如果您是FireFox用户, 在\"工具\" —> \"选项\" —> \"内容\"位置, 勾选启用Java和JavaScript即可。", "开启Active功能", "与开启Java相同。选择\"默认级别\"按钮, 系统将默认开启ActiveX。", "第五步,取消使用代理服务器, 打开IE浏览器菜单, “工具” —> “Internet选项” —>“连接”—>“局域网设置”,取消代理服务器设置。", "第六步,使用安全软件清理系统插件(清理掉流氓插件),修复浏览器到默认配置。", "第七步,进行过上述操作后仍未见效,请联系您所在地区的宽带运营商,帮您查看连接问题,并设置适合您的DNS地址。", "办法/步骤2", "设置DNS的步骤如下:", "1) 打开网络连接,右键查看网络连接属性;", "选择Internet协议版本4,查看属性;", "设定使用下面的dns地址;", "使用优酷客户端时请确认程序未被防火墙拦截禁用。", "其他情况,如果您使用的是360浏览器, 打开菜单”工具” —> ”浏览器医生”(或按下键盘上的F1),按”一键修复”, 重新启动浏览器。", "如您使用的是Opera浏览器, 并且安装了爱酷, 建议您进行如下操作:", "打开Opera浏览器菜单, ”设置” —> ”首选项” —> ”高级”(tab) —> “安全性” —> “信任的网站” —> “安全的内部主机” —> 添加127.0.0.1, “关闭”—>”确定”。", "以上内容是视频播放教程:优酷播放出错代码2001怎么解决 内容提要: 优酷2001出错怎么办?安下小编胖胖带来了优酷出错代码2001解决办法,很多朋友在使用优酷观看视频时会遇到2001出错代码,相信大家在看优酷视频的时候会频繁遇到优酷视频出错代码2001,无法观看视频,画面显示白屏或者黑屏的情况,如果你遇到这种问题不妨试一下本文中的办法来帮助大家解决问题。 第一..."], "bs_rank_pos": 5}, {"is_selected": false, "title": "为什么不能看优酷上面显示此视频暂时无法播放 播放: 未知 为什么 请", "most_related_para": -1, "segmented_title": ["为什么", "不能", "看", "优酷", "上面", "显示", "此", "视频", "暂时", "无法", "播放", "播放", ":", "未知", "为什么", "请"], "segmented_paragraphs": [["`", "`", "`", "土豆网", "也是", "这样", "么", "?", "如果", "也是", ",", "那", "就是", "网络", "问题", ",", "过", "一", "天", "就好", "了", "。", 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3Q。", "直接在页眉或页脚处双击鼠标左键,把出现在虚线内的内容删除,然后在页眉或页脚外双击鼠标左键就可以了。", "页面设置-版式-首页不同勾选 再设置后面页的页眉页脚。"], "bs_rank_pos": 1}, {"is_selected": false, "title": "怎样删除word文档中首页的页眉?_百度知道", "most_related_para": 1, "segmented_title": ["怎样", "删除", "word", "文档", "中", "首页", "的", "页眉", "?", "_", "百度", "知道"], "segmented_paragraphs": [["删除", "word", "文档", "中", "首页", "的", "页眉", "的", "操作步骤", ":", "1", "、", "单击", "页面", "布局", "选项卡", "右下角", "如图所示", "的", "按钮", ";", "2", "、", "弹出", "页面", "设置", "对话框", ",", "选择", "版式", "选项卡", ",", "选中", "首页", "不同", "复选框", ",", "首页", "的", "页眉", "即可", "被删除", "。"], ["word", "中", "的", "页眉", "是", "按", "节", "算", "的", ",", "每", "节", "都是", "一样", "的", ",", "要", "第一页", "和", "第二页", "不同", ",", "就要", "在", "第一页", "末尾", "插入", "分节符", "(", "插入", "分隔符", "选", "分节符", "中", "的", "下一页", ")", "先", "把", "第一页", "(", "节", ")", "的", "页眉", "删", "了", ",", "再", "去", "设", "第二节", "的", "页眉", "设", "的", "时候", "去掉", "工具栏", "上", "的", "“", "链接", "到", "上", "一", "个", "”", "选项", ",", "再", "设置", "第二节", "页眉"]], "paragraphs": ["删除word文档中首页的页眉的操作步骤: 1、单击页面布局选项卡右下角如图所示的按钮; 2、弹出页面设置对话框,选择版式选项卡,选中首页不同复选框,首页的页眉即可被删除。", "word中的页眉是按节算的,每节都是一样的,要第一页和第二页不同,就要在第一页末尾插入分节符(插入 分隔符 选分节符中的下一页) 先把第一页(节)的页眉删了,再去设第二节的页眉 设的时候去掉工具栏上的“链接到上一个”选项,再设置第二节页眉"], "bs_rank_pos": 2}], "answer_spans": [[7, 158]], "fake_answers": ["1、打开【页眉和页脚】工具栏打开【视图】菜单,选择【页眉和页脚】命令。　　2、编辑页眉和页脚—编辑页眉这时,将显示【页眉】框和【页眉和页脚】工具栏。　　将鼠标指针移至页眉框内,即可开始输入和编辑页眉内容。　　3、设置文档首页不显示页眉一般情况下,文档的首页不需要显示页眉。　　4、设置文档奇偶页不同页眉有的文档可能需要给奇数页和偶数页设置不同的页眉或页脚。　　5、取消设置文档不同部分显示不同的页眉内容有的文档,特别是一些内容较为复杂的文档,可能需要在文档不同部分设置不同的页眉和页脚。"], "question": "首页页眉怎么去掉", "segmented_answers": [["1", "、", "打开", "【", "页眉", "和", "页脚", "】", "工具栏", "，", "打开", "【", "视图", "】", "菜单", "，", "选择", "【", "页眉", "和", "页脚", "】", "命令", "。", "2", "、", "编辑", "页眉", "和", "页脚", "—", "编辑", "页眉", "，", "这时", "，", "将", "显示", "【", "页眉", "】", "框", "和", "【", "页眉", "和", "页脚", "】", "工具栏", "。", "将", "鼠标指针", "移", "至", "页眉", "框", "内", "，", "即可", "开始", "输入", "和", "编辑", "页眉", "内容", "。", "3", "、", "设置", "文档", "首页", "不", "显示", "页眉", "，", "一般", "情况", "下", "，", "文档", "的", "首页", "不需要", "显示", "页眉", "。", "4", "、", "设置", "文档", "奇偶", "页", "不同", "页眉", "，", "有", "的", "文档", "可能", "需要", "给", "奇数", "页", "和", "偶数", "页", "设置", "不同", "的", "页眉", "或", "页脚", "。", "5", "、", "取消", "设置", "文档", "不同", "部分", "显示", "不同", "的", "页眉", "内容", "。", "有", "的", "文档", "，", "特别", "是", "一些", "内容", "较为", "复杂", "的", "文档", "，", "可能", "需要", "在", "文档", "不同", "部分", "设置", "不同", "的", "页眉", "和", "页脚", "。"], ["在", "第一页", "的", "最后", "插入", "一", "个", "分节符", "，", "然后", "双击", "第二页", "页眉", "，", "在", "“", "页眉", "页脚", "”", "工具栏", "点击", "“", "链接", "到", "前", "一", "个", "”", "按钮", "，", "然后", "删除", "首页", "页眉", "就", "可以", "了", "。"], ["1", "、", "将", "光标", "移", "到", "首页", "的", "末尾", "。", "2", "、", "点击", "“", "页面", "布局", "”", "选项卡", "下", "的", "“", "分隔符", "”", "，", "选择", "“", "分节符", "”", "下", "的", "“", "下一页", "”", "，", "这个时候", "第二页", "开头", "会", "多", "出", "一", "个", "空行", "，", "按", "delete", "删除", "即可", "。", "3", "、", "双击", "第二页", "的", "页眉", "或", "页脚", "，", "然后", "在", "选项卡", "里", "找到", "“", "链接", "到", "前", "一", "条", "页眉", "”", "，", "点击", "去掉", "其", "激活", "状态", "。", "4", "、", "删除", "第一页", "的", "页眉", "或", "页脚", "（", "要", "是", "删除", "后", "还有", "横线", "，", "可", "多", "按", "几", "下", "delete", "）", "。", "5", "、", "双击", "正文", "，", "退出", "页眉", "编辑", "状态", "。"]], "answers": ["1、打开【页眉和页脚】工具栏，打开【视图】菜单，选择【页眉和页脚】命令。2、编辑页眉和页脚—编辑页眉，这时，将显示【页眉】框和【页眉和页脚】工具栏。将鼠标指针移至页眉框内，即可开始输入和编辑页眉内容。3、设置文档首页不显示页眉，一般情况下，文档的首页不需要显示页眉。4、设置文档奇偶页不同页眉，有的文档可能需要给奇数页和偶数页设置不同的页眉或页脚。5、取消设置文档不同部分显示不同的页眉内容。有的文档，特别是一些内容较为复杂的文档，可能需要在文档不同部分设置不同的页眉和页脚。", "在第一页的最后插入一个分节符，然后双击第二页页眉，在“页眉页脚”工具栏点击“链接到前一个”按钮，然后删除首页页眉就可以了。", "1、将光标移到首页的末尾。2、点击“页面布局”选项卡下的“分隔符”，选择“分节符”下的“下一页”，这个时候第二页开头会多出一个空行，按delete删除即可。3、双击第二页的页眉或页脚，然后在选项卡里找到“链接到前一条页眉”，点击去掉其激活状态。4、删除第一页的页眉或页脚（要是删除后还有横线，可多按几下delete）。5、双击正文，退出页眉编辑状态。"], "answer_docs": [0], "segmented_question": ["首页", "页眉", "怎么", "去掉"], "question_type": "DESCRIPTION", "question_id": 91182, "fact_or_opinion": "FACT", "match_scores": [0.9276315789473685]}
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3。然后把清洗后的粽子叶放到大锅中+水,蒸沸时间10分钟,同时加上几勺子盐,加盐能起到杀菌消毒和固色的作用。 4。倒掉煮粽子叶的盐水,加热水冲洗,然后泡在锅里备用即可。", "在使用粽叶的时候,如果采用煮粽叶的方法来杀菌杀毒的话,如果使用的粽叶是干粽叶,则需要将粽叶放于冷水中浸泡一夜,使其吸收充足的水分,变得柔软一些;而如果是用温水泡的话,则需要泡一小时即可,然后用手轻轻的清洗。如果是新鲜的粽叶,在煮之前,只需要将其清洗干净就好。如果是用开水烫粽叶的话,则最好是冷水浸泡、清洗粽叶。", "我们一般是会将粽叶放于锅中煮,或者是在锅中的开水中烫。将煮好或烫好的粽叶,从锅中拿出来后,还是需要放于清水中浸泡着,然后随用随拿,以防粽叶在包粽子的时候变得干燥易破,这样的话,就不能再用了。", "粽叶除了可以煮或烫外,还可以冰冻。采用冰冻的方法话,最好是选择新鲜的粽叶,也不需要过长时间的浸泡。将其泡于水中,用布从粽叶头到尾顺着叶子的纹理清洗粽叶,清洗干净后保留一些水分在上面,然后将其卷成圈放于干净的袋子中,然后放于冰箱的冷冻层中冰冻一晚,即可拿出让其自行解化,解化后还是需要将其放于清水中浸泡,并随用随用。", "特点:三角粽的形状比较好看而且特别,可以制作成甜、咸两种口味,蛋黄、叉烧、蜜枣都是不错的馅料。", "1、取一片宽大的粽叶,卷成一个圆锥状。 2、在圆锥的筒中装入糯米。 3、上部的粽叶向下折,直到完全盖住开口为止。 4、顺着三角的边将所有的粽叶都折好。", "5、用线绳将粽子捆绑结实即可。", "吃着美味的粽子,和家人分享最美的时光,趁着自己还没组建家庭之前多回家看看吧,端午节和家人团聚才有意义。话说吃了这么多年粽子,你造它有哪些营养价值吗?", "粽子的主料糯米富含B族维生素,能温暖脾胃,补中益气。糯米有收涩作用,对尿频、自汗也有较好的食疗效果。纯糯米的粽子热量没有那么高,也比加了馅料的粽子好消化。", "如果是黄米做粽子,其营养价值比糯米粽子还要高哦。黄米的铁含量是糯米的3倍,镁含量是、钾含量是、维生素B1含量以及膳食纤维含量都高于糯米。", "蛋黄类的粽子含有卵磷脂、维生素A,有健脑益智的功效,鸭蛋中还有铁质和钙质,少量吃一点是不错的。", "温馨提示:以上内容整理于网络,仅供参考,如果对您有帮助,留下您的阅读感言吧!", "新鲜的粽叶怎么处理 新鲜粽叶怎么保存", 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你好,突然出现得嘴唇肿胀一般是因为过敏而造成的.过敏性唇炎的原因:口唇是人体皮肤和粘膜交界的部位,地方不大,可发生的疾病却不简单。最常见的口唇疾病就是唇炎,口唇出现肿胀、疼痛或糜烂等症状,临床上唇炎分很多种,如过敏性唇炎、光线性唇炎、剥脱性唇炎、腺性唇炎、肉芽肿性唇炎和浆细胞性唇炎等,但有明确病因的只有前两种,有些还没有明确的病因。但是通过观察,总能发现一些与其发病有关或能诱发加重病情的因素,分别是过敏反应、机械刺激、感染因素、营养缺乏、遗传因素。过敏性唇炎的症状:过敏性唇炎是由于口唇或其周围的皮肤接触到了某些刺激物而引起的,在接触后数小时或数日内发病,可出现唇粘膜肿胀、水疱,甚至糜烂结痂,感染化脓形成溃疡,影响进食和说话。过敏性唇炎的治疗:中医认为口唇与脾胃关系密切,脾胃发病常反映于口唇,相反口唇的异常也多反映了脾胃的功能失调,故治疗唇炎当从脾胃入手。 指导意见: 例如,唇炎表现为口唇红肿、糜烂者,多为脾胃热盛,治疗以清泻脾胃实火为主;若口唇生疔、坚硬疼痛者,多为热毒炽盛,治疗以清热解毒为法;若病程日久,口唇肥厚、干硬者,多为脾虚痰湿凝滞,治疗当健脾化痰散结为主。", "病情分析: 这位朋友,根据您的表述个人怀疑您是上火了,上火是比较常用的中医术语,对于上火其实并不需要太过担心,只要平时注意饮食和生活节律,很快会恢复。 指导意见: 新鲜绿叶蔬菜、黄瓜、橙子、绿茶都有良好的清火作用,而胡萝卜对补充人体的维生素b、避免口唇干裂也有很好的疗效。建议您多吃一些。另外,调节好作息,放平心态,对于治疗上火也很有帮助。", "妇炎消泡腾  参考价格:47 查看说明书", "骨友灵搽剂  参考价格:26.9 查看说明书", "伸筋片  参考价格:35 查看说明书", "塞隆风湿胶囊 祛风散寒除湿,通络止痛,补益肝肾。用于风寒湿痹引...[说明书] 参考价格:￥134", "吲哚美辛胶囊 1.用于:关节炎,可缓解疼痛和肿胀;软组织损伤和...[说明书] 参考价格:￥15", "有什么办法治疗失眠 失眠多梦要煲什么吃 失眠吃什么睡 失眠多梦吃什么药好 黑眼袋是怎么形成的 有关失眠的治疗方法 头疼失眠多梦吃什么药 失眠心悸心慌是怎么回事? 睡前喝牛奶可以治疗失眠吗 治疗失眠的偏方是什么", "医生在线免费咨询", "看病 男科 妇科 癫痫 性病 养生 新闻 白癫风 牛皮癣", "就医 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指导意见: 唇风是秋季易发症状。是因风热湿邪外侵,或脾胃湿热内蕴,上蒸口唇所致。以口唇红肿、痛痒,日久破裂流水,或脱屑皮等症状!多因辛辣厚味太过,脾胃湿热内生,复受风邪侵袭;或脾气虚弱,外感燥热,致脾经血燥,熏灼唇口所发。 以上是对“早上起来上嘴唇肿了”这个问题的建议,希望对您有帮助,祝您健康!", "病情分析: 你这是唇风的症状,不可再吹风了,否则会很痒,渐渐会更加肿大!建议用盐水清洗一天2-3道可以消肿止痒,内服六味地黄丸可愈。 指导意见: 唇风是秋季易发症状。是因风热湿邪外侵,或脾胃湿热内蕴,上蒸口唇所致。以口唇红肿、痛痒,日久破裂流水,或脱屑皮等症状!", "病情分析: 你好这位朋友,根据你的描述,你可能是因为病毒引起的嘴唇肿胀。 指导意见: 根据这种情况,第一你应该注意调节饮食注意身体锻炼加强身体抗病能力,第二就是可以用一点抗病毒的外用药膏涂抹一下.", "病情分析: 你好,据你所述的情况,可能是过敏所致的局部肿胀,也可能是虫咬所致,应详细检查明确后,积极治疗, 指导意见: 日常应合理饮食,注意休息,避免不良刺激,过敏可应用扑尔敏,钙剂,维生素C,强的松等缓解,一般多可好转,具体应遵循临床医师的指导,以上供参考,", "你这是唇风的症状,不可再吹风了,否则会很痒,渐渐会更加肿大!建议用盐水清洗一天2-3道可以消肿止痒,内服六味地黄丸可愈。 唇风是秋季易发症状。是因风热湿邪外侵,或脾胃湿热内蕴,上蒸口唇所致。以口唇红肿、痛痒,日久破裂流水,或脱屑皮等症状! 多因辛辣厚味太过,脾胃湿热内生,复受风邪侵袭;或脾气虚弱,外感燥热,致脾经血燥,熏灼唇口所发。  一般唇风发为下唇此为风火湿热。若发为上唇则是风热偏重。  以上是对“早上起来上嘴唇肿了”这个问题的建议,希望对您有帮助,祝您健康!", "醋酸泼尼松  参考价格:5 查看说明书", "注射用核黄  参考价格:16 查看说明书", "四维他胶囊  参考价格:14.2 查看说明书", "六味地黄丸 滋阴补肾。用于肾阴亏损,头晕耳鸣,腰膝酸软,骨蒸...[说明书] 参考价格:￥22", "马来酸氯苯那敏片 本品适用于皮肤过敏症:荨麻疹、湿疹、皮炎、药疹、...[说明书] 参考价格:￥5", "涨智齿脸肿了怎么办 牙齿矫正最好是哪里 什么水果缓解口角炎 长智齿特别疼怎么办 牙龈肿痛喝什么茶好 长口腔溃疡吃什么药 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为一种暂时性、局限性、无痛性的皮下或粘膜下水肿。病因主要为异种蛋白或药物引起的I型变态反应所致。【诊断要点】①好发于口唇周围的疏松结缔组织,上唇多于下唇。②肿胀发展迅速,一船在10多分钟内已明显,水肿区光亮、潮红,无痛而有胀感,灼热、搔痒感。肿胀持续数小时或1～2日可消退。③水肿以上唇最多见,表现为上唇肥厚翘突,或者局限于上唇-鼻翼区。若舌、腭发生,可引起暂时性语言、进食等障碍。严重的水肿可扩散至口底及咽喉部,引起呼吸障碍。【治疗原则】①迅速找出和除去过敏源。②唇部涂用炉甘石洗剂护肤止痒。③应用抗组胺药物,必要时使用皮质类固醇。④注意扩散至口咽部引起呼吸困难病例,必要时作预防性气管切开术。", "首先你对吃的东西有过敏么/还有在这之前是否接触过可以引起过敏的介质.用毛巾把冰包起来敷十五分钟.看看效果。不行再去医院看下吧,这样放心。曾经患有鼻炎么,是过敏体制么.这都很重要.", "你是不是吃了引起过敏的事物啊?以后要注意不能吃了,严重的会引起全身皮疹水肿和喉头水肿,引起呼吸困难致命的。你最好要去医院查一下以便确诊。", "可能是上火了,我也常这样。注意点饮食就好了"], "bs_rank_pos": 2}, {"is_selected": false, "title": "吓哭!嘴唇变香肠嘴!这是肿么了?!-健康频道-手机搜狐", "most_related_para": 4, "segmented_title": ["吓哭", "!", "嘴唇", "变", "香肠嘴", "!", "这", "是", "肿", "么", "了", "?", "!", "-", "健康频道", "-", "手机", "搜狐"], "segmented_paragraphs": [["妹子", "们", "都", "想要", "一", "张", "樱桃", "小", "嘴", ",", "小巧", "可人", "。", "可", "、", "可是", "为什么", "我", "嘴巴", "开始", "肿", "起来", "了", ",", "又", "红", "有", "厚", ",", "像", "个", "香肠嘴", "。", "呜呜呜", ",", "同事", "见", "了", "我", "都", "在", "笑", ",", "都", "不能", "出去", "见", 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"客", "网", "专稿", ",", "转载", "请", "注明", "健", "客", "网", "。"], ["更多", "原创", "资讯", ",", "请", "关注", "官方", "微信", "服务", "号", ":", "健", "客", "网", "健康", "问诊", "(", "jk", "-", "zixun", ")", "!"]], "paragraphs": ["妹子们都想要一张樱桃小嘴,小巧可人。可、可是为什么我嘴巴开始肿起来了,又红有厚,像个香肠嘴。呜呜呜,同事见了我都在笑,都不能出去见人了!", "嘴唇肿还真是一个大烦恼,连出门的心思都没有了,就算出也要戴口罩,你是否有过这样的经历呢?有的唇肿过几天就自然消退,有的却死皮赖脸地赖在嘴唇上,越来越肿,还痒。这下可愁死人了。", "情侣必备的靠谱酒店预订神器 广告", "引起唇肿的原因有哪些?看这里~", "1、上火了。平时不注意饮食,大堆麻烦来找你!如果过多食用辛辣、油炸类食物而又不注意清毒,很容易导致内分泌紊乱而引起上火。不仅脸上容易长痘痘,嗓子疼,烂嘴角,嘴唇也会变肿! 温馨建议:多喝点下火茶,饮食要清淡,上火了还是少吃些辛辣刺激性食物吧。也可用淡盐水漱口,保持口腔卫生。 2、天气原因。秋冬季节因为干燥缺水,嘴肿的现象会比较频繁。但由于春天天气多变,时冷时干燥,人的嘴唇也会变得敏感,这时候如果总是舔嘴唇,越舔越干,最后周围皮肤会变粗糙,嘴唇也变得潮红肿胀。 温馨建议:小不忍则乱大谋!缺水的话多喝水就好,忍住,不要频繁舔舐、咬嘴唇。实在忍不住,掏出护唇膏涂一下,也可以炖些雪梨甜汤喝,防止嘴唇干燥。平时多吃些水果蔬菜,补充维生素。3、血管神经性水肿。是局限于皮肤和皮下组织的过敏反应,好发于上唇。症状为局部肿胀明显,感觉有轻微瘙痒、灼热和不适的异样感。可由药物、食物如鱼、虾、蟹、动物皮屑、羽毛、蚊虫叮咬、日光等引起。 温馨建议:回忆下前天是否有吃过什么引起过敏的食物或去做个检查明确过敏原,进行脱离避免接触。此外可口服抗过敏药,如扑尔敏、开瑞坦、维生素C等,多喝水,忌吃辛辣肥腻海鲜类食物。但若肿胀厉害影响呼吸,需及时前往医院进行治疗! 4、唇炎。多发于下唇,也有发生在上唇的患者。具体表现为嘴唇红肿、皲裂、脱屑和糜烂。寒冷干燥的天气、较长的日光曝晒、平时嗜好烟酒或过食辛辣食物、舐唇、营养不均衡、免疫力过低等因素都有可能引起唇炎。温馨建议:尽量避免风吹、日晒或寒冷刺激,烟酒什么的该戒就狠下心来戒了吧。时刻提醒自己不要舔咬嘴唇,清淡饮食。可以用3%的生理盐水或是其他抗菌的药水湿敷,然后涂抹抗生素软膏或是皮质激素类软膏。", "健客网专稿,转载请注明健客网。", "更多原创资讯,请关注官方微信服务号:健客网健康问诊(jk-zixun)!"], "bs_rank_pos": 3}, {"is_selected": false, "title": "上嘴唇肿了怎么办_百度经验", "most_related_para": 8, "segmented_title": ["上", "嘴唇", "肿", "了", "怎么办", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ">", "健康", "/", "养生", ">", "保健养生"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["上火", "准确", "的", "来", "说", "是", "有风", "的", "缘故", ",", "是", "风热", "上行", "!", "叫做", "唇", "风", "。", "可能会", "很", "痒", "!", "是", "因", "风热", "湿", "邪", "外", "侵", ",", "或", "脾胃湿热", "内蕴", ",", "上", "蒸", "口", "唇", "所致", "。", "以", "口", "唇", "红肿", "、", "痛痒", ",", "日久", "破裂", "流水", ",", "或", "脱屑", "皮", "等", "症状", "!"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["一", "小碗", "温", "盐水", "或是", "一", "小碗", "生理盐水", "六味地黄丸", ",", "一", "小碗", "干", "薄荷", "一", 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平常多用生理盐水常漱口,日常生活中多吃些清淡的食物,特别像白花苦菜或是蒲公英这类野菜,也可以将野菜晒干当茶喝。", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "如何吃火锅才不会上火 巧用生姜与大冲治小感冒"], "bs_rank_pos": 4}], "answer_spans": [[0, 84]], "fake_answers": ["嘴唇无缘无故的肿了可能是血管神经性水肿(angioneuroticedema)。血管神经性水肿主要是局限于皮肤和皮下组织的过敏反应,可由药物变应性,昆虫螫咬,脱敏注射或进食某些食物(特别是鸡蛋,贝壳,果仁)引起。为一种暂时性、局限性、无痛性的皮下或粘膜下水肿。病因主要为异种蛋白或药物引起的I型变态反应所致。"], "question": "嘴唇肿了", "segmented_answers": [["突然", "出现", "得", "嘴唇", "肿胀", "一般", "是因为", "过敏", "而", "造成", "的", ".", "过敏性", "唇炎", "的", "原因", "：", "口", "唇", "是", "人体", "皮肤", "和", "粘膜", "交界", "的", "部位", "，", "地方", "不大", "，", "可", "发生", "的", "疾病", "却", "不", "简单", "。", "最", "常见", "的", "口", "唇", "疾病", "就是", "唇炎", "，", "口", "唇", "出现", "肿胀", "、", "疼痛", "或", "糜烂", "等", "症状", "，", "临床", "上", "唇炎", "分", "很多", "种", "，", "如", "过敏性", "唇炎", "、", "光", "线性", "唇炎", "、", "剥脱性唇炎", "、", "腺", "性", "唇炎", "、", "肉芽肿性唇炎", "和", "浆细胞", "性", "唇炎", "等", "，", "但", "有", "明确", "病因", "的", "只有", "前", "两种", "，", "有些", "还没有", "明确", "的", "病因", "。", "但是", "通过观察", "，", "总", 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"。", "血管", "神经性", "水肿", "主要", "是", "局限", "于", "皮肤", "和", "皮下组织", "的", "过敏反应", ",", "可", "由", "药物", "变应性", ",", "昆虫", "螫", "咬", ",", "脱敏", "注射", "或", "进食", "某些", "食物", "(", "特别", "是", "鸡蛋", ",", "贝壳", ",", "果仁", ")", "引起", "。", "为", "一", "种", "暂时", "性", "、", "局限性", "、", "无痛", "性", "的", "皮下", "或", "粘膜", "下", "水肿", "。", "病因", "主要", "为", "异种", "蛋白", "或", "药物", "引起", "的", "I", "型", "变态反应", "所致", "。"]], "answers": ["突然出现得嘴唇肿胀一般是因为过敏而造成的.过敏性唇炎的原因：口唇是人体皮肤和粘膜交界的部位，地方不大，可发生的疾病却不简单。最常见的口唇疾病就是唇炎，口唇出现肿胀、疼痛或糜烂等症状，临床上唇炎分很多种，如过敏性唇炎、光线性唇炎、剥脱性唇炎、腺性唇炎、肉芽肿性唇炎和浆细胞性唇炎等，但有明确病因的只有前两种，有些还没有明确的病因。但是通过观察，总能发现一些与其发病有关或能诱发加重病情的因素，分别是过敏反应、机械刺激、感染因素、营养缺乏、遗传因素。过敏性唇炎的症状：过敏性唇炎是由于口唇或其周围的皮肤接触到了某些刺激物而引起的，在接触后数小时或数日内发病，可出现唇粘膜肿胀、水疱，甚至糜烂结痂，感染化脓形成溃疡，影响进食和说话。过敏性唇炎的治疗：中医认为口唇与脾胃关系密切，脾胃发病常反映于口唇，相反口唇的异常也多反映了脾胃的功能失调，治疗唇炎当从脾胃入手。", "这是唇风的症状，不可再吹风了，否则会很痒，渐渐会更加肿大！建议用盐水清洗一天2-3道可以消肿止痒，内服六味地黄丸可愈。", "嘴唇无缘无故的肿了可能是血管神经性水肿。血管神经性水肿主要是局限于皮肤和皮下组织的过敏反应,可由药物变应性,昆虫螫咬,脱敏注射或进食某些食物(特别是鸡蛋,贝壳,果仁)引起。为一种暂时性、局限性、无痛性的皮下或粘膜下水肿。病因主要为异种蛋白或药物引起的I型变态反应所致。"], 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成功拾取火凤之灵的玩家将无法移动,只有通过方向键传送给其他人,点击方向键后,火凤之灵会飞出去,另外一个人可以接住,然后通过队友的协作最终将火凤之灵收集到地图中间的熔炼池中。 3、击败陆元竹 第三环也分为三个阶段,陆元竹每隔一定回合都会召唤几只火凤之源,击败火凤之源后可以获得激活凤凰的能量,能量满了就可以激活强大的凤凰。同样的,陆元竹也可以通过同样的方法激活强大凤凰,所以就看你们谁能获得胜利了! 以上就是小编为大家带来的梦幻西游手游五行村之危任务攻略,希望对大家有所帮助。更多有关梦幻西游手游的最新资讯,敬请继续关注小皮手游网。", "类型:角色扮演", "评分:分", "QQ 群:"], "bs_rank_pos": 2}, {"is_selected": false, "title": "梦幻西游手游五行村之危地狱难度通关攻略_72G梦幻西游手游专区", "most_related_para": 30, "segmented_title": ["梦幻西游", "手游", "五行", "村", "之", "危", "地狱", "难度", "通关", "攻略", "_", "72G", "梦幻西游", "手游", "专区"], "segmented_paragraphs": [["梦幻西游", "手游", "五行", "村", "之", "危", "这个", "新", "出", "的", "神器", "任务", ",", "简单", "难度", "的", "关卡", "一般", "玩家", "都", "能", "通过", ",", "但是", "想要", "通关", "地狱", "级别", "是", "非常", "困难", "的", ",", "为了", "帮助", "一些", "还没有", "通关", "五行", "村", "之", "危", "地狱", "难度", "的", "玩家", "能", "顺利", "通关", ",", "下面", "小", "编", "就", "为", "玩家", "们", "带来", "梦幻西游", "手游", "五行", "村", "之", "危", "攻略", "。"], ["队伍", "配置"], ["开荒", "直接", "打的", "地狱", "难度", ",", "保守", "起", "见", "配", "的", "4", 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"会", "封印", ",", "同时", "还", "得", "抢", "泡泡", "。"], ["小", "编", "这里", "的", "打法", "是", "出现", "泡泡", "老", "套路", "击", "杀", ",", "然后", "优先", "击", "杀", "普陀", "在", "击", "杀", "方寸", ",", "避免", "复活", "增加", "压力", ",", "变身", "凤凰", "也", "还是", "优先", "使用", "大慈大悲", "抬", "血", "为", "主", ",", "主", "怪", "的", "伤害", "奇", "高", ",", "有", "方寸", "就", "封印", "吸血", ",", "最后", "留", "一", "个", "怪", "补充", "状态", "进入", "第三阶段", ",", "如果", "有", "变身", "也", "建议", "留", "着", "进入", "第三阶段", "。"], ["进入", "第三阶段", ",", "抢", "泡泡", "积分", "变身", "凤凰", "依然", "存在", ",", "其余", "回合", "只有", "陆", "元", "竹", "一", "个", "输出", ",", "别小看", "这", "一", "个", "输出", ",", "群", "秒", "法系", "技能", ",", "伤害", "每", "回合", "递增", "而且", "递增", "很快", ",", "10", "回合", "左右", "能", "递增", "到", "7000", "+", "左右", "秒伤", ",", "而且", "主", "怪", "血量", "超级", "多", "10", "+", "W", "血", ",", "双抗", "很高", ",", "碎甲术", "要", "上", ",", "第三阶段", "开始", "到", "七", "八", "回合", "罗汉", "没", "接上", "基本", "GG", ",", "所以", "抢", "泡泡", "积分", "的", "同时", ",", "记得", "对", "主", "怪", "的", "输出", "一定要", "打", "满", "打", "足", ",", "抗", "不住", "拖", "太", "久", "都", "要", "GG", "。"], ["小", "编", "这里", "的", "打法", "是", "方寸", "跟", "普陀", "都", "带", "罗汉", ",", "中间", "喝酒", ",", "在", "罗汉", "剩余", "1", "回合", "的", "时候", ",", "直接", "接", "上", "罗汉", "(", "主", "怪", "速度超快", ")", ",", "所以", "罗汉", "一定要", "接上", ",", "变身", "凤凰", "大慈大悲", "抬", "血", "跟", "普陀", "开", "大", "撑", "血", ",", "同时", "还要", "及时", "杀", "掉", ",", "不", "然", "你", "将", "会", "看到", "恐怖", "的", "伤害", "。"], ["【", "责任编辑", ":", "cloudyko", "】"], ["推荐专题"], ["精彩视频", "更多", "+"], ["本月", "热门", "更多", "+"]], "paragraphs": ["梦幻西游手游五行村之危这个新出的神器任务,简单难度的关卡一般玩家都能通过,但是想要通关地狱级别是非常困难的,为了帮助一些还没有通关五行村之危地狱难度的玩家能顺利通关,下面小编就为玩家们带来梦幻西游手游五行村之危攻略。", "队伍配置", "开荒直接打的地狱难度,保守起见配的4辅助1输出,属性特技基本全满,之后发现第三关打不过,跟主怪抢不过泡泡,后面会讲到。", "之后就换多一位输出,在重新配置火凤之灵。", "这里任务剧情不细说,任务第一关需要战胜5个火凤之灵。场内共有11只不同属性加成的火凤之灵,每战胜一只即可收集对应火凤属性,在第三关的时候会用到。", "11种不同火凤之灵的属性如下", "封印目标,持续3回合,100%命中", "增加法术防御和物理防御", "增加法术伤害", "增加物理伤害", "每回合恢复一定生命值", "全体成员回复一定生命值,目标回复量翻倍", "友方全体增加一个保护效果,有限吸收受到的伤害,持续3回合", "嘲讽目标,让目标所有技能都对自己释放,持续3回", "放出15道闪电,随机攻击场上所有敌人,每个敌人最多承受4道闪电", "所有的状态都试过,小编再三斟酌,选取如下5个状态来进行组合:大慈大悲、魔力之灵、耐力之灵、雷霆万钧、恢复之灵。", "第三关的第三阶段,主怪群秒的伤害会越来越高,小编这里推荐必带大慈大悲,雷霆万钧在第三关第一阶段伤害不足的时候用来清场使用,至于别的就看队伍配置,详细的下边会说到。", "火凤之灵的打法", "虽然是地狱难度,但是小怪的血并不是很高没有感知,不同的火凤之灵,怪物的技能跟难度也不一样,像耐力之灵,凤凰护体这些都比较容易获取,会有较多普陀跟方寸怪,而怪并不会躺尸是直接飞出场外,怪物会使用各种特技,如水清诀、破碎无双、笑里藏刀等技能,需要时刻注意自己的蓝量。", "这里需要注意的是如果带的封系,单法封印主怪要三思后行,因为主怪普攻的伤害非常之高,输出宝宝不神佑就是秒杀,打高防辅助也是两三千血量", "详细说几个比较难打的火凤之灵,凤凰挑衅会有3个狮驼岭跟龙宫,主怪每个回合都会挑衅一个我方单位,,被挑衅后该单位释放的所有技能只会对主怪释放,所以这一关建议都带法宝宝,这样一来哪怕被挑衅还是能AOE打到别的小怪。", "然后是雷霆万钧,正如开始大家所看到的动图一样,简直爆炸,完了小怪还全都是龙宫,这些兄弟上来不跟你讲道理就是秒秒秒,小编是战了再死死了再战,上齐法防宝宝使劲嗑药,计算好概率跟愤怒开罗汉开沐日慈悲,然后就赢了。", "魔力之灵,主怪群秒伤害较高,带封系的建议优先封印主,小怪基本都是群秒,提早开启罗汉,套路依然是点杀为主,开蛇阵并且注意血线的控制。", "力量之灵,5个狮驼,主怪伤害也高,小编先生建议带上隐身攻宠逐个击破,输出门派第一回合可以防御,第二回合在进行输出回复,辅助入场的愤怒要满,不然技能接不上,实在不行就带上龟速隐身打反手。", "大慈大悲,恢复之灵的主怪,回复能力很强,带上攻宠点杀小怪!", "第二关", "收集完5个火凤之灵就进入第二关,踢皮球,地狱模式有2分钟的时间,在你方才战胜火凤之灵的地方把它踢到中央的祭坛。", "踢球有方向限制,踢一次跳跃三次,玩家可以站在前边挡,前几次不熟练会失败,配好人多练几次就行,第二关还是比较简单的。", "五个皮球都踢到祭坛中央进入下一关。", "第三关", "第三关,抢火凤之源,主怪会不断召唤出3个火凤之源(泡泡)跟2个小鬼,如果下回合前你没有击杀泡泡,主怪就会把泡泡秒杀,敌方跟我方谁击杀获得5个积分(邪恶的凤凰在变身后才会行动)就可变身,一个角色点击变身下回合就可以进行变身,获得之前收集到的火凤之灵技能,大火凤之源获得2个积分血量较多,火凤之源获得1个积分。", "同时还有紧急任务需要完成,把孟度的血量恢复到90%,小编的打法是,双普陀配速对孟度一个灭灯一个点灯,主怪物理伤害很高,放下屠刀、光辉之甲空闲回合都要加上,小编是第一回合方寸直接给的技能,孟度是大唐伤害挺高,而且是血量越高伤害越高可以帮助清理小鬼,这里需要注意的是主怪伤害很高而且速度很快,打过几次就可以知道如何预判拉残血人。", "由于之前小编带的是1输出4辅助,对方主怪又会不断召唤,同时还得跟主怪抢泡泡,输出的不足导致对方已经拼成台球……", "之后换成双输出1狮驼跟1龙宫(魔王也带过,小编觉得魔王比龙宫好用,空闲回合能协助回复),狮驼连环大火凤之灵,龙宫单腾小火凤之灵加物理宝宝输出就可抢到泡泡,泡泡隔一回合召唤一次,狮驼可以休息变身后在继续连环是可以接上的,其余回合清理小怪。", "之后方寸山变成凤凰,可使用雷霆万钧跟大慈大悲,2回合使用次数,伤害跟血量恢复非常可观,可以根据情况使用技能,大慈大悲还能用来抬孟度的血量,建议别马上变身,出泡泡的那回合再变身。", "孟度血量抬满的下回合进入第二阶段,击杀5个五行村的村民,同时抢夺泡泡积分依然存在,不再召唤小鬼,5个村民的门派如下,孤魂野鬼-普陀山,吸血鬼-方寸山,万年僵尸-地府,夜叉-大唐官府,鬼娃娃-龙宫,击杀的村民会躺尸,普陀山会复活,在承受伤害的同时吸血鬼又会封印,同时还得抢泡泡。", "小编这里的打法是出现泡泡老套路击杀,然后优先击杀普陀在击杀方寸,避免复活增加压力,变身凤凰也还是优先使用大慈大悲抬血为主,主怪的伤害奇高,有方寸就封印吸血,最后留一个怪补充状态进入第三阶段,如果有变身也建议留着进入第三阶段。", "进入第三阶段,抢泡泡积分变身凤凰依然存在,其余回合只有陆元竹一个输出,别小看这一个输出,群秒法系技能,伤害每回合递增而且递增很快,10回合左右能递增到7000+左右秒伤,而且主怪血量超级多10+W血,双抗很高,碎甲术要上,第三阶段开始到七八回合罗汉没接上基本GG,所以抢泡泡积分的同时,记得对主怪的输出一定要打满打足,抗不住拖太久都要GG。", "小编这里的打法是方寸跟普陀都带罗汉,中间喝酒,在罗汉剩余1回合的时候,直接接上罗汉(主怪速度超快),所以罗汉一定要接上,变身凤凰大慈大悲抬血跟普陀开大撑血,同时还要及时杀掉,不然你将会看到恐怖的伤害。", "【责任编辑:cloudyko】", "推荐专题", "精彩视频更多+", "本月热门更多+"], "bs_rank_pos": 3}, {"is_selected": false, "title": "梦幻西游手游五行村之危地狱难度过关解析_梦幻西游手游_游戏狗", "most_related_para": 0, "segmented_title": ["梦幻西游", "手游", "五行", "村", "之", "危", "地狱", "难度", "过关", "解析", "_", "梦幻西游", "手游", "_", "游戏", "狗"], "segmented_paragraphs": [["梦幻西游", "手游", "五行", "村", "之", "危", "地狱", "难度", "过关", "解析", ",", "又", "到", "了", "开荒", "的", "季节", ",", "让", "阿飘", "先生", "带", "大家", "来", "看看", "这", "梦幻西游", "手游", "五行", "村", "之", "危", "的", "玩法", "。", "先", "感谢", "好朋友", "们", "修", "了", "一", "天", "的", "装备", "开荒", "。", "开荒", "直接", "打的", "地狱", "难度", ",", "这", "是", "队伍", "配置", ",", "保守", "起", "见", "配", "的", "4", "辅助", "1", "输出", ",", "属性", "特技", "基本", "全", "满", "。"], ["之后", "发现", 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"力量之灵:增加物理伤害", "敏捷之灵:增加速度", "恢复之灵:每回合恢复一定生命值", "大慈大悲:全体成员回复一定生命值,目标回复量翻倍", "凤凰护体:友方全体增加一个保护效果,有限吸收受到的伤害,持续3回合", "凤凰挑衅:嘲讽目标,让目标所有技能都对自己释放,持续3回合", "雷霆万钧:放出15道闪电,随机攻击场上所有敌人,每个敌人最多承受4道闪电", "法天象地:攻击目标三次,降低目标的物理伤害", "--------------------------------------", "游戏内火凤之灵截图", "所有的状态都试过,阿飘再三斟酌,选取如下5个状态来进行组合", "大慈大悲,魔力之灵,耐力之灵,雷霆万钧,恢复之灵", "第三关的第三阶段,主怪群秒的伤害会越来越高,阿飘这里推荐必带大慈大悲", "雷霆万钧在第三关第一阶段伤害不足的时候用来清场使用,至于别的就看队伍配置", "详细的下边会说到", "火凤之灵的打法", "虽然是地狱难度,但是小怪的血并不是很高没有感知,不同的火凤之灵,怪物的技能跟难度也不一样", "像耐力之灵,凤凰护体这些都比较容易获取,会有较多普陀跟方寸怪,而怪并不会躺尸是直接飞出场外", "怪物会使用各种特技如水清决、破碎无双、笑里藏刀等技能,需要时刻注意自己的蓝量", "这里需要注意的是如果带的封系,单法封印主怪要三思后行,因为主怪普攻的伤害非常之高", "输出宝宝不神佑就是秒杀,打高防辅助也是两三千血量", "详细说几个比较难打的火凤之灵", "凤凰挑衅会有3个狮驼岭跟龙宫,主怪每个回合都会挑衅一个我方单位,", "被挑衅后该单位释放的所有技能只会对主怪释放,所以这一关建议都带法宝宝", "这样一来哪怕被挑衅还是能AOE打到别的小怪", "然后是雷霆万钧,正如开始大家所看到的动图一样,简直爆炸", "完了小怪还全都是龙宫,这些兄弟上来不跟你讲道理就是秒秒秒", "阿飘是战了再死死了再战,上齐法防宝宝使劲嗑药,计算好概率跟愤怒开罗汉开沐日慈悲", "突然,灵光一闪!封印主怪?", "阿飘简直是……", "其实就想多看看天宫的雷霆万钧", "魔力之灵,主怪群秒伤害较高,带封系的建议优先封印主,小怪基本都是群秒,提早开启罗汉", "套路依然是点杀为主,开蛇阵并且注意血线的控制", "力量之灵,5个狮驼,主怪伤害也高,阿飘先生建议带上隐身攻宠逐个击破,输出门派第一回合可以防御,", "第二回合在进行输出回复,辅助入场的愤怒要满,不然技能接不上,实在不行就带上龟速隐身打反手", "大慈大悲,恢复之灵的主怪回复能力很强,带上攻宠点杀小怪!", "收集完5个火凤之灵就进入第二关,踢皮球", "地狱模式有2分钟的时间,在你方才战胜火凤之灵的地方把它踢到中央的祭坛", "踢球有方向限制,踢一次跳跃三次,玩家可以站在前边挡,", "前几次不熟练会失败,分配好人多练几次就行,第二关还是比较简单的", "五个皮球都踢到祭坛中央进入下一关", "第三关,抢火凤之源", "主怪会不断召唤出3个火凤之源(泡泡)跟2个小鬼,如果下回合前你没有击杀泡泡", "主怪就会把泡泡秒杀,敌方跟我方谁击杀获得5个积分(邪恶的凤凰在变身后才会行动)就可变身", "一个角色点击变身下回合就可以进行变身,获得之前收集到的火凤之灵技能", "大火凤之源获得2个积分血量较多,火凤之源获得1个积分", "同时还有紧急任务需要完成,把孟度的血量恢复到90%,", "阿飘的打法是双普陀配速对孟度一个灭灯一个点灯,主怪物理伤害很高,", "放下屠刀、光辉之甲空闲回合都要加上,阿飘是第一回合方寸直接给的技能", "孟度是大唐伤害挺高,而且是血量越高伤害越高可以帮助清理小鬼", "这里需要注意的是主怪伤害很高而且速度很快,打过几次就可以知道如何预判拉残血人", "由于之前阿飘带的是1输出4辅助,对方主怪又会不断召唤,同时还得跟主怪抢泡泡", "输出的不足导致对方已经拼成台球……", "之后换成双输出1狮驼跟1龙宫(魔王也带过,阿飘觉得魔王比龙宫好用,空闲回合能协助回复)", "狮驼连环大火凤之灵,龙宫单腾小火凤之灵加物理宝宝输出就可抢到泡泡", "泡泡隔一回合召唤一次,狮驼可以休息变身后在继续连环是可以接上的,其余回合清理小怪", "之后方寸山变成凤凰,可使用雷霆万钧跟大慈大悲,2回合使用次数,伤害跟血量恢复非常可观", "可以根据情况使用技能,大慈大悲还能用来抬孟度的血量,建议别马上变身,出泡泡的那回合在变身", "孟度血量抬满的下回合进入第二阶段,击杀5个五行村的村民,", "同时抢夺泡泡积分依然存在,不再召唤小鬼,5个村民的门派如下,", "孤魂野鬼-普陀山,吸血鬼-方寸山,万年僵尸-地府,夜叉-大唐官府,鬼娃娃-龙宫,", "击杀的村民会躺尸,普陀山会复活,在承受伤害的同时吸血鬼又会封印,同时还得抢泡泡。", "阿飘这里的打法是出现泡泡老套路击杀,然后优先击杀普陀在击杀方寸,避免复活增加压力", "变身凤凰也还是优先使用大慈大悲抬血为主,主怪的伤害奇高,有方寸就封印吸血鬼", "最后留一个怪补充状态进入第三阶段,如果有变身也建议留着进入第三阶段", "进入第三阶段,抢泡泡积分变身凤凰依然存在,其余回合只有陆元竹一个输出,", "别小看这一个输出,群秒法系技能,伤害每回合递增而且递增很快,10回合左右能递增到7000+左右秒伤,", "而且主怪血量超级多10+W血,双抗很高,碎甲术要上,第三阶段开始到七八回合罗汉没接上基本GG,", "所以抢泡泡积分的同时记得对主怪的输出一定要打满打足,抗不住拖太久都要GG。", "阿飘这里的打法是方寸跟普陀都带罗汉,中间喝酒,在罗汉剩余1回合的时候直接接上罗汉(主怪速度超快),", "所以罗汉一定要接上,变身凤凰大慈大悲抬血跟普陀开大撑血,"], "bs_rank_pos": 4}], "answer_spans": [[0, 240]], "fake_answers": ["1、传说中的凤凰这是五行村之危的第一环,需要玩家击败火凤之灵。在五行村周围有很多火凤之灵,不同的火凤之灵拥有不同的技能,玩家在击败火凤之灵后,可以在挑战BOSS时获得对应的技能。这一关没什么难度,只要击杀5个火凤之灵,就可以轻松过关了。2、集合火凤之灵这一环不需要战斗,只需要将集合火凤之灵收集到地图中间的熔炼池即可。不过因为火凤之灵能量异常强大,所以不能通过一般的方式收集。成功拾取火凤之灵的玩家将无法移动,只有通过方向键传送给其他人,点击方向键后,火凤之灵会飞出去,另外一个人可以接住,然后通过队友的协作最终将火凤之灵收集到地图中间的熔炼池中。3、击败陆元竹第三环也分为三个阶段,陆元竹每隔一定回合都会召唤几只火凤之源,击败火凤之源后可以获得激活凤凰的能量,能量满了就可以激活强大的凤凰。同样的,陆元竹也可以通过同样的方法激活强大凤凰,所以就看你们谁能获得胜利"], "question": "梦幻西游手游五行村之危攻略", "segmented_answers": [["1", "、", "传说", 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Plus也不例外。Galaxy S8在延续了S7 Edge/Note 7双曲面设计的同时,极大地提升了屏占比。三星Galaxy S8的屏占比高达84%,堪比采用全面屏设计的小米MIX(91%)。", "而且,得益于其所采用的18.5:9屏幕,三星S8的屏幕尺寸达到了惊人的5.8英寸,手机大小与5.15英寸的小米5几乎一致。更大的S8 Plus屏幕尺寸6.2英寸,但整机宽度甚至要比5.5英寸的苹果iPhone 7 Plus还要窄小一些。", "● 更加智能-Bixby语音助手", "在Galaxy S8上,三星用全新的Bixby取代了原来的S Voice服务。将手机的语音助手服务带到了一个新的高度。成了继苹果Siri、Google Assistant、微软Cortana和亚马逊Alexa之后,又一个将人工智能助手看做重点发力点的手机巨头。", "用户可以通过语音或者侧边键的方式唤醒语音助手,在使用上与iPhone上的Siri类似,除了可以和Bixby进行交谈之外,还可以进行文字输入、图片搜索、翻译等功能。从而为用户带来更加方便的手机使用体验。", "● 多种识别方式-指纹+虹膜+人脸", "在此前发布的三星Note 7上,三星就已经将指纹和虹膜两种识别方式。这次,三星在S8 & S8 Plus上为我们带来了全新的人脸识别方案。从实际体验上来看,全新的人脸识别精度极高,虹膜识别速度也有所提升,都可以实现秒解。", "● 依旧防水", "值得一提的是,三星在S8系列上依旧保留了防尘防水这项实用的功能。和三星S7以及Note7一样,三星S8系列的防护级别同样是IP68级,无论是防尘,还是防水都是目前最高的级别。", "实际上,IP68已经是移动设备所能达到的最高防水级别了。其前缀IP是Ingress Protection的缩写,代表设备可以对外物浸入的防护级别,首位数字代表防尘级别,大小范围0-6,数字越大防护能力越高;末位数字代表防水级别,大小范围0-8,同样数字越大防护能力越高。IP68代表可以几乎可以完全防止外物及灰尘侵入以及可以在10米深水中工作两星期不受损。", "● 顶级性能-骁龙835/Exynos8895处理器", "当然,三星S8系列在性能方面同样没有让人失望。三星S8搭载了骁龙835/Exynos8895处理器(根据市场进行投放),全新的处理器架构以及10nm工艺为手机的性能和功耗都带来了不小的提升。", "不仅如此,这次的三星S8系列还提供了4GB+64GB、4GB+128GB以及6GB+128GB版本供用户选择。对于一直在期待三星6GB内存旗舰手机的用户来说,Galaxy S8绝对是一大福音。", "就像上文说的,虽然Galaxy S8/S8 Plus上优点多多,但仍然有不少缺点无法掩盖。下面,笔者就带大家一起来细数Galaxy S8上的五个不足之处。", "● 高屏占比所带来的隐忧-易碎,维修成本高", "三星S8系列上,若论哪一个优点最为亮眼,那么一定非高屏占比莫属了。但这一优点从另一方面考虑,其实也是S8上面的一大隐忧---高屏占比的Galaxy S8在跌落时屏幕碎裂的概率相较于S7/S7 Edge也会提升。", "不仅如此,目前双曲面AMOLED屏幕仍属于稀缺货源,加上Galaxy S8系列所采用的双曲面屏幕还是独特的18.5:9的比例,屏幕的维修成本也要较普通手机高出一大截。而且,因为仍然采用双面玻璃设计,沾染指纹的问题在S8系列上依然无法避免。", "● 高屏占比&多种交互方式的代价-后置指纹识别和开孔过多", "三星为了提高手机的屏占比,将原本安置在正面的指纹识别移到了背面摄像头附近。对于不喜欢后置指纹识别的用户来说,这甚至可以成为他们不买S8的一个理由。而且,因为与摄像头距离过近,也可能会出现经常性按错的问题,拍照时可能就需要经常擦摄像头了。", "另外,因为支持了更多的识别交互方式,除了听筒之外,Galaxy S8的屏幕上方的开孔数量多达6个,虽然采用了黑色的前面板设计,但仍然难以掩盖千疮百孔的观感。", "当然,因为取消了正面按键设计,用户也不得不忍受虚拟按键的痛苦了。", "● 相机的升级并不大", "在三星S7系列发布时,凭借着独家定制的1200万像素索尼IMX260传感器,三星旗舰的拍照素质可谓是称霸一时。不过在最新的Galaxy S8系列上,三星对这款产品的相机并没有带来太大的升级。", "● 续航依旧感人,快充依旧没有升级", "在手机的电池容量方面,三星Galaxy S8电池容量为3000mAh,三星Galaxy S8 Plus电池容量为3500mAh。也就是说,为了更加紧凑的机身设计,三星在电池容量上做了妥协,加上它更大的屏幕,即使新处理器功耗表现更出色,但可以预料到,和去年的S7 Edge(3600mAh)以及Note 7(3500mAh)相比,S8的续航可能要退步了。", "不仅如此,可能是仍然没有从Note 7的阴影中走出来的原因,三星Galaxy S8系列在快充方面采取了保守的策略,继续标配了9V/1.67A的适配器,对于想要快速充电的用户来说,可能是一场噩梦。", "● iPhone 8同样值得期待", "另外一个可能会影响到用户选择Galaxy S8的因素就是今年的iPhone新机了。因为是iPhone十周年的缘故,从目前外界曝光出来的消息来看,iPhone 8的外观设计/功能将会迎来极大的进步,很多用户可能会因为这个原因,选择再等半年买iPhone 8。", "不仅如此,Galaxy S8还承担着带领三星从Note 7自燃阴影中走出来的使命。笔者预计,这很可能会影响到S8系列的初期销量。也就是说,S8系列上市后的用户认可度很可能会影响到这款产品的命运,这一点同样属于一个不稳定因素。", "笔者观点:", "优缺点列了一大堆,笔者认为,虽然Galaxy S8系列上仍有一些不足没能解决,但无论是外观设计,还是在交互上所带来的改变,都足以使其成为目前市面上最值得选择的一款安卓手机。而且,从创新的层面上讲,S8/S8 Plus也为如今创新乏力的手机行业注入了新的活力。相信不久的将来,我们将能看到更多采用类似设计的手机产品,整个手机行业的发展可能也会因此而更进一步。", "屏幕尺寸:5.8英寸 后置摄像头:1200万像素 运行内存(RAM):6GB 电池容量:3000mAh 机身存储(ROM):128GB"], "bs_rank_pos": 0}, {"is_selected": false, "title": "三星S8 vs iPhone 8:七大方面详细分析谁值得买_数码_腾讯网", "most_related_para": 32, "segmented_title": ["三星", "S8", "vs", "iPhone", "8", ":", "七大", "方面", "详细", "分析", "谁", "值得", "买", "_", "数码", "_", 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"07", ":", "26", ":", "48"], ["时", "长", "2", "′", "34", "″", "播放", "量", ":", "46"], ["时", "长", "2", "′", "34", "″", "播放", "量", ":", "46", "数码"], ["时", "长", "41", "″", "播放", "量", ":", "157"], ["时", "长", "41", "″", "播放", "量", ":", "157", "数码"]], "paragraphs": ["[摘要]三星、苹果今年将发布的旗舰机型在设计方面各有千秋,它们的处理器平分秋色,在屏幕方面似乎三星更胜一筹。它们可能都支持无线充电、防水功能。", "腾讯数码讯(文心)据Tech Times网站报道,智能手机爱好者对今年将发布的部分高端手机满怀期待,其中包括三星和苹果的旗舰机型。", "Galaxy S8和iPhone 8位居今年传言最多和最备受期待的智能手机之列,无疑也是传言眼中的宠儿。", "Tech Times将分析Galaxy S7和iPhone 7后续产品可能的规格,帮助用户决定哪款手机更符合自己的需求。", "设计", "在iPhone发展史中,iPhone 8将首次取消实体Home按键。苹果把这一决策作为减小屏幕顶、底部边框的一个途径。因此,iPhone 8边框可能远小于之前的历代产品。如果媒体报道属实,iPhone 8可能采用全玻璃机身设计。", "三星新款旗舰机型采用双曲边屏和窄边框设计。", "两款旗舰手机的焦点似乎是更漂亮的设计:更大的屏幕尺寸和更窄的边框,目前,这种设计已经成为智能手机的标准。苹果在智能手机上采用这种设计还是首次。", "屏幕", "泄露的规格信息显示,Galaxy S8配置5.8英寸Quad HD+分辨率Super AMOLED屏幕,更大尺寸的Galaxy S8+将配置6.2英寸屏幕。", "另一方面,iPhone 8将有3个型号,分别配置4.7、5和5.5英寸屏幕。但是,更重要的是,业界消息人士猜测,这可能是苹果有史以来首款配置曲面OLED屏幕的iPhone。", "Galaxy S8将轻松胜过基本款iPhone 8——据称配置4.7英寸屏幕;iPhone 8 Plus可能配置5.5英寸屏幕,Galaxy S8+在这一参数上占有上风。", "处理器", "苹果可能把新款iPhone处理器升级到全新10纳米工艺的A11芯片——据称比A10运行速度更快和更有效率。", "有媒体报道称美国版Galaxy S8配置高通骁龙835芯片,其他版本则配置Exynos 8895芯片。两款芯片也采用10纳米工艺制造,据称10纳米工艺能提升芯片运行效率。", "两款旗舰机型的芯片处理能力似乎相当,要评估哪款手机表现更好,用户需要使用两款手机进行直接对比。", "运行内存和机身内存", "当泄露的规格清单显示Galaxy S8可能配置4GB而非许多人期望的6GB运行内存时,粉丝深感失望。Galaxy S8机身内存将为64GB,支持通过microSD内存卡扩展存储容量。", "预计苹果将为iPhone 8配置3GB运行内存——这也是最大的iPhone运行内存容量。iPhone 8将有两种容量的机身内存——分别是64GB和256GB。但是,与之前所有型号一样,iPhone 8机身内存是不能扩展的。", "运行内存和机身内存从来都不是iPhone强项,这意味着希望购买存储容量更大手机的用户,应该选择三星旗舰机型。", "相机和电池", "预计iPhone 8将配置双摄3D相机——据称是在LG帮助下开发的。如果这一传言属实,新款iPhone可能能够拍摄3D照片。相机可能还配置脸部识别和虹膜扫描技术。", "iPhone 8电池也进行了改进,以延长电池续航时间。凯基证券分析师郭明錤(Ming-Chi Kuo,音译)发表报告称,iPhone 8可能配置容量为1700毫安时的电池。", "另一方面,Galaxy S8配置1200万像素后置相机和800万像素前置相机——也具备虹膜扫描功能。有媒体报道称Galaxy S8将配置3000毫安时电池。", "让粉丝感兴趣和吸引他们购买iPhone 8的一个元素是3D功能,更大容量的电池和更好的成像质量可能吸引其他人选择三星旗舰机型。", "其他功能", "Galaxy S8已经获得IP68认证,这意味着它将防水、防尘。据称三星还在开发代号为Bixby的新款语音助手。Galaxy S8还可能支持无线充电功能。", "另一方面,iPhone 8也支持无线充电功能。不过,目前尚不清楚苹果单独销售无线充电器还是包含在iPhone 8包装盒中。还有传言称,苹果在开发改进Siri的技术,使之赶超市场上的其他语音助手。iPhone 8也可能获得IP68认证。", "最新传言称三星Galaxy S8将采用人脸识别技术,这将使它获得针对苹果的优势。", "价格和销售", "预计发售之初Galaxy S8价格约为970美元(约合人民币6705元)。三星已经披露计划在3月29日的新产品发布会上发布Galaxy S8,预计其发售时间是4月28日。", "苹果旗舰机型价格可能略高,据称在1000美元(约合人民币6912元)左右。虽然发布日期尚未披露,但iPhone 8可能像以往机型那样在9月份发布,然后很快上市销售。", "推荐:关注腾讯数码微信官号(ID:qqdigi),带你玩遍各种潮酷新品,前沿资讯、一手评测、视频搞机、趣玩直播,还有各种新奇玩儿法。iPhone 8变成了史上最强?华为P10拍照超越了iPhone 7?340年前的锁比智能门锁还牛?三星S8出了一个新色?答案都在这里。", "正文已结束,您可以按alt+4进行评论", "责任编辑:vincezhong", "标签 手机 苹果 三星 iPhone", "时长5′15″ 播放量:150", "时长5′15″ 播放量:150 数码", "时长2′11″ 播放量:116", "时长2′11″ 播放量:116 数码", "2017-07-29 07:32:09", "时长3′37″ 播放量:497", "时长3′37″ 播放量:497 数码", "时长3′15″ 播放量:393", "时长3′15″ 播放量:393 数码", "2017-07-30 07:26:48", "时长2′34″ 播放量:46", "时长2′34″ 播放量:46 数码", "时长41″ 播放量:157", "时长41″ 播放量:157 数码"], "bs_rank_pos": 1}, {"is_selected": false, "title": "三星S8+值得买吗?三星Galaxy S8+全面深度评测图解_手机评测_手机", "most_related_para": 18, "segmented_title": ["三星", "S8", "+", "值得", "买", "吗", "?", "三星", "Galaxy", "S8", "+", "全面", "深度", 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"上", "也", "能", "看出", "它", "是", "一款", "典型", "的", "设计", "驱动", "型", "产品", ",", "为", "照顾", "设计", "做", "了", "一些", "极", "具", "创新", "意义", "的", "尝试", ",", "从", "某种", "方面", "来", "讲", "也", "可以", "看", "作", "是", "妥协", "。", "但", "无论如何", ",", "就", "整机", "的", "观", "感", "和", "做工", "而言", ",", "Galaxy", "S8", "+", "无愧其", "旗舰", "机", "的", "定位", "。"]], "paragraphs": ["经过Galaxy S6和S7两代旗舰手机的沉淀之后,双曲面屏终于同时应用在了Galaxy S8和S8+之上。那么很多网友朋友问三星S8+怎么样好不好?三星S8+值得买吗?下面小编就带来三星Galaxy S8+全面深度评测图解", "三星在过去的一年可谓命运多舛,原本在创新方面可以全面碾压竞品iPhone7 Plus的Galaxy Note7深陷电池门,导致三星在去年下半年的旗舰产品缺失。辗转半年的时间之后,三星Galaxy S8并未像我们想象中提前来“救火”,反而比往年延迟发布了一个月。这背后固然存在供应链的管控问题,但更重要的也是给三星更长的时间来保证产品安全问题。最重要的是,三星Galaxy S8系列手机还是来看,并且能称其实一次华丽的转身。", "经过Galaxy S6和S7两代旗舰手机的沉淀之后,双曲面屏终于同时应用在了Galaxy S8和S8+之上。众所周知,除了三星之外,双曲面屏也在多个手机厂商的旗舰产品中出现。不过这一设计并未明显推动用户体验的提升,更多的还是为手机厂商提供了一个为产品提升美感和质感的解决方案。", "除了曲面屏之外,三星Galaxy S8系列还带来了更惊艳的全视屏。三星在没有像小米MIX那样取消手机“额头”的情况下,将屏占比扩大到了83%这个惊人的数字,随之而来的还有18.5:9的屏幕比例。这样堆砌起来的参数信息可能无法令你感受到这块屏幕的冲击感,倘若能看到真机,想必你会难以拒绝它所为你带来的广阔视野。", "原本首发于Galaxy Note7上的虹膜识别在S8系列手机中也得到了继承。尽管指纹识别是消费者接受程度最高和市场应用最广泛的安全识别技术,但虹膜识别能够为用户提供安全级别更高的选择。如果你不习惯用虹膜识别解锁屏幕,但它在进入系统内部安全文件夹时,相比指纹来说确实更加便捷。", "经过笔者上面的介绍,此次三星Galaxy S8和S8+的主要卖点便是全视曲面屏和虹膜识别。关于这两点在后文中会有详细的解读,在这之前,大家不妨先看一下这两款手机的参数对比,从而更能直观的区分开两者的硬件差别。", "从上面的表格中我们能看到,三星Galaxy S8和S8+两款手机此次并未拉开配置上的差异(零售版上市后参数信息或有差别)。由于两者均采用了上去面显示屏,因此差异也就剩下机身尺寸、屏幕尺寸和电池容量了。不过有关于屏占比方面,我们其实还可以横向对比一下。", "横向对比来看,三星Galaxy S8+在机身尺寸上属于纤长型,相比于机身长度相仿的iPhone7 Plus来说,“瘦”了4.5mm,相比于小米MIX来说更是“瘦”了8.5mm。另外在屏占比方面,可谓碾压老对手苹果一条街了,仅次于去掉了“额头”的小米MIX。", "▲三星Galaxy S8+底栏有虚拟按键", "关于屏幕,三星其实也经过了一番取舍。例如为了兼容高屏占比,所以不得不将屏幕的四个角截成圆弧形。这样的做法我们在小米MIX和LG G6上都能发现。不过三星Galaxy S8和S8+采用双曲面屏,因此屏幕四个角的弧度也就更大,损失的显示面积也略有增加。", "此外,三星Galaxy S8和S8+所采用的屏幕比例为18.5:9,这与主流的视频源的屏幕比例不同,相比于常见的16:9片源来说,用三星Galaxy S8和S8+看视频会经常出现无法全屏显示的情况。", "从Galaxy S6系列开始,三星旗舰手机的外观设计和工艺就大获改观。一直到现在,曲面屏、双面玻璃成就了S系列手机的优秀质感。而在目前,高屏占比越来越成为手机设计的主流趋势。此前Galaxy S Edge系列所使用的曲面屏本身在屏占比和观感上就占有优势。经过前两代产品试水之后,Galaxy S8系列同样为双版本齐发,但这一次大胆地取消了直屏版本。也是顺市场主流趋势所动。这一部分,我们就来对三星Galaxy S8+的外观设计做解析。", "首先机身正面,Galaxy S8+使用一块6.2英寸曲面屏,柔性屏的使用使其几乎达到了两侧视觉无边的效果。特别的是,Galaxy S8+所使用的这块屏幕比例为18.5:9,加上两侧为弧度设计,所以整机远比市面上常见的16:9屏幕手机要长且窄。另外,为了适配这一屏幕比例,三星Galaxy S8+的屏幕分辨率为2960*1440。", "在外观上,Galaxy S8系列相比上代产品最大的不同就是机身正面了。除了进一步收窄的“额头”和“下巴”,Galaxy S8系列还取消了机身底部的Home键,令整机的屏占比获得了进一步提高的空间。取而代之的是屏内虚拟Home键。但在实用性和便利性上,实体键有着虚拟按键难以比拟的先天优势。作为弥补,三星通过3D Touch实现了息屏状态下按压虚拟Home键区域亮屏的操作,并模拟真实按键触感。", "Galaxy S8系列在指纹的基础上还提供了“虹膜识别”和“面部识别”作为补充。但目前这两种解锁方式受限于环境等因素的影响,实际上并没有指纹解锁来得方便快捷。", "Galaxy S8+的摄像头基本做到了与机身背部持平,这相比前代甚至于同类产品已经是一个不小的进步了。摄像头的造型与前几代产品保持了一致,仍然是圆角矩形,体现了设计的传承性。但移到机身背部的指纹识别按键的位置就显得有些不大妥帖,虽然照顾了视觉观感,但对实用性造成了妥协。这枚指纹识别按键虽然在位置上照顾了手姿(即右手正常握持时正好会触碰到指纹识别按键区域),但它与摄像头距离太近,使得手指会经常碰到摄像头,沾染指纹与污渍。", "取消实体按键、指纹识别移到背部这些改变,一方面Galaxy S8是一款非常典型的设计驱动型产品,这些是为全视曲面屏设计做出的妥协。另一方面,即便虹膜识别、面部识别的解锁体验能够媲美指纹识别,但相应的API接口还没有开放给虹膜识别和面部识别(也可理解成还形成统一、完善的标准),三星又需要推进三星智付,支持微信、支付宝等第三方支付,指纹识别在现阶段又必不可少。可以预见的是,在三星下代产品上,我们可以看到集成在玻璃下的指纹识别技术的出现。", "关于机身背部工艺,三星在发布会上并没有提及。据了解应该与上代产品相同,使用菲林贴合的玻璃背板,质感优异。其金属边框与前后玻璃之间的结合达到了很高的工艺水准。因为不同材质之间的接合处往往都是做工瑕疵的重灾区,很多厂商出于控制量产良率方面的考量,都会选择在两种材质之间再使用一种材质作为过渡,而Galaxy S8+则来得更为直接和纯粹。", "小结:在工业设计方面,三星近几年来在业界引领者的地位几乎无人能撼动,三星Galaxy S8+全视曲面屏的设计没有令人失望,达到了颇为震撼的正面观感。为了这一设计,从后置的指纹识别按键上也能看出它是一款典型的设计驱动型产品,为照顾设计做了一些极具创新意义的尝试,从某种方面来讲也可以看作是妥协。但无论如何,就整机的观感和做工而言,Galaxy S8+无愧其旗舰机的定位。"], "bs_rank_pos": 2}, {"is_selected": true, "title": "三星S8/S8+值不值得买?老外三大理由say no_TechWeb", "most_related_para": 8, "segmented_title": ["三星", "S8", "/", "S8", "+", "值", "不", "值得", "买", "?", "老外", "三大理由", "say", "no", "_", "Tech", "Web"], "segmented_paragraphs": [["Tech", "Web", "官方", "微博", ":"], ["三星", "S8", "/", "S8", "+", "值", "不", "值得", "买", "?", "老外", "三大理由", "say", "no", "三星", "昨晚", "盛大", "发布", "了", "Galaxy", "S8", "/", "S8", "+", "手机", ",", "全", "视", "曲面", "屏", "、", "骁龙", "835", "、", "千兆", "LTE", "、", "Bixby", "AI", "语音", "助手", "等", "成为", "卖点", ",", "不过", "799", "/", "899", "欧元", "的", "起", "售价", "着", "实", "不", "菲", "。"], ["下面", 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"改变", ",", "与", "此", ".", ".", "."]], "paragraphs": ["TechWeb官方微博:", "三星S8/S8+值不值得买?老外三大理由say no 三星昨晚盛大发布了Galaxy S8/S8+手机,全视曲面屏、骁龙835、千兆LTE、Bixby AI语音助手等成为卖点,不过799/899欧元的起售价着实不菲。", "下面CNET总结了令人脱离选购欲的三点,如下——", "1、摄像头 非常遗憾,S8/S8+搭载了和S7/S7 edge纸面参数完全一致的摄像头,这是有些匪夷所思的。毕竟,在如今拍照竞争如此激烈的态势下,三星万众瞩目的新旗舰在如此关键的硬件上“换汤不换药”。 2、电池续航 S8/S8+最惊艳的莫过于屏幕,两者都是2960 x 1440超2K分辨,其中前者5.8英寸,后者6.2英寸。不过,两者屏幕都比S7 edge大,但电池却双双缩水。 按照官方资料,典型场景如通话时间、视频播放等都有2小时的下滑。本来,S7e的续航就不算很好,结果这次又是一天一充甚至多充的节奏。 3、个人助理Bixby 在市面上已经有小娜、谷歌助手、苹果Siri、亚马逊Alexa情况下,三星执意自己做出Bixby。但其体验应当说最不完善,目前仅支持三星自家APP,不能从互联网收集信息。 每日头条、业界资讯、热点资讯、八卦爆料,全天跟踪微博播报。各种爆料、内幕、花边、资讯一网打尽。百万互联网粉丝互动参与,TechWeb官方微博期待您的关注。", "↑扫描二维码", "想在手机上看科技资讯和科技八卦吗?", "想第一时间看独家爆料和深度报道吗?", "请关注TechWeb官方微信公众帐号:", "1.用手机扫左侧二维码; 2.在添加朋友里,搜索关注TechWeb。", "今年的小米春季发布会上,受制于...", "近年来,随着我国经济以及医疗卫生事业的发展,人民生活水平有了很大程度的提高,但同时由于环境污染和人们生活方式的改变,与此..."], "bs_rank_pos": 4}, {"is_selected": false, "title": "三星S8很惊艳,但有5个理由不值得购买_搜狐科技_搜狐网", "most_related_para": 17, "segmented_title": ["三星", "S8", "很", "惊艳", 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S8+的指纹识别模块,单手解锁会十分糟糕,为了不出什么意外还是将手机背面翻转过来双手握持解锁吧!2.电池容量缩水三星S8/S8+最惊艳的地方莫过于屏幕,尺寸分别为5.8、6.2英寸,分辨率2960*1440,屏幕虽好,但耗电量也有了相应增加。三星S7 Edge在5.5英寸屏幕的情况下使用了3600mAh电池,但是续航尿崩,一天需要两充甚至三充。三星Note7为了增强续航强行增加电池密度,最终导致自燃事故不断而提前陨落。为了避免新的安全事故出现,5.8英寸的S8仅使用了3000mAh的电池,6.2英寸的S8+仅使用了3500mAh的电池。续航?哈哈哈见鬼去吧!3.摄像头没变化此前三星S7 Edge被誉为拍照最出色的智能手机,其搭载拥有全像素双核对焦技术的1200万像素索尼IMX260传感器。时间已过一年有余,当前市面上双摄像头手机大行其道,拍照玩法多种多样。最近索尼发布的Xperia XZ Premium更是支持高达960帧/秒的高速录像,这是因为它搭载了自家最新的IMX400传感器,有效像素1900万。之前传闻三星S8将会搭载索尼为其定制的IMX333,同样支持960高速录像,遗憾的是S8摄像头与S7 Edge一模一样,三星继续打磨了发布一年多的IMX260。换句话说就是三星S8的相机成像并不会有太大提升。三星Galaxy S8的成本大幅提升,售价也不会便宜,结合当前手机市场大幅涨价的背景,三星S8涨价也是情理之中了。此前evleaks爆料三星S8欧洲区起售价799欧元,S8+为899欧元,约合人名币5931/6631元。", "根据欧洲区售价,小编推测国行最低也要5688元起步,或者5988元,最高配版本可能要突破7000元大关,不知道这样的售价你能否接受。", "我们知道最近因为某件事,大家对某国商品有了抵触情绪,并不是因为售价过高而不选择购买。就算你没有这样的抵触情绪,你还要担心周围人的眼光,由此可见三星S8不光做出了多项妥协,就连运气都没有站在它身边。", "那么,你会做出怎样的选择呢?", "历史文章推荐", "华为P10开箱图赏丨三星S8发布会概览", "智能机器人试玩丨小米MIX白色评测", "Nokia6评测丨荣耀Magic评测", "Air Pods评测丨魅蓝X评测", "魅蓝Note5评测丨一加3T评测", "微信公众号:木蚂蚁潮流实验室(ID:mumayi999)返回搜狐,查看更多", "声明:本文由入驻搜狐号的作者撰写,除搜狐官方账号外,观点仅代表作者本人,不代表搜狐立场。", "本文相关推荐", "三星s8值得购买吗  三星s8和s7edge  三星s8指纹解锁  三星s8续航评测  三星s8怎么样  三星s8发布会  三星s8跑马灯  三星s8屏辣眼睛  三星s8港版刷国行  华为p10和三星s8  三星s8广告歌曲  三星s8隐藏功能", "不能炸买什么", "今期再卖不出,很快就变诺基亚二代了。", "不用几个,只要一个就可以了,南朝鲜。", "看电池容量就不想买了,再加上萨德,呵呵", "在我心中只有一个,,,,没钱", "只有一个理由,韩国货", "已有23人参与,点击查看更多精彩评论"], "bs_rank_pos": 5}], "answer_spans": [[18, 18]], "yesno_answers": ["No"], "fake_answers": ["。"], "question": "三星s8值得买吗", "segmented_answers": [["不值得", "。"]], "answers": ["不值得。"], "yesno_type": "OPINION", "answer_docs": [3], "segmented_question": ["三星", "s8", "值得", "买", "吗"], "question_type": "YES_NO", "question_id": 91187, "fact_or_opinion": "OPINION", "match_scores": [0.6666666666666666]}
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"【译文】邽山,蒙水从这座山发源,向南流入洋水,水中有很多黄贝;还有一种蠃鱼,长着鱼的身子却有鸟的翅膀,发出的声音像鸳鸯鸟鸣叫,在哪个地方出现那里就会有水灾。", "山海经异兽穷奇还原图", "【原文】邽山,其上有兽焉,其状如牛,猬毛,名曰穷奇,音如嗥狗,是食人。", "【译文】邽山,山上有一种野兽,形状像一般的牛,但全身长着刺猬毛,名称是穷奇,发出的声音如同狗叫,吃人。", "山海经异兽天狗还原图", "【原文】阴山,有兽焉。其状如狸而白首,名曰天狗,其音如榴榴,可以御凶。", "【译文】阴山,山中有一种野兽,形状像野猫却是白脑袋,名称是天狗,它发出的叫声与“榴榴”的读音相似,人饲养它可以辟凶邪之气。", "山海经异兽狰还原图", "【原文】章莪之山,无草木,多瑶、碧。所为甚怪。有兽焉,其状如赤豹,五尾一角,其音如击石,其名曰狰。", "【译文】章莪山,山上没有花草树木,到处是瑶、碧一类的美玉。山里常常出现十分怪异的物象。山中有一种野兽,形状像赤豹,长着五条尾巴和一只角,发出的声音如同敲击石头的响声,名称是狰。", "山海经异兽肥遗还原图", "【原文】英山,有鸟焉,其状如鹑①,黄身而赤喙,其名曰肥遗,食之已疠②,可以杀虫。", "【注释】①鹑:即“鹌鹑”的简称,是一种鸟,体形像小鸡,头小尾短,羽毛赤褐色,有黄白色条纹。雄性的鹌鹑好斗②疠:癞病,即麻风。", "【译文】英山,山中有一种禽鸟,形状像一般的鹌鹑鸟,是黄身子而红嘴巴,名称是肥遗,人吃了它的肉就能治愈麻疯病,还能杀死体内寄生虫。", "山海经异兽凤凰还原图", "【原文】丹穴之山,有鸟焉,其状如鸡,五采而文,名曰凤皇①,首文曰德,翼文曰义,背文曰礼,膺②文曰仁,腹文曰信。是鸟也,饮食自然,自歌自舞,见则天下安宁。", "【注释】①风皇:同“凤凰”,是古代传说中的鸟王。雄的叫“凤”,雌的叫“凰”。据古人说,它的形状是鸡的头,蛇的脖颈,燕子的下颔,乌龟的背,鱼的尾巴,五彩颜色,高六尺左右。②膺:胸。", "【译文】丹穴山,山中有一种鸟,形状像普通的鸡,全身上下是五彩羽毛,名称是凤凰,头上的花纹是“德”字的形状,翅膀上的花纹是“羲”字的形状,背部的花纹是“礼”字的形状,胸部的花纹是“仁”字的形状,腹部的花纹是“信”字的形状。这种叫做凤凰的鸟,吃喝很自然从容,常常是自各儿边唱边舞,一出现天下就会太平。", "山海经异兽蛊雕还原图", "【原文】鹿吴之山,上无草木,多金石。泽更之水出焉,而南流注于滂水。水有兽焉,名曰蛊雕,其状如雕而有角,其音如婴儿之音,是食人。", "【译文】再往东五百里,是座鹿吴山,山上没有花草树木,但有丰富的金属矿物和玉石。泽更水从这座山发源,然后向南流入滂水。水中有一种野兽,名称是蛊雕,形状像普通的雕鹰却头上长角,发出的声音如同婴儿啼哭,是能吃人的。", "山海经异兽九尾狐还原图", "【原文】青丘之山,有兽焉,其状如狐而九尾,其音如婴儿,能食人;食者不蛊。", "【译文】青丘山,山中有一种野兽,形状像狐狸却长着九条尾巴,吼叫的声音与婴儿啼哭相似,能吞食人;吃了它的肉就能使人不中妖邪毒气。", "山海经异兽旋龟还原图", "【原文】杻阳之山。怪水出焉,而东流注于宪翼之水。其中多玄龟,其状如龟而鸟首虺尾①,其名曰旋龟,其音如判木,佩之不聋,可以为②底。", "【注释】①虺:毒蛇。②为:治理。这里是医治、治疗的意思。底:这里与“胝”的意思相同,就是手掌或脚底因长期摩擦而生的厚皮,俗称“老茧”。", "【译文】杻阳山。怪水从这座山发源,然后向东流入宪翼水。水中有众多暗红色的龟,形状像普通乌龟却长着鸟一样的头和蛇一样的尾巴,名称是旋龟,叫声像劈开木头时发出的响声,佩带上它就能使人的耳朵不聋,还可以治愈脚底老茧。", "山海经异兽青龙还原图", "青龙,亦作“苍龙”,古代神话中的东方之神。龙是中华民族的图腾,自黄帝授命于天,威泽四方,龙就成为中华民族乃至整个中国的象征,而比较明确的定形是在汉代,从大汉朝开始,龙就被确定为皇帝的象征与代表。在东方传说中,青龙身似长蛇、麒麟首、鲤鱼尾、面有长须、犄角似鹿、有五爪、相貌威武,而在西方神话里,龙更像是长翅膀的蜥蜴。", "==========================", "世界十大战神 中国古代十大美男子 上古四大凶兽 西游记十大妖精武力排行 红楼梦贾府十大最有财力的人 中国历史上最强谋士排名 成吉思汗十大未解之谜 十大解放军惨败战役 铁处女是什么 中国历史十大古物未解之谜"], "bs_rank_pos": 0}, {"is_selected": false, "title": "山海经异兽排名实力_祥安阁历史", "most_related_para": 16, "segmented_title": 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"“", "百鸟朝凤", "”", "之", "说", "。"]], "paragraphs": ["您的位置:祥安阁风水网 > 中华民俗 > 历史 > 山海经异兽排名实力", "日期:2017-04-10 www.azg168.cn 祥安阁风水网 编辑:azg16806", "山海经所记载的神兽,都是有着奇异的外形和神奇的力量,而这些神兽不单单能够给人们带来幸福,但是同样的,也能够招来灾害,而今天我们就来讲讲,有关山海经里面的神兽到底有着怎样的传说,而这种这些神兽也是有着大家所熟悉的“毕方”、“饕餮”和“重名鸟”等等,一起来看看山海经异兽排名实力到底如何吧。", "夔(kuí)", "夔是传说中的一条腿的怪物。《山海经·大荒东经》记载:夔牛是古时代神兽,古时生于东海流波山,形状似牛,全身都是灰色的,没有长角,只长了一只脚,每次出现都会有狂风暴雨。它身上还闪耀着光芒,似日光和月光,它的吼声和雷声一样震耳欲聋。后来黄帝得到这种兽,用它的皮制成鼓并用雷兽的骨做槌,敲击鼓,鼓声响彻五百里之外,威慑天下。", "《神魔志异·灵兽篇》记载:上古奇兽,状如青牛,三足无角,吼声如雷。久居深海,三千年乃一出世,出世则风雨起,雷电作,世谓之雷神坐骑。", "《黄帝内经》载:“黄帝伐蚩尤,玄女为帝制夔牛皮鼓八十面,一震五百里,连震三千八百里。”九天玄女令军士宰夔牛制作八十面战鼓,使黄帝在得到九天玄女辅助之后,带兵与蚩尤大战于中冀。当黄帝摆下“奇门遁甲”阵之后,即令军士以雷兽之骨,大击八十面夔牛皮巨鼓,一时鼓声大作,只见整个战场地动山摇,天旋地转,喊杀冲宵汉,蚩尤兵卒神魂颠倒,冲杀无门,败倒如山。“蚩尤铜头啖石”,即把石头当饭食,且能“飞空走险”。但在夔牛鼓震声中,“九击止之,尤不能走,遂杀之”。尔后,黄帝又诛榆罔于阪泉(今河北省涿鹿县东南),经过这场大血战,天下始得大定。", "更多的古籍中则说夔是蛇状怪物。“夔,神魅也,如龙一足。”(《说文解字》)“夔,一足,踔而行。”(《六帖》)在商晚期和西周时期青铜器的装饰上,夔龙纹是主要纹饰之一,形象多为张口、卷尾的长条形,外形与青铜器饰面的结构线相适合,以直线为主,弧线为辅,具有古拙的美感。青铜器上的龙纹常被称为夔纹和夔龙纹,自宋代以来的著录中,在青铜器上凡是表现一足的、类似爬虫的物象都称之为夔,这是引用了古籍中“夔一足”的记载。其实,一足的动物是双足动物的侧面写形,故不采用夔纹一词,称为夔龙纹或龙纹。", "“白泽”", "“白泽”是传说中昆仑山上的神兽,浑身雪白,能说人话,通万物之情,很少出没,除非当时有圣人治理天下,才奉书而至,是可使人逢凶化吉的吉祥之兽。", "据《云笈七签·轩辕本纪》记载,白泽曾向皇帝讲解关于动物的种种知识。据说当时黄帝正在全国各地巡游,了解自己所辖国土的真实面貌。在东海边,他碰到了会说话的怪兽白泽。白泽博学多闻,上知天文,下知地理,通过去,晓未来,对各种动物了如指掌,知道天下所有鬼怪的名字、形貌和驱除的方术。凡是各种采天地灵气、集日月精华而产生的怪异物种,它都一一跟黄帝解释清楚,前后一共谈到1522个物种。黄帝命人将白泽所言一一画图记录,并制成《白泽精怪图》(又称《白泽图》)。书中记有各种神怪的名字、相貌和驱除的方法,并配有神怪的图画,人们一旦遇到怪物,就会按图索骥加以查找。", "浑身雪白,能说人话,通万物之情", "从很早开始,白泽就被当做驱鬼的神和祥瑞来供奉。尤其到了中古时期,对白泽的尊崇更是隆重。当时《白泽图》一书非常流行,到了几乎家手一册的程度。在禅宗语录中,也常见有“家有白泽图,妖怪自消除”“不悬肘后符,何贴白泽图”“家无白泽图,有如此妖怪”一类的语录。人们将画有白泽的图画挂在墙上,或是贴在大门上用来辟邪驱鬼。", "后世常将它的形貌使用在物品之上,当时还有做“白泽枕”的习俗,即做成白泽形象的枕头用于寝卧,其用意也是为了辟邪驱鬼。《唐书·五行志》记载中宗韦皇后的妹妹用虎豹枕以避邪,白泽枕以避魅。在军队的舆服装备中,“白泽旗”也是常见的旗号。《通典》记帝王之旗就绘有白泽的形貌,被称为白泽旗。人们甚至以“白泽”来为自己命名,出现了许多叫“白泽”的人。", "凤凰是中国神话传说中的神异动物和百鸟之王,和龙一样为汉族的民族图腾,羽毛一般被描述为赤红色,亦称为朱鸟、丹鸟、火鸟、鹍鸡等。在西方神话里又叫火鸟、不死鸟,形象一般为尾巴比较长的火烈鸟,并周身是火,估计是人们对火烈鸟加以神话加工、演化而来的。神话中说,凤凰每次死后,会周身燃起大火,然后其在烈火中获得重生,并获得较之以前更强大的生命力,称之为“凤凰涅槃”。如此周而复始,凤凰获得了永生,故有“不死鸟”的名称。", "凤凰是中国民间传说中的四灵(龙、凤凰、麒麟、龟)之一。凤凰和麒麟一样,是雌雄统称,雄为凤,雌为凰,其总称为凤凰,因此凤凰一词为合成词结构。凤凰齐飞,是吉祥和谐的象征,代表着幸福的灵物。据现存文献推断:凤鸣如箫笙,音如钟鼓。凤凰雄鸣曰即即,雌鸣曰足足,雌雄和鸣曰锵锵。", "凤凰的形象跟龙的形象一样,愈往后愈复杂。据《尔雅·释鸟》郭璞注,凤凰特征是:“鸡头、燕颔、蛇颈、龟背、鱼尾、五彩色、高六尺许。”“出于东方君子之国,翱翔四海之外,过昆仑,饮砥柱,濯羽弱水,莫宿风穴,见则天下安宁。”《山海经·图赞》说有五种像字纹:“首文曰德,翼文曰顺,背文曰义,腹文曰信,膺文曰仁。”自古以来凤凰就成了中华民族文化中的重要组成部分。", "凤凰的起源约在新石器时代,原始社会彩陶上的很多鸟纹是凤凰的雏形。距今约7400年的湖南洪江高庙文化遗址中,出土了一件白色陶罐,其颈部和肩部各戳印有东方神鸟(包括兽面、太阳)图案,一只朝向正面,一只侧面回首。据考古专家鉴定,这件陶器上的神鸟图案即凤凰,要比浙江余姚河姆渡文化遗址中发现的凤凰图案至少要早400年,是迄今为止我国发现的最早的凤凰图案。", "根据神话传说,凤是从东方殷族的鸟图腾演化而成。今日所见关于凤的最早记录,可能是在《尚书·益稷》篇中。书中叙述大禹治水后,举行庆祝盛典。由夔龙主持音乐,群鸟群兽在仪式上载歌载舞。最后,一凤凰也来了——“萧韶九成,凤皇来仪。”", "凤凰是人们心目中的瑞鸟,天下太平的象征。古人认为时逢太平盛世,便有凤凰飞来。“凤”其甲骨文和“风”的甲骨文字相同,即代表具有风的无所不在及灵性力量的意思;“凰”即“皇”字,为至高至大之意。凤凰也是中国皇权的象征,常和龙一起使用,凤从属于龙,用于皇后嫔妃,龙凤呈祥是最具中国特色的图腾。民间美术中也有大量的类似造型。凤也代表阴,尽管凤凰也分雄雌,更一般的是将其看作阴性。凤凰被认为是百鸟中最尊贵者,为鸟中之王(尽管是虚构的),有“百鸟朝凤”之说。"], "bs_rank_pos": 1}, {"is_selected": false, "title": "上古四大凶兽实力排行图片,上古四大凶兽之首,山海经异兽排名_天涯", "most_related_para": 0, "segmented_title": ["上古", "四大", "凶兽", "实力", "排行", "图片", ",", "上古", "四大", "凶兽", "之", "首", ",", "山海经", "异兽", "排名", "_", "天涯"], "segmented_paragraphs": [["中国", "古代神话", "中", "有", "四", "凶", "(", "同时", "也", "指", "是", "指", "共工", ",", "驩兜", ",", "三苗", "与", "鲧", ")", ",", "也", "就是", "四大", "恶", "兽", ",", "分别", "是", ":", "饕餮", "(", "tāo", "tiè", ")", ",", "混沌", "(", "hun", "dun", ")", ",", "穷奇", "(", "qiong", "qi", ")", "和", "梼杌", "(", "táowù", ")", "。", "上古", "四大", "凶兽", "实力", "排行", "图片", ",", "上古", "四大", "凶兽", "之", "首", ",", "山海经", "异兽", "排名", ",", "上古", "十大", "神兽", "是", "指", "古代神话", "传说", "中", "的", "神兽", ",", "这", "十种", "神兽", "分别", "是", "太阳", "烛", "照", "、", 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"《山海经之赤影传说》当中出现的朱雀、青龙引发了网友的好奇,其实除此之外,在《山海经》当中还有很多神奇的上古神兽。白泽、夔、凤凰、麒麟、梼杌、獬豸、犼、重明鸟、毕方、饕餮、腓腓、诸犍、混沌、庆忌等都是一些非常有意思的上古神兽。因为《山海经赤影传说》的开播,我们来揭秘一下《山海经》十大异兽排名、山海经凶兽排名图片,麒麟、饕餮、混沌等这些在《山海经》里最霸道的是谁呢?", "《山海经》十大异兽排名之麒麟:与凤、龟、龙共称为“四灵”", "麒麟在四灵兽中地位仅次于青龙,后由于封建皇帝对龙凤的褒扬,麒麟被排挤到民间,成为民间祥瑞的独特代表。麒麟主太平,带来丰年、福禄、长寿与美好。玄学称麒麟是岁星散开而生成,故主祥瑞,含仁怀义。", "从其外部形状上看,集龙头、鹿角、狮眼、虎背、熊腰、蛇鳞、马蹄、牛尾于一身,乃是吉祥之宝。据古籍的描述,它的身子像麕,也可以写成麇,古籍中指鹿;尾巴像牛;蹄子像马;圆的头顶,有一对角。它被古人视为神兽、仁兽。麒麟长寿,能活两千年。能吐火,声音如雷。“有毛之虫三百六十,而麒麟为之长。”(有毛之虫的意思是有毛的动物)", "麒麟是中国古人创造出的虚幻动物,这种造型是把那些备受人们珍爱的动物所具备的优点全部集中在麒麟这一幻想中的神兽的建构上。在中国众多的民间传说中,关于麒麟的故事虽然并不是很多,但其在民众生活中实实在在地无处不体现出它特有的珍贵和灵异。", "麒麟,亦作“骐麟”,雄性称麒,雌性称麟,简称“麟”。麒麟是中国古籍中记载的一种动物,与凤、龟、龙共称为“四灵”,是神的坐骑。古人把麒麟当作仁兽、瑞兽,现实中常认为长颈鹿是麒麟的原型,常用来比喻杰出的人。", "麒麟文化", "麒麟文化是中国的传统民俗文化,如麒麟送子,就是中国旧时生育民俗。传说中,麒麟为仁兽,是吉祥的象征,能为人带来子嗣。相传孔子将生之夕,有麒麟吐玉书于其家,上写“水精之子孙,衰周而素王”,意谓他有帝王之德而未居其位。此虽伪说,实为“麒麟送子”之本,见载于王充《论衡·定贤》及晋王嘉《拾遗记》。民间有“麒麟儿”、“麟儿”之美称。", "孔子释氏亲抱送,并是天上麒麟儿。”胡朴安《中华全国风俗志·湖南》引《长治新年纪俗诗》:“妇女围龙可受胎,痴心求子亦奇哉。真龙不及纸龙好,能作麟麟送子来。”(注:妇人多年不生育者,每于龙灯到家时,加送封仪,以龙身围绕妇人一次,又将龙身缩短,上骑一小孩,在堂前行绕一周,谓之麟麒送子。按麟麒属于龙族,此乃以龙灯代麒麒者。)山海经十大异兽排名、凶兽排名图片曝光,山海经里最霸道的是谁?应该非麒麟莫属了。", "南北朝时,对聪颖可爱的男孩,人们常呼为“吾家麒麟”。此后“麒麟送子图”制作为木板画,上刻对联“天上麒麟儿,地下状元郎”,以此为佳兆。民间普遍认为,求拜麒麟可以生育得子。唐杜甫《徐卿二子歌》:“君不见徐卿二子多绝奇,感应吉梦相追随。", "最八卦 观娱"], "bs_rank_pos": 3}, {"is_selected": false, "title": "山海经异兽图大全,神异灵兽能力图文详解 —【94677奇闻网】", "most_related_para": 5, "segmented_title": ["山海经", "异兽", "图", "大全", ",", "神", "异", "灵兽", "能力", "图文", "详解", "—", "【", "94677", "奇闻", "网", "】"], "segmented_paragraphs": [["当前位置", ":", "94677", "奇闻", "网", ">", "奇闻", "怪事", ">", "手机", "访问", ":", "3g", ".", "94677", ".", "com"], ["来源", ":", "www", ".", "94677", ".", "com", "时间", ":", "2017", "-", "03", "-", "22", "奇闻", "指数", ":", "229754", "编辑", ":", "admin", "手机", "版"], ["《", "山海经", "》", "中", "总是", "有", "着", "各种", "奇幻", "的", "故事", "与", "怪兽", ",", "对于", "那些", "怪兽", "的", "秒", "描述", "充满", "了", "各种", "丰富", "的", "想象", "。", "所以", "很多人", 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"矔疏是中国古代神话传说中的神兽,山海经异兽图中一角马,辟火奇兽。最早见于《山海经·北山经》,又北三百里,曰带山,其上多玉,其下多青碧。有兽焉,其状如马,一角有错,其名曰?疏,可以辟火。", "翼望山,山中有一种野兽,通过山海经异兽图可看出,形状像一般的野猫,只长着一只眼睛却是三条尾巴,名称是讙,发出的声音好像能赛过一百种动物的鸣叫,饲养它可以辟凶邪之气,人吃了它的肉就能治好黄疸病。", "中山神,又做“钟山神”,自女儿山至贾超山共十六座山,方圆三千五百里。其山神都是马身龙首神。又名中山神。", "上一篇英儿童森林发现疑为大脚怪巨大脚掌", "下一篇湖南发现 神似蹲式人体下半身“怪树”/图"], "bs_rank_pos": 4}], "answer_spans": [[0, 22]], "answer_docs": [0], "fake_answers": ["山海经异兽排名:天狗、穷奇、九尾狐、蛊雕、诸怀、蠃鱼、朱庆、梼杌、狍鴞、毕方"], "question": "山海经异兽图实力排名", "segmented_answers": [["山海经", "异兽", "排名", "：", "天狗", "、", "穷奇", "、", "九尾狐", "、", "蛊雕", "、", "诸怀", "、", "蠃鱼", "、", "朱庆", "、", "梼杌", "、", "狍鴞", "、", "毕方", "。"]], "answers": ["山海经异兽排名：天狗、穷奇、九尾狐、蛊雕、诸怀、蠃鱼、朱庆、梼杌、狍鴞、毕方。"], "entity_answers": [["天狗", "穷奇", "九尾狐", "蛊雕", "诸怀", "蠃鱼", "朱庆", "梼杌", "狍鴞", "毕方"]], "segmented_question": ["山海经", "异兽", "图", "实力", "排名"], "question_type": "ENTITY", "question_id": 91188, "fact_or_opinion": "OPINION", "match_scores": [0.9361702127659574]}
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"在他们拍摄《活色生香》的过程中,有媒体采访唐嫣:“与李易峰搭戏怎么样?”唐嫣表示:“首次和自己小的男演员拍戏,感觉压力大,但并不介意姐弟恋”,李易峰也回应称:“每次拍戏前,看到他和别的男的在一起,第二天拍戏心里就会难受。”让两人的关系更加难以猜测。", "2014年的时候李易峰甚至在微博上公开称唐嫣为“媳妇”,虽然后来被他秒删了,但还是让人浮想联翩。", "之后两人的各种照片总是有惊天巧合,网友猜测唐嫣心中是有李易峰的。", "而李易峰的绯闻太多。", "而这显然不符合糖爸爸的要求。看来有时候绯闻太多也不是好事,很可能就与自己心爱的人擦肩而过。", "你们绝对唐嫣到底更爱谁?", "本文仅代表作者观点,不代表百度立场。", "本文系作者授权百家号发表,未经许可,不得转载。", "作者近期文章", "李易峰终于公开恋爱标准了,不是唐嫣、林允儿,她笑了,原来是她! 和李易峰合作的女星,个个有绯闻,为何她没有?原来李易峰早就看出她的是他? 林心如刚当妈, 却爆出霍建华和她的恋情,令人惊讶!!", "唐嫣喜欢罗晋,眼神就能看出来,", "1111111111111111111111111111111111111111111111111111111111112", "支持罗晋唐嫣", "唐嫣根本不配,小编你别乱说!", "峰峰是俺家丽颖女神的,小编,你是不是羡慕嫉妒恨滴?脑子死机了?死也别在这是侮辱我们的女神和峰峰。", "李易峰也看不上她", "打开手机百度,查看全部评论"], "bs_rank_pos": 0}, {"is_selected": false, "title": "李易峰霍建华最怕的女星竟然是唐嫣,终于知道她为何总穿平底鞋!", "most_related_para": 13, "segmented_title": ["李易峰", "霍建华", "最", "怕", "的", "女星", "竟然", "是", "唐嫣", ",", "终于知道", "她", "为何", "总", "穿", "平底鞋", "!"], "segmented_paragraphs": [["天气", "凉", "了", ",", "唐嫣", "都是", "大衣", "出行", ",", "很", "符合", "时节", "的", "穿着", "。"], ["灰色", "超长", 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"唐嫣", ",", "不", "多", "说", "了", "!", "看图", "!", "一样", "高", "!"], ["当", "李易峰", "碰上", "唐嫣", ",", "1", "米", "81", "的", "李易峰", "和", "唐嫣", "的", "身高", "接近", "!"], ["当", "唐嫣", "碰到", "男友", "罗晋", "时", ",", "这", "身高", ",", "感觉", "挺", "般配", "啊", "!"], ["当", "唐嫣", "碰上", "前男友", "邱泽", ",", "官方", "给", "出", "的", "176", "cm", "还是", "挺", "正确", "的", "~"], ["当", "唐嫣", "穿上", "高跟鞋", "~"], ["写", "在", "雷雷", "脸上", "的", "尴尬", "!"], ["话说", ",", "李易峰", "、", "谢霆锋", "到底", "有", "多", "高", ",", "你", "是不是", "更", "期待", "?"]], "paragraphs": ["天气凉了,唐嫣都是大衣出行,很符合时节的穿着。", "灰色超长大衣", "今年的大衣越长越时髦,唐嫣轻松驾驭超长大衣,最厉害的是还是穿平底鞋配这样的超长款大衣!再一次实力验证172cm的身高绝无水分!!", "糖糖站在维秘超模刘雯的身边都没压力!", "唐嫣身材高挑是客官们都知道的事情,她在网上的官方身高是172,这个数据相信比选择绝大多数的小花旦都要高一截。", "而唐嫣虽然身材傲人,却从不给其他人难堪,因此与身高一般的明星一同出席活动时一般只会穿平底鞋,可见也是个高情商的姑娘。(好评!)而不穿高跟鞋的她却在气场上输给别人,反而给人一种平易近人的亲切感!", "她在《仙剑奇侠传三》中饰演的紫萱,《夏家三千金》中饰演的夏天美,《金玉良缘》中饰演的玉麒麟,还是去年热播大剧《锦绣未央》的未央呢?没错,唐嫣这些年越来越稳重成熟了,喜欢她的粉丝也很多,不过唐嫣最让人佩服的一点是她的身高。", "大家都知道,娱乐圈有不少明星就会谎报自己的身高,而唐嫣这172的身高就好像是娱乐圈的一把活尺子。", "众所周知唐嫣可是标准的模特身高,在娱乐圈就是活尺子一样的存在,各路男明星一和穿平底鞋的她同框,就会造成一万点伤害~", "当身高172cm的唐嫣穿上平底鞋,那就是或标尺。娱乐圈的男人们,你们敢不敢和唐嫣站一起?", "身高177cm的霍建华居然和唐嫣一样高!", "当唐嫣碰上钟汉良,钟汉良还是蛮高的,180cm肯定是有了,和唐嫣就差半个头", "当谢霆锋碰上唐嫣,不多说了!看图!一样高!", "当李易峰碰上唐嫣,1米81的李易峰和唐嫣的身高接近!", "当唐嫣碰到男友罗晋时,这身高,感觉挺般配啊!", "当唐嫣碰上前男友邱泽,官方给出的176cm还是挺正确的~", "当唐嫣穿上高跟鞋~", "写在雷雷脸上的尴尬!", "话说,李易峰、谢霆锋到底有多高,你是不是更期待?"], "bs_rank_pos": 1}, {"is_selected": false, "title": "李易峰真心配不上唐嫣!-搜狐", "most_related_para": 4, "segmented_title": ["李易峰", "真心", "配", "不", "上", "唐嫣", "!", "-", "搜狐"], "segmented_paragraphs": [["眼眸", "深邃", "、", "轮廓", "分明", "、", "身材", "颀长", ",", "活生生", "的", "一幅画", "。"], ["卓伟", "大嘴", ",", "点赞", "李易峰", "与", "唐嫣", "在一起", "帖子", ",", "引发", "外界", "遐想", "。", "遐想", "就", "遐想", "吧", ",", "万万", "没", "有", "想到", ",", "最终", "演", "变成", "双方", "粉丝", "撕逼", "大战", "。"], ["博", "主", "也是", "大嘴", ",", "虽然", "没有", "卓伟", "毒", ",", "但", "仔细", "分析", "也", "觉得", "李易峰", "配", "不", "上", "唐嫣", "。"], ["在", "八卦", "大嘴", "看", "来", ",", "李易峰", "配", "不", "上", "唐嫣", "理由", "有", "三", "。"], ["(", "一", ")", "唐嫣", "太", "老", "唐嫣", "出生于", "1983", "年", ",", "李易峰", "出生于", "1987", "年", ",", "如果", "在一起", ",", "就是", "姐弟恋", "。", "李易峰", "现在", "正", "当红", ",", "而且", "风采依旧", ",", "反", "观", "唐嫣", ",", "相比", "前", "几", "年", ",", "她", "明显", "老", "了", "很多", "。", "一", "个", "帅帅", "的", "小鲜肉", ",", "一", "个", "姐姐", "似", "御姐", ",", "唐嫣", "真心", "配", "不", "上", "大", "苏", "苏", "。", "(", "二", ")", "李易峰", "不成熟", "李易峰", "很", "小", ",", "唐嫣", "很大", ",", "一", "个", "想要", "家", ",", "一", "个", "却", "忙碌", "事业", "。", "从", "平常", "来看", ",", "李易峰", "玩", "心", "很大", ",", "没有", "成家立业", "意思", ",", "正", "因为", "这样", ",", "唐嫣", "怎么", "敢", "把", "自己", "托付", "给", "这样", "的", "男人", "。", "(", "三", ")", "离", "渣男", "只有", "一", "步", "唐嫣", "和", "邱泽", "拍拖", ",", "两个人", "分手后", ",", "外界", "一直", "报道", "男方", "如何", "如何", "渣", "。", "咱们", "不是", "当事人", ",", "不知道", "其中", "细节", ",", "但", "按照", "邱泽", "粉丝", "意思", ",", "正", "主", "完全", "被", "女方", "打压", "所致", "。", "如果", "李易峰", "与", "唐嫣", "在一起", ",", "没事", "倒", "好", ",", "有事", "李易峰", "真", "离", "渣男", "不远", 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2017清远市考点分布考场安排 2017成都交警49个高考服务点 江苏省教育考试院考前温馨提醒 2017福建泉州南安市考点分布考场安排 教育部:提醒广大考生做好考前准备 2017年全国高考报名人数940万人 2017年上海高考外语听说测试考场规则 2017年上海高考外语听力考试注意事项 2017年上海高考考场规则", "版权及免责声明", "1,\"新东方在线\"上的内容,包括文章、资料、资讯等, 本网注明\"稿件来源:新东方在线\"的,其版权 均为\"新东方在线\"或北京新东方迅程网络科技有限公司所有 ,任何公司、媒体、网站或个人未经授权不得转载、链接、转贴或以其他方式使用。已经得到 \"新东方在线\"许可 的媒体、网站,在使用时必须注明\"稿件来源:新东方\",违者本网站将依法追究责任。  2, \"新东方在线\" 未注明\"稿件来源:新东方\"的 文章、资料、资讯等 均为转载稿,本网站转载出于传递更多信息之目的,并不意味着赞同其观点或证实其内容的真实性。如其他媒体、网站或个人从本网站下载使用,必须保留本网站注明的\"稿件来源\",并自负版权等法律责任。如擅自篡改为 \" 稿件来源:新东方 \" ,本网站将依法追究其法律责任。  3,如本网转载稿涉及版权等问题,请作者见稿后在两周内与新东方在线联系。", "全国31省市2017年高考报名人数汇总 2017年高考志愿填报批次设置汇总(31省市) 2017年31省高考录取政策汇总 2017年高校专项计划招生简章汇总", "985工程大学名单及历年录取分数线 211工程大学名单及历年录取分数线 全国31省市211工程大学特色专业盘点 新东方高考英语高频词汇词组精讲汇总"], "bs_rank_pos": 0}, {"is_selected": true, "title": "2017年全国各省市高考报名人数排行榜", "most_related_para": 0, "segmented_title": ["2017", "年", "全国", "各省市", "高考", "报名人数", "排行榜"], "segmented_paragraphs": [["高考", "大战", "一触即发", ",", "2017", "年", "全国", "统一", "高考", "将", "于", "6", "月", "7", "日", 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"本文系作者授权百家号发表,未经许可,不得转载。"], "bs_rank_pos": 1}, {"is_selected": false, "title": "2017全国高考报名人数 2017年高考人数预测 2017届全国高考生人数", "most_related_para": 0, "segmented_title": ["2017", "全国", "高考", "报名人数", "2017", "年", "高考", "人数", "预测", "2017", "届", "全国", "高考生", "人数"], "segmented_paragraphs": [["2017", "年", "高考", "报名", "31", "省市", "已经", "陆续", "开始", ",", "各省市", "2017", "年", "高考", "报名人数", "正在", "陆续", "公布", ",", "2017", "全国", "高考", "报名人数", "到底", "有", "多少", "呢", "?", "下面", "是", "有", "2017", "全国", "高考", "报名人数", ",", "欢迎", "参阅", "。"], ["2017", "全国", "高考", "报名人数", "暂", "未", "更新", ",", "届时", "小", "编", "将", "持续", "跟踪报道", ",", "欢迎", "收藏", "本", "页面", "。"], ["招飞", ":", "2016", "年", "10", "月", "至", "11", "月"], ["2017", "年", "高考", "考生", "们", "所", "关心", "的", "招飞", "包括", "空军", "招飞", "和", "民航", "招飞", ",", "招飞", "一般", "包括", "初选", "、", "复", "选", "和", "定", "选", "等", "阶段", "。", "招飞", "的", "时间", "将", "在", "2016", "年", "10", "月", "至", "11", "月", ",", "具体时间安排", "要", "关注", "最新消息", "。"], ["高考", "报名", ":", "2016", "年", "12", 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"据介绍,艺术类专业统考和联考一般安排在1月进行。艺术类专业统考分为美术类专业统考、音乐类专业统考和编导类专业统考,联考专业依据当年各高校的招生情况略有变化。", "2016年的联考专业有:作曲与作曲技术理论、舞蹈表演、舞蹈表演(国标舞)、舞蹈学与舞蹈编导、表演(二人台表演)、表演(戏剧影视表演)、表演(服装设计与表演)、播音与主持艺术、播音与主持艺术(蒙语授课)。", "此外,艺术类专业校考一般安排在1至3月进行,具体时间要关注招生院校的招生信息。", "区内高职单独招生、高考体检等:2017年2至3月", "高校高职单独招生一般在每年的3月底前完成,并于4月20日前上报录取结果。考生要注意,已经被区内高校高职单招录取的考生不安排参加6月的普通高考。", "高水平艺术团和高水平运动队院校的测试一般安排在每年年初进行,具体事宜要关注各个招生院校的招生信息。考生要注意,要按照内统一要求,上交高水平运动员及各类高考加分相关证明材料,逾期未交的,无法在高考成绩中加分或无法通过高水平运动员资格审核。", "按照教育部的要求,各高校自主招生在高考后进行,但是报名仍在高考前,相对集中于2月至3月。自主招生对考生都有一些特殊的要求,考生要提前准备。 高考体检一般安排在3月至4月进行,要求所有高考报名考生均须参加体检。考生要注意,部分高校及专业对体检结果有要求,体检前要注意调整好饮食和作息。 猜你喜欢:", "【2017全国高考报名人数 2017年高考人数预测 2017届全国高考生人数】相关文章", "【学习资讯】图文精华"], "bs_rank_pos": 2}, {"is_selected": false, "title": "2017年全国高考报名人数汇总_各省市高考报名人数有多少 ", "most_related_para": 1, "segmented_title": ["2017", "年", "全国", "高考", "报名人数", "汇总", "_", "各省市", "高考", "报名人数", "有", "多少"], "segmented_paragraphs": [["牛", "校", "网", "的", "小", "编", "给", "各位", "考生", "整理", "了", ":", "2017", "年", "全国", "高考", "报名人数", "汇总", 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":", "由于", "2017", "年", "高考", "报名", "正在进行时", ",", "报考人数", "正在", "统计", "中", ",", "所有", "牛", "校", "网", "所", "提供", "的", "所有", "异地", "高考", "政策", "信息", "仅供", "考生", "及", "家长", "参考", ",", "敬请", "考生", "及", "家长", "以", "权威部门", "公布", "的", "正式", "信息", "为准", "。"]], "paragraphs": ["牛校网的小编给各位考生整理了:2017年全国高考报名人数汇总_各省市高考报名人数有多少,希望对大家有所帮助。更多的资讯请持续关注牛校网(http://www.niuxiao.net/)", "2017年全国31个省市自治区高考报名陆续开始,各地高考报名人数也将陆续公布,新高三的考生对本省市近几年报名人数需要了解,就能大致了解自己有多少的竞争对手,知己知彼才能在高考证更有胜算。", "从历年本省高考人数的变化情况能对高考形式和发展趋势进行预测,考生还能够在激烈的竞争中对自己有一个更加清晰的定位,在2017年高考中更有把握。", "小编推荐:高考成绩不理想,专科成绩也能上本科!", "2017年全国各省市高考报名人数汇总", "2017年高考报名常见问题", "未办好身份证的考生,如何参加普通高考报名?", "答:未办好或未能提供二代或三代身份证的考生,请于2015年12月7-10日携带户口薄到所在地市、县(区)招生办公室指定的报名点领取考生号和 初始登陆密码,并严格按《关于做好广东省2016年普通高校招生统一考试报名和建档工作的通知》的要求进行网上预报名和办理报名确认手续。没有二代或三代 身份证的考生,报名系统不能自动校验考生信息,因此,考生要特别注意核对输入的姓名、身份证号、指纹信息等信息是否准确。", "身份证号已经报名", "答:解决办法:在报名信息填写完成后,提交保存信息提示“该身份证号已经报名,不能重复报名”。如果确认是自己的身份证号被报过名,可以持有效证件到考生所在县级招生委员会办公室申请办理相关手续。", 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"面对我国高考目前的形势,教育部今年下达通知,确保各地高考录取率不降低,确保省际高考录取率差距进一步缩小,确保重点高校招收农村和贫困地区的学生人数进一步增加,确保中央部委所属高校本科招生总规模和投放到各省份的招生计划总量不降低,确保实现国家年度高等教育事业发展宏观管理目标。", "除了高考,升学的方式也越加多元,高考已不是高中生唯一的出路。今年,南京外国语学校有270余名同学被美国、英国、加拿大、法国等国家的大学录取,选择在本科就出国留学的高中生已渐渐多了起来。随着民众生活水准的不断提高,会有越来越多的家庭会选择出国留学这样的道路。", "浙沪最先迎来全国首批新高考考生", "2017年被称为新高考元年。浙江、上海作为在2014年就开始进行试点的省区,2017年将迎来全国首批新高考考生。", "新高考改革打破了传统文理分科的局限,它让考生和家长更为重视自身的特长学科选择而不是原先的相同学科竞争,据统计,浙江今年参加高考的学生选“纯文”的约11%,选“纯理”的近20%,选“2文1理”或“2理1文”的近70%。", "除此之外,选拔标准的多元、招生通道的拓宽、每年两次的报考机会均为考生提供了更加多元和自主的选择机会,同时也在一定程度上加大了考生和家长的负担,迫使考生和家长在报考志愿时要备足功课,详细了解报考的院校及专业。", "关于新高考,您怎么看?诚邀您做一份新高考调查问卷,点击参与调查。", "众多省市批次有所调整", "近两年,有很多省份开始逐渐实施合并录取批次的改革。", "截止2016年,全国已有浙江、四川、湖北、广东等十多个省市将二三本批次进行合并录取。上海则是直接取消了所有本科录取批次划分,实行本科普通批次统一录取。", "从2017年开始,北京、河南、江苏、安徽将合并本科二批与本科三批录取;山东、海南将合并本科一批和本科二批录取,形成本科统一录取批次;浙江新的录取方案则取消录取批次划分,所有高校实行“同台”无差别竞争,考生分段进行录取。", "国防生退出历史舞台 人工智能接棒飙戏", "同样是在2017年,国防部新闻局表示,从今年起不再从普通高中毕业生中定向招收国防生,也不再从在校大学生中考核选拔国防生,逐步调整为面向地方院校毕业生直接选拔招录。这也就意味着,存在于知名高校的国防生团体正式退出历史舞台,如果有意向报考国防生的考生要早作打算,合理应变。", "人工智能才在围棋领域大出风头,现在又开始进军高考试题了。据悉,成都某公司研发的一款人工智能系统AI-MATHS将作为“数学高考机器人”,挑战6月7日的2017年高考数学卷。而在此前,该机器人便凭靠100套试卷、1.2万道题的训练量取得了高考数学模拟卷93分的成绩。", "招飞:2016年10月至11月", "2017年高考考生们所关心的招飞包括空军招飞和民航招飞,招飞一般包括初选、复选和定选等阶段。招飞的时间将在2016年10月至11月,具体时间安排要关注最新消息。", "高考报名:2016年12月", "就考生们关心的高考时间,教育招生考试中心表示,2017年高考报名时间尚未确定,依据惯例,将于2016年12月初进行。招生考试中心强调,拟在参加高考或办理录取手续的考生均要参加高考报名,一般不安排补报名,考生一定要在规定的时间完成报名。", "艺术类专业统考和联考:2017年1月", "据介绍,艺术类专业统考和联考一般安排在1月进行。艺术类专业统考分为美术类专业统考、音乐类专业统考和编导类专业统考,联考专业依据当年各高校的招生情况略有变化。", "2016年的联考专业有:作曲与作曲技术理论、舞蹈表演、舞蹈表演(国标舞)、舞蹈学与舞蹈编导、表演(二人台表演)、表演(戏剧影视表演)、表演(服装设计与表演)、播音与主持艺术、播音与主持艺术(蒙语授课)。", "此外,艺术类专业校考一般安排在1至3月进行,具体时间要关注招生院校的招生信息。", "区内高职单独招生、高考体检等:2017年2至3月", "高校高职单独招生一般在每年的3月底前完成,并于4月20日前上报录取结果。考生要注意,已经被区内高校高职单招录取的考生不安排参加6月的普通高考。", "高水平艺术团和高水平运动队院校的测试一般安排在每年年初进行,具体事宜要关注各个招生院校的招生信息。考生要注意,要按照内统一要求,上交高水平运动员及各类高考加分相关证明材料,逾期未交的,无法在高考成绩中加分或无法通过高水平运动员资格审核。", "按照教育部的要求,各高校自主招生在高考后进行,但是报名仍在高考前,相对集中于2月至3月。自主招生对考生都有一些特殊的要求,考生要提前准备。 高考体检一般安排在3月至4月进行,要求所有高考报名考生均须参加体检。考生要注意,部分高校及专业对体检结果有要求,体检前要注意调整好饮食和作息。", ">>>点击下一页阅读更多关于2017年高考时间时间表", "上一篇:2017最新中国彩礼地图图片 中国彩礼地图对比变化 2017中国全国彩礼排行榜  下一篇:2017中国各地结婚彩礼排行榜排名表 2017最新中国彩礼地图图片", "【学习资讯】精华文章", "【学习资讯】相关文章"], "bs_rank_pos": 5}], "answer_spans": [[38, 49]], "answer_docs": [1], "fake_answers": ["。今年全国高考报考人数统计约为923.4万人"], "question": "2017年高考报名人数", "segmented_answers": [["2017", "年", "全国", "高考", "报名人数", "共", "940", "万人", "。"], ["今年", "全国", "高考", "报考人数", "统计", "约", "为", "923", ".", "4", "万人", "。"]], "answers": ["2017年全国高考报名人数共940万人。", "今年全国高考报考人数统计约为923.4万人。"], "entity_answers": [["940万"], ["923.4万"]], "segmented_question": ["2017", "年", "高考", "报名人数"], "question_type": "ENTITY", "question_id": 91190, "fact_or_opinion": "FACT", "match_scores": [1.0]}
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然后就出来一个对话框,在里面选择你要插入的文件,可以多选,再点击插入: 步骤阅读 6 最后出来效果如下,三个文档内容合并成一个文档里了: 步骤阅读 7 通过这个方法,可以很快捷的把多个word文档内容合并成一个,对于有需要的人来说真真极好的。 END", "百度经验:jingyan.baidu.com", "如有问题,可在下面评论留言讨论。 如对您有帮助,可点击右上角(或下面)的免费投票以表认可,或者收藏。 或者您可以关注我,我将不定时更新自己的经验,也许里面就有你感兴趣的呢^_^", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "bs_rank_pos": 0}, {"is_selected": false, "title": "Word2010中怎样将多个文档合成一个文档_Word联盟", "most_related_para": 0, "segmented_title": ["Word", "2010", "中", "怎样", "将", "多", "个", "文档", "合成", "一", "个", "文档", "_", "Word", "联盟"], "segmented_paragraphs": [["经常", "和", "Word", "打交道", "的", "朋友", "肯定", "会", "有许多", "的", "文件", ",", "这些", "文件", "都", "非常重要", ",", "有时候", "可能", "需要", "将", "多", "个", "文件", "合并", "成", "一", "个", "文件", ",", "将", "2", "个", "或", "3", "个", "文件", "中", "的", "内容", "全部", "放到", "一起", "。", "当然", ",", "如果", "你", "手动", "去", "复制", "的", "话", ",", "文件", "一", "多", "就", "比较", "麻烦", "。", "其实", "我", "这里", "还有", "个", "更好", "的", "方法", ",", "可以", "帮", "你", "快速", "将", "多", "个", "文档", "合并", "在一起", ",", "方法", 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"图标", "显示", "不", "正常", "解决方法"], ["Word", "打不开", "Word", "安全模式", "删除", "空白页", "Word", "上次", "启动", "时", "失败", "无法", "启动", "转换器", "Word", "遇到问题", "需要", "关闭", "发送", "错误报告", "菜单栏", "不见", "了", "工具栏", "不见", "了", "Word", "已", "停止", "工作", "自动生成", "目录", "文档", "加密", "回车", "符", "换行符", "水印", "分栏", "放大字体", "格式刷", "批注", "字数", "统计", "首字下沉", "文字", "环绕", "图片", "公式编辑器", "Word", "兼容", "包", "乱码", "修复", "工具", "Word", "快捷键", "邮件合并", "首行", "缩进", "直径", "符号", "下划线", "方框", "里", "打钩", "Word", "页码", "从", "第三页", "开始", "页码", "从", "任意", "页", "开始", "设置", "默认", "打印机", "打印机", "共享", "Word", "隐藏", "文字", "文本框", "Docx", "怎么", "打开", "Word", "2010", "官方下载", "Word", "2013", "官方下载"]], "paragraphs": ["经常和Word打交道的朋友肯定会有许多的文件,这些文件都非常重要,有时候可能需要将多个文件合并成一个文件,将2个或3个文件中的内容全部放到一起。当然,如果你手动去复制的话,文件一多就比较麻烦。其实我这里还有个更好的方法,可以帮你快速将多个文档合并在一起,方法简单而有效。下面就与Word联盟一起看看方法吧! ①单击进入“插入”选项卡; ②然后在“文本”选项组中点击“对象”旁边的小三角,在弹出的下拉菜单中选择“文件中的文字”; ③接着,选择要合并到当前文档中的文件。您可以按住 Ctrl 键来选择不止一个文档。(注意:最上面的文档将最先被合并,所以,如果您想在文档间维持某种顺序,请先对各目标文档进行排列编号。) 提示:此方法适应于Word2007和Word2010,还有一点,如果将多个文档合并成一个文件的时候,文档中的格式和样式可能会被清除,所以大家切记做好备份哟!", "上一篇:Word2010中如何调整行间距  下一篇:电脑中的Word文档图标显示不正常解决方法", "Word打不开 Word安全模式 删除空白页 Word上次启动时失败 无法启动转换器 Word遇到问题需要关闭 发送错误报告 菜单栏不见了 工具栏不见了 Word已停止工作 自动生成目录 文档加密 回车符 换行符 水印 分栏 放大字体 格式刷 批注 字数统计 首字下沉 文字环绕图片 公式编辑器 Word兼容包 乱码修复工具 Word快捷键 邮件合并 首行缩进 直径符号 下划线 方框里打钩 Word页码从第三页开始 页码从任意页开始 设置默认打印机 打印机共享 Word隐藏文字 文本框 Docx怎么打开 Word2010官方下载 Word2013官方下载"], "bs_rank_pos": 1}, {"is_selected": true, "title": "高效快速将多个WORD文档合并到一个WORD文档中", "most_related_para": 1, "segmented_title": ["高效", "快速", "将", "多", "个", "WORD", "文档", "合并", "到", "一", "个", "WORD", "文档", "中"], "segmented_paragraphs": [["如图", ",", "我们", "要", "将", "几个", "WORD", "文档", "合并", "到", "一", "个", "文档", ",", "首先", "新建", "一", "个", "空白", "文档", "。"], ["在", "“", "插入", "文件", "”", "对话框", "里", ",", "选择", "要", "插入", "的", "文件", ",", "可以", "用", "CTRL", "进行", "不", "连续", "选择", ",", "也", "可以", "通过", "SHIFT", "进行", "连续", "选择", "。"], ["看一下", "是不是", "眨眼", "的", "功夫", ",", "就", "搞好", "了", "。", "这样", "方法", "比", "起", "传统", "的", "复制", "粘贴", "效率更高", ",", "速度更快", "。"], ["经验", "内容", "仅供参考", ",", "并", "受", "版权保护", "。"], ["本", "条", "经验", "由", "auto", "hwd", "编写"], ["分享", "让", "生活", "更美好"]], "paragraphs": ["如图,我们要将几个WORD文档合并到一个文档,首先新建一个空白文档。", "在“插入文件”对话框里,选择要插入的文件,可以用CTRL进行不连续选择,也可以通过SHIFT进行连续选择。", "看一下是不是眨眼的功夫,就搞好了。这样方法比起传统的复制粘贴效率更高,速度更快。", "经验内容仅供参考,并受版权保护。", "本条经验由autohwd编写", "分享让生活更美好"], "bs_rank_pos": 2}, {"is_selected": false, "title": "在Word 中怎么样将多个文档合成一个文档_百度经验", "most_related_para": 1, "segmented_title": ["在", "Word", "中", "怎么", "样", "将", "多", "个", "文档", "合成", "一", "个", "文档", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["我们", "在", "工作", "于", "生活", "中", ",", "有时候", "会", "遇到", "这样", "的", "情况", ",", "需要", "将", "多", "个", "文件", "合并", "成", "一", "个", "文件", ",", "也", "就", "说", "将", "2", "个", "或", "3", "个", "、", "或者", "更多", "的", "文件", "中", "的", "内容", "全部", "放到", "一起", "。", "当然", ",", "如果", "你", "手动", "去", "复制", "的", "话", ",", 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"我们在工作于生活中,有时候会遇到这样的情况,需要将多个文件合并成一个文件,也就说将2个或3个、或者更多的文件中的内容全部放到一起。当然,如果你手动去复制的话,文件一多就比较麻烦。在这里笔者就简单的介绍一下,怎么在Word 中将多个文档合并成一个文档", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "1 打开Word 软件,在 插入选项卡 ——对象 步骤阅读 2 点击对象旁边的倒三角,在弹出的下拉菜单中选择文件中的文字 步骤阅读 3 接着,选择要合并的文件,用Ctrl选择,这里连选两个 步骤阅读 4 插入后,就可以看到效果了  END", "百度经验:jingyan.baidu.com", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:需要参考资料加微信httpweb520"], "bs_rank_pos": 3}, {"is_selected": false, "title": "怎么把多个word合成一个_百度知道", "most_related_para": 3, "segmented_title": ["怎么", "把", "多", "个", "word", "合成", "一", "个", "_", "百度", "知道"], "segmented_paragraphs": [["在", "Word", "中", ",", "将", "多", "个", "文档", "合并", "成", "一", "个", "文档", "的", "方法", ":", "1", "、", "在", "Word", "中", "新建", "一", "个", "文档", ";", "2", "、", "单击", "插入", "-", "-", "-", "-", "对象", "-", "-", "-", "-", "文件", "中", "的", "文字", ";", "3", "、", "弹出", "插入", "文件", "对话框", ",", "按住", "Ctrl", "键", ",", "选中", "需要", "合并", "的", "多", "个", "文档", "即可", ",", "如图所示", "。"], ["如果", "数量", "少", 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"。", "蓝色", "凹槽", "可以", "镶嵌", "防御", "类", "宝珠", ",", "上限", "也", "为", "10", "个", "。", "紫色", "凹槽", "可以", "镶嵌", "特殊", "类", "宝珠", ",", "上限", "为", "4", "个", "。"], ["(", "您", "的", "评论", "需要", "经过", "审核", "才能", "显示", ")", "字数", ":", "0", "/", "500"], ["推荐", "手机游戏专区"], ["欧洲杯", "彩票", "购买", "软件", "推荐", "安卓", "经典游戏", "推荐"]], "paragraphs": ["你的位置:首页 → 安卓游戏→ 攻略秘籍→ 腾讯弹弹堂宝珠在哪里看 弹弹堂宝珠属性介绍", "弹弹堂宝珠有什么用?如何获得弹弹堂五级宝珠?弹弹堂宝珠在哪里装备?弹弹堂宝珠要多少级可以开启,最新弹弹堂宝珠属性介绍分享给各位新老玩家朋友了,想了解更多腾讯弹弹堂手游宝珠系统攻略的可以参考一下!", "宝珠是战斗中的一种重要元素,可以极大的提升我们的战斗属性。手游中的宝珠主要分为3大类,即攻击宝珠、防御宝珠和特殊宝珠,不同类型的宝珠拥有不同属性的加成。攻击类宝珠可以增加攻击力、暴击伤害、暴击率或护甲穿透,防御类宝珠可以增加生命值、护甲、速度或怒气。特殊类宝珠虽然只拥有一种属性加成效果,但加成比例非常高。", "黄色攻击宝珠:优先考虑攻击百分比。手游中的大多数武器均以造成更高伤害为目的,攻击百分比宝珠相当百搭。其次可以选择暴击几率→击穿→暴击伤害→护甲穿透→效果命中(半数以上武器没有特殊效果,所以效果命中在这套方案中总体收益相对较低)。", "蓝色防御宝珠:优先选择生命百分比。为什么不选择防御百分比呢?简单说一下,手游中的武器基础生命相差不大,同等级武器相差比例通常保持在50%以内,而护甲的相差比例高达4-5倍,防御百分比带来的收益相当不稳定。相对来说,生命百分比的的适用面更广。选择优先级:生命百分比→抵挡伤害→韧性→防御百分比→抵挡率→效果抵抗。", "紫色特殊宝珠:优先选择敏捷百分比,特殊宝珠的类型非常多样,可以选择的空间很大,速度是每款武器都追求的属性之一,几乎适用于手游中的所有武器。当然,我们也可以根据自己偏好使用的武器类型来选择具体的特殊类宝珠。偏向进攻就选择攻击百分比或暴击率、偏向猥琐消耗则可以选择生命百分比和生命恢复。", "宝珠的获取方式多种多样,我们可以通过多种途径获取宝珠。", "1.直接在商城的“宝珠”界面下鉴定宝珠,宝珠鉴定可以分为“钻石鉴定”和“金币鉴定”,二者效果相同。如果想一次性获取大量宝珠,可以点击“鉴定五次”,这样还会有一定的优惠。每隔24小时系统会赠送给我们一次免费鉴定宝珠的机会,大家不要忘了及时领取福利。 2.在日常的签到、七日登陆中同样可以获取宝珠。 3.宝珠可以通过“宝珠碎片”合成,宝珠碎片可以通过分解不要的宝珠来获取。", "宝珠需要镶嵌在宝珠凹槽中,宝珠凹槽会随着我们等级的提升逐渐开启。如果你想要提前开启更多宝珠凹槽,更快的提升自己,则可以在宝珠界面中花费少量的钻石开启宝珠凹槽。黄色凹槽可以镶嵌攻击类宝珠,上限为10个。蓝色凹槽可以镶嵌防御类宝珠,上限也为10个。紫色凹槽可以镶嵌特殊类宝珠,上限为4个。", "(您的评论需要经过审核才能显示) 字数: 0/500", "推荐手机游戏专区", "欧洲杯彩票购买软件推荐 安卓经典游戏推荐"], "bs_rank_pos": 2}, {"is_selected": false, "title": "弹弹堂手游宝珠在哪里获取 宝珠获取方法解读_18183弹弹堂手游专区", "most_related_para": 3, "segmented_title": ["弹弹堂", "手游", "宝珠", "在", "哪里", "获取", "宝珠", "获取", "方法", "解读", "_", "18183", "弹弹堂", "手游", "专区"], "segmented_paragraphs": [["弹弹堂", "手游", "宝珠", "在", "哪里", "获取", "?", "宝珠", "是", "与", "你", "的", "战斗力", "相关", "的", ",", "可以通过", "两", "大", "途径", "得到", ",", "下面", "就", "跟", "小", "编", "一起来", "看看", "吧", 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"】"], ["虽然", "宝珠", "的", "制作过程", "如此", "简单", ",", "但", "我们", "仍然", "需要", "注意", ":", "《", "弹弹堂", "手游", "》", "部分", "3", "级", "以上", "(", "包含", "3", "级", ")", "宝珠", "拥有", "双重", "属性", "加成", "效果", ",", "这", "类", "宝珠", "无法", "通过", "宝珠", "碎片", "制作", "获得", ",", "但", "可以", "分解", "成", "宝珠", "碎片", ",", "分解", "收益", "和", "同", "等级", "、", "同类型", "单", "属性", "加成", "宝珠", "分解", "所", "获得", "的", "碎片", "数量", "相同", "。"], ["以上", "就是", "18183", "小", "编", "对", "本", "款", "游戏", "的", "解读", ",", "更多", "关于", "此", "款", "游戏", "的", "资讯", "动态", "请", "关注", "18183", "手游", "第", "一", "门户", ",", "18183", "小", "编", "将", "在", "您", "的", "游戏", "道路", "上", "为您", "保驾护航", "。"], ["转载", "请", "注明", "“", "18183", "”", "字样", "收藏", "文章"], ["游戏", "类型", ":", "休闲益智"], ["游戏", "语言", ":", "简体中文"], ["特征", ":", "Q版", ",", "竞技"], ["开发", ":", "腾讯"], ["在", "长椅", "上", "醒来后", ",", "他", "发现", "身边", "坐", "着", "的", "竟然", "是", ".", ".", ".", "在", "长椅", "上", "睡着", "了", ",", "醒来后", "他", "发现", "身边", "坐", "着", "的", "竟然", "是", "她", "。", "到底", "是", "怎么回事", "?", "泰勒", "·", "斯威夫特", "坐", "在", "旁边", "你", "居然", "还能", "睡着", "?", "你", "一定", "是", "个", "假人", "。", "关注", "右侧", "公众", "号", ",", "回复", "“", "PS", "达人", "”", "看", "全文"]], "paragraphs": ["弹弹堂手游宝珠在哪里获取?宝珠是与你的战斗力相关的,可以通过两大途径得到,下面就跟小编一起来看看吧!", "【渠道优势】", "相比较手游中的其它宝珠获取方式来说,宝珠制作有以下几点优势。", "1. 属性可控:商城鉴定中宝珠的属性完全随机,玩家无法确定每次抽取的结果,而限时商城中的宝珠礼包同样如此,到了游戏后期这两种方法已经不能满足我们精益求精的追求。但宝珠制作却非常稳定,玩家可以自由选择自己想要制作的宝珠类型; 2. 循环利用:在平时的各类游戏活动中,我们可能会获得不少低等级宝珠,这些宝珠在前期使用尚可,但到了游戏中、后期难免会连累总体属性。此时我们可以选择将这类宝珠分解,之后再用碎片合成高等级宝珠; 3. 性价比高:无论是日常签到、冒险副本还是游戏中的各类活动,我们都会得到大量的宝珠碎片,这也帮助我们省下了许多材料费用。", "【制作技巧】", "如何制作宝珠?收益如何?请看下文。", "1. 点击游戏主界面右下角的【宝珠】按钮,进入宝珠界面,随后选择【宝珠制作】即可进入相关页面。在这里,找到并点击自己需要制作的各类宝珠,在详细界面中查看制作该宝珠所需要的宝珠碎片。选择制作数量,确保碎片足够后点击【制作】即可完成宝珠制作; 2. 目前宝珠的分解与制作收益比介于0.5-0.6之间。也就是说,每分解10个宝珠,利用得到的宝珠碎片便能制作同类型、同等级的5-6个宝珠。其中,特殊类宝珠分解得到和制作所需的碎片数量都比较高,而攻击和防御类宝珠的分解、制作收益比固定为0.6。", "【注意事项】", "虽然宝珠的制作过程如此简单,但我们仍然需要注意:《弹弹堂手游》部分3级以上(包含3级)宝珠拥有双重属性加成效果,这类宝珠无法通过宝珠碎片制作获得,但可以分解成宝珠碎片,分解收益和同等级、同类型单属性加成宝珠分解所获得的碎片数量相同。", "以上就是18183小编对本款游戏的解读,更多关于此款游戏的资讯动态请关注18183手游第一门户,18183小编将在您的游戏道路上为您保驾护航。", "转载请注明“18183”字样 收藏文章", "游戏类型:休闲益智", "游戏语言:简体中文", "特征:Q版,竞技", "开发:腾讯", "在长椅上醒来后,他发现身边坐着的竟然是... 在长椅上睡着了,醒来后他发现身边坐着的竟然是她。到底是怎么回事?泰勒·斯威夫特坐在旁边你居然还能睡着?你一定是个假人。 关注右侧公众号,回复“PS达人”看全文"], "bs_rank_pos": 3}, {"is_selected": false, "title": "手游弹弹堂的宝珠系统在哪里,怎么找不到_百度知道", "most_related_para": 0, "segmented_title": ["手游", "弹弹堂", "的", "宝珠", "系统", "在哪里", ",", "怎么", "找不到", "_", "百度", "知道"], "segmented_paragraphs": [["要", "20", "级", "之后", "才会", "开通", "宝珠", "系统", "。"]], "paragraphs": ["要20级之后才会开通宝珠系统。"], "bs_rank_pos": 4}], "answer_spans": [[11, 47]], "answer_docs": [0], "fake_answers": ["20级之后才会开通宝珠系统哟,所以如果小伙伴们刚开始无法进入宝珠系统的话,那么很有可能是因为你目前还打不到宝珠系统的等级要求"], "question": "弹弹堂宝珠在哪里", "segmented_answers": [["在", "背包", "中", "，", "就", "可以看到", "左上角", "有", "一", "排", "标题栏", "，", "上面", "有", "个人资料", "，", "宝珠", "等", "，", "你", "点击", "宝珠", "，", "就", "可以", "进入", "宝珠", "页面", "了", "哦", "。", "在", "宝珠", "页面", "中", "，", "就", "可以", "去", "捣鼓", "你", "的", "专属", "宝珠", "啦", "。"], ["要", "20", "级", "之后", "才会", "开通", "宝珠", "系统", "哟", "，", "所以", "如果", "小伙伴", "们", "刚", "开始", "无法", "进入", "宝珠", "系统", "的", "话", "，", "那么", "很", "有", "可能", "是因为", "你", "目前", "还", "打", "不到", "宝珠", "系统", "的", "等级", "要求", "。"], ["宝珠", "的", "获得", "方式", "：", "1", ".", "商城", "抽取", "：", "宝珠", "的", "属性", "完全", "随机", "，", "玩家", "无法确定", "每次", "抽取", "的", "结果", "，", "而", "限时", "商城", "中", "的", "宝珠", "礼包", "同样", "如此", "，", "但是", "通过", "这个", "渠道", "，", "可以获得", "很多", "宝珠", "哦", "。", "2", ".", "活动", "获得", "：", "在", "平时", "的", "各类", "游戏", "活动", "中", "，", "可能会", "获得", "不少", "低", "等级", "宝珠", "，", "火气", "可以", "选择", "将", "这", "类", "宝珠", "分解", "，", "之后", "再", "用", "碎片", "合成", "高", "等级", "宝珠", ";", "3", ".", "冒险", "签到", "获得", "：", "无论", "是", "日常", "签到", "、", "冒险", "副本", "还是", "游戏", "中", "的", "各类", "活动", "，", "都会", "得到", "大量", "的", "宝珠", "碎片", "。"]], "answers": ["在背包中，就可以看到左上角有一排标题栏，上面有个人资料，宝珠等，你点击宝珠，就可以进入宝珠页面 了哦。在宝珠页面中，就可以去捣鼓你的专属宝珠啦。", "要20级之后才会开通宝珠系统哟，所以如果小伙伴们刚开始无法进入宝珠系统的话，那么很有可能是因为你目前还打不到宝珠系统的等级要求。", "宝珠的获得方式：1.商城抽取：宝珠的属性完全随机，玩家无法确定每次抽取的结果，而限时商城中的宝珠礼包同样如此，但是通过这个渠道，可以获得很多宝珠哦。2.活动获得：在平时的各类游戏活动中，可能会获得不少低等级宝珠，火气可以选择将这类宝珠分解，之后再用碎片合成高等级宝珠;3.冒险签到获得：无论是日常签到、冒险副本还是游戏中的各类活动，都会得到大量的宝珠碎片。"], "entity_answers": [["背包"], ["宝珠系统"], []], "segmented_question": ["弹弹堂", "宝珠", "在哪里"], "question_type": "ENTITY", "question_id": 91192, "fact_or_opinion": "FACT", "match_scores": [0.9210526315789475]}
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"看来,周星驰这部当年被西影厂艺术副厂长张子恩称为“文化垃圾”的影片,在他的不少铁杆粉丝看来,像放得越久味道越醇厚的美酒,百看不厌。但《大话西游之大圣娶亲》此次重映,是其第三次在内地上映,虽然加了11分钟内容,但要让观众当作新片一样去买单,依然给人一种炒剩饭消费情怀的感觉。希望在《美人鱼》《西游伏妖篇》里已显现出创新力下降的周星驰,能带给观众更好的新作品。", "《大话西游之大圣娶亲》是香港彩星电影公司和西安电影制片厂1994年联合摄制的一部无厘头搞笑爱情片,是《大话西游》系列的第二部,由刘镇伟导演,技安编剧,周星驰制作,周星驰、朱茵、莫文蔚、蔡少芬、陆树铭、吴孟达等人主演。1995年2月4日在香港上映,取得了2888万港币的票房,成绩不佳。", "1995年8月,《大话西游》在上海上映,又在东北三省上映,票房开始还不错,但未持续太久。在北京,《大话西游》更是被当作发行惨败的经典。偌大一个北京城《大话西游》两集才卖了40万元,有些场次观众不到10个人。有的电影院放映了两天,便决定撤下该片以避免更多损失。电影下映后,周星驰将影片的内地版权卖给了西影,并宣告彩星电影公司倒闭。", "当年《大话西游》不仅票房不好,口碑也不佳。普通观众不明白影片的情节,看得一头雾水。内地一些电影专业人士更是对该片感到不屑。据说当时刘镇伟拍《大话西游》是没有剧本的,很多地方的台词其实都是演员自己在现场的神发挥,内地合作方西影厂的人很不能理解。", "但想不到的是,1997年后,《大话西游》开始在内地高校和网络上流传并迅速走红,片中很多台词和桥段被奉为经典,“无厘头”这个词也是通过这部电影流传开来,连周星驰本人都不明白影片为啥突然起死回生、大火特火。", "《大话西游》从文化垃圾变身文化现象之后,2014年10月24日,《大话西游》在内地第二次上映,上下两部最终收入约2500万票房,票房同样一般。此次第三次在内地上映,西影厂找出了封存多年的《大话西游》拍摄胶片,找到从未曝光的近60分钟素材,从中剪出了11分钟23秒的新内容加到影片中。再加上大张旗鼓的宣传攻势,和今天提前4小时上映避开《速度与激情8》锋芒的聪明之举,首日便一举拿下1596万元票房,大幅度超过前两次上映票房毫无悬念。", "【版权提示】中商情报网倡导尊重与保护知识产权。如发现本站文章存在版权问题,烦请联系editor@askci.com、0755-82095014,我们将及时沟通与处理。", "【数据平台合作】我们的数据自媒体平台“千数头条”、数据交易所“千数堂”正式上线了!欢迎各位数据自媒体们合作入驻。免费入驻类型:各行业数据自媒体、数据企业。咨询QQ :3201971869", "中商情报网始终聚焦科技、互联网+、创业、财经、产经大数据等,目前在全国财经网站中排名居前,旗下中商产业研究院专注产业经济细分市场研究,中商可以为用您提供一手的市场数据和高价值的商业资讯,欢迎沟通交流合作!", "广告、内容合作请点这里: 寻求报道 ››", "排行榜 推荐 热门"], "bs_rank_pos": 0}, {"is_selected": false, "title": "大话西游之大圣娶亲票房 - 电影票房数据库", "most_related_para": 2, "segmented_title": ["大话西游", "之", "大圣娶亲", "票房", "-", "电影", "票房", "数据库"], "segmented_paragraphs": [["首页", "电影", "大话西游", "之", "大圣娶亲", "大话西游", "之", "大圣娶亲", "票房"], ["你", "需要", "注册", "并", "登录", "才", "可以", "浏览", "完整", "的", "统计", "数据", ":", "立即", "登录"], ["以上", "大话西游", "之", "大圣娶亲", "票房", ",", "仅供参考", ",", "若", "有", "不同", "数据", ",", "可", "至", "票房", "纠错", "提供"]], "paragraphs": ["首页 › 电影 › 大话西游之大圣娶亲 › 大话西游之大圣娶亲票房", "你需要注册并登录才可以浏览完整的统计数据:立即登录", "以上大话西游之大圣娶亲票房,仅供参考,若有不同数据,可至票房纠错提供"], "bs_rank_pos": 1}, {"is_selected": false, "title": "大话西游之大圣娶亲三天收入破亿 未来情怀将换回多少票房?", 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["这个周末,在《速度与激情8》的带动下影市终于迎来了一波票房高潮,而《大话西游之大圣娶亲》纪念加长版也在“速8”上映前4小时提前首映,最终在首日收获了1596万元,成绩仅次于“速8”,名列当天第二。", "大话西游之大圣娶亲近期电影票房", "数据来源:猫眼", "看来,周星驰这部当年被西影厂艺术副厂长张子恩称为“文化垃圾”的影片,在他的不少铁杆粉丝看来,像放得越久味道越醇厚的美酒,百看不厌。但《大话西游之大圣娶亲》此次重映,是其第三次在内地上映,虽然加了11分钟内容,但要让观众当作新片一样去买单,依然给人一种炒剩饭消费情怀的感觉。希望在《美人鱼》《西游伏妖篇》里已显现出创新力下降的周星驰,能带给观众更好的新作品。", "《大话西游之大圣娶亲》是香港彩星电影公司和西安电影制片厂1994年联合摄制的一部无厘头搞笑爱情片,是《大话西游》系列的第二部,由刘镇伟导演,技安编剧,周星驰制作,周星驰、朱茵、莫文蔚、蔡少芬、陆树铭、吴孟达等人主演。1995年2月4日在香港上映,取得了2888万港币的票房,成绩不佳。", "1995年8月,《大话西游》在上海上映,又在东北三省上映,票房开始还不错,但未持续太久。在北京,《大话西游》更是被当作发行惨败的经典。偌大一个北京城《大话西游》两集才卖了40万元,有些场次观众不到10个人。有的电影院放映了两天,便决定撤下该片以避免更多损失。电影下映后,周星驰将影片的内地版权卖给了西影,并宣告彩星电影公司倒闭。", "当年《大话西游》不仅票房不好,口碑也不佳。普通观众不明白影片的情节,看得一头雾水。内地一些电影专业人士更是对该片感到不屑。据说当时刘镇伟拍《大话西游》是没有剧本的,很多地方的台词其实都是演员自己在现场的神发挥,内地合作方西影厂的人很不能理解。", "但想不到的是,1997年后,《大话西游》开始在内地高校和网络上流传并迅速走红,片中很多台词和桥段被奉为经典,“无厘头”这个词也是通过这部电影流传开来,连周星驰本人都不明白影片为啥突然起死回生、大火特火。", "《大话西游》从文化垃圾变身文化现象之后,2014年10月24日,《大话西游》在内地第二次上映,上下两部最终收入约2500万票房,票房同样一般。此次第三次在内地上映,西影厂找出了封存多年的《大话西游》拍摄胶片,找到从未曝光的近60分钟素材,从中剪出了11分钟23秒的新内容加到影片中。再加上大张旗鼓的宣传攻势,和今天提前4小时上映避开《速度与激情8》锋芒的聪明之举,首日便一举拿下1596万元票房,大幅度超过前两次上映票房毫无悬念。", "本文仅代表作者观点,不代表百度立场。", "本文系作者授权百家号发表,未经许可,不得转载。", "还没有人评论,点击抢沙发～"], "bs_rank_pos": 2}, {"is_selected": false, "title": "疯了!这部95年票房仅20万人民币的烂片竟然重映了 - 《大话西游之", "most_related_para": 6, "segmented_title": ["疯了", "!", "这部", "95", "年", "票房", "仅", "20", "万", "人民币", "的", "烂片", "竟然", "重", "映", "了", "-", "《", "大话西游", "之"], "segmented_paragraphs": [["您", "当前", "的", "位置", ":", "Mtime", "时光", "网", ">", "大话西游", "之", "大圣娶亲", ">", "影评", ">", "疯了", "!", "这部", "95", "年", "票房", "仅", "20", "万", "人民币", "的", "烂片", "竟然", "重", "映", "了"], ["想", "看", "-", "评分", "9", ".", "0"], ["那一年", ",", "梅尔吉布森", "是", "奥斯卡", "最佳导演", "。", "奥斯卡影帝", "尼古拉斯", "凯奇", "还", "从", "不", "演", "烂片", "。", "双休日", "开始", "实行", "每周", "不再", "只", "休", "一", "天", "半", "。", "刚", "出道", "的", "汪峰", "还", "不敢", "妄言", "自己", "是", "摇滚", "歌坛", 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"那一年,梅尔吉布森是奥斯卡最佳导演。奥斯卡影帝尼古拉斯凯奇还从不演烂片。双休日开始实行每周不再只休一天半。刚出道的汪峰还不敢妄言自己是摇滚歌坛半壁江山。张惠妹那时还叫sherry,谁都想不到下一年她的那张《姐妹》专辑会大卖108万……最有趣的是,那一年,上映了一部叫做《大话西游之大圣娶亲》的烂片!", "而这部电影恰是周星驰作为投资人的第一部电影。没想到虽然算不上遭遇票房滑铁卢,但只是勉强收回成本,与周星驰出演的其他影片相比简直天壤之别。更可笑的是,第二年这部电影在国内上映,票房狂揽可怜的20万……好多影院实在无法接受这种莫名其妙无厘头的东西,上映没两天就用《离开雷锋的日子》这种口碑大片给替换了。", "直到新世纪,随着思想解放和网络风靡,人们才发现它的价值!甚至后来的《悟空传》《沙僧日记》等网络文学作品,都深受其影响……如今想来,刘镇伟的这部电影着实太过前卫,所以不为当时的人所理解,也不足为奇。时光如今转到了2017年,借着西游IP的火爆,大话西游重映,又见还没有白头发的周星驰,又看到朱茵那蜜汁性感的眨眼,还有不会发疯的蓝洁瑛,留着中分发型傻傻的莫文蔚。让时间仿佛穿梭回了那一年,那些年…", "欠周星驰的电影票,《美人鱼》的时候我们还完了。有人说是时候还朱茵一张电影票了。其实在豬姐看来,谁也不欠谁的电影票,可能在影院看到这部电影,看到里面演员当年的表演,似乎心绪总会不经意的飘散到从前的时光中……必须承认,时间不能倒流,逝去的岁月永远无法回头,然而一部老电影,却能把我们拉回到过去的回忆当中。", "无论是披着魔幻玄幻搞笑无厘头或者怎样的色彩,“大话”都是一部爱情电影。至尊宝穿越500年为了救他心爱的白晶晶,结果却意外的爱上了紫霞。两个女孩都曾进到他的心里,然而两个女孩都伤心哭泣。紫霞进去的时候他的心里只有白晶晶,而白晶晶进去的时候紫霞已经在他心里留下了一滴眼泪……眼泪,都是我体会,爱上你本是最快乐的事,却换来了最痛苦的悲。", "其实看这部电影时最大的感慨,何尝不是周星驰与朱茵的爱情呢?如今朱茵早已为人妇,然而周星驰却依旧孑然一身。我们都愿意相信,他把所有的爱都给了那时的朱茵。女神,无论长得多漂亮,迟早有一天会变成大妈,我们当年钟爱的容颜,经不起太多岁月的冲刷,一如那首歌所唱——多少人曾爱慕你年轻时的容颜,可知谁愿承受岁月无情的变迁……可相互扶持到老,哪怕爱情最后变成了亲情,难道不也是一件奇妙的事情?", "看电影时,心里不断的联想,从前的时候,爱情就能那么简单。两个陌生人,最终走到一起,从此后来不分离,人世间的感情就是如此奇妙。而如今的爱情,却是无数物质的堆砌,要房子车子还有钻戒……生活优渥了,爱情却更加难找了。我们都曾爱过一些人,我们也都失去过一些人,爱对了,我们享受人生,爱错了,我们也可以享受爱情。就像周星驰对紫霞说:“你知道我一直在骗你”。紫霞回答他:“骗就骗吧,就像飞蛾一样”。也许,这才是爱的真谛!", "该片热门影评: 十年前你绝对没有看懂的《大话西游》   十年前你绝对没有看懂的《大话西.. SerIssho评分8.0  香港电影百年100个难忘瞬间之三:爱情篇 划根火柴就是爱评分10.0  十年前你绝对没有看懂的《大话西游》,十年后你终于懂了……   这个世界上还有一个故事,叫做《大.. 小龙男3901458评分8.0  [转]工作两年后感悟的《大话西游》 这么好的文章,还是转到mtime来吧。原.. 蚂蚁蚂蚁评分8.4  【top10】静悄悄的守候--十大孤独求赏的影片 来!先给大爷笑一个! 不笑是吧,那.. 阳光下的电影评分10.0  更多314 条评论"], "bs_rank_pos": 3}, {"is_selected": false, "title": "《大话西游之大圣娶亲》创华语2D复映票房最高纪录_新浪陕西时尚_", "most_related_para": 0, "segmented_title": ["《", "大话西游", "之", "大圣娶亲", "》", "创", "华语", "2D", "复", "映", "票房", "最高纪录", "_", "新浪", "陕西", "时尚", "_"], "segmented_paragraphs": [["经典", "复", "映", ",", "在", "很多人", "印象", "里", ",", "是", "非常", "具有", "冒险精神", "的", "。", "然而", "既然", "是", "经典", ",", "就", "经得起", "时间", "一次", "次", "的", "检验", "。", "《", "大话西游", "之", "大圣娶亲", "》", "(", "以下简称", "“", "大话西游", "”", ")", "纪念", "加长版", "即将", "于", "4", "月", "14", "日", "上映", "的", "消息", "传", "出", "后", ",", "在", "“", "大话", "”", "迷", "中", "产生", "了", "很大", "反响", ",", "他们", "也", "对", "影片", "充满", "了", "期待", "。", "为了", "满足", "影", "迷", "的", "期待", ",", "片", "方", "提前", "四", "小时", "全线上映", "影片", "。", "昨晚", 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"它是一个具有两个网络适配器的主机系统,并且主机系统中的寻径功能被禁止,而对外部网络的服务和访问则由网关上的代理服务器提供。它是一种结构非常简单,但安全性很高的防火墙系统,是对双宿主主机防火墙的一个改进。", "1 简介  2 结构  3 工作原理", "双宿主主机(Dual-Homed Host)结构是围绕着至少具有两个网络接口的双宿主主机而构成的。双宿主主机内外的网络均可与双宿主主机实施通信,但内外网络之间不可直接通信,内外部网络之间的IP数据流被双宿主主机完全切断。双宿主主机可以通过代理或让用户直接注册到其上来提供很高程度的网络控制。", "双宿主机结构采用主机替代路由器执行安全控制功能,故类似于包过滤防火墙。双宿主机即一台配有多个网络接口的主机,它可以用来在内部网络和外部网络之间进行寻径。如果在一台双宿主机中寻径功能被禁止了,则这个主机可以隔离与它相连的内部网络和外部网络之间的通信,而与它相连的内部和外部网络都可以执行由它所提供的网络应用,如果这个应用允许的话,它们就可以共享数据。这样就保证内部网络和外部网络的某些节点之间可以通过双宿主机上的共享数据传递信息,但内部网络与外部网络之间却不能传递信息,从而达到保护内部网络的作用。它是外部网络用户进入内部网络的唯一通道,因此双宿主机的安全至关重要,它的用户口令控制安全是一个关键。", "双宿主主机(具有至少两个网络适配器)双宿主机防火墙双宿网关防火墙(Dual-homed Gateway Firewall)的结构。", "如图所示,双宿主主机是指具有两个网络接口的主机。双宿主主机的两个网络接口分别连接内网和外网,内网通常为局域网,外网通常为Internet。内外网络均可与双宿主主机通信,但内外网络之间不可直接通信。双宿主主机是内网和外网之间的屏障,如果入侵者得到了双宿主主机的访问权,内网就会被侵入,为了保障内网的安全,需要在双宿主主机上禁止路由功能,并且设置身份认证机制。", "双宿主机使用两种方式来提供服务,一种是用户直接登录到双宿主机上来提供服务,另一种是在双宿主机上运行代理服务器。", "第一种方式需要在双宿主机上开很多账号,这会带来一些危险:1、用户账号的存在给入侵者提供相对容易的入侵通道,每一个账号通常会有一个可重复使用的密码,这样很容易被入侵者破解。2、由于用户的行为是不可预知的,这会给入侵检测带来很大的麻烦。第二种方式的问题相对要少很多,并且有些服务(如HTTP、SMTP)本身的特点就是“存储转发”型的,很适合进行代理。在双宿主机上可以运行多种代理服务程序,当内网要访问外网时,必须先通过服务器认证,然后才可以通过代理服务程序访问外网。另外可以把包过滤路由器和双宿网关集成在一起。把包过滤路由器放在外部网络和一个屏蔽子网之间。屏蔽子网用来为外部网络用户提供一些特定的服务, 比如WWW,Gopher, FTP等。这样一来可以利用包过滤路由器的过滤保护双宿网关免受外部的攻击,例如如果禁止外部访问远程登陆到双宿网关,就可以减少外部攻击的危险。这种防火墙拒绝所有的网络服务,包括DNS等,除非应用网关有代理模块的网络服务可以允许。不灵活性是这种防火墙技术的最大缺点。另外网关主机系统的安全是双宿网关安全的关键。", "致力于权威的科学传播", "以上专家来自 中国通信学会科普中国百科科学词条评审专家委员会", "词条统计 浏览次数:次 编辑次数:5次历史版本 最近更新: 创建者:huang69", "企业信用信息"], "bs_rank_pos": 2}, {"is_selected": true, "title": "什么是宿主机?宿主机目标机的介绍_百度文库", "most_related_para": 2, "segmented_title": ["什么", "是", "宿", "主机", "?", "宿", "主机", "目标机", "的", "介绍", "_", "百度", "文库"], "segmented_paragraphs": [["什么", "是", "宿", "主机", "?", "宿", "主机", "目标机", "的", "介绍", "_", "计算机硬件", "及", "网络", "_", "IT", "/", "计算机", "_", "专业", "资料", "。", "www", ".", "guojing", ".", "net", "什么", "是", "宿", "主机", "?", "宿", "主机", "目标机", 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宿主机是什么?我们今天为大家介绍一下什么是宿主机?宿主机目标机的介绍。 简单来说,专用宿主机 (Cvm Dedicated Host", "www.guojing.net 什么是宿主机?", "宿主机目标机的介绍 宿主机是什么?我们今天为大家介绍一下什么是宿主机?宿主机目标机的介绍。 简单来说,专用宿主机 (Cvm Dedicated Host )可以让您以独享宿主机资源方式购买、创 建云主机,以满足您的资源独享、安全、合规需求;购买专用宿主机后,您可在其上灵活创 建、管理多种自定义规格的独享型云主机。", "专用宿主机有什么优势?", "一、独享资源-支持 独享宿主机资源,供您自主规划,避免资源竞争 二、创建灵活-支持指定创建、CVM 规格自定义 (1)支持指定单台、多台宿主机等方式创建 CVM; (2)CVM 规格无限制,依据宿主机资源自定义 三、安全-隔离度高 提供宿主机粒度资源隔离,网络、内存、磁盘均租户专用 四、合规-满足金融行业强监管 独享宿主资源,满足敏感业务数据保护、磁盘消磁 五、管理监控-多维度 提供云主机、宿主机多种维度管理、监控 专用宿主机优势很多啊,让大家看看现在都要哪些客户使用呢?", "什么是宿主机?宿主机目标机 企业主机有哪些?这么多企业都使用专用宿主机,肯定有选 择的理由,还有什么强悍的功能吗? 恩,必须有!", "www.guojing.net 一、计算 (1)多地域多可用区腾讯云数据中心分布在全球多个位置,覆盖国内华南、华东、华北三个地域。(2)丰富的机型配置 CDH 提出以下机型: (3)灵活创建支持指定单台、多台专用宿主机(CDH)等方式创建独享型云主机(CVM),CVM 规格无限制, 依据宿主机资源自定义,供您自主、高效实现资源规划、分配。 (4)灵活调整配置提供丰富的计算、网络和磁盘的选择和调整。支持在当前 CDH 上独享型 CVM 的 CPU、内存 的配置升级、降级,以及磁盘的配置升级。 (5)灵活的计费模式 什么是宿主机?宿主机目标机 CDH 提供包年包月计费模式,购买 CDH 后,您可以随时免费开通/销毁独享型 CVM, 与 CVM 相关的网络支持按流量计费的弹性计费模式, 网络将按实 际使用量付费。 二、镜像 (1)丰富的镜像来源 腾讯云什么是宿主机?宿主机目标机提供以下三种镜像: 公有镜像:由腾讯云官方提供,由基础操作系统和腾讯提供的初始化组件构成,所有用户均 可使用。 服务市场镜像:由第三方服务商提供,经过腾讯云审核后发布到服务市场的镜像,所有用户 均可使用。 自定义镜像:由用户通过镜像制作功能制作的镜像,用户制作的镜像只有该账户可以使用。 (2)备份服务器运行环境 自定义镜像可以用来备份服务器的系统盘,数据丢失时,快速恢复。 (3)跨地域复制镜像 为了方便能利用腾讯云宿主机是什么 嵌入式宿主机的不同地理位置区域的优势。我们提供 将自定义镜像复制到其他腾讯云地域的功能。 (4)批量部署软件环境 通过对已经部署好环境的云主机制作镜像批量创建云服务器以此达到快速批量部署软件环 境。 三、网络 www.guojing.net (1)免费的公网 IP 和内网 IP 购买大于 0 的公网带宽即可分配给您免费的公网 IP 资源。 www.guojing.net (4)漏洞扫描 提供分布式扫描系统, 当发现存在网站漏洞时, 会通过站内信件或者手机短信及时通知到您。 专业漏洞修复指引,三分钟轻松修复网站漏洞。 (5)安全组 安全组是一种有状态的包过滤功能的虚拟防火墙, 它用于设置单台或多台云服务器的网络访 问控制, 是一种重要的网络安全隔离手段。 (6)磁盘消磁 支持磁盘消磁,以满足您敏感业务数据保护、磁盘消磁等合规需求。 六、监控 (1)免费提供云监控,并支持多种实时预警 (2)多维度监控 支持专用宿主机(CDH)、独享型云主机(CVM)多维度监控。 (3)多指标监控 提供了多指标监控,如 CPU 利用率,内存利用率,磁盘利用率等。 (4)自定义告警阈值 支持配置多种指标的告警触发阈值, 每个策略可关联不同云产品。 自定义告警接收人和发送 渠道。", "(5)告警通道服务提供了自定义的告警通道服务。", "(6)健康度分析对云资源各项关键指标进行综合分析, 根据指标重要程度给予不同评分, 计算出整个系统健 康分。", "什么是宿主机?宿主机目标机的了解到这里,是不是觉得很厉害哈,想要知道此产品是否 适合您公司的需求吗?直接点击上方我们的联系方式咨询哦~"], "bs_rank_pos": 3}, {"is_selected": false, "title": "宿主机 - 必应 词典", "most_related_para": 2, 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"专用宿主机 （Cvm Dedicated Host ）可以以独享宿主机资源方式购买、创建云主机，以满足资源独享、安全、合规需求；购买专用宿主机后，可在其上灵活创建、管理多种自定义规格的独享型云主机。"], "answer_docs": [0], "segmented_question": ["宿", "主机", "是", "什么", "意思"], "question_type": "DESCRIPTION", "question_id": 91195, "fact_or_opinion": "FACT", "match_scores": [0.8571428571428572]}
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另外,如果A列用的:是半角的话(:是半角,:是全角,不一样的),就把公式中的\":\"改成\":\",否则会出错。 (2)分列法:如果不用函数的话,可以使用数据-分列,先选“分隔符”,再选“其它”,输入:号,下一步会把目标区域改成C1,按完成即把A列分成两列,C列是姓名,D列是分数。 (3)查找-替换法:还可以通过查找替换,查找中输入:*,替换中什么也不输,按全部替换就把:号和它后面的分数去掉了。"], "bs_rank_pos": 4}], "answer_spans": [[0, 44]], "fake_answers": ["提取数字前面字符串可以使用excel中的left函数,它的语法是=left(text,[num-chars]),第一参数是需要提取的文本,第二参数是提取字符串的个数"], "question": "excel取前几位", "segmented_answers": [["提取", "数字", "前面", "字符串", "可以使用", "excel", "中", "的", "left", "函数", "，", "它", "的", "语法", "是", "=", "left", "（", "text", "，", "[", "num", "-", "chars", "]", "）", "，", "第", "一", "参数", "是", "需要", "提取", "的", "文本", "，", "第", "二", "参数", "是", "提取", "字符串", "的", "个", "数", "。"], ["取", "A1", "单元格", "中", "的", "前", "6", "个", "（", "最", "左边", "6", "个", "连续", "字符", "）", "，", "在", "B1", "中", "输入", "：", "=", "LEFT", "（", "A1", "，", "6", "）", "。"]], "answers": ["提取数字前面字符串可以使用excel中的left函数，它的语法是=left（text，[num-chars]），第一参数是需要提取的文本，第二参数是提取字符串的个数。", "取A1单元格中的前6个（最左边6个连续字符），在B1中输入：=LEFT（A1，6）。"], "answer_docs": 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"角色扮演", "语言", ":", "中文", "大小", ":", "76", ".", "2", "MB", "下载", "9", "6", ".", "1"]], "paragraphs": ["当前位置:首页 → 游戏攻略→ 游戏攻略→ 巫师3狼人无限回血怎么杀 狼人打法介绍", "时间:2016/12/30 10:59:13来源:当游整理作者:当游", "巫师3狼人回血快怎么办?野性之心狼人是早起主线任务中一只野外BOSS。小编今天就为大家带来狼人打法介绍一起来看看吧。", "巫师3狼人回血快怎么办?野性之心狼人是早起主线任务中一只野外BOSS,其实不打也不会影响主线剧情,而且狼人还会光速回血。但是有些玩家还是喜欢挑战一下,小编今天就为大家带来狼人打法介绍一起来看看吧。", "【巫师3野性之心狼人打法】", "1、正常打法 首先将洞里的狼引出来清光,去和狼人单挑,否则被围攻会非常危险。 打狼人时建议采取打3下躲闪1下的节奏,不要贪刀,并且保持身上的昆恩法印不断。 在狼人开始回血时立刻用伊格尼法印烧它,如果有点燃效果的武器或者是流血武器更好。如果狼人出现硬直,直接上去不断连砍。 这个打法的好处就在可以磨练玩家的操作,让玩家更加熟悉战斗节奏。 2、控怪打法 这个打法被称之为“我的回合”,是一种容错率非常高的打法。 打狼人之前一样要将洞里的狼引出来清光,否则会被小狼打断控制,无法完美控怪。 前期一定要点出亚克席法印的玩偶大师天赋,用亚克席法印控制狼人(注意,需要长摁法印释放键才能激活玩偶大师天赋),然后上去砍3、4刀,后跳,再控怪上去砍,一直循环。 这个打法的好处就是十分安全,容错率很高。使用玩偶大师天赋加成的亚克席法印,精力槽消耗仅仅为一半,恢复也很快,所以单挑时控怪基本是无缝链接的,这样狼人就只有挨打的份了,就像打昆特牌一样。", "无论哪种打法都需要从洞里引出清光,简单难度下有了清光站着都能撸死。希望以上攻略能够帮助到大家。", "相关游戏", "网友评论", "热门评论 最新评论", "查看所有评论(0)发布评论 昵称:  表情:  字数: 0/500 (您的评论需要经过审核才能显示)", "游戏攻略推荐 1铁拳7全人物连招教学视频大全 所有人物 2大宇董事长涂俊光称《仙剑7》投资超过 3小学生占比不到3%?王者荣耀用户数据统 4和风恐怖游戏《祝姬-祀-》将延期至201 5铁拳7人物介绍大全 铁拳7全部人物故事 6《为美好的世界献上祝福》游戏首部完整 7《去月球》宣布跳票 游戏最晚将在年底 8掠食怪物属性图鉴大全 掠食有哪些敌人 9逃生2攻略大全 逃生2剧情解析结局解说 10掠食攻略大全 掠食PREY怎么玩教程图文", "美女图片  极品校花文艺居 清纯美女的一字 性感车模的妖娆 美女紧身包裙性", "游戏攻略排行榜 1饥荒营地建造心得 如何快速建造营地 2天天酷跑积分抽奖技巧 永久人物坐骑怎 3王者荣耀师徒关系怎么解除 师徒关系解 4泰拉瑞亚装备合成表图鉴 5DNF地下城与勇士闪退是什么原因 怎么解 6我的世界怎么去蘑菇岛 7我的世界龙蛋怎么孵化、用处和正确拿法 8荒岛求生游戏物品合成表大全 9GTA5 PC 版怎么购买 正版gta5多少钱 10龙纹三国答题答案一览 龙纹三国三国通", "单机游戏排行榜  周 月 总 1 5.5死亡独轮车 类型: 动作冒险 语言: 中文 大小: 7.6 MB 下载 2 7.4战地2 类型: 射击游戏 语言: 中文 大小: 2.7 GB 下载 3 7.2我的世界 类型: 模拟经营 语言: 中文 大小: 34.3 MB 下载 4 7.8打造世界 类型: 角色扮演 语言: 中文 大小: 240.9 MB 下载 5 7.2CSOL单机版神器时代9.0 类型: 射击游戏 语言: 中文 大小: 414.2 MB 下载 6 8.3烛火地牢 类型: 动作冒险 语言: 中文 大小: 101.6 MB 下载 7 6.7泰拉瑞亚 类型: 休闲益智 语言: 中文 大小: 233.0 MB 下载 8 7.5中国卡车模拟 类型: 赛车游戏 语言: 中文 大小: 439.6 MB 下载 9 4.8艾希 类型: 动作冒险 语言: 中文 大小: 1.6 GB 下载 10 6.1我的世界1.9 类型: 模拟经营 语言: 中文 大小: 158.9 MB 下载 1 7.5中国卡车模拟 类型: 赛车游戏 语言: 中文 大小: 439.6 MB 下载 2 7.2我的世界 类型: 模拟经营 语言: 中文 大小: 34.3 MB 下载 3 7.4战地2 类型: 射击游戏 语言: 中文 大小: 2.7 GB 下载 4 6.7泰拉瑞亚 类型: 休闲益智 语言: 中文 大小: 233.0 MB 下载 5 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"等级高点再去,等级差距大了什么燃烧下毒没个卵用,狼人很快就恢复正常状态然后噌噌噌回血。", "贡献度 6 金元 7449 积分 769 精华 0 注册时间 2014-7-11", "有些怪,法印还是必要的", "贡献度 602 金元 210622 积分 23470 精华 0 注册时间 2009-2-18", "用毒剑吧 只要等级相差不大都吃中毒", "对浪人丢了2发舞动之星它基本就残了,然后开个雷霆上去刚正面就把它砍死了"], "bs_rank_pos": 4}], "answer_spans": [[0, 70]], "fake_answers": ["用毒、火法烧,可以得话换个好一点的银剑,初始的那把确实难打。我点出了炼金术里那个使用对应的油时攻击有一定几率使目标中毒的。打到1/4的时候会出剧情,如果状况不好的话就让狼人杀人,然后他会自杀的。"], "question": "巫师3狼人无限回血怎么杀", "segmented_answers": [["1", "、", "最高", "难度", "。", "引", "到", "洞口", "位置", "，", "他", "会", "朝", "你", "吼叫", "，", "不会", "来", "追", "你", "，", "放", "个", "迷惑", "，", "重击", "两", "到", "三", "下", "，", "第", "三", "下", "可能会", "被", "打", "到", "，", "再", "后退", "，", "被", "退", "太后", "，", "他", "可能会", "跑", "洞", "里", "去", "，", "他", "会", "再", "吼叫", "，", "以此类推", "，", "我", "最高", "难度", "就是", "这么", "打的", "，", "或者", "走位", "不", "太行", "就", "用", "昆恩", "。", "走位", "行", "就", "用", "亚", "登", "打", ";", "2", "、", "放", "闪电", "陷阱", "，", "然后", "用", "火印", "(", "4", "级", ")", "放", "一", "个", "上去", "直接", "就是", "正面", "莽", "，", "顺便", 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放闪电陷阱，然后用火印(4级)放一个上去直接就是正面莽，顺便点燕子补血，2个一起基本10秒能干他四分之一血，不过要先清掉小狼;3、用个猫眼，看得清，进入范围放阵法减缓速度，银剑+火印配合打，阵法消失后拉开距离用炸弹，离近继续套阵法，银剑+火印，拉距离，炸弹，前提是你学了肾上腺素可以发法印的技能;4、硬砍。火烧它会硬直，劈三四下后滚，不要攻击落空，给它回血的时间，不然的话你可以直接放弃读档重来，你给它伤害,不给它休息时间，它就不回血了，被动技能但有限制，主要不要跟它游斗，你耗不起，有用的药水全往自己扔就打，看准它的攻击(大概你劈它三四下它才回一下)滚开后继续劈。", "用毒、火法烧，可以得话换个好一点的银剑，初始的那把确实难打。我点出了炼金术里那个使用对应的油时攻击有一定几率使目标中毒的。打到1/4的时候会出剧情，如果状况不好的话就让狼人杀人，然后他会自杀的。", "首先将洞里的狼引出来清光，去和狼人单挑，否则被围攻会非常危险。打狼人时建议采取打3下躲闪1下的节奏，不要贪刀，并且保持身上的昆恩法印不断。在狼人开始回血时立刻用伊格尼法印烧它，如果有点燃效果的武器或者是流血武器更好。如果狼人出现硬直，直接上去不断连砍。这个打法被称之为“我的回合”，是一种容错率非常高的打法。打狼人之前一样要将洞里的狼引出来清光，否则会被小狼打断控制，无法完美控怪。前期一定要点出亚克席法印的玩偶大师天赋，用亚克席法印控制狼人(注意，需要长摁法印释放键才能激活玩偶大师天赋)，然后上去砍3、4刀，后跳，再控怪上去砍，一直循环。这个打法的好处就是十分安全，容错率很高。使用玩偶大师天赋加成的亚克席法印，精力槽消耗仅仅为一半，恢复也很快，所以单挑时控怪基本是无缝链接的，这样狼人就只有挨打的份了，就像打昆特牌一样。"], "answer_docs": [1], "segmented_question": ["巫师", "3", "狼人", "无限", "回血", "怎么", "杀"], "question_type": "DESCRIPTION", "question_id": 91198, "fact_or_opinion": "OPINION", "match_scores": [0.9436619718309859]}
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"一、报到抽签时 考生除非已经熟悉面试考场所在地的位置,并且清楚了解考场周围的交通状况,否则一定要在考试前查看考场,并查清楚交通情况。结构化面试签到是严格按照规定时间执行的,曾经就报道过有的考生因为迟到一分钟进不了考场,给门外的工作人员下跪,但最后还是没能进入考场的事件。这就体现出了公务员结构化面试面试纪律的严肃性,不因个人意志而改变。还要提醒考生的一点是,抽签前上交通讯工具不要心存侥幸,如果没有上交一旦被发现会被取消面试资格,甚至会按照考试纪律处罚几年内不得参加考试。 二、候考时 一般情况下考生分为上午和下午两批入场考试,面试考场会为考生准备饮用水。甚至一些地区为避免考生等候时间过长体力消耗大,还准备了点心、零食等供考生补充体力。但考生最好还是自带一些适合在公共场合充饥的干粮(千万不可带瓜子之类的零食),以备不时之需。此时如果工作人员允许,可以翻看一些熟悉的题目、资料,使大脑保持适度的兴奋状态,也可以与其他候考的考生聊聊天放松,但注意千万不要去打听其他人的复习准备情况,也别主动向他人透露自身的准备情况。否则,一来可能使自己在对比他人显得准备不足的情况下,徒增紧张和消极的情绪,二来可能让别人知道了你的复习准备程度,聪明的考友可能因为你的“泄密”而调整自己的战略以应对变化。 三、室外等候时 如果考生被引考员带到小候考室,或者是考场附近的走廊、楼梯间等地方候考时,可以向引考员询问考场内的设备情况,比如是否有纸和笔,是否有麦克风,麦克风是否需要自己动手打开等,这些小细节如果引考员没有得到指示不允许透露给你,一般是会告诉你他所知道的情况的。这些小细节看似简单,但是却能帮助你进入考场后稳定心态,充分利用资源为自己助考。比如,有的考生在考场外候考时不询问清楚这些情况,进入考场之后,不知道麦克风需要自己动手打开,答题答了几句,就使劲吹、拍麦克风,甚至还询问主考官或者考场工作人员麦克风是不是已经打开。这不仅扰乱了自己的答题思路,也使庄严神圣的考场变得滑稽,甚至会影响到一部分考官对你的第一印象,实属不该。即使不出现以上状况,进场之后如果其他考生能正常使用麦克风,而你没使用,声音效果可能不理想,可能也会影响你与考官的交流效果。假如能在考场外通过正当的询问得到答案,那么进场之后就可避免出现不利的状况。 四、候分室候时 退场时不能带走草稿纸、笔以及面试题本等物品,退场后跟随引考员直接到候分室等候成绩。一般来讲,考官会在前三名考生答题时进行预评分,待三名考生答题完毕之后,考官可以自行调整自己对前三位考生的评分,正式填写分数,此后的考生不再进行预评分。因此前三位考生可能等待时间较长。接下来工作人员会发给一张面试通知单,请考生签名确认自己的面试成绩,拿到通知单即被要求离开考场了。一般来讲,当天或者隔天就能公布全部参考人员的面试成绩,心急想提前知道考试成绩的考生,则可以在考场外面等候,了解其他考友的面试情况。 要郑重提醒大家的是,面试时需要注意的一些小细节不是上考场之后自然而然就能做到的,需要在平时练习的时候尽可能地全真模拟这些场景,同时练习下一节我们即将谈到的面试礼仪与修养,让这些场景内的注意事项内化成我们的习惯,和吃饭睡觉一样的稀松平常。一些考生认为这样的训练不重要,等自己去了考场自然就能做到了,这个想法是不可取的。如果在平时没有压力的情况下你会常常忘东忘西,面试注意事项和礼仪都马马虎虎就过去了,那么在考场上紧张压力的状态下犯错的可能性会更大。如果真能在面试前做足模拟训练,到考场的紧张程度就会大大降低,更有利于考生正常甚至是超常发挥。", "知己知彼,百战不殆,早点熟识2017年省公务员结构化面试需要注意什么,早点开始进行相关备考,然后在2017省公务员结构化面试中取得好成绩!", "本文仅代表作者观点,不代表百度立场。", "本文系作者授权百家号发表,未经许可,不得转载。", "还没有人评论,点击抢沙发～"], "bs_rank_pos": 4}, {"is_selected": true, "title": "2017年湖南省公务员考试成绩公布时间及网址_公务员_新东方在线", "most_related_para": 3, "segmented_title": ["2017", "年", "湖南省", "公务员", "考试", "成绩", "公布", "时间", "及", "网址", "_", "公务员", "_", "新东方", "在线"], "segmented_paragraphs": [["2017", "年", "湖南省", "公务员", "考试", "成绩", "公布", "时间", "预计", "在", "5", "月", "下旬", ",", "各位", 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["2017年湖南省公务员考试成绩公布时间预计在5月下旬,各位考生可登陆公务员考试录用官方网站查询湖南省考笔试成绩。", "2017年湖南省公务员考试成绩公布时间及网址", "笔试成绩公布", "笔试结束后,5月下旬在公务员考试录用官方网站公布报考人员笔试成绩。", "报考人员应当参加全部规定科目的考试,成绩方为有效。", "湖南省公务员考试报名服务平台可通过下列网址进入:", "资格审查和体能测评", "(一)资格审查 1.资格审查由招录机关负责,资格审查时间另行公告。 2.资格审查对象。按照招考职位计划1∶2的比例,在报考该职位人员中按笔试成绩从高分到低分确定资格审查对象。网上报名前已通过资格审查的人员,不再复审,按照招考职位计划1∶2的比例,直接进入面试。 3.资格审查内容。报考人员需提供本人身份证、准考证、毕业证或全日制高校应届毕业生就业推荐表、户口本、职位要求的资格证书等,有工作单位的报考人员还需提供所在单位同意报考的证明。 “四项目”人员应提供由相关主管部门颁发的证书、考核鉴定意见。参加“大学生村官”项目的,要提供县级以上组织部门出具的证明;参加“三支一扶”项目的,要提供省“三支一扶”工作协调管理办公室出具的高校毕业生“三支一扶”服务证书原件和复印件;参加“农村义务教育阶段学校教师特设岗位计划”项目的,要提供省教育厅统一制作的“特岗教师”服务证书原件和复印件;参加“大学生志愿服务西部计划”项目的,要提供由共青团中央统一制作的服务证、省级或县级(我省仅醴陵、炎陵、花垣、保靖成立了项目办)大学生志愿服务西部计划项目办公室及服务单位共同盖章的鉴定表原件和复印件。 未按规定要求进行资格审查的报考人员,视为放弃资格审查。提交材料主要信息不实,或不符合招考职位条件的,资格审查不合格。 4.资格审查对象递补。因报考人员放弃资格审查和资格审查不合格而造成招考职位拟面试人数达不到招录计划1∶2比例的,在报考该职位人员中按笔试成绩从高分到低分依次递补。 报考省外事侨务办、省友协办的人员,全部参加以口译为主的专业面试,不再参加集中面试。 (二)体能测评 1.人民警察、乡镇人武专干职位资格审查合格对象在面试前需进行体能测评。 2.体能测评由招录机关负责,省、市州公务员主管部门会同同级纪检监察部门指导监督。 3.体能测评项目和标准。人民警察按照《关于印发公安机关录用人民警察体能测评项目和标准(暂行)的通知》(人社部发〔2011〕48号)执行。监狱、戒毒单位医学类人民警察按《湖南省监狱、戒毒单位医学类人民警察职位体能测评项目和标准》执行。人武专干按照《人武专干职位体能测评项目和标准》执行。 4.体能测评只能进行一次。不按规定要求进行体能测评的,视为放弃体能测评。 5.体能测评对象递补。因报考人员放弃体能测评和体能测评不合格而造成招考职位拟面试人数达不到招录计划1∶2比例的,在报考该职位资格审查合格的人员中按笔试成绩从高分到低分依次递补。递补次数不超过2次。 面试 1.面试时间及相关规定另行公告。 2.面试地点分别设在省直和市州。 3.面试由省公务员主管部门组织,省直、市州分级实施。 4.面试对象为资格审查和体能测评合格人员。 招考职位面试对象等于或少于招录计划数的,相应核减招录计划。招考职位只招1人的,不再核减招录计划数。 招考职位实际参加面试人数没有形成有效竞争的,报考人员面试成绩必须不低于当场(同一场次、同一个面试考官组、同一套面试题本)形成有效竞争职位入围体检人员的最低面试分数。 (本文节选自“2017年湖南省公务员考试公告”,具体内容以最新公告为准) 四川自贡2017年选调生资格复审|面试公告 四川内江2017选调生招录资格复审|面试 四川泸州2017选调生招录资格复审|面试 重庆北碚区2017年选聘优秀教师简章 四川绵阳师范学院2017年招聘120名公告 2017上半年重庆垫江县教育事业单位招聘", "版权及免责声明", "1,\"新东方在线\"上的内容,包括文章、资料、资讯等, 本网注明\"稿件来源:新东方在线\"的,其版权 均为\"新东方在线\"或北京新东方迅程网络科技有限公司所有 ,任何公司、媒体、网站或个人未经授权不得转载、链接、转贴或以其他方式使用。已经得到 \"新东方在线\"许可 的媒体、网站,在使用时必须注明\"稿件来源:新东方\",违者本网站将依法追究责任。  2, \"新东方在线\" 未注明\"稿件来源:新东方\"的 文章、资料、资讯等 均为转载稿,本网站转载出于传递更多信息之目的,并不意味着赞同其观点或证实其内容的真实性。如其他媒体、网站或个人从本网站下载使用,必须保留本网站注明的\"稿件来源\",并自负版权等法律责任。如擅自篡改为 \" 稿件来源:新东方 \" ,本网站将依法追究其法律责任。  3,如本网转载稿涉及版权等问题,请作者见稿后在两周内与新东方在线联系。", "2017山西公务员考试笔试合格分数线公布 2017甘肃公务员笔试成绩查询入口开通 2017上半年重庆荣昌区事业单位招聘公告 重庆市璧山区2017教育事业单位招聘简章", "2017公务员考试行测备考定义判断技巧突破 2017省考行测考试资料分析题常用统计术语 省考行测考试数量关系复习数 学运 算练习题 2016年国家公务员考试最后7天冲刺特训 2016年国家公务员考试行测答题技巧 2016年国家公务员考试行测大纲解析", "w 42分20秒  1 2016年国家公务员考试行测大纲解析  2 2016年国家公务员考试申论大纲解析  3 2016年国考申论概述  4 2016年国考申论阅读重要性  5 2016年国考申论评分规则"], "bs_rank_pos": 5}], "answer_spans": [[4, 15]], "answer_docs": [4], "fake_answers": ["5月下旬在公务员考试录用官方网站公布报考人员笔试成绩。"], "question": "2017年湖南省公务员考试成绩", "segmented_answers": [["5", "月", "下旬", "在", "公务员考试", "录用", "官方网站", "公布", "报考", "人员", "笔试成绩", "。"]], "answers": ["5月下旬在公务员考试录用官方网站公布报考人员笔试成绩。"], "entity_answers": [[]], "segmented_question": ["2017", "年", "湖南省", "公务员", "考试", "成绩"], "question_type": "ENTITY", "question_id": 91199, "fact_or_opinion": "FACT", "match_scores": [1.0]}
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"8", ",", "000", "金"]], "paragraphs": ["哪位大大发一下哪个等级可以进入血几啊?", "血色城堡等级 普通角色等级 魔剑圣导等级", "1 15~80 15~60 2 81~130 61~110 3 131~180 111~160 4 181~230 161~210", "5 231~280 211~260", "6 281~330 261~310", "7 331以上 310以上", "能看明白吗? 我觉得奇迹的装备最漂亮~~!!!", "奇迹血色城堡详解: 新增第七血色城堡:", "- 入场等级:331 ~ 400(一般角色等级), 311 ~ 400(魔剑士角色等级)", "-第七血色城堡怪物详情如下: 怪物名称 等级 生命 最小攻击力 最大攻击力 防御力 攻击成功率 防御成功率", "假面武士7 85 23000 340 390 340 520 190", "假面弓手7 87 26000 390 430 380 590 200", "骷灵战士7 90 32000 430 480 400 660 230", "骷灵兽7 93 37000 470 520 440 730 250", "骷灵骑士7 98 45000 510 560 490 800 270", "骷灵巫师7 102 55000 560 600 550 870 310", "- 第七血色城堡所需的血灵之书、血灵之骷掉出怪物等级:84(黑炎魔) 以上", "- 第七血色城堡披风合成:血灵之书(+7) + 血灵之骷(+7) + 玛雅之石 + 85万金", "- 活动点数:A(1030) B(800) C(600) D(300) E(-300)", "A = 完成者得到的活动点数 B = 完成者组队成员得到的活动点数 C = 不是组队成员,但生存下来的玩家得到的活动点数 D = 不是组队成员,又没有生存下来的玩家得到的活动点数", "E = 失败时活动点数减少", "1. 任务物品 获得方法 1) 通过打怪可以获得 [血灵之书], [血灵之骷] 2) [血灵之书]和[血灵之骷]为材料,在仙踪林的\"玛雅哥布林\"处通过合成可以获得 [透明披风] 2. 血色城堡 参加人数限制 1) 各血色城堡(共 7个) 按照先后顺序每次最多可以进入15名角色 带着与角色等级相符合的[透明披风]去找\"冰风谷\"的NPC [大天使的使者] 坐标: 冰风谷[209, 30] 或 冰风谷[219, 9] 2) 角色之间的队伍维持原先状态 3. 血色城堡 开始时间 每天24次,每小时30分钟开门 4. 血色城堡 规则 1) 入场时装备栏中的相应透明披风消失 2) 入场后可以使用移动命令 3) 入场时间结束后无法进入血色城堡 4) 在血色城堡死亡时角色回到冰风谷 5) 在血色城堡使用回城卷轴时角色回到冰风谷", "6) 在血色城堡角色死亡时不会损失经验值", "7) 在血色城堡角色死亡时不会损失金", "8) 在血色城堡角色死亡时不会掉出装备栏或身上的任何装备", "9) 在血色城堡内不能PK", "10) 进入血色城堡后,任务开始前角色不能离开安全区", "11) 血色城堡任务结束(15分钟)后角色被送回冰风谷", "12) 血色城堡限定时间(15分钟)内完成任务时,所有怪物消失、1分钟后角色被送回冰风谷", "13) 血色城堡安全区内角色可以使用如下技能:战神之力,守护之光,治疗,生命之光,守护之魂", "14) 在血色城堡内血色城堡开始前1分钟开始不能进行交易", "15) 血色城堡内不能丢弃大天使之武器(含3种)", "16) 在血色城堡内装备的'小恶魔'或'守护天使'生命值为0时,自动替换装备栏中的'小恶魔'或'守护天使'", "17) 血色城堡开始后,需要打死一定数量怪物才可以攻打城门(画面提示剩余怪物数量)", "18) 攻破血色城门后,需要打死一定数量的骷灵巫师才能开始打开水晶灵柩(画面提示剩余怪物数量),需要打死的骷灵巫师数量(最多10只)=血色城堡内的角色数量(攻破城门时)*2", "4. 血色城堡 等级限制", "血色城堡等级 普通角色等级 魔剑士等级", "1 15 ~ 80 15 ~ 60 2 81 ~ 130 61 ~ 110 3 131 ~ 180 111 ~ 160 4 181 ~ 230 161 ~ 210", "5 231 ~ 280 211 ~ 260", "6 281 ~ 330 261 ~310", "7 331 以上 310 以上", "4. 任务物品掉出怪物等级 (血灵之书, 血灵之骷)", "任务物品等级 血灵之书, 血灵之骷", "+1 等级2 (蜘蛛) 以上 +2 等级32 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CAD字体库放在哪里?... CAD2010 64位下载(... CAD标注字体大小了,... 如何显示CAD的工具栏... AUTOCAD2012破解版下... 中望CAD2010破解版下... 一些LISP程序怎样加... WIN7 64位 CAD2008下载 CAD怎么缩放,快捷键...", "1.命令格式 命令行:Rotate(V) 菜单:[修改]→[旋转(R)] 工具栏:[修改]→[旋转]通过指定的点来旋转选取的对象。2.操作步骤 用Rotate命令将图4-11(a)中正方形内的两个螺栓复制旋转90度,使得正方形每个角都有一个螺栓,如图4-11(c) 所示。操作如下:以上各项提示的含义和功能说明如下:旋转角度:指定对象绕指定的点旋转的角度。旋转轴通过指定的基点,并且平行于当前用户坐标系的 Z 轴。 复制(C):在旋转对象的同时创建对象的旋转副本。 参照(R):将对象从指定的角度旋转到新的绝对角度。 3.注意@ 对象相对于基点的旋转角度有正负之分,正角度表示沿逆", "旋转角度:指定对象绕指定的点旋转的角度。旋转轴通过指定的基点,并且平行于当前用户坐标系的 Z 轴。 复制(C):在旋转对象的同时创建对象的旋转副本。 参照(R):将对象从指定的角度旋转到新的绝对角度。 3.注意@ 对象相对于基点的旋转角度有正负之分,正角度表示沿逆时针旋转,负角度表示沿顺时针旋转。", "Email:market@zwcad.com", "4008-800-819", "4008-800-819", "产品视频教程", "中望业务 中望CAD 中望3D 中望教育 微小网 3DOne中小学社区", "产品支持 经验技巧 CAD常见问题 使用教程 安装与激活 CAD快捷键", "新闻 国内新闻 国际新闻 媒体新闻", "人才招聘 社会招聘 校园招聘 人在中望 成长在中望", "中望CAD系列", "4008-800-819", "中望3D三维软件咨询", "4008-336-663"], "bs_rank_pos": 0}, {"is_selected": true, "title": "CAD中旋转的快捷键是什么?_百度知道", "most_related_para": 0, "segmented_title": ["CAD", "中", "旋转", "的", "快捷键", "是", "什么", "?", "_", "百度", "知道"], "segmented_paragraphs": [["1", ".", "命令行", "输入", "\"", "R", "”", ",", "空格", ";", "2", ".", "选择", "要", "旋转", "的", "图形", ",", "空格", ";", "3", ".", "输入", "要", "旋转", "的", "角度", ",", "逆时针", "方向", "旋转", "输入", "正", "值", ",", "顺时针", "为", "负值", "。"], ["默认", "是", "ro", ",", "可以通过", "编辑", "acad", ".", "pgp", "自己", "设定", "。"], ["RO", "选中", "旋转", "中心", "点", "再", "输入", "旋转", "角度"], ["RO", "谢谢", "!"]], "paragraphs": ["1. 命令行输入\"R”,空格; 2. 选择要旋转的图形,空格; 3. 输入要旋转的角度,逆时针方向旋转输入正值,顺时针为负值。", "默认是ro,可以通过编辑acad.pgp自己设定。", "RO 选中旋转中心点 再输入旋转角度", "RO 谢谢!"], "bs_rank_pos": 1}, {"is_selected": false, "title": "CAD水平翻转快捷键是什么啊_百度知道", "most_related_para": 0, "segmented_title": ["CAD", "水平", "翻转", "快捷键", "是", "什么", "啊", "_", "百度", "知道"], "segmented_paragraphs": [["水平", "翻转", "快捷键", "为", "【", "Alt", "】", "+", "【", "Shift", "】", "+", "【", "Ctrl", "】", "+", "【", "B", "】", "其他", "相关", "的", "快捷键", "如", "下", ":", "　", "垂直", "(", "Vertical", ")", "翻转", "【", "Alt", "】", "+", "【", "Shift", "】", "+", "【", "Ctrl", "】", "+", "【", "V", "】", "　　", "冻结", "(", "Freeze", ")", "所选", "材质", "点", "【", "Ctrl", "】", "+", "【", "F", "】", "　　", "隐藏", "(", "Hide", ")", "所选", "材质", "点", "【", "Ctrl", "】", "+", "【", "H", "】", "　　", "全部", "解冻", "(", "unFreeze", ")", "【", "Alt", "】", "+", "【", "F", "】", "　　", "全部", "取消", "隐藏", "(", "unHide", ")", "【", "Alt", "】", "+", "【", "H", "】", "　　", "从", "堆栈", "中", "获取", "面", "选集", "【", "Alt", "】", "+", "【", "Shift", "】", "+", "【", "Ctrl", "】", "+", "【", "F", "】", "　　", "从", "面", "获取", "选集", "【", "Alt", "】", "+", "【", "Shift", "】", "+", "【", "Ctrl", "】", "+", "【", "V", "】", "　　", "锁定", "所选", "顶点", "【", "空格", "】", "　　", "水平", "镜象", "【", "Alt", "】", "+", "【", "Shift", "】", "+", "【", "Ctrl", "】", "+", "【", "N", "】", "　　", "垂直", "镜象", "【", "Alt", "】", "+", "【", "Shift", "】", "+", "【", "Ctrl", "】", "+", "【", "M", "】", "　　", "水平", "移动", "【", "Alt", "】", "+", "【", "Shift", "】", "+", "【", "Ctrl", "】", "+", "【", "J", "】", "　　", "垂直", "移动", "【", "Alt", "】", "+", "【", "Shift", "】", "+", "【", "Ctrl", "】", "+", "【", "K", "】", "　　", "平移", "视图", "【", "Ctrl", "】", "+", "【", "P", "】", "　　", "象素", "捕捉", "【", "S", "】", "　　", "平面", "贴图", "面", "/", "重", "设", "UVW", "【", "Alt", "】", "+", "【", "Shift", "】", "+", "【", "Ctrl", "】", "+", "【", "R", "】"], ["0", "~", "360", "转", "用", "RO", "快捷键", ",", "要是", "镜像", "翻转", "MI", "快捷键", "。"], ["“", "MI", "”", "镜像", "一", "下", "就", "OK", "的", "拉"], ["必须", "要", "快捷键", "有", "旋转", "和", "镜像", "命令", "ro", "-", "旋转", "MI", "-", "-", "镜像"]], "paragraphs": ["水平翻转快捷键为【Alt】+【Shift】+【Ctrl】+【B】 其他相关的快捷键如下: 　 垂直(Vertical)翻转 【Alt】+【Shift】+【Ctrl】+【V】 　　冻结(Freeze)所选材质点 【Ctrl】+【F】 　　隐藏(Hide)所选材质点 【Ctrl】+【H】 　　全部解冻(unFreeze) 【Alt】+【F】 　　全部取消隐藏(unHide) 【Alt】+【H】 　　从堆栈中获取面选集 【Alt】+【Shift】+【Ctrl】+【F】 　　从面获取选集 【Alt】+【Shift】+【Ctrl】+【V】 　　锁定所选顶点 【空格】 　　水平镜象 【Alt】+【Shift】+【Ctrl】+【N】 　　垂直镜象 【Alt】+【Shift】+【Ctrl】+【M】 　　水平移动 【Alt】+【Shift】+【Ctrl】+【J】 　　垂直移动 【Alt】+【Shift】+【Ctrl】+【K】 　　平移视图 【Ctrl】+【P】 　　象素捕捉 【S】 　　平面贴图面/重设UVW 【Alt】+【Shift】+【Ctrl】+【R】", "0~360转用RO快捷键,要是镜像翻转MI快捷键。", "“MI” 镜像一下就OK的拉", "必须要快捷键 有旋转和镜像命令 ro-旋转 MI--镜像"], "bs_rank_pos": 2}, {"is_selected": false, "title": "CAD中的旋转快捷键是什么?_百度知道", "most_related_para": 0, "segmented_title": ["CAD", "中", "的", "旋转", "快捷键", "是", "什么", "?", "_", "百度", "知道"], "segmented_paragraphs": [["RO"]], "paragraphs": ["RO"], "bs_rank_pos": 3}], "answer_spans": [[2, 38]], "fake_answers": ["命令行输入\"R”,空格;2.选择要旋转的图形,空格;3.输入要旋转的角度,逆时针方向旋转输入正值,顺时针为负值。"], "question": "cad旋转快捷键", "segmented_answers": [["命令行", "输入", "“", "R", "”", "，", "空格", "；", "选择", "要", "旋转", "的", "图形", "，", "空格", "；", "输入", "要", "旋转", "的", "图形", "，", "逆时针", "方向", "旋转", "输入", "正", "值", "，", "顺时针", "为", "负值", "。"]], "answers": ["命令行输入“R”，空格；选择要旋转的图形，空格；输入要旋转的图形，逆时针方向旋转输入正值，顺时针为负值。"], "answer_docs": [1], "segmented_question": ["cad", "旋转", "快捷键"], "question_type": "DESCRIPTION", "question_id": 91201, "fact_or_opinion": "FACT", "match_scores": [0.7142857142857142]}
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揭秘房价不会暴跌的原因 如何提升借款成功率 马上到账的贷款口子", "暂时还没有评论哦,来说两句吧!", "怎样正确投资能让你的10万迅速变100万? 易通贷:5年运营放心老平台 进入P2P超市", "风险提示:广告信息均来自平台方,不代表平台安全性,不构成投资建议!红包、体验金、理财金、大礼包、加息券、满减券、优惠券等都不直接等同于现金。参考收益说明不是收益承诺,不代表最终真实收益。理财有风险,投资需谨慎!"], "bs_rank_pos": 4}], "answer_spans": [[16, 16]], "yesno_answers": ["No"], "fake_answers": ["。"], "question": "国金宝可靠吗", "segmented_answers": [["不可靠", "。"]], "answers": ["不可靠。"], "yesno_type": "OPINION", "answer_docs": [0], "segmented_question": ["国", "金宝", "可靠", "吗"], "question_type": "YES_NO", "question_id": 91204, "fact_or_opinion": "OPINION", "match_scores": [0.6666666666666666]}
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南非的华人中,福清人占了一大部分。华人在南非经商、生活…[详细]"], "bs_rank_pos": 4}, {"is_selected": false, "title": "龙珠传奇李易欢历史上的原型是谁 结局复仇成功了吗和谁在一起--湖北", "most_related_para": 2, "segmented_title": ["龙珠", "传奇", "李易", "欢", "历史", "上", "的", "原型", "是", "谁", "结局", "复仇", "成功", "了", "吗", "和", "谁", "在一起", "-", "-", "湖北"], "segmented_paragraphs": [["原", "标题", ":", "龙珠", "传奇", "李易", "欢", "历史", "上", "的", "原型", "是", "谁", "结局", "复仇", "成功", "了", "吗", "和", "谁", "在一起"], ["据悉", ",", "结局", "中", "李易", "欢", "没有", "复仇", "成功", ",", "而是", "选择离开", "了", "皇宫", ",", "因为", "康熙", "以", "仁德", "治国", "的", "理念", "打动", "了", "她", ",", "她", "最终", "决定", "放弃", "仇恨", "并", "说服", "师", "兄妹", ",", "不要", "再", "为", "私仇", "而", "引发", "战乱", "危害", "百姓", "。", "康熙", "承诺", ",", "会", "做", "一", "代", "明君", ",", "而", "事实证明", ",", "康熙", "做到", "了", ",", "开创", "了", "康乾盛世", "的", "局面", ",", "不过", "这", "对", "昔日", "的", "恋人", "也", "只能", "相忘", "于", "世", ",", "相", "记", "于", "心", "。"], ["那么", "李易", "欢", "历史", "上", "原型", "是", "谁", "呢", "?", "据悉", ",", "李易", "欢", "的", "设定", "是", "明朝", 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解决时间:2009-6-11 12:17 提问者: gangzgy - 一级 最佳答案 一楼的 BS 一下,复制粘贴起码得找对地方啊~~~ ISP 其实就是在线编程,以往单片机下 载程序都需要烧写器之类的东东", "一级 最佳答案 一楼的 BS 一下,复制粘贴起码得找对地方啊~~~ ISP 其实就是在线编程,以往单片机下 载程序都需要烧写器之类的东东,并且需要把单片机从电路板上拔下来烧写,烧好再插上, 很麻烦. ISP 功能可以通过非常简单廉价的下载线直接在电路板上给单片机下载程序或者 擦除程序,可以在线调试,免去插来插去的麻烦. 现在出的主流单片机都支持 ISP 功能. 初学建议 AVR 或者51的 S 系列,这些都有 ISP 功能的,下载器通用,可以自制,成本不 到5块钱,这点上比烧写器便宜的多,普普通通一个烧写器就要200多.. . 一般的51单片机开发板能用 AT89S51吗?上面只说用 STC 的单片机! 悬赏分:0- 解决时间:2009-11-4 22:30 提问者: 627385312 - 二级 最佳答案 AT89S51和 STC89C51均可采用 ISP 下载,区别是: AT89S51- 是模拟 ISP,不是真正 的,最佳是采用 SPI(串行外围设备接口,SPI 是 Motorola 公司推出的一种同步串行通讯 方式) ,接到51需用到 MISO/MOSI/SCK/RST 管脚.举个例子:市场上有些用单片机做 的 USB 下载器就是这个原理方式. AT89S51- 经过试验也可以用串口232下载,但不推 荐,还是必须接到 MISO/MOSI/SCK/RST(P1.5/1.6/1.7/RST) ,而并不是接到串口 (P3.0/P3.1)下载.你看到的 AT89S51用串口下载的只是通过一个芯片转换成并行或 SPI,而不是串口(指没有用到 P3.0/3.1这个串口) STC89C51 是 ISP,用的是真正的 . 串口,其缺点是需要冷启动,虽然在程序中加入命令可以实现不需要按开关即可下载,"], "bs_rank_pos": 7}], "answer_spans": [[0, 154]], "fake_answers": ["第一,ROM89c51是4K,但89c52是8K。第二,RAM89c51是128字节,但89c52256字节就编程来说区别不大,主要是硬件资源的区别。区别如下:1、RAM空间增大:AT89C51有128字节的内部RAM,AT89C52的内部RAM扩展为256字节;2、内部FLASH变大:AT89C51有4K字节的内部FLASHPERAM,而。AT89C52的内部FLASHPERAM增加1倍,达到8K。3、中断源增加:AT89C52除了具备AT89C51的定时器/计数器T0和定时器/计数器T1,还额外增加了一个定时器/计数器T2。"], "question": "89c51和89c52的区别", "segmented_answers": [["第", "一", "，", "ROM", "89", "c", "51", "是", "4K", "，", "但", "89", "c", "52", "是", "8K", "。", "第", "二", "，", "RAM", "89", "c", "51", "是", "128", "字节", "，", "但", "89", "c", "52256", "字节", "。", "1", "、", "RAM", "空间", "增大", "：", "AT", "89", "C", "51", "有", "128", "字节", "的", "内部", "RAM", "，", "AT", "89", "C", "52", "的", "内部", "RAM", "扩展", "为", "256", "字节", "；", "2", "、", "内部", "FLASH", "变", "大", "：", "AT", "89", "C", "51", "有", "4K", "字节", "的", "内部", "FLASH", "PERAM", "，", "而", "。", "AT", "89", "C", "52", "的", "内部", "FLASH", "PERAM", "增加", "1", "倍", "，", "达到", "8", "K", "。", "3", "、", "中断", "源", "增加", "：", "AT", "89", "C", "52", "除了", "具备", "AT", "89", "C", "51", "的", "定时器", "/", "计数器", "T0", "和", "定时器", "/", "计数器", "T1", "，", "还", "额外", "增加", "了", "一", "个", "定时器", "/", "计数器", "T2", "。"]], "answers": ["第一，ROM89c51是4K，但89c52是8K。第二，RAM89c51是128字节，但89c52256字节。1、RAM空间增大：AT89C51有128字节的内部RAM，AT89C52的内部RAM扩展为256字节；2、内部FLASH变大：AT89C51有4K字节的内部FLASHPERAM，而。AT89C52的内部FLASHPERAM增加1倍，达到8K。3、中断源增加：AT89C52除了具备AT89C51的定时器/计数器T0和定时器/计数器T1，还额外增加了一个定时器/计数器T2。"], "answer_docs": [0], "segmented_question": ["89", "c", "51", "和", "89", "c", "52", "的", "区别"], "question_type": "DESCRIPTION", "question_id": 91206, "fact_or_opinion": "FACT", "match_scores": [0.8600682593856654]}
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"支付宝", "提供", "的", "交易", "号码", "(", "支付", "号", ")", ",", "以便", "日后", "核对", "和", "查询", "付款", "。"], ["了解更多", "出国留学资讯", ">", ">", ">", "留学资讯"], ["世界", "大学", "排名", "汇总"]], "paragraphs": ["2017年AP考试各科时间安排及报考须知来了!", "当前所在位置:广州新东方学校>>>出国留学>>>留学资讯", "AP,全称Advanced Placement,中文名称为美国大学预修课程,适用于全球计划前往美国读本科的高中生。由美国大学理事会(The College Board)主持,AP成绩不但可以抵扣成功申请美国大学入学后相应课程的学分,而且AP成绩也成为美国各大学录取学生的重要依据。", "目前,已有40多个国家的近3600所大学承认AP学分为其入学参考标准,其中包括哈佛、耶鲁、牛津、剑桥等世界名牌大学。", "2017年AP考试已于12月12日0时起针对AP学校在校学生开放注册报名(中国大陆地区);社会考生报名会在AP学校在校学生报名基本完成后,于2017年1月2日开放注册报名。", "社会考生定义:如果以下任何问题您的回答是YES,您就属于非AP学校考生,即社会考生:", "1.我是一名高中在校生,我的学校不提供AP课程。 2.我的学校为AP学校,但是我不在AP课程班,没有上学校的AP课程。 3.我已经高中毕业,还未申请或进入大学学习,如:休息一年。 4.我目前在中国大陆的大学就读,但计划去中国大陆以外的大学留学。 5.我是在家自学或者没有上学。", "2017年AP考试时间表", "2017年AP考试报考须知", "一、考试时间  2017年AP考试时间:5月1日—5月12日  2017年AP考试延迟考试时间:5月17日和5月18日 二、报名时间 三、开考科目  2017年开考科目及各科目的考试时间详见2017年AP考试时间表。四、考试费用  AP考试每科费用为1350元人民币(涨价了)。五、报名要求  在中国大陆地区(不含香港、澳门及台湾),只允许参加了AP课程全程学习的学生报名参加AP考试。", "参加AP考试的考生,年龄在考试当年5月31日不满21岁的方可报名。", "报名时,考生须确认所提交全部信息的真实性和准确性,如与实际证件上的信息不符,将导致考生无法参加AP考试。", "六、身份证件要求", "考试当日,考生如未携带报名时使用的证件或携带证件与报名时提交的不符,将被拒绝进入考场,考试费不予退还。", "中国大陆AP考试对身份证件的规定如下:中国大陆考生参加AP考试,必须携带唯一有效的身份证件即“中华人民共和国居民身份证”原件。根据《中华人民共和国身份证法》,任何居民无论是何年龄,均可向户籍所在派出所申领身份证。特别提醒未申领身份证的青少年考生提前办理,以免影响考试。注意:证件上的姓名和生日等个人信息必须与报名信息完全一致。确保证件在有效期内。", "七、网上报名指南", "详见http://www.apchina.net.cn/Home/RegisterFlow", "八、网上支付考试费", "自2016年起,AP考试实行网上缴纳考试费。目前提供的支付方式共有两种:首信易支付(支持具有网上支付功能的银行卡支付)或者支付宝。如果使用支付宝支付,记得记录下支付宝提供的交易号码(支付号),以便日后核对和查询付款。", "了解更多出国留学资讯>>>留学资讯", "世界大学排名汇总"], "bs_rank_pos": 4}, {"is_selected": false, "title": "2017年AP考试各科时间安排及报考须知 _搜狐教育_搜狐网", "most_related_para": 3, "segmented_title": ["2017", "年", "AP", "考试", "各科", "时间安排", "及", "报考须知", "_", "搜狐", "教育", "_", "搜狐网"], "segmented_paragraphs": [["文章", "阅读"], ["2017", "年", "AP", "考试", "将", "于", "12", "月", "12", "日", "0", "时", "起", "针对", "AP", "学校", "在校学生", "开放注册", "报名", "(", "中国", "大陆地区", ")", ";", "社会", "考生", "报名", "会", "在", "AP", "学校", "在校学生", "报名", "基本", "完成", "后", ",", "于", "2017", 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2016年12月12日——2016年12月31日 社会考生 2017年1月2日——2016年1月17日 三、开考科目 2017年开考科目及各科目的考试时间详见2017年AP考试时间表。 四、考试费用 AP考试每科费用为1350元人民币(涨价了)。 五、报名要求 在中国大陆地区(不含香港、澳门及台湾),只允许参加了AP课程全程学习的学生报名参加AP考试。 参加AP考试的考生,年龄在考试当年5月31日不满21岁的方可报名。 报名时,考生须确认所提交全部信息的真实性和准确性,如与实际证件上的信息不符,将导致考生无法参加AP考试。", "六、身份证件要求", "考试当日,考生如未携带报名时使用的证件或携带证件与报名时提交的不符,将被拒绝进入考场,考试费不予退还。 中国大陆AP考试对身份证件的规定如下:中国大陆考生参加AP考试,必须携带唯一有效的身份证件即“中华人民共和国居民身份证”原件。根据《中华人民共和国身份证法》,任何居民无论是何年龄,均可向户籍所在派出所申领身份证。特别提醒未申领身份证的青少年考生提前办理,以免影响考试。注意:证件上的姓名和生日等个人信息必须与报名信息完全一致。确保证件在有效期内。", "七、网上报名指南详见 http://www.apchina.net.cn/Home/RegisterFlow", "八、网上支付考试费:自2016年起,AP考试实行网上缴纳考试费。目前提供的支付方式共有两种:首信易支付(支持具有网上支付功能的银行卡支付)或者支付宝。如果使用支付宝支付,记得记录下支付宝提供的交易号码(支付号),以便日后核对和查询付款。返回搜狐,查看更多", "声明:本文由入驻搜狐号的作者撰写,除搜狐官方账号外,观点仅代表作者本人,不代表搜狐立场。", "留学必备书籍免费领取啦 免费领取猴哥词汇白金教材, 翻倍提升效率,翻倍提升效果。", "美国SAT封闭保分班 美国当地现学现考,1个月冲刺SAT1500分,参加北美8月SAT考试!"], "bs_rank_pos": 5}, {"is_selected": false, "title": "2016年AP考试时间表 - 小马过河", "most_related_para": 5, "segmented_title": ["2016", "年", "AP", "考试", "时间表", "-", 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雕润眉是完美医学纹绣为载体,根据人的眉毛生长而雕刻出来的眉形,根据肤色来选料,这样的眉形看起来很靓丽好看,与传统的绣眉方法相比,雕润眉是如丝发般的精细雕刻眉毛,具有三维立体感,而且纹眉后不痛不肿,不脱色,不变色,不晕色,一般半个小时就能完成的绣眉。》》》99.99%人都关注的绣眉结痂问题 2、平面绣眉 平面绣眉是传统的绣眉方式,是通过过将复合自身形象的图案纹绘在眉毛上,经过改良而形成的平面绣眉,是标准眉型图案,颜色深浅平均,跟一般的眉形没太大区别。 3、点状绣眉 点状绣眉是独具设计性的绣眉,是一种改良的损伤最小的平面绣眉,需要用针点上药水刺其眉毛的毛囊口着色,稍微比较痛,看上去的眉毛比较粗,适合眉形好,眉毛少的女生。 4、立体绣眉 立体绣眉是利用排针,一排12只,运用不同的针法衔接在眉毛上,绣出二种深浅不一的咖啡色,这样的眉形看起来很自然立体,具有长短不齐、粗细不一的毛发线条特点,,让MM看起来更秀气漂亮。 5、仿真立体绣眉 仿真立体绣眉是比较流行的绣眉方式哦,是利用排针,一排12只,运用不同的针法进行绣眉,以咖啡色为基础,这样的眉形看起来很自然动态美。", "绣眉的五种基本方法,选择适合自己的绣眉方法,打造出秀气漂亮的眉形哦。", "(想知道自己适合什么发型吗?微信添加公众号爱靓网,发型师为您解答!)"], "bs_rank_pos": 0}, {"is_selected": false, "title": "绣眉有哪几种常见方法?绣眉一般要多少钱【图】_彩妆资讯百科_美容_", "most_related_para": 3, "segmented_title": ["绣眉", "有", "哪", "几", "种", "常见", "方法", "?", "绣眉", "一般", "要", "多少", "钱", "【", "图", "】", "_", "彩妆", "资讯", "百科", "_", "美容", "_"], "segmented_paragraphs": [["2016", "-", "09", "-", "30", "10", ":", "04"], ["对于", "美", "的", "追求", "是", "很多人", "的", "天", "性", ",", "对于", "爱美", "的", "女生", "来", "说", "好看", "的", "眉", 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["六月", "护肤", "爱", "用品", "春季", "护肤", "五", "款", "高", "颜值", "萌", "物", "开启", "焕肤", "撩", "春", "模式", "实用", "百搭", "口罩", "妆容"], ["包贝尔", "新", "剧", "曝光", "表情", "冷", "姜至鹏", "出轨", "被曝", "私密", "图", "咘", "咘", "公园", "跳", "钢管舞", "萌", "翻", "白百何", "学", "龅牙兔", "神", "表情", "黄晓明", "胖", "了", "一", "圈", "曝", "真相", "周冬雨", "再", "出", "表情包", "搞笑"]], "paragraphs": ["2016-09-30 10:04", "对于美的追求是很多人的天性,对于爱美的女生来说好看的眉型不但能提升气质,还能修饰脸型,绣眉也早已成为女生完善美化眉形的一种方式。绣眉有哪几种常见方法?一般绣眉得花多少钱?一起和小编了解一种吧。", "绣眉有哪几种常见方法?一起来了解下:", "A、平面绣眉:这是一款传统的绣眉方式,是以标准的眉型为图案,通过绣眉技术纹绘在眉毛上,色泽均匀上色,给人的感觉就好像刚刚画完眉毛一样。 B、点状绣眉:点状绣眉相对来说,会更有设计性。通过药水的渗透,将有色的染料对这眉毛的毛囊口进行点刺上色,还能按照眉毛的整体轮廓勾勒出眉型,不过眉毛过粗的美眉们则不适合点状绣眉喔! C、仿真立体绣眉:这种绣眉方式是利用排针和不同的针法衔接在眉毛上,出来的眉形效果是一把一把的形状,而且常用的色料是深浅不一的咖啡色来呈现,在着色上后的眉形效果就会像自己长出来的眉毛一样自然好看。 D、立体绣眉:立体绣眉也是利用排针来进行纹绣,采取了由浅至深的形式绣出眉形,而整体的效果会呈现出雾状,非常地有立体感。", "绣眉一般要多少钱?", "一般来说,绣眉的价钱在800到5000元之间,每家的医美诊所价格都是不同,会因规模以及技术相互关联。绣眉使用的材料也是决定价格的高低,一般分为有机色料及无机色料,以及绣眉的方式也是能决定价格的关键喔!由于绣上的眉型都是因人而异的,也会导致绣眉价格的差异。", "想必经过了小编的介绍,大家都对绣眉有哪几种常见方法?以及其价格有了一定的了解!如果你也想眉形好看也不妨尝试一下绣眉,选择好的技术以及医美医院才能保证绣眉的完美效果喔!(图片来源:视觉中国)", "(责任编辑:陆薇)", "全世界都在安利的“举重妖精”李圣经,凭什么成为男神收割机? 同是新疆美女 为啥迪丽热巴观众缘最好? 从晴儿到楚乔 赵丽颖是怎么越来越美的? 滨崎步发福or水肿?3分钟快速消水肿  学刘诗诗掀起刘海 气场2米8一点也不难", "梦幻的粉嫩无瑕肌 怎能不被吸引? 怀疑自己画了个假妆?那是用错了气垫  水润透亮 让肌肤呼吸一整天  伊夫黎雪植物焕颜抚纹系列  倒春寒 给肌肤加个完整的保湿屏障", "周末好心情 慵懒  露天趴音乐节妆容", "六月护肤爱用品 春季护肤五款高颜值萌物 开启焕肤撩春模式 实用百搭口罩妆容", "包贝尔新剧曝光表情冷 姜至鹏出轨被曝私密图 咘咘公园跳钢管舞萌翻 白百何学龅牙兔神表情 黄晓明胖了一圈曝真相 周冬雨再出表情包搞笑"], "bs_rank_pos": 1}, {"is_selected": false, "title": "绣眉有哪几种_百度知道", "most_related_para": 0, "segmented_title": ["绣眉", "有", "哪", "几", "种", "_", "百度", "知道"], "segmented_paragraphs": [["平面", "绣眉", "平面", "绣眉", "是", "最", "为", "传统", "的", "绣眉", "方法", "。", "这种", 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"一、实习目的1、通过毕业实习,将理论高度上升到实践高度,更好的实现将大学期间所学的理论和实践的结合,更进一步加深对理论知识的理解,了解和掌握实际生产中的生产流程、工艺原理和技术要求,为今后学习和实际工作打下良好基础。2、培养自己善于观察、勤于思考的良好的学习习惯以及严谨的科学态度和实际动手能力,使理论与实践得到很好的结合。3、通过本次实习使我能够亲身感受到由一个学生转变到一个职业人的过程,进一步了解社会,增强对社会主义现代化建设的责任感、使命感,为离开学校、走向社会、适应社会、融入社会作好充分准备。4、本次实习对我完成毕业设计和实习报告起到很重要的作用。二、实习内容1、工厂简介赛闻工业有限公司成立于1997年,为香港与日本的合资公司,注册资本1亿港币。拥有一批专业技术人员和管理人员。做为整体运输包装方案提供商,赛闻公司主要生产加工纸、木、塑料以及多种材料复合的工业包装制品。经过多年的发展,赛闻工业有限公司现已在天津经济技术开发区、武清区和苏州分别建立工厂,并在北京、上海、南京、杭州、常州和南通设立了物流中心。随着国际业务的拓展,赛闻又在香港、加拿大注册公司、并在德国设立办事处。目前赛闻已拥有空客、丰田、摩托罗拉等近二百家稳定客户。 TEDA工厂总面积40000平方米,建筑面积14000平方米,员工总数为300人。 武清工厂(在建)总面积 40000平方米,建筑面积12000平方米。 苏州工厂总面积11000平方米,建筑面积 8300平方米,员工总数为100人。 华北地区主要客户有:Motorola, Toyota, Siemens, Simclar, Sew, Trw, Danfoss, Lohmann, Danaher, Woodward, Foxconn, Laird, Canon, Sanyo, Stanley & Denso. 华东地区主要客户有:Flextronics, Motorola, Sanmina, FCI, SEW, Danfoss, Benchmark NEC & Alcatel-Lucent.", "1、工厂简介赛闻工业有限公司成立于1997年,为香港与日本的合资公司,注册资本1亿港币。拥有一批专业技术人员和管理人员。做为整体运输包装方案提供商,赛闻公司主要生产加工纸、木、塑料以及多种材料复合的工业包装制品。经过多年的发展,赛闻工业有限公司现已在天津经济技术开发区、武清区和苏州分别建立工厂,并在北京、上海、南京、杭州、常州和南通设立了物流中心。随着国际业务的拓展,赛闻又在香港、加拿大注册公司、并在德国设立办事处。目前赛闻已拥有空客、丰田、摩托罗拉等近二百家稳定客户。 TEDA工厂总面积40000平方米,建筑面积14000平方米,员工总数为300人。 武清工厂(在建)总面积 40000平方米,建筑面积12000平方米。 苏州工厂总面积11000平方米,建筑面积 8300平方米,员工总数为100人。 华北地区主要客户有:Motorola, Toyota, Siemens, Simclar, Sew, Trw, Danfoss, Lohmann, Danaher, Woodward, Foxconn, Laird, Canon, Sanyo, Stanley & Denso. 华东地区主要客户有:Flextronics, Motorola, Sanmina, FCI, SEW, Danfoss, Benchmark NEC & Alcatel-Lucent.企业文化:(1)愿景:2020年成为中国整体运输包装解决方案的领先者。(2)使命:追求理性成长,百年经营,在员工、客户、社会责任间求取平衡及满足。(3)核心价值观: 互信:坚持互信原则,与员工、客户之间建立长久信任的关系。 快速反应:迅速理解并满足市场、客户及员工的需求。 专业:专注于整体运输包装解决方案和产品,并不断创新保持行业优势。永续承诺:公司及员工个人均应坚守百年经营之承诺。专业研发,优质设计:通过各种展会及国际交流,掌握整体包装的前沿技术和先进材料,不断优化产品;我们通过考察分析客户产品的外观、品质、保护等级,操作贮存情况,运输及环境因素等方面,向客户提供全面的运输包装解决方案,力争为客户提供优质合理的设计,并持续改进。严格品控,先进管理:赛闻将产品的质量视为企业的生命力,严格控制生产过程中的各个环节;我们是中国包装行业精益管理及JIT、VMI的先行者,拥有多年全国跨地区运营经验,在发展中不断提升能力、改善流程,与客户共同成长。快速反应,灵活服务:赛闻具有7×24小时的快速响应速度,充分满足客户精益生产模式的要求,提供JIT服务,公司的车辆都安装了GPRS系统,对运输环节实现实时电子监控。我们还可以根据客户的需要,选派经验丰富的技术工程师,上门为客户提供标准的现场包装服务。整体方案,绿色包装:赛闻优化整合了木质、纸质、塑料、金属、发泡、防锈等各种材料和技术手段,避免包装不足和包装过度;秉承绿色环保理念,在包材、设计和生产中贯彻减量、循环、可回收的方针,尽可能使用环保材料,设计折叠式包装减少空载,提高材料的利用率,减少资源浪费。技术优势:拥有多名包装专家和大量技术研发人员,现有较为完善的管理机制,有很完整的包装理论基础。设备优势:拥有环压测试仪、耐破试验仪戳穿试验、仪压力试验机、RoHS:能量色散X荧光光谱仪 、烘干箱、水分测定仪、计量器具等,从小批量到大批量均能自如生产,无最小订单限制。2、具体实习情况经多次与公司协商,我终于得到该公司允许,有幸参观了公司并在该公司进行了为期四周的实习。进入该公司,首先了解到公司机构包括业务部、生产部、财务部、采购部、管理部、技术部六大部门。据业务部介绍,现业务工作主要分为两部分,一部分是跟踪现有客户的货单情况,特别大客户每天的下单、出货、质量情况。另一部分是开发新客户,根据现有的资料向相关企业介绍公司产品,推销产品。我觉得该公司的业务部分工很合理,因为有稳定的客户的时候,发展新客户也是必要的。但是新客户的开发需要制造相关的模具,制造模具费用大,然而该客户有很多不稳定的因素,未能确保长期合作,与其把资源投入不确定的业务上,不如把更多的资源改造设备或者是与原来的客户打好关系谋求更长远的合作。所以企业的工作方针政策是由每个企业的实际情况需要决定的,并非麻木照搬。生产是该公司的基础部门,据生产部员工介绍,生产种类比较多,比较杂。只要是设计定型的产品,生产部就要负责把能生产的部分生产出来,然后把成品放入仓库,等待业务部人员安排出货。技术部门的主要工作是设计整体包装,是公司的主要核心部分。根据客户的资料、要求设计产品包装。在设计的过程中,为了使设计的更为符合客户要求,技术部门经常需要和客户进行面对面的沟通,或去客户的公司看实际产品,进行现场尺寸测量。同时,技术部的工作也包括机械的检修、维护、调整。技术人员的专业知识水平相对要求是比较高的,肩负的责任也是比较大的,我建议该公司能经常组织员工参与各种培训课程,提高生产能力。对工厂情况有所了解后,我在生产部和技术部分别进行了一段时间的实际操作。在生产部,我按照师傅的指导,开关机器,在必要的时候急停机器,在需要的时候按动相关按钮。我不得不承认我实践动手能力差,经常出错,有时还会耽误其他工序的进行,虽然如此,师傅还是很耐心的为我指出错误原因,分析改正方法,并为我多次示范操作。果然,使用师傅传授的妙招,我操作进步的很快,一步步追上了其他人的操作,没有再耽误生产的正常运行。在技术部,我扎实的理论知识和熟练使用的CAD派上了用场,我根据师傅给的图纸和客户要求,将资料在CAD图中显示出来,将我做的图打印出来后交给生产部,他们便可以照常生产了。技术部的同事们都对我很友好,经常给我更有效工作的建议,使我受益匪浅。通过对水利工程的现场参观和技术人员的讲解,了解我国及山东省治水的历史;了解水利在国民经济中的重要性;了解传统水利与现代水利、可持续发展水利的含义;了解已建和在建的著名的水利工程;了解水利工程的运用情况和除险加固情况;熟悉土石坝、水闸、渡槽、渠道、倒虹吸、堤防等水工建筑物工程的作用、布置、组成及对当地经济发展的影响;了解工程施工及组织管理情况和招、投标的情况。使同学们增加水利工程的感性认识,巩固学生的专业思想,让学生领会专业的培养目标,为下一步学习专业知识打下良好的基础。通过实习,还培养学生热爱祖国水利事业、乐于从事水利工程建设的决心和信心。(1) 听有关水利形势的报告。(2) 观看录像,了解我国及山东治水的历史及水利发展的情况,以及我国著名的水利工程情况。", "(1) 听有关水利形势的报告。(2) 观看录像,了解我国及山东治水的历史及水利发展的情况,以及我国著名的水利工程情况。(3) 参观世纪工程:工程有:", "①日照水库:枢纽工程的建设背景、工程的作用、水工建筑物的组成情况,工程存在的问题及除险加固情况和工程的综合运用管理情况,了解该工程对日照当地经济的影响,了解工程对周围环境的影响,了解工程对技术人员的业务要求和综合能力要求。"], "bs_rank_pos": 2}, {"is_selected": false, "title": "实习目的_毕业生实习目的意义", "most_related_para": 0, "segmented_title": ["实习", "目的", "_", "毕业生", "实习", "目的", "意义"], "segmented_paragraphs": [["酒店", "的", "实习", "目的", "与", "任务", "政府", "的", "实习", "目的", "与", "意义", "公安", "的", "实习", "的", "目的", "与", "任务", "支教", "的", "实习", "目的", "与", "意义", "关于", "乡镇", "的", "实习", "目的", "与", "任务", "工厂", "的", "实习", "目的", "财务", "的", "实习", "目的", "护士", "的", "实习", "目的", "证券", "的", "实习", "目的", "助理", "的", "实习", "目的"], ["地质学", "专业", "实习", "目的", "中学语文", "教育", "实习", "计划", "毕业", "实习", "目的", "是", "什么", "?", "大学生", "毕业", "实训", "目的", "工程地质", "实习", "目的", "与", "实习", "要求", "生产", "实习", "目的", "测量学", "实习", "目的", "和", "实习", "要求", "会计", "实习报告", "目的", "实习", "目的", "特点", "和", "意义", "财务会计", "实习", 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(400-887-3868,创立于1995年,广东省著名商标,旅行箱包热销品牌,以其新颖的款式和优质的品质闻名于业内,爱华仕运动用品有限公司) 【品牌招商】 [产品21] [网点8] [新闻15] [网店18]", "DELSEY大使 (大使(东莞)箱包有限公司)  (400-888-1946,始于1946年,源于法国的顶级箱包品牌,欧洲著名旅行箱包品牌,全球首创可移动行李箱品牌,大使(东莞)箱包有限公司) [产品11] [网点8] [网店7]", "雅士EMINENT (安迈特提箱(东莞)有限公司)  (06-2303952台湾,于1979年台湾,国际知名箱包品牌,以制造专业旅行箱起家,世界旅行箱大型生产商,安迈特提箱(东莞)有限公司) [产品7] [网点3] [新闻2] [网店5]", "ACE爱思 (爱思集团(中国)有限公司)  (800-820-3008,始于1940年日本,其首创轮式硬箱设计享誉国际,专业从事各类箱包产品的设计/研发/生产/销售的大型企业,爱思集团(中国)有限公司) 【品牌招商】 [产品10] [网点6] [新闻2] [网店2]", "以上数据名单由几十项数据统计计算系统自动生成,排序不分先后,仅提供给您作参考。 更多箱包-拉杆箱行业品牌等级>>", "Samsonite新秀丽 (400-8870-566,始于1910年美国,国际著名箱包品牌,旅行用品领域历史悠久的品牌,黑标Vintage系列是该品牌的极品,新秀丽(中国)有限公司)  外交官Diplomat (400-820-6065,1971年台湾,享誉全球的国际化箱包品牌,专业生产拉杆箱/旅行箱等各类高级旅行用品的企业,天盈贸易(上海)有限公司)  AmericanTourister (4008-870-566,始于1933年美国,新秀丽旗下国际著名箱包品牌,较早被主要航空公司选用的箱包品牌,新秀丽国际贸易(宁波)有限公司)  皇冠Crown (021-36528001,始于1952年台湾,享誉全球的经典品牌,箱包领域极具规模的的制造专家,致力于传输皇冠概念,强调国际流行色,上海罗杰旅游用品有限公司)  RIMOWA日默瓦 (010-82250905,始于1898年,世界著名箱包品牌,以坚固/耐用/轻巧著称的旅行箱品牌,轻巧旅行箱的先驱,秉臣联合贸易(北京)有限公司)  DELSEY大使 (400-888-1946,始于1946年,源于法国的顶级箱包品牌,欧洲著名旅行箱包品牌,全球首创可移动行李箱品牌,大使(东莞)箱包有限公司)  爱华仕OIWAS (400-887-3868,创立于1995年,广东省著名商标,旅行箱包热销品牌,以其新颖的款式和优质的品质闻名于业内,爱华仕运动用品有限公司)  雅士EMINENT (06-2303952台湾,于1979年台湾,国际知名箱包品牌,以制造专业旅行箱起家,世界旅行箱大型生产商,安迈特提箱(东莞)有限公司)", "达派dapai (0595-27302222,福建省著名商标,福建省名牌产品,集箱包产品设计/研发/生产及销售为一体的企业,达派(中国)箱包有限公司)  Samsonite新秀丽 (400-8870-566,始于1910年美国,国际著名箱包品牌,旅行用品领域历史悠久的品牌,黑标Vintage系列是该品牌的极品,新秀丽(中国)有限公司)  AmericanTourister (4008-870-566,始于1933年美国,新秀丽旗下国际著名箱包品牌,较早被主要航空公司选用的箱包品牌,新秀丽国际贸易(宁波)有限公司)  Adidas阿迪达斯 (400-880-1515,于1949年德国,全球顶级运动用品制造商,以三道平行间条为品牌标志,三叶草系列是其经典产品,阿迪达斯体育(中国)有限公司)  Nike耐克 (400-880-6453,始于1972年美国,全球著名的体育运动品牌,全球首创气垫技术,以小钩子的品牌形象著称,耐克商业(中国)有限公司)  DELSEY大使 (400-888-1946,始于1946年,源于法国的顶级箱包品牌,欧洲著名旅行箱包品牌,全球首创可移动行李箱品牌,大使(东莞)箱包有限公司)  威豹Winpard (800-830-1739,成立于1988年,广东省著名商标,高品质箱包提供商,专业从事箱包研发/设计/生产/营销的企业,广东威豹实业有限公司)  爱华仕OIWAS (400-887-3868,创立于1995年,广东省著名商标,旅行箱包热销品牌,以其新颖的款式和优质的品质闻名于业内,爱华仕运动用品有限公司)"], "bs_rank_pos": 0}, {"is_selected": false, "title": "十大拉杆箱品牌", 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Lovateam(樂福添)樂福添箱包設計師全來自國內;美國;法國;意大利;德國;韓國等多個國家和地區;他們以高端;時尚;健康爲設計理念;以最前沿思想創作美感;用形色;釋時尚;借技術制造品質;高雅不失時尚;簡約不減材料;所有産品都符合人體形態學和工業設計學;一一;外觀線條流暢;結構布局考究;選材安全可靠;産品深受商務人士和年輕一族的喜愛和信賴;其中;樂福添;絢彩;系列;更堪稱是國內唯一刮不傷;摔不破;踩不碎;五萬公裏磨不爛;萬向輪靜音王;PP環保材料拉杆箱;第二位:RIMOWA(日默瓦)1898年創立的Rimowa,百年來堅守德國傳統工藝設計,獨特的鋁鎂合金及防彈塑膠材質,輔以精確的手工製作品質,讓Rimowa成為「德國製造」的完美精工典範,亦是全球知名的高階精品旅行箱頂級品牌。Rimowa已在數不清的好萊塢電影中出現,多被用來裝載精密儀器與貴重物品,其堅固而輕便的口碑,簡約耐看的百褶溝槽設計,深受各界時尚名流與明星藝人的青睞。藝人羅志祥與大S都是Rimowa愛用者,其他如王菲、S.H.E、隋棠、孫芸芸等也都人手至少一咖呢!Rimowa已進入無數個國家,且在部分國家,例如日本,Rimowa已是豪華精品的市場領導品牌。現在香港的朋友想在出行時用上它,可以不用花好幾千塊去購買,只要付出幾百元租金就可使用最多15天,詳情可向RIMOW@RENTAL查詢。希望成為全世界人們最想「掌握」的旅行同伴。第三位:Louis Vuitton(路易威登)來自法國的Louis Vuitton,是身分地位的象徵與時尚精品的指標,其出品的古董旅行箱一直是收藏家的最愛。1854年以旅行箱起家的Louis Vuitton(路易威登),推崇旅行風尚的概念,至今擁有近160年的悠久歷史,足以作為經典代表的箱包品牌。據說,從沉沒的鐵達尼號裡打撈上來的LV旅行皮箱,不僅滴水未進,裡頭物品也保存完好,是Louis Vuitton傳奇性的故事之一。2010年11月,Louis Vuitton更出版了《100 Legendary Trunks》,載述LV經典特色旅行箱的歷史軌跡,可謂集Louis Vuitton大成的回憶錄。第四位:Samsonite(新秀麗)1910年發源於美國,如今Samsonite已是聞名國際的專業級旅行用品品牌,被譽為行李箱的第一選擇。當年它出品的是木質衣箱,在那個以車馬為主要工具的年代,新秀麗是理想的行李箱,現在則是世界上最專業的行李箱品牌之一。今日,Samsonite的新口號──“Life’s a Journey”(盡享人生旅程),象徵Samsonite能夠融入旅行人士的不同生活範疇。邁入嶄新紀元的Samsonite,運用百年經驗,將不同的創新材質及科技融入設計裏,繼續秉承品牌實用性及靈活性的優點,並以時尚及摩登的外型取代一向給人「穩重及保守」的旅行箱觀感,贏得了世界各地消費者的喜愛和認同。第五. 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"拉杆箱什么牌子的好?拉杆箱品牌有哪些?拉杆箱排名有哪些好的牌子?下面让我为大家介绍十大拉杆箱品牌排行榜吧。", "十大拉杆箱品牌排行榜", "皇冠箱包属于中山皇冠皮件有限公司,其实亚洲最大的箱包生产基地,1952年诞生在台湾,80年代CROWN 成为享誉全球的经典品牌,箱包产品畅销海内外40多个国家,每一项产品皆由日本、美国及欧洲各知名设计师设计,无论哪方面均甚称世界一流。", "乐福添箱包有限公司是一家位于广东佛山的拥有12年外销经验的优秀。公司产品系列齐全,10年外销北美经验让我们对质量有着严格的控制体系与流程,同时我司始终坚持客户第一,品质先行的原则。公司专业生产高端拉杆箱、环保PP旅行箱、万向轮旅行箱,真皮时尚包袋等。", "雅士品牌由万国通路股份有限公司创立于1979年,现已发展成为业界最大的,世界级的ABS、PP及聚酯类软袋类设计、制造及销售公司,雅士是台湾箱包第一品牌,名列全球三大箱包生产企业之一,是德、日等国的第一品牌,拥有提箱王国的地位。", "威豹品牌拉杆箱系列均是采用进口高级布料及高品质五金精致而成,于2001年导入ISO9001国际质量管理体系,现在“威豹牌”系列产品远销欧洲、美国、中东、东南亚、日本等国家和地区,“威豹”已成为中国市场著名品牌。", "森象拉杆箱是广州森茂皮具有限公司旗下的品牌,拉杆箱十大品牌,箱包十大品牌,中国箱包行业的标杆企业之一,国内箱包行业的领先品牌,集箱包产品设计、研发、生产及销售为一体的综合性企业。国内最早从事拉杆箱制作的品牌之一。", "外交官品牌自1971年创立于台湾,是专业生产ABS、EVA等材质的包括拉杆箱、旅行箱、化妆箱等品种在内的各类高级旅行用品,其产品行销遍及全球等三十多个国家和地区,是世界三大品牌之一,其中内销占30%外销占70%。", "广东啄木鸟皮具有限公司自2001年开始全权负责啄木鸟皮具在中国市场的运营,目前遍布全国各地的连锁加盟店已达数百家,在国内的影响力处于同行业领先地位。", "达派目前是国内最先进的箱包电脑检测中心,先后通过ISO9001、ISO14001、SA8000、OHSAS18001等国际认证,荣获“中国驰名商标”、“中国知名箱包十佳品牌”、“亚洲500最具价值品牌”、“2006中国箱包十二强”、“福建名牌产品”、“2009中国箱包优秀品牌”、“2008-2009年度福建省国际知名品牌”等称号。", "广东苹果实业有限公司是一家集开发、生产、销售于一体的中型民营企业,创立于1992年,曾荣获四川省市场质量信得过品牌和畅销产品、上海第十三届“服装博览会包装大类金奖”和“浙江重点市场销量主导品牌”称号;被认定为中国企业最佳形象AAA级和“苹果”及其图案为著名商标。【金猴】威海市金猴集团皮具有限公司始建于1975年,主要从事“金猴”牌系列皮具产品的生产与销售,综合实力列2006中国箱包十二强第四位,系中国皮革工业协会副理事长单位,“金猴”牌皮具多次荣获国产畅销产品“金桥奖”,还获得“国家免检产品”、“中国名牌产品”等。", "威海市金猴集团皮具有限公司始建于1975年,主要从事“金猴”牌系列皮具产品的生产与销售,综合实力列2006中国箱包十二强第四位,系中国皮革工业协会副理事长单位,“金猴”牌皮具多次荣获国产畅销产品“金桥奖”,还获得“国家免检产品”、“中国名牌产品”等。", "韩国BB霜排行榜 抗皱面霜排行榜 电热宝品牌排行榜 梳子十大品牌排行榜 牙膏品牌排行榜 进口有机奶粉排行榜 净水器十大品牌排名 羊奶粉排行榜十强 避孕套十大名牌排名 女士沐浴露十大品牌"], "bs_rank_pos": 4}, {"is_selected": false, "title": "全球10 大顶级行李箱品牌排行榜", "most_related_para": 23, "segmented_title": ["全球", "10", "大", "顶级", "行李箱", "品牌", "排行榜"], "segmented_paragraphs": [["全球", "10", "大", "顶级", "行李箱", "品牌", "排行榜"], ["买手", "客", "Buyerkey", "11", "-", "09"], ["买手", "客", "buyerkey", "(", "微信", "号", ":", "buyerkey", ")"], 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buyerkey(微信号:buyerkey)", "更多内容请关注后查看历史消息", "\" 你周游世界,家人也不在身边,你唯一能依靠的就是你自己,还有你的行李。\" 上世纪 90 年代经典好莱坞电影《魔岛仙踪》里,经验丰富的行李箱售货员对男主角 Joe Banks 如是说。", "我们经常说,来一场说走就走的旅行,离开城市,离开朋友,但是我们离不开的是旅行箱。经常出国尤其是法国、意大利等欧美国家,你就会看到那些老外拎着各种各样的经典款式的旅行箱,而在国内机场,大多是款式颜色材质相似的商务旅行箱。对旅行箱来说,一定的 \" 颜值 \" 是必须的,除了 \" 颜值 \" 还要有内涵。", "懂生活,爱旅行——世界顶级的旅行箱排行榜今天开讲!", "(《买手客》开通了评论功能,你更喜欢哪一个呢,可以拉到下面留言给小编哦。)", "品牌:RIMOWA 日默瓦", "始创于:1898 年", "RIMOWA 已经 100 多年的历史了,就连保时捷和德航也邀请 RIMOWA 为它们进行专属设计,铝镁合金材质已经成为 RIMOWA 的代名词,Topas 系列是它家的经典必败当家款,而 Topas Titanium 系列属于高调高富帅版。它的价钱在旅行箱里算得上是 \" 奢侈品 \" 了—入门级的也要 4000 元人民币,最贵的系列能卖到 1.3 万元。", "RIMOWA 的成功并非偶然,它前后植入过 200 多部影视作品,从好莱坞大片到热播美剧,《碟中谍》《史密斯夫妇》、《007》等好莱坞大片都有过 Rimowa 的身影。", "它更是明星的标配,汤姆 · 克鲁斯、莱昂纳多、王菲、范冰冰、梁朝伟、黄晓明,每人至少不下三个 RIMOWA。就最近的说,《花儿与少年》、《花样姐姐》、《爸爸去哪儿》三部真人秀,RIMOWA 扎堆。", "\" 他们有,我也要有 \",自此 Rimowa 一火冲天,2013 年,Rimowa 的全球销售额增长了 40%,其中 10%是来自中国的贡献。Rimowa 的成功在于它把旅行箱变成了像女士手包那样的时尚配饰。", "品牌:Adeele FENDI 阿黛勒 · 芬迪", "产地:意大利", "始创于:1920 年", "首先,Adeele FENDI 和 FENDI 是同一个品牌,之所以叫它 Adeele FENDI 是因为创始之初是由年轻的 AdeleCasagrande 开始经营,后随着 Adele 于 1925 年下嫁 EdoardoFENDI 而易名为芬迪 ( FENDI ) 。", "国内人们还比较热衷于它的手袋,对于它的行李箱还是知之甚少。阿黛勒芬迪的设计灵感源自于集装箱的纹路,条纹与沟壑的设计,能够让箱体呈现凹凸极具几何美感,将近 213 个零件。不仅成为了现代 \" 空中飞人 \" 标准配置,更 \" 秒杀 \" 一众明星潮人,成了他们出行街拍的必备法宝。", "\" 让我看你的旅行箱,我就可以知道你是什么样的人。\" —— Adeele FENDI 创始人阿黛勒 · 芬迪。", "品牌:LouisVuitton 路易威登", "始创于:1854 年", "LV 的旅行箱包已经成为一种文化符号,一句 \" 旅行箱是您身份的象征 \" 成为了 LV 最经典的广告语。", "1854 年,LV 第一家箱包店正式开业,店铺的橱窗展示了路易的第一件作品——白杨木圆顶旅行箱(上图)。随便一只二三万的旅行箱,人们为什么就是心甘情愿地买单?", "LV 所用的主流材质是有印花的粗帆布(Canvas),外有化工材料涂层及防水压纹,这都是 LV 的专利。使用此材质的箱包都有相当的防刮、耐磨及防水功能,并易于保养打理。和别的大牌不同,LV 就是做旅行箱起家的,在设计上是过人之处的,比那些别家出的什么羊皮、牛皮的箱子要结实多了。而且,LV 箱子不怕旧,越用越耐看,越旧越经典,直到最后变成了真正专属于你自己烙印的箱子。所以,贵一点咬咬牙一跺脚买一个也是值得的。时至今日,LV 旅行箱依然恪守过去的传统,80% 的工序靠手工制作。", "之后,姜文四年磨一剑的《一步之遥》也让人们再一次将焦点集中在 LV 旅行箱上,15 只从巴黎 LV 博物馆空运过来的古董行李箱,让爵士时代的老上海更加纸醉金迷。不提《一步之遥》,也不提《色戒》、《欲望都市》中 LV 旅行箱那华丽丽的身影,明星的现实生活中也少不了 LV 旅行箱,这时候不提范冰冰都不好意思了 ↓↓↓", "品牌:Goyard", "始创于:1853 年", "世界上最昂贵的帆布旅行箱是谁?—— Goyard。比 LV 更矜贵而且更低调的牌子,它的历史比 LV 还要早上一年,价格是它 LV 还贵,堪称世界上最昂贵的帆布旅行箱。顾客包括了英国王室、美国总统和俄国贵族,时尚老佛爷 Karl 所有的旅行行头都由这个品牌 \" 包办 \"。", "Goyard 喜欢以木材和皮革为原料,坚持手工制作,1892 年推出了由亚麻线、棉线和麻线混纺而成、经久耐用的防水帆布作为箱包的外层面料。相较于皮革的轻便与耐用,面料一经推出便惊艳世人,成为了隽永的经典之作。同一时期,Goyard 现广为人知的四色手绘 Y 字 Logo 诞生。", "Goyard 的传统制作方法,以及设计风格已传承超过 160 年不变,这是小编喜欢 Goyard 的原因,新推出了 Bourget 拉杆箱(上图),从细节到整体上都传承了其自 19 世纪的风格,采用 Goyard 经典手绘印花帆布。", "这是玛丽莲 - 梦露用过的 Goyard↓↓↓", "这是Coco Chanel 用过的 Goyard↓↓↓", "品牌:Samsonite 新秀丽", "始创于:1910 年", "又一个百年历史的牌子,也是国人最熟知的行李箱品牌,没有之一,很多商场、机场都能看到它的专柜,赢得不少国人的信任,不过,客观一点说,主要是广告宣传做的也比较足,都教授金秀贤,大长腿李敏镐,小可爱宋仲基等一大票明星都代言过 Samsonite。其次是性价比很高,比较容易接受。", "2011 年 Samsonite Cosmolite 的汽车试撞广告", "作为专门生产旅行箱包的品牌可以做为你人生 \" 第一只旅行箱 \" 的最佳选择。耐用轻便,不断更新的款式更是其它品牌所不能及的,高低不同的价格定位满足各阶层需要,入门款只需 1000 多元。不过市场上这个品牌假货比较多,商场价格水分又很大,国外代购相对实在一点。", "时尚达人们一定记得 Samsonite 与鬼才设计师 Alexander McQueen 麦昆的那次伟大合作(上图),使传统的箱包设计上升到了创意无极限的高度。以大热的鳄鱼纹现身,就连金属拉链的拉链头烙印的都是 Alexander Mcqueen 自己的指印,获得欧洲多项国际性设计大奖。", "品牌:Delsey 法国大使", "始创于:1946 年", "说起Delsey 法国大使可能很多人不认识,在欧美很有名,它创造了多个第一。1970 年,Delsey 创建了世界上第一个硬壳旅行箱,1972 年,发明了世界上第一个带轮子的行李箱。Delsey 的 Helium 系列重点是轻便,广告中一个女人单手轻松让旅行箱与地面平行的画面让人印象深刻。Delsey 还拿到了很多的行李箱界的大奖。1975 年,1987 年,分别获得了奥斯卡出口奖。", "Delsey 在全球每年卖出约 400 万个行李箱,在欧洲中东和东南亚地区都是销售第一名。"], "bs_rank_pos": 5}], "answer_spans": [[357, 367]], "answer_docs": [2], "fake_answers": ["。第三位:LouisVuitton(路易威登)來自法國"], "question": "拉杆箱十大排名", "segmented_answers": [["第一位", "：", "Lovateam", "(", "樂福", "添", ")", "；", "第", "二", "位", "：", "RIMOWA", "（", "日默瓦", "）", "；", "第三位", "：", "Louis", "Vuitton", "（", "路易", "威登", "）", "；", "第四位", "：", "Samsonite", "（", "新秀麗", "）", "第", "五", "：", "American", "Tourister", "美國", "旅行者", "；", "第", "六", "：", "Delsey", "（", "法國", "大使", "）", "；", "第", "七", "：", "Antler", "（", "安特", "麗", "）", "；", "第", "八", "：", "Hermes", "（", "愛馬仕", "）", "；", "第", "九", "：", "HIDEO", "WAKAMATSU", "（", "若松秀夫", "）", "；", "第", "十", "：", "ACE", "（", "愛思", "）", "。"], ["1", "：", "皇冠", "；", "2", "：", "乐福", "添", "；", "3", "：", "雅士", "；", "4", "：", "威豹", "；", "5", "：", "森象", "；", "6", "：", "外交官", "；", "7", "：", 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A 黄帝内经学 B 阴阳五行学 C 天地日月学 D 人体经络学 正确答案 B 16、屈原投江是哪条江 金沙江 B、长江 C、珠江 D、汨罗江 正确答案 D 17、在我国一些地区,端午节要在门上插什么东西来祛毒? A 薰衣草 B 菖蒲 C 苇叶 D 荷叶 正确答案 B 18、俗有“南龙舟,北踏青”之说,因为北方少江河,所以端午节人们大都选择踏青。踏青古时又叫什么? A、郊游 B、遍游 C、踏春 正确答案 C 19、北方人一般用什么材料包粽子? A、新鲜竹叶 B、苇叶 C 乾竹叶 正确答案 B 20、踏青的习俗不仅是端午的习俗,也是什么节日的习俗. A、中秋 B、清明 C、春节 D、重阳 正确答案 B 21、粽子多是做成角形的,这是为什么? 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"美联英语,创立于2006年,目前已在全国开设直营培训中心90多家,覆盖14个省份、25个城市。注重培养学员的语言实际应用能力,从课程开发、教学服务、环境优化、互动学习等方面为学员打造全方位英语学习环境。", "华尔街英语专注于英语教学领域,提供卓有成效、富于竞争力的解决方案,帮助非英语母语国家的人们打破语言沟通障碍,实现最美好的职业生涯和最绚丽的人生体验。", "最新英语培训机构排名,2015英语培训机构排名-听力课堂  摘要: 英语网最新推出培训频道,汇总北京英语培训机构排名,提供知名的北京英语培训机构排名查询,提供北京最优秀的英语培训学校,帮助大家能够准确,快速的找到最适合自己的培训机构。", "外教一对一电话英语、行业专业老师作业一对一批改、资深专业中教一对一助教、行业精英一对一专业英语等多项增值服务乘着暑期的热浪全新上市。此次服务的全面升级,也标志着培训频道英语的教学体系更加完善,学员将享受更多优质服务。 “为答谢广大学员对培训频道英语的厚爱,凡是报名一定级别的学员均可根据自身需求,提前免费享受一项乃至多项新的服务,包括5个月外教一对一电话英语,专业老师30次写作批改,5个月一对一中教老师助教服务。”6月20日,培训频道英语教学负责人Jason在教学服务体系升级大会上明确表示。更多详情请登录培训频道英语官网: 坚持1小时课时为基准 辅以多项服务保教学质量培训频道英语全新教学服务体系升级之后,包括4人班、8人班在内的小班课程以及精英行业欧美外教一对一VIP课程依旧以一小时课时为主体课程,辅以新增的外教一对一电话英语课后复习巩固口语互动,专业老师一对一批改作业帮助学员精准写作表达,以及一对一专业助教全线跟踪,保证英语口语的学习更加高效、轻松。 “在中国,从幼儿园开始到小学、中学,乃至大学,课堂均从45-60分钟课时为主体。这也造就了国人的学习新事物的习惯需要一定时间。而语言环境对语言学习的影响也颇为深远,要学员立马从汉语环境转入英语环境需要一定时间适应,因此,培训频道英语将坚持以60分钟的课堂为主体,而电话英语将为辅助为学员们提供课后口语互动平台。", "Copyright© 2015-2016 听力课堂 www.tingclass.net 网站备案号:冀ICP备09023732号-2", "24小时咨询电话:400-688-6148", 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华尔街在中国主打高端英语培训,小班外教口语课程。优点是,环境好服务态度也不错,小班口语课程。有去过华尔街英语的人,都会了解那学习英语的环境是绝对的舒适,同时还有昂贵的学费。如果是普通工薪阶层,面对一年大几万的学费,应该会汗如雨下吧。", "必克英语 必克英语,9年在线一对一英语培训机构,在美国、加拿大、澳大利亚以及菲律宾均有自己的教研中心,2对1的教学模式 ,全程用英语与老师一对一互动,还可以绑定微信公众号,利用零碎时间预习功课,听课堂录音回放,复习功课!另外,免预约,不用跑班,不用争着抢老师,时间点到,绑定的老师就会准时打电话过来给你上课!这是他们家的体验课程http://www.spiiker.com/daily ,不妨体验一下,看是否合适!", "英孚英语 据说有48年的历史,但具体在中国开始的培训大概就是这十几年吧。与华尔街英语相同的,一样是打的高端英语培训,自身研发的教材。小编认为,英孚也算是个老牌子了,还是算比较底气足的,师资和教学质量总体是不错的。不过近几年频频爆出英孚培训的一些黑幕,或多或少有着一点点影响,小编只能说,有些英语培训做大了,监管力度也就疲软了。此机构价格也不低,大约一年是2-3万的学费。 新东方英语 创办于1993年,相信很多人都听过这个名字,当然也不乏很多人都了解,新东方不仅学英语出国,还可以学厨师炒菜。新东方在北京是十分出名,不过很多人也知道,新东方主打的是平民大众化的路线,主要是用于考试应试英语方面的,大部分课程都是大班的大课,当然近几年也有VIP小班课程,相对于大班课程的红火,小班口语课反而显得不起眼了。小编认为如果是应试方面的英语,还是蛮推荐这个学校的。其他的暂无特别出众的地方。 韦博国际英语 成立于1998年。主要针对企业和个人提供专业的高端语言培训。小编认为韦博跟英孚还有华尔街的感觉都比较像,当然教学方面的可能还是有些许差别的。小编没有做过过多了解。至于学费,韦博貌似和英孚的价格都差不很多。课程也是小班的。个人认为,如果是要选择的话,最好是亲自去听下课程,进行对比。多做了解比较好。", "阅读完“2015年北京英语培训机构最新排名”的人还浏览过:"], "bs_rank_pos": 4}], "answer_spans": [[240, 243]], "answer_docs": [4], "fake_answers": 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"也就是说,这个数字标号越大,异辛烷所占比例越大,那它是汽油的“镇静剂”,其实就意味着,标号越大,汽油越稳定,越不容易在发动机缸体内爆燃。", "所以,小总结一下,标号越高,抗爆性越高;标号越低,抗爆性也越低。", "那为什么还要分标号呢?因为啊,咱们的发动机还有一个参数,就是他的这个压缩比,什么是压缩比?其实就是发动机为了达到一定的输出功率和扭矩,给发动机设置的一个压缩汽油和空气混合气体的程度。", "汽油的辛烷值也要根据压缩比而定,因为还存在这么几个规律:就是燃油标号越高的汽油,抗爆性越强,那就得用更高的压缩比来让汽油燃烧,相反,低标号的汽油,抗爆性弱,要用低压缩比的发动机。", "但大家可以想想这么两个极端:低压缩比的如果加高标号的汽油,会有什么后果?会出现燃油的燃烧不充分,达不到燃油的燃烧点,燃烧不充分,那就很有可能会产生积碳了。", "而如果是高压缩比的加了低标号的汽油,这个时候,燃油就会爆燃,在发动机缸体内发生爆震。", "这两种情况,无论哪种都是对发动机极其不利的。", "所以,压缩比也会影响到燃油标号。那既然有这样的影响,为什么现在很多人都开始淡化压缩比的概念了?可能听咱们节目的朋友们,许多都没听说过压缩比这个概念,更别说根据压缩比判断该加几号油了,是不是也意味着不需要看压缩比了?", "其实啊,咱们现在发动机技术的发展,压缩比已经不是影响燃油标号的唯一因素了,而且通过压缩比来决定燃油的标号已经变得不那么不准确了。比如马自达的创驰蓝天发动机的压缩比能达到13,但用92号油依然没问题。", "那讲到这里,是不是已经有朋友开始困惑了,加多少号油到底是什么决定的?我加更高标号的油到底好不好呢?", "其实啊,咱们加多少标号的汽油,只需要看一个东西,就是咱们油箱盖背面的建议值。该加多少号油,听从汽车厂商给你的建议就可以了,因为厂家在生产这款车的时候,会经过一个长时间的测试,是最科学、最官方的正确标号。", "那要加更高标号的汽油吗?一般我们其实没必要加高标号的,因为并不会真的省油,也不会减少积碳的产生。", "其实,在今年全国实行国五标准后,燃油标准也升级,很多人就开始纠结,我们应该加几号油了,如果你还有这样的疑问的话,可以在咱们微信公众号中回复“加油”两个字,这篇关于加几号油的文章就在那等着你了。", "那记得有空就来咱们的微信公众号“擎风车铺”。三味每天都会在这里为你讲一段三分钟的汽车知识小语音,这里可是最为你动擎的汽车新媒体哈 ! 觉得好的话,介绍给身边的朋友,让他们也来关注一下吧!", "开车没有劲爆车载DJ怎么行?参与“无DJ不开车”活动,工体DJ免费送给您,获取方法如下:", "关注微信公众账号:擎风车铺(ID:qingfengchepu),", "留言“DJ”两个字,最新最火爆的工体DJ等你下", "发动机脏了如何清洗? 为什么后半箱油烧的那么快?", "发表我的评论", "说客是汽车人的自媒体平台,我有我的主张,申请成为说客作者", "什么是说客? 说客有什么激励政策? 如何成为说客作者?"], "bs_rank_pos": 0}, {"is_selected": true, "title": "95号汽油比92号汽油更耐用吗_百度知道", "most_related_para": 0, "segmented_title": ["95", "号", "汽油", "比", "92", "号", "汽油", "更", "耐用", "吗", "_", "百度", "知道"], "segmented_paragraphs": [["5", "号", "汽油", "比", "92", "号", "更", "有劲", "?", "答案", "是否", "定", "的", "。", "因为", "95", "号", "和", "92", "号", "只是", "辛烷值", "不同", ",", "即", "抗", "爆", "性能", "不一样", "。", "是否", "使用", "95", "号", "汽油", ",", "取决于", "汽油", "发动机", "的", "压缩比", "。", "汽油机", "的", "压缩比", "越", "大", ",", "压缩", "过程", "终", "了", "时", "气缸", "内", "混合气", "的", "温度", "和", "压力", "就", "越", "高", ",", "而", "这", "大", "大", "加速", "了", "未", "燃", "混合气", "中", "过氧化物", "的", "生成", "和", "聚集", ",", "使", "其", "更容易", "自燃", ",", 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上文提到过,汽油标号是由辛烷值决定的,那么实际要根据发动机的压缩比来合理选择汽油标号, 所以92的车加95的汽油,如果汽车的压缩比很低,不但对发动机充分燃烧有影响,而且厂家 错误使用油品,还会减少发动机的寿命。 误区三,95#发动机的积碳更少 其实这也是一个误区,95号汽油和92号在对发动机积碳方面并无显著差异。发动机积碳主要是由于发动机长期使用或者是长期在低速下行驶,导致积碳的产生,积碳会使发动机抖动,动力减弱。减少积碳的有效办法就是使用合适的燃油清洁剂,可以有效清除积碳。加更好标号的汽油是确定不会减少积碳的。 误区四,95#汽油动力更好 95#号的汽油不会提升动力,还可能家减小动力,因为95号汽油需要更高的压缩比,如果发动机的压缩比,对导致燃烧不充分,从而导致排放上升和动力下降。 误区五,使用95#汽油油耗更低 这绝对是大错特错了,95#汽油绝对不会降低油耗,因为汽油的标号并不代表汽油热值的高92低和杂质含量的高低,因为使用更高标号的汽油并不能降低油耗,如果使用不当,还会使油耗上升。", "总结:到底使用什么汽油,答案就是看油箱盖的标签,写的多少号汽油就加多少,不会错的,千万别盲目相信以上5个说法,而加了高标号汽油。就像美女,不是越漂亮越好,太漂亮的也不适合你,你也降服不了。", "二师兄购车(微信公众号:ershixiongcar)", "转载请注明以上信息", "特别声明:以上文章内容仅代表作者本人观点,不代表新浪看点观点或立场。如有关于作品内容、版权或其它问题请于作品发布后的30日内与新浪看点联系。", "有情怀又实用的好帮手 体验宝骏310W", "热门评论", "我很负责任的告知大家:我有25年车龄的司机,进口车/国产车都开过,至今为止已使用过6台车,都是自用车,共行使里程53万公里,每台车都有行驶及保养记录,各种油耗及添加剂/清法添加剂使用情况都有记录及分析比较。这编报道完全是外行,他很不负责任地在误导大家,希望大家不要相信,新浪相关部门有没有审批就登出。", "用户6145310861", "同意兄弟说法,我也试过,所以大家不要听小编啥说", "泰新数码广场", "放他妈的小便的屁。老子感觉95号就是有劲而且省油,实测出来的!", "Sina.cn06-10 06:23", "说说你的看法 1733"], "bs_rank_pos": 4}], "answer_spans": [[9, 157]], "yesno_answers": ["No"], "fake_answers": ["答案是否定的。因为95号和92号只是辛烷值不同,即抗爆性能不一样。是否使用95号汽油,取决于汽油发动机的压缩比。汽油机的压缩比越大,压缩过程终了时气缸内混合气的温度和压力就越高,而这大大加速了未燃混合气中过氧化物的生成和聚集,使其更容易自燃,因而爆震的倾向增强。因此,只有对压缩比比较大的汽油机才选择抗爆性好的95号汽油,以避免发生爆震现象,而对于普通家用汽车,除非汽车厂家有特殊要求,根本没必要也无需加95号汽油。而随着发动机设计、制造技术的提高,对汽油辛烷值的要求会越来越低。"], "question": "95号汽油比92号耐用吗", "segmented_answers": [["答案", "是否", "定", "的", "。", "因为", "95", "号", "和", "92", "号", "只是", "辛烷值", "不同", "，", "即", "抗", "爆", "性能", "不一样", "。", "是否", "使用", "95", "号", "汽油", "，", "取决于", "汽油", "发动机", "的", "压缩比", "。", "汽油机", "的", "压缩比", "越", "大", "，", "压缩", "过程", "终", "了", "时", "气缸", "内", "混合气", "的", "温度", "和", "压力", "就", "越", "高", "，", "而", "这", "大", "大", "加速", "了", "未", "燃", "混合气", "中", "过氧化物", "的", "生成", "和", "聚集", "，", "使", "其", "更容易", "自燃", "，", "因而", "爆震", "的", "倾向", "增强", "。", "因此", "，", "只有", "对", "压缩比", "比较", "大", "的", "汽油机", "才", "选择", "抗", "爆", "性", "好", "的", "95", "号", "汽油", "，", "以", "避免", "发生", "爆震", "现象", "，", "而", "对于", "普通", "家用", "汽车", 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"国际", "影响力", "显著", "提升", "。", "在", "这", "一", "时代", "背景", "下", ",", "党", "的", "十八", "大", "胜利召开", ",", "形成", "了", "以", "习近平", "同志", "为", "总书记", "的", "党中央", "。", "时代", "的", "召唤", "和", "发展", "的", "需要", ",", "都", "要求", "中国", "对", "世界", "局势", "、", "国际格局", "提出", "主张", "、", "贡献", "智慧", "、", "发挥", "影响", "。"]], "paragraphs": ["学习啦>新闻资讯>国际>《2017北京一带一路峰会什么时候召开_2017一带一路峰会召开时间》正文", "2017一带一路国家高峰论坛到来了,“一带一路”建设从无到有、由点及面,进度和成果超出预期。那么一带一路2017开会时间什么时候?下面学习啦小编分享了2017一带一路峰会召开时间,希望你喜欢。", "时间:2017年5月14日-5月15日", "会议地点:中国 北京", "一、中国,包括东亚的蒙古国,东盟10国:(新加坡、马来西亚、印度尼西亚、缅甸、泰国、老挝、柬埔寨、越南、文莱和菲律宾),小计12国 二、西亚 18国:(伊朗、伊拉克、土耳其、叙利亚、约旦、黎巴嫩、以色列、巴勒斯坦、沙特阿拉伯、也门、阿曼、阿联酋、卡塔尔、科威特、巴林、希腊、塞浦路斯和埃及的西奈半岛) 三、南亚 8国:(印度、巴基斯坦、孟加拉、阿富汗、斯里兰卡、马尔代夫、尼泊尔和不丹) 四、中亚 5国:(哈萨克斯坦、乌兹别克斯坦、土库曼斯坦、塔吉克斯坦和吉尔吉斯斯坦)", "五、独联体 7国:(俄罗斯、乌克兰、白俄罗斯、格鲁吉亚、阿塞拜疆、亚美尼亚和摩尔多瓦)", "六、中东欧 16国:(波兰、立陶宛、爱沙尼亚、拉脱维亚、捷克、斯洛伐克、匈牙利、斯洛文尼亚、克罗地亚、波黑、黑山、塞尔维亚、阿尔巴尼亚、罗马尼亚、保加利亚和马其顿)", "蒙内铁路", "肯尼亚是中国“一带一路”战略在非洲唯一的支点,是新丝路建设中获得中国资金援助最多的国家。", "2014年5月李克强总理访问肯尼亚期间,中肯签署了关于蒙巴萨-内罗毕铁路相关合作协议,蒙内铁路是肯尼亚百年来建设的首条新铁路,是东非铁路网的咽喉,也是东非次区域互联互通重大项目,规划全长2700公里,预计总造价250亿美元。", "中匈协议", "2015年6月6日,正在匈牙利进行正式访问的外交部部长王毅,在布达佩斯同匈牙利外交与对外经济部部长西亚尔托签署了《中华人民共和国政府和匈牙利政府关于共同推进丝绸之路经济带和21世纪海上丝绸之路建设的谅解备忘录》。这是中国同欧洲国家签署的第一个此类合作文件。", "卫星通信", "为保障“一带一路”通信卫星信号无障碍,国内的相关企业和政府机构已经对“一带一路”的卫星发射进行了规划和研究,未来三年到五年内,将发射多颗通信卫星,与此同时,“一带一路”途经国家的通信信号也将逐步实现全覆盖。从而在通信领域为“一带一路”铺平道路。", "亚洲基础设施投资银行", "截至2015年4月15日,亚投行意向创始成员国确定为57个,其中域内国家37个、域外国家20个。涵盖了除美日之外的主要西方国家 ,以及亚欧区域的大部分国家,成员遍及五大洲。其他国家和地区今后仍可以作为普通成员加入亚投行。", "2015年4月28日,为期两天的亚投行第四次谈判代表会议在北京闭幕,这是亚投行57个意向创始成员国名单最终确定后首次齐聚北京,代表们对多边临时秘书处起草的《亚投行章程(草案)》修订稿进行讨论并取得显著进展。各方商定将于2015年年中完成亚投行章程谈判并签署,年底前完成章程生效程序,正式成立亚投行。", "卡拉奇-拉合尔高速公路", "2015年12月22日,中国建筑股份有限公司(“公司”)与巴基斯坦国家高速公路管理局正式签署巴基斯坦卡拉奇-拉合尔高速公路(苏库尔-木尔坦段)项目EPC总承包合同。", "卡拉奇-拉合尔高速公路项目为中巴经济走廊最大交通基础设施项目,全长约1,152公里,采用双向6车道设计,设计时速120公里/小时。公司本次签约承建的苏库尔-木尔坦段,为中巴经济走廊早期收获项目,全长392公里,建设工期36个月。合同金额2,943亿卢比,约折合人民币184.6亿元,约占公司2014年度经审计营业收入的2.31%。公司推进“一带一路”项目取得重大实质性成果。", "巴基斯坦--卡洛特水电站", "2016年1月10日,在距离巴基斯坦首都伊斯兰堡50多公里处的吉拉姆河畔,三峡集团承建的卡洛特水电站主体工程开工。这是丝路基金首个对外投资项目。", "印尼--印尼雅万高铁", "2016年1月21日,印尼雅万高铁开工奠基仪式举行。这将是印尼乃至东南亚地区的首条高铁。", "德黑兰至马什哈德高铁", "2016年2月6日,伊朗总统鲁哈尼出席了德黑兰-马什哈德铁路电气化改造项目的开工仪式,项目预计将在42个月后竣工,随后还有5年的维护期。该项目将由伊朗基础设施工程集团MAPNA和中国中机公司及苏电集团承建。", "项目全部竣工后,将有70辆中国机车以250公里的时速在该段铁路上行驶。", "区域合作秉持开放的区域合作精神,致力于维护全球自由贸易体系和开放型世界经济,符合国际社会的根本利益,彰显人类社会共同理想和美好追求,是国际合作以及全球治理新模式的积极探索,将为世界和平发展增添新的正能量。", "发展战略对接互联互通项目将推动沿线各国发展战略的对接与耦合,发掘区域内市场的潜力,促进投资和消费,创造需求和就业,增进沿线各国人民的人文交流与文明互鉴。", "中国深度融入世界当前,中国经济和世界经济高度关联。中国将一以贯之地坚持对外开放的基本国策,构建全方位开放新格局,深度融入世界经济体系。", "内部需要:中国要避免中等收入陷阱,必须开拓海外空间。", "从内部需求说,丝绸之路是中国可持续发展战略的有机部分。开放是中国过去30多年经济发展重要动力,在早期主要是对西方发达国家的开放,无论是引进技术也好,出口中国商品也好,对促进中国的经济都起到了巨大作用。现在中国国内经济增长缺乏动力,需要新形式的开放。", "中国现在人均GDP是7500美金,属于中等收入经济体。中国下一步要把自己提升为高收入经济体。但是根据世界银行统计的二战以后100多个国家看,只有十几个国家逃避中等收入陷阱,成为高收入经济体。那么这十几个国家里面,大部分是资源性国家,除此之外,能逃避中等收入陷阱的就是东亚社会,比如日本、亚洲四小龙。除了这五个经济体,其它就没有了。虽然我们有信心逃避中等收入陷阱,但是我觉得很难。", "日本能够逃避中等收入陷阱有它特殊的背景,战后它被美国占领,西方基本上把整个市场开放给了日本,日本没有花很大力气就进入了西方市场,成为西方一部分。亚洲四小龙也有特殊背景。第一,亚洲四小龙经济体比较小;第二,亚洲四小龙很多方面是属于西方治理的。中国现在的情况不一样,国际环境不好,西方对中国搞贸 易保护主义。还有中国是13亿人口的第二大经济体,要从中等收入提升到高收入水平,这是世界史上从来没有发生过的事情。", "首先,从国际政治角度看,“一带一路”是以习近平同志为总书记的党中央统筹国内国际两个大局,着眼于实现“两个一百年”奋斗目标和中华民族伟大复兴中国梦而提出的重大外交构想;是中国作为具有世界影响的大国提出的世界大战略,其意义是非凡的。20世纪80年代末以来的一段时期内,我国面临十分严峻的国际环境。由于东欧剧变、苏联解体,西方国家把“和平演变”的目标对准中国,企图通过制裁、压制等手段搞垮中国;同时,一些发展中国家对中国寄予厚望,希望中国出来当头。在这一关键时刻,邓小平同志提出,要冷静观察、稳住阵脚、沉着应付、韬光养晦、有所作为,要“埋头实干,做好一件事,我们自己的事”。邓小平所说的“一件事”,就是坚持发展经济、坚持改革开放。按照邓小平提出的这一战略思想,中国在国际政治舞台上的表现总体来讲比较低调、务实,这是由当时中国的综合实力和内外环境决定的。经过20多年的迅猛发展,中国经济总量跃居世界第二位,综合国力和国际影响力显著提升。在这一时代背景下,党的十八大胜利召开,形成了以习近平同志为总书记的党中央。时代的召唤和发展的需要,都要求中国对世界局势、国际格局提出主张、贡献智慧、发挥影响。"], "bs_rank_pos": 0}, {"is_selected": false, "title": "北京峰会2017几号开_2017年北京峰会开几天", "most_related_para": 0, "segmented_title": ["北京", "峰会", "2017", "几", "号", "开", "_", "2017", "年", "北京", "峰会", "开", "几天"], "segmented_paragraphs": [["北京", "峰会", "2017", "几", "号", "开", "文章总数", ":", "80311", "篇", "关于", 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2017年5月北京峰会放假优秀文章:55079篇 北京峰会2017几号结束经典论文85785篇 2017一带一路会议放假 一带一路峰会放假通知 一带一路峰会主会场 一带一路什么时候召开 2017一带一路北京峰会 2017一带一路峰会西安 北京峰会2017几号开 2017一带一路峰会怀柔 一带一路峰会召开时间  导读:关于北京峰会2017几号开频道整理了与2017年北京峰会开几天有关的参考资料176752篇和相关论文范文197352篇及2017年5月北京峰会放假相关写作技巧447801篇.", "解放军艺术学院学报杂志社", "实用妇产科杂志杂志社", "分析试验室杂志社", "青少年犯罪问题杂志社", "渭南师范学院学报杂志社", "专题:教育类对大学生网络素养教育的 本文作者:欧阳九根;成功正常投稿发表论文到《 教育探索 》2014年01期,引用请注明来源400期刊网! 如果您需要快速发表论文服务,请联系在线编辑! 【摘要】:针对大学生网络素养的现状,高校大学生网络素养教育,一要提高认识,加大投入;二要加强管理,强化监控...", "专题:工商管理项目教学模式在英语课程 摘要:目前随着教育体制的不断深化改革,在中职院校中进行传统的英语教学已经无法满足当前的社会需求和对人才的需求了。当前社会所需要的人才是能够在市场竞争如此激烈的环境...", "专题:矿井机电论文【红桥区风采里】红桥刮 红桥市场是中国最大的珍珠饰品贸易集散地之一,被誉为“京城珍珠第一家”。早在奥运开幕之前,红桥市场就已经开始“红火”........J起来。埃塞俄比亚议长特肖梅·托加、圣马力诺国家元首费代里科·佩蒂尼·阿马蒂和罗萨·扎费拉尼。期间,罗萨·扎费拉尼女士对红桥市场的珍珠...", "专题:雕塑艺术论文[电子商务物流配送模式论 伴随着现代信息技术的飞速发展,电子商务产生并在经济社会生活的各个领域发挥着巨大的作用,物流行业也不例外。电子商务的产生给我国物流产业的发展带来了极大的便利。但是由于电子商务模式下的物流产业才刚刚起步,还存在很多亟待完善的问题,基于这一现状,本文就电子商务模式...", "专题:医学论文投稿【ipo抑价】新制度经济学 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药学(本科类) 药学:本科(学制4年),专科(学制3年) 药学(临床药学方向):本科(学制5年) 专业要求与毕业去向:掌握药剂学、药物分析和药理学的基本...", "专题:人力资源管理天然气超声波流量计量技 摘要:天然气是我国社会生产生活中运用的重要能源,在天然气的运用中涉及到意向非常重要的内容就是流量计量,这是天然气提供者收取费用的核心依据。实际当中运用的计量装置较...", "档案管理 兵团党校学报 湖北财经高等专科学校学报 装甲兵工程学院学报 中国医疗前沿 农垦医学 传媒评论 粤海风 河北成人教育 宁波教育学院学报", "视频时长:00:41 女博士8万字论文走红 论茴香对卤鸡肉的影响[每日新闻报]  视频时长:07:25 聖密宗四歲小活佛背頌 薄伽梵智及維摩詰論文《淺\\論佛教超越界限》DVD視頻公開  视频时长:00:41 被曝剽窃他人论文表格 方舟子:一年前澄清过  视频时长:01:33 毕业临近 论文反抄袭软件卖火了  视频时长:23:44 【第二屆佛教義學研討會】用科學的方法解釋佛法_法藏法師回應趙東明博士論文  视频时长:32:45 毕业论文撰写方法与技巧(2月19日上)  视频时长:05:02 容嬷嬷教你写毕业论文  视频时长:01:54 “最年轻市长”被指抄袭论文  视频时长:00:31 京华时报:教育部介入调查厅长在清华就读论文博士事件  视频时长:02:09 搞笑,大打出手只为一份论文材料,网络时代何必呢  视频时长:01:41 视频资料《火烧圆明园》-免费课件_教案_试卷_教学设计_教学论文  视频时长:02:09 恶搞刺陵 论文大战  视频时长:02:21 16万字105页1.13兆大学生写出“史上最长毕业论文”  视频时长:09:20 都市笑口组之非一般论文  视频时长:15:01 如何写出高质量的学术论文(普刊学术中心)  视频时长:03:52 WPS Office文字 论文排版教程一:页面设置  视频时长:00:34 上海地铁色狼被指论文抄袭社保造假[天下被网罗]  视频时长:07:51 论文答辩  视频时长:13:23 论文答辩现场", "多媒体系统对机械制图教学的应用 智能化变电站技术管理论文 国际贸易和国际投资关系 教学平台无机化学教学改革 和谐社会文化艺术论文 波普艺术风格在商务酒店的应用 临床医学实验中心建设与管理 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["近日,网上有传言称:“‘一带一路’国际合作高峰论坛将于今年5月14日至15日在北京举行。近20国领导人确认参会。峰会是真,放假未知,不排除放假哈,估计规格得apec那样。”下面是小编收集整理的2017年北京一带一路峰会时间,欢迎阅读参考!!", "2017一带一路高峰论坛时间:5月14日北京举行。习近平主席在1月17日举行的达沃斯世界经济论坛年会上宣布,今年5月中国将在北京主办“一带一路”国际合作高峰论坛,共商合作大计,共建合作平台,共享合作成果,为解决当前世界和区域经济面临的问题寻找方案,为实现联动式发展注入新能量,让“一带一路”建设更好造福各国人民。", "2017一带一路国际合作高峰论坛", "时间:2017年5月14日-5月15日", "会议地点:中国 北京", "北京一带一路会议期间放假?官方:未接通知", "近日,网上有传言称:“‘一带一路’国际合作高峰论坛将于今年5月14日至15日在北京举行。近20国领导人确认参会。峰会是真,放假未知,不排除放假哈,估计规格得apec那样。”", "对此,人民网记者拨打了12345热线电话,联系到北京市人民政府便民电话中心,该中心工作人员表示:“一般假期通知都是由北京市政府下发,并且会在‘首都之窗’网站进行发布,目前中心并没有接到有关通知。”", "同时,该中心工作人员还提醒大家,网上的有关传言不要轻易相信,一切要以北京市政务门户网站“首都之窗”所发布的信息为准。", "峰会筹备工作情况介绍", "问:今年5月中国将主办“一带一路”国际合作高峰论坛,请问你们主办高峰论坛的考虑是什么?希望实现什么样的目标?", "答:习近平主席在1月17日举行的达沃斯世界经济论坛年会上宣布,今年5月中国将在北京主办“一带一路”国际合作高峰论坛,共商合作大计,共建合作平台,共享合作成果,为解决当前世界和区域经济面临的问题寻找方案,为实现联动式发展注入新能量,让“一带一路”建设更好造福各国人民。习主席关于主办高峰论坛的这段讲话勾勒出我们办会的总体设想,指明了办会方向。", "习近平主席在2013年秋天提出共建“一带一路”的合作倡议,旨在通过加强国际合作,对接彼此发展战略,实现优势互补,促进共同发展。3年多来,“一带一路”相关合作稳步推进,受到各方普遍欢迎和积极参与。现在,“一带一路”建设处在全面推进的关键节点,我们主办高峰论坛就是要总结过去、规划未来。", "高峰论坛是“一带一路”提出3年多来最高规格的论坛活动,是今年我国重要的主场外交活动,对推动国际和地区合作具有重要意义。在以习近平同志为核心的党中央领导下,我们希望通过主办高峰论坛,主要实现以下目标:一是全面总结“一带一路”建设的积极进展,展现重要早期收获成果,进一步凝聚合作共识,巩固良好的合作态势。二是共商下一阶段重要合作举措,进一步推动各方加强发展战略对接,深化伙伴关系,实现联动发展。三是在推进中国经济社会发展和结构调整的同时,推动国际合作,实现合作共赢。求木之长者,必固其根本,欲流之远者,必浚其泉源。我们期待同各方一道,通过主办高峰论坛,推进“一带一路”建设,为促进世界经济增长、深化地区合作打造更坚实的发展基础,创造更便利的联通条件,更好造福各国和各国人民。", "问:“一带一路”倡议提出已有3年多时间,请问3年多来“一带一路”建设取得了哪些成就?国际上的认可度怎么样?给各国老百姓带来了哪些实惠?", "答:“一带一路”是中国首倡,但不是中国一家的“独奏曲”,而是各国共同参与的“交响乐”,是各国共同受益的重要国际公共产品。“一带一路”不是一个空洞的口号,而是看得见、摸得着的具体举措。它抓住互联互通这个关键环节,聚焦经济合作特别是基础设施建设,契合沿线国家和本地区发展的需要。我们坚持共商、共建、共享的原则,突出务实合作、互利共赢,一步一个脚印,把中国发展同相关国家发展紧密结合,把各自发展战略和合作规划有机对接,扩大地区投资和内需,增加就业,减少贫困,从而带动提升地区整体发展水平。", "3年多来,“一带一路”建设从无到有、由点及面,进度和成果超出预期。全球100多个国家和国际组织共同参与,40多个国家和国际组织与中国签署合作协议,形成广泛国际合作共识。联合国大会、安理会、联合国亚太经社会、亚太经合组织、亚欧会议、大湄公河次区域合作等有关决议或文件都纳入或体现了“一带一路”建设内容。经济走廊建设稳步推进,互联互通网络逐步成型,贸易投资大幅增长,重要项目合作稳步实施,取得一批重要早期收获。亚投行、丝路基金的成立为金融合作提供了坚实支撑。中欧班列驰骋在广袤的亚欧大陆,运载的是琳琅满目的货物,联通的是亚欧国家的市场需求,架起的是沿线国家人民的友谊桥梁,成为“一带一路”上一道亮丽的风景线。共建“一带一路”是加强国际合作的重要途径,已经成为各方积极参与推进的重要事业,为增进各国民众福祉提供了新的发展机遇。可以说,“一带一路”倡议来自中国,成果正在惠及世界。", "问:据了解,一些国家的领导人将出席高峰论坛,国际社会高度关注。大家都很关心,目前论坛筹备工作进展如何?有哪些国家参与?高峰论坛最后将取得什么成果,你们对此有何期待?", "答:我们高度重视高峰论坛筹备工作,专门成立了筹备委员会,统筹协调各项筹备工作。在相关部门和地方共同努力下,目前筹备工作进展顺利。外方有关领导人参会意愿积极,政治准备稳步推进,主要活动安排、场地准备、会务组织、安全保障等方案都在加紧落实。下一步,我们将秉持开门办会、公开透明理念,适时通报高峰论坛筹备工作的阶段性进展。", "目前,已有近20位各国领导人确认与会,亚洲、欧洲、非洲、拉美等地区均有代表,体现了国际社会对高峰论坛和“一带一路”建设的重视和支持。此外,我们还将邀请一些国家的部长级代表团、国际组织负责人、外国前政要、知名企业家、专家学者等代表参会,共商合作大计。", "会议成果是办会成功的重要体现。高峰论坛有关成果设计正在积极推进当中,我们主要期待围绕3个方面达成重要成果:", "一是扩大国际合作共识。推动各方恪守联合国宪章的宗旨和原则,坚持和平合作、开放包容、互学互鉴、互利共赢、联动发展,把发展经济、扩大就业、消除贫困、改善民生、保护环境放在开展国际合作的优先位置,积极对接国别、区域发展战略和全球发展议程,为打造共同发展、共同繁荣的人类命运共同体作贡献。 二是推进重点领域务实合作。巩固“一带一路”各领域务实合作良好态势,围绕涉及全局性、长期性的重点领域和方向,深化基础设施互联互通、贸易投资、金融支撑、人文交流等领域务实合作,推进一批重大项目和合作协议,研究提出一些中长期重大举措。 三是规划长远合作愿景。我们愿与有关国家一道,以共建“一带一路”为契机,平等协商,兼顾各方利益,探讨建立长效合作机制。加强沟通与协调,增进互信,构建紧密务实的伙伴关系网络。加大实际投入,深化利益融合,规划符合各方共同利益的发展蓝图。", "问:听了您的介绍,感到高峰论坛将推进国际合作,深化“一带一路”与各方发展战略对接,加强中国对外经济合作。如何看待高峰论坛与国内落实“十三五”规划、全面深化改革及扩大对外开放、实现“两个一百年”奋斗目标的关系?"], "bs_rank_pos": 2}, {"is_selected": false, "title": "2017年G20峰会在哪个国家召开_2017G20峰会是哪里_2017G20峰会时间几", "most_related_para": 9, "segmented_title": ["2017", "年", "G20", "峰会", "在", "哪个", "国家", "召开", "_", "2017", "G20", "峰会", "是", "哪里", "_", "2017", "G20", "峰会", "时间", "几"], "segmented_paragraphs": [["学习", "啦", ">", "知识", "大全", ">", "十万个为什么", ">", "《", "2017", "年", "G20", "峰会", "在", "哪个", "国家", "召开", "_", "2017", "G20", "峰会", "是", "哪里", "_", "2017", "G20", "峰会", "时间", "几", "月份", "开", "》", "正文"], ["2017", "年", "G20", "峰会", "在", "哪个", "国家", "召开", "?", "2017", "G20", "峰会", "是", "哪里", "?", "2017", "年", "G20", "峰会", "将", "在", "德国", "城市", "汉堡", "举行", ",", "那么", ",", 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"峰会", "2017", "年", "在", "哪里", "开", "_", "g20", "峰会", "2017", "举办", "地", "_", "2017", "g20", "峰会", "时间", "地点", "下一篇", ":", "2017", "年", "G20", "峰会", "时间", "_", "2017", "年", "g20", "峰会", "几", "月份", "开", "_", "2017", "g20", "汉堡", "峰会", "会议", "时间", "地点"], ["【", "十万个为什么", "】", "精华", "文章"], ["【", "十万个为什么", "】", "相关文章"]], "paragraphs": ["学习啦>知识大全>十万个为什么>《2017年G20峰会在哪个国家召开_2017G20峰会是哪里_2017G20峰会时间几月份开》正文", "2017年G20峰会在哪个国家召开?2017G20峰会是哪里?2017年G20峰会将在德国城市汉堡举行,那么,2017G20峰会时间几月份开?2017G20峰会是什么时候?下面小编整理的2017G20峰会时间地点一起来看看吧", "G20峰会时间2017年地点:G20峰会将于2017年7月7日至8日在德国城市汉堡举行", "中国是2016年G20峰会主办国。本届G20峰会将于2016年9月4日至5日在中国杭州举行。之后,德国将从中国手中接任G20主席国。这将分别是中国和德国首次主办G20峰会。", "汉堡全称为“汉堡汉萨自由市”,是位于德国北部的港口城市、全国第二大城市,也是德国最大的外贸中心。易北河畔的汉堡港是欧洲第三大港口和德国最大港口。汉堡市区有约180万人口,整个都会区有超过500万人口。汉堡是德国十六个州一级行政单位之一(州级市)。", "汉堡不仅是德国著名的旅游城市,也是经济实力最强的城市之一。其人均国民生产总值(GNP)为德国各州最高。根据德国政府提供的数据,其经济实力在全欧洲仅次于伦敦、卢森堡和布鲁塞尔。", "自十二世纪开埠以来,汉堡一直保持高度的国际化。截至2013年5月,汉堡有外国领事机构99个,其中70个为名誉总领事和领事,为仅次于纽约和香港设领最多的城市,同时是国际海洋法法庭的所在地。", "2017年G20峰会预计将在汉堡国际会展中心举行。按照惯例,除了G20国家外,德国可能还将邀请国际组织、其他国家和地区组织等代表与会。", "2016年g20峰在杭州举办。中国是二十国集团的创始成员,并于2005年作为主席国成功地举办了第七届二十国集团财长和央行行长会议,先做为主办国参加2016年g20峰会。", "2016年g20峰会时间:习近平指出,中国于2016年9月4日至5日在杭州举办二十国集团领导人第十一次峰会。", "中国代表团曾在G20峰会宣布支持并决定参与国际货币基金组织增资,数额为430亿美元,随着中国世界影响力的增加,中国在g20峰会的地位也越来越重要。", "二十国集团(G20)财长和央行行长21日至22日将在美国华盛顿举行会议,议题或将聚焦全球化和自由贸易。而近期,包括国际货币基金组织(IMF)和世界贸易组织(WTO)在内的不少国际组织均对全球贸易面临的保护主义抬头等风险发出了警报。", "2017年G20峰会将于7月在汉堡举行,德国是峰会主席国。据路透社报道,有德国官员18日表示,德国将在此次于华盛顿召开的G20财长和央行行长会议上,强调全球化和自由贸易的重要性以及通过推动改革来提高未来应对危机能力的必要性。该官员表示,德国预计,包括美国在内的一些G20成员将向德国提出减少目前较高经常项目盈余水平的要求。该官员还表示,与以往财长和央行行长会议不同,此次会议之后,德国将发布一份主席国作出的摘要,而非二十国的联合声明,因为不久前,G20财长和央行行长们在德国巴登-巴登的会议后,阐述过其共同立场。", "此次G20财长和央行行长会议是在IMF和世界银行年会期间举行的。IMF和世界银行春季年会将于20日至23日在华盛顿举行,IMF总裁拉加德将在20日举行新闻发布会。一些分析认为,反对贸易保护主义很可能也是此次IMF和世行年会的重要议题之一。", "今年以来,全球贸易保护主义抬头的趋势,不断引起国际社会关注。不过,3月份于德国巴登-巴登举行的G20财长和央行行长会议发布的联合公报中,罕见地未提及反对贸易保护主义,仅提及要加强贸易在经济中的作用。这令外界担忧,G20层面的反对贸易保护主义的共同立场正在受到冲击。有报道称,由于美国继续对关键议题保留其意见,因而最终相关内容没能写入上述公报。", "美国总统特朗普自上任后,就不断强调“美国第一”的政策主张,并将美国经济面临的一些问题归咎于贸易全球化,对于美国长期以来的巨额贸易赤字也表示出不满。一些观点指出,特朗普政府的贸易主张,正显示出更多保护主义倾向。", "18日,IMF在春季年会前发布报告,上调了今年全球经济增长预期,但同时警告保护主义升温以及缺乏全球政策协调将威胁经济稳步增长势头。IMF首席经济学家莫里斯·奥布斯特费尔德指出,全球经济增长和稳定有赖于各国在贸易、金融监管、气候变化、难民政策等方面的协调与合作。他呼吁各国在推进结构性改革的同时,加大对全球多边贸易的支持。", "近年来,全球贸易形势不容乐观。2016年,全球贸易仅增长了1.3%,是自2008年金融危机以来的最慢增速。据WTO数据,长期以来,全球贸易增速与国内生产总值(GDP)之比一直维持在约1.5:1,但自2008年金融危机以来逐步下降至约1:1,去年更是降至0.6:1的低点,为2001年以来首次贸易增速低于GDP增速。", "近期,不少国际组织均对全球贸易面临的保护主义抬头等风险发出了警报。WTO上周表示,预计今年全球贸易将略微反弹,增长2.4%,与去年相比将有较大幅度的提升。但是WTO警告称,世界经济走势的不确定性以及政府货币、财政和贸易政策的不确定性,都给贸易活动带来减弱的风险,因此今年的贸易增速可能在1.8%至3.6%的区间浮动。WTO表示,一些政策方面的冲击可能将使贸易增速放缓,这包括英国脱离欧盟的潜在影响以及保护主义政策抬头的趋势。", "WTO总干事阿泽维多表示,如果货物流通和跨境服务能保持顺畅,贸易就能促进世界经济增长;但如果政策制定者为保障本国就业而对进口采取严格限制措施,贸易不但不能促进经济增长,甚至还会制约经济复苏。", "此外,IMF、世界银行和WTO三大机构本月早些时候发布联合报告,呼吁各经济体采取措施降低贸易带来的负面影响,同时继续推动全球贸易一体化。报告指出,2008年金融危机以后,全球经济增长乏力,贸易保护主义抬头,贸易自由化进程缓慢等因素导致了近年来全球贸易增速疲软。", "这份联合报告说,由于部分发达经济体缺乏相应政策来解决全球贸易带来的负面影响,当前这些经济体的民众对贸易的怀疑态度更为明显,导致反对全球贸易的思潮抬头?报告呼吁,各经济体应继续降低农业等传统贸易的壁垒、推进服务贸易和数字贸易改革,将双边和多边贸易协定中的创新内容升级为全球标准。", "上一篇:g20峰会2017年在哪里开_g20峰会2017举办地_2017g20峰会时间地点  下一篇:2017年G20峰会时间_2017年g20峰会几月份开_2017g20汉堡峰会会议时间地点", "【十万个为什么】精华文章", "【十万个为什么】相关文章"], "bs_rank_pos": 3}, {"is_selected": false, "title": "2017一带一路北京峰会时间看点有哪些- 北京本地宝", "most_related_para": 4, "segmented_title": ["2017", "一", "带", "一", "路", "北京", "峰会", "时间", "看点", "有", "哪些", "-", "北京", "本地宝"], "segmented_paragraphs": [["【", "导语", "】", ":", "【", "再", "有", "一个礼拜", ",", "这个地方", "将", "汇聚", "全球", "目光", "!", "】", "5", "月", "14", "日", "到", "15", "日", ",", "北京", "将", "召开", "非常重要", "的", "论坛", ":", "“", "一", "带", "一", "路", "”", "国际合作", "高峰论坛", "。", "据说", ",", "这次", "论坛", "是", "习近平", "主席", "提出", "“", "一", "带", "一", "路", "”", "倡议", "以来", ",", "中方", "就", "此", "召开", "的", "规格", "最高", "的", "国际性", "会议", "!", "你", "真", "的", "了解", "“", "一", "带", "一", "路", "”", "吗", "?", "这次", "论坛", "有", "哪些", "看点", "?"], ["【", "再", "有", 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此时机子会自动下载驱动 5 运作 KING ROOT, 点选 '卸载Root'6 机子自动 Unroot 成功了 希望这能帮到您 !!", "此方法是用 360超级root 来卸载Kingroot。使用Kingroot的部分安卓用户会发现Kingroot耍流氓,部分手机在“通用设置-卸载”中会显示卸载出错。用以下方法可以顺利卸载: 在“设置-应用”中找到 kingroot 选择“停用”。(如果kingroot不显示,则在浏览器重新下载一次Kingroot安装,安装完成后不要打开kingroot,这时在“设置-应用”中可看到kingroot,选择“停用”); 下载 “360超级root” 安装; 打开“360超级root”,并开启root; 然后点击“卸载预装”,再点击右上角的 删除图标 打开“回收站”,看到Kingroot并删除。 保留或者卸载360超级root。", "用360安全卫士在件管家里找到后就能卸载。"], "bs_rank_pos": 3}, {"is_selected": false, "title": "360超级root怎么解除 怎么卸载", "most_related_para": 4, "segmented_title": ["360", "超级", "root", "怎么", "解除", "怎么", "卸载"], "segmented_paragraphs": [["关键字", ":", "360", "超级", "root", "怎么", "解除", "怎么", "卸载"], ["更多精彩", "游戏攻略", ",", "敬请关注", "9", "u8u", "游戏网"], ["小伙伴", "们", ",", "你们", "对于", "360", "超级", "root", "怎么", "解除", "怎么", "卸载", "是不是", "不", "怎么", "了解", ",", "你们", "是不是", "对", "360", "超级", "root", "怎么", "解除", "怎么", "卸载", "不", "怎么", "熟悉", ",", "就让", "小", "编", "为", "你们", "讲解", "360", "超级", "root", "怎么", "解除", "怎么", "卸载", "吧", "。"], ["360", 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权限,您可以解除 Root 了。4、点击“解除 Root”按钮之后,360 Root 工具就会给出提示,“注意!解除 Root 之后将永久失去 Root 权限,并且可能无法重新获取!因此若非送修等特殊需要,不建议解除......”确认后点击“解除 Root”按钮即可解除 Root 权限。", "5、另外,您可以使用其他工具解除 Root 权限,如腾讯手机管家、卓大师等软件。还可以重新刷机来解除系统 Root 权限,不过相对较麻烦。", "360超级root怎么卸载", "您可以去各大应用市场下载卓大师的“一键Root大师”来关闭Root,恢复未Root状态。", "经过小编的介绍,小伙伴们对360超级root怎么解除 怎么卸载知道了吧,对360超级root怎么解除 怎么卸载有所了解了吧,希望可以帮助小伙伴们了解360超级root怎么解除 怎么卸载。", "游戏题材:武侠 历史 国战", "游戏题材:玄幻Q版 其它", "网友评论仅代表其个人看法,并不代表9U8U同意其观点!", "新版狂想曲《九阴绝学》三大彩蛋揭秘武侠新篇", "《群雄志》不败的传说 今日盛大公测", "《盗墓笔记》揭开重重迷影 首服惊悚开启", "攻占沙巴克扬起年少时的回忆--致经典传奇", "刀光剑影只在一刹那间滑过!剑雨江湖", "制服诱惑?不是日韩专属  你不知道的国度? 喊她AV去 可好? 穿越只为找她!!!"], "bs_rank_pos": 4}], "answer_spans": [[9, 20]], "fake_answers": ["用刷机精灵或者刷机大师这类应用软件进行解除。"], "question": "360root怎么卸载", "segmented_answers": [["下载安装", "安卓", "一键", "root", "神器", "：", "Z4", "root", "，", "清除", "已", "安装", "的", "用于", "获取", "ROOT", "权限", "的", "相关", "文件", "。"], ["进入", "手机", "的", "安全", "中心", "后", "，", "就", "看到", "了", "手机", "的", "【", "授权", "管理", "】", "，", "接着", "点击", "授权", "管理", "，", 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"。"]], "paragraphs": ["北疆知名景点介绍  相关景点费用介绍及最佳旅游时间介绍  相关北疆线路推荐", "去新疆旅行怎么可不知道南疆、北疆,首页给大家简单的扫个盲——天山山脉将新疆分为南北两大部分,习惯上称天山以南为南疆,天山以北为北疆。北疆 包括乌鲁木齐、吐鲁番地区、阿勒泰地区、塔城地区、昌吉地区、伊犁、博尔塔拉等地区等。南疆包括了库尔勒、库车、阿克苏、和田、喀什、阿图什……简单的扫盲结束后,现在就开始为大家详细介绍如何玩转北疆两条线。 北疆:乌鲁木齐—-五彩滩—-喀纳斯—-白哈巴—-禾木—–克拉玛依魔鬼城—-赛里木湖—-果子沟—-伊犁—-那拉提草原—-巴音布鲁克(天鹅湖,九曲十八弯)—-天池—乌鲁木齐。此条线路基本包括了北疆地区的不可错过的景点了。下面为了挨个详细介绍", "五彩滩 依傍额尔齐斯河,远眺南岸,有绿洲、沙漠与蓝色的天际相合,风光尽收眼底。弯弯的河滩流水潺潺,郁郁葱葱的河谷林树和对岸的五颜六色的群丘遥相辉映,一河中分两岸,胜似飞来两重天,大自然的鬼斧神工把截然不同的两种地貌巧妙地融合在一起,构成一幅浑然天成的奇景。 注意:你拿起新疆地图,你一定会觉得很晕倒,原来“五彩”这个词在地名上有多个阐释。五彩滩,五彩湾,五彩池,五彩城,我们印象中该是一个地方吧,虾米?错滴,这是四个地方,只要你跟包车师傅说错一个字,就把你带到不同的地方去了,正确的地方是—五彩滩。它位于布尔津县大概10公里处。", "五彩滩想要得到它的美,必须要获得一个重要因素:夕阳。雨中的五彩滩或者正午的五彩滩都不会使得你获得层次分明的色彩美", "喀纳斯—-白哈巴—-禾木: 这三个景点为三个临近的景点,一般游客都会一并游览。很多人以为喀纳斯、白哈巴、禾木这三个点该是统一收费才对,事实是这三个景点的收费特别的细,比如喀纳斯的门票加区间车是220,而上喀纳斯的观鱼亭则另外收费60元(区间车),而白哈巴则是门票加区间车290元。", "喀纳斯:具北国风光之雄浑,又具有南国山水之娇秀,加之这里还有“云海佛光”、“变色湖”、“浮木长堤”、“湖怪”等胜景、绝景,怎能不称得上西域之佳景、仙景!北面是白雪皑皑的奎屯山、高耸如云的友谊峰,湖周重峦叠嶂,山林犹如画屏。叶茂,枯叶朽木上苔藓、野草遍生。林间空地草甸如茵,山间百花齐放。风静波平时,湖水似一池翡翠,随着天气的变化,湖水又更换着不同的色调,自晨至夜变换着风采。每当烟云缭绕,雪峰、春山若隐若现,恍若隔世。夏季,雨后清晨,登上湖南段的骆驼峰则可观览佛光奇景。 白哈巴村:被誉为中国最美的八个小镇之一,图瓦人独特的民族服饰、宗教崇拜及风俗习惯也受到更多人的青睐,去旅行需要办理边境证,购买景区门票以及古村维护费。", "禾木:喀那斯区域最美的秋色在禾木,层林浸染,绚丽多彩,是一处典型的原始自然生态风光。在禾木村周围的小山坡上可俯视禾木村以及禾木河的全景,远观日出、雪峰与涓涓溪流,近览图瓦人家,是拍摄日出、晨雾、木屋、禾木河的绝佳取景地。", "克拉玛依魔鬼城: 关键词:雅丹地貌 “魔鬼城”是一片雅丹地貌,面积10平方公里,地面海拔350米左右。雅丹,维吾尔语“雅尔丹”的转音,原来是维吾尔族对沙土风蚀地貌的叫法,意思是“陡壁的小丘”。 9点后才能领略其中的美,阳光大好下的魔鬼城是毫无意义的,色泽偏白,酷热,在里面就是区间车,放你下去个几分钟就赶你上车,不过确实也没地方可庇荫啥的,能把你晒干。照片中任何角度你都可以在魔鬼城外围拍摄,花钱进去毫无意义。区间车全程45分钟,然后你的46块钱就没了。", "赛里木湖—-果子沟 关键词:逃票 逃票指数:完美,40元门票绝对不用给,你行驶在高速上看到一个标牌:赛里木湖出口,请不要出,继续往前开1公里,远远看到一个”赛里木湖隧道”,放慢车速,不要过隧道,隧道的右手边有一个缺口,开过去,就进入了赛里木湖。 赛里木湖是蓝宝石,阳光下那是奇美。看着阳光一点点让波光粼粼变成一片净好,湖上干净没有任何涟漪,无游船,无小舟,偶尔飞来一群水鸟,聚合,惊起,掠过,复又高飞而去。 果子沟以野果多而得名,沟内峰峦耸峙、峡谷回转、松桦繁茂、果树丛生、野花竞放、飞瀑涌泉、风光秀丽,被清人祁韵士称为“奇绝仙境”。八九月是最佳旅游时间。", "伊犁: 关键词:65团 薰衣草 伊犁为北疆最为重要的自治州,首府为伊宁市。到北疆旅行的女生是万万不能错过伊犁的,很多女孩向往的普罗旺斯的薰衣草,其实在伊犁你也能够看见,那气势一点也不比普罗旺斯差。喜欢摄影活被拍的人都不要错过这个美丽的地方。 最佳地点推荐:65团,虽然别的团场也有但是还是65团的最壮观。最佳时间:最好是6月中旬前到达 不然收割了做加工成薰衣草油了。没有赶上时间的,也可以买一些薰衣草精油之类的回来,那是相当正宗啊~~", "那拉提草原: 关键词:空中草原、夕阳 170元的门票加区间车费用 那拉提草原的精华本来就在于空中草原的夕阳,但是值得注意的是:那拉提草原居然8点下班。如果你没有足够的勇气和智慧说服园区的工作人员,那草原的夕阳基本和你无缘,同时如果去晚了你可能连进都进不去。当然了有个别凶悍的游客还是能够争取到属于自己的福利,但千万注意那只是个别的,请千万充分估计自己的实力~~", "巴音布鲁克: 关键词:天鹅、九曲十八弯 巴音布鲁克草原,蒙古语意为“富饶之泉”,当地会有成群的天鹅在此聚集,新疆人认为看见天鹅就会收获幸福,因此,这也是他们心中的圣湖。 巴音布鲁克的美,绝对是熨帖的征服,润物细无声的征服,若巴音布鲁克都不能打动你心中最深沉的情感,我已经无法从中国再寻找更绵长的深沉。", "天池: 关键词 :mark 太有名 其实真正去过天池的人十有八九是会后悔的,不去的人100%会后悔,好似没有到过天池,肿么也不能算到过新疆吧,天池的风景平心而论还是可以滴,但比起北疆的喀纳斯、巴音布鲁克真是差的太远。但是他又太有名,所以建议原道而来的朋友还是上去mark一下吧,不要给你留遗憾。", "图文来自网络,如有侵权请联系删除", "宁浩新片无人区四年终公映 引爆新疆自驾游热潮 新疆特色美食大推荐 新疆摄影线路—最佳摄影季节 新疆奇观草原石人 乌鲁木齐特色餐馆推荐", "位于乌鲁木齐西北郊的小地窝堡的民用国际机场,距离市中心16.8公里,该机场飞行区等级为4E,可满足波音747-400大型飞机全载起降。机场所属于中国国家民用航空局。"], "bs_rank_pos": 0}, {"is_selected": false, "title": "2017北疆旅游攻略,北疆自由行攻略,蚂蜂窝北疆出游攻略游记 - 蚂蜂窝", "most_related_para": 14, 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开头借用孟火火书中的一句话,此刻在写游记的前一分钟,还在看着这本《火火和他的第一本书》。我还是我,人没变,
山峰依旧不会写游记。
(欢迎关注我微博:山峰不会写游记,http://www.weibo.com/shanfeng19811104)但...", "【我的夜空幻想日记簿】------那年夏天,北疆寻梦  当我坐在电脑面前,敲击这篇游记的时候,入冬来最持久雾霾天气来临,我国多个城市已达严重污染。甚至连重庆,成都这样西南地区的城市,也已沦陷。我只能在家里,在电脑前,看着满天繁星的照片,心里一阵心悸。想起了周星驰《美人鱼》中的台词。“如果这个地球上连一滴干...", "如诗的画卷--北疆行记  这是一场值得回味很久的旅行,累并快乐着。 惯例,先上图。 乌伦古湖的日落。 贾登峪-禾木徒步途中,布拉勒汉桥附近的一处山坡 璀璨绚丽的禾木星空 禾木晨雾。 新疆怎么玩丨不到新疆不知中国之大! 新疆1/6的中国,地域辽阔。她有山脉、湖泊", "新疆怎么玩丨不到新疆不知中国之大! 新疆1/6的中国,地域辽阔。她有山脉、湖泊、草原、戈壁、沙漠、河流~~~游新疆是一种情结,怎样游可以让你在有限时间里看到更多的大美新疆呢?新疆1-30天的旅行均可,新疆的玩法多种多样。接下来就让我来为你荐几种不走回头路的环线游,一样的时间看最多的新疆风光!", "欧亚大陆的腹地【十八天自驾北京至北疆行】  《古兰经》说:世上本无什么移山之术,唯一能移动山的方法就是:山不过来,我就过去。《古兰经》又说:你见群山而以为都是固定的,其实群山都象行云样逝去。 上述《古兰经》中的经典句子,富有哲理似乎隐含喻意。其实,《古兰经》中这样带有哲理性的表述,相比《佛经...", "我曾孤单如飞鸟,看过这世界的喧嚣  我和谁都不争, 和谁争我都不屑; 我爱大自然, 其次是艺术; 我双手烤着,生命之火取暖, 火萎了, ...", "北疆故事:行万里,只为遇见你  他说任何为人称道的美丽,不及他第一次遇见你 ——马頔《南山南》 有朋友曾问我:旅行,是不是你的梦想? 我告诉他:不,旅行是我的生活。 走过很多路,看过很多景,遇过...", "我们不是荒野猎人,却也被喀纳斯的冰雪彻身净化了一把! "纳斯冬的天都已经封山无法进入,秋天才是最美的季节,雪景有什么好看的就是白茫茫一片……"这是我告诉身边的朋友我将在12月底前往新疆喀纳斯之后他们的反应。但是真的当我去到那里的时候,就觉得我们都错了,又当我敲下这篇游记的时候,我开始后悔,为什么...", "新疆之旅---乌鲁木齐市、吐鲁番、哈巴河县、白哈巴村、喀纳斯景区、禾木村、布尔津县  10天的实际行程如下: 9-19日,飞机于23:50到乌鲁木齐,订的乌鲁木齐机场芙蓉名帝酒店免费接机到酒店。 9-20日,包车司机7:30到酒店接上我们,开始吐鲁番一日游。原定当天做乌鲁木齐 K9791 20:34~北屯07:51卧铺,无奈就在当天,这趟由南站发车改为北站。本来我们到南站时间离开..."], "bs_rank_pos": 1}, {"is_selected": true, "title": "北疆景点介绍,北疆旅游景点,北疆景点推荐 - 蚂蜂窝", "most_related_para": 3, "segmented_title": ["北疆", "景点", "介绍", ",", "北疆", "旅游景点", ",", "北疆", "景点推荐", "-", "蚂", "蜂窝"], "segmented_paragraphs": [["雪山", "、", "草原", "和", "蔚蓝", "湖水", "构成", "的", "绝美", "画卷", ",", "是", "来", "新疆", "必看", "的", "美景", "之", "一"], ["湖", "水晶", "莹如玉", ",", "倒映", "着", "博格达", "峰", ",", "四周", "群山环抱", ",", "风景", "犹如", "仙境"], ["维吾尔族", "文化", "表现", "最", "集中", "、", "留存", "最", "完整", "的", "地方"], ["新疆", "最", "著名", "的", "草原", "之", "一", ",", "丘陵", "状", "的", "草原", "上", "有", "雪山", "、", "森林", "、", "野花", ",", "景色", "多样", "绝美"], ["每当", "盛夏", ",", "红日", "当", "空", ",", "地", "气", "蒸腾", ",", 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"湖水晶莹如玉,倒映着博格达峰,四周群山环抱,风景犹如仙境", "维吾尔族文化表现最集中、留存最完整的地方", "新疆最著名的草原之一,丘陵状的草原上有雪山、森林、野花,景色多样绝美", "每当盛夏,红日当空,地气蒸腾,焰云燎绕,形如飞腾的火龙十分壮观", "这里有清澈的高山湖水,茂密的原始森林,神秘的古老村落,犹如仙境", "每到傍晚夕阳照耀时,颜色绚烂奇幻,非常壮观", "世界上距离城市最近的沙漠, 娱乐项目多,景色壮丽独特", "葡萄的天地,种植了密集的葡萄,可以边吃葡萄边看维族舞蹈", "目前世界上最大、最古老、保存最完整的土建筑城市", "雪山脚下的壮美草原,景色辽阔秀美,是观光摄影的必来之地", "著名的“楼兰美女”古尸、“五星出东方”汉代织锦等国宝级文物都在此展出", "每年6-8月,这里就变成了中国的普罗旺斯,紫色的田园十分浪漫", "俯瞰乌鲁木齐市区的最佳位置,还有著名的“红山夕照”之景"], "bs_rank_pos": 2}, {"is_selected": false, "title": "新疆(北疆)两个人的穷游旅行-喀纳斯旅游攻略 | Yododo 游多多 ", "most_related_para": 13, "segmented_title": ["新疆", "(", "北疆", ")", "两个人", "的", "穷游", "旅行", "-", "喀纳斯", "旅游攻略", "|", "Yododo", "游多多"], "segmented_paragraphs": [["新疆", "(", "北疆", ")", "两个人", "的", "穷游", "旅行"], ["追太阳", "的", "画家", "13365", "浏览", ",", "0", "回复"], ["美丽", "新疆", "古老的传说", "灿烂", "的", "文化", "牵引", "着", "我", "的", "到", "来"], ["不到", "新疆", "实在", "是", "想象", "不", "出", "有多大"], ["乘坐", "西安", "至", "乌鲁木达", "齐", "的", "火车", "(", "硬座", ")", "晃晃悠悠", "的", "到达", "幸好有你", "这", "硬座", "一人", "真", "的", "无法承受"], 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"每月给公婆伙食费,因为公婆住同一小区,不用自己做饭,周末会下人均100左右的馆子,月开销有8,9千,加上养车、偶尔买买东西,平均月支出会到达1W", "每年可以出国旅游一次,人均2-3W。", "从未买过奢侈品,衣服很少买,基本在HM、优衣库、zara这种快消店里买打折的,包买过最贵的是500元。护肤品买科颜氏那样的档次,也没有办过美容院、理发店的卡。", "偶尔会买比较小贵的家居用品、生活电器来改善生活质量。比如最近在考虑入手nespressno胶囊咖啡机。放在家里的东西价格3K以下的不会太过心疼。。", "每个星期会请钟点工阿姨来打扫,我很讨厌花很多时间在家务活上 ╮(╯▽╰)╭", "理财方面,存款全部在余额宝吃利息,不炒股票期货,也没考虑过买基金和保险,在这方面我们关注的不多。", "更多的在关注如何提升自己、增加收入,目标存钱准备养孩子,多个娃后,可能将来生活质量会下降很多吧。。。。", "家庭年收入30万,如果是三位劳动力,也就是每人年薪10万,每个月1万不到。不是很难达到。但如果月收入过2万,就不是一个重量级的问题了。", "房子开销每个月大约4000-6000吧。每年就是5万左右。", "那么家庭收入缩减为25万,每位成员月薪则为7K。", "这个幅度,基本上没有改变生活的本质,可能对家庭投资和储蓄有点影响。", "生病就医,如果三人中有两人不是全部报销的话,假设一位是全裸,一位报销60%。每个月需要腾出2K一人,约3K每个月,约4万的开支。", "那么家庭收入缩减为20万,每位成员月薪为5500元。", "这个幅度,对家庭发展的影响就不仅仅是投资和储蓄的问题了,穿衣购物,添置家具,出行旅行等会受影响。吃的方面,基本不会受到影响。", "20万年收入,三分之一储蓄,剩下的三分之一投资。那么就是:每年7万的银行储蓄,4万的投资。剩下10万左右来开销。每位家庭成员的生活费用为3万每年,2500元每个月。", "基础生活费用,吃饭约3K,剩下4500元。", "购置衣物,1500元三人,剩下3000元。", "交通花费,养车,基本上剩下的2000全在里面了。", "如果保守投资,每年的收益假设为7%平均,那么就是7000K每年。每月多了600块。", "问题来了,2人世界,30万,哪怕20万都够花,会有一定的盈余。如果一旦生了孩子,对不起,分分秒秒不够。孩子教育周期太长了,除非你生了一个节能灯。", "如果30万,3口之家是2位老人,1位适龄青年,那么会宽松很多。但收益和储蓄会相对减少。", "上海的工资水平,硕士毕业6000-10000吧,算好的。本科4000起。", "每有1年的工作经验加500块工资。", "当然是说发展的好的,外加上通胀和高CPI。", "退休工人工资,3000起,干部6000", "如果是两个硕士毕业,有房无贷款,最好20万。工作3年后,40万。但前提是不生孩子。5年后结婚,两个人都100万的话,可以生孩子了。哈哈。做梦呢。", "唉,北上广生活压力之大,成本之高,我也是近1年才深有体会。", "有时候想到未来某个人,每天赚800,我却犹豫花不花20。", "除了年收入和所在地没有其它条件,无法判断。大致上如果是三口之家两人职工有收入,无房贷车贷,不炒股炒期货,那一般是这样的生活方式:父母中级管理层或中高级技术人员,每天工作比较繁忙,家务包括孩子接送由老人代为操办。孩子中学以后一般可以独自上下学。早午餐一般自行解决,晚餐一般在外面一起吃,周末偶尔自家做饭。周末和假期经常有出游计划,近处自驾远处高铁飞机,酒店都住好的,跟团积极性不高。衣食住行:衣服和配件品牌档次都在白领到金领平均线左右,男士收集衬衫女士偶尔买上万好几万的包。吃已经不是问题,看兴趣。住所一般在中环附近有二到三房小区房。出行如果顺路一般一方开车送另一方,小孩自理或跟车。只好大致描述我印象中的平均水平,其它复杂的情况无法一一列明", "广州30万家庭强行答一波。男方国企机关大爷,2015到手约140k;女方非诉律师,2016年转正后12k*16。吃饭:男方每天从单位饭堂带饭回家(快,利用餐补),周末出去吃一顿人均150,一个月净支出约1500。租房:越秀区40平老破小,2000。通勤:100*2。穿:某宝单件200以下,两人一年控制在5000以内。女方化妆品一个月控制在300左右。娱乐:单位发的电影票、游泳票、单位配的健身房,男方每周去踢一场球20块,一年净支出2000。旅游:近两年没有。2015年家庭收入227k,支出58k;2016年1-5月家庭收入130k,支出32k。来自一个准备买房的家庭手账。", "家庭起点基数不同,收入增长率不同,带来的家庭状态也肯定是千差万别。我的答案只是单纯描述目前个人家庭状态,仅是给题主一种可能性的参考。", "夫妻二人均30岁,暂无小孩。我目前税前32万,媳妇税前17万,有房有贷无车,家庭收入税后36万上下,不过算了一下今年家庭开销居然有17万(2016年数字,即使刨除非常规开支后也有11万开销)。", "两年半前用两人积蓄和父母资助加上朋友借款做首付买了外环外小两房(当时总价180万),月供6000多,一年前首付欠款已还清(在此之前手上基本没超过1万的现金),才算刚开始有积蓄。到年底目前手上现金约17万,但马上这些钱又快要清零。因为计划2017年要娃,提前需要买车,目前正在拍牌,上牌费基本不少于9万,15万的车(基本上算是最入门的了)30%首付加各种税费保险7万起,这样需要消耗现金16万,且每月车贷要3500养车1500,如果再添个娃年度开销会增加至少5万,这样一算2017年开支会增加16+0.5x12+5=27万。这样到2017年底手上现金也就再增加个10万不到。", "家庭5年计划包括房产置换,目标是中环内二居室学区房或外环外三居室,操作时间取决于限购政策变化的时间。", "至于理财,目前没有系统计划,媳妇买了短期的理财产品,年化收益率4%基本不值一提。"], "bs_rank_pos": 0}, {"is_selected": false, "title": "年薪30万在上海过的是什么样的生活?_婆媳关系_天涯论坛_天涯社区", "most_related_para": 18, "segmented_title": ["年薪", "30", "万", "在", "上海", "过", "的", "是", "什么", "样", "的", "生活", "?", "_", "婆媳关系", "_", "天涯", "论坛", "_", "天涯社区"], "segmented_paragraphs": [["字体", ":"], ["边距", ":"], ["背景", ":"], ["还原", ":"], ["年薪", "30", "万", "的", "农村", "男生", "30岁", ",", "小", "硕", ",", "没", "房", "没", "车", "在", "上海", "能", "过", "什么", "样", 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"30万在上海不买房一辈子租房住,可以过得蛮好,要买房还贷的话,甚至要攒首付的话,就是悲催的房奴,可是,你不买房肯嫁你的人真的不多哎!", "长得帅人品好这条件还是很好找带房上海女孩子的", "1 单身,可以过得很潇洒,中等水平吃吃喝喝不愁 2 结婚的话,一般买房基本是刚需了。现在上海房价这么高,郊区刚需地铁房首付也要70W左右了,估计要存几年(家里土豪的不算)", "找个年薪也二三十万的,一起奋斗吧", "我觉得他过什么样的生活第一看运气,第二看规划。 运气真是没法说,所在行业的景气度,遇到配偶的资质,甚至中国经济短中长期的发展,个人只能受着,也没啥选择。 第二看规划就得说他是个什么样的人了,要是今朝有酒今朝醉的,那还是可以潇洒快活几年的,要是未雨绸缪,责任感爆棚的那种人,那就惨了,什么父母养老医疗,自己未来养老医疗,小孩教育,成家立业等等等等,算一算全都无法规避的钱钱钱,没说的,把头一埋好好干吧,最多偶尔忙里偷闲旅游健身一下,大多数情况是紧张的工作了。", "年轻人,有希望,我看好你。", "有房一年10万也可以不错,没房一年50万估计在上海都过得苦哈哈的,除非不要房!", "找个同样年薪30万的外来女。我和我老公就是同是外来。 不过我们结婚时收入还没这么多。四年了。结婚第二年贷款买的房。目前无贷。家庭年收入50几万。不讲究奢侈,小康没问题", "刚想问有没有房和车  这样啊  可以找个城市小康女,家里有房有车的,  只不过在家里没什么话语权  或者找个同样的外来媳妇,两人租房生活,共同奋斗呗! ----------------------------- 如果在老家娶个女公务员呢,但是异地,这样的日子过得下去吗", "凤凰男啊!唉,不好找。条件好的本地姑娘不愿找你,找个条件一般,挣得没你多的本地姑娘,她也不一定看得起你,会觉得你各种土。找个跟你一样进城的农村姑娘其实最合适,可惜你可能又看不上.....", "要看你多大, 35岁以上税前30万元无房的, 不高。 30岁以下的, 还算不错。 如果你用心找的话, 外地未婚独生女, 有些也在上海买了房, 她们不会坐等男方家买房。", "不是有自称上海人的婆婆党说上海不是男方不用买房子照样娶媳妇吗?谁说的上海没房子不好娶媳妇?上海女孩有这么现实吗?不要黑我大上海。 当然,我好几个同学在上海工作,他们说在上海男方没房子不好找对象也是假得,黑你们大上海呢?哈哈…………", "你是想娶个上海本地的姑娘??现实一点吧,娶一个互相喜欢的自己家乡女孩,生活没有那么累", "我舅妈说的,,,她儿子(我表哥)上海年薪三十几万,,,表嫂一个月一万多,,,两人没有福利餐,,,吃饭一人一个月3000,,,两个人供房,,,孩子请保姆带,,,交通费电话费等等多少不清楚,,,存款不多。。。", "每月500块,满满都是爱,每月300元,幸福又团圆", "上海三十万的年薪也只够刚刚小康。", "我想说有钱人的。看了评论,我还是默默点赞。", "这个得看你村到什么程度,如果太村,你那30万顾得了老家也顾不上新房。 如果找本地妹纸,估计妹纸妈那头你得多受气,我是上海妹纸,我建议找个门当户对的外来妹纸一起奋斗更舒心。否则被人“农村驻上海办”的叫上大半辈子,你也闹心。", "请遵守天涯社区公约言论规则,不得违反国家法律法规回复(Ctrl+Enter)"], "bs_rank_pos": 1}, {"is_selected": false, "title": "[上海年薪30万什么水平]年薪30万的文案平时都在干嘛?", "most_related_para": 44, "segmented_title": ["[", "上海", "年薪", "30", "万", "什么", "水平", "]", "年薪", "30", "万", "的", "文案", "平时", "都", "在", "干嘛", "?"], "segmented_paragraphs": [["位置", ":", "美文", "网", ">", "管理者", "应该具备", "的", "素质", ">", "主持人", "应该具备", "的", "素质", ">", "[", "上海", "年薪", "30", "万", "什么", "水平", "]", "年薪", "30", "万", "的", "文案", "平时", "都", "在", "干嘛", "?"], ["台上", "一分钟", ",", "台下", "十", "年", "功", "。", "吃什么", "拉", "什么", ",", "要", "想", "写", "出来", "的", "东西", "珍贵", ",", "首先", 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"老妈", "问", "我", "工作", "的", "时候", ",", "我", "都会", "告诉", "她", ":", "我", "其实", "是", "个", "在", "网上", "贴", "小", "广告", "的", "。"], ["粉丝通", "作为", "微博", "的", "官方", "广告", "平台", ",", "和", "所有", "大", "流量", "平台", "做", "的", "其实", "是", "同", "一", "件", "事", ":", "卖", "流量", "…", "…"], ["你", "花钱", "就", "可以", "承包", "你", "想要", "的", "那", "部分", "流量", "。"], ["而", "这", "部分", "流量", "的", "利用率", "则", "决定", "了", "你", "能", "达到", "的", "所有", "效果", "。"]], "paragraphs": ["位置:美文网>管理者应该具备的素质>主持人应该具备的素质>[上海年薪30万什么水平]年薪30万的文案平时都在干嘛?", "台上一分钟,台下十年功。吃什么拉什么,要想写出来的东西珍贵,首先肚子里要装下数不清的山珍海味。那么一字千金的文案们,平时都在干吗?", "文案要讲究世界观。", "你要把对这个世界的认知,通过文字去表达出来。读者更多的是更可你的世界观,他们更会被你的观点吸引,而不是空洞的形容词和修辞手法。", "读书,可以形成自己的世界观,增加见识扩大知识圈。那些牛逼的人,几乎都是书疯子。他们做饭睡觉拉屎打点滴都会读书,他们在为一字千金做足准备。", "《后会无期》这个电影能火,下面这些文案功不可没。", "“听过很多道理,却依然过不好这一生”", "“小孩才分对错,大人只看利弊”", "再比如,某个品牌的活动主题。", "“我们是小孩,总要给大人们一点面子”。", "看电影,学会写画面感十足的故事。", "以前看电影,只是看看。现在不一样了,我知道好的电影是一个非常好的故事。看电影,可以学会怎么讲好一个故事,用最少的画面和台词。", "文案越来越重要,是因为我们对故事的不断渴望。一个产品,不仅仅是产品本身,还要藏在身后的故事。", "从小学的时候,我们就在做这个事情。小学的《看图说话》就是一个很好的文案训练方法,一张图让我们讲一个故事。一张图,有很多个故事。同样的产品,不同的故事会决定它的优劣。", "当你想不到写什么的时候,不如和客户聊聊天。客户不仅能给你提需求,还能告诉你要写的内容。关于产品,客户比我们清楚。关于特色,客户更熟悉。我们要做的,就是把他们知道的表达出来,用一种通俗易懂的方式。", "比如,当你的客户告诉你他家MP3体积小,可以装3000首歌曲。你就可能像乔布斯写出这样的。“把3000首歌装到口袋里”。客户讲述一个事实,他家的面膜是天然的,一两天是不会变白的。你就可以将劣势转化为优势。“一下子变白的面膜,来历都是不清白”。", "好的文案,有时候是别人告诉你的。我们要做的事,把这个真相告诉大家。", "假设你要替一个电影院写广告文案。", "他们想在商场中弄一个广告牌,吸引更多的人来看电影。一般文案会马上想到这些词眼,最新影片,观影环境,价格优惠等。仔细想想这些文案,它们是把其他影院做为了竞争对手,也没有考虑到真正的销售现场。能看到这个广告牌的人,都是在商场逛的人。他们一般有两种选择。一种是看电影,另外一种是继续逛。", "我们要做的事,是让客户的选择偏向看电影。所以,当你去现场跑一跑,你就可能写出这样的文案。", "“逛累了,不如来看场电影吧。”", "再来一个超级经典的案列。", "这次是关于汽车,主要表现汽车室内安静舒适。坐在办公室的文案,一般会想到“静无止静”、“用心去感受”等。当你真的坐入车内,用心也感受不了所谓的静无止静。", "就像你在高中上晚自习的时候,安静得针落有声,一枚针掉地上都能听见声音。然后,一款牛逼汽车就有一个牛逼的文案了。", "“在时速六十英里时,这款劳斯莱斯汽车上的最大噪音来自于它的电子钟”", "写文案,可以培养语感。", "开始先模仿那些经典文案,不断练习可以找到那种文字感觉。文案,其实是一个技术活。一点一滴,里面的技巧思维就会浮现在纸面上。", "本文作者:HULOO的苏酥。 个人公众号:酥酥说(ID:susu_says)", "我做产品经理的时候,平时总在想一个问题:“如果给我年薪50w,我可以胜任这样的职位吗?如果不可以,那我还差在哪里?”", "首先无论是哪个级别的产品经理,交互——需求文档——需求评估——跟进开发进度——数据反馈——产品迭代优化,这样的过程都是必须的。但把这些做好就是好的产品经理了吗?其实即使是10w的产品经理,如果他足够有悟性,那他一样会把这个过程做得很好很完美。", "但为什么他还只是10w呢?因为这还只是执行层面的工作,leader教你做什么功能,你照着做,在这个过程中,你少了思考,更多的是执行。这是命题作文,让你写什么,你就写什么。", "思考,就是10w与30w的差距。", "为什么做这个功能,这个功能是用户真正想要的吗,我是否可以用其他更好的方式代替?用户还有哪些其他的需求?我还可以做哪些其他的功能?", "一个30w的产品经理,他一定是可以单独负责一个产品,对整个产品的需求、功能、研发跟进、效果呈现、数据负责;他可以对产品做整体的时间规划,3个月后是什么样,半年后是什么样;他甚至可以带一个产品的小团队。此时,他要花更多的时间在思考,而不仅仅是执行。", "但为什么他还不是50w呢?因为更高级的产品经理,他需要站在更高的层面上来审视产品,他需要站在业务的角度去衡量产品的发展方向,他要有极宽阔的视野,了解行业,有着战略性的大局观。因为他的一个判断失误,很可能导致一整条产品线流产。", "视野,就是30w与50w的差距。", "我们大多数的人很容易做到可以单独规划一个产品,而且也可以做的不错,但如果谈产品背后的战略布局,相信很多人都并不了解,因为他没有把自己放在更高的高度上来理解产品,他对行业的理解也并不深。", "以上就是我想说的。回答我篇首的那个问题:我对行业的理解还不够深,这的确是我需要加强的地方。", "最后对于刚入行的新手,想提几个建议:", "1、多思考——执行是必须的,但思考才会成长 2、跳出来——站在总监的角度看问题,你会看到不一样的世界。", "今年上半年股市大涨,微博上出现了两个几乎一样的APP,解决的就是模拟炒股的需求,面向股市小白用户,但由于文案的差异,推广之后的效果几乎天差地别。", "我们先来看下文案A:", "这是微博广告的比较常见的写模式:一句充满诱惑的导语加一个下载APP的链接。", "表述简单明了,诉求点明确,但是并没有什么卵用……", "1、新高?新高是多高?对于不炒股的人而言,没有一个形象的概念。 2、牛人提供猛料?谁是牛人?到底有多牛?那么牛为啥自己不偷着赚钱要告诉别人?门口卖菜阿婆昨晚还和我透露一个内部消息……", "这显然是一个炒股的股民写的文案,对于大部分小白用户来说,这个文案并不能让他们了解你的APP。", "再看看文案B:", "我们已经强调过很多次了,社交媒体的文案要有趣,好玩。这样广告才能被大众所接收并传播。", "两个文案效果两极分化最重要的原因是:好坏中间只隔一条好文案。坏的文案只能带来19个转发,5个评论,243个赞,好的文章能带来4911的转发,1774个评论,2万多个赞,好的文案是坏的文案的258倍。", "牛逼文案可以带来真金白银", "在过去的一年里,每次老妈问我工作的时候,我都会告诉她:我其实是个在网上贴小广告的。", "粉丝通作为微博的官方广告平台,和所有大流量平台做的其实是同一件事:卖流量……", "你花钱就可以承包你想要的那部分流量。", "而这部分流量的利用率则决定了你能达到的所有效果。"], "bs_rank_pos": 2}, {"is_selected": true, "title": "个人年薪30万在上海或者苏州属于什么水平?【常熟吧】_百度贴吧", "most_related_para": 0, "segmented_title": ["个人", "年薪", "30", "万", "在", "上海", "或者", "苏州", "属于", "什么", "水平", "?", "【", "常熟", "吧", "】", "_", "百度贴吧"], "segmented_paragraphs": [["看", "什么", "行业", ",", "一般", "公司", "的", "话", ",", "算", "高管", ",", "但是", "很多", "行业", ",", "拿", "提成", "的", ",", "或者", "包工头", "啥", "的", ",", "不止", "30", "万", "的", "多", "了", "去", "。", "。", "另外", "有些", "公司", "高管", "薪资", "可能", "一年", "都", "没有", "30", "万", ",", "不能", "一概而论", "。"], ["一年", "三", "十", "万", ",", "是", "我", "的", 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"勒索病毒席卷全球,牵出了美国军方与情报机构。目前已经有明确的证据指出勒索病毒所使用的工具是美国国家安全局(NSA)制造的。据黑客组织“影子经纪人”披露,NSA早前遗失一大批自己制造的黑客工具,其中就包括此次wannacry使用的“永恒之蓝”。等等,NSA制造黑客工具?它不是美国国家安全局吗?为啥制造这些玩意来影响世界的网络信息安全?", "讲到这,我们必须深扒一下NSA到底是个什么组织,以及更重要的是它到底要干嘛?", "美国国家安全局又称国家保密局,隶属于美国国防部,是美国政府机构中最大的情报部门,专门负责收集和分析外国及本国通讯资料,很大程度上只对美国总统负责。是说到这,是不是脑海中联想起什么?那就是影视作品里的东厂,一群阴险毒辣的高手,搜集各种情报,为皇帝铲除异己。事实上NSA性质基本与东厂无异,早在奥巴马执政时,NSA就受命对伊朗的核设施进行了代号“震网”的病毒攻击,控制并破坏了近千台离心机,致使伊朗不得不停止浓缩铀的进程,而此次黑客工具的泄露是否意味着美国在酝酿更大的不为人知的阴谋?", "“震网”病毒充分展现了美国7年前的网络战水平。2006年,美国军方和情报部门官员向总统提交了一个对伊朗发动网络攻击的建议案,即“奥林匹克计划”,随后,由美国和以色列联合开发的“震网”病毒利用四个微软零日漏洞,对伊朗核设施进行长期而隐蔽的破坏。2010年3月,震网病毒疯狂传播,感染了伊朗将近20万台计算机。引起全球轰动。全球顶尖专家对震网病毒进行追踪,最终发现始作俑者是美国与以色列。震网病毒的彻底曝光,也宣告了美国这一网络战计划走向尽头。为了规避美国的网络攻击,2010年11月20日,伊朗纳坦兹的技术人员全面暂停了工厂的铀浓缩活动,11月22日,所有离心机都停止了运转。", "7年前的“震网”病毒具有划时代的意义,宣告了网络战从情报窃取间谍行为升级到直接进行工业物理破坏,可以直接干扰、阻断、破坏目标国家的核心工业研究进程。", "美国震网行动曝光受阻后,立马对伊朗核物理学家进行谋杀。2010年11月29日的清晨,德黑兰南部的阿泰什大街(Artesh Boulevard),40岁的著名核物理学家马吉德·沙利亚里(Majid Shahriari)在上班路上遭到炸弹袭击,当场毙命。沙利亚里是一名中子输运专家(中子输运是启动链式反应的必要步骤),西方媒体报道称,他在伊朗核计划中的地位仅次于政府高官。", "同一天,伊朗铀浓缩项目的技术领袖法雷多·艾巴西(Fereydoon Abbasi)也在上班途中遭遇了跟踪,最后死里逃生。暗杀事件当天,伊朗内贾德总统对“数字武器攻击纳坦兹核设施”一事首次做出了官方确认,并谴责美国与以色列。", "上述事实揭开了勒索病毒、“震网”病毒背后的庞大邪恶帝国—美帝国主义。这个帝国为了谋求自己的霸权统治,从网络情报窃取、病毒攻击,到直接街头谋杀,无所不用其极。", "至此我们可以看清,网络安全问题已经不简单是信息数据安全的问题,而且事关国家核心工业研发进程。美国利用所储备的大量零日漏洞与数字武器,必然将攻击目标锁定在中国核工业、高铁、大飞机、航天、机床、芯片等。利用窃取的数据地理信息,美国可以对我国核心科学家进行定点清除;利用制造的顶级数字武器,美国可以对中国的自主工业研发进行阻碍与破坏。", "我国网络对美国存在严重依附。据统计,我国86%的重要信息系统选用国外操作系统、数据库、服务器、核心路由器等,60%重要信息系统运行维护依赖国外厂商,对于这类复杂的软硬件,美国设计者往往留有作入侵的“后门”。我国现有网络接入的是美国因特网,无论从体系结构设计还是建设实践看,我国都没有实际控制权。核心网络设备、地址分配、域名解析服务都在美国为首的西方国家严密控制中。中国的“.CN”域名被置于美国因特网根域名服务器管理解析的第三级。一旦美国瘫痪、停止中国的域名解析服务,对中国必然造成毁灭性的网络信息空间和国计民生灾难。", "另一方面,我国许多网络企业的主导权控制在外国利益集团手中。国人耳熟能详的“中国互联网”前20名企业巨头,几乎清一色地被西方资本主要是美国华尔街资本控股。", "面对美国军方、情报机构、跨国资本、买办资本发动的网络战,中国应该怎么应对?", "“独立自主、自力更生”这八个字,依旧是我们应对美国帝国主义霸权的有效武器。真正的核心技术买不来。今天,勒索病毒席卷全球,中国媒体马上跟进制造恐慌,进而大肆吹捧微软,侠客岛等媒体称“微软自己也不知道漏洞存在”,还特地文字加粗,生怕别人怪罪微软,进而,许多媒体开始认为中国应该大规模引进win10,这实际上是洋奴思想在作怪。信奉洋奴哲学的势力客观上成为美国霸权主义的内应,处处限制民族自主力量的增长,处处为跨国公司在中国市场攻城略地鸣锣开道。", "对抗美国对中国全方位的政治渗透、经济侵略与技术打压,中国需要形成足够坚定的国家决心,调动足够规模的国家力量,也要调动市场的力量。中国国产CPU、操作系统等要进一步发展,仅仅依靠国家行政的力量是有限的,中国必须反垄断,诸如对垄断我国操作系统市场的微软、苹果、谷歌进行反垄断,政府采购目录向自主可控的核心技术倾斜。历史经验告诉我们,垄断往往不能通过纯粹市场竞争来打破,而需要运用“反垄断法”等手段来打破。要正确引导市场,使我国的市场同样成为我国自主操作系统、芯片的助力。", "我国是一个网络大国,成为网络强国要求我们国家网络安全的战略必须要有远见,要未雨绸缪,防患于未然,这绝不是杞人忧天。自2010年美国发布《网络安全综合计划(CNCI)》计划以来,各国均在制定各自的网络安全战略及行动计划,以应对日趋复杂的网络环境。其他国家都在做,而我们不做,不做就要落后,落后就要挨打。", "勒索病毒是中国面对的一次挑战,也是一次反思自身网络战能力的契机。如果中国在西化精英、西化媒体的误导下,继续运用“使用外国落后技术的问题、由购买外国先进技术来解决”的洋奴思维,大规模引进win10等美国新一代网络产品,让美国对中国的网络霸权进一步扩张,侵蚀中国自主产品的市场空间,打压打散中国自主研发团队,进而使我国自主研发平台发生断层,这种洋奴行为带来的灾难将比勒索病毒更加巨大。其后果,将是对整个国家的勒索,对整个民族的勒索。", "习总书记在多次讲话中提到,“没有网络安全就没有国家安全”,这话在这次黑客的袭击中再次应验,众多高校和研究机构受到影响,甚至派出所户籍管理系统和银行的ATM提款系统也出现了瘫痪,所以尽快实现国家网络信息安全是我们国防发展战略的不可或缺的一部分,也是中华民族伟大复兴不可缺少的一部分,只有实现国家网络信息安全了,国防建设就少了一份重担,我们的经济发展也才能无后顾之忧。建议尽快组织制定国家网络安全发展战略,通过国有资本投入建立扛起社会主义大旗的网络世界中国边防的哨岗,从源头上杜绝美国对我们的虎视眈眈。", "(责任编辑:张海潮 CM013)"], "bs_rank_pos": 3}, {"is_selected": false, "title": "关于勒索病毒的解决方案_百度经验", "most_related_para": 5, "segmented_title": ["关于", "勒索", "病毒", "的", "解决方案", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["“", "勒索", "病毒", "”", "全球", "爆发", ",", "通过", "蠕虫", "式", "传播", "在", "企业", "、", "校园", "内网", "大面积", "感染", ",", "一", "台", "中招", ",", "一", "片", "遭殃", "!", "针对", "此次", "危害极大", "的", "勒索", "病毒", ",", "互联网", "企业", "巨头", "加班加点", "的", "解决", "系统漏洞", ",", "下面", "分享", "360", "提供", "的", "解决方案", ",", "希望", "可以", "帮", "到", "大家", "。"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["360", "安全卫士"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["1", "针对", "未", "安装", "360", 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"经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "bs_rank_pos": 4}], "answer_spans": [[3, 21]], "yesno_answers": ["Depends", "Depends"], "fake_answers": ["病毒目前已经在一定程度上控制住了,但病毒开发者仍在持续对抗升级"], "question": "勒索病毒解决了吗", "segmented_answers": [["可以", "清除", "病毒", "，", "但", "文件", "无法", "恢复", "。"], ["目前", "已经", "在", "一定", "程度", "上", "控制", "住", "了", "，", "但", "病毒", "开发者", "仍", "在", "持续", "对抗", "升级", "病毒", "。"]], "answers": ["可以清除病毒，但文件无法恢复。", "目前已经在一定程度上控制住了，但病毒开发者仍在持续对抗升级病毒。"], "yesno_type": "OPINION", "answer_docs": [1], "segmented_question": ["勒索", "病毒", "解决", "了", "吗"], "question_type": "YES_NO", "question_id": 15, "fact_or_opinion": "OPINION", "match_scores": [0.9230769230769231]}
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并且不动明王不是佛只是菩萨 一切确实都是不动明王心中的幻想 所谓人未动 是你的心在动 片子里所有人都是明王的三念而已 结尾那把刀是个重点,明王把刀交给书生,书生又还给恶人,恶人说我来保护你,至此三念已尽斩。 其实也可以说,姑姑是明王最大的心魔,明王对姑姑说我心还系着你,怎么能成佛。 姑姑斩不断三念,明王放不下姑姑,所以明王化三念:书生书童和恶人来点化姑姑。 最后姑姑明白了不动明王的心,放下执念,救回众人,由恶转善,然后随明王而去。 为什么随明王而去呢?这就很好的说明,因为他们本是一体,三念皆斩,心魔已去,是为成佛也。 所谓的成佛只是一个象征意义,佛与菩萨又有何区别,都是具有大智慧者,何来阶级之分,万物只在一念间。 以上就是在下的一些", "最后,曾和尚说我心中还有你,我无法成佛,反过来其实就是我成不了佛,咱俩继续处对象吧!他苦于无法开口,毕竟是他先抛弃姑姑。于是我想能跟姑姑在一起的人肯定是高人,造个人肯定不在话下,主角3人其实就是被曾和尚造出来为了找借口跟姑姑复合用的,结果最后大家看到了,他成功了,在姑姑最伤心最需要人的时候他出现了,把姑姑带走了,同时也玩弄了一群女人的心。结尾看到的后夏讲故事以及为什么主角一个人出去但画面一转又出现了3个人,是因为朱孝廉一个人回去后,曾和尚看他任务完成的不错,(但完成任务就必须消失,就跟生完孩子就杀掉男人是一样的,这点跟姑姑是一个想法,不亏是夫妻)就把那2人又造出来给他做伴,让他路上不寂寞,安慰他几句就等不及了,刚出门就让他们3个消失自己去陪姑姑了。", "我认为是这样的,曾志伟在片中演得是不动明王这个是公认的,她在结尾去找闫妮的时候对她说心里没有忘了她这也是你应该知道的,至于最后主角三人为什么会消失我是这么想的,这一场梦就好似不动明王的心魔一样,不动明王之所以不能成佛是因为心中还不能看破红尘。其实朱孝廉就是不动明王,而缠绕的朱孝廉,让他最头疼的是什么?爱情!而缠绕曾哥的呢???也是爱情!这就是曾哥对心魔的一场战斗,最终朱孝廉没有得到任何爱情让冥王了解到爱情的可怕,朱孝廉就有点类似曾哥的元神,是为他解开心魔的,最终心魔解开了,他和姑姑走到了一起,而身为元神的朱孝廉自然而然的也就从曾哥的脑海中消失了,朱孝廉可以说是曾哥的一种想象罢了", "姑姑放弃了管制仙女们 跟自己的爱人走嘞 小芍药做了领导人 让仙女们可以自由去爱 不过 她却为了让朱孝廉能去现世自由发展 而放弃了自己的爱情~ 追问这我知道啊,只是为何朱孝廉回到了现世,另外两个人也回来了呢?而且为何结局他们成了一缕青烟,和尚的笑又表示什么呢? 回答那个呀 是比较有玄机~ 我觉得大致意思就是那种 虚既是实 实亦是虚吧~", "我刚开始没看懂,现在也没看懂,这是怎么一回事呢?我觉得这部剧有些无聊啊,你嘞?", "姑姑放弃了管制仙女们 跟自己的爱人走嘞 小芍药做了领导人 让仙女们可以自由去爱 不过 她却为了让朱孝廉能去现世自由发展 而放弃了自己的爱情~"], "bs_rank_pos": 1}, {"is_selected": false, "title": "画壁最后的结局是什么意思、想表达什么?_百度知道", "most_related_para": 0, "segmented_title": ["画壁", "最后", "的", "结局", "是", "什么", "意思", "、", "想", "表达", "什么", "?", "_", "百度", "知道"], "segmented_paragraphs": [["佛家", "讲", "生", "。", "道家", "谈", "灭", "(", "也", "可", "说", "是", "抑制", ")", "所以", "佛家", "的", "是", "生", "三", "尸", ",", "道家", "是", "斩三尸", "。", "@", "所谓", "幻", "由", "心", "生", ",", "曾", "和尚", "年轻的时候", "跟", "姑姑", "有", "过", "一", "段", "感情", ",", "后来", "不知道", "什么", "原因", "两个人", "无法", "在一起", ",", "和尚", "一直", "念念不忘", "着", "姑姑", ",", "曾", "和尚", "法号", "不动", ",", "实际", "就是", "不动明王", ",", "可是", "因为", "放不下", ",", "一直", "不能", "成佛", ",", "山贼", "、", "书生", "、", "仆人", ",", "就是", "他", "的", "恶", "、", "执", "、", "善", ",", "佛家", "里", "称为", 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"所以", "书生", "前后", "对待", "山贼", "的", "态度", "很大", "。"]], "paragraphs": ["佛家讲 生。道家谈 灭(也可说是抑制)所以佛家的是生三尸,道家是斩三尸 。@所谓幻由心生,曾和尚年轻的时候跟姑姑有过一段感情,后来不知道什么原因两个人无法在一起,和尚一直念念不忘着姑姑,曾和尚法号不动,实际就是不动明王,可是因为放不下,一直不能成佛,山贼、书生、仆人,就是他的恶、执、善,佛家里称为斩三尸,斩掉他们需要大智慧,也就是说抛弃了善、执、恶,无欲无求,便可成佛,所以他把三尸化为三个人,进去画壁,最后成功的放下了对姑姑的情感,有一点要说明,画壁里的一切,仙女,包括姑姑,都是和尚创造出来的,那是他的幻境。 山贼、书生、仆人最后灰飞烟灭,实际是不动尊菩萨的三种意念随风而散,他参悟得道。。", "有两个解释吧:1/创造的三个人,分别代表了善、恶、执,是为了点化仙境里的姑姑的,最后成功点化了,而三个人最后化成一道烟,和尚只是笑笑。 2、三个人是真实的存在的,而这个庙是不属于凡间的,书生最后是要干一番大事的,所以这个地方也算是一个锻炼吧,所以书生前后对待山贼的态度很大。"], "bs_rank_pos": 2}], "answer_spans": [[0, 222]], "fake_answers": ["画壁的世界其实是真实的,里面自成空间,而其中的人物,其实是姑姑画的,只是因为姑姑法力高深,所以画的人都成精了,主角的真身其实没进入,只是一缕意念进入了,你也可以理解为,主角的意念以画像的形式进入了,所以当主角他们都死后,姑姑一挥手他们就复活,反正都是画,可以抹去,亦可以画上。当主角最后会回归现实后,书童和强盗还在,并没有留在画像是因为,主角的意识从画壁中出来,回归肉体,所以他知道自己在画壁里所经历的事情,而书童和强盗的意识并没有回归肉体,一缕还停留在画壁里,所以不知道画壁里所发生的事,说白了,如果他俩和主角一起回来就会知道画壁里经历的事情了。而那和尚是不动冥王,和姑姑一样是大能,就是他送主角他们进入画壁的,目的是想点悟他们,最后只有主角也就是那书生领悟了人生道理,而书童和"], "question": "画壁结局什么意思", "segmented_answers": [["画壁", "讲", "的", "是", "一", "代", 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["画壁讲的是一代大师（曾志伟饰演的不动）突破贪、色、痴、恶等种种心结最终功德圆满、立地成佛的故事。故事中的僧人不动，在我看来指的就是佛教密宗中的五大明王之首：不动明王。而故事中的姑姑，生活在百花丛中，周围女子皆有花名，唯独姑姑剧中一直没有提及其姓名；再看其一身蓝绿色的装扮，你想到了什么？孔雀？不错！在我看来她应该是暗指佛教五大明王中的孔雀明王。而画壁的整个故事的本来面目就是：在顿悟成佛前，孔雀明王和不动明王是一对恋人，但正是由于这一段恋情，使得两人始终无法修得圆满。（不动和尚在剧中那就经典台词：“心中还有你，我怎么成得了佛”很好的印证了这一点。）所以两位明王在以壁画为界的两个空间里各自修行。不动明王需要破除的是恶念、执念和善念（佛教中称之为三尸），而孔雀明王需要破除的却是怨念和贪念。而要真正破除这些杂念，做到无欲无求、无色无相的境界，就需要一番历练和磨难。所以不动明王就将其恶、执、善三念化为三个人（孟龙潭是恶、朱孝廉是执、后夏是善），进入孔雀明王修炼的画境进行历练（难道这就是密宗的双修历练法门？）经过种种艰难的历练后，孔雀明王在不动明王的点拨下，最终抛开种种俗念顿悟成佛。（注意，这里说的是抛开各种俗念，而不是万念俱灰。因为只要有人的存在，这些欲念就无法消除。这就与剧中姑姑虽然通过自己强大的法力，将剧中的各色人等杀的灰飞烟灭，而后又在不动和尚的点拨下将所有人物恢复原形的剧情设计契合的天衣无缝。）而不动明王的恶念、执念、善念三尸，在完成了自己的使命后，随着不动明王的功德圆满、立地成佛，自然烟消云散。本剧的妙处就在于，用一个多角爱情故事为主线，暗地里却讲述了一个悟禅修佛的故事。不过这条暗线过于隐晦，没有相关宗教知识，不潜心研究的人，始终参不透其中的真相。他们三人是不动明王的化身，分别代表恶、执、善", "画壁的世界其实是真实的，里面自成空间，而其中的人物，其实是姑姑画的，只是因为姑姑法力高深，所以画的人都成精了，主角的真身其实没进入，只是一缕意念进入了，你也可以理解为，主角的意念以画像的形式进入了，所以当主角他们都死后，姑姑一挥手他们就复活，反正都是画，可以抹去，亦可以画上。当主角最后会回归现实后，书童和强盗还在，并没有留在画像是因为，主角的意识从画壁中出来，回归肉体，所以他知道自己在画壁里所经历的事情，而书童和强盗的意识并没有回归肉体，一缕还停留在画壁里，所以不知道画壁里所发生的事，说白了，如果他俩和主角一起回来就会知道画壁里经历的事情了。而那和尚是不动冥王，和姑姑一样是大能，就是他送主角他们进入画壁的，目的是想点悟他们，最后只有主角也就是那书生领悟了人生道理，而书童和强盗意念木有出来，本身就不知道画壁里的经历，没有领悟。记得最后孙丽的告白么，他说自己不想当误主角考试，说主角出去后会敢一翻大事的！如果主角没出去，是可以和自己双宿双息，但画壁外的自己就会什么也不知道在画壁里的经历，就不会领悟，考功名就没戏了"], "answer_docs": [0], "segmented_question": ["画壁", "结局", "什么", "意思"], "question_type": "DESCRIPTION", "question_id": 16, "fact_or_opinion": "OPINION", "match_scores": [0.7362428842504745]}
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"1.家装楼梯首先一个是要讲究安全,同样也都要讲究经济。其中经济包括经费上的经济以及空间经济。家装梯级的踏步高度一般设计在18MM-20MM之间(注意是高度哦),只有在公共场所的梯级踏步高度才会设计在13MM-16MM之间。其次踏步中心距,即所谓的进深(宽度)一般都会设计在230mm-270mm之间。最小不要小于220MM.踏步宽度如果设计在300MM的情况下一步迈不到,两补还不够走起来很累。但也要按照房屋的实际尺寸来量度。毕竟加厚跟直接磨边的数度是不同的!所以两者有很大的差别。", "2.踏步的长度是根据房屋的实际情况而定,一般家装的楼梯都是800mm-1200mm。可根据不同长度选择不同规格的瓷砖。如果一转楼梯的高度是192.86MM,踏步数为13踏。那么走起来是14踏的(最后一踏是直接迈到二楼的地面上的,也就是梯台)", "上面的都是小编利用手上的资料所描述的情况,但在事实上的踏步,踢脚数度还需要安装师傅量身定做出一个标准的数值。踏步跟踢脚所延伸的长宽高不同的,所以师傅要根据梯级的实际量度出每一转踏步的标准数值进行分类。小编暂时先跟你们分享这么多了,如果想了解更多。请关注我吧。我会第一时间跟你们分享更多的艺术精彩!!!!", "( 我是你们的小编陈思宁)", "精彩不断,乐趣更多哦~~亲们。。么么哒"], "bs_rank_pos": 2}, {"is_selected": false, "title": "请问室内楼梯踏步高度一般是多少?_其他_土巴兔问吧", "most_related_para": 13, "segmented_title": ["请问", "室内", "楼梯", "踏步", "高度", "一般", "是", "多少", "?", "_", "其他", "_", "土巴兔", "问", "吧"], "segmented_paragraphs": [["微信", "加", "好友", "获取", "详细", "报价"], ["装修", "顾问", "-", "馨馨", "4", "年", "行业", "经验", ",", "24", "h", "可", "咨询"], ["10", "秒", "闪电", "通过", "好友"], ["报价", "短信", "已", "发送", "到", "您", "的", "手机"], ["因", "材料", 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"1.家装楼梯一个要讲究安全,另一方面又要讲究经济.其中经济包括经费上的经济和空间的经济,家装楼梯的踏步高度一般设计在180mm-200mm之间,只有公共场所的楼梯踏步高度才会设计在130mm-160mm之间.其次是踏步中心距,即所为的进深,一般设计在230mm-250mm之间,最小不要小于220mm,踏步进深设计到300mm一步迈不到,两步还不够走起来很累. 2.踏步宽度的选择,一般家装楼梯的踏步宽度选择800mm,可根据洞口空间适量增加. 3踏步高度为192.86mm,踏步数为13踏,走起来14踏(最后一踏直接迈到二层地面上,也就是楼梯行里的“下跨”). 4、延1950mm的边有5个标准踏步,踏步中心距离为230mm,然后是一个800mm的平台(或是两个大三角或两个小三角带一个中心鱼头)作为90度转弯,下面接上7个标准踏步,中心距为230mm就可以了.", "你好,这样子的话你可以试试看下面的方法介绍 一般家里楼梯高度都在20公分左右,包括大厂家设计的楼梯,一般阁楼从地面到楼板下的高度是两米六,所以一些厂家出厂的报价单也以十三步报价,每步都是二十,因为我也是做品牌楼梯的,你的高度不高,至于休息步宽度没有限制,如果空间允许的话,和你楼梯的宽度一样大,如果楼梯是八十宽,休息步也做成80*80的平台,如果空间不允许的话,那就尽量以自己舒服为原则设计了.", "个人建议按照业主的舒适度来选择高度比较好:一般家装标准的楼梯踏步尺寸高度为210mm、宽度300mm比较好,这个高度不会太低也不会太高,对于老人小孩都没有影响,宽度300mm这个是适中的,低于300mm会感觉很窄,而且这个是根据一般的尺寸来的,当然每个业主的需求都不一样,所以并不是一定的,希望对你有帮助"], "bs_rank_pos": 3}, {"is_selected": false, "title": "楼梯踏步高度的相关规范01_图文_百度文库", "most_related_para": 2, "segmented_title": ["楼梯", "踏步", "高度", "的", "相关", "规范", "01", "_", "图文", "_", "百度", "文库"], "segmented_paragraphs": [["楼梯", "踏步", "高度", "的", "相关", "规范", "01", 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"(", "\"", "id", "\"", ")", ".", "value", ";"]], "paragraphs": ["给from一个名字然后在JavaScript的地方就可以用form的名字来调用form表单里input元素的value属性可以得到值,并把值赋给a,最后用JavaScript的alert()打印方法打印出来。 　　参考如下: 　　 　　 　　 　　 　　 　　
 　　 　　 　　
 　　 　　 　　给from一个名字然后在JavaScript的地方就可以用form的名字来调用form表单里input元素的value属性可以得到值,并把值赋给a,最后用JavaScript的alert()打印方法打印出来。", "给from一个名字然后在JavaScript的地方就可以用form的名字来调用form表单里input元素的value属性可以得到值,并把值赋给a,最后用JavaScript的alert()打印方法打印出来。 　　参考如下: 　　 　　 　　 　　 　　 　　
 　　 　　 　　
 　　 　　 　　给from一个名字然后在JavaScript的地方就可以用form的名字来调用form表单里input元素的value属性可以得到值,并把值赋给a,最后用JavaScript的alert()打印方法打印出来。", "document.getElement.ById(\"id\").value;"], "bs_rank_pos": 2}, {"is_selected": false, "title": "如何js获取当前input的value值_百度知道", "most_related_para": 0, "segmented_title": ["如何", "js", "获取", "当前", "input", "的", "value", "值", "_", "百度", "知道"], "segmented_paragraphs": [["<", "input", "type", "=", "\"", "text", "\"", "id", "=", "\"", "txt", "\"", "value", "=", "\"", "11", "\"", "/", ">", "<", "script", ">", "alert", "(", "document", ".", "get", "Element", "ById", "(", "\"", "txt", "\"", ")", ".", "value", ")", "<", "/", "script", ">", "代码", "如", "上", ",", "通过", "id", "获取", "这个", "input", ",", "然后", "直接", "获取", "value", "就", "可以", "了"]], "paragraphs": ["代码如上,通过id获取这个input,然后直接获取 value 就可以了"], "bs_rank_pos": 3}, {"is_selected": false, "title": "input value-怎么获取input里的value值——CSDN问答频道", "most_related_para": 20, "segmented_title": ["input", "value", "-", "怎么", "获取", "input", "里", 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"怎么获取input里的value值", "各位大神好, 怎么用jq获取这个input里的value值啊,我用的是$(\".st\").attr(\"value\");但是获取出来的是空,这是什么原因啊,请指教", "yuke198907 2015.07.21 11:10", "Gemini_CGB 回复qq_28195119: 我也觉得应该是这样来取值的,你可以先看下$(\".st\")拿到的标签是不是想要的标签,可能页面中有很多持有这个class的标签,如果是这样的话,你可以多加些选择器  接近2 年之前 回复", "CSDNXIAON 2015.07.21 11:13", "FF下innerHTML获取不到input标签中value的值解决办法----------------------同志你好,我是CSDN问答机器人小N,奉组织之命为你提供参考答案,编程尚未成功,同志仍需努力!", "yuyang199 2015.07.21 11:15", "input里面用$(\".st\").val();应该就可以了", "yuyang199 2015.07.21 11:15", "input里面用$(\".st\").val();应该就可以了", "qq806142724 2015.07.21 11:21", "纯js方法的话是document.getElementById(\"这里填写id\"); Jquery的话是$(\"#这里填写id\");", "strutce 2015.07.21 11:22", "document.getelementbyid", "qq_28195119 回复丵鹰: 我用的document.getelementbyclass (\"st\").value;弹出的是undefind,也就是没有获取到  接近2 年之前 回复", "strutce document.getelementbyclass 或document.getelementbyname都可以试下  接近2 年之前 回复", "showbo 2015.07.21 11:34", "你在什么位置获取的?如果是在dom之前就执行当然获取不到,此时代码执行时dom还没有生产,放到dom对象后面。", "同时检查是不是有多个class为st的对象,如果是调用val或者attr只会获取到第一个st样式的值,如果不是input对象也获取不到value属性", "showbo 回复qq_28195119: 要获取所有st对象的值?map下得到数据再join下。alert($(\".st\").map(function(){return this.value}).get().join()),有帮助记得采纳。。  接近2 年之前 回复", "qq_28195119 我是在dom之后获取的,是有多个class为st的对象,如果有多个input,那我该如何获取value呢?  接近2 年之前 回复", "save4me 2015.07.21 12:05", "你的页面上面有几个class为st的元素?如果有多个,使用$(\".st\").eq(n).val(),其中n为st元素的序号,以0开始", "longbing_85 2015.07.21 12:39", "运行页面,查看一下网页源代码,看看你的input=\"text\" ... 后面的value是什么?id是什么?class是什么 确认一下input相关属性的值和你期望的一样, 然后再用上面各位大神的方法试试~~", "插入链接  本地上传 网络图片  上传中... 浏览 上传图片  插入图片", "oSanYeCao1234567 推荐了: 从windows导入mac的android项目,下载到真机时出现空指针错误", "showbo 推荐了: JQUERY,动态加载问题,谢谢", "oSanYeCao1234567 推荐了: java为什么有些字符串转成字节数组后不能再转回字符串了", "fight_in_dl 推荐了: 问个很白痴的问题,为什么函数不能写在FOR循环里面", "caozhy 推荐了: C++赋值语句问题(最难的赋值语句)"], "bs_rank_pos": 4}], "answer_spans": [[0, 14]], "fake_answers": ["1、根据id获取input节点2、通过value获取input的值"], "question": "怎么获取input的value值", "segmented_answers": [["1", "、", "根据", "id", "获取", 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"图书馆"], ["分享", "此", "文"]], "paragraphs": ["看个人体质,有些人吸收比较好,总之吃下去肯定可以减肥", "你好,您这种情况可以考虑中药进行治疗。您最好去中医辨证后汤药联合中药外洗治疗。", "忌乱吃药!忌乱用药!忌乱吃保健品。“要对症下药”。", "排毒不需吃药,不用花钱。养生应尽量以食疗为主最好。", "要认清排毒概念:中医认为身体内多余的东西称为毒。西医没有\"毒\"的名词。", "疏通肠道、尿道、血管、气管、汗腺等,使各路排毒管道通畅,以便于进行“体内大扫除”。", "1、“宿便是百病之源”应多吃高纤维食物:粗粮,蔬菜和水果,能促进肠道蠕动,增加粪便体积和软化粪便。常吃全麦片、玉米片、或红薯、香蕉等、蜜糖、芋头、马铃薯南瓜等。具有良好的通便功效。可把肠道垃圾、毒素清除。 2、定期1个月禁食1个中午餐,只喝开水,晚餐吃蔬菜和水果,全麦片、玉米片、或红薯促使体内毒素排出。 3、多饮水促进肾脏排毒", "瘦身的话,我知道", "第一盒:肠胃好的话,zyh 138 0 153 6330饭后2个小时吃一条。肠胃不好的话,饭后半个小时吃一条。 第二盒:早饭前半个小时吃一条,晚饭前半个小时吃一条。", "吃搜啦:之前记得先喝一杯温水。多吃水果蔬菜。", "酵素果冻:60一盒。使用方法是前一个星期适应期一天一条,一盒七条,一袋15克,睡前服用最佳,开袋即食。想要减肥的便秘者,在适应期过后一天两条,饭後两小时服用,早饭後两小时或者是晚饭後两小时吃。更年期的男女、孕妇、老人小孩都可以吃,它主要是排出宿便,排出体内不需要的废弃物,从而达到减重的效果。味道是可口蓝莓味!", "躺着就可以减肥和躺着赚钱没啥区别 区别就是没减的和没赚钱的比有减和有赚钱的多了不知道多少万倍", "建议慎用减肥产品,一些减肥药会导致作呕、心跳加速、肝脏甚至肾脏受损。乱吃减肥药的危害很大,减肥不是一蹴而就的事情,一定不要乱吃药物,以免对身体的某些部位造成伤害,影响自己的身心健康!许多减肥药里面含有泻药的成分,泻药会导致服用者的肠胃退化,服用得多,服用者就无法自动排泄。", "是酵素果冻是用来排毒减肥的。", "人每天除了吃些五谷杂粮和蔬菜之外,多吃些水果对我们的身体健康有着很大的帮助。", "1.保养皮肤:水果中含丰富的抗氧化物质维生素E和微量元素,可以滋养皮肤,其美容效果可不是一般的化妆品可比的。 如:苹果中含有丰富的胡萝卜素、维生素、铁等营养元素.还含有较多的微盘元素—镁。这些物质均有利于皮肤红润、光泽、有弹性。其中维生素C能抑制黑色素的 形成,并防止黑色素在人体皮肤内沉着。 2.减缓衰老:水果除了含有我们已知的营养素外,还富含大量天然植物化合物。这些物质可以调节解毒酶的活性,改善激素 代谢,具有抗菌抗病毒的能力,并且发挥着延缓衰老的作用。如:猕猴桃。它富含维生素A,C,E以及人体必需的微量元素等营养成分,尤其是它所含的氨基酸, 能帮助人体制造激素,减缓衰老。 3.预防疾病:由于水果中含有很多维生素,微量元素,所以还可以用来预防疾病。如橘子。橘子味甘酸,性凉,具有开胃理气、止渴润肺的功效,对于口渴咽干、消化不良、干咳无痰等症有一定的治疗效果,同时还能预防心血管疾病和糖尿病。 4.降低血压:水果防治心血管病,并可阻遏强致癌物质亚硝胺的生长和癌细胞增生,产生某种酶的活性。资料分析,水果能 增进消化,改善心肌功能,对维持人体内酸碱平衡起着调节作用。 如山楂。山楂能防治心血管疾病,具有扩张血管、强心、增加冠脉血流量、改善心脏活力、兴奋中枢神经系统、降低血压和胆固醇、软化血管及利尿和镇静作用;防", "治动脉硬化,防衰老、抗癌的作用。", "5.减肥瘦身:有些水果中含有丰富的食物纤维,纤维是不能为小肠所消化的碳水化合物,在结肠内,纤维可提供给肠腔营养", "物质,这有助于促进身体的新陈代谢以及帮助抑制食欲。", "如柚子。它属于柑橘类,维生素c的含量丰富,而且纤维含量也很多,易产生饱腹感。然而它的热量却很低,可以和西瓜媲美。", "我一天吃一个就拉肚子,还没效果", "百度搜索“就爱阅读”,专业资料,生活学习,尽在就爱阅读网92to.com,您的在线图书馆", "分享此文"], "bs_rank_pos": 2}, {"is_selected": false, "title": "珍嗖啦的吃法——服用方法_搜了网", "most_related_para": 1, "segmented_title": ["珍", "嗖", "啦", "的", "吃法", "—", "—", "服用", "方法", "_", "搜", "了", "网"], 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"也", "蠢蠢欲动", "想", "加入", "微", "商", ",", "现在", "还", "不", "晚", ",", "真", "的", "不", "晚", ",", "如果", "你", "相信我", ",", "就", "趁着", "【", "ZNSOLAA", "珍", "嗖", "啦", "】", "正在", "招收", "代理", "之", "时", "赶快", "加入我们", "的", "微", "商", "队伍", "中", "吧", "!", "加入我们", ",", "全方位", "的", "培训", ",", "一对一", "的", "指导", ",", "无需", "囤货", ",", "没有", "高风险", ",", "却", "有", "高收益", ",", "真正", "让", "你", "零", "压力", "创业", "!", "时间", "自由", ",", "随时", "排毒", ",", "随时", "收钱", ",", "不", "耽误", "任何", "你", "想", "做", "的", "事情", "!", "有意", "者", "赶紧", "联系", "微信", "tytytyni", "吧", "!", "或者", "扫描", "上方", "的", "二维码", ",", "我在这里等你", "!"]], "paragraphs": ["夏季来临,马上就要露胳膊露腿了,可是还是这么胖。怎么办?天天喊着减肥减肥,却又有几个人能真正做到少吃多运动的呢!好多人认为吃减肥药就可以减肥,而这种吃了会拉肚子的产品真的健康吗?当你短期瘦下来之后减掉的不是脂肪而是水份,人体的水份大量流失导致体重变轻你的肠胃就会受到很大损伤。很明显这种传统的减肥药已经很不可取了!现在我们推出的的【ZNSOLAA珍嗖啦】一款可以促进排毒的纯酵素果冻,酸酸甜甜的蓝莓口味,非常好吃哦,关键是成份非常的安全,健康瘦身的同时也不用承受减肥的痛苦,你可以试试哦。", "【ZNSOLAA珍嗖啦】不是减肥药,是一种酶。主要排除体内多年积存的毒素,像肝毒、肺毒,从而达到一种健康瘦身的目的,即便正常排便的人也可以使用。因为体内会滞留食物残渣,肠道上也会有油,所以正常排便也可以吃哦~如果刚开始有拉肚子请不要担心哦~这是在排出毒素,排出毒素后才开始减脂哦~", "主要功效:排出毒素、降火、养颜、解酒,养颜,还能减体重。", "适宜人群有便秘的人,腰围粗大的人,口臭色斑脸上没有光泽的,养神护体的老人也可以用。还有大量消耗的体力劳动者、长期熬夜的,抽烟饮酒的,社会压力比较大的,晚上睡不着觉的,都可以用。它是纯植物,纯果实的,成分非常安全,对身体没有任何副作用,所以孕妇小孩也是可以放心食用的。", "酶存在于各种动植物的体内,正常维护我们的消化系统。因此,【ZNSOLAA珍嗖啦】能够清理肠毒,排出宿便,减少大肚腩,调理内分泌,对脸上的痘痘也有很大的作用。也有很多人脸上的雀斑,痘痘,都是因为长时间积累出来的,体内有很多的毒素排不出来,长期累积,体内的毒素慢慢累积到一定程度就会造成雀斑,痘痘,因此想要脸上颜如玉,就要体内清如水。", "【ZNSOLAA珍嗖啦】主要成分:", "诺丽果酵素、蓝莓酵素、柑橘多酚、果糖、海藻胶、水溶性膳食纤维、果汁、木糖醇、魔芋粉。", "它的调理分四个阶段:", "第一阶段:排毒,很多人调理完觉得便便畅通,量多,这是排出身体隐藏的毒素; 第二阶段:养护肠道; 第三阶段:改善皮肤; 第四阶段:改变体质,让酸性体质变成碱性体质,碱性体质的好处你们明白吧。", "最显著的特点:1、降低癌症风险;2、皮肤有光泽;3、不易疲劳;4、不易发胖。", "【ZNSOLAA珍嗖啦】的功效不只是减肥,它还有其他的功效哦!", "1、排出宿便毒素(便秘,宿便,消化障碍,胃胀等) 2、排出血液毒素(血栓,静脉曲张血脂,血液粘稠) 3、排出化妆品毒素(化妆品含有防腐剂铅汞等,长期使用加重皮肤代谢困难) 4、排出皮肤毒素(色斑,粉刺,痤疮,暗沉,发黄)", "5、排出子宫毒素(子宫毒素危害,月经不调,妇科炎症等)", "6、排出代谢毒素(肥胖,体虚,大肚子,亚健康,免疫力)", "也许你在羡慕别人月入上万,你也蠢蠢欲动想加入微商,现在还不晚,真的不晚,如果你相信我,就趁着【ZNSOLAA珍嗖啦】正在招收代理之时赶快加入我们的微商队伍中吧!加入我们,全方位的培训,一对一的指导,无需囤货,没有高风险,却有高收益,真正让你零压力创业!时间自由,随时排毒,随时收钱,不耽误任何你想做的事情!有意者赶紧联系微信 tytytyni 吧!或者扫描上方的二维码,我在这里等你!"], "bs_rank_pos": 4}], "answer_spans": [[13, 31]], "fake_answers": ["饭后2个小时吃一条。肠胃不好的话,饭后半个小时吃一条。"], "question": "珍嗖啦怎么吃法才正确", "segmented_answers": [["每日", "早", "、", "晚饭", "前", "30", "分钟", "，", "分别", "食用", "一袋", "即可", "恢复", "体", "内", "酵素", "平衡", "。", "睡前", "空腹", "服用", "效果", "也", "很好", "，", "但", "睡前", "不", "建议", "大量", "喝水", "。"], ["开始", "食用", "珍", "嗖", "啦", "时", "的", "一", "周", "，", "无论", "是", "减肥", "还是", "排毒养颜", "都", "只", "一天", "一袋", "，", "睡前", "吃", "，", "给", "身体", "一", "个", "适应期", "，", "排", "宿便", "。", "一周后", "便秘", "或", "减肥", "者", "可以", "根据", "自身", "的", "身体", "适应", "情况", "增加", "用量", "，", "一天", "两", "袋", "/", "三", "袋", "，", "饭后", "两小时", "吃", "。", "就是", "早饭", "后", "两小时", "，", "午饭", "后", "两小时", "，", "吃", "嗖", "啦", "要", "多喝水", "，", "才", "可以", "排毒养颜", "。"], ["饭后", "2", "个", "小时", "吃", "一", "条", "。", "肠胃", "不好", "的", "话", "，", "饭后", "半个小时", "吃", "一", "条", "。"]], "answers": ["每日早、晚饭前30分钟，分别食用一袋即可恢复体内酵素平衡。睡前空腹服用效果也很好，但睡前不建议大量喝水。", "开始食用珍嗖啦时的一周，无论是减肥还是排毒养颜都只一天一袋，睡前吃，给身体一个适应期，排宿便。一周后便秘或减肥者可以根据自身的身体适应情况增加用量，一天两袋/三袋，饭后两小时吃。就是早饭后两小时，午饭后两小时，吃嗖啦要多喝水，才可以排毒养颜。", "饭后2个小时吃一条。肠胃不好的话，饭后半个小时吃一条。"], "answer_docs": [2], "segmented_question": ["珍", "嗖", "啦", "怎么", "吃法", "才", "正确"], "question_type": "DESCRIPTION", "question_id": 19, "fact_or_opinion": "OPINION", "match_scores": [0.9473684210526315]}
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　　执行这条命令后,会显示当前可用内存(Free)与最大可用快(Largest)的数值,网络管理员把这两个值进行比较,就可以判断碎片对路由器性能的影响。这主要是把路由器的可用内存与最大可用快的大小进行比较。 　　二、如果路由器的可用内存与最大的可用快大小比较接近时,表示虽然路由器存在碎片但是影响不大。但是若最大可用的块很小,如只有最大可以用内存的几十分之一,那么就说明路由器内存碎片问题比较严重了,路由器的可用内存为20M,而最大可用块的大小为15M的话,则表示路由器内", "一般2M带宽使用路由器打游戏基本没什么问题,如果游戏对网速要求高的可以开4M的,其次就可能是路由器的问题了(一般使用时间长了不关就可能出现上述情况,重启即可),网线如果是自己做的可以考虑买现成的,自己做的如果能上网,基本网线没问题"], "bs_rank_pos": 1}, {"is_selected": true, "title": "我的网速很快 但是wifi很卡 什么原因_百度知道", "most_related_para": 0, "segmented_title": ["我", "的", "网速", "很快", "但是", "wifi", "很", "卡", "什么", "原因", "_", "百度", "知道"], "segmented_paragraphs": [["信号", "不好", "或", "受到", "外界", "干扰", "都会", "造成", "WIFI", "很", "卡", "。", "　　", "增强", "WIFI", "的", "办法", ":", "　　", "1", "、", "不要", "把", "路由器", "藏起来", "大部分", "人", "会", "因为", "路由器", "难看", "或者", "挡路", "而", "把", "它们", "藏起来", ",", "但", "把", "路由器", "放在", "壁橱", "或者", "柜子", "里", "会", "降", "慢", "WiFi", "的", "速度", ":", "墙壁", "和", "门", "会", "削弱", "并", "吸收", "信号", "。", "在家里", "找", "一", "个", "中心", "位置", ",", "将", "路由器", "放在", 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127ms time后面的值一般是70左右,更好的为30左右!(我现在这边的无线不太好) 如果三四百,那就是电信网络的问题。(如果time值正常,如上所说,那么估计是你电脑系统本身的问题!病毒之类……)", "电脑系统的问题,重装的系统于机器硬件的兼容性不是很好,这个很正常的,像以前的番茄花园就对有些联想的品牌机兼容不是很好,系统本身就运行慢了更别说上网了!建议换个系统试试看!", "小孩别乱搞 叫大人或者店里做吧", "那就是电信的问题了"], "bs_rank_pos": 3}], "answer_spans": [[102, 184]], "fake_answers": ["碎片对路由器性能的影响。这主要是把路由器的可用内存与最大可用快的大小进行比较。　　二、如果路由器的可用内存与最大的可用快大小比较接近时,表示虽然路由器存在碎片但是影响不大。但是若最大可用的块很小,如只有最大可以用内存的几十分之一,那么就说明路由器内存碎片问题比较严重了,路由器的"], "question": "网络卡是什么原因", "segmented_answers": [["下载", "快慢", "通常", "是", "受", "以下", "几个", "方面", "影响", "的", "。", "1", ".", "时间", "，", "当", "在", "晚上", "，", "上网", "的", "人", "比较", "多", "的", "时候", "，", "速度", "自然", "就", "不会", "快", "，", "通常", "连", "理论", "值", "都", "无法", "达到", "，", "有时", "相关", "也", "蛮", "远", "的", "，", "这", "是", "正常", "的", "。", "2", ".", "所", "下载", "的", "文件", "的", "资源", "总数", "，", "因为", "现在", "很多", "工具", "都是", "采用", "类似", "BT", "的", "原理", "了", "，", "下载", "的", "人", "越", "多", "，", "资源", "数", "越", "高", "，", "下载", "速度", "就", "越", "快", "。", "3", ".", "XP", 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["下载快慢通常是受以下几个方面影响的。1.时间，当在晚上，上网的人比较多的时候，速度自然就不会快，通常连理论值都无法达到，有时相关也蛮远的，这是正常的。2.所下载的文件的资源总数，因为现在很多工具都是采用类似BT的原理了，下载的人越多，资源数越高，下载速度就越快。3.XP系统保留带宽的限制。", "大多数情况是由于路由器丢包的原因造成的。内存碎片会导致路由器报文丢失的问题，如果路由器的可用内存与最大的可用快大小比较接近时，表示虽然路由器存在碎片但是影响不大。如果确定路由器内存中没有过多的碎片，则网络管理员就要考虑是否是因为CPU过载所导致报文丢失的。如果确认路由器CPU过载，那么网络管员需要确认到底是哪一个进程占用了大量的内存。", "信号不好或受到外界干扰都会造成WIFI很卡，不要把路由器藏起来，远离家电和金属物品，重启，更新固件版本，调整路由器天线，利用啤酒罐，用密码保护网络，错开上网高峰期，买一个中继器。"], "answer_docs": [1], "segmented_question": ["网络", "卡", "是什么", "原因"], "question_type": "DESCRIPTION", "question_id": 21, "fact_or_opinion": "OPINION", "match_scores": [0.4942528735632184]}
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"paragraphs": ["君子兰可以勤浇水,每天浇1-2次都可以,但是不要多浇水,以下为君子兰正确的浇水方法:　　 　　君子兰具有较发达的肉质根,根内存蓄着一定的水分,所以这种花比较耐旱。不过,耐旱的花也不可严重缺水,尤其在夏季高温加上空气干燥的情况下不可忘记及时浇水,君子兰否则,花卉的根、叶都会受到损伤,导致新叶萌发不出,原来的叶片焦估,不仅影响开花,甚至会引起植株死亡。但是,浇水过多又会烂根。所以要好好掌握,经常注意盆土干湿情况,出现半干就要浇一次,但浇的量不宜多,保持盆土润而不潮就是恰到好处。 　　一般情况下,春天每天浇1次;夏季浇水,可用细喷水壶将叶面及周围地面一起浇,晴天一天浇2次;秋季隔天浇1次;冬季每星期浇一次或更少。但必须注意,这里指的是“一般情况“。必须随着各种不同的具体情况,灵活掌握。", "君子兰可以勤浇水,每天浇1-2次都可以,但是不要多浇水,以下为君子兰正确的浇水方法:　　 　　君子兰具有较发达的肉质根,根内存蓄着一定的水分,所以这种花比较耐旱。不过,耐旱的花也不可严重缺水,尤其在夏季高温加上空气干燥的情况下不可忘记及时浇水,君子兰否则,花卉的根、叶都会受到损伤,导致新叶萌发不出,原来的叶片焦估,不仅影响开花,甚至会引起植株死亡。但是,浇水过多又会烂根。所以要好好掌握,经常注意盆土干湿情况,出现半干就要浇一次,但浇的量不宜多,保持盆土润而不潮就是恰到好处。 　　一般情况下,春天每天浇1次;夏季浇水,可用细喷水壶将叶面及周围地面一起浇,晴天一天浇2次;秋季隔天浇1次;冬季每星期浇一次或更少。但必须注意,这里指的是“一般情况“。必须随着各种不同的具体情况,灵活掌握。", "水是君子兰体内重要组成部分。据测定,君子兰体内有60%-80%的水分,尤其是它的这些肉质根具有强大的蓄水功能,对干旱有较强的抵抗力。君子兰喜中性水,干净、无污染的自来水、井水、河水等都可以用来浇灌,但由于新鲜的自来水和井水的水温低于土温,且含有一些杂质,应放置1-2天后再使用。至于浇水这里面也有不少的讲究。对于君子兰小苗来说最好的方法是用喷壶喷灌。将喷嘴向上往叶子上喷,这样做的好处是除了达到浇花的目的外,还能洗尘,同时在叶面上留有雾珠,保证叶面有一定的湿度,在阳光下水分蒸发可降低叶表面温度,防止日烧病。但是对正在开花的君子兰就不能采用喷灌方式浇水,以防水进入叶鞘造成烂心,对于正处花期的君子兰只能向盆内灌水。那什么时间浇、浇多少水才合适呢?这主要取决于君子兰所处的环境温度。总的浇水原则是", "现在在室外一天浇一次,市内2-3天交一次"], "bs_rank_pos": 0}, {"is_selected": true, "title": "君子兰多长时间浇一次水?_百度知道", "most_related_para": 0, "segmented_title": ["君子兰", "多长时间", "浇", "一", "次", "水", "?", "_", "百度", "知道"], "segmented_paragraphs": [["君子兰", "为", "肉质", "根", ",", "根系", "有", "一定", "的", "贮", "水", "能力", ",", "因此", "它", "比较", "耐旱", "。", "但", "耐旱", "并不等于", "可以", "缺水", ",", "干湿", "不当", "是", "养", "不好", "君子兰", "的", "主要", "原因", "。", "土壤", "含水量", "在", "15", "%", "-", "20", "%", "较", "适宜", ",", "水", "含量", "达", "30", "%", ",", "时间", "稍", "长", ",", "则", "必", "烂根", "无疑", "。", "浇水", "要", "根据", "苗", "的", "大小", "和", "季节", "、", "天气", "来", "定", ",", "一般", "四", "、", "五月份", 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["君子兰为肉质根,根系有一定的贮水能力,因此它比较耐旱。但耐旱并不等于可以缺水,干湿不当是养不好君子兰的主要原因。土壤含水量在15%-20%较适宜,水含量达30%,时间稍长,则必烂根无疑。浇水要根据苗的大小和季节、天气来定,一般四、五月份,由2-3天浇1次逐步增加到1天浇1次,入秋后则由1-2天浇1次逐步减少为3-4天浇1次。 　　浇花一般常用自来水,但是因为自来水中含有少量对君子兰有害的物质,对君子兰生长不利。再则自来水与盆土温差较大,用自来水直接浇花,刺激根部不利于君子兰的生长,所以如用自来水浇君子兰,一般应将自来水在室内静置两天。这样既把水中的有害物质如漂白粉等蒸发或沉淀除去,又能使水温达到或缩小与盆土的差距。 　　注意浇水量与浇水次数。君子兰冬季浇水,如室内温度高、湿度小,可2天-3天浇水一次。如室内"], "bs_rank_pos": 1}, {"is_selected": false, "title": "君子兰几天浇一次水呢??_百度知道", "most_related_para": 0, "segmented_title": ["君子兰", "几天", "浇", "一", "次", "水", "呢", "?", "?", "_", "百度", "知道"], "segmented_paragraphs": [["春夏", "每天", "浇", "1", "次", "水", ",", "秋", "隔天", "浇", "1", "次", "水", ",", "冬", "3", "-", "-", "5", "天", "浇", "1", "次", "君子兰", "喜", "温暖", "、", "湿润", "及", "半", "阴", "环境", ",", "怕", "酷热", "和", "阳光", "直", "晒", "。", "如果", "温度", "、", "光照", "等", "条件", "不适宜", ",", "只见", "长", "叶", ",", "不见", "开花", ",", "或", "花", "小", "色", "淡", ",", "影响", "观赏", "效果", "。", "(", "1", ")", "温度", ";", "适宜", "的", "生长", "的", "温度", "为", "15", "℃", "至", "25", 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["春夏每天浇1次水,秋隔天浇1次水,冬3--5天浇1次君子兰喜温暖、湿润及半阴环境,怕酷热和阳光直晒。如果温度、光照等条件不适宜,只见长叶,不见开花,或花小色淡,影响观赏效果。 (1)温度;适宜的生长的温度为15℃至25℃之间。 (2)光照;君子兰属半阴性植物,无光不行,光强也不行。冬季应放室内向阳处,春季出室后和秋分前后宜见半光,夏季放置通风凉爽处,避免阳光直射。天气干热时要向花盆附近地面喷水,以增湿降温。花的叶子的伸展方向于光照方向平行,同时每隔1周调换一下花盆位置,将花盆转180度,这样叶片的排列就整齐美观。 君子兰适宜森林腐殖土,这种土透气性好,渗水性强,PH值6.5左右。 (3)浇水;给君子兰浇水不能等盆土干了再浇,半干就浇,使盆土上下经常保持湿润,则有利于生长发育。春夏需每天浇1次水,秋季可隔天浇1次"], "bs_rank_pos": 2}, {"is_selected": false, "title": "君子兰多久浇一次水 君子兰几天浇一次水(图) - 花木中国网", "most_related_para": 2, "segmented_title": ["君子兰", "多久", "浇", "一", "次", "水", "君子兰", "几天", "浇", "一", "次", "水", "(", "图", ")", "-", "花木", "中国网"], "segmented_paragraphs": [["发布日期", ":", "2016", "-", "09", "-", "16", "浏览次数", ":", "4934"], ["很多", "养殖", "君子兰", "的", "新手", "都", "有", "这样", "一", "个", "疑问", ",", "那", "就是", "君子兰", "多久", "浇", "一次", "水", ",", "几天", "浇", "一", "次", "水", "比较好", ",", "有人", "说", "两天", "一", "浇", ",", "也", "有人", "说", "一", "周一", "浇", ",", "到", ".", ".", "."], ["1", "、", "君子兰", "是", "肉质", "根", ",", "根", "内", "可以", "贮存", "比较", "多", 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"注意,小编提醒您,这里我们说的是“一般情况”,必须随着各种不同的具体情况,灵活掌握。比如说,晴天要多浇;阴天要少浇,连续阴天则隔几天浇一回;雨天则不浇。气温高、空气干燥时一天要浇几次;花盆大的,因土内储水量大而不易风干,可少浇;花盆小,水分容易蒸发掉,则应适量多浇。花盆放置在通风好、容易蒸发的地方,宜适量多浇;通气差、蒸发慢、空气湿度大的地方则应少浇。苗期可以少浇;开花期需多浇。总之,要视具体情况而定,以保证盆土柔润,不使太干、太潮为原则。", "【小贴士】给君子兰浇水,水也是有讲究的哦,有条件的当然是选择用磁化水最好啦,其次是活水,比如雨水、雪水或江河里的流水等,再不行就用池塘里的水,如果能不用自来水就尽量不用,如果只能用自来水,那最后先储水放着隔2-3天再浇比较好。", "特别声明:花木中国网所有原始/编译文章及图片、图表的版权所有,如要转载需注明信息来源“花木中国网 www.huamuzg.com”,否则将追究相关法律责任", "经营:国槐、白蜡、", "供应:国槐、白蜡、", "经营:紫薇,垂柳,榉", "供应:紫薇,垂柳,榉", "经营:香樟,桂花,杨", "供应:香樟,桂花,杨", "经营:园林工程", "供应:园林工程", "经营:白玉兰,五角", "供应:白玉兰,五角"], "bs_rank_pos": 3}, {"is_selected": false, "title": "君子兰几天浇一次水呀. - 爱问知识人", "most_related_para": 4, "segmented_title": ["君子兰", "几天", "浇", "一", "次", "水", "呀", ".", "-", "爱问知识人"], "segmented_paragraphs": [["君子兰", "要", "用", "专门", "的", "\"", "君子兰", "土", "\"", ".", "(", "花鸟", "市", "大", "都", "有", ")", "成分", "主要", "是", "马粪", ",", "橡", "树叶", "(", "或", "落叶松", "的", "松针", ")", "以及", "一些", "农作物", "的", "糠", "和", "皮", ".", "于", "一定", "条件", "下", "发酵", ",", 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(夏季时要避光,冬季时可适当增加光照时间)", "君子兰原产于非洲南部,生长在大树底下,所以它既怕炎热又不耐寒,喜欢半阴而湿润的环境,畏强烈的直射阳光,生长的最佳温度在18-22度之间,5度以下,30度以上,生长受抑制。君子兰喜欢通风的环境,喜深厚肥", "君子兰有发达的肉质根,能储存较多水分并有一定的耐旱性。一般经验认为土壤含水量30%左右为宜,掌握在盆土表面见干再浇,冬季3-5天浇一次,春秋天可1-2天浇一次,夏季可适当增加浇水次数。苗期需水少,开花期需水多。浇水时勿把水浇在叶片上以防叶子腐烂。发现叶子上有灰尘时,可用软布轻擦叶面,也可用软毛刷掸刷,不要大量淋水洗刷。", "君子兰有发达的肉质根,能贮存较多水分并有一定的耐旱性,掌握在盆土表面见干再浇,在开花时间可以浇说偏多一点", "君子兰有发达的肉质根,能贮存较多水分并有一定的耐旱性。一般经验认为君子兰土壤含水量30%左右为宜,掌握在盆土表面见干再浇,冬季3-5天浇一次,春秋季节可1-2天浇一次,夏季休眠期应适当少浇水,高温高湿易引起腐烂。苗期需要水少,开花期需要水多。浇水时勿把水浇在君子兰叶片上以防叶子腐烂。盆栽君子兰最忌积水,发现排水不良应该及时更换盆土。", "出版车爱护电风扇上 有容乃大 | 05-11-16 0 0 举报", "对于君子兰浇水不要太多啊!!不然根会烂的啊!君子兰本来就是怕强光应该放在比较阴暗的地方啊!等土干了再浇啊!我教你个浇水的方法你用一个盛有水的盆子把花盆放里面待你感觉表土润而不湿时将花拿出来就可以了.(润而不湿——用手拈土感觉有水却没有水滴下来)这样可以浇透水!等再次表土干了在这样浇就可以了啊", "花鸟鱼虫相关知识", "确定举报此问题"], "bs_rank_pos": 4}], "answer_spans": [[0, 18]], "answer_docs": [0], "fake_answers": ["君子兰可以勤浇水,每天浇1-2次都可以,但是不要多浇水"], "question": "君子兰多久浇一次水", "segmented_answers": [["君子兰", "可以", "勤", "浇水", "，", "每天", "浇", "1", "-", "2", "次", "都", "可以", "，", "但是", "不", "要", "多", "浇水", "。"], ["浇水", "要", "根据", "苗", "的", "大小", "和", "季节", "、", "天气", "来", "定", "，", "一般", "四", "、", "五月份", "，", "由", "2", "-", "3", "天", "浇", "1", "次", "逐步", "增加", "到", "1", "天", "浇", "1", "次", "，", "入秋", "后", "则", "由", "1", "-", "2", "天", "浇", "1", "次", "逐步减少", "为", "3", "-", "4", "天", "浇", "1", "次", "。", "如", "室内温度", "高", "、", "湿度", "小", "，", "可", "2", "天", "-", "3", "天", "浇水", "一次", "。", "如", "室内", "很", "低", "可", "延长至", "一", "周", "浇水", "一次", "。"]], "answers": ["君子兰可以勤浇水，每天浇1-2次都可以，但是不要多浇水。", "浇水要根据苗的大小和季节、天气来定，一般四、五月份，由2-3天浇1次逐步增加到1天浇1次，入秋后则由1-2天浇1次逐步减少为3-4天浇1次。如室内温度高、湿度小，可2天-3天浇水一次。如室内很低可延长至一周浇水一次。"], "entity_answers": [[], ["2-3天", "1天", "1-2天", "3-4天", "2天-3天", "一周"]], "segmented_question": ["君子兰", "多久", "浇", "一", "次", "水"], "question_type": "ENTITY", "question_id": 22, "fact_or_opinion": "OPINION", "match_scores": [0.8717948717948718]}
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"因为授权登录只会跳转到所授权的APP进行操作,", "有些童鞋可能会觉得很奇怪,超级淘到底是从哪里营利的呢?", "没错,理论上来说时间够长的确是这样,但是如果产品够安全,我们其实不用考虑它的盈利点在哪,毕竟现在烧钱的APP还是有不少的,我们只要获得我们该获得的利益就好了。", "如果这样计算,我只消费一笔,只要时间够长,是不是可以全额返回,甚至超过商品价值。", "我来为大家分析一下超级淘的安全性能去到哪里。", "下面可能就是用户最关心得一点", "超级淘目前是可支持记录消费的平台有淘宝、天猫和京东", "但实际操作发现淘宝也是进行授权登录,所以超级淘依然只能获取到头像和昵称。", "我们用淘宝来举例,登录超级淘后在淘宝选择好商品我们会进行付款,", "然后这时候很多用户会担心支付宝的信息被超级淘窃取。", "所有的操作依然实在支付宝内进行,在最重要的付款环节,超级淘是会跳转到支付宝进行操作的,也就是超级淘仅起到跳转功能而已。", "所以傲天在这里还是总结一下吧,之前很多童鞋都担心超级淘的安全问题。经过我两个多星期的测试,超级淘作为支付宝的战略合作伙伴,安全性还是有保障。", "本文仅代表作者观点,不代表百度立场。", "本文系作者授权百家号发表,未经许可,不得转载。", "还没有人评论,点击抢沙发～"], "bs_rank_pos": 2}, {"is_selected": true, "title": "超级淘可信吗,带你一探究竟", "most_related_para": 6, "segmented_title": ["超级", "淘", "可信", "吗", ",", "带", "你", "一探究竟"], "segmented_paragraphs": [["近日", ",", "网上", "关于", "“", "超级", "淘", "可信", "吗", "”", "这样", "的", "问题", "的", "探讨", "越来越多", ",", "对", "这个", "问题", "的", "了解", ",", "大", "体", "分为", "两种", ":", "超级", "淘", "是", "可信", "的", ",", "超级", "淘", "是", "一", "个", "创新", "型", "网购", "返利", "APP", ",", 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"举个例子,你一年在淘宝消费10000,单纯利息至少就能赚到1000多,超级淘还能通过邀请好友、助力等行为与其他用户产生互动,最大化自己的年化利率,而且年化利率是无上限的,线下消费也能通过上传小票存入超级淘,这是个无底洞宝矿,只要你肯深挖。", "操作体验方面,通过笔者的几次操作,已经基本懂得操作流程,超级淘在用户体验方面下了很多功夫,简单明了的视频教程、完善的客服系统都让用户有很高的评价,超级淘还支持最低1月提现至支付宝,无需绑定银行卡,这一点非常人性化,比起其他返利周期漫长的返利平台,笔者真的觉得很满足。", "随着新零售领域的飞速发展,更多的返利模式进入人们的视野,更多的消费场景被囊括进来,网购返利越来越成为成为广大网购爱好者热衷的省钱小技巧,也将逐渐成为一种被大众接受的网购新趋势。", "如今返利网站的模式越来越多,也越来越,笔者建议消费者在选择返利平台的时候参考以下几个因素:返利平台品牌的知名度、提现金额是否有限制、会员账户安全性、及客户服务等几方面。推荐使用超级淘,其模式在市场上已经得到专家的认可,可信度还是很高的。", "本文仅代表作者观点,不代表百度立场。", "本文系作者授权百家号发表,未经许可,不得转载。", "还没有人评论,点击抢沙发～"], "bs_rank_pos": 3}, {"is_selected": false, "title": "超级淘,一款返利类APP到底安全性有多高呢?", "most_related_para": 20, "segmented_title": ["超级", "淘", ",", "一款", "返利", "类", "APP", "到底", "安全性", "有", "多", "高", "呢", "?"], "segmented_paragraphs": [["文章", "阅读"], ["为了", "照料", "没听过", "超级", "淘", "的", "小伙伴", ",", "我", "先", "来", "简单", "科普", "一", "下", "啥", "是", "超级", "淘", ","], ["超级", "淘", "本质", "上", "来", "说", "即", "是", "一", "款", "网购", "返利", "AP", ".", "P"], ["可是", "他", "与", "现在", 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"然后这时候很多用户会担心支付宝的信息被超级淘盗取。", "但实际操作发现taobao也是进行授权登录,所以超级淘仍然只能获取到头像和昵称。", "在最主要的付款环节,超级淘是会跳转到支付宝进行操作的,也即是超级淘仅起到跳转功用,一切的操作仍然真实支付宝内进行。", "所以综上来看,超级淘的安全等级仍是十分高的,由于没有你的隐私材料,所以没有盗取一说。由于没有和充值有关的功用,所以也不会有资金安全的忧虑", "声明:本文由入驻搜狐公众平台的作者撰写,除搜狐官方账号外,观点仅代表作者本人,不代表搜狐立场。"], "bs_rank_pos": 4}], "answer_spans": [[28, 39]], "yesno_answers": ["Yes", "Yes"], "fake_answers": ["超级淘作为支付宝的战略合作伙伴,安全性还是有保障。"], "question": "超级淘安全吗", "segmented_answers": [["超级", "淘", "作为", "支付宝", "的", "战略合作伙伴", "，", "安全性", "还是", "有", "保障", "。"], ["推荐", "使用", "超级", "淘", "，", "其", "模式", "在", "市场", "上", "已经", "得到", "专家", "的", "认可", "，", "可信度", "还是", "很高", "的", "。"]], "answers": ["超级淘作为支付宝的战略合作伙伴，安全性还是有保障。", "推荐使用超级淘，其模式在市场上已经得到专家的认可，可信度还是很高的。"], "yesno_type": "OPINION", "answer_docs": [2], "segmented_question": ["超级", "淘", "安全", "吗"], "question_type": "YES_NO", "question_id": 23, "fact_or_opinion": "OPINION", "match_scores": [0.9166666666666666]}
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"从第一期节目开始,节目组会安排4对男女嘉宾,在入住城堡前进行第一次选择。“可能你们选择了同一个电影,就会被组成一对CP”。随后,这4对新CP被安排住进某个房间开始3天的同居生活。每个房间可能会有一个大床房,具体怎么睡看个人选择。“我们会预设男女嘉宾的反应,但绝不干预。”", "到了第二天,8个人会聚在一起,相互认识一下对方的搭档。如果你有心仪的对象,可以展开追求、以及要求换搭档。到了第三天,每个人最终决定究竟换不换搭档,看对眼的可以牵手离开。第三次选择,每个人都必须投出“你绝对不会和谁谈恋爱”的一票,得票最高的一男一女,会被淘汰。只要有淘汰,后边会有新的选手替补进来。", "据介绍,节目总共8期、分为上下两集,每集45分钟。为此,节目组已经储备了30多名选手。至于选手的选择渠道和标准,邱子珏告诉小娱,线上找人、线下见面各种深聊,在4000多人里边,最终选中的女嘉宾要真的有高颜值,不要网红脸,男嘉宾要有极强的特质。“要真诚一点的,绝对不要太油的(笑)。”", "极品,美女下载帮助: [1] 本站大部分片源都可以使用迅雷下载,同时支持BT下载如uTorrent,比特精灵,比特彗星等. [2] 本站为非盈利性站点,所有资源均是网上搜集,仅提供带宽测试使用,请下载后于24小时内删除。 [3] 如左键点击无法下载,你可以使用右键迅雷下载,或者复制下载连接到迅雷软件新建任务下载.  [4] 1905电影网只提供Bt种子链接,不保存bt种子,也不属于bt种子的提供者、制作者、所有者,服务器也未保存任何影视、音乐、游戏等资源或文件. [5] 本站基于互联网自由分享,所有bt种子文件均来自互联网,分享于互联网,不承担任何法律责任. [6] 本站所有BT电影完全免费,须用bt软件下载,人越多下载越快,把资源分享给您的朋友可以提高下载速度."], "bs_rank_pos": 0}, {"is_selected": false, "title": "美女与极品怎么看不了_百度知道", "most_related_para": 0, "segmented_title": ["美女", "与", "极品", "怎么", "看", "不", "了", "_", "百度", "知道"], "segmented_paragraphs": [["呵呵", "我", "想", "应该", "作者", "很", "忙", "没有", "时间", "写", "了", "要", "么", "就", "剧情", "崩溃", "写", "不", "下去", "了", "。", "美女", "就是", "天生", "俊俏", "的", "脸", "。", "也", "应该", "是", "个", "高", "颜值", "的", "人生", "。", "极品", "就", "不同", "了", "。", "相当", "极品", "还能", "忍受", "。", "要是", "那些", "另类", "极品", "来", "了", "。", "你们", "的", "小", "心脏", "。", "可以", "炸开", "喽", "。"]], "paragraphs": ["呵呵 我想应该作者很忙没有时间写了 要么就剧情崩溃写不下去了。 美女就是天生俊俏的脸。也应该是个高颜值的人生。极品就不同了。相当极品还能忍受。要是那些另类极品来了。你们的小心脏。可以炸开喽。"], "bs_rank_pos": 1}, {"is_selected": true, "title": "求资源!!哪里可以看_美女与极品吧_百度贴吧", "most_related_para": 4, "segmented_title": ["求", "资源", "!", "!", "哪里", "可以", "看", "_", "美女", "与", "极品", "吧", "_", "百度贴吧"], "segmented_paragraphs": [["我", "也是", "才", "听说", "这个", "节目", "啊", "怎么", "就", "看", "不", "了", "了", "呢", "难过", "嘤", "嘤", "嘤", "哪里", "有", "资源", "啊"], ["需要", "什么", "资源", "可以", "私", "我", "fsdfr", "2017"], ["好难过", "啊", "没有人", "之前", "下载", "了", "吗"], ["微信", "Irene", "5005", "备注", "求", "资源", ",", "免费", "给"], ["下载", "贴吧", "APP", "看", "高清", "直播", "、", "视频", "!"], ["贴吧", "页面", "意见反馈", "违规", "贴吧", "举报", "反馈", "通道", "贴吧", "违规", "信息处理", "公示"]], "paragraphs": ["我也是才听说这个节目啊 怎么就看不了了呢 难过 嘤嘤嘤 哪里有资源啊", "需要什么资源可以私我fsdfr2017", "好难过啊 没有人之前下载了吗", "微信Irene5005备注求资源,免费给", "下载贴吧APP看高清直播、视频!", "贴吧页面意见反馈 违规贴吧举报反馈通道 贴吧违规信息处理公示"], "bs_rank_pos": 2}, {"is_selected": false, "title": "美女与极品不能看了-原创-高清视频-爱奇艺", "most_related_para": 5, "segmented_title": ["美女", "与", "极品", "不能", "看", "了", "-", "原创", "-", "高清", "视频", "-", "爱奇艺"], "segmented_paragraphs": [["播放", "量", "数据", ":"], ["移动", "占", "比"], ["PC", "占", "比"], ["注", ":", "数据", "来自", "爱奇艺", "(", "每日", "凌晨", "扣除", "前", "日", "非", "正常", "流量", ")"], ["精彩评论", ",", "马上", "呈现", ".", ".", "."], ["正在", "加载", ".", ".", "."]], "paragraphs": ["播放量数据:", "移动占比", "PC占比", "注:数据来自爱奇艺(每日凌晨扣除前日非正常流量)", "精彩评论,马上呈现...", "正在加载..."], "bs_rank_pos": 3}, {"is_selected": false, "title": "怎么看不到了啊_美女与极品吧_百度贴吧", "most_related_para": 0, "segmented_title": ["怎么", "看不到", "了", "啊", "_", "美女", "与", "极品", "吧", "_", "百度贴吧"], "segmented_paragraphs": [["下载", "贴吧", "APP", "看", "高清", "直播", "、", "视频", "!"], ["贴吧", "页面", "意见反馈", "违规", "贴吧", "举报", "反馈", "通道", "贴吧", "违规", "信息处理", "公示"], ["内容", ":"]], "paragraphs": ["下载贴吧APP看高清直播、视频!", "贴吧页面意见反馈 违规贴吧举报反馈通道 贴吧违规信息处理公示", "内容:"], "bs_rank_pos": 5}], "answer_spans": [[7, 8]], "answer_docs": [0], "fake_answers": ["优酷独家"], "question": "美女与极品哪里可以看", "segmented_answers": [["优酷", "独家", "。"], ["翻墙", "上", "YouTube", "看", "。"], ["迅雷", "，", "搜", "资源", "。"]], "answers": ["优酷独家。", "翻墙上 YouTube 看。", "迅雷，搜资源。"], "entity_answers": [["优酷"], ["YouTube"], ["迅雷", "搜资源"]], "segmented_question": ["美女", "与", "极品", "哪里", "可以", "看"], "question_type": "ENTITY", "question_id": 24, "fact_or_opinion": "OPINION", "match_scores": [0.8]}
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"勿施于人", "。"], ["以上", "就是", "环球", "教育", "的", "小", "编", "为", "大家", "介绍", "的", "关于", "雅思写作", "的", "高频", "句子", ",", "对于", "在", "我们", "大上海", "雅思", "培训", "一般", "多少", "钱", "这个问题", ",", "你们", "可以", "点击", "我们", "的", "官网", "进行", "在线咨询", "。"]], "paragraphs": ["文章 阅读", "在上海雅思培训一般多少钱?这是很多在上海烤鸭们的共同疑问,首先让我们来看看雅思写作中的高频句子吧。", "引经据典是英语写作中常用的方法。恰当地使用英语谚语能让阅卷老师眼前一亮,是提升英语作文逼(fēn)格(shù)的一大法宝。51出国千人免费留学计划的老师为大家带来雅思写作7分必知谚语,一起学习吧!", "1. Time flies. 光阴似箭。 2. Time and tide wait for no man. 时不我待。 3. To save time is to lengthen life. 节约时间就是延长生命。 4. Time stays not the fool’s leisure. 时间不等闲逛的傻瓜。", "5. Lost years are worse than lost dollars. 失去时光金不换。", "6. Time is money. 时间就是金钱。", "7. Time has wings. 光阴去如飞。", "8. Time lost cannot be won again. 时光一去不复返。", "2勤奋、意志与成功", "1. Where there is a will, there is a way. 有志者事竟成。 2. No pains, no gains. 没有付出就没有收获。 3. Constant dripping wears away a stone. 水滴石穿,绳锯木断。 4. Care and diligence bring luck. 谨慎和勤奋才能抓住机遇。", "5. Genius is nothing but labor and diligence. 天才不过是勤奋而已。", "6. Great hopes make great man. 伟大的抱负造就伟大的人物。", "7. Industry is the parent of success. 勤奋是成功之母。", "8. No rose without a thorn. 没有不带刺的玫瑰。", "9. There is no royal road to learning. 书山有路勤为径,学海无涯苦作舟。", "10. No way is impossible to courage. 勇者无惧。", "11. Success belongs to the persevering. 坚持就是胜利。", "12. The finest diamond must be cut. 玉不琢,不成器。", "13. Adversity makes a man wise, not rich. 逆境出人才。", "3学习方法与态度", "1. Practice makes perfect. 熟能生巧。 2. It is never too old to learn. 活到老,学到老。 3. Reading is to the mind while exercise to the body. 读书健脑,运动强身。 4. A man becomes learned by asking questions. 不耻下问才能有学问。", "5. Learn and live. 活着,为了学习。", "6. Knowledge starts with practice. 实践出真知。", "7. Complacency is the enemy of study. 学习的敌人是自己的满足。", "8. Knowledge makes humble; ignorance makes proud. 知识使人谦虚,无知使人傲慢。", "9. Knowledge advances by steps and not by leaps. 知识只能循序渐进,不能跃进。", "10. A man can do no more than he can. 凡事都应量力而行。", "11. Books and friends should be few but good. 读书如交友,应求少而精。", "12. A man cannot spin and reel at the same time. 一心不能二用。", "4金钱与财富", "1. Gold will not buy anything. 黄金不能买尽一切。 2. The chief aim of man is not to get money. 人的主要目的并不是赚钱。 3. The money the miser hoards will do him no good. 守财奴积财,对自己毫无好处。 4. What is wealth good for, if it brings melancholy? 财富如带忧郁来,有了财富有何用?", "5. Wealth makes worship. 财富能使人拜倒。", "5健康与心态", "1. An apple a day keeps the doctor away. 一天一苹果,不用请医生。 2. Prevention is better than cure. 预防胜于治疗。 3. All work and no play makes Jack a dull boy. 只工作, 不玩耍, 聪明小伙也变傻。 4. Health is not valued till sickness comes. 病时方知健康可贵。", "5. A close mouth catches no flies. 病从口入。", "6. A good medicine tastes bitter. 良药苦口。", "7. Good health is over wealth. 健康是最大的财富。", "8. Happiness lies first of all in health. 幸福首先在于健康。", "9. Cheerfulness is health; its opposite, melancholy, is disease. 欢乐就是健康, 忧郁就是病痛。", "10. He is happy that thinks himself so. 自乐者常乐。", "11. Content is better than riches. 知足者常乐。", "6择友与友谊", "1. Birds of a feather flock together. 物以类聚,人以群分。 2. A man is known by his friends. 什么人交什么朋友。 3. Keep good men company and you shall be of the number. 近朱者赤,近墨者黑。 4. A friend in need is a friend indeed. 患难见真情。", "5. A friend is easier lost than found. 得朋友难,失朋友易。", "6. A faithful friend is hard to find. 知音难觅。", "7. A friend is never known till a man has need. 需要之时方知友。", "8. Misfortune tests the sincerity of friends. 患难见真情。", "7品行与操守", "1. Honesty is the best policy. 诚实不欺为上策。 2. Respect yourself, or no one else will respect you. 要人尊敬,必须自重。 3. Patience is the best remedy. 忍耐是良药。 4. One good turn deserves another. 行善积德。", "5. He knows most who speaks least. 大智若愚。", "6. A still tongue makes a wise head. 寡言者智。", "7. A candle lights others and consumes itself. 蜡烛照亮别人,却毁灭了自己。", "8. He is not fit to command others that cannot command himself. 正人须先正己。", "9. Pride goes before, and shame comes after. 骄傲使人落后。", "10. Please the eye and plague the heart. 贪图一时快活,必然留下隐祸。", "11. Penny wise, pound foolish. 贪小便宜吃大亏。", "12. He who makes constant complaint gets little compassion. 经常诉苦,没人同情。", "13. Do as you would be done by. 己所不欲,勿施于人。", "以上就是环球教育的小编为大家介绍的关于雅思写作的高频句子,对于在我们大上海雅思培训一般多少钱这个问题,你们可以点击我们的官网进行在线咨询。"], "bs_rank_pos": 0}, {"is_selected": false, "title": "【上海雅思培训需要多少钱】 ", "most_related_para": 1, "segmented_title": ["【", "上海", "雅思培训", "需要", "多少", "钱", "】"], "segmented_paragraphs": [["来源", ":", "上海", "朗阁", "雅思", "培训"], ["上海", "雅思培训", "需要", "多少", "钱", "上海", "雅思培训", "需要", "多少", "钱", "?", "上海", "朗阁", "雅思", "培训", "最实惠", "!", "即日起", ",", "拨打", "报名热线", ":", "400", "-", "0011", "-", "653", ",", "联系", "QQ", ":", "1419731936", ",", "1726839823", "就", "有", "惊喜", "哦", "!", "一线", "城市", "的", "环境", "+", "一", "流", "的", "师资", "+", "14", "年", "专业", "雅思培训", "经验", "!", "中国", "的", "最", "的", "雅思培训", "、", "新托福", "培训", "基地", "!", "中心", "学员", "5", "分", "通过率", "98", "%", ",", "6", "分", "通过率", "90", "%", ",", "遥遥领先", "。", "考", "雅思", "高分", ",", "来", "朗阁", "雅思", "!", "自", "成立", "至今", ",", "上海", "雅思培训", "一直秉承", "“", "以人为本", "、", "专业", "一", "流", "”", "的", "办学理念", ",", "不断强化", "教学管理", ",", "时刻", "注重", "教学", "品质", ",", "凭借", "一", "流", "的", "教学", "品质", "以及", "良好", "的", "口碑", "吸引", "了", "数", "万", "名", "来自", "不同", "年龄段", "及", "教育背景", "的", "学员", "的", "加入", "!", "VIP", "一对一", "培训", ":", "雅思", "VIP", "一对一", "培训班", "托福", "VIP", "一对一", "培训班", "SAT", "VIP", "一对一", "培训班", "雅思", "65", "学习卡", "培训班", "【", "招生对象", "】", "1", "、", "英语", "基础", "不同", ",", "需要", "老师", "单独", "辅导", "的", "学员", "。", "2", "、", "学习", "时间", "不", "固定", ",", "需要", "短期", "灵活", "上课", "的", "学员", "。", "3", "、", "需要", "在", "短期内", "迅速提高", "雅思成绩", "的", "学员", "。", "4", "、", "需要", "名师指导", ",", "取得", "更", "高", "分数", "的", "学员", "。", "5", "、", "不适合", "以", "班级", "统一", "授课方式", ",", "需要", "一对一", "学习", "的", "学员", "。", "【", "课程", "简述", "】", "上海", "雅思培训", "朗阁", "品牌", "课程", ",", "具有", "丰富", "教学", "经验", "的", "专家", "执教", ",", "针对", "学员", "各项", "考试", "必备", "技巧", "和", "能力", "进行", "VIP", "尊享", "服务", "。", "极致", ":", "精品", "化", ",", "满足", "个人", "需求", ";", "舒适", ":", "精心", "打造", "每个", "学习", "细节", ",", "温馨", "感受", "每", "一", "处", ";", "独家", ":", "单独", "配备", "VIP", "名师", ",", "名师", "与", "您", "零距离", ";", "迅捷", ":", "海外", "考试", "研究中心", "专项", "提供", "最新", "雅思", "研发", "成果", ",", "细心", ":", "每位", "学员", "均", "有", "贴身学习", "辅导员", ",", "负责", "解决", "学习", "中", "遇到", "的", "任何问题", "。", "【", "APC", "教学", "】", "上海", "雅思培训", "朗阁", "VIP", "中心", "名师", "经过多年", "的", "潜心钻研", ",", "开发", "出", "能", "在", "较", "短时间内", "激发", "个人", "语言", "学习", "潜能", "的", "APC", "学习", "模型", ",", "加上", "全程", "化", "教学", "监控", "与", "服务", ",", "让", "每", "们", "参加", "学习", "的", "学员", "都", "能", "达到", "适合", "自己", "风格", "的", "学习", "效果", "!", "APC", "学习", "模型", "权威", "Authority", "发掘", "知识", "漏油", "专业化", "服务", "培养", "良好", "学习", "习惯", "精准", "Precision", "学员", "需求", "分析", "动态", "学习方案", "量化", "教学", "监控", "锁定目标", "成绩", "定制", "Customization", "拥有", "业", "内", "知名", "学术", "团队", "独立", "研发", "教材", "◇", "APC", "个性化", "课程", "定制", ",", "1", "对", "1", "教学模式", "◇", "严格", "入学测试", ",", "根据", "测试成绩", "进入", "相应", "模块", "学习", "◇", "签约", "保", "分", "承诺", "◇", "全程", "学习", "顾问", "指导", "◇", "各", "阶段", "学习", "测试", "评估", "及", "学习", "档案管理", ",", "随时", "了解", "学习", "进度", "◇", "全真试题", "模", "考", ",", "中外", "教", "点评", "◇", "考前", "1", "对", "1", "技巧", "辅导", "◇", "留学", "规划师", "海外", "择校", "指导", "上海", "雅思培训", "需要", "多少", "钱", "?", "上海", "朗阁", "雅思", "培训", "最实惠", "!", "名师名校", "!", "完善", "的", "课程体系", "、", "强大", "的", "师资力量", "、", "优质", "的", "教学质量", "、", "一", "流", "的", "学习", "环境", "以及", "客户", "为", "本", "的", "服务理念", ",", "是", "您", "学习", "雅思", ",", "考取", "高分", "的", "理想", "学府", "!", "更多", "上海", "雅思", "学校", "精彩内容", "请", "关注", ":", "上海", "雅思培训", "http", ":", "/", "/", "1345", ".", "peixun", "360", ".", "com", "/", "详情", "咨询", ":", "杨", "老师", ",", "电话", ":", "400", "-", "0011", "-", "653", ",", "联系", "QQ", ":", "1419731936", "。"], ["相关", "推荐"], ["求学", "登记"], ["最新", "加入", "学校"], ["广州", "黄埔", "技工", "汽修", "学校", "海口", "朗阁", "培训中心", "许昌", "新希望", "会计", "培训", "学校", "济南", "启航", "会计", "培训", "学校", "广州", "塞", "乐", "西班牙语", "培训学校", "上海", "塞", "乐", "西班牙语", "培训学校", "嘉兴", "仁和", "会计", "培训", "学校", "温州", "仁和", "会计", "培训", "学校", "杭州", "仁和", "会计", "培训", "学校", "上海交通大学", "继续教育学院"]], "paragraphs": ["来源:上海朗阁雅思培训", "上海雅思培训需要多少钱上海雅思培训需要多少钱?上海朗阁雅思培训最实惠!即日起,拨打报名热线:400-0011-653,联系QQ:1419731936,1726839823就有惊喜哦!一线城市的环境+一流的师资+14年专业雅思培训经验!中国的最的雅思培训、新托福培训基地!中心学员5分通过率98%,6分通过率90%,遥遥领先。考雅思高分,来朗阁雅思!  自成立至今,上海雅思培训一直秉承“以人为本、专业一流”的办学理念,不断强化教学管理,时刻注重教学品质,凭借一流的教学品质以及良好的口碑吸引了数万名来自不同年龄段及教育背景的学员的加入! VIP一对一培训: 雅思VIP一对一培训班 托福VIP一对一培训班 SAT VIP一对一培训班 雅思65学习卡培训班 【招生对象】 1、英语基础不同,需要老师单独辅导的学员。 2、学习时间不固定,需要短期灵活上课的学员。 3、需要在短期内迅速提高雅思成绩的学员。  4、需要名师指导,取得更高分数的学员。 5、不适合以班级统一授课方式,需要一对一学习的学员。  【课程简述】 上海雅思培训朗阁品牌课程,具有丰富教学经验的专家执教,针对学员各项考试必备技巧和能力进行VIP尊享服务。  极致:精品化,满足个人需求; 舒适:精心打造每个学习细节,温馨感受每一处; 独家:单独配备VIP名师,名师与您零距离;  迅捷:海外考试研究中心专项提供最新雅思研发成果, 细心:每位学员均有贴身学习辅导员,负责解决学习中遇到的任何问题。  【APC教学】 上海雅思培训朗阁VIP中心名师经过多年的潜心钻研,开发出能在较短时间内激发个人语言学习潜能的APC学习模型,加上全程化教学监控与服务,让每们参加学习的学员都能达到适合自己风格的学习效果! APC学习模型 权威Authority 发掘知识漏油 专业化服务 培养良好学习习惯 精准Precision 学员需求分析 动态学习方案 量化教学监控 锁定目标成绩 定制Customization 拥有业内知名学术团队 独立研发教材 ◇APC个性化课程定制,1对1教学模式 ◇严格入学测试,根据测试成绩进入相应模块学习 ◇签约保分承诺 ◇全程学习顾问指导 ◇各阶段学习测试评估及学习档案管理,随时了解学习进度 ◇全真试题模考,中外教点评 ◇考前1对1技巧辅导 ◇留学规划师海外择校指导 上海雅思培训需要多少钱?上海朗阁雅思培训最实惠!名师名校!完善的课程体系、强大的师资力量、优质的教学质量、一流的学习环境以及客户为本的服务理念,是您学习雅思,考取高分的理想学府! 更多上海雅思学校精彩内容请关注:上海雅思培训http://1345.peixun360.com/ 详情咨询:杨老师,电话:400-0011-653,联系QQ:1419731936。", "相关推荐", "求学登记", "最新加入学校", "广州黄埔技工汽修学校 海口朗阁培训中心 许昌新希望会计培训学校 济南启航会计培训学校 广州塞乐西班牙语培训学校 上海塞乐西班牙语培训学校 嘉兴仁和会计培训学校 温州仁和会计培训学校 杭州仁和会计培训学校 上海交通大学继续教育学院"], "bs_rank_pos": 2}, {"is_selected": false, "title": "【雅思培训班费用】雅思培训一般多少钱,上海雅思班学费 ", "most_related_para": 5, "segmented_title": ["【", "雅思", "培训班", "费用", "】", "雅思", "培训", "一般", "多少", "钱", ",", "上海", "雅思", "班", "学费"], "segmented_paragraphs": [["姓名", "电话"], ["地区", "验证码", "获取", "验证码"], ["基础", "培训", "进阶", "提升", "冲刺", "训练"], ["班", "制", ":", "全日制", "周末班", "晚间", "班", "晚间", "+", "周末班"], ["上课地点", ":", "浦东", ",", "卢湾", ",", "淮海", ",", "人民广场", ",", "中山公园", ",", "松江", ",", "嘉定", ",", "虹口", ",", "杨浦"], ["雅思", "班", "2017", "【", "雅思", "培训班", "多少", "钱", "】", "雅思", "培训班", "主要", "分为", "雅思培训", "周末班", ",", "雅思培训", "全日制", "班", ",", "雅思", "封闭", "班", ",", "雅思", "VIP", "班", ",", "雅思", "单项", "班", ",", "4", "-", "7", "分", "雅思", "课程", ",", "首先", "你", "要", "看看", "你", "目前", "的", "雅思", "水平", ",", "和", "你", "要", "达到预期", "的", "目标", ",", "然后", "才能", "算", "出", "雅思", "培训班", "全部", "雅思", "课程", "费用"], ["不同", "的", "烤鸭", "有", "不同", "的", "烦恼", ",", "来", "朗阁", "咨询", "课程", "给", "您", "合适", "指导", ",", "从", "根本", "解决", "问题"]], "paragraphs": ["姓名 电话", "地区 验证码 获取验证码", "基础培训 进阶提升 冲刺训练", "班制: 全日制 周末班 晚间班 晚间+周末班", "上课地点:浦东,卢湾,淮海,人民广场,中山公园,松江,嘉定,虹口,杨浦", "雅思班2017【雅思培训班多少钱】雅思培训班主要分为雅思培训周末班,雅思培训全日制班,雅思封闭班,雅思VIP班,雅思单项班,4-7分雅思课程,首先你要看看你目前的雅思水平,和你要达到预期的目标,然后才能算出雅思培训班全部雅思课程费用", "不同的烤鸭有不同的烦恼,来朗阁咨询课程给您合适指导,从根本解决问题"], "bs_rank_pos": 3}, {"is_selected": false, "title": "上海雅思培训一般多少钱,上海雅思培训一般需要多少钱-【中国教育", "most_related_para": 3, "segmented_title": ["上海", "雅思", "培训", "一般", "多少", "钱", ",", "上海", "雅思", "培训", "一般", "需要", "多少", "钱", "-", "【", "中国教育"], "segmented_paragraphs": [["来自", ":", "Jonny", "2017", "-", "06", "-", "04", "20", ":", "38"], ["多少", "钱", "本人", "已", "通过", "雅思", "7", "分", ",", "如果", "有", "需要", "剑桥", "真题", "或者", "各类", "复习", "用书", "的", ",", "可以", "私聊", "N1182932443", "同", "问", ",", "多少", "钱", "微信", ":", "N1182932443"], ["998", "nemo", "2017", "-", "06", "-", "04", "20", ":", "38"], ["UP", "UPUPupupUPUPUPUPUPUPUPUP", "你好", ",", "我", "正", "需要", "一", "套", "二手", "雅思", "真题", ",", "楼主", "多少", "钱", "转让", "呢", "?", "?", "?", "你好", ",", "我", "正", "需要", "一", "套", "二手", "雅思", "真题", ",", "楼主", "多少", "钱", "转让", "呢", "?", "?", "?", "85", "包邮", "转", "。", "很多", "电子", "资料", "也", "发给", "你", "UP", "UPUPUPupupup", "请问", "书", "还在", "吗", "?", "在", "的", "亲", "UP", "UPUPUP", "楼主", "还有", "吗", "?", "有", "的", "亲", "UP", "UPUPUPupup", "请问", "还有", "吗", "有", "的", "UPUPUP", "~", "~", "~", "~", "UPUPUPUP", "楼主", "书", "还在", "?", "还在"]], "paragraphs": ["来自:Jonny 2017-06-04 20:38", "多少钱本人已通过雅思7分,如果有需要剑桥真题或者各类复习用书的,可以私聊N1182932443同问,多少钱微信:N1182932443", "998nemo 2017-06-04 20:38", "UPUPUPupupUPUPUPUPUPUPUPUP你好,我正需要一套二手雅思真题,楼主多少钱转让呢???你好,我正需要一套二手雅思真题,楼主多少钱转让呢???85包邮转。很多电子资料也发给你UPUPUPUPupupup请问书还在吗?在的亲UPUPUPUP楼主还有吗?有的亲UPUPUPUPupup请问还有吗有的UPUPUP~~~~UPUPUPUP楼主书还在?还在"], "bs_rank_pos": 4}, {"is_selected": true, "title": "上海雅思培训周末班一般多少钱_百度知道", "most_related_para": 0, "segmented_title": ["上海", "雅思", "培训", "周末班", "一般", "多少", "钱", "_", "百度", "知道"], "segmented_paragraphs": [["你好", "上海", "雅思", "培训班", "周末班", "的", "价格", "一般", "是", "在", "60", "左右", "一", "个", "课时", "的", ",", "如果", "是", "一对一", "的", "话", ",", "要", "贵", "的", "哦", "。", "当然", "具体情况", "不一样", ",", "费用", "也", "会", "有", "差异", ",", "希望", "对", "你", "帮助", "!"]], "paragraphs": ["你好 上海雅思培训班周末班的价格一般是在60左右一个课时的,如果是一对一的话,要贵的哦。当然具体情况不一样,费用也会有差异,希望对你帮助!"], "bs_rank_pos": 5}], "answer_spans": [[4, 21]], "answer_docs": [4], "fake_answers": ["周末班的价格一般是在60左右一个课时的,如果是一对一的话"], "question": "上海雅思培训费用", "segmented_answers": [["周末班", "的", "价格", "一般", "是", "在", "60", "左右", "一", "个", "课时", "的", "，", "如果", "是", "一对一", "的", "话", "，", "会", "贵", "。"]], "answers": ["周末班的价格一般是在60左右一个课时的，如果是一对一的话，会贵。"], "entity_answers": [["60左右一个课时"]], "segmented_question": ["上海", "雅思培训", "费用"], "question_type": "ENTITY", "question_id": 25, "fact_or_opinion": "OPINION", "match_scores": [0.85]}
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"和", "什么", "煲汤", "丰胸"], ["煮", "鸡汤", "要", "焯水", "吗", "糖蒜", "怎么", "腌制", "最好", "吃", "红糖水", "可以", "天天", "喝", "吗", "杏鲍菇", "炒", "之前", "用", "焯", "么", "柚子皮", "能", "治", "三高", "吗", "空心菜", "怎么", "做", "汤"], ["鸡内金", "的", "吃法", "青豆", "的", "营养价值", "牛肉汤", "的", "营养价值", "银鱼", "怎么", "做", "好吃"], ["啤梨", "的", "营养价值", "通心粉", "的", "营养价值", "白薯", "的", "营养价值", "羊脑", "的", "营养价值"], ["大黄鱼", "的", "营养价值", "红花", "的", "营养价值", "花菇", "的", "营养价值", "木耳", "叶", "的", "营养价值"], ["酸辣土豆丝", "的", "营养", "价", "驴肉汤", "的", "营养价值", "花旗参", "乌鸡汤", "的", "营养", "玉兔", "海参", "什么时候", "放", "琉璃", "肺", "的", "营养价值", "五彩", "牛肉丝", "怎么", "做", "最"], ["啤梨", "的", "营养价值", "通心粉", "的", "营养价值", "白薯", "的", "营养价值", "羊脑", "的", "营养价值"], ["入夏", "吃", "糯米", "好处多", "海", "鲈鱼", "的", "营养价值", "马鲛鱼", "的", "食疗方法", "吃", "鳖", "的", "好处", "竟然", "这么多"], ["夏至", "养生", "饮食", "-", "味蕾", "在", "初夏", "绽放", "养生之道", "网", "夏至", "养生", "专题", ",", "为您", "详细", "介绍", "夏至", "习俗", "、", "夏至", "吃什么", "、", "夏至", "养生", "食疗", "、", "夏至", "节气", "饮食", "等", "与", "夏至", "节气", "有关", "的", "饮食", "养生", "知识", ",", "想要", "了解更多", "的", "与", "夏至", "节气", "有关", "的", "养生", "知识", ",", "请", "关注", "养生之道", "网", "夏至", "养生", "专题", "。", ".", ".", "."], ["端午节", "习俗", "和", "由来", "_", "端午节", "吃什么", "立夏", "节气", "养生", "-", "初夏", "饮食", "助", "你", "轻松", "过", "酷夏", "谷雨", "节气", "养生", "-", "味蕾", "花香", "齐", "绽放", "清明节", "饮食养生", "-", "清明节", "踏青", "与", "美食", "同行", "春分", "节气", "养生", "-", "春天", "从", "美食", "开始", "惊蛰", "节气", "养生", "-", "味蕾", "在", "美食", "中", "“", "惊醒", "”"], ["正确", "的", "跳绳", "减肥", "方法", "气滞血瘀", "的", "症状", "淋巴癌", "能", "治", "好", "吗", "如何", "快速", "去除", "头皮屑", "太溪", "穴位", "位置图", "睡姿", "判断", "情侣", "间", "关系"], ["紫薯", "的", "养生", "吃法", "令", "你", "流口水", "的", "鸭肉", "做法", "白", "胡椒粉", "的", "吃法", "茄子", "的", "营养价值"]], "paragraphs": ["来源:养生之道网编辑:杨琼 发表时间:2016-06-27 11:19", "养生之道网导读:银耳和什么搭配最好?银耳被称为“平民的燕窝”,它全身几乎都是胶原蛋白,深受女性朋友喜爱,女性吃银耳能美容养颜,男性吃银耳能润肺。那么,银耳和什么一起吃最好呢?", "银耳和什么搭配最好", "一、银耳+黑木耳 银耳有补肾、润肺、生津、提神及润肤的功效,对治疗慢性支气管炎和肺心病有显着的效果。木耳有益气润肺、养血的作用。两者都是营养丰富的食材,还是低脂肪的健康食品,一起吃不仅不会对身体产生坏处,黑白木耳搭配还能让各自的营养得到互补,达到双重营养的食疗功效。对久病体弱、肾虚腰背痛有很好的辅助治疗作用。 二、银耳+百合 百合的味道清香脱俗,而银耳则脆嫩爽口。此道甜品能安定心神、活络气血,还有润肺止咳、生津滋阴的功效,特别适合秋天食用。常常饮用,有调理肺、脾、肾三脏的功效。对慢性喉咽也有一定缓和作用。 三、银耳+鹌鹑蛋 银耳性味甘平,是滋补强身食品。银耳含有多种营养物质,具有阻止血液中胆固醇沉积和凝结的作用。鹌鹑蛋营养价值远高于鸡蛋,具有补气益血、强身健脑、降压降脂之功效。银耳与鹌鹑蛋同食,能使强精补肾、益气养血、健脑强身的功效更为显着。对贫血、妇婴营养不良、神经衰弱、血管硬化、心脏病等病人,均有补益作用。常吃还能防止老年性疾病。 四、银耳+鸭蛋 银耳有润肺、生津止咳、滋阴养肺、益气和血、补脑强心和补肾等功效。鸭蛋味咸、性凉,具有滋阴润肺、补中益气等功效。二者同煮共食,可治疗肺阴不足所致的咽喉干燥、声嘶、干咳等症,对阴虚、头晕和皮肤干燥所引起的瘙痒也有一定的作用。 五、银耳+蜜桔 银耳具有强精、补肾、润肠、益胃、补气、和血、强心、壮身、补脑、提神、美容、嫩肤、延年益寿之功效。蜜桔有理气健胃、燥湿化痰、下气止喘、散结止痛、促进食欲、醒酒及抗疟等多种功效。银耳冰糖蜜桔长期服用可以润肤,并有祛除脸部黄褐斑、雀斑的功效。", "六、银耳+青鱼", "青鱼养肝益肾,补气化湿,消腹胀水肿,有滋补的作用。两者相配,既可以保证食者正常营养,保健身体,又不会增重,同时可对虚胖者及时调养。", "七、银耳+猪脑", "猪脑性味甘寒,可治头风、止眩晕、益肾补脑,适合治疗肾虚、髓海不足所致的眩晕、耳鸣、健忘等症。银耳性平、味甘,能益气强志。猪脑与银耳同煮熬汤食用,可滋肾补脑,对用脑过度、头昏、记忆力减退等病症都有一定的疗效。", "八、银耳+菊花", "菊花、银耳具有滋养强壮、镇静止血的作用,两者搭配常用于治疗喉痛。而菊花还有解热、镇静作用。", "九、银耳+雪梨+川贝", "雪梨、银耳都有滋阴润肺、治疗干咳少痰之功效;川贝也有润肺、止咳的功效。雪梨、银耳、川贝三者合用,功效尤佳,对治疗久咳不愈患者效果显着。", "十、银耳+莲子", "银耳中含有较多的胶质,能润滑肠道,可以清理肠道中的毒素,莲子有固涩的功效。对于腹泻的人来说,银耳莲子的搭配既能排毒,又能有效缓解腹泻症状。", "银耳可以说是很百搭的食材,但也不是来者全收。银耳与富含铁剂的食物不能一起吃,因银耳含磷很丰富,磷与铁结合成难溶性化合物,故银耳不宜与富含铁剂的食物,如菠菜、蛋黄、动物肝脏等同食。另外,银耳不能够与白萝卜一同食用,否则容易导致皮炎的发生。", "今天初伏,冬病夏治三伏贴开 明天入伏,一年中最热的日子 心静自然凉,夏季养心切忌劳", "精蛋白锌重组人胰岛素注射液 他卡西醇软膏 博健(盐酸多奈哌齐片)", "皮肤白斑的常见并发症 皮肤颌口虫病会引起哪些并发 女子颜面黑变病引发什么疾病", "胰腺囊肿的饮食护理办法 胰腺囊肿早期症状有哪些 关注:了解自闭症儿童的世界", "核桃到底能不能补脑 有胃病的人怎么喝水好 鸡蛋怎么吃最有营养", "衣服被染色了怎么办 夏天天天洗头好吗 夏季用防晒霜要注意什么", "夏季毒蛇出没,被蛇咬后这样急救才正确,收好备用!", "这个症状提醒你是轻度宫颈糜烂,不治就晚了! 后背长粉刺,是因为你洗澡没做好这件事! 月经不调,90%的女人不知道这些调理方法! 备孕期女性看过来:这些食物增加受孕几率! 你绝对不知道,宝宝长牙居然这么痛苦! 白带颜色暗示健康问题,白带褐色是怎么回事?", "木瓜和什么煲汤丰胸", "煮鸡汤要焯水吗 糖蒜怎么腌制最好吃 红糖水可以天天喝吗 杏鲍菇炒之前用焯么 柚子皮能治三高吗 空心菜怎么做汤", "鸡内金的吃法 青豆的营养价值 牛肉汤的营养价值 银鱼怎么做好吃", "啤梨的营养价值 通心粉的营养价值 白薯的营养价值 羊脑的营养价值", "大黄鱼的营养价值 红花的营养价值 花菇的营养价值 木耳叶的营养价值", "酸辣土豆丝的营养价 驴肉汤的营养价值 花旗参乌鸡汤的营养 玉兔海参什么时候放 琉璃肺的营养价值 五彩牛肉丝怎么做最", "啤梨的营养价值 通心粉的营养价值 白薯的营养价值 羊脑的营养价值", "入夏吃糯米好处多 海鲈鱼的营养价值 马鲛鱼的食疗方法 吃鳖的好处竟然这么多", "夏至养生饮食-味蕾在初夏绽放 养生之道网夏至养生专题,为您详细介绍夏至习俗、夏至吃什么、夏至养生食疗、夏至节气饮食等与夏至节气有关的饮食养生知识,想要了解更多的与夏至节气有关的养生知识,请关注养生之道网夏至养生专题。...", "端午节习俗和由来_端午节吃什么 立夏节气养生-初夏饮食助你轻松过酷夏 谷雨节气养生-味蕾花香齐绽放 清明节饮食养生-清明节踏青与美食同行 春分节气养生-春天从美食开始 惊蛰节气养生-味蕾在美食中“惊醒”", "正确的跳绳减肥方法 气滞血瘀的症状 淋巴癌能治好吗 如何快速去除头皮屑 太溪穴位位置图 睡姿判断情侣间关系", "紫薯的养生吃法 令你流口水的鸭肉做法 白胡椒粉的吃法 茄子的营养价值"], "bs_rank_pos": 0}, {"is_selected": true, "title": "银耳和什么搭配最好_百度知道", "most_related_para": 0, "segmented_title": ["银耳", "和", "什么", "搭配", "最好", "_", "百度", "知道"], "segmented_paragraphs": [["春天", "到", "了", "适合", "养肝", "推荐", "一", "款", "银耳", "枸杞", "汤", "步骤", "1", ".", "先", "将", "银耳", "洗净", "放在", "水中", "浸泡", "至", "2", "-", "3", "小时", "(", "建议", "用", "矿泉水", "发泡", ")", "2", ".", "将", "泡", "好", "的", "银耳", "撕", "成", "小", "的", "碎片", ",", "放在", "炖盅", "中", "加入", "枸杞", "和", "300", "ml", "矿泉水", "一起", "炖", "煮", "2", "-", "3", "小时", "。", "3", ".", "炖", "完", "后", "加入", "冰糖", "就", "美味", "的", "银耳", "枸杞", "汤", "就", "可以", "食用", "啦", "。", "银耳", "除了", "可以", "和", "莲子", "、", "百合", "、", "红枣", "、", "枸杞", "、", "燕窝", "、", 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"三星", "监控", "三星", "TECH", "WIN", "成立", "于", "1977", "年", ",", "是", "韩国", "三星集团", "专业从事", "光学", "、", "航空", "、", "精密仪器", "以及", "军工", "专家点评", "|", "怎么样", "上榜", "理由", ":", "三星", "TECH", "WIN", "成立", "于", "1977", "年", ",", "是", "韩国", "三星集团", "专业从事", "光学", "、", "航空", "、", "精密仪器", "以及", "军工", "产品", "等", "事业", "的", "集团", "子公司", ",", "到", "目前", "己", "有", "32", "年", "的", "特殊", "行业", "经验", ",", "积累", "了", "雄厚", "的", "光学", "及", "数码影像", "技术", "开发", "能力", ",", "独立开发", "了", "消除", "映像", "噪点", "的", "SSNR", "芯片", "、", "多", "用", ".", ".", ".", "最新新闻", ":", "三星", "黑科技", "吸", "地", "机器人", "引领", "智能", "三星", "全力打造", "智能家居", "生活"], ["★", "海康威视", "监控", "海康威视", "是", "领先", "的", "安防产品", "及", "行业", "解决方案", "提供商", ",", "致力于", "不断", "提升", "视频", "处理", "技术", "专家点评", "|", "怎么样", "上榜", "理由", ":", "海康威视", "是", "领先", "的", "安防产品", "及", "行业", "解决方案", "提供商", ",", "致力于", "不断", "提升", "视频", "处理", "技术", "和", "视频", "分析", "技术", ",", "面向", "全球", "提供", "领先", "的", "安防产品", "、", "专业", "的", "行业解决方案", "与", "优质", "的", "服务", ",", "为", "客户", "持续", "创造", "更大", "价值", "。", "海康威视", "拥有", "业", "内", "领先", "的", "自主", "核心", ".", ".", "."], ["☆", "大华", "监控", "大华", "股份", "是", "领先", "的", "监控", "产品", "供应商", "和", "解决方案", "服务商", ",", "面向", "全球", "提供", "领先", "的", "视频", "存", "专家点评", "|", "怎么样", "上榜", "理由", ":", "大华", "股份", "是", "领先", "的", "监控", "产品", "供应商", "和", "解决方案", "服务商", ",", "面向", "全球", "提供", "领先", "的", "视频", "存储", "、", "前端", "、", "显示", "控制", "和", "智能交通", "等", "系列化", "产品", "。", "公司", "自", "2001", "年", "推出", "业", "内", "首", "台", "自主研发", "8", "路", "嵌入式", "DVR", "以来", ",", "一直", "持续", "加大", "研发", "投入", "和", "不断", "致力于", "技术创新", ".", ".", ".", "最新新闻", ":", "徐州", "优惠", "的", "大华", "玻璃制品", "供应", "哪有", "优秀", "的", "徐州", "大华", "玻璃", "制品", "有限"], ["☆", "索尼", "监控", "SONY", "索尼", "是", "横跨", "电子", "3C", "、", "游戏", "、", "金融", "、", "娱乐", "领域", "的", "世界", "巨擘", ",", "拥有", "世界", "屈指", "的", "品牌", "影响", "专家点评", "|", "怎么样", "上榜", "理由", ":", "SONY", "索尼", "是", "横跨", "电子", "3C", "、", "游戏", "、", "金融", "、", "娱乐", "领域", "的", "世界", "巨擘", ",", "拥有", "世界", "屈指", "的", "品牌", "影响力", "。", "SONY", "创立", "于", "1946", "年", "5", "月", ",", "由", "拥有", "技术", "研发", "背景", "的", "井深大", "与", "擅长", "公关", "、", "营销", "的", "盛田昭夫", "共同", "创办", "。", "公司", "原名", "「", "东京", "通信", "工业", "株式会社", "」", ",", "在", "逐渐", "迈入", "国", ".", ".", ".", "最新新闻", ":", "坐标", "东京", "索尼", "将", "开设", "第", "一", "家", "快闪", "索尼", "超", "高清", "高速", "摄像机", "FDR", "-", "AX100E"]], "paragraphs": ["上榜理由:海尔厨房设施有限公司成立于1997年,依托海尔集团位居世界白电四强、中国家电第一品牌的实力及品牌优势,致力于为消费者提供世界同步的高品质厨房生活。成套引进欧洲先进技术和设备,建成世界一流、亚洲领先的数字化厨房生...", "★ 索博智能 索博因在智能家居和电力线通讯领域的不断创新和领导地位而闻名世界。在智能家  专家点评| 怎么样  上榜理由:索博因在智能家居和电力线通讯领域的不断创新和领导地位而闻名世界。在智能家居远景的激励下,索博员工致力于帮助您与所想和所需的人实现简捷的而真智能的智能家居。我们通过向您提供最佳的产品、最好的体验、强大的销售...  最新新闻: 回顾米兰时间——揭秘劳芬设计之S 韩庚领衔 红到爆Super Junior家", "★ 安居宝门禁 广东安居宝数码科技股份有限公司是一家集研发、生产、销售、服务为一体的高科技  专家点评| 怎么样  上榜理由:广东安居宝数码科技股份有限公司是一家集研发、生产、销售、服务为一体的高科技企业。经过多年的稳定发展,已成长为具有较强自主研发和技术创新能力且经营业绩稳定增长的上市公司(股票代码为:300155)。公司位于广州市高新...", "☆ 霍尼韦尔 霍尼韦尔国际(Honeywell International)是一家营业额达300多亿美元的多元化高  专家点评| 怎么样  上榜理由:霍尼韦尔国际(Honeywell International)是一家营业额达300多亿美元的多元化高科技和制造企业,在全球,其业务涉及:航空产品和服务,楼宇、家庭和工业控制技术,汽车产品,涡轮增压器,以及特殊材料。霍尼韦尔公司总部位于美...  最新新闻: 天津霍尼韦尔防盗报警器代理,批", "☆ 瑞讯智能 瑞讯科技(亚洲)有限公司,总部在香港,主要致力于通讯与智能控制领域的产品研究  专家点评| 怎么样  上榜理由:瑞讯科技(亚洲)有限公司,总部在香港,主要致力于通讯与智能控制领域的产品研究与开发;上海瑞讯通讯服务有限公司作为中国市场的营销总部以及亚太生产基地,创建于一九九六年,坐落于上海浦东康桥工业园区,专业从事通讯和...", "☆ LG电子智能 LG(LG Electronics)是在消费类电子产品、移动通信产品和家用电器领域内的全球  专家点评| 怎么样  上榜理由:LG(LG Electronics)是在消费类电子产品、移动通信产品和家用电器领域内的全球领先者和技术创新者。LG电子以时尚设计和智能科技为理念,为消费者创造更美好的生活。 LG电子自1958年成立以来,在50多年的时间里,通过不断的...  最新新闻: 粮夏机械提供最好的LGJ500-1000 钰恒LGCN计数桌秤/电子计数桌秤", "☆ 波创智能 深圳市新和创智能科技有限公司(深圳市波创科技发展有限公司)作为国家级高新技  专家点评| 怎么样  上榜理由:深圳市新和创智能科技有限公司(深圳市波创科技发展有限公司)作为国家级高新技术企业,是最早推出数字化智能家居产品的高科技企业,作为全球专业的家居智能化、社区智能化的产品制造商和方案供应商,新和创智能科技为全球...  最新新闻: 宁波创建节水型企业昨起全面推开", "☆ 罗格朗-视得安智能 视得安罗格朗公司是罗格朗集团旗下子公司,其主营业务是在中国生产、销售楼宇对  专家点评| 怎么样  上榜理由:视得安罗格朗公司是罗格朗集团旗下子公司,其主营业务是在中国生产、销售楼宇对讲系统及产品。作为罗格朗集团的一部分,我们在日常的言行中严格遵循集团4个核心价值观:创新、职业道德、资源增值,以及关注客户。目前,视得安...", "☆ 柯帝智能 广州市聚晖电子科技有限公司成立于2004年,是一家专门从事数字城市、数字社区、  专家点评| 怎么样  上榜理由:广州市聚晖电子科技有限公司成立于2004年,是一家专门从事数字城市、数字社区、数字酒店、数字家庭的核心技术及智能化产品的研发、生产及销售的综合性高科技企业,致力于为客户提供各种先进的智能化解决方案和服务,是目前...", "☆ 快思聪智能 美国快思聪(CRESTRON)总部设立于美国新泽西Rockleigh拥有五万平方尺的现代化  专家点评| 怎么样  上榜理由:美国快思聪(CRESTRON)总部设立于美国新泽西Rockleigh拥有五万平方尺的现代化设备,是快思聪集团的销售、推广、财务及技术中心。而于新泽西Cresskill的生产厂房亦已扩展至五万平方尺,为全球供应先进的遥控设备。快思聪在洛...", "中国五金卫浴十大品牌  中国陶瓷卫浴十大品牌  中国休闲卫浴十大品牌  中国智能卫浴十大品牌  中国马桶十大品牌  中国水龙头十大品牌  中国浴室柜十大品牌  中国淋浴房十大品牌  中国花洒十大品牌  中国水槽十大品牌  中国五金挂件十大品牌  中国浴缸十大品牌  中国面盆十大品牌  中国橱柜十大品牌  电热水龙头专业品牌  不锈钢水龙头专业品牌  卫浴软管专业品牌  世界卫浴十大品牌  世界卫浴十大奢侈品牌  世界五金卫浴十大品牌  世界陶瓷卫浴十大品牌  美国卫浴十大品牌  世界浴缸十大品牌  世界水槽十大品牌  中国十大阀芯供应商  中国十大软管供应商", "卫浴十大品牌新榜上线 全新改版 更专业!更实用!", "更多品牌热销资讯", "★ 霍尼韦尔 霍尼韦尔国际(Honeywell International)是一家营业额达300多亿美元的多元化高  专家点评| 怎么样  上榜理由:霍尼韦尔国际(Honeywell International)是一家营业额达300多亿美元的多元化高科技和制造企业,在全球,其业务涉及:航空产品和服务,楼宇、家庭和工业控制技术,汽车产品,涡轮增压器,以及特殊材料。霍尼韦尔公司总部位于美...  最新新闻: 天津霍尼韦尔防盗报警器代理,批", "★ 三星监控 三星TECHWIN成立于1977年,是韩国三星集团专业从事光学、航空、精密仪器以及军工  专家点评| 怎么样  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"打", "着", "智能", "旗号", "的", "产品", "都是", "一", "个", "幌子", "而已", ",", "根本", "都", "不能", "实现", "用户", "所想", "所需", "的", "智能", "体验", "。", "特别", "是", "在", "智能家居", "领域", ",", "很多时候", "并", "不是", "说", "能够", "进行", "简单", "的", "智能控制", "就是", "智能家居", "了", "。", "我们", "所", "需要", "的", "智能", "应该", "是", "一", "种", "切实解决", "我们", "的", "实际", "需求", "并且", "使用", "方便快捷", "的", "人性化", "体验", ",", "因此", ",", "判断", "到底", "智能", "不", "智能", ",", "只有", "通过", "自己", "的", "实际", "体验", "才能", "知道", ",", "没有", "体验", "过", "的", "智能家居", "很难", "让", "用户", "产生", "信任", "。", "用户", "在", "选择", "智能", "装修", "的", "时候", ",", "一定要去", "其", "体验", "中心", "实地考察", "体验", ",", "只有", "适合", "自己", "的", "才是", "好", "产品", "。", "消费者", "安装", "智能家居", "系统", "所", "要", "追求", "的", "是", "一", "种", "高品质", "的", "生活", "体验", ",", "因此", "在", "选择", "智能", "装修", "之", "前", "一定要", "认识到", "这些", "常见", "的", "误区", ",", "避免", "造成", "资金", "的", "浪费", ",", "并且", "不能", "使", "自己", "享受", "很好", "的", "服务", "。", "一般", "原理", ",", "所有", "的", "技术", "都", "各有所长", ",", "各有所短", ",", "市场", "上", "接受", "度", "最高", ",", "使用", "量", "最大", "的", "产品", ",", "是", "值得考虑", "的", "。"], ["智能家居", "十大", "品牌", "排名", "及", "选择", "技巧", "就", "给", "大家", "介绍", "到", "这里", "了", ",", "现在", "进行", "智能家居", "装修", "的", "人", "也", "不少", ",", "花费", "也是", "很厉害", "的", ",", "但", "后期", "使用", "上", "确实", "要", "比较", "舒服", "方便", ",", "有兴趣", "的", "业主", "可以", "多", "了解", "下", ",", "更多内容", "请", "关注", "土巴兔", "学", "装修", "。", "<", "<", "<", "点", "此", "获取", "专业", "设计师", "免费", "量", "房", "、", "获取", "装修", "报价", ">", ">", ">"], ["装修", "报价", "不求人", ",", "10", "秒", "极速", "获取", "报价", "根据", "生肖", "测", "哪", "天", "开工", "最", "吉利", ",", "限时", "免费", "哦", "~"], ["家装", "施工", "工艺流程", "太", "全面", "了", "!"], ["如何", "收房", "?", "详细", "交房", "流程", "教会", "你", "收房", "!"], ["如何", "避免", "装修", "中", "的", "偷", "项", "漏项", "?"]], "paragraphs": ["微信加好友 获取详细报价", "装修顾问-馨馨 4年行业经验,24h可咨询", "10秒闪电通过好友", "报价短信已发送到您的手机", "因材料品牌及工程量不同,具体报价以量房实测为准", "稍候装修管家将回电您,免费提供装修咨询服务", "您的装修预算约 ? 万元", "装修管家将回电您,免费提供装修咨询服务", "装修怕上当?问问靠谱的人", "(四年装修行业经验)", "1. 您家的房屋现状是 : 2. 您家准备什么时候开始装修 : 3. 您家小区名称 :", "请选择您家的装修时间", "微信 手机看本文 更多装修流程、装修风水随时看", "很多人都喜欢看好莱坞科技大片,在一些科技大片里面很多主角的家里都是非常智能的,比如早上还没起床早餐就煮好了,出门空调电灯会自己关掉,有敌人进门还会自动报警等等,让我们都羡慕不已,都希望自己的家里也这样,其实这些通过智能家居也可以做到的。随着生活水平的提高很多人也开始把自己的家居装修成智能型的,今天我们来看看市面上智能家居十大品牌排名情况,以及我们该如何选择这些品牌产品等。", "智能家居十大品牌排名:", "1、海尔u-home:隶属于海尔集团,全球智能化产品的研发制造基地,中国最具价值品牌之一,青岛海尔智能家电科技有限公司。 2、索博:世界智能家居为数不多集标准智能家居协议与产品生产的企业之一,世界品牌,上海索博智能电子有限公司。 3、快思聪:世界领先的控制和自动化系统/创新科技及重塑人类生活和工作的方式的领航厂家,快思聪(亚洲)有限公司。 4、霍尼韦尔智能家居:家庭自动化控制系统领先企业,世界最大的电子保安系列产品制造商之一,霍尼韦尔安防(中国)有限公司。", "5、安居宝:高新技术企业,安防领域及智能家居领域最重要集成生产商之一,广东安居宝数码科技股份有限公司。", "6、瑞讯:致力于通讯与智能控制领域的产品研究与开发,具有较大影响力智能家居品牌,瑞讯科技(亚洲)有限公司。", "7、施耐德Schneider:拥有170多年的辉煌历史,全球能效管理专家,专业从事电力与自动化管理业,施耐德电气(中国)有限公司。", "8、罗格朗-视得安:隶属罗格朗集团,智能家居十大品牌,广东省高新技术企业,合资企业,深圳市视得安罗格朗电子股份有限公司。", "9、波创:最早推出数字化智能家居产品的高科技企业之一,安防最具影响力品牌之一,深圳市新和创智能科技有限公司。", "10、KOTI柯帝:智能家居领域最大的企业之一,知名品牌,高新技术企业,行业标准制定单位,广州市聚晖电子科技有限公司。", "智能家居选购技巧:", "1、节能环保 基于计算机技术和自动化技术,智能家居系统还可以在物业管理、消防、水、电、煤等方面提供多方位的服务。市面上很多品牌企业都说自己的智能家居产品可以营造一个节能环保的环境,那么是否真的可以做到节能呢?比如说智能家居系统是否可以做到电源控制管理等等,这些一定要了解清楚,如果做不到还怎么节能。 2、远程控制 数字化产品的优势是:技术上采用嵌入式技术,硬件功能更强大,软件开发更灵活,系统更加稳定,采用标准的以太网架构,联网更简单。作为一个家庭的智能控制中心,把家电控制、家庭安防和监控、家庭信息终端以及家庭数字娱乐都整合到一起。因此,在选择时要注意检验是否可以通过Internet来访问智能终端,并操作体验。 3、功能是否集成 智能家居是对家庭设备的集中统一控制,核心应该是有一个通讯协议,一个系统平台,一个解决方案,相当于电脑的操作系统。而对应的具体产品,如安防产品、监控产品、灯控产品、多媒体产品等,都是可以集成在这一个系统上,通过系统的通讯协议,使各个子系统相互连接,信息可以互通,操作上可以相互控制。所以选购时要作详细的分析,品牌产品的兼容性好不好,就目前的技术来说进口品牌在楼宇自动化控制方面积累了一定的经验,与国产品比较,还是高出一个档次的。 4、系统是否简洁 智能家居系统,大都是房屋装修设计的时候就要考虑进去的,与家居布线同时进行,实现相同的功能,需要配置的系统越简单,系统的集成度也就越高,技术含量也越高,后期使用也越简单方便。 5、售后服务 对智能家居系统来说服务都是少不了的,尤其是售后服务,所以选择产品时要做充分的了解,看服务是否到位,再选择恰当的产品,切不可把重点放在追求时髦、先进、绚丽的功能和外表上。 认知三大误区:", "1.有线无线区别大 国内外智能家居企业鱼龙混杂,相关智能控制技术参差不齐,传统智能家居采用有线技术,布线复杂,造价昂贵,且用户体验度非常不好。新一代物联网技术的智能家居,利用无线传感技术,结合目前最火热的移动互联网技术,采用智能终端远程控制,使整个使用体验的舒适度明显地提升了上去。相关专家表示,有线控制面临消亡,无线控制由于其免布线,移动性强,升级方便等特点迅速受到市场的欢迎,更符合智能化的需求和趋势。不过,有一些成熟的智能家居企业利用无线的优势,推出的只具有单向控制信号的无线智能家居,非常适合市场的需求,因此一些传统的低压开关厂家也纷纷进入,TCL、飞雕等等,也是采用RF433的单向技术,说明只有符合市场的,才是好产品。 2.智能家电不等于智能家居 日前,某企业推出了新一代超级智能电视,受到网民的热烈讨论。各大媒体和电视业纷纷追踪报道,一时间,智能电视以及智能家电受到了人们的强烈关注,很多人把智能家电当成了智能家居,这是一个非常大的误区,如果不认清这个,很容易被一些家电商误导。智能家电的本质还是家用电器,只不过采用了一些智能化的系统或者技术,这是相对于传统家电而言的,就像21世纪的智能手机一样,它还是手机。而智能家居是一个平台,其本身就是一个智能化的控制系统。在这个平台上,所有的家电和门窗开关都可以被远程控制,实现智能化的应用体验,可以根据用户的自定义设置,进行各种各样的智能控制,这才是真正的智能家居。我们在选择产品和品牌的时候,且不要被一些家电厂商的所谓的”智能化”字眼所迷惑和误导。 3.智能不智能,体验是关键 在21世纪智能是一个非常火热的词语,随着移动互联网的强势崛起,21世纪以后所有的产品都要和智能联系到一起,否则都不好意思出现在用户面前。然后事实却是大部分打着智能旗号的产品都是一个幌子而已,根本都不能实现用户所想所需的智能体验。特别是在智能家居领域,很多时候并不是说能够进行简单的智能控制就是智能家居了。我们所需要的智能应该是一种切实解决我们的实际需求并且使用方便快捷的人性化体验,因此,判断到底智能不智能,只有通过自己的实际体验才能知道,没有体验过的智能家居很难让用户产生信任。用户在选择智能装修的时候,一定要去其体验中心实地考察体验,只有适合自己的才是好产品。 消费者安装智能家居系统所要追求的是一种高品质的生活体验,因此在选择智能装修之前一定要认识到这些常见的误区,避免造成资金的浪费,并且不能使自己享受很好的服务。一般原理,所有的技术都各有所长,各有所短,市场上接受度最高,使用量最大的产品,是值得考虑的。", "智能家居十大品牌排名及选择技巧就给大家介绍到这里了,现在进行智能家居装修的人也不少,花费也是很厉害的,但后期使用上确实要比较舒服方便,有兴趣的业主可以多了解下,更多内容请关注土巴兔学装修。<<<点此获取专业设计师免费量房、获取装修报价>>>", "装修报价不求人,10秒极速获取报价 根据生肖测哪天开工最吉利,限时免费哦~", "家装施工工艺流程 太全面了!", "如何收房?详细交房流程教会你收房!", "如何避免装修中的偷项漏项?"], "bs_rank_pos": 2}, {"is_selected": false, "title": "世界十大智能家居", "most_related_para": 10, "segmented_title": ["世界", "十大", "智能家居"], "segmented_paragraphs": [["短信", "快捷", "登录"], ["您", "还", "可以", "使用", "合作", "账号", "登录", "网站", ":", "还没有", "一起", "网", "帐号", "?"], ["10", "秒", "钟", ",", "快速注册"], ["微信", "扫", "码", "快速登录"], ["验证", "即", "登录", ",", "未注册", "将", "自动", "创建", "一起", "网", "帐号"], ["获取", "动态", "密码"], ["微信", "账号", "登陆"], ["微信", "扫一扫", "绑定", "/", "登录"], ["从", "免费", "户型设计", "开始", "…"], ["你", "的", "装修预算", "约", "?", "万", "元"], ["设计费", ":", "?", "元", "4999", "元", "(", "测量", "、", "设计", "、", "报价", ")"], ["抢", "免费", "设计", "名额"], ["请放心", "填写", "您", "的", "隐私", "将", "被", "严格", "保密"], ["7", "年", "服务", "700", "万", "用户", ",", "中国家装", "十大", "电商", "平台"], ["亲", ",", "您", "已经", "报", "过", "名", "啦", ",", "给", "别人", "留", "点", "机会", "呗"], ["我们", "还有", "更多精彩活动", ",", "不如", "去", "看看", ">"], ["热门", "新闻"]], "paragraphs": ["短信快捷登录", "您还可以使用合作账号登录网站: 还没有一起网帐号?", "10秒钟,快速注册", "微信扫码 快速登录", "验证即登录,未注册将自动创建一起网帐号", "获取动态密码", "微信账号登陆", "微信扫一扫绑定/登录", "从免费户型设计开始…", "你的装修预算约 ? 万元", "设计费:?元4999元(测量、设计、报价)", "抢免费设计名额", "请放心填写您的隐私将被严格保密", "7年服务700万用户,中国家装十大电商平台", "亲,您已经报过名啦,给别人留点机会呗", "我们还有更多精彩活动,不如去看看>", "热门新闻"], "bs_rank_pos": 3}, {"is_selected": false, "title": "品牌榜:2016-2017年智能家居十大品牌排行榜 投票结果公布【新】_", "most_related_para": 1, "segmented_title": ["品牌", "榜", ":", "2016", "-", "2017", "年", "智能家居", "十大", "品牌", "排行榜", "投票", "结果公布", "【", "新", "】", "_"], "segmented_paragraphs": [["2016", "-", "2017", "年", "最新", "的", "智能家居", "品牌", "榜", "发布", "了", ",", "一起来", "看", "下", "本次", "发布", "的", "榜单", "的", "品牌", "数据", "情况", "吧", "。", "智能家居", "十大", "品牌", "排行榜", ",", "此次", "榜单", "收集", "了", 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"”", "完整", "生态系统", ".", ".", "."], ["(", "以上", "榜单", "排名不分先后", ",", "具体", "请", "以", "实际", "最新", "公布", "数据", "为准", "》", "查看", "完整", "智能家居", "榜单", ">", ">", ")"], ["更多", "行业", "相关", "榜单"]], "paragraphs": ["2016-2017年最新的智能家居品牌榜发布了,一起来看下本次发布的榜单的品牌数据情况吧。智能家居十大品牌排行榜,此次榜单收集了智能家居行业超过137个品牌信息及46412个网友的投票做为参考,发布的品牌榜单由CNPP大数据平台提供数据支持,综合分析了智能家居行业品牌的知名度、员工数量、企业资产规模与经营情况等各项实力数据,发布了本榜单数据,仅供方便用户找到好的品牌参考使用,具体榜单请按最新更新数据为准。", "智能家居品牌榜数据包括:广东省智能家居品牌[57家]、深圳市智能家居品牌[37家]、浙江省智能家居品牌[17家]、智能家居大品牌、智能家居著名商标、智能家居省市名牌、智能家居驰名保护、智能家居高新企业、智能家居上市公司、智能家居500强企业、智能家居中小企业等多项品牌数据查看,以下为部份大品牌数据信息。", "青岛海尔智能家电科技有限公司,u-home,智能家居十大品牌,智能化产品的研发制造基地,海尔集团在物联网时代推出的美好住居生活解决方案", "京东智能旗下针对智能硬件产品专门推出的互联开放的智能硬件平台,其App可以控制所有智能设备/远程管理/互联互通", "华为技术有限公司,始于1987年,以创新力科技著称,全球领先的信息与通信技术解决方案供应商,员工持股的民营科技公司,在电信运营商/企业/终端...", "阿里巴巴集团,创立于1999年,拥有淘宝网/天猫/聚划算/1688/阿里云/蚂蚁金服/菜鸟网络等网上及移动商贸平台,利用互联网创造了公平的竞...", "上海纯米电子科技有限公司,米家MIJIA,小米打造的全新生态链品牌,小米智能家庭的简称,专门承载其供应链产品,以电饭煲在界内引起巨大关注", "美的集团股份有限公司,美的智慧家居M-Smart,美的集团在智能家居发展战略上提出的基于美的产品群及产业群优势,致力于给消费者提供最专业的智...", 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"paragraphs": ["随笔- 150, 文章 - 0, 评论 - 144, 引用 - 0", "1.概览 普里姆算法(Prim算法),图论中的一种算法,可在加权连通图里搜索最小生成树。意即由此算法搜索到的边子集所构成的树中,不但包括了连通图里的所有顶点(英语:Vertex (graph theory)),且其所有边的权值之和亦为最小。该算法于1930年由捷克数学家沃伊捷赫·亚尔尼克(英语:Vojtěch Jarník)发现;并在1957年由美国计算机科学家罗伯特·普里姆(英语:Robert C. Prim)独立发现;1959年,艾兹格·迪科斯彻再次发现了该算法。因此,在某些场合,普里姆算法又被称为DJP算法、亚尔尼克算法或普里姆-亚尔尼克算法。 2.算法简单描述 1).输入:一个加权连通图,其中顶点集合为V,边集合为E; 2).初始化:Vnew = {x},其中x为集合V中的任一节点(起始点),Enew = {},为空; 3).重复下列操作,直到Vnew = V: a.在集合E中选取权值最小的边,其中u为集合Vnew中的元素,而v不在Vnew集合当中,并且v∈V(如果存在有多条满足前述条件即具有相同权值的边,则可任意选取其中之一); b.将v加入集合Vnew中,将边加入集合Enew中; 4).输出:使用集合Vnew和Enew来描述所得到的最小生成树。 下面对算法的图例描述 3.简单证明prim算法 反证法:假设prim生成的不是最小生成树 1).设prim生成的树为G0 2).假设存在Gmin使得cost(Gmin)不属于G0 3).将加入G0中可得一个环,且不是该环的最长边(这是因为∈Gmin) 4).这与prim每次生成最短边矛盾 5).故假设不成立,命题得证. 4.算法代码实现(未检验) 5.时间复杂度 这里记顶点数v,边数e 邻接矩阵:O(v2) 邻接表:O(elog2v) Kruskal算法 1.概览 Kruskal算法是一种用来寻找最小生成树的算法,由Joseph Kruskal在1956年发表。用来解决同样问题的还有Prim算法和Boruvka算法等。三种算法都是贪婪算法的应用。和Boruvka算法不同的地方是,Kruskal算法在图中存在相同权值的边时也有效。 2.算法简单描述 1).记Graph中有v个顶点,e个边 2).新建图Graphnew,Graphnew中拥有原图中相同的e个顶点,但没有边", "1.概览 Kruskal算法是一种用来寻找最小生成树的算法,由Joseph Kruskal在1956年发表。用来解决同样问题的还有Prim算法和Boruvka算法等。三种算法都是贪婪算法的应用。和Boruvka算法不同的地方是,Kruskal算法在图中存在相同权值的边时也有效。 2.算法简单描述 1).记Graph中有v个顶点,e个边 2).新建图Graphnew,Graphnew中拥有原图中相同的e个顶点,但没有边 3).将原图Graph中所有e个边按权值从小到大排序 4).循环:从权值最小的边开始遍历每条边 直至图Graph中所有的节点都在同一个连通分量中 if 这条边连接的两个节点于图Graphnew中不在同一个连通分量中 添加这条边到图Graphnew中 首先第一步,我们有一张图Graph,有若干点和边  将所有的边的长度排序,用排序的结果作为我们选择边的依据。这里再次体现了贪心算法的思想。资源排序,对局部最优的资源进行选择,排序完成后,我们率先选择了边AD。这样我们的图就变成了右图 在剩下的变中寻找。我们找到了CE。这里边的权重也是5 依次类推我们找到了6,7,7,即DF,AB,BE。 下面继续选择, BC或者EF尽管现在长度为8的边是最小的未选择的边。但是现在他们已经连通了(对于BC可以通过CE,EB来连接,类似的EF可以通过EB,BA,AD,DF来接连)。所以不需要选择他们。类似的BD也已经连通了(这里上图的连通线用红色表示了)。 最后就剩下EG和FG了。当然我们选择了EG。最后成功的图就是右: 3.简单证明Kruskal算法 对图的顶点数n做归纳,证明Kruskal算法对任意n阶图适用。 归纳基础: n=1,显然能够找到最小生成树。 归纳过程: 假设Kruskal算法对n≤k阶图适用,那么,在k+1阶图G中,我们把最短边的两个端点a和b做一个合并操作,即把u与v合为一个点v',把原来接在u和v的边都接到v'上去,这样就能够得到一个k阶图G'(u,v的合并是k+1少一条边),G'最小生成树T'可以用Kruskal算法得到。 我们证明T'+{}是G的最小生成树。 用反证法,如果T'+{}不是最小生成树,最小生成树是T,即W(T)})。显然T应该包含,否则,可以用加入到T中,形成一个环,删除环上原有的任意一条边,形成一棵更小权值的生成树。而T-{},是G'的生成树。所以W(T-{})<=W(T'),也就是W(T)<=W(T')+W()=W(T'+{}),产生了矛盾。于是假设不成立,T'+{}是G的最小生成树,Kruskal算法对k+1阶图也适用。 由数学归纳法,Kruskal算法得证。 4.代码算法实现 typedef struct { char vertex[VertexNum]; //顶点表 int edges[VertexNum][VertexNum]; 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最小生成树可以用kruskal(克鲁斯卡尔)算法或prim(普里姆)算法求出。[2]", "中文名 最小生成树  外文名 Minimum Spanning Tree,MST  提出者 Kruskal(克鲁斯卡尔)Prim(普里姆)", "应用学科 计算机,数学(图论),数据结构  适用领域范围 应用图论知识的实际问题  算法 Kruskal算法,Prim算法", "1 概述  2 应用  3 性质  ▪ 说明  ▪ 证明", "4 算法描述  ▪ Prim算法简述  ▪ Kruskal算法简述  ▪ 伪代码  ▪ C语言代码", "▪ Kruskal算法 - pascal语言  ▪ Prim算法 - pascal语言  ▪ C++模板", "在一给定的无向图G = (V, E) 中,(u, v) 代表连接顶点 u 与顶点 v 的边(即),而 w(u, v) 代表此边的权重,若存在 T 为 E 的子集(即)且为无循环图,使得", "的w(T) 最小,则此 T 为 G 的最小生成树。", "生成树和最小生成树有许多重要的应用。", "例如:要在n个城市之间铺设光缆,主要目标是要使这 n 个城市的任意两个之间都可以通信,但铺设光缆的费用很高,且各个城市之间铺设光缆的费用不同,因此另一个目标是要使铺设光缆的总费用最低。这就需要找到带权的最小生成树。[1]", "最小生成树性质:设G=(V,E)是一个连通网络,U是顶点集V的一个非空真子集。若(u,v)是G中一条“一个端点在U中(例如:u∈U),另一个端点不在U中的边(例如:v∈V-U),且(u,v)具有最小权值,则一定存在G的一棵最小生成树包括此边(u,v)。", "为方便说明,先作以下约定:", "①将集合U中的顶点看作是红色顶点,②而V-U中的顶点看作是蓝色顶点,③连接红点和蓝点的边看作是紫色边,④权最小的紫边称为轻边(即权重最\"轻\"的边)。于是,MST性质中所述的边(u,v)就可简称为轻边。", "用反证法证明MST性质:", "假设G中任何一棵MST都不含轻边(u,v)。则若T为G的任意一棵MST,那么它不含此轻边。", "根据树的定义,则T中必有一条从红点u到蓝点v的路径P,且P上必有一条紫边(u',v')连接红点集和蓝点集,否则u和v不连通。当把轻边(u,v)加入树T时,该轻边和P必构成了一个回路。删去紫边(u',v')后回路亦消除,由此可得另一生成树T'。", "T'和T的差别仅在于T'用轻边(u,v)取代了T中权重可能更大的紫边(u',v')。因为w(u,v)≤w(u',v'),所以", "w(T')=w(T)+w(u,v)-w(u',v')≤w(T)", "即T'是一棵比T更优的MST,所以T不是G的MST,这与假设矛盾。", "所以,MST性质成立。[1]", "求MST的一般算法可描述为:针对图G,从空树T开始,往集合T中逐条选择并加入n-1条安全边(u,v),最终生成一棵含n-1条边的MST。", "当一条边(u,v)加入T时,必须保证T∪{(u,v)}仍是MST的子集,我们将这样的边称为T的安全边。", "1).输入:一个加权连通图,其中顶点集合为V,边集合为E;2).初始化:Vnew= {x},其中x为集合V中的任一节点(起始点),Enew= {},为空;3).重复下列操作,直到Vnew= V:a.在集合E中选取权值最小的边,其中u为集合Vnew中的元素,而v不在Vnew集合当中,并且v∈V(如果存在有多条满足前述条件即具有相同权值的边,则可任意选取其中之一);b.将v加入集合Vnew中,将边加入集合Enew中;4).输出:使用集合Vnew和Enew来描述所得到的最小生成树。[1] 假设WN=(V,{E}) 是一个含有 n 个顶点的连通网,则按照克鲁斯卡尔算法构造最小生成树的过程为:先构造一个只含 n 个顶点,而边集为空的子图,若将该子图中各个顶点看成是各棵树上的根结点,则它是一个含有 n 棵树的一个森林。之后,从网的边集 E 中选取一条权值最小的边,若该条边的两个顶点分属不同的树,则将其加入子图,也就是说,将这两个顶点分别所在的两棵树合成一棵树;反之,若该条边的两个顶点已落在同一棵树上,则不可取,而应该取下一条权值最小的边再试之。依次类推,直至森林中只有一棵树,也即子图中含有 n-1条边为止。[1]", "GenerieMST(G){//求G的某棵MST", "T〈-￠; //T初始为空,是指顶点集和边集均空", "while T未形成G的生成树 do{", "找出T的一条安全边(u,v);//即T∪{(u,v)}仍为MST的子集", "T=T∪{(u,v)}; //加入安全边,扩充T", "return T; //T为生成树且是G的一棵MST", "下面给出的两种求MST的算法均是对上述的一般算法的求精,两算法的区别仅在于求安全边的方法不同。", "为简单起见,下面用序号0,1,…,n-1来表示顶点集,即是:", "V(G)={0,1,…,n-1},", "G中边上的权解释为长度,并设T=(U,TE)。"], "bs_rank_pos": 2}, {"is_selected": false, "title": "最小生成树算法及应用_图文_百度文库", "most_related_para": 9, "segmented_title": ["最小生成树", "算法", "及", "应用", "_", "图文", "_", "百度", "文库"], "segmented_paragraphs": [["暂无", "评价", "|", "0", "人", "阅读", "|", "0", "次", "下载", "|", "举报", "文档"], ["最小生成树", "算法", "及", "应用", "_", "计算机", "软件", "及", "应用", "_", "IT", "/", "计算机", "_", "专业", "资料", "。", "最小生成树", "算法", "及", "应用"], ["最小生成树", "算法", "及", "应用", "一", "、", "生成", "树", "的", "概念", "若", "图", "是", "连通", "的", "无", "向", "图", "或", "强", "连通", "的", "有向图", ",", "则", "从", "图", "中", "任意", "一", "个", "顶点", "出发", "调用", "一", "次", "bfs", "或", "dfs", "后", ",", "便", "可以", "系统", "地", "访问", "图", "中", "所有", "顶点", ";", "若", "图", "是", "有根", "的", "有向图", ",", "则", "从", "根", "出发", "通过", "调用", "一", "次", "dfs", 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"应用", "17", "页", "免费"], ["最小生成树", "算法", "及", "应用", "17", "页", "免费"]], "paragraphs": ["暂无评价|0人阅读|0次下载|举报文档", "最小生成树算法及应用_计算机软件及应用_IT/计算机_专业资料。最小生成树算法及应用", "最小生成树算法及应用一、生成树的概念 若图是连通的无向图或强连通的有向图,则从图中任意一个顶点出发调用一 次bfs或dfs后,便可以系统地访问图中所有顶点;若图是有根的有向图,则从根 出发通过调用一次dfs或bfs,亦可系统地访问所有顶点。", "在这种情况下,图中所 有顶点加上遍历过程中经过的边所构成的子图,称为原图的生成树。", "对于不连通的无向图和不是强连通的有向图,若有根或者从根外的任意顶点 出发,调用一次bfs或dfs后,一般不能系统地访问所有顶点,而只能得到以出发 点为根的连通分支(或强连通分支)的生成树。要访问其它顶点,还需要从没有 访问过的顶点中找一个顶点作为起始点,再次调用bfs或dfs,这样得到的是生成 森林。", "由此可以看出,一个图的生成树是不唯一的,不同的搜索方法可以得到不同 的生成树,即使是同一种搜索方法,出发点不同亦可导致不同的生成树。 可以证明:具有n个顶点的带权连通图,其对应的生成树有n-1条边。", "最小生成树算法及应用 最小生成树算法及应用二、求图的最小生成树算法 严格来说,如果图G=(V,E)是一个连通的无向图,则把它的全部顶点V和 一部分边E’构成一个子图G’,即G’=(V, E’),且边集E’能将图中所有顶点连通 又不形成回路,则称子图G’是图G的一棵生成树。", "最小生成树算法及应用例1、城市公交网 [问题描述] 有一张城市地图,图中的顶点为城市,无向边代表两个城市间的连通关系, 边上的权为在这两个城市之间修建高速公路的造价,研究后发现,这个地图有一 个特点,即任一对城市都是连通的。", "最小生成树算法及应用 [举例] 下面的图(A)表示一个5个城市的地图,图(B)、(C)是对图(A)分别进 行深度优先遍历和广度优先遍历得到的一棵生成树,其权和分别为20和33,前者比 后者好一些,但并不是最小生成树,最小生成树的权和为19。", "最小生成树算法及应用 1、用Prim算法求最小生成树的思想如下: ①设置一个顶点的集合S和一个边的集合TE,S和TE的初始状态均为空集; ②选定图中的一个顶点K,从K开始生成最小生成树,将K加入到集合S; ③重复下列操作,直到选取了n-1条边: 选取一条权值最小的边(X,Y),其中X∈S,not (Y∈S); 将顶点Y加入集合S,边(X,Y)加入集合TE; ④得到最小生成树T =(S,TE) 。 最小生成树算法及应用① 从文件中读入图的邻接矩阵g; ——Prim算法的实现 ② 边集数组elist初始化; For i:=1 To n-1 Do Begin elist[i].fromv:=1;elist[i].endv:=i+1;elist[i].weight:=g[1,i+1]; End; ③ 求出最小生成树的n-1条边; For k:=1 To n-1 Do Begin min:=maxint;m:=k; For j:=k To n-1 Do {查找权值最小的一条边} If elist[j].weightk Then Begin t:=elist[k];elist[k]:=elist[m];elist[m]:=t;End; {把权值最小的边调到第k个单元} j:=elist[k].endv; {j为新加入的顶点} For i:=k+1 To n-1 Do {修改未加入的边集} Begin s:=elist[i].endv; w:=g[j,s]; If w", "，", "其中", "u", "为", "集合", "Vnew", "中", "的", "元素", "，", "而", "v", "不在", "Vnew", "集合", "当", "中", "，", "并且", "v", "∈", "V", "（", "如果", "存在", "有", "多", "条", "满足", "前", "述", "条件", "即", "具有", "相同", "权值", "的", "边", "，", "则", "可", "任意", "选取", "其中", "之", "一", "）", "；", "b", ".", "将", "v", "加入", "集合", "Vnew", "中", "，", "将", "<", "u", ",", "v", ">", "边", "加入", "集合", "Enew", "中", "；", "4", "输出", "：", 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AMD Radeon Pro Duo采用了两颗顶级的Fiji核心(竟然不是两枚R9 Nano的节能版核心,颇令人意外),搭载8GHBM显存,可实现16 TeraFLOPs(每秒16兆次浮点运算)的性能,而就3个8pin辅助供电这点来看,该卡的核心主频很可能在1GHz左右,单就单精度运算性能来看,AMD Radeon Pro Duo的性能是TITAN X(7T)的2倍还要多,是AMD自家R9 390的3倍多。黄教主的核弹真的要炸!(转自太平洋电脑网)", "跑分看起来比ttx高两倍还多啊", "然而买不起 用不到╭(°A°`)╮  装电脑,出售回收旧电脑 -助手版贴吧客户端", "看起来是不是很屌炸天", "目前,AMD方面称AMD Radeon Pro Duo暂时不会面向零售市场,而是以AMD VR整机的形式出现。以上是Maingear联合AMD展示的搭载Radeon Pro Duo的主机解决方案。另外,AMD Radeon Pro Duo预计会在今年的第二季度上市,单卡售价为1499美刀(约合人民币9750元),倒是和我们之前所料不差。(转自太平洋电脑网)", "夭寿拉!AMD要出整机啦各位a饭会不会充值信仰呢", "整机的话,CPU是淫特二的么?", "我3770K+fury X 整机待机才100W左右,烤鸡也才380W左右,fury X功耗没你说的那么大", "等个10年再讨论还来得及"], "bs_rank_pos": 1}, {"is_selected": false, "title": "土豪真是多,13999的AMD Radeon Pro Duo 居然真有人买。。。。。 - ", "most_related_para": 5, "segmented_title": ["土豪", "真是", "多", ",", "13999", "的", "AMD", "Radeon", "Pro", "Duo", "居然", "真", "有人", "买", "。", "。", "。", "。", "。", "-"], "segmented_paragraphs": [["当年", "泰坦", "z", "两万多", "都", "有人", "买", "呢", "这", "算", "啥"], ["不买", "的", "话", ",", "amd", "不", "就", "倒闭", "了", "吗", "?"], ["不", "愧", "是", "正义", "的", "A", "卡", ",", "总是", "那么", "花团锦簇", ",", "大家", "都", "抢", "着", "买", "!", "那么", ",", "转帖", "机", "不", "来", "一", "张", "?"], ["去年", "九月份", "我", "就", "用", "上", "两", "块", "FURY", "X", "和", "这个", "也", "没有", "什么", "差别", "吧", "。"], ["连", "29999", "的", "某", "Z", "都", "有", "人", "买", ",", "这个", "算", "肾", "马"], ["建议", "喜欢", "捧", "这", "张", "卡", "的", "人", "去", "买", "1", "张", "测试", "4K", "VR", "效能", "和", "双", "精度", "计算", "实战", "应用", ",", "比如", "LZ", "。"], ["不是", "说", "运算", "能力", "强", "么", "。", "。", "。", "如果", "是", "拿来", "玩游戏", ",", "没理由", "不", "上", "980", "TI", "SLI", "。", "。"]], "paragraphs": ["当年泰坦z两万多都有人买呢 这算啥", "不买的话,amd不就倒闭了吗?", "不愧是正义的A卡,总是那么花团锦簇,大家都抢着买!那么,转帖机不来一张?", "去年九月份我就用上两块FURY X 和这个也没有什么差别吧。", "连29999的某Z都有人买,这个算肾马", "建议喜欢捧这张卡的人去买1张测试4K VR效能和双精度计算实战应用,比如LZ。", "不是说运算能力强么。。。如果是拿来玩游戏,没理由不上980TI SLI。。"], "bs_rank_pos": 2}, {"is_selected": true, "title": "amd radeon pro duo值得买吗_百度知道", "most_related_para": 0, "segmented_title": ["amd", "radeon", "pro", "duo", "值得", "买", "吗", "_", "百度", "知道"], "segmented_paragraphs": [["不值得", ",", "13999", "RMB", "的", "东西", "连", "NVIDIA", "的", "4599", "RMB", "的", "GTX", "1080", "都", "比", "不过", ",", "13400", "的", "3Dmark", "11", "分数", ",", "公版", "GTX", "1080", "就", "12900", "分", "了", ",", "再加上", "1080", "超频", "能力", "本来", "就", "十分", "恐怖", ",", "超一超", "随便", "吊打", "Pro", "Duo"], ["有钱", "就", "买", "吧", ",", "AMD", "现任", "卡皇", "且", "针对", "VR", "优化", "。"]], "paragraphs": ["不值得,13999RMB的东西连NVIDIA的4599RMB的GTX1080都比不过,13400的3Dmark 11分数,公版GTX1080就12900分了,再加上1080超频能力本来就十分恐怖,超一超随便吊打Pro Duo", "有钱就买吧,AMD现任卡皇且针对VR优化。"], "bs_rank_pos": 3}, {"is_selected": false, "title": "AMD Radeon Pro Duo售价几近腰斩,799美元买到就是赚到! 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Dark Rock 3散热器活动 乔思伯CR-201散热器、FR-201P/301P机箱风扇试用,玩出你灯光Style"], "bs_rank_pos": 4}], "answer_spans": [[2, 45]], "fake_answers": ["13999RMB的东西连NVIDIA的4599RMB的GTX1080都比不过,13400的3Dmark11分数,公版GTX1080就12900分了,再加上1080超频能力本来就十分恐怖,超一超随便吊打ProDuo"], "question": "pro duo为什么没人买", "segmented_answers": [["只是", "一", "个", "单纯", "的", "双卡", "交火", "卡", "而已", "，", "双卡", "性能", "比不上", "人家", "单", "卡", "，", "而且", "双卡", "现在", "优化", "兼容", "不好", "，", "不是", "什么", "游戏", "都", "支持", "的", "。"], ["两", "个", "斐济", "核心", "，", "8", "ghbm", "显存", "，", "性能", "不如", "双", "fury", "x", "。"], ["13999", "RMB", "的", "东西", "连", "NVIDIA", "的", "4599", "RMB", "的", "GTX", "1080", "都", "比", "不过", "，", "13400", "的", "3Dmark", "11", "分数", "，", "公版", "GTX", "1080", "就", "12900", "分", "了", "，", "再加上", "1080", "超频", "能力", "本来", "就", "十分", "恐怖", "，", "超一超", "随便", "吊打", "Pro", "Duo", "。"]], "answers": ["只是一个单纯的双卡交火卡而已，双卡性能比不上人家单卡，而且双卡现在优化兼容不好，不是什么游戏都支持的。", "两个斐济核心，8ghbm显存，性能不如双furyx。", "13999RMB的东西连NVIDIA的4599RMB的GTX1080都比不过，13400的3Dmark 11分数，公版GTX1080就12900分了，再加上1080超频能力本来就十分恐怖，超一超随便吊打Pro Duo。"], "answer_docs": [3], "segmented_question": ["pro", "duo", "为什么", "没", "人", "买"], "question_type": "DESCRIPTION", "question_id": 31, "fact_or_opinion": "OPINION", "match_scores": [0.8988764044943819]}
+{"documents": [{"is_selected": true, "title": "电脑的显示器灯亮着但是 却是黑屏_百度知道", "most_related_para": 0, "segmented_title": ["电脑", "的", "显示器", "灯", "亮", "着", "但是", "却", "是", "黑屏", "_", "百度", "知道"], "segmented_paragraphs": [["黑屏", "的", "原因", ":", "显卡", "和", "显示器", "之间", "的", "数据", "连线", "没有", "插", "好", "、", "VGA", "接", "连线", "有问题", "、", "分辨率", "出现错误", "、", "显卡", "失效", "等", "。", "解决", "的", "方法", ":", "第", "一", ",", "打开", "电脑", "以后", ",", "按", "CTRL", "+", "ALT", "+", "F7", "恢复", "默认", "的", "分辨率", "。", "通过", "更改", "分辨率", ",", "来", "解决", "此", "问题", "。", "第", "二", ",", "检查", "VGA", "接", "连线", "是否", "有问题", ",", "如果", "有问题", ",", "换", "一", "条", "VGA", "线", "试", "一", "下", "。", "第", "三", ",", "显卡", "需要", "重新", "安装", "。"], ["重新", "插拔", "内存条", "如果", "不成", "请", "更换", "内存", "如果", "是", "两", "条", "内存", "应该", "是", "有", "一", "条", "坏", "了"], ["内存", "接触", "不好", ",", "拆下", "用", "橡皮擦", "擦", "再", "装", "。"], ["机箱", "积", "灰", ",", "把", "机箱", "打扫", "一", "下", ",", "把", "内存条", "拔下", "重新", "插入", "。"], ["卡", "了", ",", "重启"]], "paragraphs": ["黑屏的原因:显卡和显示器之间的数据连线没有插好、VGA接连线有问题、分辨率出现错误、显卡失效等。 解决的方法: 第一,打开电脑以后,按CTRL+ALT+F7恢复默认的分辨率。通过更改分辨率,来解决此问题。 第二,检查VGA接连线是否有问题,如果有问题,换一条VGA线试一下。 第三,显卡需要重新安装。", "重新插拔内存条如果不成请更换内存如果是两条内存应该是有一条坏了", "内存接触不好,拆下用橡皮擦擦再装。", "机箱积灰,把机箱打扫一下,把内存条拔下重新插入。", "卡了,重启"], "bs_rank_pos": 0}, {"is_selected": false, "title": "电脑显示器灯亮但是黑屏_百度知道", "most_related_para": 2, "segmented_title": ["电脑", "显示器", "灯", "亮", "但是", "黑屏", "_", "百度", "知道"], "segmented_paragraphs": [["LCD", "电源", "指示灯", "亮", "、", "按键", "正常", ",", "表明", "A", "/", "D", "驱动", "板", "的", "MCU", "还能", "正常", "工作", ",", "基本上", "说明", "电源", "部分", "正常", ";", "　　", "黑屏", "可能", "是", "背光", "灯", "没有", "点亮", "(", "从", "背面", "看不到", "灯管", "发光", ")", ",", "驱动", "背光", "电路", "可能", "有", "故障", ";", "　　", "一", "、", "如果", "不能", "看到", "屏幕", "上", "的", "微", "暗", "图像", ",", "就是", "A", "/", "D", "驱动", "板", "的", "信号处理", "电路", "不", "正常", ",", "A", "/", "D", "板", "上", "的", "开关", "部分", "电路", "有", "故障", ",", "导致", "不能", "输出", "高电平", "去", "控制", "高压", "板", "的", "开关", "极", ";", "　　", "二", "、", "反", "之", ",", "如果", "能看到", "屏幕", "上", "有", "微", "暗", "的", "图像", ",", "就", "说明", "A", "/", "D", "驱动", "板", "的", "信号处理", "电路", "正常", ",", "故障", "可", "锁定", "在", "驱动", "背光", "的", "高压", "板", "上", ";", "　　", "高压", "板", "常见", "故障", "有", ":", "　　", "1", "、", "高压", "板", "自身", "保险", "管", "烧毁", ";", "　　", "2", "、", "背光", "灯管", "损坏", "(", "不过", "双灯", ",", "或", "四", "灯", "的", "背光", "同时", "损坏", "的", "可能性不大", ",", "其中", "一", "个", "损坏", "也", "会", "黑屏", ",", "高压", "板", "具有", "负载", "不平衡", "保护", "功能", ",", "如果", "一根", "灯管", "损坏", ",", "开机后", "高压", "板", "即", "进入", "负载", "不平衡", "保护", "状态", ",", "屏幕", "会", "闪烁", "一", "下", "再", "黑屏", ")", "　　", "建议", "送", "去", "维修点"], ["LCD", "电源", "指示灯", "亮", "、", "按键", "正常", ",", "表明", "A", "/", "D", "驱动", "板", "的", "MCU", "还能", "正常", "工作", ",", "基本上", "说明", "电源", "部分", "正常", ";", "　　", "黑屏", "可能", "是", "背光", "灯", "没有", "点亮", "(", "从", "背面", "看不到", "灯管", "发光", ")", ",", "驱动", "背光", "电路", "可能", "有", "故障", ";", "　　", "一", "、", "如果", "不能", "看到", "屏幕", "上", "的", "微", "暗", "图像", ",", "就是", "A", "/", "D", "驱动", "板", "的", "信号处理", "电路", "不", "正常", ",", "A", "/", "D", "板", "上", "的", "开关", "部分", "电路", "有", "故障", ",", "导致", "不能", "输出", "高电平", "去", "控制", "高压", "板", "的", "开关", "极", ";", "　　", "二", "、", "反", "之", ",", "如果", "能看到", "屏幕", "上", "有", "微", "暗", "的", "图像", ",", "就", "说明", "A", "/", "D", "驱动", "板", "的", "信号处理", "电路", "正常", ",", "故障", "可", "锁定", "在", "驱动", "背光", "的", "高压", "板", "上", ";", "　　", "高压", "板", "常见", "故障", "有", ":", "　　", "1", "、", "高压", "板", "自身", "保险", "管", "烧毁", ";", "　　", "2", "、", "背光", "灯管", "损坏", "(", "不过", "双灯", ",", "或", "四", "灯", "的", "背光", "同时", "损坏", "的", "可能性不大", ",", "其中", "一", "个", "损坏", "也", "会", "黑屏", ",", "高压", "板", "具有", "负载", "不平衡", "保护", "功能", ",", "如果", "一根", "灯管", "损坏", ",", "开机后", "高压", "板", "即", "进入", "负载", "不平衡", "保护", "状态", ",", "屏幕", "会", "闪烁", "一", "下", "再", "黑屏", ")", "　　", "建议", "送", "去", "维修点"], ["一", "个", "是", "显示器", "接", "主机", "的", "线", "没", "接", "好", "或者", "坏", "了", ",", "第二个", "就是", "内存", "和", "其它", "硬件", "出", "问题", "。", "你", "可以", "使用", "排查", "的", "办法", ",", "把", "机箱", "接", "到", "朋友", "的", "显示器", "上", "面试", "下", "。"]], "paragraphs": ["LCD电源指示灯亮、按键正常,表明A/D驱动板的MCU还能正常工作,基本上说明电源部分正常; 　　黑屏可能是背光灯没有点亮(从背面看不到灯管发光),驱动背光电路可能有故障; 　　一、如果不能看到屏幕上的微暗图像,就是A/D驱动板的信号处理电路不正常,A/D板上的开关部分电路有故障,导致不能输出高电平去控制高压板的开关极; 　　二、反之,如果能看到屏幕上有微暗的图像,就说明A/D驱动板的信号处理电路正常,故障可锁定在驱动背光的高压板上; 　　高压板常见故障有: 　　1、高压板自身保险管烧毁; 　　2、背光灯管损坏(不过双灯,或四灯的背光同时损坏的可能性不大,其中一个损坏也会黑屏,高压板具有负载不平衡保护功能,如果一根灯管损坏,开机后高压板即进入负载不平衡保护状态,屏幕会闪烁一下再黑屏) 　　建议送去维修点", "LCD电源指示灯亮、按键正常,表明A/D驱动板的MCU还能正常工作,基本上说明电源部分正常; 　　黑屏可能是背光灯没有点亮(从背面看不到灯管发光),驱动背光电路可能有故障; 　　一、如果不能看到屏幕上的微暗图像,就是A/D驱动板的信号处理电路不正常,A/D板上的开关部分电路有故障,导致不能输出高电平去控制高压板的开关极; 　　二、反之,如果能看到屏幕上有微暗的图像,就说明A/D驱动板的信号处理电路正常,故障可锁定在驱动背光的高压板上; 　　高压板常见故障有: 　　1、高压板自身保险管烧毁; 　　2、背光灯管损坏(不过双灯,或四灯的背光同时损坏的可能性不大,其中一个损坏也会黑屏,高压板具有负载不平衡保护功能,如果一根灯管损坏,开机后高压板即进入负载不平衡保护状态,屏幕会闪烁一下再黑屏) 　　建议送去维修点", "一个是显示器接主机的线没接好或者坏了,第二个就是内存和其它硬件出问题。你可以使用排查的办法,把机箱接到朋友的显示器上面试下。"], "bs_rank_pos": 1}, {"is_selected": false, "title": "我的电脑不能启动了怎么办?电源灯亮着,但是显示器是黑屏_百度知道", "most_related_para": 0, "segmented_title": ["我", "的", "电脑", "不能", "启动", "了", "怎么办", "?", "电源", "灯", "亮", "着", ",", "但是", "显示器", "是", "黑屏", "_", "百度", "知道"], "segmented_paragraphs": [["1", ".", "检查", "显示器", "电缆", "是否", "牢固", "可靠", "地", "插入", "到", "主机", "接口", "中", ",", "再", "检查", "显卡", "与", "主板", "I", "/", "O", "插槽", "之间", "的", "接触", "是否", "良好", "。", "可以", "重新", "安", "插", "一", "次", "显卡", ",", "确保", "显卡", "安", "插", "到位", ",", "接触", "良好", ";", "2", ".", "换", "一", "台", "确认", "正常", "的", "显示器", "如果", "不再", "黑屏", ",", "那么", "显示器", "可能", "损坏", ";", "3", ".", "显示器", "如", "未", "损坏", ",", "请", "检查", "CPU", "风扇", "是否", "运转", ";", "4", ".", "如", "仍", "出现", "黑屏", ",", "则", "可", "将", "除", "CPU", "、", "显卡", "、", "内存条", "之外", "的", "所有", "组件", "取下", ",", "然后", "加", "电", "启动", "电脑", "。", "如果", "内存", "有", "故障", ",", "应", "会", "有", "报警", "声", "。", "如果", "不是", "内存", "原因", ",", "请", "换", "一", "个", "正常", "的", "CPU", ",", "开机", "重新", "检测", "。", "如", "仍", "出现", "黑屏", ",", "则", "建议", "更换", "主板", ";", "5", ".", "系统", "是否", "安装", "软件", "和", "更新补丁", ",", "如", "有", "请", "卸载", "更新", "文件", ";", "6", ".", "电脑", "安装", "软件", "后", "重启", "正在", "更新", "导致", "黑屏", ",", "等待", "更新完毕", "即可", ";", "7", ".", "可", "进", "安全模式", "修复", "电脑", ",", "如果", "不能", "进", "安全模式", ",", "请", "重新", "安装", "系统", "。"], ["硬盘", "灯", "亮", "吗", ",", "显示器", "是", "电源", "灯", "亮", "却", "黑屏", "还是", "显示器", "上", "啥", "灯", "都", "不亮", ",", "若是", "后", "一", "种", "你", "将", "显示器", "电源线", "重新", "插", "一", "下", ",", "还", "不行", "的", "话", "换", "个", "显示器", "试", "试", ",", "再", "不行", "看看", "你", "的", "显示器", "输入", "信号", "线", ",", "重", "插", "一", "下", ",", "楼主", "最好", "把", "状况", "描述", "清楚", ",", "显示器", "是", "黑屏", "有", "白字", ",", "还是", "完全", "黑", "掉", "了", "。"]], "paragraphs": ["1.检查显示器电缆是否牢固可靠地插入到主机接口中,再检查显卡与主板I/O插槽之间的接触是否良好。可以重新安插一次显卡,确保显卡安插到位,接触良好; 2.换一台确认正常的显示器如果不再黑屏,那么显示器可能损坏; 3.显示器如未损坏,请检查CPU风扇是否运转; 4.如仍出现黑屏,则可将除CPU、显卡、内存条之外的所有组件取下,然后加电启动电脑。如果内存有故障,应会有报警声。如果不是内存原因,请换一个正常的CPU,开机重新检测。如仍出现黑屏,则建议更换主板; 5.系统是否安装软件和更新补丁,如有请卸载更新文件; 6.电脑安装软件后重启正在更新导致黑屏,等待更新完毕即可; 7.可进安全模式修复电脑,如果不能进安全模式,请重新安装系统。", "硬盘灯亮吗,显示器是电源灯亮却黑屏还是显示器上啥灯都不亮,若是后一种你将显示器电源线重新插一下,还不行的话换个显示器试试,再不行看看你的显示器输入信号线,重插一下,楼主最好把状况描述清楚,显示器是黑屏有白字,还是完全黑掉了。"], "bs_rank_pos": 2}], "answer_spans": [[4, 79]], "fake_answers": ["显卡和显示器之间的数据连线没有插好、VGA接连线有问题、分辨率出现错误、显卡失效等。解决的方法:第一,打开电脑以后,按CTRL+ALT+F7恢复默认的分辨率。通过更改分辨率,来解决此问题。第二,检查VGA接连线是否有问题,如果有问题,换一条VGA线试一下。"], "question": "电脑显示器黑屏但灯亮", "segmented_answers": [["显卡", "和", "显示器", "之间", "的", "数据", "连线", "没有", "插", "好", "、", "VGA", "接", "连线", "有问题", "、", "分辨率", "出现错误", "、", "显卡", "失效", "等", "。", "打开", "电脑", "以后", "，", "按", "CTRL", "+", "ALT", "+", "F7", "恢复", "默认", "的", "分辨率", "。", "通过", "更改", "分辨率", "，", "来", "解决", "此", "问题", "。", "检查", "VGA", "接", "连线", "是否", "有问题", "，", "如果", "有问题", "，", "换", "一", "条", "VGA", "线", "试", "一", "下", "。"]], "answers": ["显卡和显示器之间的数据连线没有插好、VGA接连线有问题、分辨率出现错误、显卡失效等。打开电脑以后，按CTRL+ALT+F7恢复默认的分辨率。通过更改分辨率，来解决此问题。检查VGA接连线是否有问题，如果有问题，换一条VGA线试一下。"], "answer_docs": [0], "segmented_question": ["电脑", "显示器", "黑屏", "但", "灯", "亮"], "question_type": "DESCRIPTION", "question_id": 32, "fact_or_opinion": "FACT", "match_scores": [0.8732394366197183]}
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GC:《模拟人生4》新资料片公布 11月正式推出", "《模拟人生4》高清壁纸  《模拟人生4》游戏截图  《模拟人生4》原画", "好于50%的模拟游戏", "《模拟人生4》是一款策略性人生模拟游戏,属于广受好评的《模拟人生》系列的第四代作品。游戏中玩家可以获得离线经验,打造个性化的世界,同时游戏将提供便捷的分享功能,把自己的模拟人生快速的分享给朋友。《模拟人生4》将全面支持离线单机功能!", "玩家制作模拟人生4啪啪啪Mod月入3万 解锁各种姿 《模拟人生4:城市生活》免安装中文正式版下载 《模拟人生4:童乐房间》免安装中文正式版下载 《模拟人生4》取消性别限制 可打造伪娘或金刚芭 《模拟人生4》发布新补丁 自主啪啪啪惨遭限制 《模拟人生4:欢聚一堂》免安装中文硬盘版下载 口袋妖怪版《模拟人生》演示 皮卡丘头顶绿水晶 EA公司23年元老离职 曾任《模拟人生》团队副总裁 《模拟人生4》妖魔鬼怪预告 胆小者请躲被子里 《模拟人生4》惊人数据 玩家激情啪啪啪了2亿次", "最新热门游戏"], "bs_rank_pos": 2}, {"is_selected": false, "title": "《模拟人生4》游戏全秘籍(作弊码)|Sims 4,The - 我爱秘籍", "most_related_para": 3, "segmented_title": ["《", "模拟人生", "4", "》", "游戏", "全", "秘籍", "(", "作弊码", ")", "|", "Sims", "4", ",", "The", "-", "我", "爱", "秘籍"], "segmented_paragraphs": [["在", "Sim4", "游戏", "中", "按", "[", "Ctrl", "]", "+", "[", "Shift", "]", "+", "C", "开启", "控制台", ",", "输入", "秘籍", "指令", "后", "按回车", "确认", "可获得", "对应", "效果", "。"], ["注", ":"], ["1", "、", "秘籍", "指令", "中", "的", "大括号", "中括号", "[", "]", "不用", "输入", "。"], ["在", "很多", "秘籍", "指令", "中", 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"代码大全", "《", "远征", "军", ":", "维京", "》", "游戏秘籍", "《", "中世纪", "2", ":", "全面战争", "》", "全", "秘籍", "/", "技能", "/", "属性", "代码"], ["模拟城市", "5", "英文名", ":", "SimCity", "秘籍", "数", ":", "10", "+"], ["模拟人生", "2", "秘籍", "数", ":", "52", "+"], ["《", "模拟人生", "3", "》", "秘籍", "《", "马基", "埃", "亚", "尔", "的", "传说", "》", "秘籍", "/", "作弊码", "/", "LUA", "控制台", "MD", "《", "梦幻模拟战", "2", "》", "超级", "秘籍", "《", "模拟农场", "17", "》", "存档", "修改"]], "paragraphs": ["在Sim4游戏中按 [Ctrl] + [Shift] + C 开启控制台,输入秘籍指令后按回车确认可获得对应效果。", "注:", "1、秘籍指令中的大括号 中括号 [] 不用输入。", "在很多秘籍指令中需指定对应的小人id,此时可使用 sims.get_sim_id_by_name [名] [姓] 来获得市民ID。", "输入:sims.get_sim_id_by_name 家偉 林", "即可获得获得林家偉的sim id", "模拟人生3 英文名:Sim 3,The 秘籍数:20+", "《模拟人生1》全秘籍 《模拟人生1》秘籍集锦 《模拟人生2》全秘籍 《我的模拟人生》秘籍 《模拟人生:中世纪》秘籍", "《模拟人生4 》作弊MOD:Cheater Mod v3.4.0 英文名:Sims 4,The 作者:Krys29 日期:2017.06.05", "异星工厂 英文名:Factorio 秘籍数:270+", "辐射4 英文名:Fallout 4 秘籍数:4105+", "猎天使魔女 英文名:Bayonetta 秘籍数:18+", "《方舟:生存进化》游戏秘籍指令代码大全 《远征军:维京》游戏秘籍 《中世纪2:全面战争》全秘籍/技能/属性代码", "模拟城市5 英文名:SimCity 秘籍数:10+", "模拟人生2 秘籍数:52+", "《模拟人生3》秘籍 《马基埃亚尔的传说》秘籍/作弊码/LUA控制台 MD《梦幻模拟战2》超级秘籍 《模拟农场17》存档修改"], "bs_rank_pos": 3}, {"is_selected": false, "title": "模拟人生4超迅速赚钱心得攻略_模拟人生4攻略秘籍_游侠网", "most_related_para": 2, "segmented_title": ["模拟人生", "4", "超", "迅速", "赚钱", "心得", "攻略", "_", "模拟人生", "4", "攻略秘籍", "_", "游侠网"], "segmented_paragraphs": [["不", "少", "玩家", "觉得", "赚钱", "很辛苦", ",", "今天", "小", "编", "带来", "《", "模拟人生", "4", "》", "超", "迅速", "赚钱", "心得", "攻略", ",", "一起来", "看看", "吧", "!"], ["刚", "开始", "玩", "的", "时候", "小人", "沿袭", "3", "代", "职业", "-", "画画", ",", "辛辛苦苦", "布置", "了", "非常", "启发", "的", "画室", ",", "画", "到", "后来", "几", "小时", "出", "一", "副", "也", "才", "卖", "几", "千", ",", "偶尔", "出", "个", "巨作", "也", "不过", "1W", "多", ",", "完全", "不能", "满足", "小人", "日益", "膨胀", "的", "物欲", ",", "后来", "看到", "论坛", "上", "有", "“", "大神", "”", "分享", "一", "个", "小秘密", ",", "不停", "的", "给", "经纪人", "打电话", ",", "一通电话", "几分钟", ",", "然后", "收入", "500", ",", "不停", "刷", "囧rz", "。", "。"], ["再后来", "看到", "很多人", "说", "做", "电竞", "直播", ",", "粉丝", "破百万", "钱", 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"请", "关注", ":", "模拟人生", "4", "专题"], ["更多", "相关", "讨论", "请", "前往", ":", "模拟人生", "4", "论坛"], ["扫描", "关注", "游侠网"], ["PC", "TV"]], "paragraphs": ["不少玩家觉得赚钱很辛苦,今天小编带来《模拟人生4》超迅速赚钱心得攻略,一起来看看吧!", "刚开始玩的时候小人沿袭3代职业-画画,辛辛苦苦布置了非常启发的画室,画到后来几小时出一副也才卖几千,偶尔出个巨作也不过1W多,完全不能满足小人日益膨胀的物欲,后来看到论坛上有“大神”分享一个小秘密,不停的给经纪人打电话,一通电话几分钟,然后收入500,不停刷囧rz。。", "再后来看到很多人说做电竞直播,粉丝破百万钱跳的哗啦啦的,不过副作用是必须在职电玩专业,只能趁下班那点时间自己赶直播,于是我就自己改进了下,买了奖励商店里的专业偷懒,然后天天翘班,关起门来不停直播...虽然听到钱不停的叮叮叮跳账是非常美妙的一件事,而且每小时几千的入账是我试过所有包括作家木工园艺烘焙等等无法企及的,可是这样一来就特别无聊,小人只能不停的蹲在电脑前,无法享受其他的乐趣,还要每天接到老板催命电话很是烦人...而且之前需要不停的参加电玩比赛攒粉丝,前戏特别漫长...", "在我攒了700W粉丝,赚了300多W以后,我果断受不了这种无趣的生活,毅然辞职,开始开发调酒烹饪钓鱼收藏等等新鲜玩法,为了各种黑科技玩了科学家,不停开发新科技,调戏NPC,合成各种有趣的血清,然后...制造了终极黑科技——复制机!!!有了这台机器,所有罕见收藏品从此烂大街,死亡花再不用蹲花园里天天浇水施肥,发明所需各种普通罕见宝石金属从此告别风餐露宿满世界找原料厚脸皮每个同事蹭一遍...而且这台机器可随身携带,可以带回家,而且不需要科学家职业也能操作", "然后最关键的一点来了,在玩烹饪制作仙馔密酒的时候我发现...这货竟然是能直接出售的!而且价值不菲,我第一次做出来的无懈可击品质的售价竟然高达1W3!然后开始抱着单纯偷懒的目的试试丢上去准备囤一冰箱让小人从此长生不老,然后...接下来你们都知道要怎么办了", "更多相关资讯请关注:模拟人生4专题", "更多相关讨论请前往:模拟人生4论坛", "扫描关注游侠网", "PC TV"], 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这种应该是口腔溃疡的表现,医学上又叫它单纯性疱疹,说是消化道病毒引起的,我每年秋冬都容易得,可能这个季节干燥易上火吧。我的经验是有一点红痒的时就要马上“灭火”,内治:吃维生素B2,喝“豆腐鲫鱼咸蛋皮蛋芫荽汤”;外用:用“阿昔洛韦乳膏”涂抹嘴上的泡,这种药是专门治单纯性疱疹病毒的。行动得快,有时可以不可以把它消灭在萌芽之中的。 如果您嘴上的泡已发展得非常大了,虽然晚了点,还是要用以上的方法,既可以不可以控制发展,还可以不可以帮助早点康复。我发现嘴上长过泡的人以后就非常容易长,所以平日注意有些上火就要及时清热,同时“阿昔洛韦乳膏”、B2等也要常备,实在得上了好把损失降到最低(任它发展既疼苦又难看啊~)。 当然,嘴上容易长泡的人一定要注意清淡饮食的,只是美食当前有时非常难控制~~祝早日康复! 挖,这么可怕了,是病毒啊,这个我还是第一次生. 嘴唇都肿了. “病毒说“我是在医书上查到的,我身边又不少朋友有这种“毛病”,均虚火重的人,且均有了第一次以后就非常容易长。你的嘴唇肿了说明发得挺厉害的,以后还是注意戒口吧。这种水泡从发到痊愈一般要10天前后,发展过程是:水泡--黄色的痂--黄痂脱落变成血疤--血疤脱落痊愈。结血疤的时是最难看的,黑黑的,像嘴上多了些黑痣……所以,切记防患于未然哦! 您已经评价过! 不好:0 您已经评价过! 原创:0", "大多是属于疱疹性龈口炎,这也是单纯疱疹感染所致。该病有自限性,但很容易复发。原发感染潜伏期2-12天,平均6天,如果出现了疱,可以使用一些漱口液如银离子漱口液进行含漱,防止继发感染,也可使用碘甘油涂抹水疱,促其恢复。", "蒋康伦 医师 | 2014-11-19 23:48"], "bs_rank_pos": 0}, {"is_selected": false, "title": "嘴唇上长水泡是为什么?_百度拇指医生", "most_related_para": 1, "segmented_title": ["嘴唇", "上", "长", "水泡", "是", "为什么", "?", "_", "百度", "拇指", "医生"], "segmented_paragraphs": [["?", "嘴唇", "上", "长", "水泡", "是", "为什么", "?"], ["在", "日常", "生活", "中", ",", 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"嘴唇上长水泡称这口唇疱疹,属于一种病毒性皮肤病,是由于单纯性疱疹病毒感染引起的, 中医 相关信息 用当归治疗闭经有什么方法? 怎么用胡颓子根治疗闭经? 怎么用大黄末治疗闭经? 用香附治疗闭经有什么方法? 如何用当归治疗闭经? 用大叶茜草根治疗闭经的方法是什么? 怎么用马鞭草根治疗闭经? 怎么用红土牛膝治疗闭经? 用益母草、香附子治疗闭经的方法是什么? 如何用鲜臭梧桐皮治疗闭经? 中药的汤剂 中药生女孩 扎四缝穴位 土槿皮中药 人参花泡水 被称为“上火”,好发于口唇、口角,常由感冒、发热、疲劳,睡眠不足、心情抑郁、紧张焦虑等因素,致使免疫力下降,使潜伏于体内的疱疹病毒大量繁殖,导致嘴角起泡而发病。开始有灼热感,继而群集水泡、糜烂、结痂,病程约一周左右。可复发。  治疗: 一般的抗病毒药物,如病毒唑、病毒灵等都能用于单纯疱疹的治疗,但其中以阿昔洛韦功效最为突出。阿昔洛韦对病毒有特殊的亲和力,起到抑制病毒复制的作用。  1、5%阿昔洛韦软膏或0.1%泡疹净霜外用,与皮炎平软膏交替外涂效果更好。有感染者加用金霉素软膏。 2、利巴韦林注射液(病毒唑)湿敷:用医用棉球或棉签、小块纱布饱蘸利巴韦林注射液湿敷于皮损处,每次敷15min,每天3～5次。 3、清热解毒药的中药,如牛黄解毒片、消炎解毒丸等口服。  预防口唇疱疹复发: 1、保证睡眠时间,多吃新鲜蔬菜,补充各种维生素,多饮水,以增强机体免疫力,防止疱疹的发生。 2、加强口腔卫生,进食后用淡盐水...", "临床常见,患者多为成年人。损伤部位常发生在口唇、口周、鼻孔附近。", "发病初时局部有搔痒感,随即出现红斑及簇集性红色小丘疹,迅速变为绿豆或稍大的水疱,水疱破裂后呈现溃烂面,数日后干燥结痂,全病程1-2周,愈后可留暂时性的褐色色素沉着。", "单纯疱疹可以在同一部位复发,引起复发的原因很多如发热性疾病、感冒、日晒、过劳、精神紧张、悲伤等,它们均可以引起单纯疱疹的复发。", "温馨提示:单纯疱疹目前无特效防治办法,主要尽量避免各种诱发因素。本病有自限性,约1-2周可自愈。治疗原则为缩短病程,防止继发感染和并发症,减少复发。", "上火或者是受凉都会引起水泡 上火的情况,建议多吃点水果,喝点水,水果避免火性的像橘子之类的;受凉则要注意保暖,夜里盖好睡,多喝开水,少吹凉风", "高中英语翻译给Mr.Harrison的信... 半桶水叮铛响什么意思是什么意思??? 推荐个乳液本人皮肤干性,T区比较油,T区...", "上火了,每个人上火体现的位置都不同,有的人走嘴唇,有的人走鼻子,有的人走眼睛,多吃点梨等水果、蔬菜去去火就好了。", "水泡相关知识 症状 预防 治疗", "宝宝嘴上起水泡该怎么办啊? 嘴上起水泡怎么办? 我嘴上起水泡了我怕留疤怎么办 婴儿嘴起水泡了怎么办 嘴上起水泡怎么办 宝宝嘴上起水泡该怎么办啊?  小孩的嘴上起水泡了怎么样?  嘴上起水泡怎么办 宝宝嘴上起水泡该怎么办啊?  小孩的嘴上起水泡怎么办?"], "bs_rank_pos": 2}, {"is_selected": false, "title": "嘴唇长水泡_百度拇指医生", "most_related_para": 4, "segmented_title": ["嘴唇", "长", "水泡", "_", "百度", "拇指", "医生"], "segmented_paragraphs": [["嘴唇", "每年", ",", "春夏", ",", "或", "秋冬", "的", "换季", "的", "时候", ",", "嘴角", "的", "嘴唇", "边上", "都会", "长", "出", "水泡", "(", "不会", "长", "脓", ")", ",", "很恶心", ",", "也", "很", "影响", "外观", "。", "请问", "这", "是", "什么", "原因", "引起", "的", "?", "可不可以", "根除", "?", "有", "什么", "方便", ",", "有效", "的", "办法", "根除", "?", "谢谢", "。"], ["有", "的", "人", "在", "秋末", "冬", "初", ",", "常", "在", "嘴唇", "的", "唇", "黏膜", "(", "红色", "部分", ")", "及", "口腔", "周围", "的", "皮肤", "上", "出现", "水疱", ",", "我们", "称", "它", "为", "“", "唇", "疱疹", "”", "。", "这", "是", "一", "种", "由", "单纯", "疱疹", "病毒", "引起", "的", "口腔疾病", "。", 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"有的人在秋末冬初,常在嘴唇的唇黏膜(红色部分)及口腔周围的皮肤上出现水疱,我们称它为“唇疱疹”。这是一种由单纯疱疹病毒引起的口腔疾病。这种病毒可以在唾液或皮肤上生存并通过飞沫、唾液、与疱疹液接触而传播。口唇周围是这种疾病易发的部位。当身体抵抗力减弱,如有发热、消化不良、感冒、妇女月经前期时就可促使这种疾病的复发。强阳光照射、局部机械损伤、情绪因素也可导致复发。患者老少都有。该病发病前常在唇红部或唇红缘与皮肤交界处,局部发痒、发红,有灼痛感。随后在发痒处出现二三个或多个成簇的针尖大或小米粒大小的透明小疱。出现小水疱后继续发痒。数小时后,小水疱开始破裂,随后糜烂或融合成为一个大疱。疱内有透明液体。很快大疱破溃,结成黄痂或血痂。经过1周或10天左右,痂便脱落,自然痊愈。但常因洗脸时易于将痂皮碰破,致使继发感染,甚至使病损处出现小脓包,之后再度结痂而延长了愈合时间。愈合后不留瘢痕,但可有色素沉着。一般无全身症状。受损部位在诱发的刺激因素作用下,可在数月、数周甚至数日内再次发作。(1)全身治疗。①在医生指导下应用抗病毒药物,如阿昔洛韦、利巴韦林、干扰素等,或口服中药双黄连口服液、板蓝根冲剂等。②对于复发较严重而频繁者,还应选用免疫调节剂,如胸腺素、转移因子、左旋咪唑等。③病情严重,如合并颌下淋巴腺炎、体温升高者,运用支持疗法,卧床休息,应用去痛片等解热药物,多喝水,及时补充维生素B、维生素C等,进半流质饮食或软食。(2)局部治疗。①保持口腔清洁,可用洗必泰溶液或复方硼砂液漱口。②起病时可涂3%碘酊于疱疹部位,或应用红霉素软膏局部涂擦以消炎止痛,促进愈合。③继发感染时可用生理盐水、3%硼酸液或氯己定液等局部湿敷。④氦氖激光局部照射治疗对发病早期患者有一定疗效。", "嘴唇上长水疱最常见就是单纯疱疹病毒感染所致的单纯疱疹性龈口炎。这是单纯疱疹病毒引起,临床上以簇集性水疱为特征,有自限性,但很容易复发。一般需要改善生活方式,低糖低脂饮食,不可熬夜,不可焦虑,否则容易复发。", "蒋康伦 医师 | 2014-9-30 11:30", "嘴唇上长水泡称这口唇疱疹,属于一种病毒性皮肤病,是由于单纯性疱疹病毒感染引起的,中医被称为“上火”,好发于口唇、口角,常由感冒、发热、疲劳,睡眠不足、心情抑郁、紧张焦虑等因素,致使免疫力下降,使潜伏于体内的疱疹病毒大量繁殖,导致嘴角起泡而发病。开始有灼热感,继而群集水泡、糜烂、结痂,病程约一周左右。可复发。 治疗: 一般的抗病毒药物,如病毒唑、病毒灵等都能用于单纯疱疹的治疗,但其中以阿昔洛韦功效最为突出。阿昔洛韦对病毒有特殊的亲和力,起到抑制病毒复制的作用。 1、5%阿昔洛韦软膏或0.1%泡疹净霜外用,与皮炎平软膏交替外涂效果更好。有感染者加用金霉素软膏。 2、利巴韦林注射液(病毒唑)湿敷:用医用棉球或棉签、小块纱布饱蘸利巴韦林注射液湿敷于皮损处,每次敷15min,每天3～5次。 3、清热解毒药的中药,如牛黄解毒片、消炎解毒丸等口服。 预防口唇疱疹复发: 1、保证睡眠时间,多吃新鲜蔬菜,补充各种维生素,多饮水,以增强机体免疫力,防止疱疹的发生。 2、加强口腔卫生,进食后用淡盐水漱口、睡觉前用盐水涮牙,漱口,效果很好,刷牙后不再吃东西。勤换牙刷,以免牙刷上的病毒残留,造成多重疱疹 3、避免吃含丰富精胺酸的食物,如巧克力、可乐、豌豆、榖类麦片、花生、腰果、啤酒等。 4、运动可强化免疫系统,放松心情,避免紧张情绪及缓解压力有利于防治单纯疱疹复发。", "在日常生活中,很多人会遇到嘴角烂嘴边还起小水泡的情况, 这 是口唇疱疹。口唇疱疹是由于单纯性疱疹病毒感染引起的,中医被称为\"上火\",好发于口唇,口角,常由感冒,发热,疲劳,睡眠不足,心情抑郁,紧张焦虑等因素,致使免疫力下降,使潜伏于体内的疱疹病毒大量繁殖,导致嘴角起泡而发病。开始有灼热感,继而群集水泡,糜烂,结痂,病程约一周左右,可复发。 口唇疱疹常在口唇黏膜处出现针头大小的小疱,常为一群,也有两三群,自觉有轻度烧灼感,历时一周左右可自愈,亦可反复发作。如果疱疹出现后因机体免疫机制的调整不当,或破溃后而继发感染,会延长病程。 预防口唇疱疹,首先要保证睡眠时间,中午要午睡。其次,注意调整饮食结构,要注意多吃新鲜蔬菜,补充各种维生素,不应单纯吃清淡类食品,应荤素结合,要吃饱吃杂,多饮水,保持体内营养平衡,这样才能增强机体免疫力,预防口唇疱疹的发生。发热的病人一定要注意口腔卫生,要养成进食后漱口,睡觉前刷牙,特别是刷牙后不再吃东西的好习惯,每天最好用淡盐水漱口1至3次", "你的情况可能是有上火引起的,建议服用龙胆泻肝丸和阿莫西林等药物."], "bs_rank_pos": 3}, {"is_selected": false, "title": "嘴唇上长泡该怎么办_百度经验", "most_related_para": 5, "segmented_title": ["嘴唇", "上", "长", "泡", "该怎么办", "_", "百度", "经验"], "segmented_paragraphs": [["百度", "经验", ">", "健康", "/", "养生", ">", "保健养生"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["嘴唇", "上", "长", "泡", "是", "一", "种", "常见", "的", "疾病", ",", "几乎", "每个", "人", "都", "受到", "过", "它", "的", "困扰", ",", "其实", "嘴唇", "上", "长", "泡", "是", "疱疹", "病毒感染", "的", "一", "种", "口角炎", "的", "表现", ",", "患者", "最初", "的", "主观", "感受", "为", "痒", "痛", "热", ",", "后期", "水泡", "会", "破", "皮", "然后", "结痂", ",", "最后", "就是", "脱", "痂", "的", "过程", "。"], ["嘴唇", "上", "长", "泡", ",", "我们", "首先", "应该", "调整", "饮食", ",", "少", "吃", "油腻", "性", "食物", ",", "保证", "清淡", "饮食", ",", "多", "摄取", "水果", "和", "蔬菜", "。", "如果", "嘴唇", 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"起", "到", "降火", "的", "作用", "。"], ["百度", "经验", ":", "jingyan", ".", "baidu", ".", "com"], ["嘴唇", "上", "长", "泡", "的", "时候", "千万不要", "将", "其", "扎破", "。", "这样", "会", "有", "感染", "的", "风险", ",", "也有", "可能", "会", "留下", "疤痕", ",", "影响", "美观", "。"], ["经验", "内容", "仅供参考", ",", "如果", "您", "需", "解决", "具体", "问题", "(", "尤其", "法律", "、", "医学", "等", "领域", ")", ",", "建议", "您", "详细", "咨询", "相关", "领域", "专业人士", "。"], ["脂溢性皮炎", "怎么", "治", "?"]], "paragraphs": ["百度经验 > 健康/养生 > 保健养生", "百度经验:jingyan.baidu.com", "嘴唇上长泡是一种常见的疾病,几乎每个人都受到过它的困扰,其实嘴唇上长泡是疱疹病毒感染的一种口角炎的表现,患者最初的主观感受为痒痛热,后期水泡会破皮然后结痂,最后就是脱痂的过程。", "嘴唇上长泡,我们首先应该调整饮食,少吃油腻性食物,保证清淡饮食,多摄取水果和蔬菜。如果嘴唇上长泡现象还是不能改善的话,最好借助药物或则求助医生。嘴唇上长泡具体解决办法如下:", "百度经验:jingyan.baidu.com", "1 上火起泡后,一定不要长时间的说话,说话的声音不能太大了,以免引起化脓。同时日常要多喝一些水,当然了如果白开水喝不下去的话,也可以喝一些花茶,像菊花花,玫瑰花茶,茉莉花茶都是很不错的选择。 2 吃维生素c,上火后吃点吃维生素c后上火的症状就可以明显的改善,当然了如果多吃一些含有吃维生素c的食物效果会更好,而且还能补充由于上火原因引起的营养不良的问题。 3 当嘴唇上的水泡已经发展为鼓鼓的状态时,简单的菊花茶已经不能有明显的效果了,这时候需要借助药物来解毒。比如:广西黄药复方金花油。复方金花油既可疏通唇之经络、使唇部气血充盈、改善唇表微循环,同时又能清除坏死唇粘膜组织、修复受损细胞,并形成新的唇粘膜组织,可从根本上痊愈。 4 多吃一些苦味食物,多吃苦,更有利于健康,其实吃苦味食品,对人体的健康还有很多的好处,由其是对于一些由于实火症引起的嘴巴起泡,效果更是不错。 5 多吃一些利尿除湿的食物,像冬瓜,西瓜,等瓜果蔬菜就有利湿的食物了,生活中一些清淡的饮食方式对有利于缓解嘴巴起泡泡的问题的。 6 多喝绿豆汤。一直以来,绿豆都有解毒清火的功效。所以,嘴唇上出现长泡现象时,可以多喝绿豆汤。如果能将绿豆煮烂,更能起到降火的作用。", "百度经验:jingyan.baidu.com", "嘴唇上长泡的时候千万不要将其扎破。这样会有感染的风险,也有可能会留下疤痕,影响美观。", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "脂溢性皮炎怎么治?"], "bs_rank_pos": 4}], "answer_spans": [[121, 359]], "fake_answers": ["被称为“上火”,好发于口唇、口角,常由感冒、发热、疲劳,睡眠不足、心情抑郁、紧张焦虑等因素,致使免疫力下降,使潜伏于体内的疱疹病毒大量繁殖,导致嘴角起泡而发病。开始有灼热感,继而群集水泡、糜烂、结痂,病程约一周左右。可复发。治疗:一般的抗病毒药物,如病毒唑、病毒灵等都能用于单纯疱疹的治疗,但其中以阿昔洛韦功效最为突出。阿昔洛韦对病毒有特殊的亲和力,起到抑制病毒复制的作用。1、5%阿昔洛韦软膏或0.1%泡疹净霜外用,与皮炎平软膏交替外涂效果更好。有感染者加用金霉素软膏。2、利巴韦林注射液(病毒唑)湿敷:用医用棉球或棉签、小块纱布饱蘸利巴韦林注射液湿敷于皮损处,每次敷15min,每天3～5次。3、清热解毒药的中药,如牛黄解毒片、消炎解毒丸等口服。预防口唇疱疹复发:1、保证睡眠时间,多吃新鲜蔬菜,补充各种维生素,多饮水,以增强机体免疫力,防止疱疹的发生。2、加强口腔卫生"], "question": "嘴唇长水泡", "segmented_answers": [["嘴唇", "上", "长", "水泡", "称", "这", "为", "口", "唇", "疱疹", "，", "属于", "一", "种", "病毒性", "皮肤病", "，", "是", "由于", "单纯性", "疱疹", "病毒感染", "引起", "的", "，", "中医", "被", "称为", "“", "上火", "”", "，", "好", "发", "于", "口", "唇", "、", "口角", "，", "常", "由", "感冒", "、", "发热", "、", "疲劳", "，", "睡眠不足", "等", "因素", "，", "致使", "免疫力下降", "，", "使", "潜伏", "于", "体", "内", "的", "疱疹", "病毒", "大量", "繁殖", "，", "导致", "嘴角", "起泡", "而", "发病", "。", "开始", "有", "灼热", "感", "，", "继而", "群集", "水泡", "、", "糜烂", "、", "结痂", "，", "病程", "约", "一", "周", "左右", "。", "治疗", "：", "一般", "的", "抗病毒药物", "，", "如", "病毒唑", "、", "病毒灵", "等", "都", "能", "用于", "单纯", "疱疹", "的", "治疗", "，", "但", "其中", "以", "阿昔洛韦", "功效", "最为", "突出", "。", "1", "、", "5", "%", "阿昔洛韦", "软膏", "或", "0", ".", "1", "%", "泡", "疹", "净", "霜", "外", "用", "，", "与", "皮炎平", "软膏", "交替", "外", "涂", "效果", "更好", "。", "有", "感染者", "加", "用", "金霉素", "软膏", "。", "2", "、", "利巴韦林", "注射液", "（", "病毒唑", "）", "湿敷", "：", "用", "医用", "棉球", "或", "棉签", "、", "小", "块", "纱布", "饱蘸", "利巴韦林", "注射液", "湿敷", "于", "皮损", "处", "，", "每次", "敷", "15", 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　　这么多行业的职业资格证书都不用考了,考证党无疑成为最受益的人群。对于那些正在为考证而发愁的考生而言,再也不用烦恼了。而对于那些本身正在观望的人群也大有好处。 　　“资格证书的取消可以降低就业和创业的门槛,也可以鼓励市场的合理竞争”高顿财经职业发展研究中心赵老师发表了在自己的看法。对于一些已经考了相关资格证书的考生,本以为比其他竞争者有更好的优势,但其实不然。拥有证书却不具备相应的操作能力,让其他人失去了公平合理的机会。 　　职业资格“挂羊头卖狗肉”", "一、2014年至2016年12月国务院共对职业资格证进行了7批清理。 二、详细目录可以去国务院网站文件库点击国发查询文件。 三、2016年12月12日人力资源和社会保证部达记者问说明职业资格证的的详细目录还没有出。", "北京可道智慧教育咨询中心开授的《职业发展咨询教练》认证培训课程是跟劳动部联合颁发的,可以作为被劳动部门承认的从业证明,是由刘美兰、徐斌、林嘉怡、廖满媛、徐英哲、于彬彬合力组建的针对职业发展规划(职业规划)、职业发展咨询、职业培训认证方面的指导和培训,非常吃香的。"], "bs_rank_pos": 2}, {"is_selected": true, "title": "国家职业资格证书有哪些_查查吧", "most_related_para": 3, "segmented_title": ["国家", "职业资格", "证书", "有", "哪些", "_", "查查", "吧"], "segmented_paragraphs": [["主页", ">", "教育培训", ">", "职业", "培训", ">", "国家", "职业资格", "证书", "有", "哪些"], ["作者", ":", "grcoi", "2017", "-", "03", "-", "10", "11", ":", "13", "[", "查查", "吧", "]", "来源", ":", "www", ".", "chachaba", ".", "com"], ["国家", "职业资格", "证书", "有", "哪些", "?", "职业资格证", "代表", "着", "拥有者", "具备", "一定", "的", "技能", ",", "但是", "有", "哪些", "是", "被", "国家", 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职业资格证即职业资格证书,国家职业资格证书,是劳动从业者具有从事某一职业所必备的学识和技能的证明。它是劳动者求职、任职、开业的资格凭证,是用人单位招聘、录用劳动者的主要依据,也是境外就业、对外劳务合作人员办理技能水平公证的有效证件。人保部门规定先培训再考证再必须持证上岗。我校考取的职业资格证书查询都可以上官方网站查询。 二、职业资格考试、国家职业标准、国家职业技能鉴定 职业资格证书与职业劳动活动密切相联,反映特定职业的实际工作标准和规范。职业资格证书,它是劳动就业制度的一项重要内容,也是一种特殊形式的国家考试制度。它是指按照国家制定的职业技能标准或任职资格条件,通过政府认定的考核鉴定机构,对劳动者的技能水平或职业资格进行客观公正、科学规范的评价和鉴定,对合格者授予相应的国家职业资格证书。国家职业资格证书由中华人民共和国人力资源和社会保障部统一印制,所有职业资格证书均由人力资源和社会保障部门或国务院有关部门按规定办理和核发,且职业资格证书查询均可上发证机关官方网站查询。 三、职业资格证书等级划分 国家职业资格证书一般分为五个等级,即分为初级技能(五级)、中级技能(四级)、高级技能(三级)、技师(二级)、高级技师(一级)共五个等级。 四、职业资格证书各级别报考条件 国家职业资格证书三级(高级)申报条件(具备以下条件之一者) 1、取得本职业中级资格证书后,连续从事本职业2年以上,经高级正规培训达到规定标准学时数,并取得结业证书。 2、对未取得职业资格证书的人员在本职业工作满5年,经本单位出具从事本职业工作年限证明,可申报高级资格鉴定。 3、具有高级技校或取得经人保部门政审核认定的,以高级技能为培养目标的高等职业学校本职业(专业)毕业生。", "1、取得本职业中级资格证书后,连续从事本职业2年以上,经高级正规培训达到规定标准学时数,并取得结业证书。 2、对未取得职业资格证书的人员在本职业工作满5年,经本单位出具从事本职业工作年限证明,可申报高级资格鉴定。 3、具有高级技校或取得经人保部门政审核认定的,以高级技能为培养目标的高等职业学校本职业(专业)毕业生。 4、高职高专院校毕业班学生和大学本科毕业班学生可申报相应职业(工种)高级资格鉴定。", "2017婚前保证书大全 婚前保证书怎么写2017-03-14 15:48:31 固定资产管理办法有哪些 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主要依据", "技能 对外劳务合作  任职 职业劳动活动  法律 《劳动法》、《职业教育法》", "1 概念  2 规定制度  ▪ 法律依据  ▪ 意义  ▪ 样本  3 等级分类", "4 办理方式  ▪ 如何报名  ▪ 考试内容  ▪ 鉴定方式  ▪ 获证程序  ▪ 技能鉴定  5 申报要求", "6 注意事项  7 取消范围  ▪ 历次取消  ▪ 最新进展", "职业资格是对从事某一职业所必备的学识、技术和能力的基本要求,反映了劳动者为适应职业劳动需要而运用特定的知识、技术和技能的能力。与学历文凭不同,学历文凭主要反映学生学习的经历,是文化理论知识水平的证明。职业资格与职业劳动的具体要求密切结合,更直接、更准确地反映了特定职业的实际工作标准和操作规范,以及劳动者从事该职业所达到的实际工作能力水平。", "职业资格证书图片(2张)", "职业资格证书皮", "术复杂、通用性广、涉及到国家财产、人民生命安全和消费者利益的职业(工种)的劳动者,必须经过培训,并取得职业资格证书后,方可就业上岗。实行就业准入的职业范围由劳动和社会保障部确定并向社会发布。", "职业介绍机构要在显著位置公告实行就业准入的职业范围;各地印制的求职登记表中要有登记职业资格证书的栏目;用人单位招聘广告栏中也应有相应职业资格要求。职业介绍机构的工作人员在工作过程中,对国家规定实行就业准入的职业,应要求求职者出示职业资格证书并进行查验,凭证推荐就业;用人单位要凭证招聘用工。", "从事就业准入职业的新生劳动力,就业前必须经过一到三年的职业培训,并取得职业资格证书;对招收未取得相应职业资格证书人员的用人单位,劳动监察机构应依法查处,并责令其改正;对从事个体工商经营的人员,要取得职业资格证书后工商部门才办理开业手续。", "《劳动法》第八章第六十九条规定:“国家确定职业分类,对规定的职业制定职业技能标准,实行职业资格证书制度,由经过政府批准的考核鉴定机构负责对劳动者实施职业技能考核鉴定。”", "《职业教育法》第一章第八条明确指出:“实施职业教育应当根据实际需要,同国家制定的职业分类和职业等级标准相适应,实行学历文凭、培训证书和职业资格证书制度。”这些法规确定了国家推行职业资格证书制度和开展职业技能鉴定的法律依据。", "汽车维修工证书样本", "国家汽车维修工证书", "我国职业资格证书分为五个等级:初级(五级)、中级(四级)、高级(三级)、技师(二级)和高级技师(一级)。", "劳动和社会保障部依据《中华人民共和国职业分类大典》确定了实行就业准入的87个职业目录。分别是:测量放线工、铁路线路工、铁路桥隧工、车工、铣工、磨工、镗工、组合机床操作工、加工中心操作工、数控车工、数控铣工、铸造工、锻造工、焊工、金属热处理工、冷作钣金工、涂装工、装配钳工、工具钳工、锅炉设备装配工、电机装配工、高低压电器装配工、电子仪器仪表装配工、电工仪器仪表转配工、机修钳工、汽车修", "职业资格证书样本(三级)", "理工、摩托车维修工、精密仪器仪表维修工、锅炉设备安装工、变电设备安装工、维修电工、计算机维修工、手工木工、精细木工、音响调音员、贵金属首饰手工制作工、土石方机械操作工、砌筑工、混凝土工、钢筋工、架子工、防水工、装饰装修工、电气设备安装工、管工、汽车驾驶员、起重装卸机械操作工、化学检验工、食品检验工、纺织纤维检验工、贵金属首饰钻石宝玉石检验员、防腐蚀工、锁具修理工。", "营业员、推销员、出版物发行员、中药购销员、鉴定估价师、医药商品购销员、心理咨询师、中药调剂员、冷藏工、中式烹调师、中式面点师、西式烹调师、西式面点师、调酒师、营养配餐员、前厅服务员、客房服务员、保健按摩师、职业指导员、物业管理员、锅炉操作工、美容师、美发师、摄影师、眼镜验光员、眼镜定配工、家用电子产品维修工、家用电器产品维修工、钟表维修工、办公设备维修工、养老护理员、人力资源管理师、营销师、理财规划师、客户服务管理师等", "根据国家有关规定,办理职业资格证书的程序为:职业技能鉴定所(站)将考核合格人员名单报经当地职业技能鉴定指导中心审核,再报经同级劳动保障行政部门或行业部门劳动保障工作机构批准后,由职业技能鉴定指导中心按照国家规定的证书编码方案和填写格式要求统一办理证书加盖职业技能鉴定机构专用印章经同级劳动保障行政部门或行业部门劳动保障工作机构验印后,由职业技能鉴定所(站)送交本人。", "申请职业技能鉴定的人员,可向当地职业技能鉴定所(站)提出申请,填写职业技能鉴定 申请表。报名时应出示本人身份证、培训毕(结)业证书、《技术等级证书》或工作单位劳资部门出具的工作年限证明等。申报技师、高级技师任职资格的人员,还须出具本人的技术成果和工作业绩证明,并提交本人的技术总结和论文资料等。", "国家实施职业技能鉴定分为理论知识考试和操作技能考核两部分。理论知识考试一般采取笔试。操作技能考核可采取工作现场操作、模拟现场操作、问题答辩等方式进行。这些内容是依据国家职业(技能)标准、职业技能鉴定规范(即考试大纲)和相应教材", "职业技能鉴定", "来确定的,并通过编制试卷来进行鉴定考核。", "职业技能鉴定分为知识要求考试和操作技能考核两部分。知识要求考试一般采用笔试,技能要求考核一般采用现场操作加工典型工件、生产作业项目、模拟操作等方式进行。计分一般采用百分制,两部分成绩都在60分以上为合格,80分以上为良好,95分以上为优秀。", "职业技能鉴定所(站)将考核合格人员名单报经当地职业技能鉴定指导中心审核,再报经同级劳动保障行政部门或行业部门劳动保障工作机构批准后,由职业技能鉴定指导中心按照国家规定的证书编码方案和填写格式要求统一办理证书,加盖职业技能鉴定机构专用印章,经同级劳动保障行政部门或行业部门劳动保障工作机构验印后由职业技能鉴定所(站)送交本人。", "民办职业培训机构", "劳动部门已经基本建立了社会化的培训与资格认证体制,有资格和能力的企业、厂矿、学校经劳动部门的严格审查后,可以获得认证资格。据劳动部门统计,有资格进行职业培训的学校一共有3类,包括3400多所技工学校,20000余所职业培训机构,其中民办职业培训机构16000多所。", "任何符合条件的个人均可自主申请参加职业技能鉴定,申请人根据所申报职业的资格条件,确定自己申报鉴定的等级。职业技能鉴定分为知识要求考试和操作技能考核两部分。经鉴定合格者,由劳动保障部门颁发相应的职业资格证书。", "职业技能鉴定是一项基于职业技能水平的考核活动,属于标准参照型考试。它是由考试考核机构对劳动者从事某种职业所应掌握的技术理论知识和实际操作能力做出客观的测量和评价。职业技能鉴定是国家职业资格证书制度的重要组成部分。"], "bs_rank_pos": 5}, {"is_selected": false, "title": "全国职业资格证书都有哪些_百度文库", 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"百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "1 如果手机可以使用,可以进入设置选择恢复出厂设置, 步骤阅读 2 如果手机无法进入系统运行,可以强制关机,长摁开关机键15秒,即可进入关机状态 步骤阅读 3 手机强制关机以后,同时按住手机的开机键+音量向上键+主屏幕键,屏幕亮起进入Recovery模式。 步骤阅读 4 在Recovery模式下,音量上下键移动光标,选择:wipe data/factory reset 这个选项! 步骤阅读 5 接着进入这个界面,选择:YES 步骤阅读 6 然后手机就自行恢复出厂了 步骤阅读 7 恢复出厂完毕后,返回这个界面,选择:reboot system now(重启) 步骤阅读 8 正常情况下已解决问题,如果没解决就需要固件了,新手不建议个人刷机。 END", "百度经验:jingyan.baidu.com", "个人刷机后手机不再享受保修 如未解决请到专业维修点询问", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "bs_rank_pos": 4}], "answer_spans": [[0, 99]], "fake_answers": ["智能手机故障的出现会受到很多因素的影响。如第三方软件冲突,病毒,缓存过量的占用,都会引起自动重启现象。建议尝试以下方法:1、卸载近期安装的第三方软件,初步排除系统软件冲突原因。2、取出外部SD卡后进行测试是否还有重启现象3、备份手机数据(电话簿、短信、图片等),将手机恢复出厂设置若通过以上方法问题依然存在,建议将手机送至就近的三星服务中心进行检测。"], "question": "三星a8一直重启怎么办", "segmented_answers": [["1", "、", "在", "关机", "状态", "下", "同时", "按住", "音量", "上", "，", "HOME", "键", "，", "电源", "键", "等待", "屏幕", "亮", "起", "后", "即可", "放开", "，", "进入", "Recovery", "模式", "。", "2", "、", "进入", "recovery", "模式", "后", "，", "会出现", 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"平时在家烤肉吃的少,没买烤肉盘,让电饼铛临时当下小助手,这也算节约能源吧。偶尔做一顿烤肉大餐,给一成不变的餐桌来点儿特别的吧。淘宝店:蜜悦手工私房甜点http://miyuecake.taobao.com微信公众账号:蜜悦甜点新浪微博:悦来悦快乐", "五花肉或牛里脊 500克 110克的烤肉酱 1包", "五花肉切大片后倒入烤肉酱,拌匀后腌制半小时。Ps:肉在冰箱里冻的半硬时切,可以切的薄一些。 电饼铛关闭上盖电源,底盘预热后刷薄油,放上腌好的肉烤吧,一面变色后翻另一面,焦黄有少许焦斑即可(上盖不用盖哟,便于观察,别烤糊了)。 烤完肉后,可以烤一些鱼、虾、香菇,这些都不用腌,本身就很鲜美。 最后再烤一些蔬菜吧,也不用腌,烤的时候,可以薄薄的刷一层烤肉酱,我烤了先生爱吃的韭菜和美人椒,大家可以根据自己口味,茄子、西葫芦切片后烤烤,口感都很不错... 看看我烤的成品吧,肉片微卷,有少许焦斑,肉香四溢,即成。配韩式辣椒酱,或者干脆留少许腌肉酱,切点儿蒜片和美人椒圈,摆盘享用吧。 空口,或者配上泡菜都很好吃。我喜欢用生菜卷入五花肉、蒜片和辣椒圈,一口吃下去,超爽。", "～～推荐大喜大的烤肉酱,有好几种口味可选择,口感都不错。～～肉腌好,烤的方法有以下四种选择,都不影响美味哟!1,家用烤肉盘 2,平底锅煎3,烤箱180度上下火烤12分钟,中间翻次面 4,电饼铛", "中餐也是不可替代的", "该菜谱创建于 2015-02-28", "版权归作者所有,没有作者本人的书面许可任何人不得转载或使用整体或任何部分的内容。", "希洛大大大大人 18568 做过", "《Tinrry下午茶》教你做芒果流心慕斯", "Tinrry甜悦 10393 做过", "思-37 16014 做过", "小諾妈 16543 做过", "十分钟就做好的懒人双皮奶", "悦来悦快乐 6440 做过", "详细步骤制作泡泡浴娃娃蛋糕", "悦来悦快乐 5148 做过", "悦来悦快乐 1800 做过", "悦来悦快乐 561 做过", "转印法制作超萌的巧虎蛋糕", "悦来悦快乐 515 做过", "酸奶慕斯HelloKitty蛋糕", "悦来悦快乐 428 做过", "悦来悦快乐 250 做过", "苗家酸汤鱼火锅", "悦来悦快乐 178 做过"], "bs_rank_pos": 3}, 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["1.将所有小龙虾用劲水冲洗数遍后,再一只只处理:左手捏住背部,右手用牙刷对着水龙头刷洗,并剪去长须2.所有虾刷洗净后浸泡在有少许醋的清水中,10分钟后捞出沥干,要注意防止其爬出3.葱白洗净切成葱花。大蒜去根部洗净切成片。干姜刮皮洗净切成丝。干辣椒用剪刀除去根蒂后剪成细丝4.大火烧热炒锅中的油,加入花椒,改小火慢炸。当油小滚时,加入干辣椒丝并不时翻动。当辣椒丝刚刚呈黄红色时,改大火,依次加入姜丝,蒜片,葱花爆出香味"], "question": "爆炒小龙虾的做法", "segmented_answers": [["1", "、", "将", "所有", "小龙虾", "用劲", "水", "冲洗", "数", "遍", "后", "，", "再", "一只", "只", "处理", "：", "左手", "捏", "住", "背部", "，", "右手", "用", "牙刷", "对着", "水龙头", "刷", "洗", "，", "并", "剪", "去", "长", "须", "；", "2", "、", "所有", "虾", "刷", "洗净", "后", "浸泡", "在", "有", "少许", "醋", "的", "清水", "中", "，", "10", "分钟", "后", "捞", "出", "沥干", "，", "要", "注意", "防止", "其", "爬", "出", "；", "3", "、", "葱白", "洗净", "切成", "葱花", "。", "大蒜", "去", "根部", "洗净", "切成", "片", "。", "干姜", "刮", "皮", "洗净", "切", "成", "丝", "。", "干辣椒", "用", "剪刀", "除去", "根", "蒂", "后", "剪", "成", "细丝", "；", "4", "、", "大火", "烧", "热", "炒锅", "中", "的", "油", "，", "加入", "花椒", "，", "改", "小", "火", "慢", "炸", "。", "当", "油", "小", "滚", "时", "，", "加入", "干辣椒", "丝", "并", "不", "时", "翻动", "。", "当", "辣椒", "丝", "刚刚", "呈", "黄", 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"食用", "。"]], "answers": ["1、将所有小龙虾用劲水冲洗数遍后，再一只只处理：左手捏住背部，右手用牙刷对着水龙头刷洗，并剪去长须；2、所有虾刷洗净后浸泡在有少许醋的清水中，10分钟后捞出沥干，要注意防止其爬出；3、葱白洗净切成葱花。大蒜去根部洗净切成片。干姜刮皮洗净切成丝。干辣椒用剪刀除去根蒂后剪成细丝；4、大火烧热炒锅中的油，加入花椒，改小火慢炸。当油小滚时，加入干辣椒丝并不时翻动。当辣椒丝刚刚呈黄红色时，改大火，依次加入姜丝，蒜片，葱花爆出香味；5、将沥干的小龙虾入锅，加料酒炝锅后加酱油、盐、白砂糖一同翻炒5分钟；6、加入1碗开水加盖改小火慢炖入味，8分钟后汤汁基本收干时，改大火加鸡精、白胡椒粉、椒盐粉炒匀即可。", "1、先准备好配料，姜切片，蒜拍一下。2、干辣椒剪成半节备用。3、先把小龙虾尾巴那里的肠子拉出来丢了，接着把头摘掉。用牙刷把肚子刷干净。最后用清水反复洗清过后放入盐水里面侵泡半个小时以上。4、然后锅里把水烧沸，倒入清洗干净过后的龙虾。煮上5-10分钟，龙虾颜色基本红透了就可以捞起备用。5、锅里倒油，倒入姜，蒜，花椒，干辣椒，爆炒出香味过后。放入麻辣鱼底料，接着爆炒出香味。6、接着倒入煮好过后的小龙虾，翻炒2分钟。7、接着倒入高汤或者清水，放入大葱，五香。盖上锅盖，用中火把水份焖干接可以了。主要是为了能让小龙虾更入味。待锅里的水份差不多焖干了，就揭开锅盖，翻炒一下。就可以起锅了。", "1、小龙虾养在清水中半小时，看见龙虾爬行的水浑了就反复换清水；2、之后用牙刷清洗龙虾腹部；3、龙虾尾部有3片取中间的那一片拉出肠泥；4、放入清水中反复清洗；5、烧小龙虾用菜籽油，红油豆瓣酱；6、调料（花椒，干辣椒，生姜片，大蒜）；7、锅内倒入菜籽油，要要多一点，8分热时转小火，加入干辣椒和花椒爆出香味捞起；8、加入八角，生姜片，大蒜爆炒出香味；9、加入红油豆瓣酱；10、转小火煸炒出香味；11、转大火倒入龙虾反复煸炒至龙虾全部变色；12、料酒3大勺子加入；13、2调羹生抽和一调羹老抽加入；14、开水一杯，差不多和食物持平；15、倒入爆香的花椒和干辣椒；16、盖上大火烧至熟透；17、打开加入葱段后熄火，尝试了一下味道，很鲜美，不要任何调料再调味了。小贴士：让龙虾在清水中爬行能清除龙虾本身的赃物，中间一定要把浑水换掉的。虾肠一定要去掉再烹饪，麻辣味可以根据自家的喜好度调整，一定要煮透了再食用。"], "answer_docs": [0], "segmented_question": ["爆炒", "小龙虾", "的", "做法"], "question_type": "DESCRIPTION", "question_id": 40, "fact_or_opinion": "OPINION", "match_scores": [0.7282608695652174]}
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"不能", "一起", "愉快", "地", "装", "13", "了", "?"], ["于是", ",", "你", "能", "在", "《", "小时代", "》", "前", "两", "部", "的", "电影", "开篇", ",", "看到", "TINY", "TIMES", "璀璨夺目", "地", "闪耀", "了", "大", "半", "个", "屏幕", ",", "而", "中文", "“", "小时代", "”", "则", "很", "没", "存在", "感", "地缩", "在", "底下", "。"], ["在", "片名", "里", "打", "出", "times", ",", "而", "不是", "era", "或者", "epoch", ",", "大概", "是", "向", "狄更斯", "《", "双城记", "》", "的", "开篇", "致敬", "(", "It", "was", "the", "best", "of", "times", ",", "it", "was", "the", "worst", "of", "times", ")", "。", "至于", "说", "电影", "里", "有没有", "反映", "出", "中国", "当下", "的", "“", "时代", "潮流", "”", "(", "times", ")", ",", "反正", "小", "编", "我", "没", "太", "看", "出来", "。", "但", "不管怎么说", ",", "这部", "系列", "电影", "的", "前", "两", "部", "登上", "了", "包括", "《", "时代周刊", "》", "和", "《", "纽约时报", "》", "在", "内", "的", "各种", "Times", ",", "在", "国际", "上", "挣", "足", "了", "曝光", "率", ",", "也", "算", "是", "功德圆满", "了", "。"], ["相比之下", ",", "韩寒", "的", "《", "后会无期", "》", "就", "低调", "得", "多", ",", "英文名", "Continent", "在", "海报", "上", "小", "到", "快", "看不清", "。", "你", "问", "“", "大洲", "”", "和", "“", "后会无期", "”", "有啥", "关系", "?", "哦", ",", "因为", "影片", "讲", "的", "是", "几个", "生活", "在", "岛上", "的", "年轻人", ",", "驾车", "深入", "大陆", "(", "continent", ")", "去", "旅行", "和", "感悟", "的", "故事", "。"], ["原来", "中文", "负责", "文艺", ",", "英文", "负责", "点", "题", "。", "(", "小", "编", "无", "责任", "猜想", ":", "如果", "这部片子", "取名", "叫", "mainland", "的", "话", ",", "会不会", "更", "吸引", "台湾", "小伙伴", "的", "眼球", "呢", "?", ")"], ["这些年", ",", "凡是", "能", "挤", "进", "院线", "的", "国产", "电影", ",", "不", "论", "大小", "优劣", ",", "取", "一", "个", "“", "高端", "大气", "上档次", "”", "的", "英文名", "似乎", "都", "成", "了", "必", "选项", "。", "没有", "英文", "护", "体", ",", "不仅", "难以", "登上", "外媒", "报道", ",", "取悦", "国外市场", ",", "就", "连", "在", "国内", "观众", "面前", "装", "13", "都会", "变得", "很", "困难", "呢", "。"], ["可", "别", "低估", "英文名", "的", "作用", ":", "虽然", "传统", "如", "《", "西游", "降魔", "篇", "》", "(", "Journey", "to", "the", "West", ":", "Conquer", "-", "ing", "the", "Demons", ")", "《", "分手大师", "》", "(", "The", "Breakup", "Guru", ")", "只是", "老老实实", "地", "把", "中文翻译", "了", "一", "下", ",", "或者", "当", "中文名", "太", "过", "文艺", "晦涩", "时", ",", "帮", "西方", "观众", "概括", "一", "下", "影片", "的", "中心思想", ",", "但", "一些", "聪明", "的", "导演", "和", "制片人", "还", "发现", ",", "当", "中文名", "把", "概括", "剧情", "的", "脏", "活", "累", "活", "给", "包", "了", "的", "时候", ",", "英文名", "可以", "风雅", "地", "跟", "你", "一起", "坐", "下来", ",", "谈谈", "人生", ",", "谈谈", "理想", ",", "谈谈", "影片", "里", "那些", "有", "的", "没", "的", "的", "内涵", "与", "格调", "。"], ["于是", "越来越", "经常", "地", ",", "你", "就会", "看到", "这些", "文艺", "范", "儿", "十足", ",", "但", "与", "“", "原文", "”", "没有", "半点", "关系", "的", "英文名", ":"]], "paragraphs": ["参考消息网10月21日报道 外媒称,如今很多中国人都有英文名字,与一些西方人打交道的时候,多数人都喜欢用自己的英文名。", "据英国广播公司网站10月21日报道,中国的官方媒体建议说,中国人在取英文名字的时候也要讲究学问,尽量小心,避开一些意思不雅的名字。", "中央电视台的网站上给那些想取英文名的国人提出建议,不要取一些可能冒犯别人的名字。", "央视网表示,人们最好不要使用一些小说中的人物名字,一些带有色情含义的名字,或者不知就里地使用“Dragon(龙)”,“Fish (鱼)”或者 “Lawyer(律师)”这样的名字。这些名字可能会在事后工作中出现麻烦。", "该网站还建议说,英文名应该带有“感情色彩”,或者显示个人性格。所以像“Satan(撒旦)”或者“Dumbledore(邓布利多)”这种名字不适合取。", "另外该网站还建议说,女孩子如果要取和食物有关的名字的时候要小心,比如Candy(糖果),Lolly(棒棒糖)或者Sugar(糖) 这类名字经常被认为是“脱衣舞女的名字”", "另外该网站还特别对于一些带有色情内涵的名字提出了警告。", "网站建议说,伊丽莎白、凯瑟琳、威廉或者乔治这样的“传统的英文名字”是好的英文名选择。文章说,“如果你想要一个安全的英文名,那么就取此类的名字吧。”", "点击图片进入下一页", "【延伸阅读】美华人移民取英文名不按常理出牌 奇葩名频酿笑话", "中新网9月10日电 据美国《世界日报》报道,美国在线知识市场Quora上日前有人提问“中国人起过那些在中国以外很少用的奇葩英文名”?引发不少网友的积极讨论,同时随着美国华人新移民的增加,一些华人出产自中国的奇怪英文名在美国也闹出不少笑话。", "新移民华人有的喜欢起自己中文名字谐音或意思接近的名字,但有时难免过于牵强闹笑话,空姐孙小姐说,她认识一个华人女孩中文名字叫依婷,就起了一个英文名字Eating。还有一个女孩中文名字叫蜜儿,就给自己起名Honey,她的同事每次叫她的英文名字都觉得很奇怪,让她改名成了Sweetie。名字里有倩或蕊字的女孩多数喜欢叫Cheer或者Cherry,但Cherry除了有樱桃的意思也有处女膜的意思。", "孙小姐说,她还认识一个中国来的男生英文名字叫Elephant,原因是他名字里有一个Xiang字,他的朋友叫他Da Xiang,所以给自己起了这样的英文名。这也让孙小姐哭笑不得。", "有的是名字没问题,但是和姓氏搭配就闹笑话,一名从事物流业的华人Jolly Wang,每次她报上自己的名字都被别人笑,后来才知道因为Wang在南方俚语是“小弟弟”的意思,Jolly就是开心的“小弟弟”。", "网友Amanda说,最常见的华人女生名字绝对是Cherry、Apple、Happy、Sunny还有Coco和其表姐妹Cece和Cici。网友Mona说,她认识三个年龄相近的女孩分别叫香奈儿(Chanel)、蒂芬妮(Tiffany)和古驰(Gucci),这是美国人无法编出来的笑话。还有一个女生,用她在这个世界上最喜欢的东西给自己命名,Money。", "有网友说,Ruby也是华人女孩喜欢用的名字,但有美国人认为这个名字是美国奶奶辈的人才叫的。还有一些华人为了追求独特,喜欢起一些让美国人闻风色变的名字,如美杜莎、希特勒、萨达姆、Tory(保守党)等。", "就和老外起中文名一样,有些华人取英文名也经常不按理出牌,如网友Nancy说,他们有一个顾客叫Anyway,大家都猜他有个妹妹叫Whatever。还有Alpha陈和Omega陈是一对双胞胎名字。有一个姑娘叫Cylinder(圆柱体),大家花了好几个月劝她改成发音一样的Selina。网友Michael说,Chlorophyll Wong(叶绿素王)这个人的名字在他心目中始终占据一个特殊的位置。还有一个女孩说自己的英文名是Easy,当时就有人和她解释说对正经女人来讲,Easy是一个非常糟糕的名字,她当时脸就红了,估计很快改了名字。", "华人齐先生说,其实在美国不一定要有英文名,保持自己的中文名字挺好,而且有些发音虽然美国人刚开始会不习惯,但叫多了发音就会很准确,这才是文化的尊重。 (张宏)", "(2014-09-10 10:09:09)", "【延伸阅读】吐槽国产电影的英文名字", "点击图片进入下一页", "姚远", "在这样一个“标题党”横行的年代,一部商业电影想要卖座,没有一个拉轰的名字恐怕不行。", "尤其在中国,多少人都是懵懂地走进电影院,45度角仰望显示屏,看看场次再扫一眼海报,就完成了选片。而你偏偏要叫《赛德克·巴莱》、《三傻大闹宝莱坞》,不是诚心和自己的票房过不去吗?(小编要负责任地说一句:这两部都是非常优秀的电影作品。)", "这不,七月要上映的两部国产片,也是未见其人先闻其名:一部叫《小时代3:刺金时代》,似乎不看它你就错过了一个时代;后一部更吓人,叫《后会无期》,还没见面呢就要诀别,仿佛没买票去捧场的话,你就要错过了导演的一生。", "但是小伙伴们,你们有留意过这些闪亮的中文片名旁,那些很努力刷存在感的英文名吗?", "中文负责文艺,英文负责点题", "论英文名的重要性,新晋导演郭敬明可能最有发言权。这位中国最富有的作家一直在用他的一头金发,CK内裤,和小说里不时蹦出的英文对白提醒着我们,这是个全球化的时代,要是连英文都说不好,还能不能一起愉快地装13了?", "于是,你能在《小时代》前两部的电影开篇,看到TINY TIMES璀璨夺目地闪耀了大半个屏幕,而中文“小时代”则很没存在感地缩在底下。", "在片名里打出times,而不是era或者epoch,大概是向狄更斯《双城记》的开篇致敬(It was the best of times,it was the worst of times)。至于说电影里有没有反映出中国当下的“时代潮流”(times),反正小编我没太看出来。但不管怎么说,这部系列电影的前两部登上了包括《时代周刊》和《纽约时报》在内的各种Times,在国际上挣足了曝光率,也算是功德圆满了。", "相比之下,韩寒的《后会无期》就低调得多,英文名Continent在海报上小到快看不清。你问“大洲”和“后会无期”有啥关系?哦,因为影片讲的是几个生活在岛上的年轻人,驾车深入大陆(continent)去旅行和感悟的故事。", "原来中文负责文艺,英文负责点题。(小编无责任猜想:如果这部片子取名叫mainland的话,会不会更吸引台湾小伙伴的眼球呢?)", "这些年,凡是能挤进院线的国产电影,不论大小优劣,取一个“高端大气上档次”的英文名似乎都成了必选项。没有英文护体,不仅难以登上外媒报道,取悦国外市场,就连在国内观众面前装13都会变得很困难呢。", "可别低估英文名的作用:虽然传统如《西游降魔篇》(Journey to the West:Conquer-ing the Demons)《分手大师》(The Breakup Guru)只是老老实实地把中文翻译了一下,或者当中文名太过文艺晦涩时,帮西方观众概括一下影片的中心思想,但一些聪明的导演和制片人还发现,当中文名把概括剧情的脏活累活给包了的时候,英文名可以风雅地跟你一起坐下来,谈谈人生,谈谈理想,谈谈影片里那些有的没的的内涵与格调。", "于是越来越经常地,你就会看到这些文艺范儿十足,但与“原文”没有半点关系的英文名:"], "bs_rank_pos": 4}], "answer_spans": [[0, 43]], "answer_docs": [0], "fake_answers": ["Mia,女子名,说英文名其实不是很合适,因为这个名字主要用于德国和北欧等地,英美人几乎不用的。这个名字是Marie的简化形式,源于希伯来语,含义为“苦涩”。"], "question": "mia英文名", "segmented_answers": [["Mia", "，", "女子", "名", "，", "说", "英文名", "其实", "不是", "很", "合适", "，", "因为", "这个名字", "主要", "用于", "德国", "和", "北欧", "等", "地", "，", "英美", "人", "几乎", "不用", "的", "。", "这个名字", "是", "Marie", "的", "简化", "形式", "，", "源于", "希伯来语", "，", "含义", "为", "“", "苦涩", "”", "。"], ["米娅", "，", "女性", "用", "名", "。"], ["女", "名", "，", "比较", "常见", "。"]], "answers": ["Mia，女子名，说英文名其实不是很合适，因为这个名字主要用于德国和北欧等地，英美人几乎不用的。这个名字是Marie的简化形式，源于希伯来语，含义为“苦涩”。", "米娅，女性用名。", "女名，比较常见。"], "entity_answers": [[], [], []], "segmented_question": ["mia", "英文名"], "question_type": "ENTITY", "question_id": 41, "fact_or_opinion": "FACT", "match_scores": [0.8636363636363636]}
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"LOL(英雄联盟)是腾讯旗下一款非常火爆的游戏,上至70岁老人,下至幼儿园小盆友,都有LOL的粉丝,因为英雄联盟是一款团队协作的游戏,由5个人共同作战,所以当己方有一个或者一个以上的人挂机的时候,场面就会出现一面倒的局面,即便前期己方优势大,拖到后期也是非常难赢的,针对这一现象,英雄联盟本次的更新出现了解决该问题的方法,有挂机可以三分钟内直接投降,接下来小编就跟大家聊聊LOL三分钟内如何投降。", "百度经验:jingyan.baidu.com", "百度经验:jingyan.baidu.com", "1 在S6赛季以前,所有的投降模式都是只有到20分钟以后才可以在菜单栏处点击投降按钮,之后由团队投票决定是否投降,这个机制比较死板,因为如果有人挂机也必须等到20分钟后才可以投降,那么不仅浪费时间,也浪费胜点。 步骤阅读 2 在本次更新中,LOL更新了最新机制,如果本局比赛有人挂机,三分钟内可以直接投降,并且投降后不会掉胜点,也不会计入失败。所以算是非常人性化的一个设计。 步骤阅读 3 如果想要投降,也要满足一些条件,队伍必须有人掉线,并且没有发生击杀以及所有未掉线玩家都必须点确定投降,才可以投降。不然的话还是要继续比赛,因为只能点一次,所以提前一定要在三分钟内跟同伴商量好。 步骤阅读 4 需要投降可以直接点开会话框窗口:输入:/remark。之后在屏幕右侧下方就会出现投降,只需要点击投降,并且队伍中其他人都点投降后,队伍就会显示失败,退出后也会发现没有获得经验以及金币,也没有任何损失。 步骤阅读 5 投降后,未掉线玩家是不会扣分的,掉线玩家扣分多并且会给予警告,严重会给予封号,所以这种机制也会很好的解决掉LOL游戏非常严重的挂机现象,警告哪些动不动就挂机的玩家。 步骤阅读", "百度经验:jingyan.baidu.com", "注意/remark前面有个/一定要打上。 只能投降一次,所以前期一定要跟同伴商量好,避免点错。", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "bs_rank_pos": 2}, {"is_selected": false, "title": "LOL怎么15分钟投降 LOL15分钟投降指令是什么-無趣", "most_related_para": 3, 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"我会想你,但我不会再爱你阅读(76) 父母说话的语气,决定了子女的情商阅读(16) 在辞职之前,老板还认为我们工作得很开心阅读(10) 30岁,你该担心的是失业,而不是失恋或失婚阅读(16) 和你出门不用带脑子阅读(13) 十年前一起搬砖,现在身价相差300亿阅读(11) 《我爱二次元》今晚首播 陈赫、孙艺洲燃爆全场阅读(0) 我们总以为来日方长,可一不小心就是后会无期阅读(13) 半年韶光:EDG战队如何从国之骄傲到人人黑阅读(1) 【兰珂酱】夺冠后的SKT竟被一粉丝“团灭”?阅读(9)", "LOL未来战士头像怎么得 伊泽瑞尔头像领取地址  LOL本周半价皮肤有哪些 5月26日半价英雄介绍  LOL限定皮肤中龙瞎和卧虎藏龙贾克斯哪个值钱  LOL5月阿卡丽的神秘商店地址 2017一折抽奖技巧  LOL熊猫提莫是限定吗 熊猫提莫半价购买地址  LOL盲僧龙瞎皮肤新特效是什么 龙瞎皮肤重做归来  lol2017召唤师招募令是什么 召唤师招募令地址  LOL招募令怎么邀请好友 召唤师招募令活动网址  LOL冠军典狱长是限定吗 冠军典狱长锤石多少钱  LOL未来战士凯特琳是不是终极皮肤吗"], "bs_rank_pos": 3}, {"is_selected": false, "title": "LOL怎么在15分钟投降 LOL15分钟投降方法介绍 - 游戏攻略", "most_related_para": 4, "segmented_title": ["LOL", "怎么", "在", "15", "分钟", "投降", "LOL", "15", "分钟", "投降", "方法", "介绍", "-", "游戏攻略"], "segmented_paragraphs": [["LOL", "怎么", "在", "15", "分钟", "投降", ",", "LOL", "游戏", "进行", "了", "更新", ",", "可以", "在", "15", "分钟", "就", "可以", "发起", "投降", "了", ",", "那么", "这个", "投降", "怎么", "操作", "呢", "?", "下面", "我们", "一起来", "看看", "吧", "!"], ["lol", "怎么", "在", "15", "分钟", "投降"], 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平时一副神游的样子,一旦进入角色就马力全开。虽然主演的作品不算太多,但不论是经典的《魔王》,还是在小朋友中拥有绝对人气的《怪物小王子》,反差再大的角色他都能很好地驾驭。 第四名:中居正广(3535票) 身为国民天团的领袖,中居正广演起戏来也丝毫不含糊。从早期的作品中,大家就能看出他对角色的准确把握。在目前热播的《ATARU》中,他更是通过自己精湛的演技塑造了一个十分富有个性的人物。 第五名:木村拓哉(3529票) 这位日剧大神的实力不需赘述。《悠长假期》、《HERO》、《华丽一族》等等,木村拓哉的经典作品数不胜数。甚至早年还有“木村在剧中演什么职业,这个职业就开始火爆”的传说。", "川妹子邓家佳既受观众追捧,又得专家肯定。 第30届电影金鸡奖颁奖典礼,于9月19日晚,在吉林省吉林市人民艺术剧院隆重举行。从四川内江市走向全国的川妹子邓家佳,凭借在电"], "bs_rank_pos": 2}, {"is_selected": false, "title": "偶像中也有学霸!扒一扒各个杰尼斯艺人的学历-微众圈", "most_related_para": 45, "segmented_title": ["偶像", "中", "也有", "学霸", "!", "扒一扒", "各", "个", "杰尼斯", "艺人", "的", "学历", "-", "微众", "圈"], "segmented_paragraphs": [["下", "文", "提到", "的", "通信制", "也", "就是", "函授"], ["SMAP", "的", "名字", "来源于", "四", "个", "英文", "单词", ":", "Sports", "Music", "Assemble", "People", ",", "意思", "是", "运动", "音乐", "召集", "人民", ",", "体现", "了", "健康", "、", "活泼", "的", "特点", "。", "现", "由", "木村拓哉", "、", "中居正广", "、", "稻垣吾郎", "、", "香取慎吾", "、", "草", "彅", "刚", "五位", "成员", "组成", "。", "他们", "在", "1988", "年", "结成", "并", "于", "1991", 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",", "平均年龄", "15", ".", "2", "岁", ",", "创", "下", "当时", "东京", "巨蛋", "最", "年轻", "艺人", "开", "唱", "记录", "。", "薮宏太", ":", "早稻田大学", "人间", "科学", "部", "人间情报", "科学", "科", "(", "通信", "教育", ")", "高木", "雄", "也", ":", "神奈川县", "立", "横浜", "平", "沼", "高中", "(", "中途", "退学", ")", "八乙女光", ":", "堀越", "高中", "伊野尾慧", ":", "明治大学", "理工", "学", "部", "建筑", "学科", "(", "研究生", "在", "读", ")", "有冈大贵", ":", "堀越", "高中", "冈本圭人", ":", "上", "智", "大学", "国际", "教养", "学", "部", "中岛裕翔", ":", "堀越", "高中", "知念侑李", ":", "堀越", "高中", "森本龙太郎", ":", "堀越", "高中", "Kis", "-", "My", "-", "Ft2", "于", "2005", "年", "7", "月", "26", "日", "结成", ",", "2011", "年", "8", "月", "10", "日", "出道", "。", "出道", "单曲", "《", "Everybody", "Go", "》", "发售", "首", "日", "销售量", "达", "17", ".", "5", "万", "张", ",", "首次", "登场", "便", "获得", "8", "月", "9", "日", "发布", "的", "Oricon", "日", "单曲", "排行榜", "第", "1", "名", "。", "北山宏光", ":", "亚细亚", "大学", "经济学", "部", "千", "贺", "健", "永", ":", "日出", "高中", "宫田", "俊", "哉", ":", "国", "士", "馆", "大学", "政", "经", "学", "部", "经济", "学科", "横尾", 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"2012", "年", "2", "月", "1", "日", "正式", "DVD", "出道", "。", "团", "名", "由", "Acrobat", "Boys", "Club", "而来", ",", "前身", "A", ".", "B", ".", "C", ".", ",", "主要", "以", "舞台剧", "活跃", ",", "同时", "也", "常", "出现", "在", "音乐", "节目", "少年", "俱乐部", "。", "五", "关", "晃", "一", ":", "立志", "舍", "高中", "河", "合", "郁", "人", ":", "立志", "舍", "高中", "桥本良亮", ":", "克拉克", "记念", "国际", "高中", "通信", "制", "(", "中途", "退学", ")", "冢", "田僚一", ":", "立志", "舍", "高中", "户", "冢", "祥", "太", ":", "立志", "舍", "高中"]], "paragraphs": ["下文提到的通信制也就是函授", "SMAP的名字来源于四个英文单词:Sports Music Assemble People,意思是运动音乐召集人民,体现了健康、活泼的特点。现由木村拓哉、中居正广、稻垣吾郎、香取慎吾、草彅刚五位成员组成。他们在1988年结成并于1991年以单曲出道,他们一直是日本最受欢迎的青春偶像组合之一。成员的个人气质、才艺、魅力以及造就了SMAP这个当之无愧的亚洲天团。", "中居正广:立代代木高中", "木村拓哉:立代代木高中", "香取慎吾:日本放送协会学园高中(通信制)(中途退学)", "稻垣吾郎:堀越高中", "草剪刚:堀越高中", "5人男子乐团组合TOKIO(东京小子)于1994年9月21日正式出道。出道两个月后就在日本武道馆举办了演唱会,并于同年受邀参加了NHK红白歌会,是与前辈SMAP一样固定参加红白歌会的杰尼斯艺人代表。", "城岛茂:奈良县立奈良商业高中", "山口达也:埼玉県立草加西高中", "松冈昌宏:堀越高中", "长濑智也:堀越高中(中途退学)", "国分太一:豊岛学院高中(中途退学)", "V6之名取自于“Victory six”,V也代表Vicycle及Veteran。V6是继“光GENJI”之后,第二组分组形式的组合。在1995年8月份成立, 并在1995年11月1日正式出道。", "坂本昌行:昭和第一高中商业科", "长野博:神奈川县立绫濑西高中", "井之原快彦:科学技术学园高中", "森田刚:埼玉县立岩槻北陵高中(中途退学)", "冈田准一:堀越学校", "三宅健:东海大学付属望星高中(中途退学)", "KinKi Kids(近畿小子)于1993年8月6日结成,1997年7月21日正式出道,有堂本光一及堂本刚两名成员。出道之前,曾以“W堂本”及“Kanzai Boya”名字出现。因为出生近畿地方而以“近畿小子”作为团体名称。KinKi Kids被誉为J家最具音乐气息的组合,他们在音乐上取得的成绩缔造了很多日本乐坛的传奇。", "堂本光一:东海大学附属望星高中(通信制)", "堂本刚:堀越高中", "岚(ARASHI)是由大野智、樱井翔、相叶雅纪、二宫和也、松本润5名成员组成的偶像团体,于1999年出道。至2014年出道十五周年,岚的单曲总销量超过2000万张,音乐DVD销量位居日本历代第一。岚是现今日本最有人气的组合之一,成员除了唱歌更是在综艺节目以及影视剧方面表现优异。", "大野智: 东海大学附属望星高中(通信制)(中途退学)", "樱井翔: 庆应义塾大学经济部", "相叶雅纪: 东海大学附属望星高中业(通信制)", "二宫和也: 立志舍高中(通信制)", "松本润: 堀越高中", "泷与翼(简称“T&T”)是一个日本双人偶像团体,团名是取两名成员泷泽秀明和今井翼名字的首字组成,组合于2002年9月11日正式出道。", "泷泽秀明:八王子实践高中(中途退学)", "今井翼:八洲学园高中新宿校(通信制)", "NEWS,2003年9月15日成立并于2004年5月12日正式出道。“NEWS”代表North(北)、East(东)、West(西)、South(南)四个方向,寓意NEWS能向国际四方发展,并有“新讯息”的意味。团体成员:小山庆一郎(成员色:紫色)、加藤成亮(成员色:绿色)、增田贵久(成员色:黄色)、手越祐也(成员色:粉色)。", "小山庆一郎:明治大学文学部", "加藤成亮:青山学院大学法学部", "增田贵久:克拉克记念国际高中(通信制)", "手越佑也:早稻田大学人间科学部人间情报科学科(通信教育)", "关8官方翻译为关杰尼∞,原名关杰尼8,出道后将8写作∞,意为无穷大。由横山裕、涉谷昴、村上信五、锦户亮、安田章大、丸山隆平和大仓忠义等7人组成。关8成员因为都来自关西,歌曲风格都带着浓浓的关西风,被歌迷戏称“搞笑团”。", "横山裕:大阪市立此花中学校", "涉谷昴:大阪学院大阪高中(中途退学)", "村上信五:金光大阪高中(中途退学)", "锦户亮:京都艺术高中", "安田章大:尼崎市立尼崎高中(中途退学)", "丸山隆平:关西文化艺术学院", "大仓忠义:大阪商业大学高中(中途退学)", "当红偶像团体KAT-TUN于2001年3月结成,同年4月初登场,当时团体内部平均年龄14岁,成军五年后于2006年3月22日全员年满20岁后宣布全员正式出道。 一出道便以百万销量荣获Oricon年度桂冠。目前正在充电期。 亀梨和也:都立水元高中 (中途退学) 上田竜也:私立昭和第一高中 中丸雄一:早稻田大学人间科学部人间情报科学科 (通信教育) Hey!Say!JUMP Hey! Say! JUMP是Hey! Say! Johnny's Ultra Music Power的缩写,意思是可以在平成年代飞得更高更远。组合由Hey! Say! 7与Hey! Say! BEST组成,2007年9月24日结成,并担任11月开幕的“世界杯排球赛”应援大使,同年11月14日,以单曲《Ultra Music Power》正式出道。2007年12月22日,首次在东京巨蛋举办出道演唱会,平均年龄15.2岁,创下当时东京巨蛋最年轻艺人开唱记录。 薮宏太:早稻田大学人间科学部人间情报科学科(通信教育) 高木雄也:神奈川县立横浜平沼高中(中途退学) 八乙女光:堀越高中 伊野尾慧:明治大学理工学部建筑学科(研究生在读) 有冈大贵:堀越高中 冈本圭人:上智大学国际教养学部 中岛裕翔:堀越高中 知念侑李:堀越高中 森本龙太郎:堀越高中 Kis-My-Ft2于2005年7月26日结成,2011年8月10日出道。出道单曲《Everybody Go》发售首日销售量达17.5万张,首次登场便获得8月9日发布的Oricon日单曲排行榜第1名。 北山宏光:亚细亚大学经济学部 千贺健永:日出高中 宫田俊哉:国士馆大学政经学部经济学科 横尾渉:克拉克记念国际高中 藤谷太辅:明海大学经济学部 玉森裕太:日出高中 二阶堂高嗣:克拉克记念国际高中(中途退学) Sexy Zone是2011年9月29日正式宣布推出的5人新团体,并于同年11月16日发行单曲《Sexy Zone》实现CD出道。这也是杰尼斯首次全面推出的性感组合,出道时平均年龄只有14.2岁。 中岛健人:明治学院大学社会学部(在读) 菊池风磨:庆应义塾大学综合政策学部(在读) 佐藤胜利:堀越高中(在读) 松岛聪:静冈县内高中(在读) MARIUSU叶:东京横浜独逸学园中等科(在读) A.B.C.-Z是由五关晃一、河合郁人、桥本良亮、冢田僚一和户冢祥太组成的五人偶像团体,于2012年2月1日正式DVD出道。团名由Acrobat Boys Club 而来,前身A.B.C.,主要以舞台剧活跃,同时也常出现在音乐节目少年俱乐部。 五关晃一:立志舍高中 河合郁人:立志舍高中 桥本良亮:克拉克记念国际高中通信制(中途退学) 冢田僚一:立志舍高中 户冢祥太:立志舍高中"], "bs_rank_pos": 3}, {"is_selected": false, "title": "杰尼斯事务所_日本人选出的2016好感度艺人TOP10_沪江日语", "most_related_para": 6, "segmented_title": 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"推荐", ":"], ["2016", "年", "11", "月", "17", "日", "06", "点", "30", "分", ":", "ウ", "サ", "ギ"]], "paragraphs": ["编辑点评:第13届音乐祭由2万名粉丝甄选出的最喜欢的歌手排行榜已出炉,除了达成七连霸的岚、常驻组之外,SMAP因宣布年内解散而名次大幅提升,复出的宇多田光亦名列前茅。", "岚达成7连霸!", "2004年にスタートし、毎年発表している『音楽ファン2万人が選ぶ“好きなアーティストランキング”2016』だが、嵐の7年連続総合首位は史上初であり、最多記録。今回は、世代別ランキング「10代」「20代」で1位を獲得している。", "“由两万音乐粉选出的喜欢的艺人排行榜”已经举行了12年。加上今年,岚连续七年获得综合排名第一位的高纪录,达成史上首次七连霸的好成绩。并且本次在世代排行“10代”“20代”中均获得了第一名。", "特に支持の高い若年層からは、「仲が良いし メンバー全員が輝いている」(千葉/10代女性)というメンバー同士の仲の良さに惹かれる声はもちろん、「雰囲気、考え方や姿勢、見ているだけで笑顔にさせてくれる存在」(愛知/10代女性)、「仕事に対する姿勢も尊敬できる」(千葉/20代女性)など、その人間性に魅力を感じるという意見も多かった。", "尤其是支持特别高的年轻层,有“关系好的成员全部都熠熠生辉啊”(千叶/10代女性)这类因他们成员之间关系好而受到感染的粉丝,也有“气氛、思想和姿态、仅仅是看着他们就能让我们微笑的存在”(爱知/10代女性)、“他们对于工作的态度十分值得尊敬”(千叶/20代女性)等感受到他们人性魅力的意见也很多。", "今年は、アリーナクラスの会場をめぐるツアー『ARASHI“Japonism Show”in ARENA』を開催、先ごろ新たなドームツアーもスタートさせた嵐。シングル「復活LOVE」と「I seek/Daylight」、今年最高の初動売上を記録したアルバム『Are You Happy?』の発売など、バラエティやドラマでの活躍はもちろん、コンスタントな音楽活動にも力を入れている。それだけに、「ライブは最高の演出で感動するし、飽きない」(神奈川/10代女性)、「5人のパフォーマンスにまとまりがある」(東京/20代男性)、「歌、ダンス、パフォーマンスが素晴らしい。様々なジャンルを歌いこなせる」(埼玉/30代女性)と、年々ライブや音楽面を評価する声も増加しているようだ。", "今年的《ARASHI\"Japonism Show\"in ARENA》巡演,之后紧接着开始了巨蛋巡演的岚,发售了单曲《复活LOVE》和《I seek/Daylight》,以及今年初动最高的专辑《Are You Happy?》,五人在综艺和电视剧中也相当活跃,常规音乐活动也十分给力。“对于演唱会相当感动,完全不会腻”(神奈川/10代女性)、“5人的表演非常整齐划一很和谐”(东京/20代男性)、“演唱、舞蹈、表演都很棒,每个类型的歌曲都能唱得很好”(埼玉/30代女性),粉丝们每年都增加着对于他们音乐方面高评价的呼声。", "声明:本双语文章的中文翻译系沪江日语原创内容,转载请注明出处。中文翻译仅代表译者个人观点,仅供参考。如有不妥之处,欢迎指正。", "相关阅读推荐:", "2016年11月17日06点30分 : ウサギ"], "bs_rank_pos": 4}], "answer_spans": [[75, 81]], "answer_docs": [2], "fake_answers": ["。第二名:生田斗真"], "question": "杰尼斯事务所艺人排名", "segmented_answers": [["1", "岚", "。", "2", "关", "杰尼", "。", "3", "Kis", "-", "My", "-", "Ft2", "。", "4", "Hey", "!", "Say", "!", "JUMP", "。", "5", "Sexy", "Zone", "。", "6S", "MAP", "（", "解散", "）", "。", "7", "NEWS", "。", "8", "KAT", "-", "TUN", "。", "9", "杰尼斯", "WEST", "。", "10", "V6", "。"], ["第一名", "：", 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"魅族外观可以,手机做的比较薄,但摄像头突出,机身手感可以。手机系统是基于阿里云的flyme6,系统简洁,功能扩展可玩性不高,相对于魅族自身的差点,电池耗电方面可以,待机时间长,对于轻度使用者一天以上基本没问题。魅族的快冲可以,从20%开始,基本1小时多就可以冲满,但冲电的时候机身温度有点烫。屏幕一般,相机拍照一般,千元机的水准,不过扫二维码识别挺快的。通话声音还可以,外放音乐声音大,音质表现可以。指纹解锁速度挺快的,识别率挺好,可以支持支Fu宝和微信支付,使用方便。电信联通信号表现还可以,wifi上网可以,目前没发现掉线情况。手机运行内存说3G,实际2.6多G,系统占用1.5G左右,实际可用少,不过满足日常软件使用。总体表现还可以详细内容>", "第一次使用努比亚手1.外观:大小正好,屏幕很清晰,屏幕色彩比较平淡,但是色彩可以自己设置,我设置的艳丽,设置完成后,显示效果好很多!整体做工很精致,很圆润,手感很好!2.系统流畅度还可以,不能说非常流畅,但很顺手!应用启动速度还可以,虽然,不是飞一般的快,但也没有明显的延时,日常使用绝对够用!64G存储,除了系统占用,还剩50个G。运行内存用安兔兔测试,还剩1.8个G指纹解锁速度一般,不快也不慢中规中矩!3.照相:索尼318传感器,2300万像素,成像效果还可以,风格属于还原真是色彩,没有非常艳丽的感觉,不是很讨好眼球,但清晰度,噪点控制都还可以,个人觉得还有待提高,没有发挥索尼318的真实水平,前置1300万像素,成像效果也还可以,美颜功能很自然,没有像别的手机那样修饰的那么夸张!4:电池待机还算比较耐用,坚持一天没问题!没有明显发热!5.触摸屏下方有时候不灵,把分屏关掉后就没问题了6.网上说的虚拟按键不灵敏,没遇到!7.信号还可以,比我以前用的360手机强多了!8:边缘触控功能很酷总体来说一款手机各方面都中规中矩,很均衡!下面有几张我的拍摄样张,供大家欣赏!详细内容>", "用户 梦***城 于 2016/11/7 发布的评价", "一直都在用酷派手机,大神F1也都在用,刚帮亲戚也买了大神F2。确实很让我信赖的品牌,性价比高,手机优化的也很不错,用得得心应手。选这款电信手机也是因为刚办了张电信4G卡,加上喜欢酷派手机的手感和外形。这款酷派Y70电信手机外形简约也显大方,刚拿到手开机确实给我很大的惊喜,5寸的720p屏幕,色彩鲜艳,显示非常细腻,亮度高,可视角度大。整个手感恰到好处,5寸屏手机我觉得是最适合手握或者是放在裤兜里的了。试了下照相功能,对焦速度很精准,拍摄的照片还是蛮不错的,毕竟800万像素不会让人失望的。再说系统优化的确实流畅,没有一点卡顿。预装了几个常用软件,如果不喜欢也都可以删除,全由自己选择。自带的酷派社区软件很不错,可以随时登陆论坛与酷派机友互动,掌握最新的酷派活动信息,还能第一时间更新升级系统,做的非常人性化。手机4G信号很稳定,通话清晰,上网速度让人很惊讶,配置的20毫安电池,偶尔上上网,接打几个电话,待机一天刚刚好。电池后盖有些难拿下,需要指甲沿后盖转一圈才可以轻松拿下,需要联系几次就很熟练了。总体来说是个很不错双卡双待电信手机,性价比高,让人很满意。值得推荐朋友来买。详细内容>", "用户h***y 于 2014/10/31 发布的评价", "1、薄,屏幕稍大,但是厚度小,所以比较轻;2、系统很流畅,切换没卡顿,指纹解锁反应快,方便了开屏时间;3、相机一般,别让像素忽悠了,光线透过镜头量不好,低光直接拍照不好,但自带相机能调曝光时间等设置,可以稍微改善照片质量。日常拍照可以满足,如有摄影爱好又不怕麻烦钻研的话,建议还是微单或单反,手机摄影无论多智能都因大小尺寸有局限;4、省电。没详细留意,无wifi只4g网络状态下待机一白天用电不到15%。充电稍慢,但不发热。5、外观很好。现在出来的手机都讲究外观设计,所以一般都不会难看得难以接受。颜色看个人喜欢吧。6、最基本的打电话。通话质量很好。华为嘛,靠得住。4g网络信号接受良好。7、所用的卡是最小的卡。8、买手机,如果要考虑资金问题,就建议买一千至两千的手机,太贵喊着高配置双摄像头太浮。日常拍照用不着什么双摄像头,手机配双摄像头也不会专业哪里去,拍不好多高配置还是不会拍的。还有,大多数的游戏也没几个要用到什么八核的,你玩个连连看、植物大战僵尸四核都用不着,但基于安卓系统配个四核也情有可原。别广告喊什么就信什么。谢谢详细内容>", "用户w***r 于 2016/9/16 发布的评价", "手机性价比太高了,运行速度很快,不卡顿,支持扩展内存,信号真心不错,充电挺快的,续航也不错,屏幕大小正适合老年人,不会太大也不会太小,屏幕清晰,屏幕占比大,一贯的窄边框,还有这个下方的小圆点改成按压式的home键看起来简洁大方,有些人说是跟iphone学的,管他呢,学了好了就行。 flyme系统棒棒的,在某些细节做得比较到位,如:拨打运营商的电话直接弹出数字键盘,语音的识别也很准,也可以把长按home改为锁屏,有访客模式,做得很好,特别是那个悬浮球,用熟了之后更是非常方便。不太喜欢就是 一个是发热,果然魅族很容易发热。还有外形塑料感重,但是谁让他这么便宜呢。还有摄像头照相不敢抖动,一抖动就发虚,不信有1300万像素哈哈。外放音质不行。 总的来说系统简单易用 才*真心便宜 适合备用机或者给老人使用。详细内容>", "用户l***s 于 2016/4/27 发布的评价", "手机相关信息", "手机比价导购 手机十大品牌 手机口碑大全 手机降价排行 手机导购知识 销量上升最快的手机"], "bs_rank_pos": 2}, {"is_selected": false, "title": "【手机大全】手机报价及图片大全-ZOL中关村在线", "most_related_para": 1, "segmented_title": ["【", "手机", "大全", "】", "手机", "报价", "及", "图片", "大全", "-", "ZOL", "中关村在线"], "segmented_paragraphs": [["已", "选", "条件", ":"], ["更多", "选项", "(", "RAM", "容量", ",", "ROM", "容量", ")", "等", "[", "高级搜索", "]"]], "paragraphs": ["已选条件:", "更多选项(RAM容量,ROM容量)等 [高级搜索]"], "bs_rank_pos": 6}, {"is_selected": false, "title": "高性价比手机推荐_手机销量排行榜 - 手机推荐网", "most_related_para": 17, "segmented_title": ["高", "性价比", "手机", "推荐", "_", "手机", "销量", "排行榜", "-", "手机", "推荐", "网"], "segmented_paragraphs": [["2017", "年", "4", "月", "7", "日", "1000", "左右", "手机", "推荐"], ["手机", "是", "我们", "每天", "都会", "用", "的", "电子产品", ",", "而且", "使用", "的", "频率", "非常", "的", "高", ",", "点", "餐", "、", "购物", "、", "支付", "、", "导航", "几乎", "生活", "中", "的", "方方面面", "都", "离不开", "手机", "。", "手机", "对", "现", ".", ".", "."], ["随着", "手机", "技术", "的", "发展", ",", "现在", "的", "手机", "在", "各方面", "相对", "之", "前", "都", "有", "很大", "提升", ",", "不管", "是", "千元", "级", "的", "产品", ",", "还是", "定位", 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每次咳嗽医生都是让做雾化,做一次都要几十块,两个小朋友一起做的话就去掉了百十块了~~还要每天跑医院,来回车费又去了十几块,花钱又浪费时间,听说可以自己买个雾化机回来自己可以做,但不知道那个牌比较好、价格怎么样?这些都不了解,现请妈妈们帮忙帮忙~~~谢谢了~`", "1,英华融泰挺不错的,在医院看到的这个牌子,医生说还不错,是个老品牌。送了两套雾化配件,包括面罩、杯子、鼻插什么的特别全,可以调节雾量,这点很不错,机器运行有点声音,这个都这样可以接受,雾喷出来也挺细腻的,相信能带来好的效果 2,我看广州市第八人民医院那里用的是英华融泰的卖五百多,淘宝有一家卖四百多.还看了百瑞的两千多, 评价也不错~ 另外所有雾化器都可以配英华融泰的雾化杯等配件 ,质量不错价格也不错评价也很好,具体还得你自己用才知道~", "可以买个压缩式\"雾化器\".百瑞品牌的.大人小孩都可以用,现在一般感冒都是上呼吸道感染,急支气管等急性喉炎,医生都会建议雾化的.总比打点滴好,而且这样雾化也比较有效.药物直接可喷入呼吸道,也减少药物浪费.价钱大概在2000左右.便宜点的可以看看英华融泰,质量也很不错用起来没太大区别,出雾量好雾化颗粒小比较适合家用。", "普元,英华融泰的都比较好,您像一代雾化器是超声波的,二代是压缩式,三代的是筛网式的,像您你这种是给孩子用的建议您买普元的雾化器,方便携带,而且雾化的颗粒是5微毫的比较均匀更容易达到肺泡,我在安贞那里上班旁边有康复之家安贞店的连锁医疗器械里面的雾化器种类类比较多您可以去咨询下。地址:安贞医院急诊北行200米 电话:64432569", "你可以买台湾产的普元质量不错价格适中性价比很高咨询电话022-27371200", "买台湾产的普元雾化器吧质量不错经济实惠", "雾化吸入治疗的优势 (1)药物作用直接,雾化后的药物可直接送达呼吸道患病部位,对缓解支气管哮喘效果显著且迅速,优于其他治疗方法; (2)用药量少,最多只有其他给药方式的十分之一,明显地减少了药物的毒副作用; (3)减少全身用药的机会及用量,可避免打针吃药的痛苦,尤其适用于儿童患者; (4)对患者协同性无要求,潮式呼吸即有效; (5)可调整剂量,可同时辅助供氧,可实现联合药物治疗(若药物之间无配伍禁忌) 康复之家连锁安贞店有卖各种品牌的雾化器像台湾普元的、德国百瑞的等均有销售,价格合理公道,治疗效果好。地址:安贞医院急诊北行200米 电话:64432569", "北京协和医院东门康复之家有多种品牌的雾化器,雾化颗粒的大小直接影响药效的吸收,一个雾化器可全家使用,65135538", "台湾产的普元雾化器质量不错价格适中", "买台湾产的普元雾化器吧价格适中治疗效果不错", "买台湾产的普元雾化器质量不错经济实惠", "来福士儿童雾化器,雾化颗粒细腻均匀,吸收好关键吸引小朋友喜欢,雾化不抗拒", "买台湾普元雾化器吧性价比挺高的", "百瑞,欧姆龙,普元的雾化器都不错。你可以去天津市和平区鞍山道111号咨询", "老人和孩子适合用压缩式雾化器,比如德国的百瑞,台湾的普元,国产的英华荣泰,鱼跃,凯亚的雾化器都是质量很不错的,有需要可以咨询0-2-7-8-7-8-3-0-5-1-6", "买台湾产的普元雾化器吧性价比高咨询电话022-27371200", "下载贴吧APP看高清直播、视频!", "贴吧页面意见反馈 违规贴吧举报反馈通道 贴吧违规信息处理公示"], "bs_rank_pos": 1}, {"is_selected": false, "title": "雾化机什么牌子的好 十大品牌排行榜新鲜出炉-土巴兔装修大学", "most_related_para": 15, "segmented_title": ["雾化", "机", "什么", "牌子", "的", "好", "十大", "品牌", "排行榜", "新鲜出炉", "-", "土巴兔", "装修", "大学"], "segmented_paragraphs": [["微信", "加", "好友", "获取", "详细", "报价"], ["装修", "顾问", "-", "馨馨", "4", "年", "行业", "经验", ",", "24", "h", "可", "咨询"], ["10", "秒", "闪电", "通过", "好友"], ["报价", "短信", "已", "发送", "到", "您", "的", "手机"], ["因", "材料", "品牌", "及", "工程", "量", "不同", ",", "具体", "报价", "以量房", "实测", "为准"], ["稍候", 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"目前随着人们健康意识的增强,对自己的健康问题越来越关注了。雾霾成为目前影响健康的一大障碍,为了对抗雾霾对身体的影响,许多人选择购买一台家用雾化机。今天,小编要介绍给大家的就是关于雾化机的一些知识,包括雾化机的类型以及雾化机的排名问题,希望通过这篇文章能够对想要购买雾化机的朋友提供一些帮助。好了,下面要开始介绍雾化机什么牌子的好。", "作为雾化器的中国小小传道士,我们进行了国内几大电商平台雾化器品牌的调查。至少发现以下几点:", "1. 众多雾化器品牌已经入驻天猫,不下50家;而压缩雾化器”、“超声雾化器”、“儿童哮喘雾化器”、“家用雾化器等产品搜索词作为天猫的主推搜索词,同时也体现了平台用户的选购倾向; 2. 京东商场合作的雾化器品牌相对要少很多,包括欧姆龙、百瑞等雾化器品牌,仅在20家以内;其相关搜索“雾化器 家用 儿童”、“雾化器医用”也是雾化器品类搜索的top2热词。 3. 而全球最大的电商平台亚马逊,入驻品牌则更为狭窄,但也囊括了国际知名雾化器品牌,诸如压缩雾化器”、“超声雾化器”、“儿童哮喘雾化器”、“家用雾化器", "根据各个品牌的市场份额和用户关注度,2013年雾化器十大品牌排行榜如下:", "1. Omron欧姆龙雾化器:日本欧姆龙,多种雾化器产品,满足各类需求; 2. Yuyue鱼跃雾化器:江苏出品,超声雾化器和压缩空气式雾化器; 3. Scian西恩雾化器:产地不详,由上海雷乐实业有限公司代理; 4. Folee富林雾化器:江苏出品;", "5. 氧气盒子O2BOX:广东出品;", "6. 德国百瑞雾化器:德国出品,拥有国际领先可控技术;", "7. 德维比斯DEVILBISS:美国品牌,是全球呼吸系统用医疗保健设备的最主要供应商之一。", "8. 凯亚雾化器:来自广东佛山,KYWH1004;", "9. 米勒mele雾化器:来自广东深圳,雾尔康(Welcon)医用雾化器NW-5001;", "10. 英华融泰雾化器:产品出自内蒙;", "雾化机怎么使用", "第一步:准备电源 1.连接雾化器和电源适配器 2.将电源适配器的插头插入电源插座 第二步:拧开雾化杯 1.适当的药液量约为2~7ml(切勿超过8ml) 2.药液过少——无法将药液吸上来,也无法进行雾化。 3.药液过多——将导致药液雾化的部分被药液覆盖,进而无法雾化。 第三步:将雾化杯与雾化面罩连接,输氧管与雾化杯连接。 第四步:另一端连接雾化器出气口 第五步:通电后按下开关键,进行正常雾化。 雾化机保养维护 请保持产品的最佳状态并遵循以下七大注意指导事项,以免使产品遭受破坏:", "1、请勿将产品放置于婴儿或无意识患者触及的地方。 2、请勿使产品受到任何强烈的冲撞,例如从高处摔落本产品。 3、当药液瓶内存在药液时,请勿搬运或存放产品。 4、请勿把产品放在高温、低温、高压或阳光直射的地方。", "5、请勿弄折送气管。", "6、关于废弃的本体、附件和可选部件的处理方法,请遵照当地政府的规定执行。", "7、请勿使用苯、稀释剂和易燃化学药品来清洗产品。", "二、更换过滤片", "当过滤片变色时,请更换新的过滤片,操作步骤如下。", "1、将过滤片盖向外拉出后取下,即可更换过滤片。 2、用牙签等尖锐的工具将过滤片取出。请将取出后的过滤片废弃,更换新的过滤片。 3、安装过滤片盖。", "仅使用本产品专用的过滤片。请勿在未安装过滤片的情况下使用本产品。", "过滤片不分正反面。", "更换前请确认过滤片清洁且无灰尘。", "请勿清洗过滤片。如果过滤片沾湿,请更换过滤片。过滤片沾湿时将引起堵塞。", "为防止过滤片盖发生堵塞,请定期清洗过滤片盖。请勿对过滤片盖煮沸。清洗时,请务必先将过滤片盖取出。清洗后充分干燥,然后按照正确的方法安装过滤片盖。", "小编建议大家在选购之前一定要做足功课,仔细的对各个品牌的雾化机进行对比分析,从而选择到一款物美价廉的雾化机。如果,你身边的朋友也打算购买雾化机的化,你可以把这篇文章转发给你的朋友们,让他们也了解下这方面的知识。", "以上就是有关雾化机什么牌子的好的相关内容,希望能对大家有所帮助!土巴兔装修网,全行业的领导者。在此页面点击下列链接,即可获得大品牌主材供应,专业设计团队提供的免费优质设计、免费验房等,装修费用立省40%!", "装修报价不求人,10秒极速获取报价 根据生肖测哪天开工最吉利,限时免费哦~", "家装施工工艺流程 太全面了!", "如何收房?详细交房流程教会你收房!", "如何避免装修中的偷项漏项?"], "bs_rank_pos": 2}, {"is_selected": false, "title": "【十大雾化器品牌】【雾化机品牌】雾化器什么牌子好?→ 买购网", "most_related_para": 9, "segmented_title": ["【", "十大", "雾化器", "品牌", "】", "【", "雾化", "机", "品牌", "】", "雾化器", "什么", "牌子", "好", "?", "→", "买", "购", "网"], "segmented_paragraphs": [["综合搜索", ">", "综合搜索"], ["导航", "相关行业", "雾化器", "呼吸机", "制氧机", "理疗", "仪", "血压计", "血糖仪", "按摩椅", "颈椎", "牵引", "器", "血氧", "仪", "静脉曲张", "袜", "拔罐", "器", "足浴", "桶", "胎心仪", "颈椎", "按摩器", "按摩器", "足浴盆", "助听器", "体温计", "脂肪", "测量仪", "健康", "秤", "计步器", "轮椅", "空气净化器", "保健养生", "二级", "行业", "按", "人群", "体育运动", "/", "运动户外", "保健", "按摩", "器材", "保健品", "营养品", "滋补", "食材", "药材", "成人用品", "医药", "/", "医院", "身体", "部位", "健康", "/", "养生", "医疗", "护理", "婴幼儿用品", 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"1. Omron欧姆龙雾化器:日本欧姆龙,多种雾化器产品,满足各类需求; 2. Yuyue鱼跃雾化器:江苏出品,超声雾化器和压缩空气式雾化器; 3. Scian西恩雾化器:产地不详,由上海雷乐实业有限公司代理; 4. Folee富林雾化器:江苏出品;", "5. 氧气盒子O2BOX:广东出品;", "6. 德国百瑞雾化器:德国出品,拥有国际领先可控技术;", "7. 德维比斯DEVILBISS:美国品牌,是全球呼吸系统用医疗保健设备的最主要供应商之一。", "8. 凯亚雾化器:来自广东佛山,KYWH1004;", "9. 米勒mele雾化器:来自广东深圳,雾尔康(Welcon)医用雾化器NW-5001;", "10. 英华融泰雾化器:产品出自内蒙;", "可见目前只有三大国际品牌进驻中国,包括欧姆龙、百瑞以及德维比斯,相对而言更多的雾化器市场份额由国内品牌占领,主要分布于江苏和广东两大雾化器国内生产基地。", "调查发现,作为雾化器产品的研发和推动者,以及家庭雾化治疗理念的传播者,欧姆龙雾化器的市场份额在逐年走高,其不仅拥有最为丰富的产品线:主流的超声雾化器和压缩雾化器,还开发了独有的网式雾化器,其价格也分低中高三个档次,可以满足不同家庭的价位需要。", "据悉,2013年欧姆龙新上市了NE-C802低噪儿童哮喘家用压缩式雾化器,携带方便,能满足随时随地实现雾化吸入治疗;此款产品仅网络专供,不在线下售卖,因此其价格更为亲民,是同类价格中的一款高性价比雾化器,是众多妈妈们的放心选择。", "事实上,专家们也从用户角度给了些雾化器品牌选购的建议:操作简单、方便携带也是衡量雾化器的重要指标,而欧姆龙压缩式雾化器设计了单键操作,即使儿童也可轻松使用。并且体积小巧,非常适合外出携带。NE-C802型压缩式雾化器还专门配有充电器和充电电池,以及便携式手提包,外出游玩、旅行都能带在身边使用,实现随时随地进行吸入治疗,是家用雾化器品牌产品的不二选择。", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。"], "bs_rank_pos": 5}], "answer_spans": [[2, 75]], "answer_docs": [0], "fake_answers": ["英华融泰挺不错的,在医院看到的这个牌子,医生说还不错,是个老品牌。送了两套雾化配件,包括面罩、杯子、鼻插什么的特别全,可以调节雾量,这点很不错,机器运行有点声音,这个都这样可以接受,雾喷出来也挺细腻的,相信能带来好的效果"], "question": "雾化器哪个牌子好", "segmented_answers": [["英华", "融", "泰", "挺不错", "的", "，", "医生", "说", "还不错", "，", "是", "个", "老", "品牌", "。", "送", "了", "两", "套", "雾化", "配件", "，", "包括", "面罩", "、", "杯子", "、", "鼻", "插", "什么", "的", "特别", "全", "，", "可以", "调节", "雾", "量", "，", "这", "点", "很不错", "，", "机器", "运行", "有点", "声音", "，", "这个", "都", "这样", "可以", "接受", "，", "雾", "喷", "出来", "也", "挺", "细腻", "的", "，", "相信", "能", "带来", "好", "的", "效果", "。"], ["雾化器", "还是", "进口", "百瑞", "的", "好", "。"], ["台湾", "产", "的", "普元", "雾化器", "。", "质量", "不错", "，", "经济实惠", "。"]], "answers": ["英华融泰挺不错的，医生说还不错，是个老品牌。送了两套雾化配件，包括面罩、杯子、鼻插什么的特别全，可以调节雾量，这点很不错，机器运行有点声音，这个都这样可以接受，雾喷出来也挺细腻的，相信能带来好的效果。", "雾化器还是进口百瑞的好。", "台湾产的普元雾化器。质量不错，经济实惠。"], "entity_answers": [["英华融泰"], ["百瑞"], ["普元雾化器"]], "segmented_question": ["雾化器", "哪个", "牌子", "好"], "question_type": "ENTITY", "question_id": 46, "fact_or_opinion": "OPINION", "match_scores": [0.8251748251748252]}
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提供的一层额外安全保护,旨在确保只有您可以访问自己的帐户,即使其他人知道您的密码也是如此。", "有了双重认证,只能通过您信任的设备(如 iPhone、iPad 或 Mac)才能访问您的帐户。首次登录一部新设备时,您需要提供两种信息:您的密码和自动显示在您的受信任设备上的六位验证码。输入验证码后,您即确认您信任这部新设备。例如,如果您有一部 iPhone 并且要在新购的 Mac 上首次登录您的帐户,您将收到提示信息,要求您输入密码和自动显示在您 iPhone 上的验证码。", "由于只输入密码不再能够访问您的帐户,因此双重认证显著增强了 Apple ID 以及所有通过 Apple 储存的个人信息的安全性。", "登录后,系统将不会再次要求您在该设备上输入验证码,除非您完全注销帐户、抹掉设备数据或出于安全原因而需要更改密码。当您在 Web 上登录时,可以选择信任您的浏览器,这样当您下次从该电脑登录时,系统就不会要求您输入验证码。", "受信任的设备是您已使用双重认证登录的设备,可以是装有 iOS 9 及更高版本的 iPhone、iPad 或 iPod touch 或者装有 OS X El Capitan 及更高版本的 Mac。我们知道这是您的设备,并且当您在其他设备或浏览器上登录时,这个受信任设备将显示 Apple 提供的验证码来验证您的身份。", "受信任的电话号码是指可通过短信或电话接收验证码的号码。您必须至少验证一个受信任的电话号码才能注册双重认证。", "在中国,您的其中一个受信任的电话号码必须是在中国注册且国家/地区代码是 +86 的电话号码。您还应考虑验证您可以使用的其他电话号码,如家庭电话号码或亲朋好友所用的号码。当您暂时无法使用自己的设备时,就可以使用这些号码。", "验证码是您使用 Apple ID 登录新设备或浏览器时,发送到您的受信任设备或电话号码的临时代码。您也可以从受信任设备的“设置”中获取验证码。", "验证码不同于您输入以用于解锁 iPhone、iPad 或 iPod touch 的设备密码。", "如果iCloud 用户至少拥有一个装有 iOS 9 或者 OS X El Capitan 或更高版本的设备,现在就可以使用双重认证。进一步了解。", "您可以在 iPhone、iPad 或 iPod touch 上按照以下步骤来开启双重认证。", "如果使用的是 iOS 10.3 或更高版本:", "前往“设置”>“[您的姓名]”>“密码与安全性”。 轻点“打开双重认证”。 轻点“继续”。", "如果使用的是 iOS 10.2 或更低版本:", "前往“设置”>“iCloud”。 轻点您的 Apple ID >“密码与安全性”。 轻点“打开双重认证”。 轻点“继续”。", "系统可能会要求您回答 Apple ID 安全提示问题。", "输入您想要在登录时用来接收验证码的电话号码。您可以选择是通过短信还是通过电话接收验证码。", "轻点“下一步”后,Apple 会向您提供的电话号码发送验证码。", "输入验证码以验证您的电话号码,然后开启双重认证。", "在装有 OS X El Capitan 或更高版本的 Mac 上,请按照以下步骤操作:", "前往苹果 () 菜单 >“系统偏好设置”>“iCloud”>“帐户详细信息”。 点按“安全性”。 点按“打开双重认证”。", "在iOS 10.3 或 macOS 10.12.4 及更高版本中创建的某些 Apple ID 默认受双重认证保护。如果是这样,您会发现这些设备上已开启双重认证。", "如果您已经在使用两步验证并想要升级,请将两步验证关闭,然后开启双重认证。", "如果您的帐户不符合双重认证条件,您仍可使用两步验证来保护您的信息。", "双重认证会显著提升 Apple ID 的安全性。开启双重认证后,您需要使用密码以及受信任设备或受信任的电话号码才能登录您的帐户。为了尽可能保护帐户的安全性并确保您永远不会失去访问权限,您应遵循以下几条简单准则:", "牢记Apple ID 密码。 在您的所有设备上均使用一个设备密码。 确保受信任的电话号码始终为最新号码。 确保受信任设备的安全。", "您可以通过 Apple ID 帐户页面管理受信任的电话号码、受信任的设备以及其他帐户信息。", "要使用双重认证,您需要至少登记一个受信任的电话号码以便接收验证码。您可以按照以下步骤来更新受信任的电话号码:", "前往您的 Apple ID 帐户页面。 使用您的 Apple ID 登录。 前往“安全”部分,然后点按“编辑”。", "如果要添加电话号码,请点按“添加受信任的电话号码”,然后输入电话号码。选择是以短信还是电话方式验证号码,然后点按“继续”。要移除受信任的电话号码,请点按要移除的电话号码旁边的 。在中国,您的其中一个受信任的电话号码必须是在中国注册且国家/地区代码是 +86 的电话号码。", "您可以在您的 Apple ID 帐户页面的“设备”部分中查看并管理受信任设备的列表。", "前往您的 Apple ID 帐户页面。 使用您的 Apple ID 登录。 前往“设备”部分。", "设备列表将显示您当前使用 Apple ID 登录的设备。选择一台设备以查看型号、序列号和其他有用信息,包括该设备是否受信任以及是否可以接收 Apple ID 验证码。", "您还可以移除受信任设备。移除受信任设备是为了确保它不再显示验证码,以及阻止其访问 iCloud 以及设备上的其他 Apple 服务(除非您再次使用双重认证登录)。如果需要查找设备或抹掉设备数据,然后将其从受信任设备列表中移除,您可以使用“查找我的 iPhone”。", "开启双重认证后,您需要使用应用专用密码在非Apple 提供的第三方应用或服务(如电子邮件、通讯录或日历应用)中登录您的帐户。请按照以下步骤生成应用专用密码:", "登录Apple ID 帐户页面。 点按“应用专用密码”下的“生成密码”。 按照屏幕上的步骤操作。", "生成应用专用密码后,请像往常一样将它输入或粘贴到相关应用的密码栏位中。", "需要帮助?您可从下文中找到您问题的答案。", "是的。双重认证是一种直接内建在 iOS、macOS、tvOS、watchOS 和 Apple 网站中的新服务。它使用不同的方法来对设备授信和发送验证码,而且提供了更为简化的用户体验。要使用一些具有较高安全要求的功能,必须通过双重认证。", "以前的两步验证功能会继续为已注册的用户单独提供服务。", "您可以按照以下步骤,通过受信任的 iPhone、iPad 或 iPod touch 来重设或更改密码:", "前往“设置”>“[您的姓名]”。如果使用的是 iOS 10.2 或更低版本,请前往“设置”>“iCloud”> 轻点您的 Apple ID。 轻点“密码与安全性”。 轻点“更改密码”。 输入新密码。", "这些步骤只能在已启用设备密码的受信任设备上完成。如果您无法访问受信任设备,请前往 iforgot.apple.com,输入您的 Apple ID,然后按照提示重设密码。如果需要帮助,请尝试这些步骤。", "如果您要登录,但是手边没有可显示验证码的受信任设备,您可以选择以短信或电话方式将验证码发送至受信任的电话号码。点按登录屏幕上的“没有收到验证码吗”,然后选择将验证码发送至受信任的电话号码。您还可以直接从受信任设备的“设置”中获取验证码。了解如何获取验证码。"], "bs_rank_pos": 4}], "answer_spans": [[0, 93]], "fake_answers": ["1、首先登入以下网址:appleid.apple.com在打开的页面上点击【管理您的AppleID】2、然后登入账号和密码3、然后需要发送验证码到手机或者绑定的设备4、然后点击【密码和账户安全】5.在右侧拉到最下面,可以看到【关闭两步验证】6.然后需要重新设置安全问题,出生日期和救援邮箱7.然后确认关闭两步验证8、关闭成功"], "question": "双重验证怎么关闭", "segmented_answers": [["1", ".", "首先", "登入", "以下", "网址", "：", "appleid", ".", "apple", ".", "com", "。", "在", "打开", "的", "页面", "上", "点击", "【", "管理", "您", "的", "Apple", "ID", "】", "2", ".", "然后", "登入", "账号", "和", "密码", "。", "3", ".", "然后", "需要", "发送", "验证码", "到", "手机", "或者", "绑定", "的", "设备", "。", "4", ".", "然后", "点击", "【", "密码", "和", "账户", "安全", "】", "。", "5", ".", "在", "右侧", "拉", "到", "最", "下面", "，", "可以看到", "【", "关闭", "两", "步", "验证", "】", "。", "6", ".", "然后", "需要", "重新", "设置", "安全问题", "，", "出生日期", "和", 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在打开的弹窗中,我们勾选我们需要清理的选项,然后勾选下方的”每次关闭浏览器后自动清除所有数据“,如下图所示。 步骤阅读 6 然后点击立即清除,就可以将搜狗高速浏览器设置为无痕模式了。 7 在没有关闭浏览器前,我们也是可以查看我们的历史记录的,同时也可以清除我们的历史记录。 在”工具“菜单栏下,我们可以看到”历史记录\",如下图所示。 步骤阅读 8 点击”历史记录“,我们进入到“历史记录”的页面,如下图所示。 步骤阅读 9 在历史记录的页面,我们直接点击清除历史记录就可以了。 步骤阅读 END", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:平面设计学习,平面设计爱好者", "ps制作烟花效果动态图,gif动态... 零基础学AI绘图:怎么绘制卡通女..."], "bs_rank_pos": 2}, {"is_selected": false, "title": "搜狗浏览器怎么设置无痕浏览模式?搜狗浏览器无痕浏览功能使用方法_", "most_related_para": 3, "segmented_title": ["搜狗", "浏览器", "怎么", "设置", "无痕", "浏览", "模式", "?", "搜狗", "浏览器", "无痕", "浏览", "功能", "使用方法", "_"], "segmented_paragraphs": [["搜狗", "浏览器", "无痕", "浏览", "是", "一", "种", "关闭", "浏览器", "后", "就会", "立即", "清除", "浏览", "记录", "的", "功能", ",", "搜狗", "浏览器", "是", "我们", "经常", "使用", "的", "一", "款", "浏览器", "。", "如果", "我们", "不", "想", "让", "别人", "看到", "我", "浏览", "过", "什么", "网页", ",", "我们", "可以", "设置", "浏览器", "无痕", "浏览", ",", "下面", "小", "编", "就", "告诉", "大家", "怎么", "设置", "搜狗", "浏览器", "无痕", "浏览"], ["搜狗", "浏览器", "是", "我们", 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"用户", "”"]], "paragraphs": ["搜狗浏览器无痕浏览是一种关闭浏览器后就会立即清除浏览记录的功能,搜狗浏览器是我们经常使用的一款浏览器。如果我们不想让别人看到我浏览过什么网页,我们可以设置浏览器无痕浏览,下面小编就告诉大家怎么设置搜狗浏览器无痕浏览", "搜狗浏览器是我们经常使用的一款浏览器。如果我们不想让别人看到我浏览过什么网页,我们可以设置浏览器无痕浏览,下面小编就告诉大家怎么设置搜狗浏览器无痕浏览", "软件名称: 搜狗浏览器 搜狗高速浏览器 V6.4.6.21806 正式官方安装版 目前最快速最流畅的浏览器 软件大小: 43.4MB 更新时间: 2016-08-29", "步骤一:我们首先打开浏览器,点击“工具”找到“搜狗高速浏览器选项” 步骤二:进入“搜狗高速浏览器选项”找到”隐私保护选项“在右侧可以看到“每次关闭浏览器清除”我们把他打勾,然后点击“应用”点击“确定”。 步骤三:然后重启我们的浏览器再次开启后就看不到之前的浏览记录了。", "如果你有时候不想清除我们可以勾选下面的“每次关闭浏览器清楚提示用户”"], "bs_rank_pos": 3}, {"is_selected": false, "title": "搜狗浏览器如何打开隐私窗口?搜狗浏览器的无痕浏览在哪? - 比克尔", "most_related_para": 2, "segmented_title": ["搜狗", "浏览器", "如何", "打开", "隐私", "窗口", "?", "搜狗", "浏览器", "的", "无痕", "浏览", "在", "哪", "?", "-", "比", "克", "尔"], "segmented_paragraphs": [["您", "当前", "所在", "位置", ":", "资讯", "教程", "中心", "-", ">", "软件", "教程", "-", ">", "工具", "软件", "-", ">", "搜狗", "浏览器", "如何", "打开", "隐私", "窗口", "?", "搜狗", "浏览器", "的", "无痕", "浏览", "在", "哪", "?"], ["无论", "是", "哪款", "浏览器", "都会", "拥有", "一", "个", "无痕", "模式", ",", 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"首先打开搜狗高速浏览器，打开搜狗高速浏览器后，在右上方找到：工具，如下图所示。点击菜单栏中的工具，点击工具后，可以打开工具的下拉菜单，在下拉菜单中，找到清除浏览记录。找到清除浏览记录“后，点击”清除浏览记录，在打开的弹窗中，勾选我们需要清理的选项，然后勾选下方的每次关闭浏览器后自动清除所有数据，然后点击立即清除，就可以将搜狗高速浏览器设置为无痕模式了。在工具菜单栏下，可以看到历史记录。点击历史记录，进入到“历史记录”的页面，在历史记录的页面，直接点击清除历史记录就可以了。"], "answer_docs": [1], "segmented_question": ["搜狗", "浏览器", "无痕", "浏览", "怎么", "开启"], "question_type": "DESCRIPTION", "question_id": 48, "fact_or_opinion": "FACT", "match_scores": [0.8284023668639054]}
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"临床", "说", "有", "一些人", ",", "因为", "虚火", "旺", "盛", ",", "也", "出现", "了", "一些", "在", "下", "焦", "这块", "有", "虚火", ",", "再加上", "吃", "一些", "辛辣", "食品", "等等", ",", "结果", "又", "有", "一些", "湿热", ",", "湿热", "和", "这种", "虚火", "交织在一起", ",", "就", "出现", "了", "前列腺炎", "的", "症状", ",", "尿频", "等", "的", "一些", "情况", ",", "小便", "黄", ",", "甚至", "也有", "一些", "小腹", "坠", "胀", "的", "一些", "情况", "。", "我们", "在", "治疗", "慢性前列腺炎", "过程", "中", ",", "如果", "有", "肾虚", "的", "情况", ",", "我们", "就", "应该", "考虑", "进去", ",", "这", "就是", "中医", "治病", "的", "特征", "所在", "。"]], "paragraphs": ["肾阴阳两虚型编辑 锁定", "肾虚主要是四方面,一个是肾阴虚,一个是肾阳虚,一个是肾精亏虚,一个是肾气虚,临床上更多的是肾阴虚和肾阳虚,肾阴虚当然是肾虚的一种,而不是肾虚都是肾阴虚,肾虚是一种总称,肾阴虚和肾阳虚都是肾虚的一部分,代表了某一种症型。", "中文名 肾阴阳两虚型  四方面 一个是肾阴虚,一个是肾阳虚", "原因 肾阴是物质上的缺乏  治疗方法 多吃一些补肾阴的药物", "1 区别  2 治疗方法  3 分类特征", "肾阴虚和肾阳虚的区别在于,肾阴是物质上的缺乏,肾阳就是功能上的匮乏,物质上的缺乏就是腰膝酸软,头晕,还有如果在年轻人的身上出现早泄、遗精,还有出现五心烦热,失眠、心烦,中医有阴虚生内热,阳虚生外寒,如果在老年人出现的肾阴虚就容易脱发或发白的比较早,容易耳鸣、耳背,牙齿松动,眼花的比较早一些,所以说,这是肾阴虚,肾阴虚一般说在中青年发生的比较多,我们在临床上的症状比较多,那么我再说肾阳虚,肾阳虚有一些症状和肾阴虚也是有共性的,比如说腰膝酸软,四肢乏力,但是肾阳虚更体现是功能性的所谓出现的四肢不温,那个是五心烦热,这个是手脚发凉,面色容易有一些黄白畏寒怕冷,如果是中青年出现性欲减退、阳痿,人感觉经常有一种神气疲惫的感觉,因为是功能性的,更多的体现在这方面,我们说肾阳虚和肾阴虚不是截然分开的,在临床上经常遇到这样的人,我究竟是肾阴虚还是肾阳虚,我就讲了,我说肾阴虚、肾阳虚也好,如果匮乏到一定程度了,就是阴损积阳,阳损积阴,肾阳虚时间长了会有肾阴虚的症状,所以我们再补肾阳的时候,一定要补肾阴,而且要阴中求阳,而且在补肾阴的时候也不是一味的只用补肾阴的药物。", "六味地黄丸是宋朝一个大医学家耳科专家叫钱乙为小孩创立的一个方子,主要儿童囟门不合,说话晚等等,小儿发育不好,畸形龟背,他认为是肾阴虚所造成的,实际脱胎于汉朝,去调了肉桂,热性就少了,我们社会发展到今天了,一般说,我们在衣食温饱都解决了,我们的住的房屋都比较好,穿戴也比较好,阳气虚的就比较少了,但是我们比较劳累,物质的匮乏就显示出来了,所以现代的男人主要是肾阴虚的倾向,所以说滋阴补肾,阴中求阳是中医补肾的主要切向, 中医认为,任何事物都分为阴阳两个方面,人的肾也有肾阴和肾阳之分,肾功能障碍产生的原因可能是肾阴虚造成的,也可能是肾阳虚造成的,所以,补肾要查明原因,分型而补。", "提示:肾阴虚的临床表现:1.腰酸腿软。2.口干、烦躁。3.手心发热。4.爱出汗。", "由肾阴虚导致性功能障碍的男性,平常可以多吃一些补肾阴的药物,另外食补也是很好的办法,例如,用桑葚子、枸杞煮粥,也有不错的效果。", "肾阳虚首先会出现腰痛,因为腰为肾之府,这种痛一般会有冷的感觉。病人痛的同时,觉得腰里面发凉,这是肾阳虚的一个典型表现。另外,它扩展到全身,会出现全身也怕冷。因为肾阳是一身阳气的根本,如果根虚弱了,整个身体的阳气的温煦作用就会下降。", "提示:肾阳虚的临床表现:1.腰痛而且发凉。2.手脚冰凉、尿频。", "肾阳虚的人适合吃的食物有海产品、韭菜籽。", "肾阴虚的情况下火就容易旺,阴虚火旺,如果这时再使用那些温热的壮阳药物,等于是火上浇油,病人热性就更大了。反过来说,假如病人肾阳虚、怕冷,你再用一些滋阴药,等于是雪上加霜,病人同样也受不了。", "肾阴虚、肾阳虚是中医的一个术语,肾阴虚和肾阳虚是指中医讲的肾脏功能方面的症,我们平常讲肾虚,实际包括了肾阴虚和肾阳虚,也包括了阴阳两虚,但是现在在宣传上不是很细致,所以说解释不清,把有些问题混淆了。我简单的讲一下,这个涉及到中医基础理论,内容很多。肾阴虚是一个物质的,肾阴和肾经是属于物质的,肾阳和肾气是功能的。如果我们消耗的物质比较多,比如说肾精这块,或者是性生活过于频繁,或者是由于劳动,包括脑力、体力的过度,或者是生下来以后身体就弱,这样他的物质基础比较低,而出现的一些症状,比如说出现了一些腰膝酸软、四肢乏力、头晕耳鸣,包括一些脱发,还有一些牙齿松动,记忆力减退,容易衰老的也比较早,也还包括临床上的性欲减退,还有容易遗精、早泄等等,这都归为肾阴虚的症状,肾阴虚还容易出现虚热,中医有一个理论阴虚生内热,阳虚生外寒,所以有时候容易出现五心烦热,就是两个手心,两个脚心,心口感觉热的感觉,还有睡觉的时候,容易盗汗,就是睡着了,一醒身体有汗。", "他的特点,肾阴虚多发生在中青年的时候,中青年活动量比较大,无论是学习、锻炼,精力上物质耗损比较多,对性功能这块,性欲不是特别低,但是射精容易快,有遗精现象。那么肾阳虚,大多数多少功能性的差,也有和肾阴虚相同的情况,比如说腰膝酸软,四肢乏力,还有性欲减退,但是因为它是阳分虚了,肾气虚了,所以出现一些怕冷的一些症状,中医有句话,阳虚生外寒,这样的人面色比较虚白,比较畏寒怕冷,手脚冰凉、小便清长,大便汤薄。这种人性欲比较低,和现代医学说的雄性激素低有相同之处。所以阳虚表现的是外在的,一般也可能由于身体比较弱,有肾阳虚的情况,但是大多数的是在中老年容易出现这种肾阳虚的情况,中青年肾阴虚比较多。", "所以说,我想是那样,一般就记住了,这两个特征,一个是年龄上的特征,肾阴虚多发生在中青年,肾阳虚多发生在中老年,肾阴虚容易有虚热的一些症状,肾阳虚有怕冷的症状。在性功能这块,肾阴虚是容易早泄遗精的比较多,肾阳虚的阳痿的比较多。当然有共同之处,比如说腰酸乏力,四肢酸软等等,这个是共同的症状。前列腺炎是一个现代医学术语,和这种肾阴虚、肾阳虚有交叉的地方,确实在临床说有一些人,因为虚火旺盛,也出现了一些在下焦这块有虚火,再加上吃一些辛辣食品等等,结果又有一些湿热,湿热和这种虚火交织在一起,就出现了前列腺炎的症状,尿频等的一些情况,小便黄,甚至也有一些小腹坠胀的一些情况。我们在治疗慢性前列腺炎过程中,如果有肾虚的情况,我们就应该考虑进去,这就是中医治病的特征所在。"], "bs_rank_pos": 0}, {"is_selected": true, "title": "【肾阴阳两虚怎么补】阴阳两虚能同时补吗 -有问必答-快速问医生", "most_related_para": 2, "segmented_title": ["【", "肾", "阴阳", "两", "虚", "怎么", "补", "】", "阴阳", "两", "虚", "能", "同时", "补", "吗", "-", "有问必答", "-", "快速", "问", "医生"], "segmented_paragraphs": [["您", "的", "位置", ":", "首页", "→", "热门", "问答", "→", "肾", "阴阳", "两", "虚", "怎么", "补"], ["经常", "感冒", 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"会导致", "尿频", "、", "尿急", "和", "尿", "不", "净", "等", "症状", ",", "一般", "伴有", "阴虚", "的", "症状", "意见建议", ":", "你", "的", "情况", "我", "月", "建议", "首先", "就要", "注意休息", ",", "清淡", "饮食", ",", "多吃蔬菜", "和", "水果", ",", "注意", "外阴", "卫生", ",", "不要", "长时间", "坐", "着", ",", "先", "服药", "治疗", ",", "比如", "服用", "氧氟沙星", "片", "和", "清开灵", "颗粒", "和", "每天", "服用", "100", "克", "南瓜子", "治疗", ",", "实在", "不行", "的", "话", "最好", "到", "医院", "进行", "输", "注", "菌必治", "和", "甲硝唑", "治疗", ",", "多", "参加", "锻炼", "很重要"], ["你", "讲", "的", "如此", "多", "的", "病症", "和", "手淫", "史", "密切相关", "原因", "先前", "的", "医生", "已经", "回答", "不再赘述", "至于", "你", "的", "这个", "年龄", "的", "性交", "次数", "应", "为", "每隔", "3", "天", "或", "以上", "一", "回", "建议", "在", "治疗", "期间", "杜绝", "手淫", "花钱", "少", "的", "方法", "很简单", "第", "一", "是", "炎症", "就", "得", "抗炎", "因", "前列腺", "有", "软膜", "虽然", "药物", "很难", "到达", "但是", "可以", "有", "针对性", "的", "用", "你", "的", "情况", "可用", "左", "客", "的", "抗生素", "口服", "治疗", "是", "泌尿系感染", "的", "金", "标准", "的", "抗生素", "第", "二", "为", "避免", "多", "花钱", "不", "建议", "再", "做", "手术", "治疗", "那个", "抗生素", "肾", "功", "可能会", "有些", "损害", "所以", "建议", "联合", "中药治疗", "减毒", "而", "增效", "可以", "口服", "金", "贵", "肾气", "丸", "是", "张仲景", "的", "古方", "其", "妙难言", "需", "口服", "月", "余", "买", "同仁堂", "的", "最好", "三", "保持", "良好", "心态", "多", "运动", "多", "吃", "水果", "尿", "黄", "可以", "喝", "点", "啤酒", "或者", "口服", "氢氯噻嗪", "一", "片", "一", "日", "就", "行", "15", "天", "即", "止", "四", "阴"]], "paragraphs": ["您的位置:首页→ 热门问答→ 肾阴阳两虚怎么补", "经常感冒、阴阳两虚医生你好,大夫说我因经常熬夜,吸烟喝酒,不注意锻炼,引起抵抗力差,阴阳两虚、气虚,是经常感冒、上火的主要原因。应该服用中药调理,我觉得也很有道理,您能为我开个中药方吗?请问六味地黄丸、知柏地黄丸和金匮肾气丸能同时服用,同时补阴虚和阳虚吗?您能为我开个中药方吗?请问六味地黄丸、知柏地黄丸和金匮肾气丸能同时服用,同时补阴虚和阳虚吗?", "病情分析:阴阳两虚:既有阴虚又有阳虚,称阴阳两虚,其主要表现为;既怕冷又怕热,冬天特别怕冷,夏天又特别怕热,这是阴阳失调或阴阳两虚之体质,进补宜采用阴阳并补,养阴温阳和滋阴壮阳等补法 意见建议:补阴虚食品:甲鱼、燕窝、百合、鸭肉、黑鱼、海蜇、藕、金针菇、枸杞头、荸荠、生梨等,可经常交替选服。 补阴虚药膳食疗方举例: 银耳红枣羹(或百合莲子羹):银耳、红枣(或百合、莲子)适量共煮羹当点心服食,可补阴虚。 甲鱼二子汤:甲鱼1只与女贞子、枸杞子各20g同煮汤,加调味,食甲鱼饮汤,连食数剂,可补阴虚和治肝肾阴虚所致的腰痛,遗精、头晕、目花等症。 石斛河鱼;石斛6g,河鱼1条共蒸食,可滋阴。 虫草老雄鸭", "病情分析:根据你的症状,经常感冒,抵抗力差,平时还有经常熬夜,吸烟喝酒的习惯,诊断你为阳虚是可以的,但是仅凭你爱上火就说你有阴虚,感觉欠妥意见建议:我感觉你现在最主要的还是阳虚,虽然有阴阳两虚的,可以同时阴阳双补,但是你应该先补阳,阳气升起来了,阴液自然就会慢慢恢复正常,况且金匮肾气丸就是在六味地黄丸的基础上加了少量肉桂和附子,取‘少火生气’的意思,所以六味地黄丸、知柏地黄丸和金匮肾气丸你不用同时服用,只服用一段时间金匮肾气丸就可以了,祝你早日康复", "的中药好多了,就是现在稍微吃点油腻的或者是凉的,早上呕吐的时候就能看见胆汁色,我人为是脾虚导致的胆汁反流性胃炎(去医院做过胃镜)之后又去把脉,医生说胆汁反流性胃炎可以了,得平时注意点保养,然后把脉说肾阴阳两虚(之前经常手淫过)然后当时去看的时候咽喉发炎。。是不是虚火导致的? 大便不是很成型,有点胆汁色,舌苔中间有点胆汁色 舌头有齿痕 应该是脾虚,想问下 脾虚肾阴阳两虚 到底该怎么办?而且现在脖子以上发热腰以下是冷的 是不是所谓的心肾不交?每次睡觉起来都没精神手心和脚心发热出汗,口干,喝水之后也口干,身上也没事就出汗,该怎么办?现在老中医给我开阴阳双补的中药 只是还没开始喝药呢!谁能给个合理补身体的方案!!跪求吃过中药 胆汁反流性胃炎好转,没有根治,只能抱养,现在阴阳两虚 开始补肾 医生给了个海狗肾宝丸 吃完了 感觉有点用处 只是现在老中医给我开的阴阳双补的中药还没吃呢!现在一直在喝普洱茶 感觉胆汁反流性胃炎好多了!应该有点用,然后每天下午4点用买的枸杞放在舌头下面含着,能治疗口干的问题 请给个明确的方案想吃过中药 胆汁反流性胃炎好转,没有根治,只能抱养,现在阴阳两虚 开始补肾 医生给了个海狗肾宝丸 吃完了 感觉有点用处 只是现在老中医给我开的阴阳双补的中药还没吃呢!", "病情分析:你好,胃病需要养,慢性胃炎是个长期的过程,也需要你长期注意保养。具体来说就是,饮食要有规律,一日三餐准时均匀,很多人不吃早饭,这个习惯是非常对胃病不利的。食物中,生,冷,硬,辣,的少吃,甚至不吃。指导意见:这个需要点毅力,不吃辣椒,而且再热的天坚决不喝冰水,不吃冰激凌。少和碳酸饮料,少和茶。睡觉前喝杯热牛奶。如此保养长期形成习惯后你的胃炎自然就好了! 提问人的追问 2013-07-17 10:31:17我想问下治疗方案优先治疗什么都需要注意什么!", "病情分析:1,反流性胃炎的表现,胃病的治疗,用药时间较长,因为每天三次刺激胃酸分泌.所以治疗的关键是按疗程治疗,建议如下治疗;;指导意见:奥美拉唑胶囊.每天2次,早晚饭前服用,吗丁啉片.1片,三次.饭前服用,糖铝片,4片,三次,饭前服用.多酶片,三片,三次.饭前服用.加上氨苄青霉素胶囊,2片,三次.饭前服用.甲硝唑片,2片,三次.饭前服用.最多服用一周可有明显效果,建议连续服用一个月,然后再复查. 提问人的追问 2013-07-17 10:30:25我想问下治疗方案优先治疗什么都需要注意什么!", "口臭,不敢吃凉的,吃了恶心,立刻就拉肚子。口有时苦,就是臭,还有肝火,肾阴阳两虚,舌体胖大有齿痕,请问吃啥药去掉臭味呢?谢谢。女49岁:需要医生帮助提供远程诊断:需要医生帮助提供远程诊断", "病情分析:你好:这种情况是与内火大有关。指导意见:建议:你吃点健胃消食片、附子理中丸、龙胆泻肝丸试试,不要吃生冷食物。喝点菊花茶。", "病情分析:口臭多是因为消化系统胃肠道出现机能紊乱,肠胃失调引起的。指导意见:建议:生姜大枣煮水喝,晚上睡觉前逆时针揉揉肚子,多吃蔬菜水果,饮食清淡,保证大便正常。", "腰酸痛腿软,气短乏力无精神易困倦,心悸喘咳,小便清长,冬天手脚一天到晚是冰凉的,心跳快,健忘,头眩晕时有耳鸣,失眠多梦,遗精,面色黄,眼圈深黑,脱发。阴囊潮湿有异味,阴茎举而不坚、早泄,尿黄,尿口红,尿灼热,尿频短少,尿无力滴沥,射精无力已经有4年以上的时间了,以前有手淫史前年在医院用前列腺可视枪介入法治疗过几次,每次做过后医生都说差不多快好了。做了几次钱花了近2万还没好我就没信心了就放弃治疗了。我想是不是要先治好肾虚才能治好前列腺吧帮想个找种最方便,花钱少的方法。中医优先,最好能开个妙方,西医也可。", "病情分析:你好,你的情况很有可能是由于慢性前列腺炎引起的症状,一般会导致尿频、尿急和尿不净等症状,一般伴有阴虚的症状意见建议:你的情况我月建议首先就要注意休息,清淡饮食,多吃蔬菜和水果,注意外阴卫生,不要长时间坐着,先服药治疗,比如服用氧氟沙星片和清开灵颗粒和每天服用100克南瓜子治疗,实在不行的话最好到医院进行输注菌必治和甲硝唑治疗,多参加锻炼很重要", "你讲的如此多的病症 和手淫史密切相关 原因先前的医生已经回答 不再赘述 至于你的这个年龄的性交次数应为 每隔3天或以上一回 建议在治疗期间杜绝手淫 花钱少的方法 很简单 第一 是炎症 就得抗炎 因前列腺有软膜 虽然药物很难到达 但是可以有针对性的用 你的情况可用 左客 的抗生素口服治疗 是泌尿系感染的金标准的抗生素 第二 为避免多花钱 不建议再做手术治疗 那个 抗生素肾功可能会有些损害 所以建议联合中药治疗 减毒而增效 可以口服 金贵肾气丸 是张仲景的古方 其妙难言 需口服月余 买同仁堂的最好 三保持良好心态 多运动 多吃水果 尿黄可以喝点啤酒或者 口服氢氯噻嗪 一片一日就行 15天即止 四阴"], "bs_rank_pos": 1}, {"is_selected": false, "title": "肾阴阳两虚治疗,阴阳双补 ", "most_related_para": 12, "segmented_title": ["肾", "阴阳", "两", "虚", "治疗", ",", "阴阳", "双", "补"], "segmented_paragraphs": [["肾", "阴阳", "两", "虚", "则", "有", "五心烦热", "、", "盗汗", "或", "自汗", "、", "四肢", "发", "凉", "、", "遗精", "失眠", "、", "多梦", "、", "舌", "红", "无苔", "、", "脉", "细", "数", "或", "舌", "淡", "苔", "白", "、", "脉", "沉", "迟", "。", "肾", "的", "阴阳", "俱", "虚", ",", "以", "畏冷", "肢", "凉", ",", "五心烦热", ",", "眩晕", "耳鸣", ",", "腰膝酸痛", ",", "男子", "遗精", "早泄", ",", "女子", "经", "少", "难", "孕", ",", "尺", "脉", "弱", "等", "为", "常见", "症", "的", "证", "候", "。"], ["中医", "认为", ",", "任何", "事物", "都", "分为", "阴阳", "两", "个", "方面", ",", "人", "的", "肾", "也有", "肾阴", "和", "肾阳", "之", "分", 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"的", "效果", "。"], ["肾阳虚", "首先", "会出现", "腰痛", ",", "因为", "腰", "为", "肾", "之", "府", ",", "这种", "痛", "一般", "会", "有", "冷", "的", "感觉", "。", "病人", "痛", "的", "同时", ",", "觉得", "腰", "里面", "发", "凉", ",", "这", "是", "肾阳虚", "的", "一", "个", "典型", "表现", "。", "另外", ",", "它", "扩展", "到", "全身", ",", "会出现", "全身", "也", "怕冷", "。", "因为", "肾阳", "是", "一", "身", "阳气", "的", "根本", ",", "如果", "根", "虚弱", "了", ",", "整个", "身体", "的", "阳气", "的", "温煦", "作用", "就会", "下降", "。"], ["肾阳虚", "的", "人", "适合", "吃", "的", "食物", "有", "海产品", "、", "韭菜籽", "。", "补肾", "可以", "选用", "金匮肾气丸", "、", "五子", "衍", "宗", "丸", "等", "。"], ["肾阴虚", "的", "情况", "下", "火", "就", "容易", "旺", ",", "阴虚火旺", ",", "如果", "这时", "再使用", "那些", "温", "热", "的", "壮阳", "药物", ",", "等于", "是", "火上浇油", ",", "病人", "热", "性", "就", "更大", "了", "。", "反过来说", ",", "假如", "病人", "肾阳虚", "、", "怕冷", ",", "你", "再", "用", "一些", "滋阴", "药", ",", "等于", "是", "雪上加霜", ",", "病人", "同样", "也", "受不了", "。"], ["治疗", "法则", ":", "补肾", "温", "阳", "滋阴", ",", "阴阳", "气血双补", "。"], ["1", ".", "肾", "内", "藏", "元", "阴", "元阳", ",", "为", "水火", "之", "脏", ",", "宜", "于", "封", "藏", ",", "忌", "于", "泄漏", ",", "故", "肾病", "多", "见", "虚", "证", "。", "肾", "之", "阴阳", "两", "虚", ",", "多", "由", "阴虎", "或", "阳虚", "证", ",", "日久", "发展", "所致", "。", "治疗", "之", "时", ",", "还", "应", "明确", "阴虚", "偏重", ",", "抑或", "阳虚", "偏重", ",", "然后", "确定", "在", "阴阳", "双", "补", "之", "时", ",", "以", "滋阴", "为", "主", ",", "或", "以", "温", "阳", "为", "要", "。", "2", ".", "肾", "之", "阴阳", "两", "虚", ",", "乃", "精气", "两", "伤", ",", "在", "临", "证", "之", "时", ",", "除", "用", "温补", "元阳", ",", "填补", "真", "阴", ",", "阴阳", "并", "补", "之外", ";", "还", "须", "取", "“", "精", "不足", "者", ",", "补", "之", "以", "味", "”", "之", "义", ",", "酌情", "加", "用", "紫河车", "、", "龟板胶", "、", "鹿角胶", "等", "血肉", "有情", "之", "品", ",", "以", "填补", "精血", "。", "由于", "肾", "与", "其他", "脏腑", "的", "关系", "至", "为", "密切", ",", "还", "须", "结合", "辨证", ",", "明确", "相关", "脏腑", "的", "病", "证", ",", "治", "肾", "及", "参", "治", "他", "脏", ",", "以", "利", "提高", "疗效", "。", "3", ".", "肾", "阴阳", "两", "虚", "为", "慢性", "虚弱", "性病证", ",", "病程", "冗长", ",", "病情", "复杂", ",", "难", "取", "速效", ",", "治疗", "用药", ",", "贵", "在", "坚持", ",", "只要", "辨证", "准确", ",", "应", "守", "方", "不", "移", ",", "不宜", "变动", "过", "多", "。", "综合", "疗法", ",", "摄", "生", "养", "性", ",", "饮食调理", "办", "不可少", "。"], ["肾气丸", "合", "龟", "鹿", "二", "仙", "膏", ",", "可以", "阴阳", "双", "补", "。"], ["龟", "鹿", "二", "仙", "膏", "为", "古方", ",", "明代", "(", "1569", "年", ")", "王", "三才", "所", "著", "《", "医", "便", "》", "记", "述", "详细", ",", "为", "胶", "剂", ",", "现", "被", "列入", "第一批", "国家", "非处方药", "目录", ",", "具有", "温", "肾", "益精", ",", "补气", "养血", "作用", ",", "临床", "用于", "久", "病", "肾虚", "、", "腰酸", "膝", "软", "、", "精血", "不足", "、", "遗精", "阳痿", "等", ",", "被", "称为", "“", "男", "版", "阿胶", "”", "。"], ["龟", "鹿", "二", "仙", "膏", "组", "方", "精", "当", ",", "由", "龟板", "、", "鹿角", "、", "党参", "、", "枸杞子", "四药", "组成", "。", "虚劳", "是", "虚", "损", "劳伤", "的", "意思", ",", "也是", "五脏", "诸", "虚", "不足", "而", "产生", "的", "多种疾病", "的", "概括", ",", "凡", "先天不足", ",", "后天", "失调", ",", "病", "久", "失", "养", ",", "正气", "损伤", ",", "久", "虚", "不复", ",", "表现", "各种", "虚弱", "证", "候", "的", "均", "属", "虚劳", "范畴", "。", "祖国医学", "认为", ",", "龟板", "通", "任脉", "而", "滋阴", "潜阳", ",", "益肾", "生津", ";", "鹿角", "以", "通", "督脉", "而", "壮阳", "补肾", ",", "强", "筋", "健", "骨", ";", "“", "二", "仙", "”", "乃", "“", "血肉", "有情", "之", "品", "”", ",", "合", "用", "则", "沟通", "任", "督", ",", "峻", "补", "阴阳", ";", "另", "配", "党参", "补气", "以", "健脾", "养胃", ",", "益", "肺", "生津", ";", "枸杞子", "养血", "以", "滋补", "肝肾", ",", "益精", "明目", "。", "全", "方", "合", "用", ",", "阳", "生", "阴", "长", ",", "气", "固", "血", "充", ",", "故", "对", "虚劳", "患者", "尤", "能", "取", "效", "。", "实验", "证明", "龟", "鹿", "二", "仙", "膏", "具有", "补血", "、", "补肾", "、", "壮阳", "、", "抗辐射", "、", "抗衰老", "作用", ",", "能", "提高", "细胞", "和", "体液免疫", "功能", "。", "龟", "鹿", "二", "仙", "膏", "的", "药理学", "、", "药效学", "研究", "为", "临床治疗", "诸", "虚", "百损", ",", "抗", "老", "防", "衰", "提供", "了", "科学依据", "。"], ["龟", "鹿", "二", "仙", "膏方", "药", ":", "由", "鹿角", "500", "克", "、", "龟板", "250", "克", "、", "枸杞", "100", "克", "、", "人参", "50", "克", ",", "先", "熬", "龟板", "、", "鹿角", "为", "胶", ",", "再", "将", "人参", "、", "枸杞", "熬", "膏", "和", "入", "而", "成", "。", "每", "晨", "酒", "服", "10", "克", "。"]], "paragraphs": ["肾阴阳两虚则有五心烦热、盗汗或自汗、四肢发凉、遗精失眠、多梦、舌红无苔、脉细数或舌淡苔白、脉沉迟。肾的阴阳俱虚,以畏冷肢凉,五心烦热,眩晕耳鸣,腰膝酸痛,男子遗精早泄,女子经少难孕,尺脉弱等为常见症的证候。", "中医认为,任何事物都分为阴阳两个方面,人的肾也有肾阴和肾阳之分,肾功能障碍产生的原因可能是肾阴虚造成的,也可能是肾阳虚造成的,所以,补肾要查明原因,分型而补。", "一.肾阴阳虚的临床表现 腰脊痠病,胫痠膝软,足跟痠痛,耳鸣耳聋,发焦脱落,齿摇稀松,遗精阳痿早泄,月经失调崩漏,或开心烦热,口干咽燥,失眠盗汗,或畏寒肢冷,面目虚浮,夜尿频数。舌红或淡,少苔,脉双尺沉细。主要症状如下: 二.肾阴阳两虚容易与哪些症状混淆? 俗称命门火衰,则有阳虚的临床表现:如畏寒、肢冷、小便清长、面色晃白、性欲减退、阳痿早泄、舌淡苔白、脉沉迟。  俗称肾水不足,除有肾虚的表现外还有阴虚的临床表现:如五心烦热、潮热盗汗、口干舌燥、尿黄便干、舌红少苔、脉细数。  气短自汗、倦怠无力、面色晃白、小便频多、遗精早泄、舌苔淡白、脉细弱。  则有五心烦热、盗汗或自汗、四肢发凉、遗精失眠、多梦、舌红无苔、脉细数或舌淡苔白、脉沉迟。 三.肾阴阳两虚的病因 中医认为:虚劳,消渴,阳痿,遗精,水肿,咳嗽,喘证,月经不调,崩漏,绝经前后诸证,西医:慢性气管炎,糖尿病,高血压,神经衰弱,肺结核,肾病综合征,慢性肾功能衰竭,甲状腺功能减退,再生障碍性贫血,月经失调,更年期综合征。 本证或由肾阴先虚,日久阴损及阳,发展而来,或由肾阳不足,日久阳损及阴,逐渐而成。 或由肾阴先虚,日久阴损及阳,发展而来,或由肾阳不足,日久阳损及阴,逐渐而成。肾为先天之本,藏真阴而寓元阳,若肾中元阳不足,命门火衰与肾中元阴不足,阴精亏损同时并见,即为肾阴阳两虚证。其常见原因为禀赋不足,或久病不愈,或房室劳伤,或老年休衰等,以致阴精、阳气两虑,不能濡养、温照脏腑经络,而形成此证。病仗在肾,其性质属虚,不过临床常见阴阳各有偏重的不同,这须权衡阴虎阳虚的程度而定。  四.肾阴阳两虚的诊断 肾阴阳两虚,则有五心烦热、盗汗或自汗、四肢发凉、遗精失眠、多梦、舌红无苔、脉细数或舌淡苔白、脉沉迟。传统医学所讲的“肾虚”概念中的“肾”,不仅指解剖学上的肾脏,而且是一个生理作用相当广泛,与人体生殖、生长发育、消化、内分泌代谢等都有直接或间接关系的重要脏器。“肾虚症状”也就是一宽泛的概念,他包括泌尿系统、生殖系统、内分泌代谢系统、神经精神系统及消化、血液、呼吸等诸多系统的相关疾病。所以,肾虚的症状很多,概括如下: 记忆力下降记忆力减退,注意力不集中,精力不足,工作效率降低。", "情绪不佳情绪常难以自控,头晕,易怒,烦躁,焦虑,抑郁等。", "3.意志方面表现为;", "信心不足,缺乏自信,工作没热情,生活没激情,没有目标和方向。", "4.性功能方面表现为:", "性功能降低男子性兴趣降低,性欲降低,阳萎或阳物举而不坚,遗精、滑精、早泄,显微镜检查可见精子减少或精子活动力减低,不育。女子子宫发育不良,如幼稚子宫、卵巢早衰闭经、月经不调,性欲减退,不孕等。", "尿频,尿等待,小便清长等症状。", "肾虚的症状还可能有早衰,健忘失眠,食欲不振,骨骼与关节疼痛,腰膝酸软,不耐疲劳,乏力,视力减退,听力衰减。脱发白发、头发脱落或须发早白,牙齿松动易落等。容颜早衰、眼袋、黑眼圈,肤色晦暗无光泽,肤质粗糙、干燥,出现皱纹,色斑,中年暗疮,肌肤缺乏弹性;嗓音逐渐粗哑,女性乳房开始下垂,腰、腹脂肪堆积;男性早秃等。", "一.中医治疗原则", "由肾阴虚导致性功能障碍的男性,平常可以多吃一些六味地黄丸类的补肾阴的药物,另外食补也是很好的办法,例如,用桑葚子、枸杞煮粥,也有不错的效果。", "肾阳虚首先会出现腰痛,因为腰为肾之府,这种痛一般会有冷的感觉。病人痛的同时,觉得腰里面发凉,这是肾阳虚的一个典型表现。另外,它扩展到全身,会出现全身也怕冷。因为肾阳是一身阳气的根本,如果根虚弱了,整个身体的阳气的温煦作用就会下降。", "肾阳虚的人适合吃的食物有海产品、韭菜籽。补肾可以选用金匮肾气丸、五子衍宗丸等。", "肾阴虚的情况下火就容易旺,阴虚火旺,如果这时再使用那些温热的壮阳药物,等于是火上浇油,病人热性就更大了。反过来说,假如病人肾阳虚、怕冷,你再用一些滋阴药,等于是雪上加霜,病人同样也受不了。", "治疗法则:补肾温阳滋阴,阴阳气血双补。", "1.肾内藏元阴元阳,为水火之脏,宜于封藏,忌于泄漏,故肾病多见虚证。肾之阴阳两虚,多由阴虎或阳虚证,日久发展所致。治疗之时,还应明确阴虚偏重,抑或阳虚偏重,然后确定在阴阳双补之时,以滋阴为主,或以温阳为要。 2.肾之阴阳两虚,乃精气两伤,在临证之时,除用温补元阳,填补真阴,阴阳并补之外;还须取“精不足者,补之以味”之义,酌情加用紫河车、龟板胶、鹿角胶等血肉有情之品,以填补精血。由于肾与其他脏腑的关系至为密切,还须结合辨证,明确相关脏腑的病证,治肾及参治他脏,以利提高疗效。 3. 肾阴阳两虚为慢性虚弱性病证,病程冗长,病情复杂,难取速效,治疗用药,贵在坚持,只要辨证准确,应守方不移,不宜变动过多。综合疗法,摄生养性,饮食调理办不可少。", "肾气丸合龟鹿二仙膏,可以阴阳双补。", "龟鹿二仙膏为古方,明代(1569年)王三才所著《医便》记述详细,为胶剂,现被列入第一批国家非处方药目录,具有温肾益精,补气养血作用,临床用于久病肾虚、腰酸膝软、精血不足、遗精阳痿等,被称为“男版阿胶”。", "龟鹿二仙膏组方精当,由龟板、鹿角、党参、枸杞子四药组成。虚劳是虚损劳伤的意思,也是五脏诸虚不足而产生的多种疾病的概括,凡先天不足,后天失调,病久失养,正气损伤,久虚不复,表现各种虚弱证候的均属虚劳范畴。祖国医学认为,龟板通任脉而滋阴潜阳,益肾生津;鹿角以通督脉而壮阳补肾,强筋健骨;“二仙”乃“血肉有情之品”,合用则沟通任督,峻补阴阳;另配党参补气以健脾养胃,益肺生津;枸杞子养血以滋补肝肾,益精明目。全方合用,阳生阴长,气固血充,故对虚劳患者尤能取效。实验证明龟鹿二仙膏具有补血、补肾、壮阳、抗辐射、抗衰老作用,能提高细胞和体液免疫功能。龟鹿二仙膏的药理学、药效学研究为临床治疗诸虚百损,抗老防衰提供了科学依据。", "龟鹿二仙膏方药:由鹿角 500克、龟板250克、枸杞100克、人参50克,先熬龟板、鹿角为胶,再将人参、枸杞熬膏和入而成。每晨酒服10克。"], "bs_rank_pos": 2}, {"is_selected": false, "title": "手淫引起的,肾阴阳两虚怎么调理_寻医问药网_xywy.com", "most_related_para": 18, "segmented_title": ["手淫", "引起", "的", ",", "肾", "阴阳", "两", "虚", "怎么", "调理", "_", "寻医问药", "网", "_", "xywy", ".", "com"], "segmented_paragraphs": [["手淫", "引起", "的", ",", "肾", "阴阳", "两", "虚", "怎么", "调理", "相关问题"], ["提问", "时间", ":", "2015", "-", "09", "-", "16", "10", ":", "42", ":", "39"], ["患者", "性别", ":", "男", "患者", "年龄", ":", "19", "-", "29"], ["补肾", ",", "由于", "肾", "有", "阴虚", "、", "阳虚", "、", "精", "虚", "、", "气虚", "的", "不同", ",", "补肾", "就", "有", "补肾", "阳", "、", "滋", "肾", "阴", "、", "益", "肾气", "、", "填", 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["手淫引起的,肾阴阳两虚怎么调理相关问题", "提问时间:2015-09-16 10:42:39", "患者性别:男患者年龄:19-29", "补肾,由于肾有阴虚、阳虚、精虚、气虚的不同,补肾就有补肾阳、滋肾阴、益肾气、填肾精等等不同的途径和不同的用药.然而在当前却有一种错误趋向,即保健品中以补虚为主,补虚以补肾为主,补肾又以补肾阳为主,导", "提问时间:2014-07-29 23:33:49", "患者性别:男患者年龄:27岁", "问题分析:手淫引起的早泄怎样调理?以前手淫着急射精引起的早泄,怎么调理啊意见建议:可以吃点中成药调理一下,常用的中成药有复方玄驹胶囊,固阳锁精丸,五子衍宗丸,参茸鞭丸等。中药副作用小,但服用的时间比", "提问时间:2015-09-14 05:47:45", "患者性别:男患者年龄:22", "病情分析: 你好:肾虚分肾阳虚与肾阴虚:肾阳虚的症状为腰酸,四肢发冷,畏寒,,也就是说表现为“寒”的症状, 肾阳虚可以吃狗肉,羊肉,韭菜,泥鳅来进行食补,药物则有肾宝,或金匮肾气丸, 肾阴虚的症状为“热”,主要", "提问时间:2014-12-21 16:41:32", "患者性别:男患者年龄:26岁", "问题分析:经常的手淫现象在这种情况有可能会导致,肾气亏虚出现阳痿和早泄的现象。我们需要保持饮食清淡多运动锻炼。意见建议:同时戒掉这种不良习惯 配合口服一些葡萄糖钙加锌口服溶液 多种维生素片 配合口服金", "提问时间:2013-11-10 10:27:03", "患者性别:男患者年龄:24", "意见建议:气血两虚 可以 饮食调理的。可以采取当归、黄芪、大枣、乳鸽一只煲汤服用,可以滋补气血。", "提问时间:2013-04-10 10:12:17", "患者性别:女患者年龄:24岁", "病情分析: 你好,肾阴阳两虚则有五心烦热、盗汗或自汗、四肢发凉、遗精失眠、多梦、舌红无苔、脉细数或舌淡苔白、脉沉迟。意见建议:调理时宜采用阴阳并补,养阴温阳和滋阴壮阳等补法。临床可用补天大造丸加减调理", "提问时间:2014-04-18 14:22:06", "患者性别:男患者年龄:69", "指导意见:肾阴阳两虚,建议滋阴补肾,可以服用六味地黄丸,滋阴补肾。用于肾阴亏损,头晕耳鸣,腰膝酸软,骨蒸潮热,盗汗遗精。但感冒发热病人不宜服用。", "提问时间:2014-01-22 22:01:01", "患者性别:男患者年龄:33", "指导意见:朋友, 中医有较好的治疗效果,建议找专业的中医师进行辨证施治,可达到标本兼治的治疗目的。", "提问时间:2014-03-16 19:51:18", "患者性别:男患者年龄:22岁", "问题分析:您好:依据您所提供的情况来看,肾阴阳两虚可以服用炙甘草汤来治疗。炙甘草汤是滋补肾阴阳两虚的中药方剂。意见建议:建议您在服用中药的同时可以摄入坚果类的食物,例如腰果板栗核桃黑芝麻等。尽量的保", "提问时间:2014-12-14 19:34:37", "患者性别:女患者年龄:22岁", "病情分析:肾虚分为阴虚和阳虚两种,主要是肾脏精气阴阳不足引起的意见建议:一般治疗采用中药和饮食治疗会比较好,饮食可以吃猪腰,黄精枸杞牛尾汤,药物可以使用六味地黄丸,左归丸,右归丸,金贵肾气丸等药物"], "bs_rank_pos": 3}, {"is_selected": false, "title": "什么叫肾阴阳两虚?怎么治疗?_百度知道", "most_related_para": 0, "segmented_title": ["什么叫", "肾", "阴阳", "两", "虚", "?", "怎么", "治疗", "?", "_", "百度", "知道"], "segmented_paragraphs": [["肾", "阴阳", "两", "虚", ":", "则", "有", "五心烦热", "、", "盗汗", "或", "自汗", "、", "四肢", "发", "凉", "、", "遗精", "失眠", "、", "多梦", "、", "舌", "红", "无苔", "、", "脉", "细", "数", "或", "舌", "淡", "苔", "白", "、", "脉", "沉", "迟", "。", "肾", "阴阳", "两", "虚", ",", "先", "治疗", "肾阴虚", "还是", "先", "治疗", "肾阳虚", "好", ".", "疾病", "的", "发生", ",", "从", "根本", "上", "说", ",", "即时", "阴阳", "的", "相对", "平衡", "遭到", "破坏", ",", "出现", "偏", 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"在", "微微", "补", "火", ",", "即", "生", "肾气", ",", "佐", "以", "茯苓", "渗湿"]], "paragraphs": ["肾阴阳两虚:则有五心烦热、盗汗或自汗、四肢发凉、遗精失眠、多梦、舌红无苔、脉细数或舌淡苔白、脉沉迟。 肾阴阳两虚,先治疗肾阴虚还是先治疗肾阳虚好. 疾病的发生,从根本上说,即时阴阳的相对平衡遭到破坏,出现偏盛偏衰的结 果.对以阴阳的偏盛偏衰,中医学指出应\"谨察阴阳所在而调之,以平为期.\" 阴阳两虚,应当指出阴阳是互根互用,阴阳偏衰亦可互根,因此在治疗阴阳偏 衰病症时,还应注意\"阳中求阴\"或\"阴中求阳\"的原则,即在补阴同时适当配用 补阳药物,补阳时适当配用补阴药物. <<景岳全书.新八方略>>中说:'此阴阳相济之妙用也.故善补阳者必阴中求阳 ,则阳得阴助而生化无穷,善补阴者必阳中求阴,则阴得阳升而源泉不竭.\" 在具体应用中肾阴阳两虚则阴阳双补,其代表方剂如桂附地黄丸. 方中桂附用量少,意不在补火,而在微微补火,即生肾气,佐以茯苓渗湿"], "bs_rank_pos": 4}], "answer_spans": [[7, 206]], "fake_answers": ["既有阴虚又有阳虚,称阴阳两虚,其主要表现为;既怕冷又怕热,冬天特别怕冷,夏天又特别怕热,这是阴阳失调或阴阳两虚之体质,进补宜采用阴阳并补,养阴温阳和滋阴壮阳等补法意见建议:补阴虚食品:甲鱼、燕窝、百合、鸭肉、黑鱼、海蜇、藕、金针菇、枸杞头、荸荠、生梨等,可经常交替选服。补阴虚药膳食疗方举例:银耳红枣羹(或百合莲子羹):银耳、红枣(或百合、莲子)适量共煮羹当点心服食,可补阴虚。甲鱼二子汤:甲鱼1只与女贞子、枸杞子各20g同煮汤,加调味,食甲鱼饮汤,连食数剂,可补阴虚和治肝肾阴虚所致的腰痛,遗精、头晕、目花等症。石斛河鱼;石斛6g,河鱼1条共蒸食,可滋阴。虫草老雄鸭"], "question": "肾阴阳两虚怎么治", "segmented_answers": [["由", "肾阴虚", "导致", "性功能障碍", "的", "男性", "，", "平常", "可以", "多", "吃", "一些", "补肾", "阴", "的", "药物", "，", "另外", "食补", "也是", "很好", "的", 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"；", "石斛", "6g", "，", "河鱼", "1", "条", "共", "蒸", "食", "，", "可", "滋阴", "。", "虫草", "老", "雄", "鸭", "。"]], "answers": ["由肾阴虚导致性功能障碍的男性，平常可以多吃一些补肾阴的药物，另外食补也是很好的办法，例如，用桑葚子、枸杞煮粥，也有不错的效果。肾阳虚首先会出现腰痛，因为腰为肾之府，这种痛一般会有冷的感觉。病人痛的同时，觉得腰里面发凉，这是肾阳虚的一个典型表现。另外，它扩展到全身，会出现全身也怕冷。因为肾阳是一身阳气的根本，如果根虚弱了，整个身体的阳气的温煦作用就会下降。提示：肾阳虚的临床表现：1．腰痛而且发凉。2．手脚冰凉、尿频。肾阳虚的人适合吃的食物有海产品、韭菜籽。肾阴虚的情况下火就容易旺，阴虚火旺，如果这时再使用那些温热的壮阳药物，等于是火上浇油，病人热性就更大了。反过来说，假如病人肾阳虚、怕冷，你再用一些滋阴药，等于是雪上加霜，病人同样也受不了。", "既有阴虚又有阳虚，称阴阳两虚，其主要表现为；既怕冷又怕热，冬天特别怕冷，夏天又特别怕热，这是阴阳失调或阴阳两虚之体质，进补宜采用阴阳并补，养阴温阳和滋阴壮阳等补法。意见建议：补阴虚食品：甲鱼、燕窝、百合、鸭肉、黑鱼、海蜇、藕、金针菇、枸杞头、荸荠、生梨等，可经常交替选服。补阴虚药膳食疗方举例：银耳红枣羹（或百合莲子羹）：银耳、红枣（或百合、莲子）适量共煮羹当点心服食，可补阴虚。 甲鱼二子汤：甲鱼1只与女贞子、枸杞子各20g同煮汤，加调味，食甲鱼饮汤，连食数剂，可补阴虚和治肝肾阴虚所致的腰痛，遗精、头晕、目花等症。石斛河鱼；石斛6g，河鱼1条共蒸食，可滋阴。虫草老雄鸭。"], "answer_docs": [1], "segmented_question": ["肾", "阴阳", "两", "虚", "怎么", "治"], "question_type": "DESCRIPTION", "question_id": 49, "fact_or_opinion": "FACT", "match_scores": [0.8606965174129352]}
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方法二:有些同学介绍用冰或冰水,其实用冷水是可以的,但是冰水最好就不要了,因为强烈刺激会伤皮肤的,建议可以用毛巾包着冰块来按,最好就是用冰袋.冰袋大约几块钱一个,网上一块钱也买得到,往里面灌点水,里面就变成凝胶状了,放冰箱里冻起来,好了以后用软布或纱布包着按在肿的地方就可以了,因为是凝胶状,所以时间长点,不像冰块一样化得快,而且不会太冻,不会伤皮肤,医院一般现在都用这个,也许医院也有得卖的,可以去问一下. 指导意见: 方法三:如果不太靠近眼部,可以擦一点去淤消肿的药油,但是不要太用力揉,轻揉,之后要擦掉或洗掉脸上多余的油.  以上是对“脸部摔肿,如何快速消肿”这个问题的建议,希望对您有帮助,祝您健康!", "病情分析: 刚摔伤的时候需要进行冷敷,一两天后需要进行热敷来促进肿的消散. 指导意见: 建议您进行热敷,同时涂些活血化瘀的中药液.", "病情分析: 最好用鸡蛋来快速消肿. 指导意见: 温水洁面后,将煮好的鸡蛋趁热剥皮,手执温热的鸡蛋在脸上滚动,滚动时按照肌肉生长的规律进行,额部,眼部,嘴部,鼻部,颊部都要照顾到,直到鸡蛋完全冷却下来.由于蛋白的主要成分是蛋白质,在按摩过程中,其营养物质可以通过毛孔直接被皮肤吸收,营养肌肤,清热消肿.鸡蛋按摩后再用冷毛巾敷面几分钟,这样,刚煮熟的热鸡蛋促使面部皮肤血管舒张,增强血液循环,再用冷毛巾敷面可使毛孔和血管收缩,一张一弛令皮肤富于光泽和弹性.", "病情分析: 消肿的办法;用冰块敷在肿起的地方,睡一觉会好很多.  如果有淤血情况发生,不要紧张,用热鸡蛋在肿的地方滚.超有效,本人亲身使用.  指导意见: 消肿的办法;用冰块敷在肿起的地方,睡一觉会好很多.  如果有淤血情况发生,不要紧张,用热鸡蛋在肿的地方滚.超有效,本人亲身使用.", "氨苯蝶啶氢  参考价格:56 查看说明书", "呋塞米片  参考价格:5 查看说明书", "呋塞米片  参考价格:5.2 查看说明书", "金匮肾气丸 温补肾阳,化气行水。用于肾虚水肿,腰膝酸软,小便...[说明书] 参考价格:￥12", "来曲唑片 本品用于抗雌激素治疗无效的晚期乳腺癌绝经后患者。...[说明书] 参考价格:￥510", "脸上长雀斑吃什么 激光祛雀斑后怎么护理 美容院激光点痣可靠吗 祛斑药膏怎么样 美容院可以收缩毛孔吗 怎样可以嫩肤 激光祛斑后恢复过程 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"百度经验:jingyan.baidu.com", "1 生活要有规律不要熬夜 脸肿的原因之一就是生活没规律,经常熬夜会影响代谢。经常熬夜和工作压力很大的人,会减慢身体的新陈代谢,使体内废物易于积聚。或者饮食不定时,也有的是因为压力过大引起脸部浮肿,因为亚健康问题会令淋巴系统运作减慢,因此会让过剩的水分囤积在体内无法及时排出,造成身体浮肿、脸部浮肿等问题。所以,生活要回到正常的轨道上,偶尔也给自己精神方面多放松。 步骤阅读 2 睡前不要喝太多水 喝水固然重要,但在睡觉前不能喝太多的水,水分会囤积在体内,第二天就会出现浮肿的现象。 步骤阅读 3 消肿动作 1:将耳朵向上拉。一只手将同边的耳朵向上拉,而另一只手就按住拉伸耳朵的那边脸,向耳朵方向推按。2:双手手指放在额头的中央,然后向太阳穴的方向分别推进,可以帮助脸部水分地排出,最后移动到太阳穴的位置而按摩太阳穴。 步骤阅读 步骤阅读 4 热毛巾敷脸 用热毛巾敷脸也能消肿,热毛巾敷脸可以缓解脸部的肌肉,疏通脸部的毛孔和淋巴,让脸上多余的水分得到彻底的排出。一般敷热毛巾的时间是15-20分钟,每5分钟换一次毛巾。 步骤阅读", "百度经验:jingyan.baidu.com", "经验内容仅供参考,如果您需解决具体问题(尤其法律、医学等领域),建议您详细咨询相关领域专业人士。", "个性签名:微信:sxy08120921"], "bs_rank_pos": 1}, {"is_selected": false, "title": "脸肿了怎么快速消肿,脸肿了怎么办能消肿,脸肿怎么消肿-乐哈健康网 ", "most_related_para": 4, "segmented_title": ["脸", "肿", "了", "怎么", "快速", "消肿", ",", "脸", "肿", "了", "怎么办", "能", "消肿", ",", "脸", "肿", "怎么", "消肿", "-", "乐", "哈", "健康网"], "segmented_paragraphs": [["美容护肤", ">", "脸", "肿", "了", "怎么", "快速", "消肿"], ["引起", "脸", "肿", "的", "原因", "很多", ",", "可能", "是", "病理性", "的", ",", "也", "可能", 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"做法:用指腹按压脸部,特别是浮肿位置周围。", "如果是脸部水肿的话可以通过喝黑咖啡的方法来帮助快速消肿。黑咖啡中含有的咖啡因具有很好的排水利尿、加速身体热量消耗的作用,能够帮助快速消除脸部水肿。", "做法:吃完早饭后喝一杯约100毫克的黑咖啡即可,一般半小时~1小时左右能消肿。", "如果是脸部水肿的话可以通过利用热毛巾敷脸的方法来帮助快速消肿。用热毛巾敷脸能够帮助舒缓脸部的肌肉,扩张脸部的毛孔和淋巴细胞,这样能够帮助脸部多余的水分排出体外,从而有效消肿。", "做法:用热毛巾敷在脸部约15分钟左右就可以帮助消肿,一般敷5分钟左右就需要换一次毛巾。要注意温度不宜过高以免烫伤。"], "bs_rank_pos": 2}, {"is_selected": false, "title": "早上起来脸浮肿怎样快速消肿?_百度拇指医生", "most_related_para": 2, "segmented_title": ["早上起来", "脸", "浮肿", "怎样", "快速", "消肿", "?", "_", "百度", "拇指", "医生"], "segmented_paragraphs": [["?", "早上起来", "脸", "浮肿", "怎样", "快速", "消肿", "?"], ["跪求", "高人", "指点", "。", "。", "。"], ["早晨", "起床", "脸", "肿", "的", "原因", "非常", "多", ",", "但", "大部分", "是因为", "错误", "的", "生活习惯", ",", "正常", "的", "血液循环", "和", "新陈代谢", "受阻", "。", "1", "、", "晚上", "睡觉", "前", "不要", "喝", "太多", "水", ",", "不要", "吃", "太多", "零食", ",", "尤其", "是", "带", "咸味", "的", "食品", "。", "2", "、", "晚上", "不要", "睡眠", "太", "晚", ",", "睡前", "用", "热水", "洗脸", ",", "保持", "面部", "清洁", "。", "3", "、", "要", "纠正", "生活习惯", "=", "身体", "肿", "是因为", "体", 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"非常", "有害", "的", ",", "因为", "有些", "食物", "是", "不宜", "空腹", "食用", "的", ",", "否则", "会", "给", "你", "的", "健康", "埋下", "隐患", "。", "回答", "人", "的", "补充", "还有", "一", "个", "方法", "挺", "有效", "的", ",", "早晨", "起床", "后", "先", "喝一杯", "黑咖啡", "(", "一定要", "是", "纯", "的", "黑咖啡", "哦", ")", ",", "可以", "不可以", "有效", "去除", "水肿", ",", "且", "对", "解决", "便秘", "也", "非常", "有效", "。"]], "paragraphs": ["?早上起来脸浮肿怎样快速消肿?", "跪求高人指点。。。", "早晨起床脸肿的原因非常多,但大部分是因为错误的生活习惯,正常的血液循环和新陈代谢受阻。 1、晚上睡觉前不要喝太多水,不要吃太多零食,尤其是带咸味的食品。 2、晚上不要睡眠太晚,睡前用热水洗脸,保持面部清洁。 3、要纠正生活习惯=身体肿是因为体内积累不必要的水分。男性体内适当水分应占体重的60%,女性应占50%。正常情况是体内水分的三分之二储存在细胞内。如果水分泄露涤行└胞外,在细胞之间积累,身体就会浮肿。如果要消肿,首先要避免吃辣、咸、刺激性的食物。抱川中医大学盆唐家庭医学系教授朴基宪说:“如果吃的咸,就会喝非常多水。这样不仅是脸,且在体内也会积累非常多水分。且睡觉前最好不要喝水,也不要超过必要量(每天平均1.5升)。” 4、如果把脸埋在枕头里睡觉或枕头太高,脸也会肿起来。如果因为压力和失眠症睡眠不足,也会使身体肿起来。尤其是,精神压力大就会分泌出抗利尿荷尔蒙,导致小便不畅,身体出现浮肿。 5、在用绝食和饮食减肥法减肥后,只要暴饮暴食,身体就会浮肿。因为,突然暴食会使水分吸收的速度比以前加快。 6、如果保证充足的睡眠,就能促进小便畅通,就可以不可以预防浮肿。如果穿有弹性的袜子和长筒袜,就会压迫身体,有助于体液通过静脉排出。 7、利用休息时间进行简单的体操、伸展运动、走路等运动,就能促进血液循环,可以不可以预防浮肿。 缓解办法: 1、如果觉得眼部浮肿,将一茶勺的盐溶于一杯暖水中,把棉花浸湿后压干水分,敷在眼睛上数分钟,就能有效减低浮肿。 2、清晨起床后,喝一大杯温热的柠檬水,可以不可以消除体内毒素,预防眼部松弛。 3、若是前一晚喝太多水导致眼部浮肿,可以不可以先用冷水洗洗脸,然后用冷毛巾敷眼睛;若是前一天晚上喝酒或熬夜造成眼部浮肿,应该用温毛巾覆盖眼部,以促进眼部血液循环,缓解眼部肌肉疲劳。 4、挑选适合我的眼霜质地非常重要。一般,膏状的眼霜营养丰厚,眼部啫哩则侧重于补水。在眼肿的日子,一定要选用有消除浮肿功能的眼霜。 眼部消肿按摩法1: 1、玩了整整一晚,虽然非常疲惫,也要坚持给眼部额外的护理,用棉棒蘸取稀释后的盐水,帮助缓解眼部肌肤的水肿情况。 眼部消肿按摩法2: 10:00 a.m.:工作的间隙,做一些简单的眼部运动,舒缓紧盯镜头造成的眼部疲劳。简单的几个动作,就可以不可以让你僵了的双眸焕然一新,随时以靓丽的姿态接受镜头的考验。 1、头部保持不动,只移动双眼,尽量向上望,5秒后转向下再望5秒。 2、眼睛向左望,5秒后再转向右望。 3、从左至右慢慢转动眼球,重复5次。 4、从右至左慢慢转动眼球,重复5次。 2:00 p.m.:吃过午饭,略做休息后,做个简单的眼部按摩,帮助眼周血液流畅地循环。 1、用中指和无名指轻按眼眶,舒缓眼部组织。 2、由鼻梁处开始,用中指轻柔地按压眼睑,由内眼角按转至眼尾。 3、从外眼角开始,用中指轻柔地按压眼睑,由眼尾按至内眼角。 “饥不择食”,是说人饿的时,食欲强烈。但是如果抓到什么吃什么,急于填饱肚子对健康是非常有害的,因为有些食物是不宜空腹食用的,否则会给你的健康埋下隐患。 回答人的补充 还有一个方法挺有效的,早晨起床后先喝一杯黑咖啡(一定要是纯的黑咖啡哦),可以不可以有效去除水肿,且对解决便秘也非常有效。"], "bs_rank_pos": 3}, {"is_selected": false, "title": "脸肿怎么快速消肿_百度知道", "most_related_para": 0, "segmented_title": ["脸", "肿", "怎么", "快速", "消肿", "_", "百度", "知道"], "segmented_paragraphs": [["睡眠不足", ",", "睡前", "喝水", "过", "多", ",", "还有", "睡前", "吃", "的", "一些", "药物", "也有", "可能", "引起", ",", "引致", "身体", "浮肿", "的", "主", "因", "是", "体", "内", "积存", "太多", "水分", "排", "不", "出去", "。", "早上", "眼睛", "浮肿", "的", "常见原因", "是", "睡前", "喝水", "太多", "或", "饮食", "的", "盐分", "过高", "。", "。", "饮食", "消肿", "法", "正常", "来", "说", ",", "由", "食物", "和", "饮料", "中", "吸收", "的", "水分", "经", "血液", "和", "淋巴", "循环", "后", "会", "由", "汗腺", "和", "尿道", "排出体外", ",", "容易", "浮肿", "的", "人", "可能", "是", "这", "两", "个", "系统", "出了毛病", "。", "摄取", "过量", "的", "盐", "会", "令", "水分", "滞留", "体", "内", ",", "也", "会出现", "浮肿", "现象", "。", "所以", "要", "维持", "水分", "平衡", ",", "就", "必须", "将", "多余", "的", "水", "排出体外", ",", "多", "吃", "加强", "水分", "循环", "的", "食物", "可", "帮助", "消肿", "。", "还有", "平", "日", "要", "减少", "摄取", "使", "身体", "易", "受", "寒冷", "的", "食物", "、", "冷饮", "或", "增加", "肠胃", "负担", "的", "食物", ",", "因此", "举", "易", "使", "肠胃", "疲惫", ",", "使", "体", "内", "的", "水分", "滞留", "下来", "。", "此外", ",", "要", "对付", "浮肿", ",", "温和", "或", "有", "利尿", "作用", "的", "食物", "要", "均衡", "摄取", ",", "而", "要", "使", "胃部", "得到", "休息", ",", "晚上", "也", "应", "减少", "饮食", ",", "不应", "大量", "摄入", "水分", "。", "平", "日", "多", "吸收", "钙质", "也", "能", "帮助", "排出", "体", "内", "由", "盐分", "产生", "的", "多余", "水分", "。", "含"]], "paragraphs": ["睡眠不足,睡前喝水过多,还有睡前吃的一些药物也有可能引起,引致身体浮肿的主因是体内积存太多水分排不出去。早上眼睛浮肿的常见原因是睡前喝水太多或饮食的盐分过高。。 饮食消肿法正常来说,由食物和饮料中吸收的水分经血液和淋巴循环后会由汗腺和尿道排出体外,容易浮肿的人可能是这两个系统出了毛病。摄取过量的盐会令水分滞留体内,也会出现浮肿现象。所以要维持水分平衡,就必须将多余的水排出体外,多吃加强水分循环的食物可帮助消肿。还有平日要减少摄取使身体易受寒冷的食物、冷饮或增加肠胃负担的食物,因此举易使肠胃疲惫,使体内的水分滞留下来。此外,要对付浮肿,温和或有利尿作用的食物要均衡摄取,而要使胃部得到休息,晚上也应减少饮食,不应大量摄入水分。平日多吸收钙质也能帮助排出体内由盐分产生的多余水分。含"], "bs_rank_pos": 4}], "answer_spans": [[3, 244]], "fake_answers": ["方法一:用去壳的鸡蛋滚来滚去的.这方法地球人都知道,不光是消肿,就重要的是去淤,一般摔了或者打肿的地方都会有些淤血的,刚煮好有点烫的鸡蛋最快,不过要小心不要烫了皮肤.方法二:有些同学介绍用冰或冰水,其实用冷水是可以的,但是冰水最好就不要了,因为强烈刺激会伤皮肤的,建议可以用毛巾包着冰块来按,最好就是用冰袋.冰袋大约几块钱一个,网上一块钱也买得到,往里面灌点水,里面就变成凝胶状了,放冰箱里冻起来,好了以后用软布或纱布包着按在肿的地方就可以了,因为是凝胶状,所以时间长点,不像冰块一样化得快,而且不会太冻,不会伤皮肤,医院一般现在都用这个,也许医院也有得卖的,可以去问一下.指导意见:方法三:如果不太靠近眼部,可以擦一点去淤消肿的药油,但是不要太用力揉,轻揉,之后要擦掉或洗掉脸上多余的油"], "question": "脸肿怎么消肿", "segmented_answers": [["方法", "一", "：", "用", "去", "壳", "的", "鸡蛋", "滚", "来", "滚", "去", "的", ".", "这", "方法", "地球人", "都", "知道", ",", "不", "光", "是", "消肿", ",", "就", "重要", "的", "是", "去", "淤", ",", "一般", "摔", "了", "或者", "打", "肿", "的", "地方", "都会", "有些", "淤血", "的", ",", "刚", "煮", "好", "有点", "烫", "的", "鸡蛋", "最", "快", ",", "不过", "要", "小心", "不要", "烫", "了", "皮肤", "。", "方法", "二", "：", "有些", "同学", "介绍", "用", "冰", "或", "冰水", ",", "其实用", "冷水", "是", "可以", "的", ",", "但是", "冰水", "最好", "就", "不要", "了", ",", "因为", "强烈", "刺激", "会", "伤", "皮肤", "的", ",", "建议", "可以", "用", "毛巾", "包", "着", "冰块", "来", "按", ",", "最好", "就是", "用", "冰袋", ".", "冰袋", "大约", "几块钱", "一", "个", ",", "网上", "一块钱", "也", "买", "得到", ",", "往", "里面", "灌", "点", "水", ",", "里面", "就", "变成", "凝胶", "状", "了", ",", "放", "冰箱", "里", "冻", "起来", ",", "好", "了", "以后", "用", "软", "布", "或", "纱布", "包", "着", "按", "在", "肿", "的", "地方", "就", "可以", "了", ",", "因为", "是", "凝胶", "状", ",", "所以", "时间长", "点", ",", "不", "像", "冰块", "一样", "化", "得", "快", ",", "而且", "不会", "太", "冻", ",", "不会", "伤", "皮肤", ",", "医院", "一般", "现在", "都", "用", "这个", ",", "也许", "医院", "也有", "得", "卖", "的", ",", "可以", "去", "问一下", "。", "方法", "三", "：", "如果", "不太", "靠近", "眼部", ",", "可以", "擦", "一点", "去", "淤", "消肿", "的", "药", "油", ",", "但是", "不要", "太", "用力", "揉", ",", "轻", "揉", ",", "之后", "要", "擦掉", "或", "洗掉", "脸上", "多余", "的", "油", "。"], ["建议", "损伤", "后", "24", "小时", "内", "首先", "进行", "冰敷", "，", "24", "小时", "后", "才", "可以", "进行", "热敷", "的", "，", "如果", "一开始", "就", "热敷", "会", "加重", "水肿", "渗血", "的", "，", "同时", "在", "生活", "上", "注意休息", "，", "不要", "吃", "辛辣", "刺激", "的", "食物", "，", "一般", "两天内", "水肿", "都是", "会", "消退", "的", "。"], ["1", "将", "耳朵", "向上", "拉", "。", "一只手", "将", "同", "边", "的", "耳朵", "向上", "拉", "，", "而", "另一只手", "就", "按住", "拉伸", "耳朵", "的", "那边", "脸", "，", "向", "耳朵", "方向", "推", "按", "。", "2", "双手", "手指", "放在", "额头", "的", "中央", "，", "然后", "向", "太阳穴", "的", "方向", "分别", "推进", "，", "可以", "帮助", "脸部", "水分", "地", "排出", "，", "最后", "移动", "到", "太阳穴", "的", "位置", "而", "按摩", "太阳穴", "。", "热", "毛巾", "敷脸", "、", "喝", "蜂蜜", "水", "、", "喝", "黑咖啡", "、", "冷热水", "交换", "敷脸", "、", "最好", "垫", "高", "枕头", "睡觉", "、", "减少", "摄取", "易", "受", "寒冷", "的", "食物", "、", "补充营养", "、", "适量", "运动", "，", "保持乐观", "情绪", "、", "泡", "温水", "澡", "。"]], "answers": ["方法一：用去壳的鸡蛋滚来滚去的.这方法地球人都知道,不光是消肿,就重要的是去淤,一般摔了或者打肿的地方都会有些淤血的,刚煮好有点烫的鸡蛋最快,不过要小心不要烫了皮肤。 方法二：有些同学介绍用冰或冰水,其实用冷水是可以的,但是冰水最好就不要了,因为强烈刺激会伤皮肤的,建议可以用毛巾包着冰块来按,最好就是用冰袋.冰袋大约几块钱一个,网上一块钱也买得到,往里面灌点水,里面就变成凝胶状了,放冰箱里冻起来,好了以后用软布或纱布包着按在肿的地方就可以了,因为是凝胶状,所以时间长点,不像冰块一样化得快,而且不会太冻,不会伤皮肤,医院一般现在都用这个,也许医院也有得卖的,可以去问一下。方法三：如果不太靠近眼部,可以擦一点去淤消肿的药油,但是不要太用力揉,轻揉,之后要擦掉或洗掉脸上多余的油。", "建议损伤后24小时内首先进行冰敷，24小时后才可以进行热敷的，如果一开始就热敷会加重水肿渗血的，同时在生活上注意休息，不要吃辛辣刺激的食物，一般两天内水肿都是会消退的。", "1将耳朵向上拉。一只手将同边的耳朵向上拉，而另一只手就按住拉伸耳朵的那边脸，向耳朵方向推按。2双手手指放在额头的中央，然后向太阳穴的方向分别推进，可以帮助脸部水分地排出，最后移动到太阳穴的位置而按摩太阳穴。热毛巾敷脸、喝蜂蜜水、喝黑咖啡、冷热水交换敷脸、最好垫高枕头睡觉、减少摄取易受寒冷的食物、补充营养、适量运动，保持乐观情绪、泡温水澡。"], "answer_docs": [0], "segmented_question": ["脸", "肿", "怎么", "消肿"], "question_type": "DESCRIPTION", "question_id": 50, "fact_or_opinion": "FACT", "match_scores": [0.9730848861283643]}
diff --git a/PaddleNLP/reading_comprehension/data/download.sh b/PaddleNLP/reading_comprehension/data/download.sh
new file mode 100644
index 0000000000000000000000000000000000000000..70ee0cf57d1cca0ee55c726c1a0d6911cb15d596
--- /dev/null
+++ b/PaddleNLP/reading_comprehension/data/download.sh
@@ -0,0 +1,30 @@
+#!/bin/bash
+# ==============================================================================
+# Copyright 2017 Baidu.com, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+
+# download preprocessed data
+wget -c --no-check-certificate https://baidu-nlp.bj.bcebos.com/dureader_machine_reading-dataset-2.0.0.tar.gz
+# download trained model parameters and vocabulary
+wget -c --no-check-certificate https://baidu-nlp.bj.bcebos.com/dureader_machine_reading-bidaf-1.0.0.tar.gz 
+
+# decompression
+tar -zxvf dureader_machine_reading-dataset-2.0.0.tar.gz
+tar -zxvf dureader_machine_reading-bidaf-1.0.0.tar.gz 
+
+ln -s trained_model_para/vocab ./
+ln -s trained_model_para/saved_model ./
+
diff --git a/PaddleNLP/reading_comprehension/data/md5sum.txt b/PaddleNLP/reading_comprehension/data/md5sum.txt
new file mode 100644
index 0000000000000000000000000000000000000000..32d109e9c99b4124240b0110bb443a80d1fb4662
--- /dev/null
+++ b/PaddleNLP/reading_comprehension/data/md5sum.txt
@@ -0,0 +1,2 @@
+02065c4adec24860c52b0192a3615d8d  dureader_machine_reading-bidaf-1.0.0.tar.gz
+a6b4678ccf319a0c8812b80aed71c24b  dureader_machine_reading-dataset-2.0.0.tar.gz
diff --git a/PaddleNLP/reading_comprehension/src/UPDATES.md b/PaddleNLP/reading_comprehension/src/UPDATES.md
new file mode 100644
index 0000000000000000000000000000000000000000..cc0904c0292536462733e618bbb73c8149c67af1
--- /dev/null
+++ b/PaddleNLP/reading_comprehension/src/UPDATES.md
@@ -0,0 +1,28 @@
+# The notes on the updates of PaddlePaddle baseline
+
+## Updates
+
+We implement a BiDAF model with PaddlePaddle. Note that we have an update on the PaddlePaddle baseline (Feb 25, 2019). In this document, we give the details of the major updates:
+
+### 1 Paragraph Extraction
+
+The first update is that we incorporate a strategy of paragraph extraction to improve the model performance (see the file `paddle/para_extraction.py`). A similar strategy has been used in the Top-1 system (Liu et al. 2018) at [2018 Machine Reading Challenge](http://mrc2018.cipsc.org.cn/). 
+
+The original baseline of DuReader (He et al. 2018) employed a simple strategy to select paragraphs for model training and testing. However, the paragraphs that includes the true answers may not be selected. Hence, we want to incorporate as much information for the answer extraction as possible. 
+
+The detail of the new strategy of paragraph extraction is as follows. We apply the new paragraph extraction strategy on each document. For each document, 
+ - We remove the duplicated paragraphs in the document.
+ - We concatenate the title and all paragraphs in the document with a pre-defined splitter if it is shorter than a predefined maximum length. Otherwise, 
+	- We compute F1 score of each paragraph relative to the question; 
+	- We concatenate the title and the top-K paragraphs (by F1 score) with a pre-defined splitter to form an extracted paragraph that should be shorter than the predefined maximum length.
+
+### 2 The Prior of Document Ranking
+
+We also introduce the prior of document ranking from search engine (see line #176 in `paddle/run.py`). The documents in DuReader are collected from the search results. Hence, the prior scores of document ranking is an important feature. We compute the prior scores from the training data and apply the prior scores in the testing stage.  
+
+## Reference
+
+- Liu, J., Wei, W., Sun, M., Chen, H., Du, Y. and Lin, D., 2018. A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 2109-2118).
+
+- He, W., Liu, K., Liu, J., Lyu, Y., Zhao, S., Xiao, X., Liu, Y., Wang, Y., Wu, H., She, Q. and Liu, X., 2017. Dureader: a chinese machine reading comprehension dataset from real-world applications. arXiv preprint arXiv:1711.05073.
+
diff --git a/PaddleNLP/machine_reading_comprehension/args.py b/PaddleNLP/reading_comprehension/src/args.py
similarity index 90%
rename from PaddleNLP/machine_reading_comprehension/args.py
rename to PaddleNLP/reading_comprehension/src/args.py
index 96c4e35cf3fe822e2794b41a385c106882935906..8b3be2b4677cb92e4ead45bdf7b61d3a2fa859c0 100644
--- a/PaddleNLP/machine_reading_comprehension/args.py
+++ b/PaddleNLP/reading_comprehension/src/args.py
@@ -58,8 +58,8 @@ def parse_args():
     parser.add_argument('--max_q_len', type=int, default=60)
     parser.add_argument('--doc_num', type=int, default=5)
 
-    parser.add_argument('--vocab_dir', default='data/vocab', help='vocabulary')
-    parser.add_argument("--save_dir", type=str, default="data/models",
+    parser.add_argument('--vocab_dir', default='../data/vocab', help='vocabulary')
+    parser.add_argument("--save_dir", type=str, default="../data/models",
                         help="Specify the path to save trained models.")
     parser.add_argument("--save_interval", type=int, default=1,
                         help="Save the trained model every n passes. (default: %(default)d)")
@@ -67,19 +67,19 @@ def parse_args():
                         help="Specify the path to load trained models.")
     parser.add_argument('--log_path',
                         help='path of the log file. If not set, logs are printed to console')
-    parser.add_argument('--result_dir', default='data/results/',
+    parser.add_argument('--result_dir', default='../data/results/',
                         help='the dir to output the results')
     parser.add_argument('--result_name', default='test_result',
                         help='the file name of the predicted results')
 
     parser.add_argument('--trainset', nargs='+',
-                        default=['data/demo/trainset/search.train.json'],
+                        default=['../data/demo/trainset/search.train.json'],
                         help='train dataset')
     parser.add_argument('--devset', nargs='+',
-                        default=['data/demo/devset/search.dev.json'],
+                        default=['../data/demo/devset/search.dev.json'],
                         help='dev dataset')
     parser.add_argument('--testset', nargs='+',
-                        default=['data/demo/testset/search.test.json'],
+                        default=['../data/demo/testset/search.test.json'],
                         help='test dataset')
 
     parser.add_argument("--enable_ce", action='store_true',
diff --git a/PaddleNLP/machine_reading_comprehension/dataset.py b/PaddleNLP/reading_comprehension/src/dataset.py
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/dataset.py
rename to PaddleNLP/reading_comprehension/src/dataset.py
diff --git a/PaddleNLP/machine_reading_comprehension/paragraph_extraction.py b/PaddleNLP/reading_comprehension/src/paragraph_extraction.py
similarity index 99%
rename from PaddleNLP/machine_reading_comprehension/paragraph_extraction.py
rename to PaddleNLP/reading_comprehension/src/paragraph_extraction.py
index 414d745d2deee5ece1df52b90d86b6e84c622c76..0267eb1f8367b850b2c4436026ca0d1ebdab615c 100644
--- a/PaddleNLP/machine_reading_comprehension/paragraph_extraction.py
+++ b/PaddleNLP/reading_comprehension/src/paragraph_extraction.py
@@ -27,7 +27,7 @@ def compute_paragraph_score(sample):
             if len(question) > 0:
                 related_score = metric_max_over_ground_truths(f1_score,
                         para_tokens,
-                        question)
+                        [question])
             else:
                 related_score = 0.0
             doc['segmented_paragraphs_scores'].append(related_score)
diff --git a/PaddleNLP/machine_reading_comprehension/preprocess.py b/PaddleNLP/reading_comprehension/src/preprocess.py
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/preprocess.py
rename to PaddleNLP/reading_comprehension/src/preprocess.py
diff --git a/PaddleNLP/machine_reading_comprehension/run.py b/PaddleNLP/reading_comprehension/src/run.py
similarity index 99%
rename from PaddleNLP/machine_reading_comprehension/run.py
rename to PaddleNLP/reading_comprehension/src/run.py
index 19b637cd41d228e3a3e4bcebd734dbf66043c7ab..04379557d60b037e6c84788e642f454767bbd49c 100644
--- a/PaddleNLP/machine_reading_comprehension/run.py
+++ b/PaddleNLP/reading_comprehension/src/run.py
@@ -35,9 +35,11 @@ if sys.version[0] == '2':
     reload(sys)
     sys.setdefaultencoding("utf-8")
 sys.path.append('..')
+sys.path.append('../../models/reading_comprehension/')
+
 
 from args import *
-import rc_model
+import bidaf_model as rc_model
 from dataset import BRCDataset
 import logging
 import pickle
@@ -449,7 +451,6 @@ def train(logger, args):
                                 inference_program, avg_cost, s_probs, e_probs,
                                 match, feed_order, place, dev_count, vocab,
                                 brc_data, logger, args)
-                            logger.info('Dev eval loss {}'.format(eval_loss))
                             logger.info('Dev eval result: {}'.format(
                                 bleu_rouge))
                 pass_end_time = time.time()
@@ -463,7 +464,6 @@ def train(logger, args):
                         inference_program, avg_cost, s_probs, e_probs, match,
                         feed_order, place, dev_count, vocab, brc_data, logger,
                         args)
-                    logger.info('Dev eval loss {}'.format(eval_loss))
                     logger.info('Dev eval result: {}'.format(bleu_rouge))
                 else:
                     logger.warning(
@@ -533,7 +533,6 @@ def evaluate(logger, args):
             eval_loss, bleu_rouge = validation(
                 inference_program, avg_cost, s_probs, e_probs, match, feed_order,
                 place, dev_count, vocab, brc_data, logger, args)
-            logger.info('Dev eval loss {}'.format(eval_loss))
             logger.info('Dev eval result: {}'.format(bleu_rouge))
             logger.info('Predicted answers are saved to {}'.format(
                 os.path.join(args.result_dir)))
diff --git a/PaddleNLP/reading_comprehension/src/run.sh b/PaddleNLP/reading_comprehension/src/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..332ecf34ff2bdbc226f52a1572a024c61d615aba
--- /dev/null
+++ b/PaddleNLP/reading_comprehension/src/run.sh
@@ -0,0 +1,52 @@
+#!/bin/bash
+export CUDA_VISIBLE_DEVICES=0
+
+paragraph_extraction ()
+{
+    SOURCE_DIR=$1
+    TARGET_DIR=$2
+    echo "Start paragraph extraction, this may take a few hours"
+    echo "Source dir: $SOURCE_DIR"
+    echo "Target dir: $TARGET_DIR"
+    mkdir -p $TARGET_DIR/trainset
+    mkdir -p $TARGET_DIR/devset
+    mkdir -p $TARGET_DIR/testset
+
+    echo "Processing trainset"
+    cat $SOURCE_DIR/trainset/search.train.json | python paragraph_extraction.py train \
+            > $TARGET_DIR/trainset/search.train.json
+    cat $SOURCE_DIR/trainset/zhidao.train.json | python paragraph_extraction.py train \
+            > $TARGET_DIR/trainset/zhidao.train.json
+
+    echo "Processing devset"
+    cat $SOURCE_DIR/devset/search.dev.json | python paragraph_extraction.py dev \
+            > $TARGET_DIR/devset/search.dev.json
+    cat $SOURCE_DIR/devset/zhidao.dev.json | python paragraph_extraction.py dev \
+            > $TARGET_DIR/devset/zhidao.dev.json
+
+    echo "Processing testset"
+    cat $SOURCE_DIR/testset/search.test.json | python paragraph_extraction.py test \
+            > $TARGET_DIR/testset/search.test.json
+    cat $SOURCE_DIR/testset/zhidao.test.json | python paragraph_extraction.py test \
+            > $TARGET_DIR/testset/zhidao.test.json
+    echo "Paragraph extraction done!"
+}
+
+
+PROCESS_NAME="$1"
+case $PROCESS_NAME in
+    --para_extraction)
+    # Start paragraph extraction 
+    if [ ! -d ../data/preprocessed ]; then
+        echo "Please download the preprocessed data first (See README - Preprocess)"
+        exit 1
+    fi
+    paragraph_extraction ../data/preprocessed ../data/extracted
+    ;;
+    --prepare|--train|--evaluate|--predict)
+        # Start Paddle baseline
+        python run.py $@
+    ;;
+    *)
+        echo $"Usage: $0 {--para_extraction|--prepare|--train|--evaluate|--predict}"
+esac
diff --git a/PaddleNLP/machine_reading_comprehension/vocab.py b/PaddleNLP/reading_comprehension/src/vocab.py
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/vocab.py
rename to PaddleNLP/reading_comprehension/src/vocab.py
diff --git a/PaddleNLP/reading_comprehension/utils/__init__.py b/PaddleNLP/reading_comprehension/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c4c8ce95fb22a23dec00649465d192d7793869a
--- /dev/null
+++ b/PaddleNLP/reading_comprehension/utils/__init__.py
@@ -0,0 +1,36 @@
+# coding:utf8
+# ==============================================================================
+# Copyright 2017 Baidu.com, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+This package implements some utility functions shared by PaddlePaddle
+and Tensorflow model implementations.
+
+Authors: liuyuan(liuyuan04@baidu.com)
+Date:    2017/10/06 18:23:06
+"""
+
+
+from .dureader_eval import compute_bleu_rouge
+from .dureader_eval import normalize
+from .preprocess import find_fake_answer
+from .preprocess import find_best_question_match
+
+__all__ = [
+    'compute_bleu_rouge',
+    'normalize',
+    'find_fake_answer',
+    'find_best_question_match',
+    ]
diff --git a/PaddleNLP/machine_reading_comprehension/utils/download_thirdparty.sh b/PaddleNLP/reading_comprehension/utils/download_thirdparty.sh
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/utils/download_thirdparty.sh
rename to PaddleNLP/reading_comprehension/utils/download_thirdparty.sh
diff --git a/PaddleNLP/reading_comprehension/utils/dureader_eval.py b/PaddleNLP/reading_comprehension/utils/dureader_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3839a46c9c63c83eccc332bbf385b0ce5fd5d46
--- /dev/null
+++ b/PaddleNLP/reading_comprehension/utils/dureader_eval.py
@@ -0,0 +1,546 @@
+# -*- coding:utf8 -*-
+# ==============================================================================
+# Copyright 2017 Baidu.com, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+This module computes evaluation metrics for DuReader dataset.
+"""
+
+
+import argparse
+import json
+import sys
+import zipfile
+
+from collections import Counter
+from .bleu_metric.bleu import Bleu
+from .rouge_metric.rouge import Rouge
+
+EMPTY = ''
+YESNO_LABELS = set(['Yes', 'No', 'Depends'])
+
+
+def normalize(s):
+    """
+    Normalize strings to space joined chars.
+
+    Args:
+        s: a list of strings.
+
+    Returns:
+        A list of normalized strings.
+    """
+    if not s:
+        return s
+    normalized = []
+    for ss in s:
+        tokens = [c for c in list(ss) if len(c.strip()) != 0]
+        normalized.append(' '.join(tokens))
+    return normalized
+
+
+def data_check(obj, task):
+    """
+    Check data.
+
+    Raises:
+        Raises AssertionError when data is not legal.
+    """
+    assert 'question_id' in obj, "Missing 'question_id' field."
+    assert 'question_type' in obj, \
+            "Missing 'question_type' field. question_id: {}".format(obj['question_type'])
+
+    assert 'yesno_answers' in obj, \
+            "Missing 'yesno_answers' field. question_id: {}".format(obj['question_id'])
+    assert isinstance(obj['yesno_answers'], list), \
+            r"""'yesno_answers' field must be a list, if the 'question_type' is not
+            'YES_NO', then this field should be an empty list.
+            question_id: {}""".format(obj['question_id'])
+
+    assert 'entity_answers' in obj, \
+            "Missing 'entity_answers' field. question_id: {}".format(obj['question_id'])
+    assert isinstance(obj['entity_answers'], list) \
+            and len(obj['entity_answers']) > 0, \
+            r"""'entity_answers' field must be a list, and has at least one element,
+            which can be a empty list. question_id: {}""".format(obj['question_id'])
+
+
+def read_file(file_name, task, is_ref=False):
+    """
+    Read predict answers or reference answers from file.
+
+    Args:
+        file_name: the name of the file containing predict result or reference
+                   result.
+
+    Returns:
+        A dictionary mapping question_id to the result information. The result
+        information itself is also a dictionary with has four keys:
+        - question_type: type of the query.
+        - yesno_answers: A list of yesno answers corresponding to 'answers'.
+        - answers: A list of predicted answers.
+        - entity_answers: A list, each element is also a list containing the entities
+                    tagged out from the corresponding answer string.
+    """
+    def _open(file_name, mode, zip_obj=None):
+        if zip_obj is not None:
+            return zip_obj.open(file_name, mode)
+        return open(file_name, mode)
+
+    results = {}
+    keys = ['answers', 'yesno_answers', 'entity_answers', 'question_type']
+    if is_ref:
+        keys += ['source']
+
+    zf = zipfile.ZipFile(file_name, 'r') if file_name.endswith('.zip') else None
+    file_list = [file_name] if zf is None else zf.namelist()
+
+    for fn in file_list:
+        for line in _open(fn, 'r', zip_obj=zf):
+            try:
+                obj = json.loads(line.strip())
+            except ValueError:
+                raise ValueError("Every line of data should be legal json")
+            data_check(obj, task)
+            qid = obj['question_id']
+            assert qid not in results, "Duplicate question_id: {}".format(qid)
+            results[qid] = {}
+            for k in keys:
+                results[qid][k] = obj[k]
+    return results
+
+
+def compute_bleu_rouge(pred_dict, ref_dict, bleu_order=4):
+    """
+    Compute bleu and rouge scores.
+    """
+    assert set(pred_dict.keys()) == set(ref_dict.keys()), \
+            "missing keys: {}".format(set(ref_dict.keys()) - set(pred_dict.keys()))
+    scores = {}
+    bleu_scores, _ = Bleu(bleu_order).compute_score(ref_dict, pred_dict)
+    for i, bleu_score in enumerate(bleu_scores):
+        scores['Bleu-%d' % (i + 1)] = bleu_score
+    rouge_score, _ = Rouge().compute_score(ref_dict, pred_dict)
+    scores['Rouge-L'] = rouge_score
+    return scores
+
+
+def local_prf(pred_list, ref_list):
+    """
+    Compute local precision recall and f1-score,
+    given only one prediction list and one reference list
+    """
+    common = Counter(pred_list) & Counter(ref_list)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return 0, 0, 0
+    p = 1.0 * num_same / len(pred_list)
+    r = 1.0 * num_same / len(ref_list)
+    f1 = (2 * p * r) / (p + r)
+    return p, r, f1
+
+
+def compute_prf(pred_dict, ref_dict):
+    """
+    Compute precision recall and f1-score.
+    """
+    pred_question_ids = set(pred_dict.keys())
+    ref_question_ids = set(ref_dict.keys())
+    correct_preds, total_correct, total_preds = 0, 0, 0
+    for question_id in ref_question_ids:
+        pred_entity_list = pred_dict.get(question_id, [[]])
+        assert len(pred_entity_list) == 1, \
+            'the number of entity list for question_id {} is not 1.'.format(question_id)
+        pred_entity_list = pred_entity_list[0]
+        all_ref_entity_lists = ref_dict[question_id]
+        best_local_f1 = 0
+        best_ref_entity_list = None
+        for ref_entity_list in all_ref_entity_lists:
+            local_f1 = local_prf(pred_entity_list, ref_entity_list)[2]
+            if local_f1 > best_local_f1:
+                best_ref_entity_list = ref_entity_list
+                best_local_f1 = local_f1
+        if best_ref_entity_list is None:
+            if len(all_ref_entity_lists) > 0:
+                best_ref_entity_list = sorted(all_ref_entity_lists,
+                        key=lambda x: len(x))[0]
+            else:
+                best_ref_entity_list = []
+        gold_entities = set(best_ref_entity_list)
+        pred_entities = set(pred_entity_list)
+        correct_preds += len(gold_entities & pred_entities)
+        total_preds += len(pred_entities)
+        total_correct += len(gold_entities)
+    p = float(correct_preds) / total_preds if correct_preds > 0 else 0
+    r = float(correct_preds) / total_correct if correct_preds > 0 else 0
+    f1 = 2 * p * r / (p + r) if correct_preds > 0 else 0
+    return {'Precision': p, 'Recall': r, 'F1': f1}
+
+
+def prepare_prf(pred_dict, ref_dict):
+    """
+    Prepares data for calculation of prf scores.
+    """
+    preds = {k: v['entity_answers'] for k, v in pred_dict.items()}
+    refs = {k: v['entity_answers'] for k, v in ref_dict.items()}
+    return preds, refs
+
+
+def filter_dict(result_dict, key_tag):
+    """
+    Filter a subset of the result_dict, where keys ends with 'key_tag'.
+    """
+    filtered = {}
+    for k, v in result_dict.items():
+        if k.endswith(key_tag):
+            filtered[k] = v
+    return filtered
+
+
+def get_metrics(pred_result, ref_result, task, source):
+    """
+    Computes metrics.
+    """
+    metrics = {}
+
+    ref_result_filtered = {}
+    pred_result_filtered = {}
+    if source == 'both':
+        ref_result_filtered = ref_result
+        pred_result_filtered = pred_result
+    else:
+        for question_id, info in ref_result.items():
+            if info['source'] == source:
+                ref_result_filtered[question_id] = info
+                if question_id in pred_result:
+                    pred_result_filtered[question_id] = pred_result[question_id]
+
+    if task == 'main' or task == 'all' \
+            or task == 'description':
+        pred_dict, ref_dict = prepare_bleu(pred_result_filtered,
+                ref_result_filtered,
+                task)
+        metrics = compute_bleu_rouge(pred_dict, ref_dict)
+    elif task == 'yesno':
+        pred_dict, ref_dict = prepare_bleu(pred_result_filtered,
+                ref_result_filtered,
+                task)
+        keys = ['Yes', 'No', 'Depends']
+        preds = [filter_dict(pred_dict, k) for k in keys]
+        refs = [filter_dict(ref_dict, k) for k in keys]
+
+        metrics = compute_bleu_rouge(pred_dict, ref_dict)
+
+        for k, pred, ref in zip(keys, preds, refs):
+            m = compute_bleu_rouge(pred, ref)
+            k_metric = [(k + '|' + key, v) for key, v in m.items()]
+            metrics.update(k_metric)
+
+    elif task == 'entity':
+        pred_dict, ref_dict = prepare_prf(pred_result_filtered,
+                ref_result_filtered)
+        pred_dict_bleu, ref_dict_bleu = prepare_bleu(pred_result_filtered,
+                ref_result_filtered,
+                task)
+        metrics = compute_prf(pred_dict, ref_dict)
+        metrics.update(compute_bleu_rouge(pred_dict_bleu, ref_dict_bleu))
+    else:
+        raise ValueError("Illegal task name: {}".format(task))
+
+    return metrics
+
+
+def prepare_bleu(pred_result, ref_result, task):
+    """
+    Prepares data for calculation of bleu and rouge scores.
+    """
+    pred_list, ref_list = [], []
+    qids = ref_result.keys()
+    for qid in qids:
+        if task == 'main':
+            pred, ref = get_main_result(qid, pred_result, ref_result)
+        elif task == 'yesno':
+            pred, ref = get_yesno_result(qid, pred_result, ref_result)
+        elif task == 'all':
+            pred, ref = get_all_result(qid, pred_result, ref_result)
+        elif task == 'entity':
+            pred, ref = get_entity_result(qid, pred_result, ref_result)
+        elif task == 'description':
+            pred, ref = get_desc_result(qid, pred_result, ref_result)
+        else:
+            raise ValueError("Illegal task name: {}".format(task))
+        if pred and ref:
+            pred_list += pred
+            ref_list += ref
+    pred_dict = dict(pred_list)
+    ref_dict = dict(ref_list)
+    for qid, ans in ref_dict.items():
+        ref_dict[qid] = normalize(ref_dict[qid])
+        pred_dict[qid] = normalize(pred_dict.get(qid, [EMPTY]))
+        if not ans or ans == [EMPTY]:
+            del ref_dict[qid]
+            del pred_dict[qid]
+
+    for k, v in pred_dict.items():
+        assert len(v) == 1, \
+            "There should be only one predict answer. question_id: {}".format(k)
+    return pred_dict, ref_dict
+
+
+def get_main_result(qid, pred_result, ref_result):
+    """
+    Prepare answers for task 'main'.
+
+    Args:
+        qid: question_id.
+        pred_result: A dict include all question_id's result information read
+                     from args.pred_file.
+        ref_result: A dict incluce all question_id's result information read
+                    from args.ref_file.
+    Returns:
+        Two lists, the first one contains predict result, the second
+        one contains reference result of the same question_id. Each list has
+        elements of tuple (question_id, answers), 'answers' is a list of strings.
+    """
+    ref_ans = ref_result[qid]['answers']
+    if not ref_ans:
+        ref_ans = [EMPTY]
+    pred_ans = pred_result.get(qid, {}).get('answers', [])[:1]
+    if not pred_ans:
+        pred_ans = [EMPTY]
+
+    return [(qid, pred_ans)], [(qid, ref_ans)]
+
+
+def get_entity_result(qid, pred_result, ref_result):
+    """
+    Prepare answers for task 'entity'.
+
+    Args:
+        qid: question_id.
+        pred_result: A dict include all question_id's result information read
+                     from args.pred_file.
+        ref_result: A dict incluce all question_id's result information read
+                    from args.ref_file.
+    Returns:
+        Two lists, the first one contains predict result, the second
+        one contains reference result of the same question_id. Each list has
+        elements of tuple (question_id, answers), 'answers' is a list of strings.
+    """
+    if ref_result[qid]['question_type'] != 'ENTITY':
+        return None, None
+    return get_main_result(qid, pred_result, ref_result)
+
+
+def get_desc_result(qid, pred_result, ref_result):
+    """
+    Prepare answers for task 'description'.
+
+    Args:
+        qid: question_id.
+        pred_result: A dict include all question_id's result information read
+                     from args.pred_file.
+        ref_result: A dict incluce all question_id's result information read
+                    from args.ref_file.
+    Returns:
+        Two lists, the first one contains predict result, the second
+        one contains reference result of the same question_id. Each list has
+        elements of tuple (question_id, answers), 'answers' is a list of strings.
+    """
+    if ref_result[qid]['question_type'] != 'DESCRIPTION':
+        return None, None
+    return get_main_result(qid, pred_result, ref_result)
+
+
+def get_yesno_result(qid, pred_result, ref_result):
+    """
+    Prepare answers for task 'yesno'.
+
+    Args:
+        qid: question_id.
+        pred_result: A dict include all question_id's result information read
+                     from args.pred_file.
+        ref_result: A dict incluce all question_id's result information read
+                    from args.ref_file.
+    Returns:
+        Two lists, the first one contains predict result, the second
+        one contains reference result of the same question_id. Each list has
+        elements of tuple (question_id, answers), 'answers' is a list of strings.
+    """
+    def _uniq(li, is_ref):
+        uniq_li = []
+        left = []
+        keys = set()
+        for k, v in li:
+            if k not in keys:
+                uniq_li.append((k, v))
+                keys.add(k)
+            else:
+                left.append((k, v))
+
+        if is_ref:
+            dict_li = dict(uniq_li)
+            for k, v in left:
+                dict_li[k] += v
+            uniq_li = [(k, v) for k, v in dict_li.items()]
+        return uniq_li
+
+    def _expand_result(uniq_li):
+        expanded = uniq_li[:]
+        keys = set([x[0] for x in uniq_li])
+        for k in YESNO_LABELS - keys:
+            expanded.append((k, [EMPTY]))
+        return expanded
+
+    def _get_yesno_ans(qid, result_dict, is_ref=False):
+        if qid not in result_dict:
+            return [(str(qid) + '_' + k, v) for k, v in _expand_result([])]
+        yesno_answers = result_dict[qid]['yesno_answers']
+        answers = result_dict[qid]['answers']
+        lbl_ans = _uniq([(k, [v]) for k, v in zip(yesno_answers, answers)], is_ref)
+        ret = [(str(qid) + '_' + k, v) for k, v in _expand_result(lbl_ans)]
+        return ret
+
+    if ref_result[qid]['question_type'] != 'YES_NO':
+        return None, None
+
+    ref_ans = _get_yesno_ans(qid, ref_result, is_ref=True)
+    pred_ans = _get_yesno_ans(qid, pred_result)
+    return pred_ans, ref_ans
+
+
+def get_all_result(qid, pred_result, ref_result):
+    """
+    Prepare answers for task 'all'.
+
+    Args:
+        qid: question_id.
+        pred_result: A dict include all question_id's result information read
+                     from args.pred_file.
+        ref_result: A dict incluce all question_id's result information read
+                    from args.ref_file.
+    Returns:
+        Two lists, the first one contains predict result, the second
+        one contains reference result of the same question_id. Each list has
+        elements of tuple (question_id, answers), 'answers' is a list of strings.
+    """
+    if ref_result[qid]['question_type'] == 'YES_NO':
+        return get_yesno_result(qid, pred_result, ref_result)
+    return get_main_result(qid, pred_result, ref_result)
+
+
+def format_metrics(metrics, task, err_msg):
+    """
+    Format metrics. 'err' field returns any error occured during evaluation.
+
+    Args:
+        metrics: A dict object contains metrics for different tasks.
+        task: Task name.
+        err_msg: Exception raised during evaluation.
+    Returns:
+        Formatted result.
+    """
+    result = {}
+    sources = ["both", "search", "zhidao"]
+    if err_msg is not None:
+        return {'errorMsg': str(err_msg), 'errorCode': 1, 'data': []}
+    data = []
+    if task != 'all' and task != 'main':
+        sources = ["both"]
+
+    if task == 'entity':
+        metric_names = ["Bleu-4", "Rouge-L"]
+        metric_names_prf = ["F1", "Precision", "Recall"]
+        for name in metric_names + metric_names_prf:
+            for src in sources:
+                obj = {
+                    "name": name,
+                    "value": round(metrics[src].get(name, 0) * 100, 2),
+                    "type": src,
+                    }
+                data.append(obj)
+    elif task == 'yesno':
+        metric_names = ["Bleu-4", "Rouge-L"]
+        details = ["Yes", "No", "Depends"]
+        src = sources[0]
+        for name in metric_names:
+            obj = {
+                "name": name,
+                "value": round(metrics[src].get(name, 0) * 100, 2),
+                "type": 'All',
+                }
+            data.append(obj)
+            for d in details:
+                obj = {
+                    "name": name,
+                    "value": \
+                        round(metrics[src].get(d + '|' + name, 0) * 100, 2),
+                    "type": d,
+                    }
+                data.append(obj)
+    else:
+        metric_names = ["Bleu-4", "Rouge-L"]
+        for name in metric_names:
+            for src in sources:
+                obj = {
+                    "name": name,
+                    "value": \
+                        round(metrics[src].get(name, 0) * 100, 2),
+                    "type": src,
+                    }
+                data.append(obj)
+
+    result["data"] = data
+    result["errorCode"] = 0
+    result["errorMsg"] = "success"
+
+    return result
+
+
+def main(args):
+    """
+    Do evaluation.
+    """
+    err = None
+    metrics = {}
+    try:
+        pred_result = read_file(args.pred_file, args.task)
+        ref_result = read_file(args.ref_file, args.task, is_ref=True)
+        sources = ['both', 'search', 'zhidao']
+        if args.task not in set(['main', 'all']):
+            sources = sources[:1]
+        for source in sources:
+            metrics[source] = get_metrics(
+                    pred_result, ref_result, args.task, source)
+    except ValueError as ve:
+        err = ve
+    except AssertionError as ae:
+        err = ae
+
+    print(json.dumps(
+            format_metrics(metrics, args.task, err),
+            ensure_ascii=False).encode('utf8'))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('pred_file', help='predict file')
+    parser.add_argument('ref_file', help='reference file')
+    parser.add_argument('task',
+            help='task name: Main|Yes_No|All|Entity|Description')
+
+    args = parser.parse_args()
+    args.task = args.task.lower().replace('_', '')
+    main(args)
diff --git a/PaddleNLP/reading_comprehension/utils/get_vocab.py b/PaddleNLP/reading_comprehension/utils/get_vocab.py
new file mode 100644
index 0000000000000000000000000000000000000000..4dc62a2410ffd37b3bf2be8c1c8df8b588dd5862
--- /dev/null
+++ b/PaddleNLP/reading_comprehension/utils/get_vocab.py
@@ -0,0 +1,67 @@
+# -*- coding:utf8 -*-
+# ==============================================================================
+# Copyright 2017 Baidu.com, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+Utility function to generate vocabulary file.
+"""
+
+
+import argparse
+import sys
+import json
+
+from itertools import chain
+
+
+def get_vocab(files, vocab_file):
+    """
+    Builds vocabulary file from field 'segmented_paragraphs'
+    and 'segmented_question'.
+
+    Args:
+        files: A list of file names.
+        vocab_file: The file that stores the vocabulary.
+    """
+    vocab = {}
+    for f in files:
+        with open(f, 'r') as fin:
+            for line in fin:
+                obj = json.loads(line.strip())
+                paras = [
+                        chain(*d['segmented_paragraphs'])
+                        for d in obj['documents']]
+                doc_tokens = chain(*paras)
+                question_tokens = obj['segmented_question']
+                for t in list(doc_tokens) + question_tokens:
+                    vocab[t] = vocab.get(t, 0) + 1
+    # output
+    sorted_vocab = sorted([(v, c) for v, c in vocab.items()],
+            key=lambda x: x[1],
+            reverse=True)
+    with open(vocab_file, 'w') as outf:
+        for w, c in sorted_vocab:
+            print >> outf, '{}\t{}'.format(w.encode('utf8'), c)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--files', nargs='+', required=True,
+            help='file list to count vocab from.')
+    parser.add_argument('--vocab', required=True,
+            help='file to store counted vocab.')
+    args = parser.parse_args()
+    get_vocab(args.files, args.vocab)
+
diff --git a/PaddleNLP/reading_comprehension/utils/marco_tokenize_data.py b/PaddleNLP/reading_comprehension/utils/marco_tokenize_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..7273e7046e82cd816298af81c175ab10136b16d0
--- /dev/null
+++ b/PaddleNLP/reading_comprehension/utils/marco_tokenize_data.py
@@ -0,0 +1,43 @@
+#coding=utf8
+
+import os, sys, json
+import nltk
+
+def _nltk_tokenize(sequence):
+    tokens = nltk.word_tokenize(sequence)
+
+    cur_char_offset = 0
+    token_offsets = []
+    token_words = []
+    for token in tokens:
+        cur_char_offset = sequence.find(token, cur_char_offset)
+        token_offsets.append([cur_char_offset, cur_char_offset + len(token) - 1])
+        token_words.append(token)
+    return token_offsets, token_words
+
+def segment(input_js):
+    _, input_js['segmented_question'] = _nltk_tokenize(input_js['question'])
+    for doc_id, doc in enumerate(input_js['documents']):
+        doc['segmented_title'] = []
+        doc['segmented_paragraphs'] = []
+        for para_id, para in enumerate(doc['paragraphs']):
+            _, seg_para = _nltk_tokenize(para)
+            doc['segmented_paragraphs'].append(seg_para)
+    if 'answers' in input_js:
+        input_js['segmented_answers'] = []
+        for answer_id, answer in enumerate(input_js['answers']):
+            _, seg_answer = _nltk_tokenize(answer)
+            input_js['segmented_answers'].append(seg_answer)
+
+
+if __name__ == '__main__':
+    if len(sys.argv) != 2:
+        print('Usage: tokenize_data.py ')
+        exit()
+
+    nltk.download('punkt')
+    
+    for line in open(sys.argv[1]):
+        dureader_js = json.loads(line.strip())
+        segment(dureader_js)
+        print(json.dumps(dureader_js))
diff --git a/PaddleNLP/machine_reading_comprehension/utils/marcov1_to_dureader.py b/PaddleNLP/reading_comprehension/utils/marcov1_to_dureader.py
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/utils/marcov1_to_dureader.py
rename to PaddleNLP/reading_comprehension/utils/marcov1_to_dureader.py
diff --git a/PaddleNLP/reading_comprehension/utils/marcov2_to_v1_tojsonl.py b/PaddleNLP/reading_comprehension/utils/marcov2_to_v1_tojsonl.py
new file mode 100644
index 0000000000000000000000000000000000000000..c301e12f6edd566af2ede63c3c8780daedd75468
--- /dev/null
+++ b/PaddleNLP/reading_comprehension/utils/marcov2_to_v1_tojsonl.py
@@ -0,0 +1,14 @@
+import sys
+import json
+import pandas as pd 
+
+if __name__ == '__main__':
+    if len(sys.argv) != 3:
+        print('Usage: tojson.py  ')
+        exit()
+    infile = sys.argv[1]
+    outfile = sys.argv[2]
+    df = pd.read_json(infile)
+    with open(outfile, 'w') as f:
+        for row in df.iterrows():
+            f.write(row[1].to_json() + '\n')
\ No newline at end of file
diff --git a/PaddleNLP/reading_comprehension/utils/preprocess.py b/PaddleNLP/reading_comprehension/utils/preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..af76549f4427a341fed341a7d09fd5bb7230de7b
--- /dev/null
+++ b/PaddleNLP/reading_comprehension/utils/preprocess.py
@@ -0,0 +1,218 @@
+###############################################################################
+# ==============================================================================
+# Copyright 2017 Baidu.com, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+This module finds the most related paragraph of each document according to recall.
+"""
+
+import sys
+if sys.version[0] == '2':
+    reload(sys)
+    sys.setdefaultencoding("utf-8")
+import json
+from collections import Counter
+
+
+def precision_recall_f1(prediction, ground_truth):
+    """
+    This function calculates and returns the precision, recall and f1-score
+    Args:
+        prediction: prediction string or list to be matched
+        ground_truth: golden string or list reference
+    Returns:
+        floats of (p, r, f1)
+    Raises:
+        None
+    """
+    if not isinstance(prediction, list):
+        prediction_tokens = prediction.split()
+    else:
+        prediction_tokens = prediction
+    if not isinstance(ground_truth, list):
+        ground_truth_tokens = ground_truth.split()
+    else:
+        ground_truth_tokens = ground_truth
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return 0, 0, 0
+    p = 1.0 * num_same / len(prediction_tokens)
+    r = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * p * r) / (p + r)
+    return p, r, f1
+
+
+def recall(prediction, ground_truth):
+    """
+    This function calculates and returns the recall
+    Args:
+        prediction: prediction string or list to be matched
+        ground_truth: golden string or list reference
+    Returns:
+        floats of recall
+    Raises:
+        None
+    """
+    return precision_recall_f1(prediction, ground_truth)[1]
+
+
+def f1_score(prediction, ground_truth):
+    """
+    This function calculates and returns the f1-score
+    Args:
+        prediction: prediction string or list to be matched
+        ground_truth: golden string or list reference
+    Returns:
+        floats of f1
+    Raises:
+        None
+    """
+    return precision_recall_f1(prediction, ground_truth)[2]
+
+
+def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
+    """
+    This function calculates and returns the precision, recall and f1-score
+    Args:
+        metric_fn: metric function pointer which calculates scores according to corresponding logic.
+        prediction: prediction string or list to be matched
+        ground_truth: golden string or list reference
+    Returns:
+        floats of (p, r, f1)
+    Raises:
+        None
+    """
+    scores_for_ground_truths = []
+    for ground_truth in ground_truths:
+        score = metric_fn(prediction, ground_truth)
+        scores_for_ground_truths.append(score)
+    return max(scores_for_ground_truths)
+
+
+def find_best_question_match(doc, question, with_score=False):
+    """
+    For each document, find the paragraph that matches best to the question.
+    Args:
+        doc: The document object.
+        question: The question tokens.
+        with_score: If True then the match score will be returned,
+            otherwise False.
+    Returns:
+        The index of the best match paragraph, if with_score=False,
+        otherwise returns a tuple of the index of the best match paragraph
+        and the match score of that paragraph.
+    """
+    most_related_para = -1
+    max_related_score = 0
+    most_related_para_len = 0
+    for p_idx, para_tokens in enumerate(doc['segmented_paragraphs']):
+        if len(question) > 0:
+            related_score = metric_max_over_ground_truths(recall,
+                    para_tokens,
+                    question)
+        else:
+            related_score = 0
+
+        if related_score > max_related_score \
+                or (related_score == max_related_score \
+                and len(para_tokens) < most_related_para_len):
+            most_related_para = p_idx
+            max_related_score = related_score
+            most_related_para_len = len(para_tokens)
+    if most_related_para == -1:
+        most_related_para = 0
+    if with_score:
+        return most_related_para, max_related_score
+    return most_related_para
+
+
+def find_fake_answer(sample):
+    """
+    For each document, finds the most related paragraph based on recall,
+    then finds a span that maximize the f1_score compared with the gold answers
+    and uses this span as a fake answer span
+    Args:
+        sample: a sample in the dataset
+    Returns:
+        None
+    Raises:
+        None
+    """
+    for doc in sample['documents']:
+        most_related_para = -1
+        most_related_para_len = 999999
+        max_related_score = 0
+        for p_idx, para_tokens in enumerate(doc['segmented_paragraphs']):
+            if len(sample['segmented_answers']) > 0:
+                related_score = metric_max_over_ground_truths(recall,
+                                                              para_tokens,
+                                                              sample['segmented_answers'])
+            else:
+                continue
+            if related_score > max_related_score \
+                    or (related_score == max_related_score
+                        and len(para_tokens) < most_related_para_len):
+                most_related_para = p_idx
+                most_related_para_len = len(para_tokens)
+                max_related_score = related_score
+        doc['most_related_para'] = most_related_para
+
+    sample['answer_docs'] = []
+    sample['answer_spans'] = []
+    sample['fake_answers'] = []
+    sample['match_scores'] = []
+
+    best_match_score = 0
+    best_match_d_idx, best_match_span = -1, [-1, -1]
+    best_fake_answer = None
+    answer_tokens = set()
+    for segmented_answer in sample['segmented_answers']:
+        answer_tokens = answer_tokens | set([token for token in segmented_answer])
+    for d_idx, doc in enumerate(sample['documents']):
+        if not doc['is_selected']:
+            continue
+        if doc['most_related_para'] == -1:
+            doc['most_related_para'] = 0
+        most_related_para_tokens = doc['segmented_paragraphs'][doc['most_related_para']][:1000]
+        for start_tidx in range(len(most_related_para_tokens)):
+            if most_related_para_tokens[start_tidx] not in answer_tokens:
+                continue
+            for end_tidx in range(len(most_related_para_tokens) - 1, start_tidx - 1, -1):
+                span_tokens = most_related_para_tokens[start_tidx: end_tidx + 1]
+                if len(sample['segmented_answers']) > 0:
+                    match_score = metric_max_over_ground_truths(f1_score, span_tokens,
+                                                                sample['segmented_answers'])
+                else:
+                    match_score = 0
+                if match_score == 0:
+                    break
+                if match_score > best_match_score:
+                    best_match_d_idx = d_idx
+                    best_match_span = [start_tidx, end_tidx]
+                    best_match_score = match_score
+                    best_fake_answer = ''.join(span_tokens)
+    if best_match_score > 0:
+        sample['answer_docs'].append(best_match_d_idx)
+        sample['answer_spans'].append(best_match_span)
+        sample['fake_answers'].append(best_fake_answer)
+        sample['match_scores'].append(best_match_score)
+
+
+if __name__ == '__main__':
+    for line in sys.stdin:
+        sample = json.loads(line)
+        find_fake_answer(sample)
+        print(json.dumps(sample, encoding='utf8', ensure_ascii=False))
diff --git a/PaddleNLP/machine_reading_comprehension/utils/run_marco2dureader_preprocess.sh b/PaddleNLP/reading_comprehension/utils/run_marco2dureader_preprocess.sh
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/utils/run_marco2dureader_preprocess.sh
rename to PaddleNLP/reading_comprehension/utils/run_marco2dureader_preprocess.sh
diff --git a/PaddleNLP/sentiment_classification/README.md b/PaddleNLP/sentiment_classification/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ab50cdb2c533c0d068e67f3df24e56af78a69c2c
--- /dev/null
+++ b/PaddleNLP/sentiment_classification/README.md
@@ -0,0 +1,184 @@
+## 简介
+
+情感倾向分析（Sentiment Classification，简称Senta）针对带有主观描述的中文文本，可自动判断该文本的情感极性类别并给出相应的置信度。情感类型分为积极、消极。情感倾向分析能够帮助企业理解用户消费习惯、分析热点话题和危机舆情监控，为企业提供有利的决策支持。可通过[AI开放平台-情感倾向分析](http://ai.baidu.com/tech/nlp_apply/sentiment_classify) 线上体验。
+
+情感是人类的一种高级智能行为，为了识别文本的情感倾向，需要深入的语义建模。另外，不同领域（如餐饮、体育）在情感的表达各不相同，因而需要有大规模覆盖各个领域的数据进行模型训练。为此，我们通过基于深度学习的语义模型和大规模数据挖掘解决上述两个问题。效果上，我们基于开源情感倾向分类数据集ChnSentiCorp进行评测；此外，我们还开源了百度基于海量数据训练好的模型，该模型在ChnSentiCorp数据集上fine-tune之后（基于开源模型进行Finetune的方法请见下面章节），可以得到更好的效果。具体数据如下所示：
+ 
+| 模型 | dev | test | 模型（finetune） |dev | test | 
+| :------| :------ | :------ | :------ |:------ | :------
+| BOW | 89.8% | 90.0% | BOW |91.3% | 90.6% |
+| CNN | 90.6% | 89.9% | CNN |92.4% | 91.8% |
+| LSTM | 90.0% | 91.0% | LSTM |93.3% | 92.2% |
+| GRU | 90.0% | 89.8% | GRU |93.3% | 93.2% |
+| BI-LSTM | 88.5% | 88.3% | BI-LSTM |92.8% | 91.4% |
+| ERNIE | 95.1% | 95.4% | ERNIE |95.4% | 95.5% |
+| ERNIE+BI-LSTM | 95.3% | 95.2% | ERNIE+BI-LSTM |95.7% | 95.6% |
+
+
+
+## 快速开始
+
+本项目依赖于 Paddlepaddle Fluid 1.3.2，请参考 [安装指南](http://www.paddlepaddle.org/#quick-start) 进行安装
+python版本依赖python 2.7
+
+#### 安装代码
+
+克隆数据集代码库到本地
+```shell
+git clone https://github.com/PaddlePaddle/models.git
+cd models/PaddleNLP/sentiment_classification 
+```
+
+#### 数据准备
+
+下载经过预处理的数据，运行该脚本之后，data目录下会存在训练数据（train.tsv）、开发集数据（dev.tsv）、测试集数据（test.tsv）以及对应的词典（word_dict.txt）
+```shell
+wget https://baidu-nlp.bj.bcebos.com/sentiment_classification-dataset-1.0.0.tar.gz
+tar -zxvf sentiment_classification-dataset-1.0.0.tar.gz
+```
+
+#### 模型下载
+
+我们开源了基于ChnSentiCorp数据训练的情感倾向性分类模型（基于BOW、CNN、LSTM、ERNIE多种模型训练），可供用户直接使用。我们提供了两种下载方式：
+
+方式一：基于PaddleHub命令行工具（PaddleHub安装方式 https://github.com/PaddlePaddle/PaddleHub )
+```shell
+hub download sentiment_classification --output_path ./
+tar -zxvf sentiment_classification-1.0.0.tar.gz
+```
+
+方式二：直接下载
+```shell
+wget https://baidu-nlp.bj.bcebos.com/sentiment_classification-1.0.0.tar.gz
+tar -zxvf sentiment_classification-1.0.0.tar.gz
+```
+
+#### 模型评估
+
+基于上面的预训练模型和数据，可以运行下面的命令进行测试，查看预训练模型在开发集（dev.tsv）上的评测效果
+```shell
+# BOW、CNN、LSTM、BI-LSTM、GRU模型
+sh run.sh eval
+# ERNIE、ERNIE+BI-LSTM模型
+sh run_ernie.sh eval
+```
+注：如果用户需要使用预训练的BI-LSTM模型，需要修改run.sh和senta_config.json中的配置。run.sh脚本修改如下：
+```shell
+MODEL_PATH=senta_model/bilstm_model/
+# 在eval()函数中，修改如下参数：
+--vocab_path $MODEL_PATH/word_dict.txt
+--init_checkpoint $MODEL_PATH/params
+```
+senta_config.json中需要修改如下：
+```shell
+# vob_size大小对应为上面senta_model/bilstm_model//word_dict.txt，词典大小
+"vocab_size": 1256606
+```
+如果用户需要使用预训练的ERNIE+BI-LSTM模型，需要修改run_ernie.sh中的配置如下：
+```shell
+# 在eval()函数中，修改如下参数：
+--init_checkpoint senta_model/ernie_bilstm_model/
+--model_type "ernie_bilstm"
+```
+
+#### 模型训练
+
+基于示例的数据集，可以运行下面的命令，在训练集（train.tsv）上进行模型训练，并在开发集（dev.tsv）验证
+```shell
+# BOW、CNN、LSTM、BI-LSTM、GRU模型
+sh run.sh train
+# ERNIE、ERNIE+BI-LSTM模型
+sh run_ernie.sh train
+```
+训练完成后，可修改```run.sh```中init_checkpoint参数，进行模型评估和预测
+
+#### 模型预测
+
+利用已有模型，可以运行下面命令，对未知label的数据（test.tsv）进行预测
+```shell
+# BOW、CNN、LSTM、BI-LSTM、GRU模型
+sh run.sh infer
+#ERNIE+BI-LSTM模型
+sh run_ernie.sh infer
+```
+
+## 进阶使用
+
+#### 任务定义
+
+传统的情感分类主要基于词典或者特征工程的方式进行分类，这种方法需要繁琐的人工特征设计和先验知识，理解停留于浅层并且扩展泛化能力差。为了避免传统方法的局限，我们采用近年来飞速发展的深度学习技术。基于深度学习的情感分类不依赖于人工特征，它能够端到端的对输入文本进行语义理解，并基于语义表示进行情感倾向的判断。
+#### 模型原理介绍
+
+本项目针对情感倾向性分类问题，开源了一系列模型，供用户可配置地使用：
+
++ BOW（Bag Of Words）模型，是一个非序列模型，使用基本的全连接结构；
++ CNN（Convolutional Neural Networks），是一个基础的序列模型，能处理变长序列输入，提取局部区域之内的特征；
++ GRU（Gated Recurrent Unit），序列模型，能够较好地解决序列文本中长距离依赖的问题；
++ LSTM（Long Short Term Memory），序列模型，能够较好地解决序列文本中长距离依赖的问题；
++ BI-LSTM（Bidirectional Long Short Term Memory），序列模型，采用双向LSTM结构，更好地捕获句子中的语义特征；
++ ERNIE（Enhanced Representation through kNowledge IntEgration），百度自研基于海量数据和先验知识训练的通用文本语义表示模型，并基于此在情感倾向分类数据集上进行fine-tune获得。
++ ERNIE+BI-LSTM，基于ERNIE语义表示对接上层BI-LSTM模型，并基于此在情感倾向分类数据集上进行Fine-tune获得；
+
+#### 数据格式说明
+
+训练、预测、评估使用的数据可以由用户根据实际的应用场景，自己组织数据。数据由两列组成，以制表符分隔，第一列是以空格分词的中文文本（分词预处理方法将在下文具体说明），文件为utf8编码；第二列是情感倾向分类的类别（0表示消极；1表示积极），注意数据文件第一行固定表示为"text_a\tlabel"
+
+```text
+特 喜欢 这种 好看的 狗狗	              1
+这 真是 惊艳 世界 的 中国 黑科技	      1
+环境 特别 差 ，脏兮兮 的，再也 不去 了     0
+```
+注：本项目额外提供了分词预处理脚本（在本项目的preprocess目录下），可供用户使用，具体使用方法如下：
+```shell
+python tokenizer.py --test_data_dir ./test.txt.utf8 --batch_size 1 > test.txt.utf8.seg
+
+#其中test.txt.utf8为待分词的文件，一条文本数据一行，utf8编码，分词结果存放在test.txt.utf8.seg文件中。
+```
+
+#### 代码结构说明
+
+```text
+.
+├── senta_config.json       # 模型配置文件
+├── config.py               # 定义了该项目模型的相关配置，包括具体模型类别、以及模型的超参数
+├── reader.py               # 定义了读入数据，加载词典的功能
+├── run_classifier.py       # 该项目的主函数，封装包括训练、预测、评估的部分
+├── run_ernie_classifier.py # 基于ERNIE表示的项目的主函数
+├── run_ernie.sh            # 基于ERNIE的训练、预测、评估运行脚本
+├── run.sh                  # 训练、预测、评估运行脚本
+├── utils.py                # 定义了其他常用的功能函数
+```
+
+#### 如何组建自己的模型
+
+可以根据自己的需求，组建自定义的模型，具体方法如下所示：
+
+1. 定义自己的网络结构 
+用户可以在 ```models/classification/nets.py``` 中，定义自己的模型，只需要增加新的函数即可。假设用户自定义的函数名为```user_net```
+2. 更改模型配置
+在 ```senta_config.json``` 中需要将 ```model_type``` 改为用户自定义的 ```user_net```
+3. 模型训练、评估、预测需要在 run.sh 、run_ernie.sh 中将模型、数据、词典路径等配置进行修改
+
+#### 如何基于百度开源模型进行Finetune
+用户可基于百度开源模型在自有数据上实现Finetune训练，以期获得更好的效果提升；如『简介』部分中，我们基于百度开源模型在ChnSentiCorp数据集上Finetune后可以得到更好的效果，具体模型Finetune方法如下所示，如果用户基于开源BI-LSTM模型进行Finetune，需要修改run.sh和senta_config.json文件；
+run.sh脚本修改如下：
+```shell
+# 在train()函数中，增加--init_checkpoint选项；修改--vocab_path
+--init_checkpoint senta_model/bilstm_model/params
+--vocab_path senta_model/bilstm_model/word_dict.txt
+```
+senta_config.json中需要修改如下：
+```shell
+# vob_size大小对应为上面senta_model/bilstm_model//word_dict.txt，词典大小
+"vocab_size": 1256606
+```
+如果用户基于开源ERNIE+BI-LSTM模型进行Finetune，需要更新run_ernie.sh脚本，具体修改如下：
+```shell
+# 在train()函数中，修改--init_checkpoint选项；修改--model_type
+--init_checkpoint senta_model/ernie_bilstm_model
+--model_type "ernie_bilstm"
+```
+
+## 如何贡献代码
+
+如果你可以修复某个issue或者增加一个新功能，欢迎给我们提交PR。如果对应的PR被接受了，我们将根据贡献的质量和难度进行打分（0-5分，越高越好）。如果你累计获得了10分，可以联系我们获得面试机会或者为你写推荐信。
diff --git a/PaddleNLP/sentiment_classification/config.py b/PaddleNLP/sentiment_classification/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..49023d27e1c363f63b75d4ec1caa797564e34788
--- /dev/null
+++ b/PaddleNLP/sentiment_classification/config.py
@@ -0,0 +1,41 @@
+"""
+Senta model.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import six
+import json
+import numpy as np
+import paddle.fluid as fluid
+
+class SentaConfig(object):
+    """
+    Senta Config
+    """
+    def __init__(self, config_path):
+        self._config_dict = self._parse(config_path)
+
+    def _parse(self, config_path):
+        try:
+            with open(config_path) as json_file:
+                config_dict = json.load(json_file)
+        except Exception:
+            raise IOError("Error in parsing bert model config file '%s'" %
+                config_path)
+
+        else:
+            return config_dict
+
+    def __getitem__(self, key):
+        return self._config_dict[key]
+
+    def print_config(self):
+        """
+        Print Config
+        """
+        for arg, value in sorted(six.iteritems(self._config_dict)):
+            print('%s: %s' % (arg, value))
+        print('------------------------------------------------')
diff --git a/PaddleNLP/sentiment_classification/reader.py b/PaddleNLP/sentiment_classification/reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..38a0f6e95c8640155f4369f8547279ccb01d5ff3
--- /dev/null
+++ b/PaddleNLP/sentiment_classification/reader.py
@@ -0,0 +1,80 @@
+"""
+Senta Reader
+"""
+
+import os
+import types
+import csv
+import numpy as np
+from utils import load_vocab
+from utils import data_reader
+
+import paddle
+import paddle.fluid as fluid
+
+class SentaProcessor(object):
+    """
+    Processor class for data convertors for senta
+    """
+
+    def __init__(self,
+                 data_dir,
+                 vocab_path,
+                 random_seed=None):
+        self.data_dir = data_dir
+        self.vocab = load_vocab(vocab_path)
+        self.num_examples = {"train": -1, "dev": -1, "infer": -1}
+        np.random.seed(random_seed)
+
+    def get_train_examples(self, data_dir, epoch):
+        """
+        Load training examples
+        """
+        return data_reader((self.data_dir + "/train.tsv"), self.vocab, self.num_examples, "train", epoch)
+
+    def get_dev_examples(self, data_dir, epoch):
+        """
+        Load dev examples
+        """
+        return data_reader((self.data_dir + "/dev.tsv"), self.vocab, self.num_examples, "dev", epoch)
+
+    def get_test_examples(self, data_dir, epoch):
+        """
+        Load test examples
+        """
+        return data_reader((self.data_dir + "/test.tsv"), self.vocab, self.num_examples, "infer", epoch)
+
+    def get_labels(self):
+        """
+        Return Labels
+        """
+        return ["0", "1"]
+
+    def get_num_examples(self, phase):
+        """
+        Return num of examples in train, dev, test set
+        """
+        if phase not in ['train', 'dev', 'infer']:
+            raise ValueError(
+                "Unknown phase, which should be in ['train', 'dev', 'infer'].")
+        return self.num_examples[phase]
+
+    def get_train_progress(self):
+        """
+        Get train progress
+        """
+        return self.current_train_example, self.current_train_epoch
+
+    def data_generator(self, batch_size, phase='train', epoch=1, shuffle=True):
+        """
+        Generate data for train, dev or infer
+        """
+        if phase == "train":
+            return paddle.batch(self.get_train_examples(self.data_dir, epoch), batch_size)
+        elif phase == "dev":
+            return paddle.batch(self.get_dev_examples(self.data_dir, epoch), batch_size)
+        elif phase == "infer":
+            return paddle.batch(self.get_test_examples(self.data_dir, epoch), batch_size)
+        else:
+            raise ValueError(
+                "Unknown phase, which should be in ['train', 'dev', 'infer'].")
diff --git a/PaddleNLP/sentiment_classification/run.sh b/PaddleNLP/sentiment_classification/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a1f79a4b80ca2167cb12089e952bfa87bd7baf6a
--- /dev/null
+++ b/PaddleNLP/sentiment_classification/run.sh
@@ -0,0 +1,85 @@
+#! /bin/bash
+export FLAGS_enable_parallel_graph=1
+export FLAGS_sync_nccl_allreduce=1
+export CUDA_VISIBLE_DEVICES=3
+export FLAGS_fraction_of_gpu_memory_to_use=0.95
+export CPU_NUM=1
+
+TASK_NAME='senta'
+DATA_PATH=./senta_data/
+CKPT_PATH=./save_models
+MODEL_PATH=./save_models/step_1800/
+
+# run_train on train.tsv and do_val on test.tsv
+train() {
+    python -u run_classifier.py \
+        --task_name ${TASK_NAME} \
+        --use_cuda true \
+        --do_train true \
+        --do_val true \
+        --do_infer false \
+        --batch_size 16 \
+        --data_dir ${DATA_PATH} \
+        --vocab_path ${DATA_PATH}/word_dict.txt \
+        --checkpoints ${CKPT_PATH} \
+        --save_steps 50 \
+        --validation_steps 50 \
+        --epoch 3 \
+        --senta_config_path ./senta_config.json \
+        --skip_steps 10
+}
+
+# run_eval on test.tsv
+evaluate() {
+    python -u run_classifier.py \
+        --task_name ${TASK_NAME} \
+        --use_cuda true \
+        --do_train false \
+        --do_val true \
+        --do_infer false \
+        --batch_size 10 \
+        --data_dir ${DATA_PATH} \
+        --vocab_path ${DATA_PATH}/word_dict.txt \
+        --init_checkpoint ${MODEL_PATH} \
+        --senta_config_path ./senta_config.json 
+}
+
+# run_infer on infer.tsv
+infer() {
+    python -u run_classifier.py \
+        --task_name ${TASK_NAME} \
+        --use_cuda true \
+        --do_train false \
+        --do_val false \
+        --do_infer true \
+        --batch_size 10 \
+        --data_dir ${DATA_PATH} \
+        --vocab_path ${DATA_PATH}/word_dict.txt \
+        --init_checkpoint ${MODEL_PATH} \
+        --senta_config_path ./senta_config.json
+}
+
+main() {
+    local cmd=${1:-help}
+    case "${cmd}" in
+        train)
+            train "$@";
+            ;;
+        eval)
+            evaluate "$@";
+            ;;
+        infer)
+            infer "$@";
+            ;;
+        help)
+            echo "Usage: ${BASH_SOURCE} {train|eval|infer}";
+            return 0;
+            ;;
+        *)
+            echo "Unsupport commend [${cmd}]";
+            echo "Usage: ${BASH_SOURCE} {train|eval|infer}";
+            return 1;
+            ;;
+    esac
+}
+main "$@"
diff --git a/PaddleNLP/sentiment_classification/run_classifier.py b/PaddleNLP/sentiment_classification/run_classifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..e93a56376919f3d95f72fbe644153325062081a7
--- /dev/null
+++ b/PaddleNLP/sentiment_classification/run_classifier.py
@@ -0,0 +1,369 @@
+"""
+Sentiment Classification Task
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import time
+import argparse
+import numpy as np
+import multiprocessing
+import sys
+sys.path.append("../models/classification/")
+
+from nets import bow_net
+from nets import lstm_net
+from nets import cnn_net
+from nets import bilstm_net
+from nets import gru_net
+
+import paddle
+import paddle.fluid as fluid
+
+import reader
+from config import SentaConfig
+from utils import ArgumentGroup, print_arguments
+from utils import init_checkpoint
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+model_g = ArgumentGroup(parser, "model", "model configuration and paths.")
+model_g.add_arg("senta_config_path", str, None, "Path to the json file for senta model config.")
+model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.")
+model_g.add_arg("checkpoints", str, "checkpoints", "Path to save checkpoints")
+
+train_g = ArgumentGroup(parser, "training", "training options.")
+train_g.add_arg("epoch", int, 10, "Number of epoches for training.")
+train_g.add_arg("save_steps", int, 10000, "The steps interval to save checkpoints.")
+train_g.add_arg("validation_steps", int, 1000, "The steps interval to evaluate model performance.")
+train_g.add_arg("lr", float, 0.002, "The Learning rate value for training.")
+
+log_g = ArgumentGroup(parser, "logging", "logging related")
+log_g.add_arg("skip_steps", int, 10, "The steps interval to print loss.")
+log_g.add_arg("verbose", bool, False, "Whether to output verbose log")
+
+data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options")
+data_g.add_arg("data_dir", str, None, "Path to training data.")
+data_g.add_arg("vocab_path", str, None, "Vocabulary path.")
+data_g.add_arg("batch_size", int, 256, "Total examples' number in batch for training.")
+data_g.add_arg("random_seed", int, 0, "Random seed.")
+
+run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
+run_type_g.add_arg("use_cuda", bool, True, "If set, use GPU for training.")
+run_type_g.add_arg("task_name", str, None,
+    "The name of task to perform sentiment classification.")
+run_type_g.add_arg("do_train", bool, True, "Whether to perform training.")
+run_type_g.add_arg("do_val", bool, True, "Whether to perform evaluation.")
+run_type_g.add_arg("do_infer", bool, True, "Whether to perform inference.")
+
+args = parser.parse_args()
+# yapf: enable.
+
+def create_model(args,
+                 pyreader_name,
+                 senta_config,
+                 num_labels,
+                 is_inference=False):
+
+    """
+    Create Model for sentiment classification
+    """
+
+    pyreader = fluid.layers.py_reader(
+        capacity=16,
+        shapes=([-1, 1], [-1, 1]),
+        dtypes=('int64', 'int64'),
+        lod_levels=(1, 0),
+        name=pyreader_name,
+        use_double_buffer=False)
+
+    if senta_config['model_type'] == "bilstm_net":
+        network = bilstm_net
+    elif senta_config['model_type'] == "bow_net":
+        network = bow_net
+    elif senta_config['model_type'] == "cnn_net":
+        network = cnn_net
+    elif senta_config['model_type'] == "lstm_net":
+        network = lstm_net
+    elif senta_config['model_type'] == "gru_net":
+        network = gru_net
+    else:
+        raise ValueError("Unknown network type!")
+
+    if is_inference:
+        data, label = fluid.layers.read_file(pyreader)
+        probs = network(data, None, senta_config["vocab_size"], is_infer=is_inference)
+        print("create inference model...")
+        return pyreader, probs
+
+    data, label = fluid.layers.read_file(pyreader)
+    ce_loss, probs = network(data, label, senta_config["vocab_size"], is_infer=is_inference)
+    loss = fluid.layers.mean(x=ce_loss)
+    num_seqs = fluid.layers.create_tensor(dtype='int64')
+    accuracy = fluid.layers.accuracy(input=probs, label=label, total=num_seqs)
+    return pyreader, loss, accuracy, num_seqs
+
+
+
+def evaluate(exe, test_program, test_pyreader, fetch_list, eval_phase):
+    """
+    Evaluation Function
+    """
+    test_pyreader.start()
+    total_cost, total_acc, total_num_seqs = [], [], []
+    time_begin = time.time()
+    while True:
+        try:
+            np_loss, np_acc, np_num_seqs = exe.run(program=test_program,
+                                                   fetch_list=fetch_list,
+                                                   return_numpy=False)
+            np_loss = np.array(np_loss)
+            np_acc = np.array(np_acc)
+            np_num_seqs = np.array(np_num_seqs)
+            total_cost.extend(np_loss * np_num_seqs)
+            total_acc.extend(np_acc * np_num_seqs)
+            total_num_seqs.extend(np_num_seqs)
+        except fluid.core.EOFException:
+            test_pyreader.reset()
+            break
+    time_end = time.time()
+    print("[%s evaluation] ave loss: %f, ave acc: %f, elapsed time: %f s" %
+        (eval_phase, np.sum(total_cost) / np.sum(total_num_seqs),
+        np.sum(total_acc) / np.sum(total_num_seqs), time_end - time_begin))
+
+
+def inference(exe, test_program, test_pyreader, fetch_list, infer_phrase):
+    """
+    Inference Function
+    """
+    test_pyreader.start()
+    time_begin = time.time()
+    while True:
+        try:
+            np_props = exe.run(program=test_program, fetch_list=fetch_list,
+                                return_numpy=True)
+            for probs in np_props[0]:
+                print("%d\t%f\t%f" % (np.argmax(probs), probs[0], probs[1]))
+        except fluid.core.EOFException:
+            test_pyreader.reset()
+            break
+    time_end = time.time()
+    print("[%s] elapsed time: %f s" % (infer_phrase, time_end - time_begin))
+
+
+def main(args):
+    """
+    Main Function
+    """
+    senta_config = SentaConfig(args.senta_config_path)
+
+    if args.use_cuda:
+        place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0')))
+        dev_count = fluid.core.get_cuda_device_count()
+    else:
+        place = fluid.CPUPlace()
+        dev_count = 1
+    exe = fluid.Executor(place)
+
+    task_name = args.task_name.lower()
+    processor = reader.SentaProcessor(data_dir=args.data_dir,
+                                      vocab_path=args.vocab_path,
+                                      random_seed=args.random_seed)
+    num_labels = len(processor.get_labels())
+
+
+    if not (args.do_train or args.do_val or args.do_infer):
+        raise ValueError("For args `do_train`, `do_val` and `do_infer`, at "
+                         "least one of them must be True.")
+
+    startup_prog = fluid.Program()
+    if args.random_seed is not None:
+        startup_prog.random_seed = args.random_seed
+
+    if args.do_train:
+        train_data_generator = processor.data_generator(
+            batch_size=args.batch_size,
+            phase='train',
+            epoch=args.epoch,
+            shuffle=True)
+
+        num_train_examples = processor.get_num_examples(phase="train")
+
+        max_train_steps = args.epoch * num_train_examples // args.batch_size // dev_count
+
+        print("Device count: %d" % dev_count)
+        print("Num train examples: %d" % num_train_examples)
+        print("Max train steps: %d" % max_train_steps)
+
+        train_program = fluid.Program()
+
+        with fluid.program_guard(train_program, startup_prog):
+            with fluid.unique_name.guard():
+                train_pyreader, loss, accuracy, num_seqs = create_model(
+                    args,
+                    pyreader_name='train_reader',
+                    senta_config=senta_config,
+                    num_labels=num_labels,
+                    is_inference=False)
+
+                sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=args.lr)
+                sgd_optimizer.minimize(loss)
+
+        if args.verbose:
+            lower_mem, upper_mem, unit = fluid.contrib.memory_usage(
+                program=train_program, batch_size=args.batch_size)
+            print("Theoretical memory usage in training: %.3f - %.3f %s" %
+                (lower_mem, upper_mem, unit))
+
+    if args.do_val:
+        test_prog = fluid.Program()
+        with fluid.program_guard(test_prog, startup_prog):
+            with fluid.unique_name.guard():
+                test_pyreader, loss, accuracy, num_seqs = create_model(
+                    args,
+                    pyreader_name='test_reader',
+                    senta_config=senta_config,
+                    num_labels=num_labels,
+                    is_inference=False)
+
+        test_prog = test_prog.clone(for_test=True)
+
+    if args.do_infer:
+        infer_prog = fluid.Program()
+        with fluid.program_guard(infer_prog, startup_prog):
+            with fluid.unique_name.guard():
+                infer_pyreader, prop = create_model(
+                    args,
+                    pyreader_name='infer_reader',
+                    senta_config=senta_config,
+                    num_labels=num_labels,
+                    is_inference=True)
+        infer_prog = infer_prog.clone(for_test=True)
+
+    exe.run(startup_prog)
+
+    if args.do_train:
+        if args.init_checkpoint:
+            init_checkpoint(
+                exe,
+                args.init_checkpoint,
+                main_program=startup_prog)
+
+    elif args.do_val or args.do_infer:
+        if not args.init_checkpoint:
+            raise ValueError("args 'init_checkpoint' should be set if"
+                             "only doing validation or testing!")
+        init_checkpoint(
+            exe,
+            args.init_checkpoint,
+            main_program=startup_prog)
+
+    if args.do_train:
+        train_exe = exe
+        train_pyreader.decorate_paddle_reader(train_data_generator)
+    else:
+        train_exe = None
+    if args.do_val or args.do_infer:
+        test_exe = exe
+
+    if args.do_train:
+        train_pyreader.start()
+        steps = 0
+        total_cost, total_acc, total_num_seqs = [], [], []
+        time_begin = time.time()
+        while True:
+            try:
+                steps += 1
+                #print("steps...")
+                if steps % args.skip_steps == 0:
+                    fetch_list = [loss.name, accuracy.name, num_seqs.name]
+                else:
+                    fetch_list = []
+
+                outputs = train_exe.run(program=train_program, fetch_list=fetch_list, return_numpy=False)
+                #print("finished one step")
+                if steps % args.skip_steps == 0:
+                    np_loss, np_acc, np_num_seqs = outputs
+                    np_loss = np.array(np_loss)
+                    np_acc = np.array(np_acc)
+                    np_num_seqs = np.array(np_num_seqs)
+                    total_cost.extend(np_loss * np_num_seqs)
+                    total_acc.extend(np_acc * np_num_seqs)
+                    total_num_seqs.extend(np_num_seqs)
+
+                    if args.verbose:
+                        verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size()
+                        print(verbose)
+
+                    time_end = time.time()
+                    used_time = time_end - time_begin
+                    print("step: %d, ave loss: %f, "
+                        "ave acc: %f, speed: %f steps/s" %
+                        (steps, np.sum(total_cost) / np.sum(total_num_seqs),
+                        np.sum(total_acc) / np.sum(total_num_seqs),
+                        args.skip_steps / used_time))
+                    total_cost, total_acc, total_num_seqs = [], [], []
+                    time_begin = time.time()
+
+                if steps % args.save_steps == 0:
+                    save_path = os.path.join(args.checkpoints,
+                                         "step_" + str(steps))
+                    fluid.io.save_persistables(exe, save_path, train_program)
+
+                if steps % args.validation_steps == 0:
+                    # evaluate dev set
+                    if args.do_val:
+                        print("do evalatation")
+                        test_pyreader.decorate_paddle_reader(
+                            processor.data_generator(
+                                batch_size=args.batch_size,
+                                phase='dev',
+                                epoch=1,
+                                shuffle=False))
+
+                        evaluate(exe, test_prog, test_pyreader,
+                                [loss.name, accuracy.name, num_seqs.name],
+                                "dev")
+
+            except fluid.core.EOFException:
+                save_path = os.path.join(args.checkpoints, "step_" + str(steps))
+                fluid.io.save_persistables(exe, save_path, train_program)
+                train_pyreader.reset()
+                break
+
+    # final eval on dev set
+    if args.do_val:
+        test_pyreader.decorate_paddle_reader(
+            processor.data_generator(
+                batch_size=args.batch_size, phase='dev', epoch=1,
+                shuffle=False))
+        print("Final validation result:")
+        evaluate(exe, test_prog, test_pyreader,
+            [loss.name, accuracy.name, num_seqs.name], "dev")
+
+        test_pyreader.decorate_paddle_reader(
+            processor.data_generator(
+                batch_size=args.batch_size, phase='infer', epoch=1,
+                shuffle=False))
+        evaluate(exe, test_prog, test_pyreader,
+            [loss.name, accuracy.name, num_seqs.name], "infer")
+
+
+    # final eval on test set
+    if args.do_infer:
+        infer_pyreader.decorate_paddle_reader(
+            processor.data_generator(
+                batch_size=args.batch_size,
+                phase='infer',
+                epoch=1,
+                shuffle=False))
+        print("Final test result:")
+        inference(exe, infer_prog, infer_pyreader,
+            [prop.name], "infer")
+
+if __name__ == "__main__":
+    print_arguments(args)
+    main(args)
diff --git a/PaddleNLP/sentiment_classification/run_ernie.sh b/PaddleNLP/sentiment_classification/run_ernie.sh
new file mode 100644
index 0000000000000000000000000000000000000000..46571243b344790229fbe3023be84d76af887c7a
--- /dev/null
+++ b/PaddleNLP/sentiment_classification/run_ernie.sh
@@ -0,0 +1,112 @@
+#! /bin/bash
+export FLAGS_fraction_of_gpu_memory_to_use=0.95
+export FLAGS_enable_parallel_graph=1
+export FLAGS_sync_nccl_allreduce=1
+export CUDA_VISIBLE_DEVICES=3
+export CPU_NUM=1
+ERNIE_PRETRAIN=./senta_model/ernie_pretrain_model/
+DATA_PATH=./senta_data
+MODEL_SAVE_PATH=./save_models
+
+# run_train
+train() {
+    python -u run_ernie_classifier.py \
+        --use_cuda true \
+        --verbose true \
+        --do_train true \
+        --do_val true \
+        --do_infer false \
+        --batch_size 24 \
+        --init_checkpoint $ERNIE_PRETRAIN/params \
+        --train_set $DATA_PATH/train.tsv \
+        --dev_set $DATA_PATH/dev.tsv \
+        --test_set $DATA_PATH/test.tsv \
+        --vocab_path $ERNIE_PRETRAIN/vocab.txt \
+        --checkpoints $MODEL_SAVE_PATH \
+        --save_steps 5000 \
+        --validation_steps 100 \
+        --epoch 10 \
+        --max_seq_len 256 \
+        --ernie_config_path $ERNIE_PRETRAIN/ernie_config.json \
+        --model_type "ernie_base" \
+        --lr 5e-5 \
+        --skip_steps 10 \
+        --num_labels 2 \
+        --random_seed 1
+}
+
+# run_eval
+evaluate() {
+    python -u run_ernie_classifier.py \
+        --use_cuda true \
+        --verbose true \
+        --do_train false \
+        --do_val true \
+        --do_infer false \
+        --batch_size 24 \
+        --init_checkpoint ./save_models/step_5000/ \
+        --dev_set $DATA_PATH/dev.tsv \
+        --vocab_path $ERNIE_PRETRAIN/vocab.txt \
+        --max_seq_len 256 \
+        --ernie_config_path $ERNIE_PRETRAIN/ernie_config.json \
+        --model_type "ernie_base" \
+        --num_labels 2
+    
+    python -u run_ernie_classifier.py \
+        --use_cuda true \
+        --verbose true \
+        --do_train false \
+        --do_val true \
+        --do_infer false \
+        --batch_size 24 \
+        --init_checkpoint ./save_models/step_5000/ \
+        --dev_set $DATA_PATH/test.tsv \
+        --vocab_path $ERNIE_PRETRAIN/vocab.txt \
+        --max_seq_len 256 \
+        --ernie_config_path $ERNIE_PRETRAIN/ernie_config.json \
+        --model_type "ernie_base" \
+        --num_labels 2
+}
+
+# run_infer
+infer() {
+    python -u run_ernie_classifier.py \
+        --use_cuda true \
+        --verbose true \
+        --do_train false \
+        --do_val false \
+        --do_infer true \
+        --batch_size 24 \
+        --init_checkpoint ./save_models/step_5000 \
+        --test_set $DATA_PATH/test.tsv \
+        --vocab_path $ERNIE_PRETRAIN/vocab.txt \
+        --max_seq_len 256 \
+        --ernie_config_path $ERNIE_PRETRAIN/ernie_config.json \
+        --model_type "ernie_base" \
+        --num_labels 2
+}
+
+main() {
+    local cmd=${1:-help}
+    case "${cmd}" in
+        train)
+            train "$@";
+            ;;
+        eval)
+            evaluate "$@";
+            ;;
+        infer)
+            infer "$@";
+            ;;
+        help)
+            echo "Usage: ${BASH_SOURCE} {train|eval|infer}";
+            return 0;
+            ;;
+        *)
+            echo "Unsupport commend [${cmd}]";
+            echo "Usage: ${BASH_SOURCE} {train|eval|infer}";
+            return 1;
+            ;;
+    esac
+}
+main "$@"
diff --git a/PaddleNLP/sentiment_classification/run_ernie_classifier.py b/PaddleNLP/sentiment_classification/run_ernie_classifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a9820e563f0cd20d0d7f35a6638fdae75240fde
--- /dev/null
+++ b/PaddleNLP/sentiment_classification/run_ernie_classifier.py
@@ -0,0 +1,410 @@
+"""
+Sentiment Classification Task
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import time
+import argparse
+import numpy as np
+import multiprocessing
+import sys
+
+import paddle
+import paddle.fluid as fluid
+
+sys.path.append("../models/classification/")
+sys.path.append("..")
+print(sys.path)
+
+from nets import bow_net
+from nets import lstm_net
+from nets import cnn_net
+from nets import bilstm_net
+from nets import gru_net
+from nets import ernie_base_net
+from nets import ernie_bilstm_net
+from preprocess.ernie import task_reader
+from models.representation.ernie import ErnieConfig
+from models.representation.ernie import ernie_encoder
+from models.representation.ernie import ernie_pyreader
+from utils import ArgumentGroup
+from utils import print_arguments
+from utils import init_checkpoint
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+model_g = ArgumentGroup(parser, "model", "model configuration and paths.")
+model_g.add_arg("ernie_config_path",         str,  None,           "Path to the json file for ernie model config.")
+model_g.add_arg("senta_config_path", str, None, "Path to the json file for senta model config.")
+model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.")
+model_g.add_arg("checkpoints", str, "checkpoints", "Path to save checkpoints")
+model_g.add_arg("model_type", str, "ernie_base", "Type of current ernie model")
+
+train_g = ArgumentGroup(parser, "training", "training options.")
+train_g.add_arg("epoch", int, 10, "Number of epoches for training.")
+train_g.add_arg("save_steps", int, 10000, "The steps interval to save checkpoints.")
+train_g.add_arg("validation_steps", int, 1000, "The steps interval to evaluate model performance.")
+train_g.add_arg("lr", float, 0.002, "The Learning rate value for training.")
+
+log_g = ArgumentGroup(parser, "logging", "logging related")
+log_g.add_arg("skip_steps", int, 10, "The steps interval to print loss.")
+log_g.add_arg("verbose", bool, False, "Whether to output verbose log")
+
+data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options")
+data_g.add_arg("data_dir", str, None, "Path to training data.")
+data_g.add_arg("vocab_path", str, None, "Vocabulary path.")
+data_g.add_arg("batch_size", int, 256, "Total examples' number in batch for training.")
+data_g.add_arg("random_seed", int, 0, "Random seed.")
+data_g.add_arg("num_labels",  int,  2,     "label number")
+data_g.add_arg("max_seq_len", int,  512,   "Number of words of the longest seqence.")
+data_g.add_arg("train_set",           str,  None,  "Path to training data.")
+data_g.add_arg("test_set",            str,  None,  "Path to test data.")
+data_g.add_arg("dev_set",             str,  None,  "Path to validation data.")
+data_g.add_arg("label_map_config",    str,  None,  "label_map_path.")
+data_g.add_arg("do_lower_case",       bool, True,
+               "Whether to lower case the input text. Should be True for uncased models and False for cased models.")
+
+run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
+run_type_g.add_arg("use_cuda", bool, True, "If set, use GPU for training.")
+run_type_g.add_arg("do_train", bool, True, "Whether to perform training.")
+run_type_g.add_arg("do_val", bool, True, "Whether to perform evaluation.")
+run_type_g.add_arg("do_infer", bool, True, "Whether to perform inference.")
+
+args = parser.parse_args()
+# yapf: enable.
+
+def create_model(args,
+                 embeddings,
+                 labels,
+                 is_prediction=False):
+
+    """
+    Create Model for sentiment classification based on ERNIE encoder
+    """
+    sentence_embeddings = embeddings["sentence_embeddings"]
+    token_embeddings = embeddings["token_embeddings"]
+
+    if args.model_type == "ernie_base":
+        ce_loss, probs = ernie_base_net(sentence_embeddings, labels,
+            args.num_labels)
+
+    elif args.model_type == "ernie_bilstm":
+        ce_loss, probs = ernie_bilstm_net(token_embeddings, labels,
+            args.num_labels)
+
+    else:
+        raise ValueError("Unknown network type!")
+
+    if is_prediction:
+        return probs
+    loss = fluid.layers.mean(x=ce_loss)
+
+    num_seqs = fluid.layers.create_tensor(dtype='int64')
+    accuracy = fluid.layers.accuracy(input=probs, label=labels, total=num_seqs)
+
+    return loss, accuracy, num_seqs
+
+
+def evaluate(exe, test_program, test_pyreader, fetch_list, eval_phase):
+    """
+    Evaluation Function
+    """
+    test_pyreader.start()
+    total_cost, total_acc, total_num_seqs = [], [], []
+    time_begin = time.time()
+    while True:
+        try:
+            np_loss, np_acc, np_num_seqs = exe.run(program=test_program,
+                                                   fetch_list=fetch_list,
+                                                   return_numpy=False)
+            np_loss = np.array(np_loss)
+            np_acc = np.array(np_acc)
+            np_num_seqs = np.array(np_num_seqs)
+            total_cost.extend(np_loss * np_num_seqs)
+            total_acc.extend(np_acc * np_num_seqs)
+            total_num_seqs.extend(np_num_seqs)
+        except fluid.core.EOFException:
+            test_pyreader.reset()
+            break
+    time_end = time.time()
+    print("[%s evaluation] ave loss: %f, ave acc: %f, elapsed time: %f s" %
+        (eval_phase, np.sum(total_cost) / np.sum(total_num_seqs),
+        np.sum(total_acc) / np.sum(total_num_seqs), time_end - time_begin))
+
+
+def infer(exe, infer_program, infer_pyreader, fetch_list, infer_phase):
+    """
+    Inference Function
+    """
+    infer_pyreader.start()
+    time_begin = time.time()
+    while True:
+        try:
+            batch_probs = exe.run(program=infer_program, fetch_list=fetch_list,
+                                return_numpy=True)
+            for probs in batch_probs[0]:
+                print("%d\t%f\t%f" % (np.argmax(probs), probs[0], probs[1]))
+        except fluid.core.EOFException:
+            infer_pyreader.reset()
+            break
+    time_end = time.time()
+    print("[%s] elapsed time: %f s" % (infer_phase, time_end - time_begin))
+
+
+def main(args):
+    """
+    Main Function
+    """
+    args = parser.parse_args()
+    ernie_config = ErnieConfig(args.ernie_config_path)
+    ernie_config.print_config()
+
+    if args.use_cuda:
+        place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0')))
+        dev_count = fluid.core.get_cuda_device_count()
+    else:
+        place = fluid.CPUPlace()
+        dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+    exe = fluid.Executor(place)
+
+    reader = task_reader.ClassifyReader(
+        vocab_path=args.vocab_path,
+        label_map_config=args.label_map_config,
+        max_seq_len=args.max_seq_len,
+        do_lower_case=args.do_lower_case,
+        random_seed=args.random_seed)
+
+    if not (args.do_train or args.do_val or args.do_infer):
+        raise ValueError("For args `do_train`, `do_val` and `do_infer`, at "
+                         "least one of them must be True.")
+
+    startup_prog = fluid.Program()
+    if args.random_seed is not None:
+        startup_prog.random_seed = args.random_seed
+
+    if args.do_train:
+        train_data_generator = reader.data_generator(
+            input_file=args.train_set,
+            batch_size=args.batch_size,
+            epoch=args.epoch,
+            shuffle=True,
+            phase="train")
+
+        num_train_examples = reader.get_num_examples(args.train_set)
+
+        max_train_steps = args.epoch * num_train_examples // args.batch_size // dev_count
+
+        print("Device count: %d" % dev_count)
+        print("Num train examples: %d" % num_train_examples)
+        print("Max train steps: %d" % max_train_steps)
+
+        train_program = fluid.Program()
+
+        with fluid.program_guard(train_program, startup_prog):
+            with fluid.unique_name.guard():
+                # create ernie_pyreader
+                train_pyreader, ernie_inputs, labels = ernie_pyreader(
+                    args,
+                    pyreader_name='train_reader')
+
+                # get ernie_embeddings
+                embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config)
+
+                # user defined model based on ernie embeddings
+                loss, accuracy, num_seqs = create_model(
+                args,
+                embeddings,
+                labels=labels,
+                is_prediction=False)
+
+                """
+                sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=args.lr)
+                sgd_optimizer.minimize(loss)
+                """
+                optimizer = fluid.optimizer.Adam(learning_rate=args.lr)
+                optimizer.minimize(loss)
+
+        if args.verbose:
+            lower_mem, upper_mem, unit = fluid.contrib.memory_usage(
+                program=train_program, batch_size=args.batch_size)
+            print("Theoretical memory usage in training: %.3f - %.3f %s" %
+                (lower_mem, upper_mem, unit))
+
+    if args.do_val:
+        test_prog = fluid.Program()
+        with fluid.program_guard(test_prog, startup_prog):
+            with fluid.unique_name.guard():
+                # create ernie_pyreader
+                test_pyreader, ernie_inputs, labels = ernie_pyreader(
+                    args,
+                    pyreader_name='eval_reader')
+
+                # get ernie_embeddings
+                embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config)
+
+                # user defined model based on ernie embeddings
+                loss, accuracy, num_seqs = create_model(
+                args,
+                embeddings,
+                labels=labels,
+                is_prediction=False)
+
+        test_prog = test_prog.clone(for_test=True)
+
+    if args.do_infer:
+        infer_prog = fluid.Program()
+        with fluid.program_guard(infer_prog, startup_prog):
+            with fluid.unique_name.guard():
+                infer_pyreader, ernie_inputs, labels = ernie_pyreader(
+                    args,
+                    pyreader_name="infer_reader")
+
+                # get ernie_embeddings
+                embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config)
+
+                probs = create_model(args,
+                                    embeddings,
+                                    labels=labels,
+                                    is_prediction=True)
+
+        infer_prog = infer_prog.clone(for_test=True)
+
+    exe.run(startup_prog)
+
+    if args.do_train:
+        if args.init_checkpoint:
+            init_checkpoint(
+                exe,
+                args.init_checkpoint,
+                main_program=startup_prog)
+    elif args.do_val or args.do_infer:
+        if not args.init_checkpoint:
+            raise ValueError("args 'init_checkpoint' should be set if"
+                             "only doing validation or testing!")
+        init_checkpoint(
+            exe,
+            args.init_checkpoint,
+            main_program=startup_prog)
+
+    if args.do_train:
+        exec_strategy = fluid.ExecutionStrategy()
+        exec_strategy.num_iteration_per_drop_scope = 1
+        
+        train_exe = fluid.ParallelExecutor(
+            use_cuda=args.use_cuda,
+            loss_name=loss.name,
+            exec_strategy=exec_strategy,
+            main_program=train_program)
+        
+        train_pyreader.decorate_tensor_provider(train_data_generator)
+    else:
+        train_exe = None
+    if args.do_val or args.do_infer:
+        test_exe = exe
+
+    if args.do_train:
+        train_pyreader.start()
+        steps = 0
+        total_cost, total_acc, total_num_seqs = [], [], []
+        time_begin = time.time()
+        while True:
+            try:
+                steps += 1
+                if steps % args.skip_steps == 0:
+                    fetch_list = [loss.name, accuracy.name, num_seqs.name]
+                else:
+                    fetch_list = []
+
+                outputs = train_exe.run(fetch_list=fetch_list, return_numpy=False)
+                if steps % args.skip_steps == 0:
+                    np_loss, np_acc, np_num_seqs = outputs
+                    np_loss = np.array(np_loss)
+                    np_acc = np.array(np_acc)
+                    np_num_seqs = np.array(np_num_seqs)
+                    total_cost.extend(np_loss * np_num_seqs)
+                    total_acc.extend(np_acc * np_num_seqs)
+                    total_num_seqs.extend(np_num_seqs)
+
+                    if args.verbose:
+                        verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size()
+                        print(verbose)
+
+                    time_end = time.time()
+                    used_time = time_end - time_begin
+                    print("step: %d, ave loss: %f, "
+                        "ave acc: %f, speed: %f steps/s" %
+                        (steps, np.sum(total_cost) / np.sum(total_num_seqs),
+                        np.sum(total_acc) / np.sum(total_num_seqs),
+                        args.skip_steps / used_time))
+                    total_cost, total_acc, total_num_seqs = [], [], []
+                    time_begin = time.time()
+
+                if steps % args.save_steps == 0:
+                    save_path = os.path.join(args.checkpoints,
+                                         "step_" + str(steps))
+                    fluid.io.save_persistables(exe, save_path, train_program)
+
+                if steps % args.validation_steps == 0:
+                    # evaluate dev set
+                    if args.do_val:
+                        test_pyreader.decorate_tensor_provider(
+                            reader.data_generator(
+                                input_file=args.dev_set,
+                                batch_size=args.batch_size,
+                                phase='dev',
+                                epoch=1,
+                                shuffle=False))
+
+                        evaluate(exe, test_prog, test_pyreader,
+                                [loss.name, accuracy.name, num_seqs.name],
+                                "dev")
+                        
+                        test_pyreader.decorate_tensor_provider(
+                            reader.data_generator(
+                                input_file=args.test_set,
+                                batch_size=args.batch_size,
+                                phase='infer',
+                                epoch=1,
+                                shuffle=False))
+                        
+                        evaluate(exe, test_prog, test_pyreader,
+                                 [loss.name, accuracy.name, num_seqs.name],
+                                 "infer")
+
+            except fluid.core.EOFException:
+                save_path = os.path.join(args.checkpoints, "step_" + str(steps))
+                fluid.io.save_persistables(exe, save_path, train_program)
+                train_pyreader.reset()
+                break
+
+    # final eval on dev set
+    if args.do_val:
+        test_pyreader.decorate_tensor_provider(
+            reader.data_generator(
+                input_file=args.dev_set,
+                batch_size=args.batch_size, phase='dev', epoch=1,
+                shuffle=False))
+        print("Final validation result:")
+        evaluate(exe, test_prog, test_pyreader,
+            [loss.name, accuracy.name, num_seqs.name], "dev")
+
+    # final eval on test set
+    if args.do_infer:
+        infer_pyreader.decorate_tensor_provider(
+            reader.data_generator(
+                input_file=args.test_set,
+                batch_size=args.batch_size,
+                phase='infer',
+                epoch=1,
+                shuffle=False))
+        print("Final test result:")
+        infer(exe, infer_prog, infer_pyreader,
+            [probs.name], "infer")
+
+if __name__ == "__main__":
+    print_arguments(args)
+    main(args)
diff --git a/PaddleNLP/sentiment_classification/senta_config.json b/PaddleNLP/sentiment_classification/senta_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..825345849626ec31976396bdd8a3b2eabdeba1b6
--- /dev/null
+++ b/PaddleNLP/sentiment_classification/senta_config.json
@@ -0,0 +1,4 @@
+{
+    "model_type": "bilstm_net",
+    "vocab_size": 33256
+}
diff --git a/PaddleNLP/sentiment_classification/utils.py b/PaddleNLP/sentiment_classification/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c117178b224ecf77b05e724c7849444d65729f51
--- /dev/null
+++ b/PaddleNLP/sentiment_classification/utils.py
@@ -0,0 +1,150 @@
+"""
+Arguments for configuration
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import six
+import argparse
+import io
+import sys
+import random
+import numpy as np
+import os
+
+import paddle
+import paddle.fluid as fluid
+
+
+def str2bool(v):
+    """
+    String to Boolean
+    """
+    # because argparse does not support to parse "true, False" as python
+    # boolean directly
+    return v.lower() in ("true", "t", "1")
+
+
+class ArgumentGroup(object):
+    """
+    Argument Class
+    """
+    def __init__(self, parser, title, des):
+        self._group = parser.add_argument_group(title=title, description=des)
+
+    def add_arg(self, name, type, default, help, **kwargs):
+        """
+        Add argument
+        """
+        type = str2bool if type == bool else type
+        self._group.add_argument(
+            "--" + name,
+            default=default,
+            type=type,
+            help=help + ' Default: %(default)s.',
+            **kwargs)
+
+
+def print_arguments(args):
+    """
+    Print Arguments
+    """
+    print('-----------  Configuration Arguments -----------')
+    for arg, value in sorted(six.iteritems(vars(args))):
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------------')
+
+
+def init_checkpoint(exe, init_checkpoint_path, main_program):
+    """
+    Init CheckPoint
+    """
+    assert os.path.exists(
+        init_checkpoint_path), "[%s] cann't be found." % init_checkpoint_path
+
+    def existed_persitables(var):
+        """
+        If existed presitabels
+        """
+        if not fluid.io.is_persistable(var):
+            return False
+        return os.path.exists(os.path.join(init_checkpoint_path, var.name))
+
+    fluid.io.load_vars(
+        exe,
+        init_checkpoint_path,
+        main_program=main_program,
+        predicate=existed_persitables)
+    print("Load model from {}".format(init_checkpoint_path))
+
+    
+def data_reader(file_path, word_dict, num_examples, phrase, epoch):
+    """
+    Convert word sequence into slot
+    """
+    unk_id = len(word_dict)
+    all_data = []
+    with io.open(file_path, "r", encoding='utf8') as fin:
+        for line in fin:
+            if line.startswith('text_a'):
+                continue
+            cols = line.strip().split("\t")
+            if len(cols) != 2:
+                sys.stderr.write("[NOTICE] Error Format Line!")
+                continue
+            label = int(cols[1])
+            wids = [word_dict[x] if x in word_dict else unk_id
+                    for x in cols[0].split(" ")]
+            all_data.append((wids, label))
+
+    if phrase == "train":
+        random.shuffle(all_data)
+
+    num_examples[phrase] = len(all_data)
+        
+    def reader():
+        """
+        Reader Function
+        """
+        for epoch_index in range(epoch):
+            for doc, label in all_data:
+                yield doc, label
+    return reader
+
+def load_vocab(file_path):
+    """
+    load the given vocabulary
+    """
+    vocab = {}
+    with io.open(file_path, 'r', encoding='utf8') as f:
+        wid = 0
+        for line in f:
+            if line.strip() not in vocab:
+                vocab[line.strip()] = wid
+                wid += 1
+    vocab[""] = len(vocab)
+    return vocab
+
+
+def init_pretraining_params(exe,
+                            pretraining_params_path,
+                            main_program,
+                            use_fp16=False):
+    """load params of pretrained model, NOT including moment, learning_rate"""
+    assert os.path.exists(pretraining_params_path
+                          ), "[%s] cann't be found." % pretraining_params_path
+
+    def _existed_params(var):
+        if not isinstance(var, fluid.framework.Parameter):
+            return False
+        return os.path.exists(os.path.join(pretraining_params_path, var.name))
+
+    fluid.io.load_vars(
+        exe,
+        pretraining_params_path,
+        main_program=main_program,
+        predicate=_existed_params)
+    print("Load pretraining parameters from {}.".format(
+        pretraining_params_path))
diff --git a/PaddleNLP/similarity_net/README.md b/PaddleNLP/similarity_net/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4e31a73f3a6d99b38b2a190eefc0118542aca8e3
--- /dev/null
+++ b/PaddleNLP/similarity_net/README.md
@@ -0,0 +1,169 @@
+# 短文本语义匹配
+## 简介
+### 任务说明
+短文本语义匹配(SimilarityNet, SimNet)是一个计算短文本相似度的框架，可以根据用户输入的两个文本，计算出相似度得分。SimNet框架在百度各产品上广泛应用，主要包括BOW、CNN、RNN、MMDNN等核心网络结构形式，提供语义相似度计算训练和预测框架，适用于信息检索、新闻推荐、智能客服等多个应用场景，帮助企业解决语义匹配问题。可通过[AI开放平台-短文本相似度](https://ai.baidu.com/tech/nlp_basic/simnet)线上体验。
+### 效果说明
+基于百度海量搜索数据，我们训练了一个SimNet-BOW-Pairwise语义匹配模型，在一些真实的FAQ问答场景中，该模型效果比基于字面的相似度方法AUC提升5%以上，我们基于百度自建测试集（包含聊天、客服等数据集）和语义匹配数据集(LCQMC)进行评测，效果如下表所示。LCQMC数据集以Accuracy为评测指标，而pairwise模型的输出为相似度，因此我们采用0.91作为分类阈值，相比于基线模型中网络结构同等复杂的CBOW模型（准确率为0.737），我们模型的准确率为0.7517。
+
+
+| 模型       | 百度知道         | ECOM        |QQSIM | UNICOM | LCQMC |
+|:-----------:|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|
+|   | AUC  | AUC | AUC|正逆序比|Accuracy|
+|BOW_Pairwise|0.6766|0.7308|0.7643|1.5630|0.7517|
+## 快速开始
+#### 版本依赖
+本项目依赖于 Paddlepaddle Fluid 1.3.1，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装。
+
+python版本依赖python 2.7
+#### 安装代码
+克隆工具集代码库到本地
+```shell
+git clone https://github.com/PaddlePaddle/models.git
+cd models/PaddleNLP/similarity_net
+```
+#### 数据准备
+下载经过预处理的数据，运行命令后，data目录下会存在训练集数据示例、集数据示例、测试集数据示例，以及对应词索引字典（term2id.dict）。
+```shell
+wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/simnet_dataset-1.0.0.tar.gz
+tar xzf simnet_dataset-1.0.0.tar.gz
+```
+#### 模型准备
+我们开源了基于大规模数据训练好的pairwise模型（基于bow模型训练），我们提供两种下载方式，模型保在./model_files/simnet_bow_pairwise_pretrained_model/下。
+
+##### 方式一：基于PaddleHub命令行工具（PaddleHub[安装方式](https://github.com/PaddlePaddle/PaddleHub)）
+```shell
+mkdir model_files
+hub download simnet_bow_pairwise --output_path ./
+tar xzf simnet_bow-pairwise-1.0.0.tar.gz -C ./model_files
+```
+##### 方式二：直接下载
+```shell
+mkdir model_files
+wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/simnet_bow-pairwise-1.0.0.tar.gz
+tar xzf simnet_bow-pairwise-1.0.0.tar.gz -C ./model_files
+```
+#### 评估
+我们公开了自建的测试集，包括百度知道、ECOM、QQSIM、UNICOM四个数据集，基于上面的预训练模型，用户可以进入evaluate目录下依次执行下列命令获取测试集评估结果。
+```shell
+sh evaluate_ecom.sh  
+sh evaluate_qqsim.sh   
+sh evaluate_zhidao.sh  
+sh evaluate_unicom.sh  
+```
+用户也可以指定./run.sh中的TEST_DATA_PATH的值，通过下列命令评估自己指定的测试集。
+```shell
+sh run.sh eval
+```
+
+#### 推测
+基于上面的预训练模型，可以运行下面的命令进行推测，并保存推测结果到本地。
+```shell
+sh run.sh infer  
+```
+#### 训练与验证
+用户可以基于示例数据构建训练集和开发集，可以运行下面的命令，进行模型训练和开发集验证。
+```shell
+sh run.sh train  
+```
+## 进阶使用
+### 任务定义与建模
+传统的文本匹配技术如信息检索中的向量空间模型 VSM、BM25 等算法，主要解决词汇层面的相似度问题，这种方法的效果在实际应用中受到语言的多义词和语言结构等问题影响。SimNet 在语义表示上沿袭了隐式连续向量表示的方式，但对语义匹配问题在深度学习框架下进行了 End-to-End 的建模，将point-wise与 pair-wise 两种有监督学习方式全部统一在一个整体框架内。在实际应用场景下，将海量的用户点击行为数据转化为大规模的弱标记数据，在网页搜索任务上的初次使用即展现出极大威力，带来了相关性的明显提升。
+### 模型原理介绍
+SimNet如下图所示：
+![struct](https://github.com/PaddlePaddle/models/blob/paddle-nlp/PaddleNLP/similarity_net/struct.jpg)
+
+### 数据格式说明
+训练模式一共分为pairwise和pointwise两种模式。
+#### pairwise模式：
+训练集格式如下： query \t pos_query \t neg_query。
+query、pos_query和neg_query是以空格分词的中文文本，中间使用制表符'\t'隔开，pos_query表示与query相似的正例，neg_query表示与query不相似的随机负例，文本编码为utf-8。

+```
+现在 安卓模拟器 哪个 好 用     电脑 安卓模拟器 哪个 更好      电信 手机 可以 用 腾讯 大王 卡 吗 ?
+土豆 一亩地 能 收 多少 斤      一亩 地土豆 产 多少 斤        一亩 地 用 多少 斤 土豆 种子
+```
+
+开发集和测试集格式：query1 \t query2 \t label。

+
+query1和query2表示以空格分词的中文文本，label为0或1，1表示query1与query2相似，0表示query1与query2不相似，query1、query2和label中间以制表符'\t'隔开，文本编码为utf-8。

+```
+现在 安卓模拟器 哪个 好 用    电脑 安卓模拟器 哪个 更好      1
+为什么 头发 掉 得 很厉害      我 头发 为什么 掉 得 厉害    1
+常喝 薏米 水 有 副 作用 吗    女生 可以 长期 喝 薏米 水养生 么    0
+长 的 清新 是 什么 意思      小 清新 的 意思 是 什么 0
+```
+
+#### pointwise模式：
+
+训练集、开发集和测试集数据格式相同：query1和query2表示以空格分词的中文文本，label为0或1，1表示query1与query2相似，0表示query1与query2不相似，query1、query2和label中间以制表符'\t'隔开，文本编码为utf-8。
+```
+现在 安卓模拟器 哪个 好 用    电脑 安卓模拟器 哪个 更好      1
+为什么 头发 掉 得 很厉害      我 头发 为什么 掉 得 厉害    1
+常喝 薏米 水 有 副 作用 吗    女生 可以 长期 喝 薏米 水养生 么    0
+长 的 清新 是 什么 意思     小 清新 的 意思 是 什么 0
+```
+
+#### infer数据集：
+
+pairwise和pointwise的infer数据集格式相同：query1 \t query2。

+
+query1和query2为以空格分词的中文文本。
+```
+怎么 调理 湿热 体质 ？   湿热 体质 怎样 调理 啊
+搞笑 电影 美国   搞笑 的 美国 电影
+```
+
+__注__：本项目额外提供了分词预处理脚本（在preprocess目录下），可供用户使用，具体使用方法如下：
+
+```shell
+python tokenizer.py --test_data_dir ./test.txt.utf8 --batch_size 1 > test.txt.utf8.seg
+```
+其中test.txt.utf8为待分词的文件，一条文本数据一行，utf8编码，分词结果存放在test.txt.utf8.seg文件中
+
+### 代码结构说明
+- run_classifier.py：该项目的主函数，封装包括训练、预测、评估的部分

+- config.py：定义该项目模型的配置类，读取具体模型类别、以及模型的超参数等

+- reader.py：定义了读入数据的相关函数

+- utils.py：定义了其他常用的功能函数

+- Config: 定义多种模型的配置文件

+### 如何训练
+```shell
+python run_classifier.py \
+   --task_name ${TASK_NAME} \ 
+   --use_cuda false \ #是否使用GPU
+   --do_train True \  #是否训练
+   --do_valid True \  #是否在训练中测试开发集
+   --do_test True \   #是否验证测试集
+   --do_infer False \ #是否预测
+   --batch_size 128 \ #batch_size的值
+   --train_data_dir ${TRAIN_DATA_kPATH} \ #训练集的路径
+   --valid_data_dir ${VALID_DATA_PATH} \ #开发集的路径
+   --test_data_dir ${TEST_DATA_PATH} \   #测试集的路径
+   --infer_data_dir ${INFER_DATA_PATH} \ #待推测数据的路径
+   --output_dir ${CKPT_PATH} \           #模型存放的路径
+   --config_path ${CONFIG_PATH} \        #配置文件路径
+   --vocab_path ${VOCAB_PATH} \          #字典路径
+   --epoch 10 \    #epoch值
+   --save_steps 1000 \  #每save_steps保存一次模型
+   --validation_steps 100 \ #每validation_steps验证一次开发集结果
+   --task_mode ${TASK_MODE} #训练模式，pairwise或pointwise，与相应的配置文件匹配。
+   --compute_accuracy False \   #是否计算accuracy
+   --lamda 0.91 \    #pairwise模式计算accuracy时的阈值
+```
+### 如何组建自己的模型
+用户可以根据自己的需求，组建自定义的模型，具体方法如下所示：
+
+i. 定义自己的网络结构
+
+用户可以在nlp_tools/models/matching下定义自己的模型；
+
+ii. 更改模型配置
+
+用户仿照config中的文件生成自定义模型的配置文件。
+
+用户需要保留配置文件中的net、loss、optimizer、task_mode和model_path字段。net为用户自定义的模型参数，task_mode表示训练模式，为pairwise或pointwise，要与训练命令中的--task_mode命令保持一致，model_path为模型保存路径，loss和optimizer依据自定义模型的需要仿照config下的其他文件填写。
+
+iii.模型训练，运行训练、评估、预测脚本即可（具体方法同上）。
+
+## 其他
+### 如何贡献代码
+如果你可以修复某个issue或者增加一个新功能，欢迎给我们提交PR。如果对应的PR被接受了，我们将根据贡献的质量和难度进行打分（0-5分，越高越好）。如果你累计获得了10分，可以联系我们获得面试机会或者为你写推荐信。
diff --git a/PaddleNLP/similarity_net/config.py b/PaddleNLP/similarity_net/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9cc1544f4df552935372949394b891a5caadc31
--- /dev/null
+++ b/PaddleNLP/similarity_net/config.py
@@ -0,0 +1,44 @@
+"""
+SimNet config
+"""
+
+import six
+import json
+
+
+class SimNetConfig(object):
+    """
+    simnet Config
+    """
+
+    def __init__(self, args):
+        self.task_mode = args.task_mode
+        self.config_path = args.config_path
+        self._config_dict = self._parse(args.config_path)
+
+    def _parse(self, config_path):
+        try:
+            with open(config_path) as json_file:
+                config_dict = json.load(json_file)
+        except Exception:
+            raise IOError("Error in parsing simnet model config file '%s'" % config_path)
+
+        else:
+            if config_dict["task_mode"] != self.task_mode:
+                raise ValueError(
+                    "the config '{}' does not match the task_mode '{}'".format(self.config_path, self.task_mode))
+            return config_dict
+
+    def __getitem__(self, key):
+        return self._config_dict[key]
+
+    def __setitem__(self, key, value):
+        self._config_dict[key] = value
+
+    def print_config(self):
+        """
+        Print Config
+        """
+        for arg, value in sorted(six.iteritems(self._config_dict)):
+            print('%s: %s' % (arg, value))
+        print('------------------------------------------------')
diff --git a/PaddleNLP/similarity_net/config/bow_pairwise.json b/PaddleNLP/similarity_net/config/bow_pairwise.json
new file mode 100644
index 0000000000000000000000000000000000000000..6c6fc9a62c356a21ff9996454014e5420acb1078
--- /dev/null
+++ b/PaddleNLP/similarity_net/config/bow_pairwise.json
@@ -0,0 +1,23 @@
+{
+    "net": {
+        "module_name": "bow",
+        "class_name": "BOW",
+        "emb_dim": 128, 
+        "bow_dim": 128,
+        "hidden_dim": 128
+    },
+    "loss": {
+        "module_name": "hinge_loss",
+        "class_name": "HingeLoss",
+        "margin": 0.1
+    },
+    "optimizer": {
+        "class_name": "AdamOptimizer",
+        "learning_rate": 0.001,
+        "beta1": 0.9,
+        "beta2": 0.999,
+        "epsilon": 1e-08
+    },
+    "task_mode": "pairwise",
+    "model_path": "bow_pairwise"
+}
diff --git a/PaddleNLP/similarity_net/config/bow_pointwise.json b/PaddleNLP/similarity_net/config/bow_pointwise.json
new file mode 100644
index 0000000000000000000000000000000000000000..c9a438ddb8a79aec9e9d997f6d9a5cbdf90cadcd
--- /dev/null
+++ b/PaddleNLP/similarity_net/config/bow_pointwise.json
@@ -0,0 +1,18 @@
+{
+    "net": {
+        "module_name": "bow",
+        "class_name": "BOW",
+        "emb_dim": 128, 
+        "bow_dim": 128
+    },
+    "loss": {
+        "module_name": "softmax_cross_entropy_loss",
+        "class_name": "SoftmaxCrossEntropyLoss"
+    },
+    "optimizer": {
+        "class_name": "SGDOptimizer",
+        "learning_rate" : 0.001
+    },
+    "task_mode": "pointwise",
+    "model_path": "bow_pointwise"
+}
diff --git a/PaddleNLP/similarity_net/config/cnn_pairwise.json b/PaddleNLP/similarity_net/config/cnn_pairwise.json
new file mode 100644
index 0000000000000000000000000000000000000000..88ca6523984702e983727e71d3ce63d06b4db968
--- /dev/null
+++ b/PaddleNLP/similarity_net/config/cnn_pairwise.json
@@ -0,0 +1,24 @@
+{
+    "net": {
+        "module_name": "cnn",
+        "class_name": "CNN",
+        "emb_dim": 128, 
+        "filter_size": 3,
+        "num_filters": 256,
+        "hidden_dim": 128
+    },
+    "loss": {
+        "module_name": "hinge_loss",
+        "class_name": "HingeLoss",
+        "margin": 0.1
+    },
+    "optimizer": {
+        "class_name": "AdamOptimizer",
+        "learning_rate": 0.001,
+        "beta1": 0.9,
+        "beta2": 0.999,
+        "epsilon": 1e-08
+    },
+    "task_mode": "pairwise",
+    "model_path": "cnn_pairwise"
+}
diff --git a/PaddleNLP/similarity_net/config/cnn_pointwise.json b/PaddleNLP/similarity_net/config/cnn_pointwise.json
new file mode 100644
index 0000000000000000000000000000000000000000..019697015a39724a5c4064abb8c2946c3b527718
--- /dev/null
+++ b/PaddleNLP/similarity_net/config/cnn_pointwise.json
@@ -0,0 +1,20 @@
+{
+    "net": {
+        "module_name": "cnn",
+        "class_name": "CNN",
+        "emb_dim": 128, 
+        "filter_size": 3,
+        "num_filters": 256,
+        "hidden_dim": 128
+    },
+    "loss": {
+        "module_name": "softmax_cross_entropy_loss",
+        "class_name": "SoftmaxCrossEntropyLoss"
+    },
+    "optimizer": {
+        "class_name": "SGDOptimizer",
+        "learning_rate" : 0.001
+    },
+    "task_mode": "pointwise",
+    "model_path": "cnn_pointwise"
+}
diff --git a/PaddleNLP/similarity_net/config/gru_pairwise.json b/PaddleNLP/similarity_net/config/gru_pairwise.json
new file mode 100644
index 0000000000000000000000000000000000000000..5977a3374744f4f5a79e7ec4426c0197ec147996
--- /dev/null
+++ b/PaddleNLP/similarity_net/config/gru_pairwise.json
@@ -0,0 +1,23 @@
+{
+    "net": {
+        "module_name": "gru",
+        "class_name": "GRU",
+        "emb_dim": 128, 
+        "gru_dim": 128,
+        "hidden_dim": 128
+    },
+    "loss": {
+        "module_name": "hinge_loss",
+        "class_name": "HingeLoss",
+        "margin": 0.1
+    },
+    "optimizer": {
+        "class_name": "AdamOptimizer",
+        "learning_rate": 0.001,
+        "beta1": 0.9,
+        "beta2": 0.999,
+        "epsilon": 1e-08
+    },
+    "task_mode": "pairwise",
+    "model_path": "gru_pairwise"
+}
diff --git a/PaddleNLP/similarity_net/config/gru_pointwise.json b/PaddleNLP/similarity_net/config/gru_pointwise.json
new file mode 100644
index 0000000000000000000000000000000000000000..eaf0b7c9809b3d5880c1a24db9cc4781709f7661
--- /dev/null
+++ b/PaddleNLP/similarity_net/config/gru_pointwise.json
@@ -0,0 +1,19 @@
+{
+    "net": {
+        "module_name": "gru",
+        "class_name": "GRU",
+        "emb_dim": 128, 
+        "gru_dim": 128,
+        "hidden_dim": 128
+    },
+    "loss": {
+        "module_name": "softmax_cross_entropy_loss",
+        "class_name": "SoftmaxCrossEntropyLoss"
+    },
+    "optimizer": {
+        "class_name": "SGDOptimizer",
+        "learning_rate" : 0.001
+    },
+    "task_mode": "pointwise",
+    "model_path": "gru_pointwise"
+}
diff --git a/PaddleNLP/similarity_net/config/lstm_pairwise.json b/PaddleNLP/similarity_net/config/lstm_pairwise.json
new file mode 100644
index 0000000000000000000000000000000000000000..c78e41c168d5c5dd77b8875939ad36d107b40afb
--- /dev/null
+++ b/PaddleNLP/similarity_net/config/lstm_pairwise.json
@@ -0,0 +1,23 @@
+{
+    "net": {
+        "module_name": "lstm",
+        "class_name": "LSTM",
+        "emb_dim": 128, 
+        "lstm_dim": 128,
+        "hidden_dim": 128
+    },
+    "loss": {
+        "module_name": "hinge_loss",
+        "class_name": "HingeLoss",
+        "margin": 0.1
+    },
+    "optimizer": {
+        "class_name": "AdamOptimizer",
+        "learning_rate": 0.001,
+        "beta1": 0.9,
+        "beta2": 0.999,
+        "epsilon": 1e-08
+    },
+    "task_mode": "pairwise",
+    "model_path": "lstm_pairwise"
+}
diff --git a/PaddleNLP/similarity_net/config/lstm_pointwise.json b/PaddleNLP/similarity_net/config/lstm_pointwise.json
new file mode 100644
index 0000000000000000000000000000000000000000..e38921be06c9805b4486d937ad125f2be83f3160
--- /dev/null
+++ b/PaddleNLP/similarity_net/config/lstm_pointwise.json
@@ -0,0 +1,19 @@
+{
+    "net": {
+        "module_name": "lstm",
+        "class_name": "LSTM",
+        "emb_dim": 128, 
+        "lstm_dim": 128,
+        "hidden_dim": 128
+    },
+    "loss": {
+        "module_name": "softmax_cross_entropy_loss",
+        "class_name": "SoftmaxCrossEntropyLoss"
+    },
+    "optimizer": {
+        "class_name": "SGDOptimizer",
+        "learning_rate" : 0.001
+    },
+    "task_mode": "pointwise",
+    "model_path": "lstm_pointwise"
+}
diff --git a/PaddleNLP/similarity_net/config/mmdnn_pointwise.json b/PaddleNLP/similarity_net/config/mmdnn_pointwise.json
new file mode 100644
index 0000000000000000000000000000000000000000..0239f80883bf12bebb2378a0b923fdeb5eb42601
--- /dev/null
+++ b/PaddleNLP/similarity_net/config/mmdnn_pointwise.json
@@ -0,0 +1,32 @@
+{
+    "net": {
+        "module_name": "mm_dnn",
+        "class_name": "MMDNN",
+        "embedding_dim": 128,
+        "num_filters": 256,
+        "lstm_dim": 128,
+        "hidden_size": 128,
+        "window_size_left": 3,
+        "window_size_right": 3,
+        "dpool_size_left": 2,
+        "dpool_size_right": 2
+    },
+    "loss": {
+        "module_name": "softmax_cross_entropy_loss",
+        "class_name": "SoftmaxCrossEntropyLoss"
+    },
+    "optimizer": {
+        "class_name": "AdamOptimizer",
+        "learning_rate": 0.001,
+        "beta1": 0.9,
+        "beta2": 0.999,
+        "epsilon": 1e-08
+    },
+    "max_len_left": 32,
+    "max_len_right": 32,
+    "n_class": 2,
+    "task_mode": "pointwise",
+    "match_mask" : 1,
+    "model_path": "mm_dnn_pointwise"
+
+}
diff --git a/PaddleNLP/similarity_net/download.sh b/PaddleNLP/similarity_net/download.sh
new file mode 100644
index 0000000000000000000000000000000000000000..96e63f1c9ca3fe26821ee7b04784aea78b3d265c
--- /dev/null
+++ b/PaddleNLP/similarity_net/download.sh
@@ -0,0 +1,16 @@
+#!/usr/bin/env bash
+model_files_path="./model_files"
+
+#get pretrained_bow_pairwise_model
+wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/simnet_bow-pairwise-1.0.0.tar.gz
+if [ ! -d $model_files_path ]; then
+    mkdir $model_files_path
+fi
+tar xzf simnet_bow-pairwise-1.0.0.tar.gz -C $model_files_path
+rm simnet_bow-pairwise-1.0.0.tar.gz
+
+#get data
+wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/simnet_dataset-1.0.0.tar.gz
+tar xzf simnet_dataset-1.0.0.tar.gz
+rm simnet_dataset-1.0.0.tar.gz
+
diff --git a/PaddleNLP/similarity_net/evaluate/evaluate_ecom.sh b/PaddleNLP/similarity_net/evaluate/evaluate_ecom.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4a00efabae4f6bf07c8a7e73c3d214d1c8a4276f
--- /dev/null
+++ b/PaddleNLP/similarity_net/evaluate/evaluate_ecom.sh
@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+export FLAGS_enable_parallel_graph=1
+export FLAGS_sync_nccl_allreduce=1
+export CUDA_VISIBLE_DEVICES=3
+export FLAGS_fraction_of_gpu_memory_to_use=0.95
+TASK_NAME='simnet'
+TEST_DATA_PATH=./data/ecom
+VOCAB_PATH=./data/term2id.dict
+CKPT_PATH=./model_files
+TEST_RESULT_PATH=./evaluate/ecom_test_result
+TASK_MODE='pairwise'
+CONFIG_PATH=./config/bow_pairwise.json
+INIT_CHECKPOINT=./model_files/simnet_bow_pairwise_pretrained_model/
+cd ..
+
+python ./run_classifier.py \
+    --task_name ${TASK_NAME} \
+    --use_cuda false \
+    --do_test True \
+    --verbose_result True \
+    --batch_size 128 \
+    --test_data_dir ${TEST_DATA_PATH} \
+    --test_result_path ${TEST_RESULT_PATH} \
+    --config_path ${CONFIG_PATH} \
+    --vocab_path ${VOCAB_PATH} \
+    --task_mode ${TASK_MODE} \
+    --init_checkpoint ${INIT_CHECKPOINT}
diff --git a/PaddleNLP/similarity_net/evaluate/evaluate_qqsim.sh b/PaddleNLP/similarity_net/evaluate/evaluate_qqsim.sh
new file mode 100644
index 0000000000000000000000000000000000000000..fa8bdcc034af4c77a199e52af1e0d23471fefeec
--- /dev/null
+++ b/PaddleNLP/similarity_net/evaluate/evaluate_qqsim.sh
@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+export FLAGS_enable_parallel_graph=1
+export FLAGS_sync_nccl_allreduce=1
+export CUDA_VISIBLE_DEVICES=3
+export FLAGS_fraction_of_gpu_memory_to_use=0.95
+TASK_NAME='simnet'
+TEST_DATA_PATH=./data/qqsim
+VOCAB_PATH=./data/term2id.dict
+CKPT_PATH=./model_files
+TEST_RESULT_PATH=./evaluate/qqsim_test_result
+TASK_MODE='pairwise'
+CONFIG_PATH=./config/bow_pairwise.json
+INIT_CHECKPOINT=./model_files/simnet_bow_pairwise_pretrained_model/
+cd ..
+
+python ./run_classifier.py \
+    --task_name ${TASK_NAME} \
+    --use_cuda false \
+    --do_test True \
+    --verbose_result True \
+    --batch_size 128 \
+    --test_data_dir ${TEST_DATA_PATH} \
+    --test_result_path ${TEST_RESULT_PATH} \
+    --config_path ${CONFIG_PATH} \
+    --vocab_path ${VOCAB_PATH} \
+    --task_mode ${TASK_MODE} \
+    --init_checkpoint ${INIT_CHECKPOINT}
diff --git a/PaddleNLP/similarity_net/evaluate/evaluate_unicom.sh b/PaddleNLP/similarity_net/evaluate/evaluate_unicom.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a93aaa4bc2c7a75dfad1e4f9d1f59ae9711947f1
--- /dev/null
+++ b/PaddleNLP/similarity_net/evaluate/evaluate_unicom.sh
@@ -0,0 +1,30 @@
+#!/usr/bin/env bash
+export FLAGS_enable_parallel_graph=1
+export FLAGS_sync_nccl_allreduce=1
+export CUDA_VISIBLE_DEVICES=3
+export FLAGS_fraction_of_gpu_memory_to_use=0.95
+TASK_NAME='simnet'
+INFER_DATA_PATH=./evaluate/unicom_infer
+VOCAB_PATH=./data/term2id.dict
+CKPT_PATH=./model_files
+INFER_RESULT_PATH=./evaluate/unicom_infer_result
+TASK_MODE='pairwise'
+CONFIG_PATH=./config/bow_pairwise.json
+INIT_CHECKPOINT=./model_files/simnet_bow_pairwise_pretrained_model/
+
+python unicom_split.py
+cd ..
+python ./run_classifier.py \
+    --task_name ${TASK_NAME} \
+    --use_cuda false \
+    --do_infer True \
+    --batch_size 128 \
+    --infer_data_dir ${INFER_DATA_PATH} \
+    --infer_result_path ${INFER_RESULT_PATH} \
+    --config_path ${CONFIG_PATH} \
+    --vocab_path ${VOCAB_PATH} \
+    --task_mode ${TASK_MODE} \
+    --init_checkpoint ${INIT_CHECKPOINT}
+cd evaluate
+python unicom_compute_pos_neg.py
+
diff --git a/PaddleNLP/similarity_net/evaluate/evaluate_zhidao.sh b/PaddleNLP/similarity_net/evaluate/evaluate_zhidao.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9e6346104c8bbb8540f39e58a404eb566f43b0ed
--- /dev/null
+++ b/PaddleNLP/similarity_net/evaluate/evaluate_zhidao.sh
@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+export FLAGS_enable_parallel_graph=1
+export FLAGS_sync_nccl_allreduce=1
+export CUDA_VISIBLE_DEVICES=3
+export FLAGS_fraction_of_gpu_memory_to_use=0.95
+TASK_NAME='simnet'
+TEST_DATA_PATH=./data/zhidao
+VOCAB_PATH=./data/term2id.dict
+CKPT_PATH=./model_files
+TEST_RESULT_PATH=./evaluate/zhidao_test_result
+TASK_MODE='pairwise'
+CONFIG_PATH=./config/bow_pairwise.json
+INIT_CHECKPOINT=./model_files/simnet_bow_pairwise_pretrained_model/
+cd ..
+
+python ./run_classifier.py \
+    --task_name ${TASK_NAME} \
+    --use_cuda false \
+    --do_test True \
+    --verbose_result True \
+    --batch_size 128 \
+    --test_data_dir ${TEST_DATA_PATH} \
+    --test_result_path ${TEST_RESULT_PATH} \
+    --config_path ${CONFIG_PATH} \
+    --vocab_path ${VOCAB_PATH} \
+    --task_mode ${TASK_MODE} \
+    --init_checkpoint ${INIT_CHECKPOINT}
diff --git a/PaddleNLP/similarity_net/evaluate/unicom_compute_pos_neg.py b/PaddleNLP/similarity_net/evaluate/unicom_compute_pos_neg.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9ba7a5130d3acd4451f4eb76559082461941788
--- /dev/null
+++ b/PaddleNLP/similarity_net/evaluate/unicom_compute_pos_neg.py
@@ -0,0 +1,44 @@
+"""
+comput unicom
+"""
+
+infer_results = []
+labels = []
+result = []
+temp_reuslt = []
+temp_query = ""
+pos_num = 0.0
+neg_num = 0.0
+
+with open("./unicom_infer_result", "r") as infer_result_file:
+    for line in infer_result_file:
+        infer_results.append(line.strip().split("\t"))
+
+with open("./unicom_label", "r") as label_file:
+    for line in label_file:
+        labels.append(line.strip().split("\t"))
+
+for infer_result, label in zip(infer_results, labels):
+    if infer_result[0] != temp_query and temp_query != "":
+        result.append(temp_reuslt)
+        temp_query = infer_result[0]
+        temp_reuslt = []
+        temp_reuslt.append(infer_result + label)
+    else:
+        if temp_query == '':
+            temp_query = infer_result[0]
+        temp_reuslt.append(infer_result + label)
+else:
+    result.append(temp_reuslt)
+
+for _result in result:
+    for n, i in enumerate(_result, start=1):
+        for j in _result[n:]:
+            if (int(j[-1]) > int(i[-1]) and float(j[-2]) < float(i[-2])) or (
+                    int(j[-1]) < int(i[-1]) and float(j[-2]) > float(i[-2])):
+                neg_num += 1
+            elif (int(j[-1]) > int(i[-1]) and float(j[-2]) > float(i[-2])) or (
+                    int(j[-1]) < int(i[-1]) and float(j[-2]) < float(i[-2])):
+                pos_num += 1
+
+print("pos/neg of unicom data is %f" % (pos_num / neg_num))
diff --git a/PaddleNLP/similarity_net/evaluate/unicom_split.py b/PaddleNLP/similarity_net/evaluate/unicom_split.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d93116d48a45697690ddda23e93c93f85069cb9
--- /dev/null
+++ b/PaddleNLP/similarity_net/evaluate/unicom_split.py
@@ -0,0 +1,11 @@
+"""
+split unicom file
+"""
+
+with open("../data/unicom", "r") as unicom_file:
+    with open("./unicom_infer", "w") as infer_file:
+        with open("./unicom_label", "w") as label_file:
+            for line in unicom_file:
+                line = line.strip().split('\t')
+                infer_file.write("\t".join(line[:2]) + '\n')
+                label_file.write(line[2] + '\n')
diff --git a/PaddleNLP/similarity_net/reader.py b/PaddleNLP/similarity_net/reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd953d8fc7dedb6bbbbd07f6ffd9d68d3c003c5f
--- /dev/null
+++ b/PaddleNLP/similarity_net/reader.py
@@ -0,0 +1,182 @@
+"""
+SimNet reader
+"""
+
+import logging
+import numpy as np
+
+
+class SimNetProcessor(object):
+    def __init__(self, args, vocab):
+        self.args = args
+        # load vocab
+        self.vocab = vocab
+        self.valid_label = np.array([])
+        self.test_label = np.array([])
+
+    def get_reader(self, mode):
+        """
+        Get Reader
+        """
+
+        def reader_with_pairwise():
+            """
+                Reader with Pairwise
+            """
+            if mode == "valid":
+                with open(self.args.valid_data_dir) as file:
+                    for line in file:
+                        query, title, label = line.strip().split("\t")
+                        if len(query) == 0 or len(title) == 0 or len(label) == 0 or not label.isdigit() or int(
+                                label) not in [0, 1]:
+                            logging.warning("line not match format in test file")
+                            continue
+                        query = [self.vocab[word] for word in query.split(" ") if word in self.vocab]
+                        title = [self.vocab[word] for word in title.split(" ") if word in self.vocab]
+                        if len(query) == 0:
+                            query = [0]
+                        if len(title) == 0:
+                            title = [0]
+                        yield [query, title]
+            elif mode == "test":
+                with open(self.args.test_data_dir) as file:
+                    for line in file:
+                        query, title, label = line.strip().split("\t")
+                        if len(query) == 0 or len(title) == 0 or len(label) == 0 or not label.isdigit() or int(
+                                label) not in [0, 1]:
+                            logging.warning("line not match format in test file")
+                            continue
+                        query = [self.vocab[word] for word in query.split(" ") if word in self.vocab]
+                        title = [self.vocab[word] for word in title.split(" ") if word in self.vocab]
+                        if len(query) == 0:
+                            query = [0]
+                        if len(title) == 0:
+                            title = [0]
+                        yield [query, title]
+            else:
+                with open(self.args.train_data_dir) as file:
+                    for line in file:
+                        query, pos_title, neg_title = line.strip().split("\t")
+                        if len(query) == 0 or len(pos_title) == 0 or len(neg_title) == 0:
+                            logging.warning("line not match format in test file")
+                            continue
+                        query = [self.vocab[word] for word in query.split(" ") if word in self.vocab]
+                        pos_title = [self.vocab[word] for word in pos_title.split(" ") if word in self.vocab]
+                        neg_title = [self.vocab[word] for word in neg_title.split(" ") if word in self.vocab]
+                        if len(query) == 0:
+                            query = [0]
+                        if len(pos_title) == 0:
+                            pos_title = [0]
+                        if len(neg_title) == 0:
+                            neg_title = [0]
+                        yield [query, pos_title, neg_title]
+
+        def reader_with_pointwise():
+            """
+            Reader with Pointwise
+            """
+            if mode == "valid":
+                with open(self.args.valid_data_dir) as file:
+                    for line in file:
+                        query, title, label = line.strip().split("\t")
+                        if len(query) == 0 or len(title) == 0 or len(label) == 0 or not label.isdigit() or int(
+                                label) not in [0, 1]:
+                            logging.warning("line not match format in test file")
+                            continue
+                        query = [self.vocab[word] for word in query.split(" ") if word in self.vocab]
+                        title = [self.vocab[word] for word in title.split(" ") if word in self.vocab]
+                        if len(query) == 0:
+                            query = [0]
+                        if len(title) == 0:
+                            title = [0]
+                        yield [query, title]
+            elif mode == "test":
+                with open(self.args.test_data_dir) as file:
+                    for line in file:
+                        query, title, label = line.strip().split("\t")
+                        if len(query) == 0 or len(title) == 0 or len(label) == 0 or not label.isdigit() or int(
+                                label) not in [0, 1]:
+                            logging.warning("line not match format in test file")
+                            continue
+                        query = [self.vocab[word] for word in query.split(" ") if word in self.vocab]
+                        title = [self.vocab[word] for word in title.split(" ") if word in self.vocab]
+                        if len(query) == 0:
+                            query = [0]
+                        if len(title) == 0:
+                            title = [0]
+                        yield [query, title]
+            else:
+                with open(self.args.train_data_dir) as file:
+                    for line in file:
+                        query, title, label = line.strip().split("\t")
+                        if len(query) == 0 or len(title) == 0 or len(label) == 0 or not label.isdigit() or int(
+                                label) not in [0, 1]:
+                            logging.warning("line not match format in test file")
+                            continue
+                        query = [self.vocab[word] for word in query.split(" ") if word in self.vocab]
+                        title = [self.vocab[word] for word in title.split(" ") if word in self.vocab]
+                        label = int(label)
+                        if len(query) == 0:
+                            query = [0]
+                        if len(title) == 0:
+                            title = [0]
+                        yield [query, title, label]
+
+        if self.args.task_mode == "pairwise":
+            return reader_with_pairwise
+        else:
+            return reader_with_pointwise
+
+    def get_infer_reader(self):
+        """
+        get infer reader
+        """
+        with open(self.args.infer_data_dir, "r") as file:
+            for line in file:
+                query, title = line.strip().split("\t")
+                if len(query) == 0 or len(title) == 0:
+                    logging.warning("line not match format in test file")
+                    continue
+                query = [self.vocab[word] for word in query.split(" ") if word in self.vocab]
+                title = [self.vocab[word] for word in title.split(" ") if word in self.vocab]
+                if len(query) == 0:
+                    query = [0]
+                if len(title) == 0:
+                    title = [0]
+                yield [query, title]
+
+    def get_infer_data(self):
+        """
+        get infer data
+        """
+        with open(self.args.infer_data_dir, "r") as file:
+            for line in file:
+                query, title = line.strip().split("\t")
+                if len(query) == 0 or len(title) == 0:
+                    logging.warning("line not match format in test file")
+                    continue
+                yield line.strip()
+
+    def get_valid_label(self):
+        """
+        get valid data label
+        """
+        if self.valid_label.size == 0:
+            labels = []
+            with open(self.args.valid_data_dir, "r") as f:
+                for line in f:
+                    labels.append([int(line.strip().split("\t")[-1])])
+            self.valid_label = np.array(labels)
+        return self.valid_label
+
+    def get_test_label(self):
+        """
+        get test data label
+        """
+        if self.test_label.size == 0:
+            labels = []
+            with open(self.args.test_data_dir, "r") as f:
+                for line in f:
+                    labels.append([int(line.strip().split("\t")[-1])])
+            self.test_label = np.array(labels)
+        return self.test_label
diff --git a/PaddleNLP/similarity_net/run.sh b/PaddleNLP/similarity_net/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..cc1ee5a50d47d580a0e8249992a7f8816cf1d3b4
--- /dev/null
+++ b/PaddleNLP/similarity_net/run.sh
@@ -0,0 +1,99 @@
+#!/usr/bin/env bash
+export FLAGS_enable_parallel_graph=1
+export FLAGS_sync_nccl_allreduce=1
+export CUDA_VISIBLE_DEVICES=3
+export FLAGS_fraction_of_gpu_memory_to_use=0.95
+TASK_NAME='simnet'
+TRAIN_DATA_PATH=./data/train_pairwise_data
+VALID_DATA_PATH=./data/test_pairwise_data
+TEST_DATA_PATH=./data/test_pairwise_data
+INFER_DATA_PATH=./data/infer_data
+VOCAB_PATH=./data/term2id.dict
+CKPT_PATH=./model_files
+TEST_RESULT_PATH=./test_result
+INFER_RESULT_PATH=./infer_result
+TASK_MODE='pairwise'
+CONFIG_PATH=./config/bow_pairwise.json
+INIT_CHECKPOINT=./model_files/simnet_bow_pairwise_pretrained_model/
+
+
+# run_train
+train() {
+	python run_classifier.py \
+		--task_name ${TASK_NAME} \
+		--use_cuda false \
+		--do_train True \
+		--do_valid True \
+		--do_test True \
+		--do_infer False \
+		--batch_size 128 \
+		--train_data_dir ${TRAIN_DATA_PATH} \
+		--valid_data_dir ${VALID_DATA_PATH} \
+		--test_data_dir ${TEST_DATA_PATH} \
+		--infer_data_dir ${INFER_DATA_PATH} \
+		--output_dir ${CKPT_PATH} \
+		--config_path ${CONFIG_PATH} \
+		--vocab_path ${VOCAB_PATH} \
+		--epoch 10 \
+		--save_steps 1000 \
+		--validation_steps 100 \
+		--compute_accuracy False \
+		--lamda 0.91 \
+		--task_mode ${TASK_MODE}
+}
+#run_evaluate
+evaluate() {
+	python run_classifier.py \
+		--task_name ${TASK_NAME} \
+		--use_cuda false \
+		--do_test True \
+		--verbose_result True \
+		--batch_size 128 \
+		--test_data_dir ${TEST_DATA_PATH} \
+		--test_result_path ${TEST_RESULT_PATH} \
+		--config_path ${CONFIG_PATH} \
+		--vocab_path ${VOCAB_PATH} \
+		--task_mode ${TASK_MODE} \
+		--compute_accuracy False \
+		--lamda 0.91 \
+		--init_checkpoint ${INIT_CHECKPOINT}
+}
+# run_infer
+infer() {
+	python run_classifier.py \
+		--task_name ${TASK_NAME} \
+		--use_cuda false \
+		--do_infer True \
+		--batch_size 128 \
+		--infer_data_dir ${INFER_DATA_PATH} \
+		--infer_result_path ${INFER_RESULT_PATH} \
+		--config_path ${CONFIG_PATH} \
+		--vocab_path ${VOCAB_PATH} \
+		--task_mode ${TASK_MODE} \
+		--init_checkpoint ${INIT_CHECKPOINT}
+}
+
+main() {
+    local cmd=${1:-help}
+    case "${cmd}" in
+        train)
+            train "$@";
+            ;;
+        eval)
+            evaluate "$@";
+            ;;
+        infer)
+            infer "$@";
+            ;;
+        help)
+            echo "Usage: ${BASH_SOURCE} {train|eval|infer}";
+            return 0;
+            ;;
+        *)
+            echo "Unsupport commend [${cmd}]";
+            echo "Usage: ${BASH_SOURCE} {train|eval|infer}";
+            return 1;
+            ;;
+    esac
+}
+main "$@"
diff --git a/PaddleNLP/similarity_net/run_classifier.py b/PaddleNLP/similarity_net/run_classifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf93adac6683a1fecb27a2aa9f79b206b6408bb6
--- /dev/null
+++ b/PaddleNLP/similarity_net/run_classifier.py
@@ -0,0 +1,354 @@
+"""
+SimNet Task
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import time
+import argparse
+import multiprocessing
+import sys
+
+sys.path.append("..")
+
+import paddle
+import paddle.fluid as fluid
+import numpy as np
+import config
+import utils
+import reader
+import models.matching.paddle_layers as layers
+
+import logging
+
+parser = argparse.ArgumentParser(__doc__)
+model_g = utils.ArgumentGroup(parser, "model", "model configuration and paths.")
+model_g.add_arg("config_path", str, None, "Path to the json file for EmoTect model config.")
+model_g.add_arg("init_checkpoint", str, "examples/cnn_pointwise.json", "Init checkpoint to resume training from.")
+model_g.add_arg("output_dir", str, None, "Directory path to save checkpoints")
+model_g.add_arg("task_mode", str, None, "task mode: pairwise or pointwise")
+
+train_g = utils.ArgumentGroup(parser, "training", "training options.")
+train_g.add_arg("epoch", int, 10, "Number of epoches for training.")
+train_g.add_arg("save_steps", int, 200, "The steps interval to save checkpoints.")
+train_g.add_arg("validation_steps", int, 100, "The steps interval to evaluate model performance.")
+
+log_g = utils.ArgumentGroup(parser, "logging", "logging related")
+log_g.add_arg("skip_steps", int, 10, "The steps interval to print loss.")
+log_g.add_arg("verbose_result", bool, True, "Whether to output verbose result.")
+log_g.add_arg("test_result_path", str, "test_result", "Directory path to test result.")
+log_g.add_arg("infer_result_path", str, "infer_result", "Directory path to infer result.")
+
+data_g = utils.ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options")
+data_g.add_arg("train_data_dir", str, None, "Directory path to training data.")
+data_g.add_arg("valid_data_dir", str, None, "Directory path to valid data.")
+data_g.add_arg("test_data_dir", str, None, "Directory path to testing data.")
+data_g.add_arg("infer_data_dir", str, None, "Directory path to infer data.")
+data_g.add_arg("vocab_path", str, None, "Vocabulary path.")
+data_g.add_arg("batch_size", int, 32, "Total examples' number in batch for training.")
+
+run_type_g = utils.ArgumentGroup(parser, "run_type", "running type options.")
+run_type_g.add_arg("use_cuda", bool, False, "If set, use GPU for training.")
+run_type_g.add_arg("task_name", str, None, "The name of task to perform sentiment classification.")
+run_type_g.add_arg("do_train", bool, False, "Whether to perform training.")
+run_type_g.add_arg("do_valid", bool, False, "Whether to perform dev.")
+run_type_g.add_arg("do_test", bool, False, "Whether to perform testing.")
+run_type_g.add_arg("do_infer", bool, False, "Whether to perform inference.")
+run_type_g.add_arg("compute_accuracy", bool, False, "Whether to compute accuracy.")
+run_type_g.add_arg("lamda", float, 0.91,
+                   "When task_mode is pairwise, lamda is the threshold for calculating the accuracy.")
+
+args = parser.parse_args()
+
+
+def train(conf_dict, args):
+    """
+    train processic
+    """
+    # loading vocabulary
+    vocab = utils.load_vocab(args.vocab_path)
+    # get vocab size
+    conf_dict['dict_size'] = len(vocab)
+    # Get data layer
+    data = layers.DataLayer()
+    # Load network structure dynamically
+    net = utils.import_class(
+        "../models/matching", conf_dict["net"]["module_name"], conf_dict["net"]["class_name"])(conf_dict)
+    # Load loss function dynamically
+    loss = utils.import_class(
+        "../models/matching/losses", conf_dict["loss"]["module_name"], conf_dict["loss"]["class_name"])(conf_dict)
+    # Load Optimization method
+    optimizer = utils.import_class(
+        "../models/matching/optimizers", "paddle_optimizers", conf_dict["optimizer"]["class_name"])(conf_dict)
+    # load auc method
+    metric = fluid.metrics.Auc(name="auc")
+    # Get device
+    if args.use_cuda:
+        place = fluid.core.CUDAPlace(0)
+    else:
+        place = fluid.core.CPUPlace()
+
+    simnet_process = reader.SimNetProcessor(args, vocab)
+    if args.task_mode == "pairwise":
+        # Build network
+        left = data.ops(name="left", shape=[1], dtype="int64", lod_level=1)
+        pos_right = data.ops(name="right", shape=[1], dtype="int64", lod_level=1)
+        neg_right = data.ops(name="neg_right", shape=[1], dtype="int64", lod_level=1)
+        left_feat, pos_score = net.predict(left, pos_right)
+
+        # Get Feeder and Reader
+        train_feeder = fluid.DataFeeder(place=place, feed_list=[left.name, pos_right.name, neg_right.name])
+        train_reader = simnet_process.get_reader("train")
+        if args.do_valid:
+            valid_feeder = fluid.DataFeeder(place=place, feed_list=[left.name, pos_right.name])
+            valid_reader = simnet_process.get_reader("valid")
+            pred = pos_score
+        # Save Infer model
+        infer_program = fluid.default_main_program().clone(for_test=True)
+        _, neg_score = net.predict(left, neg_right)
+        avg_cost = loss.compute(pos_score, neg_score)
+        avg_cost.persistable = True
+    else:
+        # Build network
+        left = data.ops(name="left", shape=[1], dtype="int64", lod_level=1)
+        right = data.ops(name="right", shape=[1], dtype="int64", lod_level=1)
+        label = data.ops(name="label", shape=[1], dtype="int64", lod_level=0)
+        left_feat, pred = net.predict(left, right)
+
+        # Get Feeder and Reader
+        train_feeder = fluid.DataFeeder(place=place, feed_list=[left.name, right.name, label.name])
+        train_reader = simnet_process.get_reader("train")
+        if args.do_valid:
+            valid_feeder = fluid.DataFeeder(place=place, feed_list=[left.name, right.name])
+            valid_reader = simnet_process.get_reader("valid")
+        # Save Infer model
+        infer_program = fluid.default_main_program().clone(for_test=True)
+        avg_cost = loss.compute(pred, label)
+        avg_cost.persistable = True
+
+    # operate Optimization
+    optimizer.ops(avg_cost)
+    executor = fluid.Executor(place)
+    executor.run(fluid.default_startup_program())
+    # Get and run executor
+    parallel_executor = fluid.ParallelExecutor(use_cuda=args.use_cuda, loss_name=avg_cost.name,
+                                               main_program=fluid.default_main_program())
+    # Get device number
+    device_count = parallel_executor.device_count
+    logging.info("device count: %d" % device_count)
+
+    def valid_and_test(program, feeder, reader, process, mode="test"):
+        """
+        return auc and acc
+        """
+        # Get Batch Data
+        batch_data = paddle.batch(reader, args.batch_size, drop_last=False)
+        pred_list = []
+        for data in batch_data():
+            _pred = executor.run(program=program, feed=feeder.feed(data), fetch_list=[pred.name])
+            pred_list += list(_pred)
+        pred_list = np.vstack(pred_list)
+        if mode == "test":
+            label_list = process.get_test_label()
+        elif mode == "valid":
+            label_list = process.get_valid_label()
+        if conf_dict['net']['class_name'] == 'MMDNN':
+            pred_list = utils.deal_preds_of_mmdnn(conf_dict, pred_list)
+        if args.task_mode == "pairwise":
+            pred_list = np.hstack((np.ones_like(pred_list) - pred_list, pred_list))
+        metric.reset()
+        metric.update(pred_list, label_list)
+        auc = metric.eval()
+        if args.compute_accuracy:
+            acc = utils.get_accuracy(pred_list, label_list, args.task_mode, args.lamda)
+            return auc, acc
+        else:
+            return auc
+
+    # run train
+    logging.info("start train process ...")
+    # set global step
+    global_step = 0
+    for epoch_id in range(args.epoch):
+        losses = []
+        # Get batch data iterator
+        train_batch_data = paddle.batch(paddle.reader.shuffle(train_reader, buf_size=10000),
+                                        args.batch_size, drop_last=False)
+        start_time = time.time()
+        for iter, data in enumerate(train_batch_data()):
+            if len(data) < device_count:
+                logging.info("the size of batch data is less than device_count(%d)" % device_count)
+                continue
+            global_step += 1
+            avg_loss = parallel_executor.run([avg_cost.name], feed=train_feeder.feed(data))
+            if args.do_valid and global_step % args.validation_steps == 0:
+
+                valid_result = valid_and_test(program=infer_program, feeder=valid_feeder, reader=valid_reader,
+                                              process=simnet_process, mode="valid")
+                if args.compute_accuracy:
+                    valid_auc, valid_acc = valid_result
+                    logging.info("global_steps: %d, valid_auc: %f, valid_acc: %f" % (global_step, valid_auc, valid_acc))
+                else:
+                    valid_auc = valid_result
+                    logging.info("global_steps: %d, valid_auc: %f" % (global_step, valid_auc))
+            if global_step % args.save_steps == 0:
+                model_save_dir = os.path.join(args.output_dir, conf_dict["model_path"])
+                model_path = os.path.join(model_save_dir, str(global_step))
+
+                if not os.path.exists(model_save_dir):
+                    os.makedirs(model_save_dir)
+                if args.task_mode == "pairwise":
+                    feed_var_names = [left.name, pos_right.name]
+                    target_vars = [left_feat, pos_score]
+                else:
+                    feed_var_names = [left.name, right.name, ]
+                    target_vars = [left_feat, pred]
+                fluid.io.save_inference_model(
+                    model_path, feed_var_names, target_vars, executor, infer_program)
+                logging.info("saving infer model in %s" % model_path)
+            losses.append(np.mean(avg_loss[0]))
+        end_time = time.time()
+        logging.info("epoch: %d, loss: %f, used time: %d sec" % (epoch_id, np.mean(losses), end_time - start_time))
+    if args.do_test:
+        if args.task_mode == "pairwise":
+            # Get Feeder and Reader
+            test_feeder = fluid.DataFeeder(place=place, feed_list=[left.name, pos_right.name])
+            test_reader = simnet_process.get_reader("test")
+        else:
+            # Get Feeder and Reader
+            test_feeder = fluid.DataFeeder(place=place, feed_list=[left.name, right.name])
+            test_reader = simnet_process.get_reader("test")
+        test_result = valid_and_test(program=infer_program, feeder=test_feeder, reader=test_reader,
+                                     process=simnet_process, mode="test")
+        if args.compute_accuracy:
+            test_auc, test_acc = test_result
+            logging.info("AUC of test is %f, Accuracy of test is %f" % (test_auc, test_acc))
+        else:
+            test_auc = test_result
+            logging.info("AUC of test is %f" % test_auc)
+
+
+def test(conf_dict, args):
+    """
+    run predict
+    """
+    vocab = utils.load_vocab(args.vocab_path)
+    simnet_process = reader.SimNetProcessor(args, vocab)
+    # load auc method
+    metric = fluid.metrics.Auc(name="auc")
+    with open("predictions.txt", "w") as predictions_file:
+        # Get model path
+        model_path = args.init_checkpoint
+        # Get device
+        if args.use_cuda:
+            place = fluid.core.CUDAPlace(0)
+        else:
+            place = fluid.core.CPUPlace()
+        # Get executor
+        executor = fluid.Executor(place=place)
+        # Load model
+        program, feed_var_names, fetch_targets = fluid.io.load_inference_model(model_path, executor)
+        if args.task_mode == "pairwise":
+            # Get Feeder and Reader
+            feeder = fluid.DataFeeder(place=place, feed_list=feed_var_names, program=program)
+            test_reader = simnet_process.get_reader("test")
+        else:
+            # Get Feeder and Reader
+            feeder = fluid.DataFeeder(place=place, feed_list=feed_var_names, program=program)
+            test_reader = simnet_process.get_reader("test")
+        # Get batch data iterator
+        batch_data = paddle.batch(test_reader, args.batch_size, drop_last=False)
+        logging.info("start test process ...")
+        pred_list = []
+        for iter, data in enumerate(batch_data()):
+            output = executor.run(program, feed=feeder.feed(data), fetch_list=fetch_targets)
+            if args.task_mode == "pairwise":
+                pred_list += list(map(lambda item: float(item[0]), output[1]))
+                predictions_file.write("\n".join(map(lambda item: str(item[0]), output[1])) + "\n")
+            else:
+                pred_list += map(lambda item: item, output[1])
+                predictions_file.write("\n".join(map(lambda item: str(np.argmax(item)), output[1])) + "\n")
+        if conf_dict['net']['class_name'] == 'MMDNN':
+            pred_list = utils.deal_preds_of_mmdnn(conf_dict, pred_list)
+        if args.task_mode == "pairwise":
+            pred_list = np.array(pred_list).reshape((-1, 1))
+            pred_list = np.hstack((np.ones_like(pred_list) - pred_list, pred_list))
+        else:
+            pred_list = np.array(pred_list)
+        labels = simnet_process.get_test_label()
+
+        metric.update(pred_list, labels)
+        if args.compute_accuracy:
+            acc = utils.get_accuracy(pred_list, labels, args.task_mode, args.lamda)
+            logging.info("AUC of test is %f, Accuracy of test is %f" % (metric.eval(), acc))
+        else:
+            logging.info("AUC of test is %f" % metric.eval())
+
+    if args.verbose_result:
+        utils.get_result_file(args)
+        logging.info("test result saved in %s" % os.path.join(os.getcwd(), args.test_result_path))
+
+
+def infer(args):
+    """
+    run predict
+    """
+    vocab = utils.load_vocab(args.vocab_path)
+    simnet_process = reader.SimNetProcessor(args, vocab)
+    # Get model path
+    model_path = args.init_checkpoint
+    # Get device
+    if args.use_cuda:
+        place = fluid.core.CUDAPlace(0)
+    else:
+        place = fluid.core.CPUPlace()
+    # Get executor
+    executor = fluid.Executor(place=place)
+    # Load model
+    program, feed_var_names, fetch_targets = fluid.io.load_inference_model(model_path, executor)
+    if args.task_mode == "pairwise":
+        # Get Feeder and Reader
+        infer_feeder = fluid.DataFeeder(place=place, feed_list=feed_var_names, program=program)
+        infer_reader = simnet_process.get_infer_reader
+    else:
+        # Get Feeder and Reader
+        infer_feeder = fluid.DataFeeder(place=place, feed_list=feed_var_names, program=program)
+        infer_reader = simnet_process.get_infer_reader
+    # Get batch data iterator
+    batch_data = paddle.batch(infer_reader, args.batch_size, drop_last=False)
+    logging.info("start test process ...")
+    preds_list = []
+    for iter, data in enumerate(batch_data()):
+        output = executor.run(program, feed=infer_feeder.feed(data), fetch_list=fetch_targets)
+        if args.task_mode == "pairwise":
+            preds_list += list(map(lambda item: str(item[0]), output[1]))
+        else:
+            preds_list += map(lambda item: str(np.argmax(item)), output[1])
+    with open(args.infer_result_path, "w") as infer_file:
+        for _data, _pred in zip(simnet_process.get_infer_data(), preds_list):
+            infer_file.write(_data + "\t" + _pred + "\n")
+    logging.info("infer result saved in %s" % os.path.join(os.getcwd(), args.infer_result_path))
+
+
+def main(conf_dict, args):
+    """
+    main
+    """
+    if args.do_train:
+        train(conf_dict, args)
+    elif args.do_test:
+        test(conf_dict, args)
+    elif args.do_infer:
+        infer(args)
+    else:
+        raise ValueError("one of do_train and do_test and do_infer must be True")
+
+
+if __name__ == "__main__":
+    utils.print_arguments(args)
+    utils.init_log("./log/TextSimilarityNet")
+    conf_dict = config.SimNetConfig(args)
+    main(conf_dict, args)
diff --git a/PaddleNLP/similarity_net/struct.jpg b/PaddleNLP/similarity_net/struct.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..46987779af2ff59aaa4a7bb0a8943f86524acbe6
Binary files /dev/null and b/PaddleNLP/similarity_net/struct.jpg differ
diff --git a/PaddleNLP/similarity_net/utils.py b/PaddleNLP/similarity_net/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cbb61cad44a263778f3a6aeb43da1c0b58a81b9
--- /dev/null
+++ b/PaddleNLP/similarity_net/utils.py
@@ -0,0 +1,278 @@
+# --coding=utf-8
+"""
+SimNet utilities.
+"""
+
+import time
+import sys
+import re
+import os
+import six
+import numpy as np
+import logging
+import logging.handlers
+
+"""
+******functions for file processing******
+"""
+
+
+def load_vocab(file_path):
+    """
+    load the given vocabulary
+    """
+    vocab = {}
+    if not os.path.isfile(file_path):
+        raise ValueError("vocabulary dose not exist under %s" % file_path)
+    with open(file_path, 'r') as f:
+        for line in f:
+            items = line.strip('\n').split("\t")
+            if items[0] not in vocab:
+                vocab[items[0]] = int(items[1])
+    vocab[""] = 0
+    return vocab
+
+
+def get_result_file(args):
+    """
+    Get Result File
+    Args:
+      conf_dict: Input path config
+      samples_file_path: Data path of real training
+      predictions_file_path: Prediction results path
+    Returns:
+      result_file: merge sample and predict result
+
+    """
+    with open(args.test_data_dir, "r") as test_file:
+        with open("predictions.txt", "r") as predictions_file:
+            with open(args.test_result_path, "w") as test_result_file:
+                test_datas = [line.strip("\n") for line in test_file]
+                predictions = [line.strip("\n") for line in predictions_file]
+                for test_data, prediction in zip(test_datas, predictions):
+                    test_result_file.write(test_data + "\t" + prediction + "\n")
+    os.remove("predictions.txt")
+
+
+"""
+******functions for string processing******
+"""
+
+
+def pattern_match(pattern, line):
+    """
+    Check whether a string is matched
+    Args:
+      pattern: mathing pattern
+      line : input string
+    Returns:
+      True/False
+    """
+    if re.match(pattern, line):
+        return True
+    else:
+        return False
+
+
+"""
+******functions for parameter processing******
+"""
+
+
+def print_progress(task_name, percentage, style=0):
+    """
+    Print progress bar
+    Args:
+      task_name: The name of the current task
+      percentage: Current progress
+      style: Progress bar form
+    """
+    styles = ['#', '█']
+    mark = styles[style] * percentage
+    mark += ' ' * (100 - percentage)
+    status = '%d%%' % percentage if percentage < 100 else 'Finished'
+    sys.stdout.write('%+20s [%s] %s\r' % (task_name, mark, status))
+    sys.stdout.flush()
+    time.sleep(0.002)
+
+
+def display_args(name, args):
+    """
+    Print parameter information
+    Args:
+      name: logger instance name
+      args: Input parameter dictionary
+    """
+    logger = logging.getLogger(name)
+    logger.info("The arguments passed by command line is :")
+    for k, v in sorted(v for v in vars(args).items()):
+        logger.info("{}:\t{}".format(k, v))
+
+
+def import_class(module_path, module_name, class_name):
+    """
+    Load class dynamically
+    Args:
+      module_path: The current path of the module
+      module_name: The module name
+      class_name: The name of class in the import module
+    Return:
+      Return the attribute value of the class object
+    """
+    if module_path:
+        sys.path.append(module_path)
+    module = __import__(module_name)
+    return getattr(module, class_name)
+
+
+def str2bool(v):
+    """
+    String to Boolean
+    """
+    # because argparse does not support to parse "true, False" as python
+    # boolean directly
+    return v.lower() in ("true", "t", "1")
+
+
+class ArgumentGroup(object):
+    """
+    Argument Class
+    """
+
+    def __init__(self, parser, title, des):
+        self._group = parser.add_argument_group(title=title, description=des)
+
+    def add_arg(self, name, type, default, help, **kwargs):
+        """
+        Add argument
+        """
+        type = str2bool if type == bool else type
+        self._group.add_argument(
+            "--" + name,
+            default=default,
+            type=type,
+            help=help + ' Default: %(default)s.',
+            **kwargs)
+
+
+def print_arguments(args):
+    """
+    Print Arguments
+    """
+    print('-----------  Configuration Arguments -----------')
+    for arg, value in sorted(six.iteritems(vars(args))):
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------------')
+
+
+def init_log(log_path, level=logging.INFO, when="D", backup=7,
+             format="%(levelname)s: %(asctime)s - %(filename)s:%(lineno)d * %(thread)d %(message)s",
+             datefmt=None):
+    """
+    init_log - initialize log module
+
+    Args:
+      log_path      - Log file path prefix.
+                      Log data will go to two files: log_path.log and log_path.log.wf
+                      Any non-exist parent directories will be created automatically
+      level         - msg above the level will be displayed
+                      DEBUG < INFO < WARNING < ERROR < CRITICAL
+                      the default value is logging.INFO
+      when          - how to split the log file by time interval
+                      'S' : Seconds
+                      'M' : Minutes
+                      'H' : Hours
+                      'D' : Days
+                      'W' : Week day
+                      default value: 'D'
+      format        - format of the log
+                      default format:
+                      %(levelname)s: %(asctime)s: %(filename)s:%(lineno)d * %(thread)d %(message)s
+                      INFO: 12-09 18:02:42: log.py:40 * 139814749787872 HELLO WORLD
+      backup        - how many backup file to keep
+                      default value: 7
+
+    Raises:
+        OSError: fail to create log directories
+        IOError: fail to open log file
+    """
+    formatter = logging.Formatter(format, datefmt)
+    logger = logging.getLogger()
+    logger.setLevel(level)
+
+    # console Handler
+    consoleHandler = logging.StreamHandler()
+    consoleHandler.setLevel(logging.DEBUG)
+    logger.addHandler(consoleHandler)
+
+    dir = os.path.dirname(log_path)
+    if not os.path.isdir(dir):
+        os.makedirs(dir)
+
+    handler = logging.handlers.TimedRotatingFileHandler(log_path + ".log",
+                                                        when=when,
+                                                        backupCount=backup)
+    handler.setLevel(level)
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+
+    handler = logging.handlers.TimedRotatingFileHandler(log_path + ".log.wf",
+                                                        when=when,
+                                                        backupCount=backup)
+    handler.setLevel(logging.WARNING)
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+
+
+def set_level(level):
+    """
+    Reak-time set log level
+    """
+    logger = logging.getLogger()
+    logger.setLevel(level)
+    logging.info('log level is set to : %d' % level)
+
+
+def get_level():
+    """
+    get Real-time log level
+    """
+    logger = logging.getLogger()
+    return logger.level
+
+
+def get_accuracy(preds, labels, mode, lamda=0.91):
+    """
+    compute accuracy
+    """
+    if mode == "pairwise":
+        preds = np.array(list(map(lambda x: 1 if x[1] >= lamda else 0, preds)))
+    else:
+        preds = np.array(list(map(lambda x: np.argmax(x), preds)))
+    labels = np.squeeze(labels)
+    return np.mean(preds == labels)
+
+
+def get_softmax(preds):
+    """
+    compute sotfmax
+    """
+    _exp = np.exp(preds)
+    return _exp / np.sum(_exp, axis=1, keepdims=True)
+
+
+def get_sigmoid(preds):
+    """
+    compute sigmoid
+    """
+    return 1 / (1 + np.exp(-preds))
+
+
+def deal_preds_of_mmdnn(conf_dict, preds):
+    """
+    deal preds of mmdnn
+    """
+    if conf_dict['task_mode'] == 'pairwise':
+        return get_sigmoid(preds)
+    else:
+        return get_softmax(preds)
diff --git a/PaddleNLP/chinese_ner/.run_ce.sh b/PaddleNLP/unarchived/chinese_ner/.run_ce.sh
similarity index 100%
rename from PaddleNLP/chinese_ner/.run_ce.sh
rename to PaddleNLP/unarchived/chinese_ner/.run_ce.sh
diff --git a/PaddleNLP/chinese_ner/README.md b/PaddleNLP/unarchived/chinese_ner/README.md
similarity index 100%
rename from PaddleNLP/chinese_ner/README.md
rename to PaddleNLP/unarchived/chinese_ner/README.md
diff --git a/PaddleNLP/unarchived/chinese_ner/__init__.py b/PaddleNLP/unarchived/chinese_ner/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/PaddleNLP/chinese_ner/_ce.py b/PaddleNLP/unarchived/chinese_ner/_ce.py
similarity index 71%
rename from PaddleNLP/chinese_ner/_ce.py
rename to PaddleNLP/unarchived/chinese_ner/_ce.py
index 7afa65d346459dd691d93765ffbd6e02406e2e93..a53d6fe5be564044a00c98bdcfeaec7f6884822e 100644
--- a/PaddleNLP/chinese_ner/_ce.py
+++ b/PaddleNLP/unarchived/chinese_ner/_ce.py
@@ -7,22 +7,24 @@ from kpi import CostKpi
 from kpi import DurationKpi
 from kpi import AccKpi
 
-
-each_pass_duration_cpu1_thread1_kpi = DurationKpi('each_pass_duration_cpu1_thread1', 0.08, 0, actived=True)
+each_pass_duration_cpu1_thread1_kpi = DurationKpi(
+    'each_pass_duration_cpu1_thread1', 0.08, 0, actived=True)
 train_recall_cpu1_thread1_kpi = AccKpi('train_recall_cpu1_thread1', 0.08, 0)
-each_pass_duration_gpu1_kpi = DurationKpi('each_pass_duration_gpu1', 0.08, 0, actived=True)
+each_pass_duration_gpu1_kpi = DurationKpi(
+    'each_pass_duration_gpu1', 0.08, 0, actived=True)
 train_recall_gpu1_kpi = AccKpi('train_recall_gpu1', 0.08, 0)
-each_pass_duration_gpu4_kpi = DurationKpi('each_pass_duration_gpu4', 0.08, 0, actived=True)
+each_pass_duration_gpu4_kpi = DurationKpi(
+    'each_pass_duration_gpu4', 0.08, 0, actived=True)
 train_recall_gpu4_kpi = AccKpi('train_recall_gpu4', 0.08, 0)
 
 tracking_kpis = [
-        each_pass_duration_cpu1_thread1_kpi,
-        train_recall_cpu1_thread1_kpi,
-        each_pass_duration_gpu1_kpi,
-        train_recall_gpu1_kpi,
-        each_pass_duration_gpu4_kpi,
-        train_recall_gpu4_kpi,
-        ]
+    each_pass_duration_cpu1_thread1_kpi,
+    train_recall_cpu1_thread1_kpi,
+    each_pass_duration_gpu1_kpi,
+    train_recall_gpu1_kpi,
+    each_pass_duration_gpu4_kpi,
+    train_recall_gpu4_kpi,
+]
 
 
 def parse_log(log):
diff --git a/PaddleNLP/chinese_ner/data/label_dict b/PaddleNLP/unarchived/chinese_ner/data/label_dict
similarity index 100%
rename from PaddleNLP/chinese_ner/data/label_dict
rename to PaddleNLP/unarchived/chinese_ner/data/label_dict
diff --git a/PaddleNLP/chinese_ner/data/test_files/test_part_1 b/PaddleNLP/unarchived/chinese_ner/data/test_files/test_part_1
similarity index 100%
rename from PaddleNLP/chinese_ner/data/test_files/test_part_1
rename to PaddleNLP/unarchived/chinese_ner/data/test_files/test_part_1
diff --git a/PaddleNLP/chinese_ner/data/test_files/test_part_2 b/PaddleNLP/unarchived/chinese_ner/data/test_files/test_part_2
similarity index 100%
rename from PaddleNLP/chinese_ner/data/test_files/test_part_2
rename to PaddleNLP/unarchived/chinese_ner/data/test_files/test_part_2
diff --git a/PaddleNLP/chinese_ner/data/train_files/train_part_1 b/PaddleNLP/unarchived/chinese_ner/data/train_files/train_part_1
similarity index 100%
rename from PaddleNLP/chinese_ner/data/train_files/train_part_1
rename to PaddleNLP/unarchived/chinese_ner/data/train_files/train_part_1
diff --git a/PaddleNLP/chinese_ner/data/train_files/train_part_2 b/PaddleNLP/unarchived/chinese_ner/data/train_files/train_part_2
similarity index 100%
rename from PaddleNLP/chinese_ner/data/train_files/train_part_2
rename to PaddleNLP/unarchived/chinese_ner/data/train_files/train_part_2
diff --git a/PaddleNLP/chinese_ner/infer.py b/PaddleNLP/unarchived/chinese_ner/infer.py
similarity index 100%
rename from PaddleNLP/chinese_ner/infer.py
rename to PaddleNLP/unarchived/chinese_ner/infer.py
diff --git a/PaddleNLP/chinese_ner/reader.py b/PaddleNLP/unarchived/chinese_ner/reader.py
similarity index 100%
rename from PaddleNLP/chinese_ner/reader.py
rename to PaddleNLP/unarchived/chinese_ner/reader.py
diff --git a/PaddleNLP/chinese_ner/scripts/README.md b/PaddleNLP/unarchived/chinese_ner/scripts/README.md
similarity index 100%
rename from PaddleNLP/chinese_ner/scripts/README.md
rename to PaddleNLP/unarchived/chinese_ner/scripts/README.md
diff --git a/PaddleNLP/chinese_ner/scripts/infer.sh b/PaddleNLP/unarchived/chinese_ner/scripts/infer.sh
similarity index 100%
rename from PaddleNLP/chinese_ner/scripts/infer.sh
rename to PaddleNLP/unarchived/chinese_ner/scripts/infer.sh
diff --git a/PaddleNLP/chinese_ner/scripts/train.sh b/PaddleNLP/unarchived/chinese_ner/scripts/train.sh
similarity index 100%
rename from PaddleNLP/chinese_ner/scripts/train.sh
rename to PaddleNLP/unarchived/chinese_ner/scripts/train.sh
diff --git a/PaddleNLP/chinese_ner/train.py b/PaddleNLP/unarchived/chinese_ner/train.py
similarity index 98%
rename from PaddleNLP/chinese_ner/train.py
rename to PaddleNLP/unarchived/chinese_ner/train.py
index 4a6a61b34390b4c25c0f6042604b25878f0bc134..894260d399a4957cf6fed6b573eca6d12ebdee6d 100644
--- a/PaddleNLP/chinese_ner/train.py
+++ b/PaddleNLP/unarchived/chinese_ner/train.py
@@ -374,17 +374,16 @@ def main(args):
             if args.device == "GPU":
                 gpu_num = device[1]
                 print("kpis\teach_pass_duration_gpu%s\t%s" %
-                    (gpu_num, total_time / epoch_idx))
-                print("kpis\ttrain_recall_gpu%s\t%s" %
-                    (gpu_num, ce_recall))
+                      (gpu_num, total_time / epoch_idx))
+                print("kpis\ttrain_recall_gpu%s\t%s" % (gpu_num, ce_recall))
             else:
                 cpu_num = device[1]
                 threads_num = device[2]
                 print("kpis\teach_pass_duration_cpu%s_thread%s\t%s" %
-                    (cpu_num, threads_num, total_time / epoch_idx))
+                      (cpu_num, threads_num, total_time / epoch_idx))
                 print("kpis\ttrain_recall_cpu%s_thread%s\t%s" %
-                    (cpu_num, threads_num, ce_recall))
-        
+                      (cpu_num, threads_num, ce_recall))
+
 
 def get_device(args):
     if args.device == "GPU":
@@ -395,7 +394,7 @@ def get_device(args):
         threads_num = os.environ.get('NUM_THREADS', 1)
         cpu_num = os.environ.get('CPU_NUM', 1)
         return "cpu", int(cpu_num), int(threads_num)
-        
+
 
 if __name__ == "__main__":
     args = parse_args()
diff --git a/PaddleNLP/deep_attention_matching_net/.run_ce.sh b/PaddleNLP/unarchived/deep_attention_matching_net/.run_ce.sh
similarity index 100%
rename from PaddleNLP/deep_attention_matching_net/.run_ce.sh
rename to PaddleNLP/unarchived/deep_attention_matching_net/.run_ce.sh
diff --git a/PaddleNLP/deep_attention_matching_net/README.md b/PaddleNLP/unarchived/deep_attention_matching_net/README.md
similarity index 100%
rename from PaddleNLP/deep_attention_matching_net/README.md
rename to PaddleNLP/unarchived/deep_attention_matching_net/README.md
diff --git a/PaddleNLP/deep_attention_matching_net/_ce.py b/PaddleNLP/unarchived/deep_attention_matching_net/_ce.py
similarity index 100%
rename from PaddleNLP/deep_attention_matching_net/_ce.py
rename to PaddleNLP/unarchived/deep_attention_matching_net/_ce.py
diff --git a/PaddleNLP/deep_attention_matching_net/douban/download_data.sh b/PaddleNLP/unarchived/deep_attention_matching_net/douban/download_data.sh
similarity index 100%
rename from PaddleNLP/deep_attention_matching_net/douban/download_data.sh
rename to PaddleNLP/unarchived/deep_attention_matching_net/douban/download_data.sh
diff --git a/PaddleNLP/deep_attention_matching_net/douban/test.sh b/PaddleNLP/unarchived/deep_attention_matching_net/douban/test.sh
similarity index 100%
rename from PaddleNLP/deep_attention_matching_net/douban/test.sh
rename to PaddleNLP/unarchived/deep_attention_matching_net/douban/test.sh
diff --git a/PaddleNLP/deep_attention_matching_net/douban/train.sh b/PaddleNLP/unarchived/deep_attention_matching_net/douban/train.sh
similarity index 100%
rename from PaddleNLP/deep_attention_matching_net/douban/train.sh
rename to PaddleNLP/unarchived/deep_attention_matching_net/douban/train.sh
diff --git a/PaddleNLP/unarchived/deep_attention_matching_net/images/Figure1.png b/PaddleNLP/unarchived/deep_attention_matching_net/images/Figure1.png
new file mode 100644
index 0000000000000000000000000000000000000000..c02c3e4fb1c7d5c50da4b50d19f5d182d8291f98
Binary files /dev/null and b/PaddleNLP/unarchived/deep_attention_matching_net/images/Figure1.png differ
diff --git a/PaddleNLP/unarchived/deep_attention_matching_net/images/Figure2.png b/PaddleNLP/unarchived/deep_attention_matching_net/images/Figure2.png
new file mode 100644
index 0000000000000000000000000000000000000000..b225afc1205fd03bc61301a112628447e142bb3e
Binary files /dev/null and b/PaddleNLP/unarchived/deep_attention_matching_net/images/Figure2.png differ
diff --git a/PaddleNLP/deep_attention_matching_net/model.py b/PaddleNLP/unarchived/deep_attention_matching_net/model.py
similarity index 100%
rename from PaddleNLP/deep_attention_matching_net/model.py
rename to PaddleNLP/unarchived/deep_attention_matching_net/model.py
diff --git a/PaddleNLP/deep_attention_matching_net/test_and_evaluate.py b/PaddleNLP/unarchived/deep_attention_matching_net/test_and_evaluate.py
similarity index 100%
rename from PaddleNLP/deep_attention_matching_net/test_and_evaluate.py
rename to PaddleNLP/unarchived/deep_attention_matching_net/test_and_evaluate.py
diff --git a/PaddleNLP/deep_attention_matching_net/train_and_evaluate.py b/PaddleNLP/unarchived/deep_attention_matching_net/train_and_evaluate.py
similarity index 97%
rename from PaddleNLP/deep_attention_matching_net/train_and_evaluate.py
rename to PaddleNLP/unarchived/deep_attention_matching_net/train_and_evaluate.py
index 823d13de9facb2537fbb6358e16c339f2ea5354b..332fcb9d5d38721eb99e5c71a4dc852791e3580a 100644
--- a/PaddleNLP/deep_attention_matching_net/train_and_evaluate.py
+++ b/PaddleNLP/unarchived/deep_attention_matching_net/train_and_evaluate.py
@@ -248,9 +248,8 @@ def train(args):
 
     print("device count %d" % dev_count)
     print("theoretical memory usage: ")
-    print(
-        fluid.contrib.memory_usage(
-            program=train_program, batch_size=args.batch_size))
+    print(fluid.contrib.memory_usage(
+        program=train_program, batch_size=args.batch_size))
 
     exe = fluid.Executor(place)
     exe.run(train_startup)
@@ -319,9 +318,8 @@ def train(args):
             if (args.save_path is not None) and (step % save_step == 0):
                 save_path = os.path.join(args.save_path, "step_" + str(step))
                 print("Save model at step %d ... " % step)
-                print(
-                    time.strftime('%Y-%m-%d %H:%M:%S',
-                                  time.localtime(time.time())))
+                print(time.strftime('%Y-%m-%d %H:%M:%S',
+                                    time.localtime(time.time())))
                 fluid.io.save_persistables(exe, save_path, train_program)
 
                 score_path = os.path.join(args.save_path, 'score.' + str(step))
@@ -360,9 +358,8 @@ def train(args):
                     save_path = os.path.join(args.save_path,
                                              "step_" + str(step))
                     print("Save model at step %d ... " % step)
-                    print(
-                        time.strftime('%Y-%m-%d %H:%M:%S',
-                                      time.localtime(time.time())))
+                    print(time.strftime('%Y-%m-%d %H:%M:%S',
+                                        time.localtime(time.time())))
                     fluid.io.save_persistables(exe, save_path, train_program)
 
                     score_path = os.path.join(args.save_path,
diff --git a/PaddleNLP/deep_attention_matching_net/ubuntu/download_data.sh b/PaddleNLP/unarchived/deep_attention_matching_net/ubuntu/download_data.sh
similarity index 100%
rename from PaddleNLP/deep_attention_matching_net/ubuntu/download_data.sh
rename to PaddleNLP/unarchived/deep_attention_matching_net/ubuntu/download_data.sh
diff --git a/PaddleNLP/deep_attention_matching_net/ubuntu/test.sh b/PaddleNLP/unarchived/deep_attention_matching_net/ubuntu/test.sh
similarity index 100%
rename from PaddleNLP/deep_attention_matching_net/ubuntu/test.sh
rename to PaddleNLP/unarchived/deep_attention_matching_net/ubuntu/test.sh
diff --git a/PaddleNLP/deep_attention_matching_net/ubuntu/train.sh b/PaddleNLP/unarchived/deep_attention_matching_net/ubuntu/train.sh
similarity index 100%
rename from PaddleNLP/deep_attention_matching_net/ubuntu/train.sh
rename to PaddleNLP/unarchived/deep_attention_matching_net/ubuntu/train.sh
diff --git a/PaddleNLP/unarchived/deep_attention_matching_net/utils/__init__.py b/PaddleNLP/unarchived/deep_attention_matching_net/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/PaddleNLP/deep_attention_matching_net/utils/douban_evaluation.py b/PaddleNLP/unarchived/deep_attention_matching_net/utils/douban_evaluation.py
similarity index 100%
rename from PaddleNLP/deep_attention_matching_net/utils/douban_evaluation.py
rename to PaddleNLP/unarchived/deep_attention_matching_net/utils/douban_evaluation.py
diff --git a/PaddleNLP/deep_attention_matching_net/utils/evaluation.py b/PaddleNLP/unarchived/deep_attention_matching_net/utils/evaluation.py
similarity index 100%
rename from PaddleNLP/deep_attention_matching_net/utils/evaluation.py
rename to PaddleNLP/unarchived/deep_attention_matching_net/utils/evaluation.py
diff --git a/PaddleNLP/deep_attention_matching_net/utils/layers.py b/PaddleNLP/unarchived/deep_attention_matching_net/utils/layers.py
similarity index 100%
rename from PaddleNLP/deep_attention_matching_net/utils/layers.py
rename to PaddleNLP/unarchived/deep_attention_matching_net/utils/layers.py
diff --git a/PaddleNLP/deep_attention_matching_net/utils/reader.py b/PaddleNLP/unarchived/deep_attention_matching_net/utils/reader.py
similarity index 100%
rename from PaddleNLP/deep_attention_matching_net/utils/reader.py
rename to PaddleNLP/unarchived/deep_attention_matching_net/utils/reader.py
diff --git a/PaddleNLP/deep_attention_matching_net/utils/util.py b/PaddleNLP/unarchived/deep_attention_matching_net/utils/util.py
similarity index 100%
rename from PaddleNLP/deep_attention_matching_net/utils/util.py
rename to PaddleNLP/unarchived/deep_attention_matching_net/utils/util.py
diff --git a/PaddleNLP/language_model/gru/.run_ce.sh b/PaddleNLP/unarchived/language_model/gru/.run_ce.sh
similarity index 100%
rename from PaddleNLP/language_model/gru/.run_ce.sh
rename to PaddleNLP/unarchived/language_model/gru/.run_ce.sh
diff --git a/PaddleNLP/language_model/gru/README.md b/PaddleNLP/unarchived/language_model/gru/README.md
similarity index 100%
rename from PaddleNLP/language_model/gru/README.md
rename to PaddleNLP/unarchived/language_model/gru/README.md
diff --git a/PaddleNLP/language_model/gru/_ce.py b/PaddleNLP/unarchived/language_model/gru/_ce.py
similarity index 100%
rename from PaddleNLP/language_model/gru/_ce.py
rename to PaddleNLP/unarchived/language_model/gru/_ce.py
diff --git a/PaddleNLP/language_model/gru/infer.py b/PaddleNLP/unarchived/language_model/gru/infer.py
similarity index 100%
rename from PaddleNLP/language_model/gru/infer.py
rename to PaddleNLP/unarchived/language_model/gru/infer.py
diff --git a/PaddleNLP/language_model/gru/train.py b/PaddleNLP/unarchived/language_model/gru/train.py
similarity index 100%
rename from PaddleNLP/language_model/gru/train.py
rename to PaddleNLP/unarchived/language_model/gru/train.py
diff --git a/PaddleNLP/language_model/gru/train_on_cloud.py b/PaddleNLP/unarchived/language_model/gru/train_on_cloud.py
similarity index 100%
rename from PaddleNLP/language_model/gru/train_on_cloud.py
rename to PaddleNLP/unarchived/language_model/gru/train_on_cloud.py
diff --git a/PaddleNLP/language_model/gru/utils.py b/PaddleNLP/unarchived/language_model/gru/utils.py
similarity index 100%
rename from PaddleNLP/language_model/gru/utils.py
rename to PaddleNLP/unarchived/language_model/gru/utils.py
diff --git a/PaddleNLP/language_model/lstm/.run_ce.sh b/PaddleNLP/unarchived/language_model/lstm/.run_ce.sh
similarity index 100%
rename from PaddleNLP/language_model/lstm/.run_ce.sh
rename to PaddleNLP/unarchived/language_model/lstm/.run_ce.sh
diff --git a/PaddleNLP/language_model/lstm/README.md b/PaddleNLP/unarchived/language_model/lstm/README.md
similarity index 100%
rename from PaddleNLP/language_model/lstm/README.md
rename to PaddleNLP/unarchived/language_model/lstm/README.md
diff --git a/PaddleNLP/language_model/lstm/_ce.py b/PaddleNLP/unarchived/language_model/lstm/_ce.py
similarity index 100%
rename from PaddleNLP/language_model/lstm/_ce.py
rename to PaddleNLP/unarchived/language_model/lstm/_ce.py
diff --git a/PaddleNLP/unarchived/language_model/lstm/args.py b/PaddleNLP/unarchived/language_model/lstm/args.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c6957b2bf90eb45bf009e022e7ee27cc4742f4a
--- /dev/null
+++ b/PaddleNLP/unarchived/language_model/lstm/args.py
@@ -0,0 +1,45 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import distutils.util
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--model_type",
+        type=str,
+        default="small",
+        help="model_type [test|small|medium|large]")
+    parser.add_argument(
+        "--rnn_model",
+        type=str,
+        default="static",
+        help="model_type [static|padding|cudnn]")
+    parser.add_argument(
+        "--data_path", type=str, help="all the data for train,valid,test")
+    parser.add_argument('--para_init', action='store_true')
+    parser.add_argument(
+        '--use_gpu', type=bool, default=False, help='whether using gpu')
+    parser.add_argument(
+        '--log_path',
+        help='path of the log file. If not set, logs are printed to console')
+    parser.add_argument('--enable_ce', action='store_true')
+    args = parser.parse_args()
+    return args
diff --git a/PaddleNLP/unarchived/language_model/lstm/data/download_data.sh b/PaddleNLP/unarchived/language_model/lstm/data/download_data.sh
new file mode 100644
index 0000000000000000000000000000000000000000..29966c7663a60ad802e273727496acb6381c98ca
--- /dev/null
+++ b/PaddleNLP/unarchived/language_model/lstm/data/download_data.sh
@@ -0,0 +1,4 @@
+
+wget http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz
+
+tar -xzvf simple-examples.tgz
diff --git a/PaddleNLP/unarchived/language_model/lstm/lm_model.py b/PaddleNLP/unarchived/language_model/lstm/lm_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..6110b48ba37fb7c20ca71d541e1448b772729a1d
--- /dev/null
+++ b/PaddleNLP/unarchived/language_model/lstm/lm_model.py
@@ -0,0 +1,299 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle.fluid.layers as layers
+import paddle.fluid as fluid
+from paddle.fluid.layers.control_flow import StaticRNN as PaddingRNN
+import numpy as np
+
+
+def lm_model(hidden_size,
+             vocab_size,
+             batch_size,
+             num_layers=2,
+             num_steps=20,
+             init_scale=0.1,
+             dropout=None,
+             rnn_model='static'):
+    def padding_rnn(input_embedding, len=3, init_hidden=None, init_cell=None):
+        weight_1_arr = []
+        weight_2_arr = []
+        bias_arr = []
+        hidden_array = []
+        cell_array = []
+        mask_array = []
+        for i in range(num_layers):
+            weight_1 = layers.create_parameter([hidden_size * 2, hidden_size*4], dtype="float32", name="fc_weight1_"+str(i), \
+                    default_initializer=fluid.initializer.UniformInitializer(low=-init_scale, high=init_scale))
+            weight_1_arr.append(weight_1)
+            bias_1 = layers.create_parameter(
+                [hidden_size * 4],
+                dtype="float32",
+                name="fc_bias1_" + str(i),
+                default_initializer=fluid.initializer.Constant(0.0))
+            bias_arr.append(bias_1)
+
+            pre_hidden = layers.slice(
+                init_hidden, axes=[0], starts=[i], ends=[i + 1])
+            pre_cell = layers.slice(
+                init_cell, axes=[0], starts=[i], ends=[i + 1])
+            pre_hidden = layers.reshape(pre_hidden, shape=[-1, hidden_size])
+            pre_cell = layers.reshape(pre_cell, shape=[-1, hidden_size])
+            hidden_array.append(pre_hidden)
+            cell_array.append(pre_cell)
+
+        input_embedding = layers.transpose(input_embedding, perm=[1, 0, 2])
+        rnn = PaddingRNN()
+
+        with rnn.step():
+            input = rnn.step_input(input_embedding)
+            for k in range(num_layers):
+                pre_hidden = rnn.memory(init=hidden_array[k])
+                pre_cell = rnn.memory(init=cell_array[k])
+                weight_1 = weight_1_arr[k]
+                bias = bias_arr[k]
+
+                nn = layers.concat([input, pre_hidden], 1)
+                gate_input = layers.matmul(x=nn, y=weight_1)
+
+                gate_input = layers.elementwise_add(gate_input, bias)
+                #i, j, f, o = layers.split(gate_input, num_or_sections=4, dim=-1)
+                i = layers.slice(
+                    gate_input, axes=[1], starts=[0], ends=[hidden_size])
+                j = layers.slice(
+                    gate_input,
+                    axes=[1],
+                    starts=[hidden_size],
+                    ends=[hidden_size * 2])
+                f = layers.slice(
+                    gate_input,
+                    axes=[1],
+                    starts=[hidden_size * 2],
+                    ends=[hidden_size * 3])
+                o = layers.slice(
+                    gate_input,
+                    axes=[1],
+                    starts=[hidden_size * 3],
+                    ends=[hidden_size * 4])
+
+                c = pre_cell * layers.sigmoid(f) + layers.sigmoid(
+                    i) * layers.tanh(j)
+                m = layers.tanh(c) * layers.sigmoid(o)
+
+                rnn.update_memory(pre_hidden, m)
+                rnn.update_memory(pre_cell, c)
+
+                rnn.step_output(m)
+                rnn.step_output(c)
+
+                input = m
+
+                if dropout != None and dropout > 0.0:
+                    input = layers.dropout(
+                        input,
+                        dropout_prob=dropout,
+                        dropout_implementation='upscale_in_train')
+
+            rnn.step_output(input)
+        #real_res = layers.concat(res, 0)
+        rnnout = rnn()
+
+        last_hidden_array = []
+        last_cell_array = []
+        real_res = rnnout[-1]
+        for i in range(num_layers):
+            m = rnnout[i * 2]
+            c = rnnout[i * 2 + 1]
+            m.stop_gradient = True
+            c.stop_gradient = True
+            last_h = layers.slice(
+                m, axes=[0], starts=[num_steps - 1], ends=[num_steps])
+            last_hidden_array.append(last_h)
+            last_c = layers.slice(
+                c, axes=[0], starts=[num_steps - 1], ends=[num_steps])
+            last_cell_array.append(last_c)
+        '''
+        else:
+            real_res = rnnout[-1]
+            for i in range( num_layers ):
+
+            m1, c1, m2, c2 = rnnout
+            real_res = m2
+            m1.stop_gradient = True
+            c1.stop_gradient = True
+            c2.stop_gradient = True
+        '''
+
+        #layers.Print( first_hidden, message="22", summarize=10)
+        #layers.Print( rnnout[1], message="11", summarize=10)
+        #real_res = ( rnnout[1] + rnnout[2] + rnnout[3] + rnnout[4]) / 4.0
+        real_res = layers.transpose(x=real_res, perm=[1, 0, 2])
+        last_hidden = layers.concat(last_hidden_array, 0)
+        last_cell = layers.concat(last_cell_array, 0)
+        '''
+        last_hidden = layers.concat( hidden_array, 1 )
+        last_hidden = layers.reshape( last_hidden, shape=[-1, num_layers, hidden_size])
+        last_hidden = layers.transpose( x = last_hidden, perm = [1, 0, 2])
+        last_cell = layers.concat( cell_array, 1)
+        last_cell = layers.reshape( last_cell, shape=[ -1, num_layers, hidden_size])
+        last_cell = layers.transpose( x = last_cell, perm = [1, 0, 2])
+        '''
+
+        return real_res, last_hidden, last_cell
+
+    def encoder_static(input_embedding, len=3, init_hidden=None,
+                       init_cell=None):
+
+        weight_1_arr = []
+        weight_2_arr = []
+        bias_arr = []
+        hidden_array = []
+        cell_array = []
+        mask_array = []
+        for i in range(num_layers):
+            weight_1 = layers.create_parameter([hidden_size * 2, hidden_size*4], dtype="float32", name="fc_weight1_"+str(i), \
+                    default_initializer=fluid.initializer.UniformInitializer(low=-init_scale, high=init_scale))
+            weight_1_arr.append(weight_1)
+            bias_1 = layers.create_parameter(
+                [hidden_size * 4],
+                dtype="float32",
+                name="fc_bias1_" + str(i),
+                default_initializer=fluid.initializer.Constant(0.0))
+            bias_arr.append(bias_1)
+
+            pre_hidden = layers.slice(
+                init_hidden, axes=[0], starts=[i], ends=[i + 1])
+            pre_cell = layers.slice(
+                init_cell, axes=[0], starts=[i], ends=[i + 1])
+            pre_hidden = layers.reshape(pre_hidden, shape=[-1, hidden_size])
+            pre_cell = layers.reshape(pre_cell, shape=[-1, hidden_size])
+            hidden_array.append(pre_hidden)
+            cell_array.append(pre_cell)
+
+        res = []
+        for index in range(len):
+            input = layers.slice(
+                input_embedding, axes=[1], starts=[index], ends=[index + 1])
+            input = layers.reshape(input, shape=[-1, hidden_size])
+            for k in range(num_layers):
+                pre_hidden = hidden_array[k]
+                pre_cell = cell_array[k]
+                weight_1 = weight_1_arr[k]
+                bias = bias_arr[k]
+
+                nn = layers.concat([input, pre_hidden], 1)
+                gate_input = layers.matmul(x=nn, y=weight_1)
+
+                gate_input = layers.elementwise_add(gate_input, bias)
+                i, j, f, o = layers.split(gate_input, num_or_sections=4, dim=-1)
+
+                c = pre_cell * layers.sigmoid(f) + layers.sigmoid(
+                    i) * layers.tanh(j)
+                m = layers.tanh(c) * layers.sigmoid(o)
+
+                hidden_array[k] = m
+                cell_array[k] = c
+                input = m
+
+                if dropout != None and dropout > 0.0:
+                    input = layers.dropout(
+                        input,
+                        dropout_prob=dropout,
+                        dropout_implementation='upscale_in_train')
+
+            res.append(layers.reshape(input, shape=[1, -1, hidden_size]))
+        real_res = layers.concat(res, 0)
+        real_res = layers.transpose(x=real_res, perm=[1, 0, 2])
+        last_hidden = layers.concat(hidden_array, 1)
+        last_hidden = layers.reshape(
+            last_hidden, shape=[-1, num_layers, hidden_size])
+        last_hidden = layers.transpose(x=last_hidden, perm=[1, 0, 2])
+        last_cell = layers.concat(cell_array, 1)
+        last_cell = layers.reshape(
+            last_cell, shape=[-1, num_layers, hidden_size])
+        last_cell = layers.transpose(x=last_cell, perm=[1, 0, 2])
+
+        return real_res, last_hidden, last_cell
+
+    x = layers.data(name="x", shape=[-1, 1, 1], dtype='int64')
+    y = layers.data(name="y", shape=[-1, 1], dtype='float32')
+
+    init_hidden = layers.data(name="init_hidden", shape=[1], dtype='float32')
+    init_cell = layers.data(name="init_cell", shape=[1], dtype='float32')
+
+    init_hidden = layers.reshape(
+        init_hidden, shape=[num_layers, -1, hidden_size])
+    init_cell = layers.reshape(init_cell, shape=[num_layers, -1, hidden_size])
+
+    x_emb = layers.embedding(
+        input=x,
+        size=[vocab_size, hidden_size],
+        dtype='float32',
+        is_sparse=False,
+        param_attr=fluid.ParamAttr(
+            name='embedding_para',
+            initializer=fluid.initializer.UniformInitializer(
+                low=-init_scale, high=init_scale)))
+
+    x_emb = layers.reshape(x_emb, shape=[-1, num_steps, hidden_size])
+    if dropout != None and dropout > 0.0:
+        x_emb = layers.dropout(
+            x_emb,
+            dropout_prob=dropout,
+            dropout_implementation='upscale_in_train')
+
+    if rnn_model == "padding":
+        rnn_out, last_hidden, last_cell = padding_rnn(
+            x_emb, len=num_steps, init_hidden=init_hidden, init_cell=init_cell)
+    elif rnn_model == "static":
+        rnn_out, last_hidden, last_cell = encoder_static(
+            x_emb, len=num_steps, init_hidden=init_hidden, init_cell=init_cell)
+    elif rnn_model == "cudnn":
+        x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
+        rnn_out, last_hidden, last_cell = layers.lstm( x_emb, init_hidden, init_cell,  num_steps, hidden_size, num_layers, \
+                is_bidirec=False, \
+                default_initializer=fluid.initializer.UniformInitializer(low=-init_scale, high=init_scale) )
+        rnn_out = layers.transpose(rnn_out, perm=[1, 0, 2])
+    else:
+        print("type not support")
+        return
+    rnn_out = layers.reshape(rnn_out, shape=[-1, num_steps, hidden_size])
+
+
+    softmax_weight = layers.create_parameter([hidden_size, vocab_size], dtype="float32", name="softmax_weight", \
+            default_initializer=fluid.initializer.UniformInitializer(low=-init_scale, high=init_scale))
+    softmax_bias = layers.create_parameter([vocab_size], dtype="float32", name='softmax_bias', \
+            default_initializer=fluid.initializer.UniformInitializer(low=-init_scale, high=init_scale))
+
+    projection = layers.matmul(rnn_out, softmax_weight)
+    projection = layers.elementwise_add(projection, softmax_bias)
+
+    projection = layers.reshape(projection, shape=[-1, vocab_size])
+    #y = layers.reshape( y, shape=[-1, vocab_size])
+
+    loss = layers.softmax_with_cross_entropy(
+        logits=projection, label=y, soft_label=False)
+
+    loss = layers.reshape(loss, shape=[-1, num_steps])
+    loss = layers.reduce_mean(loss, dim=[0])
+    loss = layers.reduce_sum(loss)
+
+    loss.permissions = True
+
+    feeding_list = ['x', 'y', 'init_hidden', 'init_cell']
+    return loss, last_hidden, last_cell, feeding_list
diff --git a/PaddleNLP/unarchived/language_model/lstm/reader.py b/PaddleNLP/unarchived/language_model/lstm/reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c0551d84f253d9bfd8420e56185780bee7c3145
--- /dev/null
+++ b/PaddleNLP/unarchived/language_model/lstm/reader.py
@@ -0,0 +1,108 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities for parsing PTB text files."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import os
+import sys
+import numpy as np
+
+Py3 = sys.version_info[0] == 3
+
+
+def _read_words(filename):
+    data = []
+    with open(filename, "r") as f:
+        if Py3:
+            return f.read().replace("\n", "").split()
+        else:
+            return f.read().decode("utf-8").replace("\n", "").split()
+
+
+def _build_vocab(filename):
+    data = _read_words(filename)
+
+    counter = collections.Counter(data)
+    count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))
+
+    words, _ = list(zip(*count_pairs))
+
+    print("vocab word num", len(words))
+    word_to_id = dict(zip(words, range(len(words))))
+
+    return word_to_id
+
+
+def _file_to_word_ids(filename, word_to_id):
+    data = _read_words(filename)
+    return [word_to_id[word] for word in data if word in word_to_id]
+
+
+def ptb_raw_data(data_path=None):
+    """Load PTB raw data from data directory "data_path".
+
+  Reads PTB text files, converts strings to integer ids,
+  and performs mini-batching of the inputs.
+
+  The PTB dataset comes from Tomas Mikolov's webpage:
+
+  http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz
+
+  Args:
+    data_path: string path to the directory where simple-examples.tgz has
+      been extracted.
+
+  Returns:
+    tuple (train_data, valid_data, test_data, vocabulary)
+    where each of the data objects can be passed to PTBIterator.
+  """
+
+    train_path = os.path.join(data_path, "ptb.train.txt")
+    #train_path = os.path.join(data_path, "train.fake")
+    valid_path = os.path.join(data_path, "ptb.valid.txt")
+    test_path = os.path.join(data_path, "ptb.test.txt")
+
+    word_to_id = _build_vocab(train_path)
+    train_data = _file_to_word_ids(train_path, word_to_id)
+    valid_data = _file_to_word_ids(valid_path, word_to_id)
+    test_data = _file_to_word_ids(test_path, word_to_id)
+    vocabulary = len(word_to_id)
+    return train_data, valid_data, test_data, vocabulary
+
+
+def get_data_iter(raw_data, batch_size, num_steps):
+    data_len = len(raw_data)
+    raw_data = np.asarray(raw_data, dtype="int64")
+
+    #print( "raw", raw_data[:20] )
+
+    batch_len = data_len // batch_size
+
+    data = raw_data[0:batch_size * batch_len].reshape((batch_size, batch_len))
+
+    #h = data.reshape( (-1))
+    #print( "h", h[:20])
+
+    epoch_size = (batch_len - 1) // num_steps
+    for i in range(epoch_size):
+        start = i * num_steps
+        #print( i * num_steps )
+        x = np.copy(data[:, i * num_steps:(i + 1) * num_steps])
+        y = np.copy(data[:, i * num_steps + 1:(i + 1) * num_steps + 1])
+
+        yield (x, y)
diff --git a/PaddleNLP/unarchived/language_model/lstm/train.py b/PaddleNLP/unarchived/language_model/lstm/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c22b30f13738566c8a7670f44e2485fc8938404
--- /dev/null
+++ b/PaddleNLP/unarchived/language_model/lstm/train.py
@@ -0,0 +1,307 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import time
+import os
+import random
+
+import math
+
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import paddle.fluid.framework as framework
+from paddle.fluid.executor import Executor
+
+import reader
+
+import sys
+if sys.version[0] == '2':
+    reload(sys)
+    sys.setdefaultencoding("utf-8")
+sys.path.append('..')
+import os
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+from args import *
+import lm_model
+import logging
+import pickle
+
+SEED = 123
+
+
+def get_current_model_para(train_prog, train_exe):
+    param_list = train_prog.block(0).all_parameters()
+    param_name_list = [p.name for p in param_list]
+
+    vals = {}
+    for p_name in param_name_list:
+        p_array = np.array(fluid.global_scope().find_var(p_name).get_tensor())
+        vals[p_name] = p_array
+
+    return vals
+
+
+def save_para_npz(train_prog, train_exe):
+    print("begin to save model to model_base")
+    param_list = train_prog.block(0).all_parameters()
+    param_name_list = [p.name for p in param_list]
+
+    vals = {}
+    for p_name in param_name_list:
+        p_array = np.array(fluid.global_scope().find_var(p_name).get_tensor())
+        vals[p_name] = p_array
+
+    emb = vals["embedding_para"]
+    print("begin to save model to model_base")
+    np.savez("mode_base", **vals)
+
+
+def train():
+    args = parse_args()
+    model_type = args.model_type
+    rnn_model = args.rnn_model
+    logger = logging.getLogger("lm")
+    logger.setLevel(logging.INFO)
+    formatter = logging.Formatter(
+        '%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    if args.enable_ce:
+        fluid.default_startup_program().random_seed = SEED
+    if args.log_path:
+        file_handler = logging.FileHandler(args.log_path)
+        file_handler.setLevel(logging.INFO)
+        file_handler.setFormatter(formatter)
+        logger.addHandler(file_handler)
+    else:
+        console_handler = logging.StreamHandler()
+        console_handler.setLevel(logging.INFO)
+        console_handler.setFormatter(formatter)
+        logger.addHandler(console_handler)
+
+    logger.info('Running with args : {}'.format(args))
+
+    vocab_size = 10000
+    if model_type == "test":
+        num_layers = 1
+        batch_size = 2
+        hidden_size = 10
+        num_steps = 3
+        init_scale = 0.1
+        max_grad_norm = 5.0
+        epoch_start_decay = 1
+        max_epoch = 1
+        dropout = 0.0
+        lr_decay = 0.5
+        base_learning_rate = 1.0
+    elif model_type == "small":
+        num_layers = 2
+        batch_size = 20
+        hidden_size = 200
+        num_steps = 20
+        init_scale = 0.1
+        max_grad_norm = 5.0
+        epoch_start_decay = 4
+        max_epoch = 13
+        dropout = 0.0
+        lr_decay = 0.5
+        base_learning_rate = 1.0
+    elif model_type == "medium":
+        num_layers = 2
+        batch_size = 20
+        hidden_size = 650
+        num_steps = 35
+        init_scale = 0.05
+        max_grad_norm = 5.0
+        epoch_start_decay = 6
+        max_epoch = 39
+        dropout = 0.5
+        lr_decay = 0.8
+        base_learning_rate = 1.0
+    elif model_type == "large":
+        num_layers = 2
+        batch_size = 20
+        hidden_size = 1500
+        num_steps = 35
+        init_scale = 0.04
+        max_grad_norm = 10.0
+        epoch_start_decay = 14
+        max_epoch = 55
+        dropout = 0.65
+        lr_decay = 1.0 / 1.15
+        base_learning_rate = 1.0
+    else:
+        print("model type not support")
+        return
+
+    # Training process
+    loss, last_hidden, last_cell, feed_order = lm_model.lm_model(
+        hidden_size,
+        vocab_size,
+        batch_size,
+        num_layers=num_layers,
+        num_steps=num_steps,
+        init_scale=init_scale,
+        dropout=dropout,
+        rnn_model=rnn_model)
+    # clone from default main program and use it as the validation program
+    main_program = fluid.default_main_program()
+    inference_program = fluid.default_main_program().clone(for_test=True)
+
+    fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByGlobalNorm(
+        clip_norm=max_grad_norm))
+
+    learning_rate = fluid.layers.create_global_var(
+        name="learning_rate",
+        shape=[1],
+        value=1.0,
+        dtype='float32',
+        persistable=True)
+
+    optimizer = fluid.optimizer.SGD(learning_rate=learning_rate)
+
+    optimizer.minimize(loss)
+
+    place = core.CUDAPlace(0) if args.use_gpu else core.CPUPlace()
+    exe = Executor(place)
+    exe.run(framework.default_startup_program())
+
+    data_path = args.data_path
+    print("begin to load data")
+    raw_data = reader.ptb_raw_data(data_path)
+    print("finished load data")
+    train_data, valid_data, test_data, _ = raw_data
+
+    def prepare_input(batch, init_hidden, init_cell, epoch_id=0, with_lr=True):
+        x, y = batch
+        new_lr = base_learning_rate * (lr_decay**max(
+            epoch_id + 1 - epoch_start_decay, 0.0))
+        lr = np.ones((1), dtype='float32') * new_lr
+        res = {}
+        x = x.reshape((-1, num_steps, 1))
+        y = y.reshape((-1, 1))
+
+        res['x'] = x
+        res['y'] = y
+        res['init_hidden'] = init_hidden
+        res['init_cell'] = init_cell
+        if with_lr:
+            res['learning_rate'] = lr
+
+        return res
+
+    def eval(data):
+        # when eval the batch_size set to 1
+        eval_data_iter = reader.get_data_iter(data, batch_size, num_steps)
+        total_loss = 0.0
+        iters = 0
+        init_hidden = np.zeros(
+            (num_layers, batch_size, hidden_size), dtype='float32')
+        init_cell = np.zeros(
+            (num_layers, batch_size, hidden_size), dtype='float32')
+        for batch_id, batch in enumerate(eval_data_iter):
+            input_data_feed = prepare_input(
+                batch, init_hidden, init_cell, epoch_id, with_lr=False)
+            fetch_outs = exe.run(
+                inference_program,
+                feed=input_data_feed,
+                fetch_list=[loss.name, last_hidden.name, last_cell.name],
+                use_program_cache=True)
+
+            cost_train = np.array(fetch_outs[0])
+            init_hidden = np.array(fetch_outs[1])
+            init_cell = np.array(fetch_outs[2])
+
+            total_loss += cost_train
+            iters += num_steps
+
+        ppl = np.exp(total_loss / iters)
+        return ppl
+
+    # get train epoch size
+    batch_len = len(train_data) // batch_size
+    epoch_size = (batch_len - 1) // num_steps
+    log_interval = epoch_size // 10
+    total_time = 0.0
+    for epoch_id in range(max_epoch):
+        start_time = time.time()
+        print("epoch id", epoch_id)
+        train_data_iter = reader.get_data_iter(train_data, batch_size,
+                                               num_steps)
+
+        total_loss = 0
+
+        init_hidden = None
+        init_cell = None
+        #debug_para(fluid.framework.default_main_program(), parallel_executor)
+        total_loss = 0
+        iters = 0
+        init_hidden = np.zeros(
+            (num_layers, batch_size, hidden_size), dtype='float32')
+        init_cell = np.zeros(
+            (num_layers, batch_size, hidden_size), dtype='float32')
+        for batch_id, batch in enumerate(train_data_iter):
+            input_data_feed = prepare_input(
+                batch, init_hidden, init_cell, epoch_id=epoch_id)
+            fetch_outs = exe.run(feed=input_data_feed,
+                                 fetch_list=[
+                                     loss.name, last_hidden.name,
+                                     last_cell.name, 'learning_rate'
+                                 ],
+                                 use_program_cache=True)
+
+            cost_train = np.array(fetch_outs[0])
+            init_hidden = np.array(fetch_outs[1])
+            init_cell = np.array(fetch_outs[2])
+
+            lr = np.array(fetch_outs[3])
+
+            total_loss += cost_train
+            iters += num_steps
+            if batch_id > 0 and batch_id % log_interval == 0:
+                ppl = np.exp(total_loss / iters)
+                print("ppl ", batch_id, ppl[0], lr[0])
+
+        ppl = np.exp(total_loss / iters)
+        if epoch_id == 0 and ppl[0] > 1000:
+            # for bad init, after first epoch, the loss is over 1000
+            # no more need to continue
+            return
+        end_time = time.time()
+        total_time += end_time - start_time
+        print("train ppl", ppl[0])
+
+        if epoch_id == max_epoch - 1 and args.enable_ce:
+            print("ptblm\tlstm_language_model_duration\t%s" %
+                  (total_time / max_epoch))
+            print("ptblm\tlstm_language_model_loss\t%s" % ppl[0])
+
+        model_path = os.path.join("model_new/", str(epoch_id))
+        if not os.path.isdir(model_path):
+            os.makedirs(model_path)
+        fluid.io.save_persistables(
+            executor=exe, dirname=model_path, main_program=main_program)
+        valid_ppl = eval(valid_data)
+        print("valid ppl", valid_ppl[0])
+    test_ppl = eval(test_data)
+    print("test ppl", test_ppl[0])
+
+
+if __name__ == '__main__':
+    train()
diff --git a/PaddleNLP/machine_reading_comprehension/.run_ce.sh b/PaddleNLP/unarchived/machine_reading_comprehension/.run_ce.sh
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/.run_ce.sh
rename to PaddleNLP/unarchived/machine_reading_comprehension/.run_ce.sh
diff --git a/PaddleNLP/machine_reading_comprehension/README.md b/PaddleNLP/unarchived/machine_reading_comprehension/README.md
similarity index 99%
rename from PaddleNLP/machine_reading_comprehension/README.md
rename to PaddleNLP/unarchived/machine_reading_comprehension/README.md
index 228291d47c9a350c5f515f60e46c40ba4d1be507..3031511fb6af25e300a06440de633cac615269f4 100644
--- a/PaddleNLP/machine_reading_comprehension/README.md
+++ b/PaddleNLP/unarchived/machine_reading_comprehension/README.md
@@ -30,7 +30,7 @@ cd utils && bash download_thirdparty.sh
 
 ## 模型训练
 ### 段落抽取
-在段落抽取阶段，主要是使用文档相关性score对文档内容进行优化， 抽取的结果将会放到`data/extracted/`目录下。如果你用demo数据测试，可以跳过这一步。如果你用dureader数据，需要指定抽取的数据目录，命令如下： 
+在段落抽取阶段，主要是使用文档相关性score对文档内容进行优化， 抽取的结果将会放到`data/extracted/`目录下。如果你用demo数据测试，可以跳过这一步。如果你用dureader数据，需要指定抽取的数据目录，命令如下：
 ```
 bash run.sh --para_extraction --trainset data/preprocessed/trainset/zhidao.train.json data/preprocessed/trainset/search.train.json --devset data/preprocessed/devset/zhidao.dev.json data/preprocessed/devset/search.dev.json --testset data/preprocessed/testset/zhidao.test.json data/preprocessed/testset/search.test.json
 ```
diff --git a/PaddleNLP/machine_reading_comprehension/_ce.py b/PaddleNLP/unarchived/machine_reading_comprehension/_ce.py
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/_ce.py
rename to PaddleNLP/unarchived/machine_reading_comprehension/_ce.py
diff --git a/PaddleNLP/unarchived/machine_reading_comprehension/args.py b/PaddleNLP/unarchived/machine_reading_comprehension/args.py
new file mode 100644
index 0000000000000000000000000000000000000000..81829c712e76d59ed4e25ddfe6b379782275db8b
--- /dev/null
+++ b/PaddleNLP/unarchived/machine_reading_comprehension/args.py
@@ -0,0 +1,145 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import distutils.util
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        '--prepare',
+        action='store_true',
+        help='create the directories, prepare the vocabulary and embeddings')
+    parser.add_argument('--train', action='store_true', help='train the model')
+    parser.add_argument(
+        '--evaluate', action='store_true', help='evaluate the model on dev set')
+    parser.add_argument(
+        '--predict',
+        action='store_true',
+        help='predict the answers for test set with trained model')
+
+    parser.add_argument(
+        "--embed_size",
+        type=int,
+        default=300,
+        help="The dimension of embedding table. (default: %(default)d)")
+    parser.add_argument(
+        "--hidden_size",
+        type=int,
+        default=150,
+        help="The size of rnn hidden unit. (default: %(default)d)")
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=0.001,
+        help="Learning rate used to train the model. (default: %(default)f)")
+    parser.add_argument('--optim', default='adam', help='optimizer type')
+    parser.add_argument(
+        "--weight_decay",
+        type=float,
+        default=0.0001,
+        help="Weight decay. (default: %(default)f)")
+
+    parser.add_argument(
+        '--drop_rate', type=float, default=0.0, help="Dropout probability")
+    parser.add_argument('--random_seed', type=int, default=123)
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=32,
+        help="The sequence number of a mini-batch data. (default: %(default)d)")
+    parser.add_argument(
+        "--pass_num",
+        type=int,
+        default=5,
+        help="The number epochs to train. (default: %(default)d)")
+    parser.add_argument(
+        "--use_gpu",
+        type=distutils.util.strtobool,
+        default=True,
+        help="Whether to use gpu. (default: %(default)d)")
+    parser.add_argument(
+        "--log_interval",
+        type=int,
+        default=50,
+        help="log the train loss every n batches. (default: %(default)d)")
+
+    parser.add_argument('--max_p_num', type=int, default=5)
+    parser.add_argument('--max_a_len', type=int, default=200)
+    parser.add_argument('--max_p_len', type=int, default=500)
+    parser.add_argument('--max_q_len', type=int, default=60)
+    parser.add_argument('--doc_num', type=int, default=5)
+
+    parser.add_argument('--vocab_dir', default='data/vocab', help='vocabulary')
+    parser.add_argument(
+        "--save_dir",
+        type=str,
+        default="data/models",
+        help="Specify the path to save trained models.")
+    parser.add_argument(
+        "--save_interval",
+        type=int,
+        default=1,
+        help="Save the trained model every n passes. (default: %(default)d)")
+    parser.add_argument(
+        "--load_dir",
+        type=str,
+        default="",
+        help="Specify the path to load trained models.")
+    parser.add_argument(
+        '--log_path',
+        help='path of the log file. If not set, logs are printed to console')
+    parser.add_argument(
+        '--result_dir',
+        default='data/results/',
+        help='the dir to output the results')
+    parser.add_argument(
+        '--result_name',
+        default='test_result',
+        help='the file name of the predicted results')
+
+    parser.add_argument(
+        '--trainset',
+        nargs='+',
+        default=['data/demo/trainset/search.train.json'],
+        help='train dataset')
+    parser.add_argument(
+        '--devset',
+        nargs='+',
+        default=['data/demo/devset/search.dev.json'],
+        help='dev dataset')
+    parser.add_argument(
+        '--testset',
+        nargs='+',
+        default=['data/demo/testset/search.test.json'],
+        help='test dataset')
+
+    parser.add_argument(
+        "--enable_ce",
+        action='store_true',
+        help="If set, run the task with continuous evaluation logs.")
+    parser.add_argument(
+        '--para_print', action='store_true', help="Print debug info")
+    parser.add_argument(
+        "--dev_interval",
+        type=int,
+        default=-1,
+        help="evaluate on dev set loss every n batches. (default: %(default)d)")
+    args = parser.parse_args()
+    return args
diff --git a/PaddleNLP/machine_reading_comprehension/data/download.sh b/PaddleNLP/unarchived/machine_reading_comprehension/data/download.sh
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/data/download.sh
rename to PaddleNLP/unarchived/machine_reading_comprehension/data/download.sh
diff --git a/PaddleNLP/machine_reading_comprehension/data/md5sum.txt b/PaddleNLP/unarchived/machine_reading_comprehension/data/md5sum.txt
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/data/md5sum.txt
rename to PaddleNLP/unarchived/machine_reading_comprehension/data/md5sum.txt
diff --git a/PaddleNLP/unarchived/machine_reading_comprehension/dataset.py b/PaddleNLP/unarchived/machine_reading_comprehension/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c583e100997b41aa5d5229c4c3f8fee59d7539f
--- /dev/null
+++ b/PaddleNLP/unarchived/machine_reading_comprehension/dataset.py
@@ -0,0 +1,244 @@
+# -*- coding:utf8 -*-
+# ==============================================================================
+# Copyright 2017 Baidu.com, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+This module implements data process strategies.
+"""
+
+import os
+import json
+import logging
+import numpy as np
+from collections import Counter
+import io
+
+
+class BRCDataset(object):
+    """
+    This module implements the APIs for loading and using baidu reading comprehension dataset
+    """
+
+    def __init__(self,
+                 max_p_num,
+                 max_p_len,
+                 max_q_len,
+                 train_files=[],
+                 dev_files=[],
+                 test_files=[]):
+        self.logger = logging.getLogger("brc")
+        self.max_p_num = max_p_num
+        self.max_p_len = max_p_len
+        self.max_q_len = max_q_len
+
+        self.train_set, self.dev_set, self.test_set = [], [], []
+        if train_files:
+            for train_file in train_files:
+                self.train_set += self._load_dataset(train_file, train=True)
+            self.logger.info('Train set size: {} questions.'.format(
+                len(self.train_set)))
+
+        if dev_files:
+            for dev_file in dev_files:
+                self.dev_set += self._load_dataset(dev_file)
+            self.logger.info('Dev set size: {} questions.'.format(
+                len(self.dev_set)))
+
+        if test_files:
+            for test_file in test_files:
+                self.test_set += self._load_dataset(test_file)
+            self.logger.info('Test set size: {} questions.'.format(
+                len(self.test_set)))
+
+    def _load_dataset(self, data_path, train=False):
+        """
+        Loads the dataset
+        Args:
+            data_path: the data file to load
+        """
+        with io.open(data_path, 'r', encoding='utf-8') as fin:
+            data_set = []
+            for lidx, line in enumerate(fin):
+                sample = json.loads(line.strip())
+                if train:
+                    if len(sample['answer_spans']) == 0:
+                        continue
+                    if sample['answer_spans'][0][1] >= self.max_p_len:
+                        continue
+
+                if 'answer_docs' in sample:
+                    sample['answer_passages'] = sample['answer_docs']
+
+                sample['question_tokens'] = sample['segmented_question']
+
+                sample['passages'] = []
+                for d_idx, doc in enumerate(sample['documents']):
+                    if train:
+                        most_related_para = doc['most_related_para']
+                        sample['passages'].append({
+                            'passage_tokens':
+                            doc['segmented_paragraphs'][most_related_para],
+                            'is_selected': doc['is_selected']
+                        })
+                    else:
+                        para_infos = []
+                        for para_tokens in doc['segmented_paragraphs']:
+                            question_tokens = sample['segmented_question']
+                            common_with_question = Counter(
+                                para_tokens) & Counter(question_tokens)
+                            correct_preds = sum(common_with_question.values())
+                            if correct_preds == 0:
+                                recall_wrt_question = 0
+                            else:
+                                recall_wrt_question = float(
+                                    correct_preds) / len(question_tokens)
+                            para_infos.append((para_tokens, recall_wrt_question,
+                                               len(para_tokens)))
+                        para_infos.sort(key=lambda x: (-x[1], x[2]))
+                        fake_passage_tokens = []
+                        for para_info in para_infos[:1]:
+                            fake_passage_tokens += para_info[0]
+                        sample['passages'].append({
+                            'passage_tokens': fake_passage_tokens
+                        })
+                data_set.append(sample)
+        return data_set
+
+    def _one_mini_batch(self, data, indices, pad_id):
+        """
+        Get one mini batch
+        Args:
+            data: all data
+            indices: the indices of the samples to be selected
+            pad_id:
+        Returns:
+            one batch of data
+        """
+        batch_data = {
+            'raw_data': [data[i] for i in indices],
+            'question_token_ids': [],
+            'question_length': [],
+            'passage_token_ids': [],
+            'passage_length': [],
+            'start_id': [],
+            'end_id': [],
+            'passage_num': []
+        }
+        max_passage_num = max(
+            [len(sample['passages']) for sample in batch_data['raw_data']])
+        max_passage_num = min(self.max_p_num, max_passage_num)
+        for sidx, sample in enumerate(batch_data['raw_data']):
+            count = 0
+            for pidx in range(max_passage_num):
+                if pidx < len(sample['passages']):
+                    count += 1
+                    batch_data['question_token_ids'].append(sample[
+                        'question_token_ids'][0:self.max_q_len])
+                    batch_data['question_length'].append(
+                        min(len(sample['question_token_ids']), self.max_q_len))
+                    passage_token_ids = sample['passages'][pidx][
+                        'passage_token_ids'][0:self.max_p_len]
+                    batch_data['passage_token_ids'].append(passage_token_ids)
+                    batch_data['passage_length'].append(
+                        min(len(passage_token_ids), self.max_p_len))
+            # record the start passage index of current sample
+            passade_idx_offset = sum(batch_data['passage_num'])
+            batch_data['passage_num'].append(count)
+            gold_passage_offset = 0
+            if 'answer_passages' in sample and len(sample['answer_passages']) and \
+                    sample['answer_passages'][0] < len(sample['documents']):
+                for i in range(sample['answer_passages'][0]):
+                    gold_passage_offset += len(batch_data['passage_token_ids'][
+                        passade_idx_offset + i])
+                start_id = min(sample['answer_spans'][0][0], self.max_p_len)
+                end_id = min(sample['answer_spans'][0][1], self.max_p_len)
+                batch_data['start_id'].append(gold_passage_offset + start_id)
+                batch_data['end_id'].append(gold_passage_offset + end_id)
+            else:
+                # fake span for some samples, only valid for testing
+                batch_data['start_id'].append(0)
+                batch_data['end_id'].append(0)
+        return batch_data
+
+    def word_iter(self, set_name=None):
+        """
+        Iterates over all the words in the dataset
+        Args:
+            set_name: if it is set, then the specific set will be used
+        Returns:
+            a generator
+        """
+        if set_name is None:
+            data_set = self.train_set + self.dev_set + self.test_set
+        elif set_name == 'train':
+            data_set = self.train_set
+        elif set_name == 'dev':
+            data_set = self.dev_set
+        elif set_name == 'test':
+            data_set = self.test_set
+        else:
+            raise NotImplementedError('No data set named as {}'.format(
+                set_name))
+        if data_set is not None:
+            for sample in data_set:
+                for token in sample['question_tokens']:
+                    yield token
+                for passage in sample['passages']:
+                    for token in passage['passage_tokens']:
+                        yield token
+
+    def convert_to_ids(self, vocab):
+        """
+        Convert the question and passage in the original dataset to ids
+        Args:
+            vocab: the vocabulary on this dataset
+        """
+        for data_set in [self.train_set, self.dev_set, self.test_set]:
+            if data_set is None:
+                continue
+            for sample in data_set:
+                sample['question_token_ids'] = vocab.convert_to_ids(sample[
+                    'question_tokens'])
+                for passage in sample['passages']:
+                    passage['passage_token_ids'] = vocab.convert_to_ids(passage[
+                        'passage_tokens'])
+
+    def gen_mini_batches(self, set_name, batch_size, pad_id, shuffle=True):
+        """
+        Generate data batches for a specific dataset (train/dev/test)
+        Args:
+            set_name: train/dev/test to indicate the set
+            batch_size: number of samples in one batch
+            pad_id: pad id
+            shuffle: if set to be true, the data is shuffled.
+        Returns:
+            a generator for all batches
+        """
+        if set_name == 'train':
+            data = self.train_set
+        elif set_name == 'dev':
+            data = self.dev_set
+        elif set_name == 'test':
+            data = self.test_set
+        else:
+            raise NotImplementedError('No data set named as {}'.format(
+                set_name))
+        data_size = len(data)
+        indices = np.arange(data_size)
+        if shuffle:
+            np.random.shuffle(indices)
+        for batch_start in np.arange(0, data_size, batch_size):
+            batch_indices = indices[batch_start:batch_start + batch_size]
+            yield self._one_mini_batch(data, batch_indices, pad_id)
diff --git a/PaddleNLP/unarchived/machine_reading_comprehension/paragraph_extraction.py b/PaddleNLP/unarchived/machine_reading_comprehension/paragraph_extraction.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a74a9bae639e9d5f607f8ba5f0c66ba93577772
--- /dev/null
+++ b/PaddleNLP/unarchived/machine_reading_comprehension/paragraph_extraction.py
@@ -0,0 +1,200 @@
+#!/usr/bin/python
+#-*- coding:utf-8 -*-
+
+import sys
+if sys.version[0] == '2':
+    reload(sys)
+    sys.setdefaultencoding("utf-8")
+import json
+import copy
+from preprocess import metric_max_over_ground_truths, f1_score
+
+
+def compute_paragraph_score(sample):
+    """
+    For each paragraph, compute the f1 score compared with the question
+    Args:
+        sample: a sample in the dataset.
+    Returns:
+        None
+    Raises:
+        None
+    """
+    question = sample["segmented_question"]
+    for doc in sample['documents']:
+        doc['segmented_paragraphs_scores'] = []
+        for p_idx, para_tokens in enumerate(doc['segmented_paragraphs']):
+            if len(question) > 0:
+                related_score = metric_max_over_ground_truths(
+                    f1_score, para_tokens, question)
+            else:
+                related_score = 0.0
+            doc['segmented_paragraphs_scores'].append(related_score)
+
+
+def dup_remove(doc):
+    """
+    For each document, remove the duplicated paragraphs
+    Args:
+        doc: a doc in the sample
+    Returns:
+        bool
+    Raises:
+        None
+    """
+    paragraphs_his = {}
+    del_ids = []
+    para_id = None
+    if 'most_related_para' in doc:
+        para_id = doc['most_related_para']
+    doc['paragraphs_length'] = []
+    for p_idx, (segmented_paragraph, paragraph_score) in \
+        enumerate(zip(doc["segmented_paragraphs"], doc["segmented_paragraphs_scores"])):
+        doc['paragraphs_length'].append(len(segmented_paragraph))
+        paragraph = ''.join(segmented_paragraph)
+        if paragraph in paragraphs_his:
+            del_ids.append(p_idx)
+            if p_idx == para_id:
+                para_id = paragraphs_his[paragraph]
+            continue
+        paragraphs_his[paragraph] = p_idx
+    # delete
+    prev_del_num = 0
+    del_num = 0
+    for p_idx in del_ids:
+        if p_idx < para_id:
+            prev_del_num += 1
+        del doc["segmented_paragraphs"][p_idx - del_num]
+        del doc["segmented_paragraphs_scores"][p_idx - del_num]
+        del doc['paragraphs_length'][p_idx - del_num]
+        del_num += 1
+    if len(del_ids) != 0:
+        if 'most_related_para' in doc:
+            doc['most_related_para'] = para_id - prev_del_num
+        doc['paragraphs'] = []
+        for segmented_para in doc["segmented_paragraphs"]:
+            paragraph = ''.join(segmented_para)
+            doc['paragraphs'].append(paragraph)
+        return True
+    else:
+        return False
+
+
+def paragraph_selection(sample, mode):
+    """
+    For each document, select paragraphs that includes as much information as possible
+    Args:
+        sample: a sample in the dataset.
+        mode: string of ("train", "dev", "test"), indicate the type of dataset to process.
+    Returns:
+        None
+    Raises:
+        None
+    """
+    # predefined maximum length of paragraph
+    MAX_P_LEN = 500
+    # predefined splitter
+    splitter = u''
+    # topN of related paragraph to choose
+    topN = 3
+    doc_id = None
+    if 'answer_docs' in sample and len(sample['answer_docs']) > 0:
+        doc_id = sample['answer_docs'][0]
+        if doc_id >= len(sample['documents']):
+            # Data error, answer doc ID > number of documents, this sample
+            # will be filtered by dataset.py
+            return
+    for d_idx, doc in enumerate(sample['documents']):
+        if 'segmented_paragraphs_scores' not in doc:
+            continue
+        status = dup_remove(doc)
+        segmented_title = doc["segmented_title"]
+        title_len = len(segmented_title)
+        para_id = None
+        if doc_id is not None:
+            para_id = sample['documents'][doc_id]['most_related_para']
+        total_len = title_len + sum(doc['paragraphs_length'])
+        # add splitter
+        para_num = len(doc["segmented_paragraphs"])
+        total_len += para_num
+        if total_len <= MAX_P_LEN:
+            incre_len = title_len
+            total_segmented_content = copy.deepcopy(segmented_title)
+            for p_idx, segmented_para in enumerate(doc["segmented_paragraphs"]):
+                if doc_id == d_idx and para_id > p_idx:
+                    incre_len += len([splitter] + segmented_para)
+                if doc_id == d_idx and para_id == p_idx:
+                    incre_len += 1
+                total_segmented_content += [splitter] + segmented_para
+            if doc_id == d_idx:
+                answer_start = incre_len + sample['answer_spans'][0][0]
+                answer_end = incre_len + sample['answer_spans'][0][1]
+                sample['answer_spans'][0][0] = answer_start
+                sample['answer_spans'][0][1] = answer_end
+            doc["segmented_paragraphs"] = [total_segmented_content]
+            doc["segmented_paragraphs_scores"] = [1.0]
+            doc['paragraphs_length'] = [total_len]
+            doc['paragraphs'] = [''.join(total_segmented_content)]
+            doc['most_related_para'] = 0
+            continue
+        # find topN paragraph id
+        para_infos = []
+        for p_idx, (para_tokens, para_scores) in \
+                enumerate(zip(doc['segmented_paragraphs'], doc['segmented_paragraphs_scores'])):
+            para_infos.append(
+                (para_tokens, para_scores, len(para_tokens), p_idx))
+        para_infos.sort(key=lambda x: (-x[1], x[2]))
+        topN_idx = []
+        for para_info in para_infos[:topN]:
+            topN_idx.append(para_info[-1])
+        final_idx = []
+        total_len = title_len
+        if doc_id == d_idx:
+            if mode == "train":
+                final_idx.append(para_id)
+                total_len = title_len + 1 + doc['paragraphs_length'][para_id]
+        for id in topN_idx:
+            if total_len > MAX_P_LEN:
+                break
+            if doc_id == d_idx and id == para_id and mode == "train":
+                continue
+            total_len += 1 + doc['paragraphs_length'][id]
+            final_idx.append(id)
+        total_segmented_content = copy.deepcopy(segmented_title)
+        final_idx.sort()
+        incre_len = title_len
+        for id in final_idx:
+            if doc_id == d_idx and id < para_id:
+                incre_len += 1 + doc['paragraphs_length'][id]
+            if doc_id == d_idx and id == para_id:
+                incre_len += 1
+            total_segmented_content += [splitter] + doc['segmented_paragraphs'][
+                id]
+        if doc_id == d_idx:
+            answer_start = incre_len + sample['answer_spans'][0][0]
+            answer_end = incre_len + sample['answer_spans'][0][1]
+            sample['answer_spans'][0][0] = answer_start
+            sample['answer_spans'][0][1] = answer_end
+        doc["segmented_paragraphs"] = [total_segmented_content]
+        doc["segmented_paragraphs_scores"] = [1.0]
+        doc['paragraphs_length'] = [total_len]
+        doc['paragraphs'] = [''.join(total_segmented_content)]
+        doc['most_related_para'] = 0
+
+
+if __name__ == "__main__":
+    # mode="train"/"dev"/"test"
+    mode = sys.argv[1]
+    for line in sys.stdin:
+        line = line.strip()
+        if line == "":
+            continue
+        try:
+            sample = json.loads(line, encoding='utf8')
+        except:
+            print >> sys.stderr, "Invalid input json format - '{}' will be ignored".format(
+                line)
+            continue
+        compute_paragraph_score(sample)
+        paragraph_selection(sample, mode)
+        print(json.dumps(sample, encoding='utf8', ensure_ascii=False))
diff --git a/PaddleNLP/unarchived/machine_reading_comprehension/preprocess.py b/PaddleNLP/unarchived/machine_reading_comprehension/preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..65842e7dd5fad312627292756dfcb4d7f026d3b3
--- /dev/null
+++ b/PaddleNLP/unarchived/machine_reading_comprehension/preprocess.py
@@ -0,0 +1,219 @@
+###############################################################################
+# ==============================================================================
+# Copyright 2017 Baidu.com, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+This module finds the most related paragraph of each document according to recall.
+"""
+
+import sys
+if sys.version[0] == '2':
+    reload(sys)
+    sys.setdefaultencoding("utf-8")
+import json
+from collections import Counter
+
+
+def precision_recall_f1(prediction, ground_truth):
+    """
+    This function calculates and returns the precision, recall and f1-score
+    Args:
+        prediction: prediction string or list to be matched
+        ground_truth: golden string or list reference
+    Returns:
+        floats of (p, r, f1)
+    Raises:
+        None
+    """
+    if not isinstance(prediction, list):
+        prediction_tokens = prediction.split()
+    else:
+        prediction_tokens = prediction
+    if not isinstance(ground_truth, list):
+        ground_truth_tokens = ground_truth.split()
+    else:
+        ground_truth_tokens = ground_truth
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return 0, 0, 0
+    p = 1.0 * num_same / len(prediction_tokens)
+    r = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * p * r) / (p + r)
+    return p, r, f1
+
+
+def recall(prediction, ground_truth):
+    """
+    This function calculates and returns the recall
+    Args:
+        prediction: prediction string or list to be matched
+        ground_truth: golden string or list reference
+    Returns:
+        floats of recall
+    Raises:
+        None
+    """
+    return precision_recall_f1(prediction, ground_truth)[1]
+
+
+def f1_score(prediction, ground_truth):
+    """
+    This function calculates and returns the f1-score
+    Args:
+        prediction: prediction string or list to be matched
+        ground_truth: golden string or list reference
+    Returns:
+        floats of f1
+    Raises:
+        None
+    """
+    return precision_recall_f1(prediction, ground_truth)[2]
+
+
+def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
+    """
+    This function calculates and returns the precision, recall and f1-score
+    Args:
+        metric_fn: metric function pointer which calculates scores according to corresponding logic.
+        prediction: prediction string or list to be matched
+        ground_truth: golden string or list reference
+    Returns:
+        floats of (p, r, f1)
+    Raises:
+        None
+    """
+    scores_for_ground_truths = []
+    for ground_truth in ground_truths:
+        score = metric_fn(prediction, ground_truth)
+        scores_for_ground_truths.append(score)
+    return max(scores_for_ground_truths)
+
+
+def find_best_question_match(doc, question, with_score=False):
+    """
+    For each document, find the paragraph that matches best to the question.
+    Args:
+        doc: The document object.
+        question: The question tokens.
+        with_score: If True then the match score will be returned,
+            otherwise False.
+    Returns:
+        The index of the best match paragraph, if with_score=False,
+        otherwise returns a tuple of the index of the best match paragraph
+        and the match score of that paragraph.
+    """
+    most_related_para = -1
+    max_related_score = 0
+    most_related_para_len = 0
+    for p_idx, para_tokens in enumerate(doc['segmented_paragraphs']):
+        if len(question) > 0:
+            related_score = metric_max_over_ground_truths(recall, para_tokens,
+                                                          question)
+        else:
+            related_score = 0
+
+        if related_score > max_related_score \
+                or (related_score == max_related_score \
+                and len(para_tokens) < most_related_para_len):
+            most_related_para = p_idx
+            max_related_score = related_score
+            most_related_para_len = len(para_tokens)
+    if most_related_para == -1:
+        most_related_para = 0
+    if with_score:
+        return most_related_para, max_related_score
+    return most_related_para
+
+
+def find_fake_answer(sample):
+    """
+    For each document, finds the most related paragraph based on recall,
+    then finds a span that maximize the f1_score compared with the gold answers
+    and uses this span as a fake answer span
+    Args:
+        sample: a sample in the dataset
+    Returns:
+        None
+    Raises:
+        None
+    """
+    for doc in sample['documents']:
+        most_related_para = -1
+        most_related_para_len = 999999
+        max_related_score = 0
+        for p_idx, para_tokens in enumerate(doc['segmented_paragraphs']):
+            if len(sample['segmented_answers']) > 0:
+                related_score = metric_max_over_ground_truths(
+                    recall, para_tokens, sample['segmented_answers'])
+            else:
+                continue
+            if related_score > max_related_score \
+                    or (related_score == max_related_score
+                        and len(para_tokens) < most_related_para_len):
+                most_related_para = p_idx
+                most_related_para_len = len(para_tokens)
+                max_related_score = related_score
+        doc['most_related_para'] = most_related_para
+
+    sample['answer_docs'] = []
+    sample['answer_spans'] = []
+    sample['fake_answers'] = []
+    sample['match_scores'] = []
+
+    best_match_score = 0
+    best_match_d_idx, best_match_span = -1, [-1, -1]
+    best_fake_answer = None
+    answer_tokens = set()
+    for segmented_answer in sample['segmented_answers']:
+        answer_tokens = answer_tokens | set(
+            [token for token in segmented_answer])
+    for d_idx, doc in enumerate(sample['documents']):
+        if not doc['is_selected']:
+            continue
+        if doc['most_related_para'] == -1:
+            doc['most_related_para'] = 0
+        most_related_para_tokens = doc['segmented_paragraphs'][doc[
+            'most_related_para']][:1000]
+        for start_tidx in range(len(most_related_para_tokens)):
+            if most_related_para_tokens[start_tidx] not in answer_tokens:
+                continue
+            for end_tidx in range(
+                    len(most_related_para_tokens) - 1, start_tidx - 1, -1):
+                span_tokens = most_related_para_tokens[start_tidx:end_tidx + 1]
+                if len(sample['segmented_answers']) > 0:
+                    match_score = metric_max_over_ground_truths(
+                        f1_score, span_tokens, sample['segmented_answers'])
+                else:
+                    match_score = 0
+                if match_score == 0:
+                    break
+                if match_score > best_match_score:
+                    best_match_d_idx = d_idx
+                    best_match_span = [start_tidx, end_tidx]
+                    best_match_score = match_score
+                    best_fake_answer = ''.join(span_tokens)
+    if best_match_score > 0:
+        sample['answer_docs'].append(best_match_d_idx)
+        sample['answer_spans'].append(best_match_span)
+        sample['fake_answers'].append(best_fake_answer)
+        sample['match_scores'].append(best_match_score)
+
+
+if __name__ == '__main__':
+    for line in sys.stdin:
+        sample = json.loads(line)
+        find_fake_answer(sample)
+        print(json.dumps(sample, encoding='utf8', ensure_ascii=False))
diff --git a/PaddleNLP/unarchived/machine_reading_comprehension/rc_model.py b/PaddleNLP/unarchived/machine_reading_comprehension/rc_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..edc009c26f8554c63964d09db59d45e65505b46a
--- /dev/null
+++ b/PaddleNLP/unarchived/machine_reading_comprehension/rc_model.py
@@ -0,0 +1,331 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle.fluid.layers as layers
+import paddle.fluid as fluid
+import numpy as np
+
+
+def dropout(input, args):
+    """Dropout function"""
+    if args.drop_rate:
+        return layers.dropout(
+            input,
+            dropout_prob=args.drop_rate,
+            seed=args.random_seed,
+            is_test=False)
+    else:
+        return input
+
+
+def bi_lstm_encoder(input_seq, gate_size, para_name, args):
+    """
+    A bi-directional lstm encoder implementation.
+    Linear transformation part for input gate, output gate, forget gate
+    and cell activation vectors need be done outside of dynamic_lstm.
+    So the output size is 4 times of gate_size.
+    """
+
+    input_forward_proj = layers.fc(
+        input=input_seq,
+        param_attr=fluid.ParamAttr(name=para_name + '_fw_gate_w'),
+        size=gate_size * 4,
+        act=None,
+        bias_attr=False)
+    input_reversed_proj = layers.fc(
+        input=input_seq,
+        param_attr=fluid.ParamAttr(name=para_name + '_bw_gate_w'),
+        size=gate_size * 4,
+        act=None,
+        bias_attr=False)
+    forward, _ = layers.dynamic_lstm(
+        input=input_forward_proj,
+        size=gate_size * 4,
+        use_peepholes=False,
+        param_attr=fluid.ParamAttr(name=para_name + '_fw_lstm_w'),
+        bias_attr=fluid.ParamAttr(name=para_name + '_fw_lstm_b'))
+    reversed, _ = layers.dynamic_lstm(
+        input=input_reversed_proj,
+        param_attr=fluid.ParamAttr(name=para_name + '_bw_lstm_w'),
+        bias_attr=fluid.ParamAttr(name=para_name + '_bw_lstm_b'),
+        size=gate_size * 4,
+        is_reverse=True,
+        use_peepholes=False)
+
+    encoder_out = layers.concat(input=[forward, reversed], axis=1)
+    return encoder_out
+
+
+def get_data(input_name, lod_level, args):
+    input_ids = layers.data(
+        name=input_name, shape=[1], dtype='int64', lod_level=lod_level)
+    return input_ids
+
+
+def embedding(input_ids, shape, args):
+    """Embedding layer"""
+    input_embedding = layers.embedding(
+        input=input_ids,
+        size=shape,
+        dtype='float32',
+        is_sparse=True,
+        param_attr=fluid.ParamAttr(name='embedding_para'))
+    return input_embedding
+
+
+def encoder(input_embedding, para_name, hidden_size, args):
+    """Encoding layer"""
+    encoder_out = bi_lstm_encoder(
+        input_seq=input_embedding,
+        gate_size=hidden_size,
+        para_name=para_name,
+        args=args)
+    return dropout(encoder_out, args)
+
+
+def attn_flow(q_enc, p_enc, p_ids_name, args):
+    """Bidirectional Attention layer"""
+    tag = p_ids_name + "::"
+    drnn = layers.DynamicRNN()
+    with drnn.block():
+        h_cur = drnn.step_input(p_enc)
+        u_all = drnn.static_input(q_enc)
+        h_expd = layers.sequence_expand(x=h_cur, y=u_all)
+        s_t_mul = layers.elementwise_mul(x=u_all, y=h_expd, axis=0)
+        s_t_sum = layers.reduce_sum(input=s_t_mul, dim=1, keep_dim=True)
+        s_t_re = layers.reshape(s_t_sum, shape=[-1, 0])
+        s_t = layers.sequence_softmax(input=s_t_re)
+        u_expr = layers.elementwise_mul(x=u_all, y=s_t, axis=0)
+        u_expr = layers.sequence_pool(input=u_expr, pool_type='sum')
+
+        b_t = layers.sequence_pool(input=s_t_sum, pool_type='max')
+        drnn.output(u_expr, b_t)
+    U_expr, b = drnn()
+    b_norm = layers.sequence_softmax(input=b)
+    h_expr = layers.elementwise_mul(x=p_enc, y=b_norm, axis=0)
+    h_expr = layers.sequence_pool(input=h_expr, pool_type='sum')
+
+    H_expr = layers.sequence_expand(x=h_expr, y=p_enc)
+    H_expr = layers.lod_reset(x=H_expr, y=p_enc)
+    h_u = layers.elementwise_mul(x=p_enc, y=U_expr, axis=0)
+    h_h = layers.elementwise_mul(x=p_enc, y=H_expr, axis=0)
+
+    g = layers.concat(input=[p_enc, U_expr, h_u, h_h], axis=1)
+    return dropout(g, args)
+
+
+def fusion(g, args):
+    """Fusion layer"""
+    m = bi_lstm_encoder(
+        input_seq=g, gate_size=args.hidden_size, para_name='fusion', args=args)
+    return dropout(m, args)
+
+
+def lstm_step(x_t, hidden_t_prev, cell_t_prev, size, para_name, args):
+    """Util function for pointer network"""
+
+    def linear(inputs, para_name, args):
+        return layers.fc(input=inputs,
+                         size=size,
+                         param_attr=fluid.ParamAttr(name=para_name + '_w'),
+                         bias_attr=fluid.ParamAttr(name=para_name + '_b'))
+
+    input_cat = layers.concat([hidden_t_prev, x_t], axis=1)
+    forget_gate = layers.sigmoid(x=linear(input_cat, para_name + '_lstm_f',
+                                          args))
+    input_gate = layers.sigmoid(x=linear(input_cat, para_name + '_lstm_i',
+                                         args))
+    output_gate = layers.sigmoid(x=linear(input_cat, para_name + '_lstm_o',
+                                          args))
+    cell_tilde = layers.tanh(x=linear(input_cat, para_name + '_lstm_c', args))
+
+    cell_t = layers.sums(input=[
+        layers.elementwise_mul(
+            x=forget_gate, y=cell_t_prev), layers.elementwise_mul(
+                x=input_gate, y=cell_tilde)
+    ])
+
+    hidden_t = layers.elementwise_mul(x=output_gate, y=layers.tanh(x=cell_t))
+
+    return hidden_t, cell_t
+
+
+def point_network_decoder(p_vec, q_vec, hidden_size, args):
+    """Output layer - pointer network"""
+    tag = 'pn_decoder:'
+    init_random = fluid.initializer.Normal(loc=0.0, scale=1.0)
+
+    random_attn = layers.create_parameter(
+        shape=[1, hidden_size],
+        dtype='float32',
+        default_initializer=init_random)
+    random_attn = layers.fc(
+        input=random_attn,
+        size=hidden_size,
+        act=None,
+        param_attr=fluid.ParamAttr(name=tag + 'random_attn_fc_w'),
+        bias_attr=fluid.ParamAttr(name=tag + 'random_attn_fc_b'))
+    random_attn = layers.reshape(random_attn, shape=[-1])
+    U = layers.fc(input=q_vec,
+                  param_attr=fluid.ParamAttr(name=tag + 'q_vec_fc_w'),
+                  bias_attr=False,
+                  size=hidden_size,
+                  act=None) + random_attn
+    U = layers.tanh(U)
+
+    logits = layers.fc(input=U,
+                       param_attr=fluid.ParamAttr(name=tag + 'logits_fc_w'),
+                       bias_attr=fluid.ParamAttr(name=tag + 'logits_fc_b'),
+                       size=1,
+                       act=None)
+    scores = layers.sequence_softmax(input=logits)
+    pooled_vec = layers.elementwise_mul(x=q_vec, y=scores, axis=0)
+    pooled_vec = layers.sequence_pool(input=pooled_vec, pool_type='sum')
+
+    init_state = layers.fc(
+        input=pooled_vec,
+        param_attr=fluid.ParamAttr(name=tag + 'init_state_fc_w'),
+        bias_attr=fluid.ParamAttr(name=tag + 'init_state_fc_b'),
+        size=hidden_size,
+        act=None)
+
+    def custom_dynamic_rnn(p_vec, init_state, hidden_size, para_name, args):
+        tag = para_name + "custom_dynamic_rnn:"
+
+        def static_rnn(step,
+                       p_vec=p_vec,
+                       init_state=None,
+                       para_name='',
+                       args=args):
+            tag = para_name + "static_rnn:"
+            ctx = layers.fc(
+                input=p_vec,
+                param_attr=fluid.ParamAttr(name=tag + 'context_fc_w'),
+                bias_attr=fluid.ParamAttr(name=tag + 'context_fc_b'),
+                size=hidden_size,
+                act=None)
+
+            beta = []
+            c_prev = init_state
+            m_prev = init_state
+            for i in range(step):
+                m_prev0 = layers.fc(
+                    input=m_prev,
+                    size=hidden_size,
+                    act=None,
+                    param_attr=fluid.ParamAttr(name=tag + 'm_prev0_fc_w'),
+                    bias_attr=fluid.ParamAttr(name=tag + 'm_prev0_fc_b'))
+                m_prev1 = layers.sequence_expand(x=m_prev0, y=ctx)
+
+                Fk = ctx + m_prev1
+                Fk = layers.tanh(Fk)
+                logits = layers.fc(
+                    input=Fk,
+                    size=1,
+                    act=None,
+                    param_attr=fluid.ParamAttr(name=tag + 'logits_fc_w'),
+                    bias_attr=fluid.ParamAttr(name=tag + 'logits_fc_b'))
+
+                scores = layers.sequence_softmax(input=logits)
+                attn_ctx = layers.elementwise_mul(x=p_vec, y=scores, axis=0)
+                attn_ctx = layers.sequence_pool(input=attn_ctx, pool_type='sum')
+
+                hidden_t, cell_t = lstm_step(
+                    attn_ctx,
+                    hidden_t_prev=m_prev,
+                    cell_t_prev=c_prev,
+                    size=hidden_size,
+                    para_name=tag,
+                    args=args)
+                m_prev = hidden_t
+                c_prev = cell_t
+                beta.append(scores)
+            return beta
+
+        return static_rnn(
+            2, p_vec=p_vec, init_state=init_state, para_name=para_name)
+
+    fw_outputs = custom_dynamic_rnn(p_vec, init_state, hidden_size, tag + "fw:",
+                                    args)
+    bw_outputs = custom_dynamic_rnn(p_vec, init_state, hidden_size, tag + "bw:",
+                                    args)
+
+    start_prob = layers.elementwise_add(
+        x=fw_outputs[0], y=bw_outputs[1], axis=0) / 2
+    end_prob = layers.elementwise_add(
+        x=fw_outputs[1], y=bw_outputs[0], axis=0) / 2
+
+    return start_prob, end_prob
+
+
+def rc_model(hidden_size, vocab, args):
+    """This function build the whole BiDAF network"""
+    emb_shape = [vocab.size(), vocab.embed_dim]
+    start_labels = layers.data(
+        name="start_lables", shape=[1], dtype='float32', lod_level=1)
+    end_labels = layers.data(
+        name="end_lables", shape=[1], dtype='float32', lod_level=1)
+
+    # stage 1:setup input data, embedding table & encode
+    q_id0 = get_data('q_id0', 1, args)
+
+    q_ids = get_data('q_ids', 2, args)
+    p_ids_name = 'p_ids'
+
+    p_ids = get_data('p_ids', 2, args)
+    p_embs = embedding(p_ids, emb_shape, args)
+    q_embs = embedding(q_ids, emb_shape, args)
+    drnn = layers.DynamicRNN()
+    with drnn.block():
+        p_emb = drnn.step_input(p_embs)
+        q_emb = drnn.step_input(q_embs)
+
+        p_enc = encoder(p_emb, 'p_enc', hidden_size, args)
+        q_enc = encoder(q_emb, 'q_enc', hidden_size, args)
+
+        # stage 2:match
+        g_i = attn_flow(q_enc, p_enc, p_ids_name, args)
+        # stage 3:fusion
+        m_i = fusion(g_i, args)
+        drnn.output(m_i, q_enc)
+
+    ms, q_encs = drnn()
+    p_vec = layers.lod_reset(x=ms, y=start_labels)
+    q_vec = layers.lod_reset(x=q_encs, y=q_id0)
+
+    # stage 4:decode 
+    start_probs, end_probs = point_network_decoder(
+        p_vec=p_vec, q_vec=q_vec, hidden_size=hidden_size, args=args)
+
+    # calculate model loss
+    cost0 = layers.sequence_pool(
+        layers.cross_entropy(
+            input=start_probs, label=start_labels, soft_label=True),
+        'sum')
+    cost1 = layers.sequence_pool(
+        layers.cross_entropy(
+            input=end_probs, label=end_labels, soft_label=True),
+        'sum')
+
+    cost0 = layers.mean(cost0)
+    cost1 = layers.mean(cost1)
+    cost = cost0 + cost1
+    cost.persistable = True
+
+    feeding_list = ["q_ids", "start_lables", "end_lables", "p_ids", "q_id0"]
+    return cost, start_probs, end_probs, ms, feeding_list
diff --git a/PaddleNLP/unarchived/machine_reading_comprehension/run.py b/PaddleNLP/unarchived/machine_reading_comprehension/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..b202baecbf99e0f57c8240ccbc26e4e646979583
--- /dev/null
+++ b/PaddleNLP/unarchived/machine_reading_comprehension/run.py
@@ -0,0 +1,655 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import time
+import os
+import random
+import json
+import six
+import multiprocessing
+
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import paddle.fluid.framework as framework
+from paddle.fluid.executor import Executor
+
+import sys
+if sys.version[0] == '2':
+    reload(sys)
+    sys.setdefaultencoding("utf-8")
+sys.path.append('..')
+
+from args import *
+import rc_model
+from dataset import BRCDataset
+import logging
+import pickle
+from utils import normalize
+from utils import compute_bleu_rouge
+from vocab import Vocab
+
+
+def prepare_batch_input(insts, args):
+    batch_size = len(insts['raw_data'])
+    inst_num = len(insts['passage_num'])
+    if batch_size != inst_num:
+        print("data error %d, %d" % (batch_size, inst_num))
+        return None
+    new_insts = []
+
+    passage_idx = 0
+    for i in range(batch_size):
+        p_len = 0
+        p_id = []
+        p_ids = []
+        q_ids = []
+        q_id = []
+        p_id_r = []
+        p_ids_r = []
+        q_ids_r = []
+        q_id_r = []
+
+        for j in range(insts['passage_num'][i]):
+            p_ids.append(insts['passage_token_ids'][passage_idx + j])
+            p_id = p_id + insts['passage_token_ids'][passage_idx + j]
+            q_ids.append(insts['question_token_ids'][passage_idx + j])
+            q_id = q_id + insts['question_token_ids'][passage_idx + j]
+
+        passage_idx += insts['passage_num'][i]
+        p_len = len(p_id)
+
+        def _get_label(idx, ref_len):
+            ret = [0.0] * ref_len
+            if idx >= 0 and idx < ref_len:
+                ret[idx] = 1.0
+            return [[x] for x in ret]
+
+        start_label = _get_label(insts['start_id'][i], p_len)
+        end_label = _get_label(insts['end_id'][i], p_len)
+        new_inst = [q_ids, start_label, end_label, p_ids, q_id]
+        new_insts.append(new_inst)
+    return new_insts
+
+
+def batch_reader(batch_list, args):
+    res = []
+    for batch in batch_list:
+        res.append(prepare_batch_input(batch, args))
+    return res
+
+
+def read_multiple(reader, count, clip_last=True):
+    """
+    Stack data from reader for multi-devices.
+    """
+
+    def __impl__():
+        res = []
+        for item in reader():
+            res.append(item)
+            if len(res) == count:
+                yield res
+                res = []
+        if len(res) == count:
+            yield res
+        elif not clip_last:
+            data = []
+            for item in res:
+                data += item
+            if len(data) > count:
+                inst_num_per_part = len(data) // count
+                yield [
+                    data[inst_num_per_part * i:inst_num_per_part * (i + 1)]
+                    for i in range(count)
+                ]
+
+    return __impl__
+
+
+def LodTensor_Array(lod_tensor):
+    lod = lod_tensor.lod()
+    array = np.array(lod_tensor)
+    new_array = []
+    for i in range(len(lod[0]) - 1):
+        new_array.append(array[lod[0][i]:lod[0][i + 1]])
+    return new_array
+
+
+def print_para(train_prog, train_exe, logger, args):
+    """Print para info for debug purpose"""
+    if args.para_print:
+        param_list = train_prog.block(0).all_parameters()
+        param_name_list = [p.name for p in param_list]
+        num_sum = 0
+        for p_name in param_name_list:
+            p_array = np.array(train_exe.scope.find_var(p_name).get_tensor())
+            param_num = np.prod(p_array.shape)
+            num_sum = num_sum + param_num
+            logger.info(
+                "param: {0},  mean={1}  max={2}  min={3}  num={4} {5}".format(
+                    p_name,
+                    p_array.mean(),
+                    p_array.max(), p_array.min(), p_array.shape, param_num))
+        logger.info("total param num: {0}".format(num_sum))
+
+
+def find_best_answer_for_passage(start_probs, end_probs, passage_len):
+    """
+    Finds the best answer with the maximum start_prob * end_prob from a single passage
+    """
+    if passage_len is None:
+        passage_len = len(start_probs)
+    else:
+        passage_len = min(len(start_probs), passage_len)
+    best_start, best_end, max_prob = -1, -1, 0
+    for start_idx in range(passage_len):
+        for ans_len in range(args.max_a_len):
+            end_idx = start_idx + ans_len
+            if end_idx >= passage_len:
+                continue
+            prob = start_probs[start_idx] * end_probs[end_idx]
+            if prob > max_prob:
+                best_start = start_idx
+                best_end = end_idx
+                max_prob = prob
+    return (best_start, best_end), max_prob
+
+
+def find_best_answer_for_inst(sample,
+                              start_prob,
+                              end_prob,
+                              inst_lod,
+                              para_prior_scores=(0.44, 0.23, 0.15, 0.09, 0.07)):
+    """
+    Finds the best answer for a sample given start_prob and end_prob for each position.
+    This will call find_best_answer_for_passage because there are multiple passages in a sample
+    """
+    best_p_idx, best_span, best_score = None, None, 0
+    for p_idx, passage in enumerate(sample['passages']):
+        if p_idx >= args.max_p_num:
+            continue
+        if len(start_prob) != len(end_prob):
+            logger.info('error: {}'.format(sample['question']))
+            continue
+        passage_start = inst_lod[p_idx] - inst_lod[0]
+        passage_end = inst_lod[p_idx + 1] - inst_lod[0]
+        passage_len = passage_end - passage_start
+        passage_len = min(args.max_p_len, len(passage['passage_tokens']))
+        answer_span, score = find_best_answer_for_passage(
+            start_prob[passage_start:passage_end],
+            end_prob[passage_start:passage_end], passage_len)
+        if para_prior_scores is not None:
+            # the Nth prior score = the Number of training samples whose gold answer comes
+            #  from the Nth paragraph / the number of the training samples
+            score *= para_prior_scores[p_idx]
+        if score > best_score:
+            best_score = score
+            best_p_idx = p_idx
+            best_span = answer_span
+    if best_p_idx is None or best_span is None:
+        best_answer = ''
+    else:
+        best_answer = ''.join(sample['passages'][best_p_idx]['passage_tokens'][
+            best_span[0]:best_span[1] + 1])
+    return best_answer, best_span
+
+
+def validation(inference_program, avg_cost, s_probs, e_probs, match, feed_order,
+               place, dev_count, vocab, brc_data, logger, args):
+    """
+    do inference with given inference_program
+    """
+    parallel_executor = fluid.ParallelExecutor(
+        main_program=inference_program,
+        use_cuda=bool(args.use_gpu),
+        loss_name=avg_cost.name)
+    print_para(inference_program, parallel_executor, logger, args)
+
+    # Use test set as validation each pass
+    total_loss = 0.0
+    count = 0
+    n_batch_cnt = 0
+    n_batch_loss = 0.0
+    pred_answers, ref_answers = [], []
+    val_feed_list = [
+        inference_program.global_block().var(var_name)
+        for var_name in feed_order
+    ]
+    val_feeder = fluid.DataFeeder(val_feed_list, place)
+    pad_id = vocab.get_id(vocab.pad_token)
+    dev_reader = lambda:brc_data.gen_mini_batches('dev', args.batch_size, pad_id, shuffle=False)
+    dev_reader = read_multiple(dev_reader, dev_count)
+
+    for batch_id, batch_list in enumerate(dev_reader(), 1):
+        feed_data = batch_reader(batch_list, args)
+        val_fetch_outs = parallel_executor.run(
+            feed=list(val_feeder.feed_parallel(feed_data, dev_count)),
+            fetch_list=[avg_cost.name, s_probs.name, e_probs.name, match.name],
+            return_numpy=False)
+        total_loss += np.array(val_fetch_outs[0]).sum()
+        start_probs_m = LodTensor_Array(val_fetch_outs[1])
+        end_probs_m = LodTensor_Array(val_fetch_outs[2])
+        match_lod = val_fetch_outs[3].lod()
+        count += len(np.array(val_fetch_outs[0]))
+
+        n_batch_cnt += len(np.array(val_fetch_outs[0]))
+        n_batch_loss += np.array(val_fetch_outs[0]).sum()
+        log_every_n_batch = args.log_interval
+        if log_every_n_batch > 0 and batch_id % log_every_n_batch == 0:
+            logger.info('Average dev loss from batch {} to {} is {}'.format(
+                batch_id - log_every_n_batch + 1, batch_id, "%.10f" % (
+                    n_batch_loss / n_batch_cnt)))
+            n_batch_loss = 0.0
+            n_batch_cnt = 0
+        batch_offset = 0
+        for idx, batch in enumerate(batch_list):
+            #one batch
+            batch_size = len(batch['raw_data'])
+            batch_range = match_lod[0][batch_offset:batch_offset + batch_size +
+                                       1]
+            batch_lod = [[batch_range[x], batch_range[x + 1]]
+                         for x in range(len(batch_range[:-1]))]
+            start_prob_batch = start_probs_m[batch_offset:batch_offset +
+                                             batch_size + 1]
+            end_prob_batch = end_probs_m[batch_offset:batch_offset + batch_size
+                                         + 1]
+            for sample, start_prob_inst, end_prob_inst, inst_range in zip(
+                    batch['raw_data'], start_prob_batch, end_prob_batch,
+                    batch_lod):
+                #one instance
+                inst_lod = match_lod[1][inst_range[0]:inst_range[1] + 1]
+                best_answer, best_span = find_best_answer_for_inst(
+                    sample, start_prob_inst, end_prob_inst, inst_lod)
+                pred = {
+                    'question_id': sample['question_id'],
+                    'question_type': sample['question_type'],
+                    'answers': [best_answer],
+                    'entity_answers': [[]],
+                    'yesno_answers': []
+                }
+                pred_answers.append(pred)
+                if 'answers' in sample:
+                    ref = {
+                        'question_id': sample['question_id'],
+                        'question_type': sample['question_type'],
+                        'answers': sample['answers'],
+                        'entity_answers': [[]],
+                        'yesno_answers': []
+                    }
+                    ref_answers.append(ref)
+            batch_offset = batch_offset + batch_size
+
+    result_dir = args.result_dir
+    result_prefix = args.result_name
+    if result_dir is not None and result_prefix is not None:
+        if not os.path.exists(args.result_dir):
+            os.makedirs(args.result_dir)
+        result_file = os.path.join(result_dir, result_prefix + '.json')
+        with open(result_file, 'w') as fout:
+            for pred_answer in pred_answers:
+                fout.write(json.dumps(pred_answer, ensure_ascii=False) + '\n')
+        logger.info('Saving {} results to {}'.format(result_prefix,
+                                                     result_file))
+
+    ave_loss = 1.0 * total_loss / count
+    # compute the bleu and rouge scores if reference answers is provided
+    if len(ref_answers) > 0:
+        pred_dict, ref_dict = {}, {}
+        for pred, ref in zip(pred_answers, ref_answers):
+            question_id = ref['question_id']
+            if len(ref['answers']) > 0:
+                pred_dict[question_id] = normalize(pred['answers'])
+                ref_dict[question_id] = normalize(ref['answers'])
+        bleu_rouge = compute_bleu_rouge(pred_dict, ref_dict)
+    else:
+        bleu_rouge = None
+    return ave_loss, bleu_rouge
+
+
+def l2_loss(train_prog):
+    param_list = train_prog.block(0).all_parameters()
+    para_sum = []
+    for para in param_list:
+        para_mul = fluid.layers.elementwise_mul(x=para, y=para, axis=0)
+        para_sum.append(fluid.layers.reduce_sum(input=para_mul, dim=None))
+    return fluid.layers.sums(para_sum) * 0.5
+
+
+def train(logger, args):
+    """train a model"""
+    logger.info('Load data_set and vocab...')
+    with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
+        if six.PY2:
+            vocab = pickle.load(fin)
+        else:
+            vocab = pickle.load(fin, encoding='bytes')
+        logger.info('vocab size is {} and embed dim is {}'.format(vocab.size(
+        ), vocab.embed_dim))
+    brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len,
+                          args.trainset, args.devset)
+    logger.info('Converting text into ids...')
+    brc_data.convert_to_ids(vocab)
+    logger.info('Initialize the model...')
+
+    if not args.use_gpu:
+        place = fluid.CPUPlace()
+        dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+    else:
+        place = fluid.CUDAPlace(0)
+        dev_count = fluid.core.get_cuda_device_count()
+
+    # build model
+    main_program = fluid.Program()
+    startup_prog = fluid.Program()
+    if args.enable_ce:
+        main_program.random_seed = args.random_seed
+        startup_prog.random_seed = args.random_seed
+    with fluid.program_guard(main_program, startup_prog):
+        with fluid.unique_name.guard():
+            avg_cost, s_probs, e_probs, match, feed_order = rc_model.rc_model(
+                args.hidden_size, vocab, args)
+            # clone from default main program and use it as the validation program
+            inference_program = main_program.clone(for_test=True)
+
+            # build optimizer
+            if args.optim == 'sgd':
+                optimizer = fluid.optimizer.SGD(
+                    learning_rate=args.learning_rate)
+            elif args.optim == 'adam':
+                optimizer = fluid.optimizer.Adam(
+                    learning_rate=args.learning_rate)
+            elif args.optim == 'rprop':
+                optimizer = fluid.optimizer.RMSPropOptimizer(
+                    learning_rate=args.learning_rate)
+            else:
+                logger.error('Unsupported optimizer: {}'.format(args.optim))
+                exit(-1)
+            if args.weight_decay > 0.0:
+                obj_func = avg_cost + args.weight_decay * l2_loss(main_program)
+                optimizer.minimize(obj_func)
+            else:
+                obj_func = avg_cost
+                optimizer.minimize(obj_func)
+
+            # initialize parameters
+            place = core.CUDAPlace(0) if args.use_gpu else core.CPUPlace()
+            exe = Executor(place)
+            if args.load_dir:
+                logger.info('load from {}'.format(args.load_dir))
+                fluid.io.load_persistables(
+                    exe, args.load_dir, main_program=main_program)
+            else:
+                exe.run(startup_prog)
+                embedding_para = fluid.global_scope().find_var(
+                    'embedding_para').get_tensor()
+                embedding_para.set(vocab.embeddings.astype(np.float32), place)
+
+            # prepare data
+            feed_list = [
+                main_program.global_block().var(var_name)
+                for var_name in feed_order
+            ]
+            feeder = fluid.DataFeeder(feed_list, place)
+
+            logger.info('Training the model...')
+            parallel_executor = fluid.ParallelExecutor(
+                main_program=main_program,
+                use_cuda=bool(args.use_gpu),
+                loss_name=avg_cost.name)
+            print_para(main_program, parallel_executor, logger, args)
+
+            for pass_id in range(1, args.pass_num + 1):
+                pass_start_time = time.time()
+                pad_id = vocab.get_id(vocab.pad_token)
+                if args.enable_ce:
+                    train_reader = lambda:brc_data.gen_mini_batches('train', args.batch_size, pad_id, shuffle=False)
+                else:
+                    train_reader = lambda:brc_data.gen_mini_batches('train', args.batch_size, pad_id, shuffle=True)
+                train_reader = read_multiple(train_reader, dev_count)
+                log_every_n_batch, n_batch_loss = args.log_interval, 0
+                total_num, total_loss = 0, 0
+                for batch_id, batch_list in enumerate(train_reader(), 1):
+                    feed_data = batch_reader(batch_list, args)
+                    fetch_outs = parallel_executor.run(
+                        feed=list(feeder.feed_parallel(feed_data, dev_count)),
+                        fetch_list=[obj_func.name],
+                        return_numpy=False)
+                    cost_train = np.array(fetch_outs[0]).mean()
+                    total_num += args.batch_size * dev_count
+                    n_batch_loss += cost_train
+                    total_loss += cost_train * args.batch_size * dev_count
+
+                    if args.enable_ce and batch_id >= 100:
+                        break
+                    if log_every_n_batch > 0 and batch_id % log_every_n_batch == 0:
+                        print_para(main_program, parallel_executor, logger,
+                                   args)
+                        logger.info(
+                            'Average loss from batch {} to {} is {}'.format(
+                                batch_id - log_every_n_batch + 1, batch_id,
+                                "%.10f" % (n_batch_loss / log_every_n_batch)))
+                        n_batch_loss = 0
+                    if args.dev_interval > 0 and batch_id % args.dev_interval == 0:
+                        if brc_data.dev_set is not None:
+                            eval_loss, bleu_rouge = validation(
+                                inference_program, avg_cost, s_probs, e_probs,
+                                match, feed_order, place, dev_count, vocab,
+                                brc_data, logger, args)
+                            logger.info('Dev eval loss {}'.format(eval_loss))
+                            logger.info('Dev eval result: {}'.format(
+                                bleu_rouge))
+                pass_end_time = time.time()
+                time_consumed = pass_end_time - pass_start_time
+                logger.info('epoch: {0}, epoch_time_cost: {1:.2f}'.format(
+                    pass_id, time_consumed))
+                logger.info('Evaluating the model after epoch {}'.format(
+                    pass_id))
+                if brc_data.dev_set is not None:
+                    eval_loss, bleu_rouge = validation(
+                        inference_program, avg_cost, s_probs, e_probs, match,
+                        feed_order, place, dev_count, vocab, brc_data, logger,
+                        args)
+                    logger.info('Dev eval loss {}'.format(eval_loss))
+                    logger.info('Dev eval result: {}'.format(bleu_rouge))
+                else:
+                    logger.warning(
+                        'No dev set is loaded for evaluation in the dataset!')
+
+                logger.info('Average train loss for epoch {} is {}'.format(
+                    pass_id, "%.10f" % (1.0 * total_loss / total_num)))
+
+                if pass_id % args.save_interval == 0:
+                    model_path = os.path.join(args.save_dir, str(pass_id))
+                    if not os.path.isdir(model_path):
+                        os.makedirs(model_path)
+
+                    fluid.io.save_persistables(
+                        executor=exe,
+                        dirname=model_path,
+                        main_program=main_program)
+                if args.enable_ce:  # For CE
+                    print("kpis\ttrain_cost_card%d\t%f" %
+                          (dev_count, total_loss / total_num))
+                    if brc_data.dev_set is not None:
+                        print("kpis\ttest_cost_card%d\t%f" %
+                              (dev_count, eval_loss))
+                    print("kpis\ttrain_duration_card%d\t%f" %
+                          (dev_count, time_consumed))
+
+
+def evaluate(logger, args):
+    """evaluate a specific model using devset"""
+    logger.info('Load data_set and vocab...')
+    with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
+        vocab = pickle.load(fin)
+        logger.info('vocab size is {} and embed dim is {}'.format(vocab.size(
+        ), vocab.embed_dim))
+    brc_data = BRCDataset(
+        args.max_p_num, args.max_p_len, args.max_q_len, dev_files=args.devset)
+    logger.info('Converting text into ids...')
+    brc_data.convert_to_ids(vocab)
+    logger.info('Initialize the model...')
+
+    # build model
+    main_program = fluid.Program()
+    startup_prog = fluid.Program()
+    with fluid.program_guard(main_program, startup_prog):
+        with fluid.unique_name.guard():
+            avg_cost, s_probs, e_probs, match, feed_order = rc_model.rc_model(
+                args.hidden_size, vocab, args)
+            # initialize parameters
+            if not args.use_gpu:
+                place = fluid.CPUPlace()
+                dev_count = int(
+                    os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+            else:
+                place = fluid.CUDAPlace(0)
+                dev_count = fluid.core.get_cuda_device_count()
+
+            exe = Executor(place)
+            if args.load_dir:
+                logger.info('load from {}'.format(args.load_dir))
+                fluid.io.load_persistables(
+                    exe, args.load_dir, main_program=main_program)
+            else:
+                logger.error('No model file to load ...')
+                return
+
+            inference_program = main_program.clone(for_test=True)
+            eval_loss, bleu_rouge = validation(
+                inference_program, avg_cost, s_probs, e_probs, match,
+                feed_order, place, dev_count, vocab, brc_data, logger, args)
+            logger.info('Dev eval loss {}'.format(eval_loss))
+            logger.info('Dev eval result: {}'.format(bleu_rouge))
+            logger.info('Predicted answers are saved to {}'.format(
+                os.path.join(args.result_dir)))
+
+
+def predict(logger, args):
+    """do inference on the test dataset """
+    logger.info('Load data_set and vocab...')
+    with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
+        vocab = pickle.load(fin)
+        logger.info('vocab size is {} and embed dim is {}'.format(vocab.size(
+        ), vocab.embed_dim))
+    brc_data = BRCDataset(
+        args.max_p_num, args.max_p_len, args.max_q_len, dev_files=args.testset)
+    logger.info('Converting text into ids...')
+    brc_data.convert_to_ids(vocab)
+    logger.info('Initialize the model...')
+
+    # build model
+    main_program = fluid.Program()
+    startup_prog = fluid.Program()
+    with fluid.program_guard(main_program, startup_prog):
+        with fluid.unique_name.guard():
+            avg_cost, s_probs, e_probs, match, feed_order = rc_model.rc_model(
+                args.hidden_size, vocab, args)
+            # initialize parameters
+            if not args.use_gpu:
+                place = fluid.CPUPlace()
+                dev_count = int(
+                    os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+            else:
+                place = fluid.CUDAPlace(0)
+                dev_count = fluid.core.get_cuda_device_count()
+
+            exe = Executor(place)
+            if args.load_dir:
+                logger.info('load from {}'.format(args.load_dir))
+                fluid.io.load_persistables(
+                    exe, args.load_dir, main_program=main_program)
+            else:
+                logger.error('No model file to load ...')
+                return
+
+            inference_program = main_program.clone(for_test=True)
+            eval_loss, bleu_rouge = validation(
+                inference_program, avg_cost, s_probs, e_probs, match,
+                feed_order, place, dev_count, vocab, brc_data, logger, args)
+
+
+def prepare(logger, args):
+    """
+    checks data, creates the directories, prepare the vocabulary and embeddings
+    """
+    logger.info('Checking the data files...')
+    for data_path in args.trainset + args.devset + args.testset:
+        assert os.path.exists(data_path), '{} file does not exist.'.format(
+            data_path)
+    logger.info('Preparing the directories...')
+    for dir_path in [args.vocab_dir, args.save_dir, args.result_dir]:
+        if not os.path.exists(dir_path):
+            os.makedirs(dir_path)
+
+    logger.info('Building vocabulary...')
+    brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len,
+                          args.trainset, args.devset, args.testset)
+    vocab = Vocab(lower=True)
+    for word in brc_data.word_iter('train'):
+        vocab.add(word)
+
+    unfiltered_vocab_size = vocab.size()
+    vocab.filter_tokens_by_cnt(min_cnt=2)
+    filtered_num = unfiltered_vocab_size - vocab.size()
+    logger.info('After filter {} tokens, the final vocab size is {}'.format(
+        filtered_num, vocab.size()))
+
+    logger.info('Assigning embeddings...')
+    vocab.randomly_init_embeddings(args.embed_size)
+
+    logger.info('Saving vocab...')
+    with open(os.path.join(args.vocab_dir, 'vocab.data'), 'wb') as fout:
+        pickle.dump(vocab, fout)
+
+    logger.info('Done with preparing!')
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    if args.enable_ce:
+        random.seed(args.random_seed)
+        np.random.seed(args.random_seed)
+
+    logger = logging.getLogger("brc")
+    logger.setLevel(logging.INFO)
+    formatter = logging.Formatter(
+        '%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    if args.log_path:
+        file_handler = logging.FileHandler(args.log_path)
+        file_handler.setLevel(logging.INFO)
+        file_handler.setFormatter(formatter)
+        logger.addHandler(file_handler)
+    else:
+        console_handler = logging.StreamHandler()
+        console_handler.setLevel(logging.INFO)
+        console_handler.setFormatter(formatter)
+        logger.addHandler(console_handler)
+    args = parse_args()
+    logger.info('Running with args : {}'.format(args))
+    if args.prepare:
+        prepare(logger, args)
+    if args.train:
+        train(logger, args)
+    if args.evaluate:
+        evaluate(logger, args)
+    if args.predict:
+        predict(logger, args)
diff --git a/PaddleNLP/machine_reading_comprehension/run.sh b/PaddleNLP/unarchived/machine_reading_comprehension/run.sh
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/run.sh
rename to PaddleNLP/unarchived/machine_reading_comprehension/run.sh
diff --git a/PaddleNLP/machine_reading_comprehension/utils/__init__.py b/PaddleNLP/unarchived/machine_reading_comprehension/utils/__init__.py
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/utils/__init__.py
rename to PaddleNLP/unarchived/machine_reading_comprehension/utils/__init__.py
diff --git a/PaddleNLP/unarchived/machine_reading_comprehension/utils/download_thirdparty.sh b/PaddleNLP/unarchived/machine_reading_comprehension/utils/download_thirdparty.sh
new file mode 100755
index 0000000000000000000000000000000000000000..cc37da879971c2279edab220485d08d9b20c35fa
--- /dev/null
+++ b/PaddleNLP/unarchived/machine_reading_comprehension/utils/download_thirdparty.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+# ==============================================================================
+# Copyright 2017 Baidu.com, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# We use Bleu and Rouge as evaluation metrics, the calculation of these metrics
+# relies on the scoring scripts under "https://github.com/tylin/coco-caption"
+
+bleu_base_url='https://raw.githubusercontent.com/tylin/coco-caption/master/pycocoevalcap/bleu'
+bleu_files=("LICENSE" "__init__.py" "bleu.py" "bleu_scorer.py")
+
+rouge_base_url="https://raw.githubusercontent.com/tylin/coco-caption/master/pycocoevalcap/rouge"
+rouge_files=("__init__.py" "rouge.py")
+
+download() {
+    local metric=$1; shift;
+    local base_url=$1; shift;
+    local fnames=($@);
+
+    mkdir -p ${metric}
+    for fname in ${fnames[@]};
+    do
+        printf "downloading: %s\n" ${base_url}/${fname}
+        wget --no-check-certificate ${base_url}/${fname} -O ${metric}/${fname}
+    done
+}
+
+# prepare rouge
+download "rouge_metric" ${rouge_base_url} ${rouge_files[@]}
+
+# prepare bleu
+download "bleu_metric" ${bleu_base_url} ${bleu_files[@]}
+
+# convert python 2.x source code to python 3.x
+2to3 -w "../utils/bleu_metric/bleu_scorer.py"
+2to3 -w "../utils/bleu_metric/bleu.py"
diff --git a/PaddleNLP/machine_reading_comprehension/utils/dureader_eval.py b/PaddleNLP/unarchived/machine_reading_comprehension/utils/dureader_eval.py
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/utils/dureader_eval.py
rename to PaddleNLP/unarchived/machine_reading_comprehension/utils/dureader_eval.py
diff --git a/PaddleNLP/machine_reading_comprehension/utils/get_vocab.py b/PaddleNLP/unarchived/machine_reading_comprehension/utils/get_vocab.py
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/utils/get_vocab.py
rename to PaddleNLP/unarchived/machine_reading_comprehension/utils/get_vocab.py
diff --git a/PaddleNLP/machine_reading_comprehension/utils/marco_tokenize_data.py b/PaddleNLP/unarchived/machine_reading_comprehension/utils/marco_tokenize_data.py
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/utils/marco_tokenize_data.py
rename to PaddleNLP/unarchived/machine_reading_comprehension/utils/marco_tokenize_data.py
diff --git a/PaddleNLP/unarchived/machine_reading_comprehension/utils/marcov1_to_dureader.py b/PaddleNLP/unarchived/machine_reading_comprehension/utils/marcov1_to_dureader.py
new file mode 100644
index 0000000000000000000000000000000000000000..022db4dd1bdf98d2a7e0ead659e988ff109b59e9
--- /dev/null
+++ b/PaddleNLP/unarchived/machine_reading_comprehension/utils/marcov1_to_dureader.py
@@ -0,0 +1,37 @@
+#coding=utf8
+
+import sys
+import json
+import pandas as pd
+
+
+def trans(input_js):
+    output_js = {}
+    output_js['question'] = input_js['query']
+    output_js['question_type'] = input_js['query_type']
+    output_js['question_id'] = input_js['query_id']
+    output_js['fact_or_opinion'] = ""
+    output_js['documents'] = []
+    for para_id, para in enumerate(input_js['passages']):
+        doc = {}
+        doc['title'] = ""
+        if 'is_selected' in para:
+            doc['is_selected'] = True if para['is_selected'] != 0 else False
+        doc['paragraphs'] = [para['passage_text']]
+        output_js['documents'].append(doc)
+
+    if 'answers' in input_js:
+        output_js['answers'] = input_js['answers']
+    return output_js
+
+
+if __name__ == '__main__':
+    if len(sys.argv) != 2:
+        print('Usage: marcov1_to_dureader.py ')
+        exit()
+
+    df = pd.read_json(sys.argv[1])
+    for row in df.iterrows():
+        marco_js = json.loads(row[1].to_json())
+        dureader_js = trans(marco_js)
+        print(json.dumps(dureader_js))
diff --git a/PaddleNLP/machine_reading_comprehension/utils/marcov2_to_v1_tojsonl.py b/PaddleNLP/unarchived/machine_reading_comprehension/utils/marcov2_to_v1_tojsonl.py
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/utils/marcov2_to_v1_tojsonl.py
rename to PaddleNLP/unarchived/machine_reading_comprehension/utils/marcov2_to_v1_tojsonl.py
diff --git a/PaddleNLP/machine_reading_comprehension/utils/preprocess.py b/PaddleNLP/unarchived/machine_reading_comprehension/utils/preprocess.py
similarity index 100%
rename from PaddleNLP/machine_reading_comprehension/utils/preprocess.py
rename to PaddleNLP/unarchived/machine_reading_comprehension/utils/preprocess.py
diff --git a/PaddleNLP/unarchived/machine_reading_comprehension/utils/run_marco2dureader_preprocess.sh b/PaddleNLP/unarchived/machine_reading_comprehension/utils/run_marco2dureader_preprocess.sh
new file mode 100644
index 0000000000000000000000000000000000000000..fcb7d67a002ef15384b7c725eb14f3a4dd64ec9e
--- /dev/null
+++ b/PaddleNLP/unarchived/machine_reading_comprehension/utils/run_marco2dureader_preprocess.sh
@@ -0,0 +1,22 @@
+#!/bin/bash
+
+input_file=$1
+output_file=$2
+
+# convert the data from MARCO V2 (json) format to MARCO V1 (jsonl) format. 
+# the script was forked from MARCO repo. 
+# the format of MARCO V1 is much more easier to explore. 
+python3 marcov2_to_v1_tojsonl.py $input_file $input_file.marcov1
+
+# convert the data from MARCO V1 format to DuReader format. 
+python3 marcov1_to_dureader.py $input_file.marcov1 >$input_file.dureader_raw
+
+# tokenize the data. 
+python3 marco_tokenize_data.py $input_file.dureader_raw >$input_file.segmented
+
+# find fake answers (indicating the start and end positions of answers in the document) for train and dev sets. 
+# note that this should not be applied for test set, since there is no ground truth in test set. 
+python preprocess.py $input_file.segmented >$output_file
+
+# remove the temporal data files. 
+rm -rf $input_file.dureader_raw $input_file.segmented
diff --git a/PaddleNLP/unarchived/machine_reading_comprehension/vocab.py b/PaddleNLP/unarchived/machine_reading_comprehension/vocab.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4ebb07ad710b7101858841bd0e9c7a4b90589a7
--- /dev/null
+++ b/PaddleNLP/unarchived/machine_reading_comprehension/vocab.py
@@ -0,0 +1,201 @@
+# -*- coding:utf8 -*-
+# ==============================================================================
+# Copyright 2017 Baidu.com, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+This module implements the Vocab class for converting string to id and back
+"""
+
+import numpy as np
+
+
+class Vocab(object):
+    """
+    Implements a vocabulary to store the tokens in the data, with their corresponding embeddings.
+    """
+
+    def __init__(self, filename=None, initial_tokens=None, lower=False):
+        self.id2token = {}
+        self.token2id = {}
+        self.token_cnt = {}
+        self.lower = lower
+
+        self.embed_dim = None
+        self.embeddings = None
+
+        self.pad_token = ''
+        self.unk_token = ''
+        self.split_token = ''
+
+        self.initial_tokens = initial_tokens if initial_tokens is not None else []
+        self.initial_tokens.extend(
+            [self.pad_token, self.unk_token, self.split_token])
+        for token in self.initial_tokens:
+            self.add(token)
+
+        if filename is not None:
+            self.load_from_file(filename)
+
+    def size(self):
+        """
+        get the size of vocabulary
+        Returns:
+            an integer indicating the size
+        """
+        return len(self.id2token)
+
+    def load_from_file(self, file_path):
+        """
+        loads the vocab from file_path
+        Args:
+            file_path: a file with a word in each line
+        """
+        for line in open(file_path, 'r'):
+            token = line.rstrip('\n')
+            self.add(token)
+
+    def get_id(self, token):
+        """
+        gets the id of a token, returns the id of unk token if token is not in vocab
+        Args:
+            key: a string indicating the word
+        Returns:
+            an integer
+        """
+        token = token.lower() if self.lower else token
+        try:
+            return self.token2id[token]
+        except KeyError:
+            return self.token2id[self.unk_token]
+
+    def get_token(self, idx):
+        """
+        gets the token corresponding to idx, returns unk token if idx is not in vocab
+        Args:
+            idx: an integer
+        returns:
+            a token string
+        """
+        try:
+            return self.id2token[idx]
+        except KeyError:
+            return self.unk_token
+
+    def add(self, token, cnt=1):
+        """
+        adds the token to vocab
+        Args:
+            token: a string
+            cnt: a num indicating the count of the token to add, default is 1
+        """
+        token = token.lower() if self.lower else token
+        if token in self.token2id:
+            idx = self.token2id[token]
+        else:
+            idx = len(self.id2token)
+            self.id2token[idx] = token
+            self.token2id[token] = idx
+        if cnt > 0:
+            if token in self.token_cnt:
+                self.token_cnt[token] += cnt
+            else:
+                self.token_cnt[token] = cnt
+        return idx
+
+    def filter_tokens_by_cnt(self, min_cnt):
+        """
+        filter the tokens in vocab by their count
+        Args:
+            min_cnt: tokens with frequency less than min_cnt is filtered
+        """
+        filtered_tokens = [
+            token for token in self.token2id if self.token_cnt[token] >= min_cnt
+        ]
+        # rebuild the token x id map
+        self.token2id = {}
+        self.id2token = {}
+        for token in self.initial_tokens:
+            self.add(token, cnt=0)
+        for token in filtered_tokens:
+            self.add(token, cnt=0)
+
+    def randomly_init_embeddings(self, embed_dim):
+        """
+        randomly initializes the embeddings for each token
+        Args:
+            embed_dim: the size of the embedding for each token
+        """
+        self.embed_dim = embed_dim
+        self.embeddings = np.random.rand(self.size(), embed_dim)
+        for token in [self.pad_token, self.unk_token, self.split_token]:
+            self.embeddings[self.get_id(token)] = np.zeros([self.embed_dim])
+
+    def load_pretrained_embeddings(self, embedding_path):
+        """
+        loads the pretrained embeddings from embedding_path,
+        tokens not in pretrained embeddings will be filtered
+        Args:
+            embedding_path: the path of the pretrained embedding file
+        """
+        trained_embeddings = {}
+        with open(embedding_path, 'r') as fin:
+            for line in fin:
+                contents = line.strip().split()
+                token = contents[0].decode('utf8')
+                if token not in self.token2id:
+                    continue
+                trained_embeddings[token] = list(map(float, contents[1:]))
+                if self.embed_dim is None:
+                    self.embed_dim = len(contents) - 1
+        filtered_tokens = trained_embeddings.keys()
+        # rebuild the token x id map
+        self.token2id = {}
+        self.id2token = {}
+        for token in self.initial_tokens:
+            self.add(token, cnt=0)
+        for token in filtered_tokens:
+            self.add(token, cnt=0)
+        # load embeddings
+        self.embeddings = np.zeros([self.size(), self.embed_dim])
+        for token in self.token2id.keys():
+            if token in trained_embeddings:
+                self.embeddings[self.get_id(token)] = trained_embeddings[token]
+
+    def convert_to_ids(self, tokens):
+        """
+        Convert a list of tokens to ids, use unk_token if the token is not in vocab.
+        Args:
+            tokens: a list of token
+        Returns:
+            a list of ids
+        """
+        vec = [self.get_id(label) for label in tokens]
+        return vec
+
+    def recover_from_ids(self, ids, stop_id=None):
+        """
+        Convert a list of ids to tokens, stop converting if the stop_id is encountered
+        Args:
+            ids: a list of ids to convert
+            stop_id: the stop id, default is None
+        Returns:
+            a list of tokens
+        """
+        tokens = []
+        for i in ids:
+            tokens += [self.get_token(i)]
+            if stop_id is not None and i == stop_id:
+                break
+        return tokens
diff --git a/PaddleNLP/neural_machine_translation/README.md b/PaddleNLP/unarchived/neural_machine_translation/README.md
similarity index 100%
rename from PaddleNLP/neural_machine_translation/README.md
rename to PaddleNLP/unarchived/neural_machine_translation/README.md
diff --git a/PaddleNLP/neural_machine_translation/rnn_search/.run_ce.sh b/PaddleNLP/unarchived/neural_machine_translation/rnn_search/.run_ce.sh
similarity index 100%
rename from PaddleNLP/neural_machine_translation/rnn_search/.run_ce.sh
rename to PaddleNLP/unarchived/neural_machine_translation/rnn_search/.run_ce.sh
diff --git a/PaddleNLP/neural_machine_translation/rnn_search/README.md b/PaddleNLP/unarchived/neural_machine_translation/rnn_search/README.md
similarity index 99%
rename from PaddleNLP/neural_machine_translation/rnn_search/README.md
rename to PaddleNLP/unarchived/neural_machine_translation/rnn_search/README.md
index 86d4a021baf11e04a9fd07c05dbf50425451efab..556ea6f5fc481a120bcca67e4c1c7b9c28856b7f 100644
--- a/PaddleNLP/neural_machine_translation/rnn_search/README.md
+++ b/PaddleNLP/unarchived/neural_machine_translation/rnn_search/README.md
@@ -19,7 +19,7 @@
 
 近年来，深度学习技术的发展不断为机器翻译任务带来新的突破。直接用神经网络将源语言映射到目标语言，即端到端的神经网络机器翻译（End-to-End Neural Machine Translation, End-to-End NMT）模型逐渐成为主流，此类模型一般简称为NMT模型。
 
-本目录包含一个经典的机器翻译模型[RNN Search](https://arxiv.org/pdf/1409.0473.pdf)的Paddle Fluid实现。事实上，RNN search是一个较为传统的NMT模型，在现阶段，其表现已被很多新模型（如[Transformer](https://arxiv.org/abs/1706.03762)）超越。但除机器翻译外，该模型是许多序列到序列（sequence to sequence, 以下简称Seq2Seq）类模型的基础，很多解决其他NLP问题的模型均以此模型为基础；因此其在NLP领域具有重要意义，并被广泛用作Baseline. 
+本目录包含一个经典的机器翻译模型[RNN Search](https://arxiv.org/pdf/1409.0473.pdf)的Paddle Fluid实现。事实上，RNN search是一个较为传统的NMT模型，在现阶段，其表现已被很多新模型（如[Transformer](https://arxiv.org/abs/1706.03762)）超越。但除机器翻译外，该模型是许多序列到序列（sequence to sequence, 以下简称Seq2Seq）类模型的基础，很多解决其他NLP问题的模型均以此模型为基础；因此其在NLP领域具有重要意义，并被广泛用作Baseline.
 
 本目录下此范例模型的实现，旨在展示如何用Paddle Fluid实现一个带有注意力机制（Attention）的RNN模型来解决Seq2Seq类问题，以及如何使用带有Beam Search算法的解码器。如果您仅仅只是需要在机器翻译方面有着较好翻译效果的模型，则建议您参考[Transformer的Paddle Fluid实现](https://github.com/PaddlePaddle/models/tree/develop/fluid/neural_machine_translation/transformer)。
 
diff --git a/PaddleNLP/neural_machine_translation/rnn_search/_ce.py b/PaddleNLP/unarchived/neural_machine_translation/rnn_search/_ce.py
similarity index 100%
rename from PaddleNLP/neural_machine_translation/rnn_search/_ce.py
rename to PaddleNLP/unarchived/neural_machine_translation/rnn_search/_ce.py
diff --git a/PaddleNLP/neural_machine_translation/rnn_search/args.py b/PaddleNLP/unarchived/neural_machine_translation/rnn_search/args.py
similarity index 100%
rename from PaddleNLP/neural_machine_translation/rnn_search/args.py
rename to PaddleNLP/unarchived/neural_machine_translation/rnn_search/args.py
diff --git a/PaddleNLP/neural_machine_translation/rnn_search/attention_model.py b/PaddleNLP/unarchived/neural_machine_translation/rnn_search/attention_model.py
similarity index 100%
rename from PaddleNLP/neural_machine_translation/rnn_search/attention_model.py
rename to PaddleNLP/unarchived/neural_machine_translation/rnn_search/attention_model.py
diff --git a/PaddleNLP/neural_machine_translation/rnn_search/images/bi_rnn.png b/PaddleNLP/unarchived/neural_machine_translation/rnn_search/images/bi_rnn.png
similarity index 100%
rename from PaddleNLP/neural_machine_translation/rnn_search/images/bi_rnn.png
rename to PaddleNLP/unarchived/neural_machine_translation/rnn_search/images/bi_rnn.png
diff --git a/PaddleNLP/neural_machine_translation/rnn_search/images/decoder_attention.png b/PaddleNLP/unarchived/neural_machine_translation/rnn_search/images/decoder_attention.png
similarity index 100%
rename from PaddleNLP/neural_machine_translation/rnn_search/images/decoder_attention.png
rename to PaddleNLP/unarchived/neural_machine_translation/rnn_search/images/decoder_attention.png
diff --git a/PaddleNLP/neural_machine_translation/rnn_search/images/encoder_attention.png b/PaddleNLP/unarchived/neural_machine_translation/rnn_search/images/encoder_attention.png
similarity index 100%
rename from PaddleNLP/neural_machine_translation/rnn_search/images/encoder_attention.png
rename to PaddleNLP/unarchived/neural_machine_translation/rnn_search/images/encoder_attention.png
diff --git a/PaddleNLP/neural_machine_translation/rnn_search/infer.py b/PaddleNLP/unarchived/neural_machine_translation/rnn_search/infer.py
similarity index 100%
rename from PaddleNLP/neural_machine_translation/rnn_search/infer.py
rename to PaddleNLP/unarchived/neural_machine_translation/rnn_search/infer.py
diff --git a/PaddleNLP/neural_machine_translation/rnn_search/no_attention_model.py b/PaddleNLP/unarchived/neural_machine_translation/rnn_search/no_attention_model.py
similarity index 100%
rename from PaddleNLP/neural_machine_translation/rnn_search/no_attention_model.py
rename to PaddleNLP/unarchived/neural_machine_translation/rnn_search/no_attention_model.py
diff --git a/PaddleNLP/neural_machine_translation/rnn_search/train.py b/PaddleNLP/unarchived/neural_machine_translation/rnn_search/train.py
similarity index 100%
rename from PaddleNLP/neural_machine_translation/rnn_search/train.py
rename to PaddleNLP/unarchived/neural_machine_translation/rnn_search/train.py
diff --git a/PaddleNLP/neural_machine_translation/transformer/.gitignore b/PaddleNLP/unarchived/neural_machine_translation/transformer/.gitignore
similarity index 100%
rename from PaddleNLP/neural_machine_translation/transformer/.gitignore
rename to PaddleNLP/unarchived/neural_machine_translation/transformer/.gitignore
diff --git a/PaddleNLP/neural_machine_translation/transformer/.run_ce.sh b/PaddleNLP/unarchived/neural_machine_translation/transformer/.run_ce.sh
similarity index 100%
rename from PaddleNLP/neural_machine_translation/transformer/.run_ce.sh
rename to PaddleNLP/unarchived/neural_machine_translation/transformer/.run_ce.sh
diff --git a/PaddleNLP/unarchived/neural_machine_translation/transformer/README.md b/PaddleNLP/unarchived/neural_machine_translation/transformer/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..6fea167b5e7c3e9dd759ef30d9225b451350e889
--- /dev/null
+++ b/PaddleNLP/unarchived/neural_machine_translation/transformer/README.md
@@ -0,0 +1,23 @@
+The minimum PaddlePaddle version needed for the code sample in this directory is the lastest develop branch. If you are on a version of PaddlePaddle earlier than this, [please update your installation](http://www.paddlepaddle.org/docs/develop/documentation/en/build_and_install/pip_install_en.html).
+
+---
+
+# Attention is All You Need: A Paddle Fluid implementation
+
+This is a Paddle Fluid implementation of the Transformer model in [Attention is All You Need]() (Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, arxiv, 2017).
+
+If you use the dataset/code in your research, please cite the paper:
+
+```text
+@inproceedings{vaswani2017attention,
+  title={Attention is all you need},
+  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
+  booktitle={Advances in Neural Information Processing Systems},
+  pages={6000--6010},
+  year={2017}
+}
+```
+
+### TODO
+
+This project is still under active development.
diff --git a/PaddleNLP/neural_machine_translation/transformer/README_cn.md b/PaddleNLP/unarchived/neural_machine_translation/transformer/README_cn.md
similarity index 100%
rename from PaddleNLP/neural_machine_translation/transformer/README_cn.md
rename to PaddleNLP/unarchived/neural_machine_translation/transformer/README_cn.md
diff --git a/PaddleNLP/neural_machine_translation/transformer/_ce.py b/PaddleNLP/unarchived/neural_machine_translation/transformer/_ce.py
similarity index 100%
rename from PaddleNLP/neural_machine_translation/transformer/_ce.py
rename to PaddleNLP/unarchived/neural_machine_translation/transformer/_ce.py
diff --git a/PaddleNLP/unarchived/neural_machine_translation/transformer/config.py b/PaddleNLP/unarchived/neural_machine_translation/transformer/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..823341ed9084e80b5fe74655bf8db897d72175f0
--- /dev/null
+++ b/PaddleNLP/unarchived/neural_machine_translation/transformer/config.py
@@ -0,0 +1,201 @@
+class TrainTaskConfig(object):
+    # support both CPU and GPU now.
+    use_gpu = True
+    # the epoch number to train.
+    pass_num = 30
+    # the number of sequences contained in a mini-batch.
+    # deprecated, set batch_size in args.
+    batch_size = 32
+    # the hyper parameters for Adam optimizer.
+    # This static learning_rate will be multiplied to the LearningRateScheduler
+    # derived learning rate the to get the final learning rate.
+    learning_rate = 2.0
+    beta1 = 0.9
+    beta2 = 0.997
+    eps = 1e-9
+    # the parameters for learning rate scheduling.
+    warmup_steps = 8000
+    # the weight used to mix up the ground-truth distribution and the fixed
+    # uniform distribution in label smoothing when training.
+    # Set this as zero if label smoothing is not wanted.
+    label_smooth_eps = 0.1
+    # the directory for saving trained models.
+    model_dir = "trained_models"
+    # the directory for saving checkpoints.
+    ckpt_dir = "trained_ckpts"
+    # the directory for loading checkpoint.
+    # If provided, continue training from the checkpoint.
+    ckpt_path = None
+    # the parameter to initialize the learning rate scheduler.
+    # It should be provided if use checkpoints, since the checkpoint doesn't
+    # include the training step counter currently.
+    start_step = 0
+    # the frequency to save trained models.
+    save_freq = 10000
+
+
+class InferTaskConfig(object):
+    use_gpu = True
+    # the number of examples in one run for sequence generation.
+    batch_size = 10
+    # the parameters for beam search.
+    beam_size = 5
+    max_out_len = 256
+    # the number of decoded sentences to output.
+    n_best = 1
+    # the flags indicating whether to output the special tokens.
+    output_bos = False
+    output_eos = False
+    output_unk = True
+    # the directory for loading the trained model.
+    model_path = "trained_models/pass_1.infer.model"
+
+
+class ModelHyperParams(object):
+    # These following five vocabularies related configurations will be set
+    # automatically according to the passed vocabulary path and special tokens.
+    # size of source word dictionary.
+    src_vocab_size = 10000
+    # size of target word dictionay
+    trg_vocab_size = 10000
+    # index for  token
+    bos_idx = 0
+    # index for  token
+    eos_idx = 1
+    # index for  token
+    unk_idx = 2
+    # max length of sequences deciding the size of position encoding table.
+    max_length = 256
+    # the dimension for word embeddings, which is also the last dimension of
+    # the input and output of multi-head attention, position-wise feed-forward
+    # networks, encoder and decoder.
+    d_model = 512
+    # size of the hidden layer in position-wise feed-forward networks.
+    d_inner_hid = 2048
+    # the dimension that keys are projected to for dot-product attention.
+    d_key = 64
+    # the dimension that values are projected to for dot-product attention.
+    d_value = 64
+    # number of head used in multi-head attention.
+    n_head = 8
+    # number of sub-layers to be stacked in the encoder and decoder.
+    n_layer = 6
+    # dropout rates of different modules.
+    prepostprocess_dropout = 0.1
+    attention_dropout = 0.1
+    relu_dropout = 0.1
+    # to process before each sub-layer
+    preprocess_cmd = "n"  # layer normalization
+    # to process after each sub-layer
+    postprocess_cmd = "da"  # dropout + residual connection
+    # random seed used in dropout for CE.
+    dropout_seed = None
+    # the flag indicating whether to share embedding and softmax weights.
+    # vocabularies in source and target should be same for weight sharing.
+    weight_sharing = True
+
+
+def merge_cfg_from_list(cfg_list, g_cfgs):
+    """
+    Set the above global configurations using the cfg_list. 
+    """
+    assert len(cfg_list) % 2 == 0
+    for key, value in zip(cfg_list[0::2], cfg_list[1::2]):
+        for g_cfg in g_cfgs:
+            if hasattr(g_cfg, key):
+                try:
+                    value = eval(value)
+                except Exception:  # for file path
+                    pass
+                setattr(g_cfg, key, value)
+                break
+
+
+# The placeholder for batch_size in compile time. Must be -1 currently to be
+# consistent with some ops' infer-shape output in compile time, such as the
+# sequence_expand op used in beamsearch decoder.
+batch_size = -1
+# The placeholder for squence length in compile time.
+seq_len = ModelHyperParams.max_length
+# Here list the data shapes and data types of all inputs.
+# The shapes here act as placeholder and are set to pass the infer-shape in
+# compile time.
+input_descs = {
+    # The actual data shape of src_word is:
+    # [batch_size, max_src_len_in_batch, 1]
+    "src_word": [(batch_size, seq_len, 1), "int64", 2],
+    # The actual data shape of src_pos is:
+    # [batch_size, max_src_len_in_batch, 1]
+    "src_pos": [(batch_size, seq_len, 1), "int64"],
+    # This input is used to remove attention weights on paddings in the
+    # encoder.
+    # The actual data shape of src_slf_attn_bias is:
+    # [batch_size, n_head, max_src_len_in_batch, max_src_len_in_batch]
+    "src_slf_attn_bias": [(batch_size, ModelHyperParams.n_head, seq_len,
+                           seq_len), "float32"],
+    # The actual data shape of trg_word is:
+    # [batch_size, max_trg_len_in_batch, 1]
+    "trg_word": [(batch_size, seq_len, 1), "int64",
+                 2],  # lod_level is only used in fast decoder.
+    # The actual data shape of trg_pos is:
+    # [batch_size, max_trg_len_in_batch, 1]
+    "trg_pos": [(batch_size, seq_len, 1), "int64"],
+    # This input is used to remove attention weights on paddings and
+    # subsequent words in the decoder.
+    # The actual data shape of trg_slf_attn_bias is:
+    # [batch_size, n_head, max_trg_len_in_batch, max_trg_len_in_batch]
+    "trg_slf_attn_bias": [(batch_size, ModelHyperParams.n_head, seq_len,
+                           seq_len), "float32"],
+    # This input is used to remove attention weights on paddings of the source
+    # input in the encoder-decoder attention.
+    # The actual data shape of trg_src_attn_bias is:
+    # [batch_size, n_head, max_trg_len_in_batch, max_src_len_in_batch]
+    "trg_src_attn_bias": [(batch_size, ModelHyperParams.n_head, seq_len,
+                           seq_len), "float32"],
+    # This input is used in independent decoder program for inference.
+    # The actual data shape of enc_output is:
+    # [batch_size, max_src_len_in_batch, d_model]
+    "enc_output": [(batch_size, seq_len, ModelHyperParams.d_model), "float32"],
+    # The actual data shape of label_word is:
+    # [batch_size * max_trg_len_in_batch, 1]
+    "lbl_word": [(batch_size * seq_len, 1), "int64"],
+    # This input is used to mask out the loss of paddding tokens.
+    # The actual data shape of label_weight is:
+    # [batch_size * max_trg_len_in_batch, 1]
+    "lbl_weight": [(batch_size * seq_len, 1), "float32"],
+    # This input is used in beam-search decoder.
+    "init_score": [(batch_size, 1), "float32", 2],
+    # This input is used in beam-search decoder for the first gather
+    # (cell states updation)
+    "init_idx": [(batch_size, ), "int32"],
+}
+
+# Names of word embedding table which might be reused for weight sharing.
+word_emb_param_names = (
+    "src_word_emb_table",
+    "trg_word_emb_table", )
+# Names of position encoding table which will be initialized externally.
+pos_enc_param_names = (
+    "src_pos_enc_table",
+    "trg_pos_enc_table", )
+# separated inputs for different usages.
+encoder_data_input_fields = (
+    "src_word",
+    "src_pos",
+    "src_slf_attn_bias", )
+decoder_data_input_fields = (
+    "trg_word",
+    "trg_pos",
+    "trg_slf_attn_bias",
+    "trg_src_attn_bias",
+    "enc_output", )
+label_data_input_fields = (
+    "lbl_word",
+    "lbl_weight", )
+# In fast decoder, trg_pos (only containing the current time step) is generated
+# by ops and trg_slf_attn_bias is not needed.
+fast_decoder_data_input_fields = (
+    "trg_word",
+    "init_score",
+    "init_idx",
+    "trg_src_attn_bias", )
diff --git a/PaddleNLP/unarchived/neural_machine_translation/transformer/gen_data.sh b/PaddleNLP/unarchived/neural_machine_translation/transformer/gen_data.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e00ae05d9c5cc59b7b401428f6e1252397debfe9
--- /dev/null
+++ b/PaddleNLP/unarchived/neural_machine_translation/transformer/gen_data.sh
@@ -0,0 +1,220 @@
+#! /usr/bin/env bash
+
+set -e
+
+OUTPUT_DIR=$PWD/gen_data
+
+###############################################################################
+# change these variables for other WMT data
+###############################################################################
+OUTPUT_DIR_DATA="${OUTPUT_DIR}/wmt16_ende_data"
+OUTPUT_DIR_BPE_DATA="${OUTPUT_DIR}/wmt16_ende_data_bpe"
+LANG1="en"
+LANG2="de"
+# each of TRAIN_DATA: data_url data_file_lang1 data_file_lang2
+TRAIN_DATA=(
+'http://www.statmt.org/europarl/v7/de-en.tgz'
+'europarl-v7.de-en.en' 'europarl-v7.de-en.de'
+'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz'
+'commoncrawl.de-en.en' 'commoncrawl.de-en.de'
+'http://data.statmt.org/wmt16/translation-task/training-parallel-nc-v11.tgz'
+'news-commentary-v11.de-en.en' 'news-commentary-v11.de-en.de'
+)
+# each of DEV_TEST_DATA: data_url data_file_lang1 data_file_lang2
+DEV_TEST_DATA=(
+'http://data.statmt.org/wmt16/translation-task/dev.tgz'
+'newstest201[45]-deen-ref.en.sgm' 'newstest201[45]-deen-src.de.sgm'
+'http://data.statmt.org/wmt16/translation-task/test.tgz'
+'newstest2016-deen-ref.en.sgm' 'newstest2016-deen-src.de.sgm'
+)
+###############################################################################
+
+###############################################################################
+# change these variables for other WMT data
+###############################################################################
+# OUTPUT_DIR_DATA="${OUTPUT_DIR}/wmt14_enfr_data"
+# OUTPUT_DIR_BPE_DATA="${OUTPUT_DIR}/wmt14_enfr_data_bpe"
+# LANG1="en"
+# LANG2="fr"
+# # each of TRAIN_DATA: ata_url data_tgz data_file 
+# TRAIN_DATA=(
+# 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz'
+# 'commoncrawl.fr-en.en' 'commoncrawl.fr-en.fr'
+# 'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz'
+# 'training/europarl-v7.fr-en.en' 'training/europarl-v7.fr-en.fr'
+# 'http://www.statmt.org/wmt14/training-parallel-nc-v9.tgz'
+# 'training/news-commentary-v9.fr-en.en' 'training/news-commentary-v9.fr-en.fr'
+# 'http://www.statmt.org/wmt10/training-giga-fren.tar'
+# 'giga-fren.release2.fixed.en.*' 'giga-fren.release2.fixed.fr.*'
+# 'http://www.statmt.org/wmt13/training-parallel-un.tgz'
+# 'un/undoc.2000.fr-en.en' 'un/undoc.2000.fr-en.fr'
+# )
+# # each of DEV_TEST_DATA: data_url data_tgz data_file_lang1 data_file_lang2
+# DEV_TEST_DATA=(
+# 'http://data.statmt.org/wmt16/translation-task/dev.tgz'
+# '.*/newstest201[45]-fren-ref.en.sgm' '.*/newstest201[45]-fren-src.fr.sgm'
+# 'http://data.statmt.org/wmt16/translation-task/test.tgz'
+# '.*/newstest2016-fren-ref.en.sgm' '.*/newstest2016-fren-src.fr.sgm'
+# )
+###############################################################################
+
+mkdir -p $OUTPUT_DIR_DATA $OUTPUT_DIR_BPE_DATA
+
+# Extract training data
+for ((i=0;i<${#TRAIN_DATA[@]};i+=3)); do
+  data_url=${TRAIN_DATA[i]}
+  data_tgz=${data_url##*/}  # training-parallel-commoncrawl.tgz
+  data=${data_tgz%.*}  # training-parallel-commoncrawl
+  data_lang1=${TRAIN_DATA[i+1]}
+  data_lang2=${TRAIN_DATA[i+2]}
+  if [ ! -e ${OUTPUT_DIR_DATA}/${data_tgz} ]; then
+    echo "Download "${data_url}
+    wget -O ${OUTPUT_DIR_DATA}/${data_tgz} ${data_url}
+  fi
+
+  if [ ! -d ${OUTPUT_DIR_DATA}/${data} ]; then
+    echo "Extract "${data_tgz}
+    mkdir -p ${OUTPUT_DIR_DATA}/${data}
+    tar_type=${data_tgz:0-3}
+    if [ ${tar_type} == "tar" ]; then
+      tar -xvf ${OUTPUT_DIR_DATA}/${data_tgz} -C ${OUTPUT_DIR_DATA}/${data}
+    else
+      tar -xvzf ${OUTPUT_DIR_DATA}/${data_tgz} -C ${OUTPUT_DIR_DATA}/${data}
+    fi
+  fi
+  # concatenate all training data
+  for data_lang in $data_lang1 $data_lang2; do
+    for f in `find ${OUTPUT_DIR_DATA}/${data} -regex ".*/${data_lang}"`; do
+      data_dir=`dirname $f`
+      data_file=`basename $f`
+      f_base=${f%.*}
+      f_ext=${f##*.}
+      if [ $f_ext == "gz" ]; then
+        gunzip $f
+        l=${f_base##*.}
+        f_base=${f_base%.*}
+      else
+        l=${f_ext}
+      fi
+      
+      if [ $i -eq 0 ]; then
+        cat ${f_base}.$l > ${OUTPUT_DIR_DATA}/train.$l
+      else
+        cat ${f_base}.$l >> ${OUTPUT_DIR_DATA}/train.$l
+      fi
+    done
+  done
+done
+
+# Clone mosesdecoder
+if [ ! -d ${OUTPUT_DIR}/mosesdecoder ]; then
+  echo "Cloning moses for data processing"
+  git clone https://github.com/moses-smt/mosesdecoder.git ${OUTPUT_DIR}/mosesdecoder
+fi
+
+# Extract develop and test data
+dev_test_data=""
+for ((i=0;i<${#DEV_TEST_DATA[@]};i+=3)); do
+  data_url=${DEV_TEST_DATA[i]}
+  data_tgz=${data_url##*/}  # training-parallel-commoncrawl.tgz
+  data=${data_tgz%.*}  # training-parallel-commoncrawl
+  data_lang1=${DEV_TEST_DATA[i+1]}
+  data_lang2=${DEV_TEST_DATA[i+2]}
+  if [ ! -e ${OUTPUT_DIR_DATA}/${data_tgz} ]; then
+    echo "Download "${data_url}
+    wget -O ${OUTPUT_DIR_DATA}/${data_tgz} ${data_url}
+  fi
+
+  if [ ! -d ${OUTPUT_DIR_DATA}/${data} ]; then
+    echo "Extract "${data_tgz}
+    mkdir -p ${OUTPUT_DIR_DATA}/${data}
+    tar_type=${data_tgz:0-3}
+    if [ ${tar_type} == "tar" ]; then
+      tar -xvf ${OUTPUT_DIR_DATA}/${data_tgz} -C ${OUTPUT_DIR_DATA}/${data}
+    else
+      tar -xvzf ${OUTPUT_DIR_DATA}/${data_tgz} -C ${OUTPUT_DIR_DATA}/${data}
+    fi
+  fi
+
+  for data_lang in $data_lang1 $data_lang2; do
+    for f in `find ${OUTPUT_DIR_DATA}/${data} -regex ".*/${data_lang}"`; do
+      data_dir=`dirname $f`
+      data_file=`basename $f`
+      data_out=`echo ${data_file} | cut -d '-' -f 1`  # newstest2016
+      l=`echo ${data_file} | cut -d '.' -f 2`  # en
+      dev_test_data="${dev_test_data}\|${data_out}"  # to make regexp
+      if [ ! -e ${OUTPUT_DIR_DATA}/${data_out}.$l ]; then
+        ${OUTPUT_DIR}/mosesdecoder/scripts/ems/support/input-from-sgm.perl \
+          < $f > ${OUTPUT_DIR_DATA}/${data_out}.$l
+      fi
+    done
+  done
+done
+
+# Tokenize data
+for l in ${LANG1} ${LANG2}; do
+  for f in `ls ${OUTPUT_DIR_DATA}/*.$l | grep "\(train${dev_test_data}\)\.$l$"`; do
+    f_base=${f%.*}  # dir/train dir/newstest2016
+    f_out=$f_base.tok.$l
+    if [ ! -e $f_out ]; then
+      echo "Tokenize "$f
+      ${OUTPUT_DIR}/mosesdecoder/scripts/tokenizer/tokenizer.perl -q -l $l -threads 8 < $f > $f_out
+    fi
+  done
+done
+
+# Clean data
+for f in ${OUTPUT_DIR_DATA}/train.${LANG1} ${OUTPUT_DIR_DATA}/train.tok.${LANG1}; do
+  f_base=${f%.*}  # dir/train dir/train.tok
+  f_out=${f_base}.clean
+  if [ ! -e $f_out.${LANG1} ] && [ ! -e $f_out.${LANG2} ]; then
+    echo "Clean "${f_base}
+    ${OUTPUT_DIR}/mosesdecoder/scripts/training/clean-corpus-n.perl $f_base ${LANG1} ${LANG2} ${f_out} 1 80
+  fi
+done
+
+# Clone subword-nmt and generate BPE data
+if [ ! -d ${OUTPUT_DIR}/subword-nmt ]; then
+  git clone https://github.com/rsennrich/subword-nmt.git ${OUTPUT_DIR}/subword-nmt
+fi
+
+# Generate BPE data and vocabulary
+for num_operations in 32000; do
+  if [ ! -e ${OUTPUT_DIR_BPE_DATA}/bpe.${num_operations} ]; then
+    echo "Learn BPE with ${num_operations} merge operations"
+    cat ${OUTPUT_DIR_DATA}/train.tok.clean.${LANG1} ${OUTPUT_DIR_DATA}/train.tok.clean.${LANG2} | \
+      ${OUTPUT_DIR}/subword-nmt/learn_bpe.py -s $num_operations > ${OUTPUT_DIR_BPE_DATA}/bpe.${num_operations}
+  fi
+
+  for l in ${LANG1} ${LANG2}; do
+    for f in `ls ${OUTPUT_DIR_DATA}/*.$l | grep "\(train${dev_test_data}\)\.tok\(\.clean\)\?\.$l$"`; do
+      f_base=${f%.*}  # dir/train.tok dir/train.tok.clean dir/newstest2016.tok
+      f_base=${f_base##*/}  # train.tok train.tok.clean newstest2016.tok
+      f_out=${OUTPUT_DIR_BPE_DATA}/${f_base}.bpe.${num_operations}.$l
+      if [ ! -e $f_out ]; then
+        echo "Apply BPE to "$f
+        ${OUTPUT_DIR}/subword-nmt/apply_bpe.py -c ${OUTPUT_DIR_BPE_DATA}/bpe.${num_operations} < $f > $f_out
+      fi
+    done
+  done
+
+  if [ ! -e ${OUTPUT_DIR_BPE_DATA}/vocab.bpe.${num_operations} ]; then
+    echo "Create vocabulary for BPE data"
+    cat ${OUTPUT_DIR_BPE_DATA}/train.tok.clean.bpe.${num_operations}.${LANG1} ${OUTPUT_DIR_BPE_DATA}/train.tok.clean.bpe.${num_operations}.${LANG2} | \
+      ${OUTPUT_DIR}/subword-nmt/get_vocab.py | cut -f1 -d ' ' > ${OUTPUT_DIR_BPE_DATA}/vocab.bpe.${num_operations}
+  fi
+done
+
+# Adapt to the reader
+for f in ${OUTPUT_DIR_BPE_DATA}/*.bpe.${num_operations}.${LANG1}; do
+  f_base=${f%.*}  # dir/train.tok.clean.bpe.32000 dir/newstest2016.tok.bpe.32000
+  f_out=${f_base}.${LANG1}-${LANG2}
+  if [ ! -e $f_out ]; then
+    paste -d '\t' $f_base.${LANG1} $f_base.${LANG2} > $f_out
+  fi
+done
+if [ ! -e ${OUTPUT_DIR_BPE_DATA}/vocab_all.bpe.${num_operations} ]; then
+  sed '1i\\n\n' ${OUTPUT_DIR_BPE_DATA}/vocab.bpe.${num_operations} > ${OUTPUT_DIR_BPE_DATA}/vocab_all.bpe.${num_operations}
+fi
+
+echo "All done."
diff --git a/PaddleNLP/neural_machine_translation/transformer/images/attention_formula.png b/PaddleNLP/unarchived/neural_machine_translation/transformer/images/attention_formula.png
similarity index 100%
rename from PaddleNLP/neural_machine_translation/transformer/images/attention_formula.png
rename to PaddleNLP/unarchived/neural_machine_translation/transformer/images/attention_formula.png
diff --git a/PaddleNLP/unarchived/neural_machine_translation/transformer/images/multi_head_attention.png b/PaddleNLP/unarchived/neural_machine_translation/transformer/images/multi_head_attention.png
new file mode 100644
index 0000000000000000000000000000000000000000..427fb6b32aaeb7013066a167aab4fb97c024c2d6
Binary files /dev/null and b/PaddleNLP/unarchived/neural_machine_translation/transformer/images/multi_head_attention.png differ
diff --git a/PaddleNLP/unarchived/neural_machine_translation/transformer/images/transformer_network.png b/PaddleNLP/unarchived/neural_machine_translation/transformer/images/transformer_network.png
new file mode 100644
index 0000000000000000000000000000000000000000..34be0e5c7e2b08f858683d86353db5e81049c7ca
Binary files /dev/null and b/PaddleNLP/unarchived/neural_machine_translation/transformer/images/transformer_network.png differ
diff --git a/PaddleNLP/unarchived/neural_machine_translation/transformer/infer.py b/PaddleNLP/unarchived/neural_machine_translation/transformer/infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..96b8e0a14fcc225e11e63a7604c8bf8e6db8e45e
--- /dev/null
+++ b/PaddleNLP/unarchived/neural_machine_translation/transformer/infer.py
@@ -0,0 +1,324 @@
+import argparse
+import ast
+import multiprocessing
+import numpy as np
+import os
+from functools import partial
+
+import paddle
+import paddle.fluid as fluid
+
+import model
+import reader
+from config import *
+from model import wrap_encoder as encoder
+from model import wrap_decoder as decoder
+from model import fast_decode as fast_decoder
+from train import pad_batch_data, prepare_data_generator
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("Training for Transformer.")
+    parser.add_argument(
+        "--src_vocab_fpath",
+        type=str,
+        required=True,
+        help="The path of vocabulary file of source language.")
+    parser.add_argument(
+        "--trg_vocab_fpath",
+        type=str,
+        required=True,
+        help="The path of vocabulary file of target language.")
+    parser.add_argument(
+        "--test_file_pattern",
+        type=str,
+        required=True,
+        help="The pattern to match test data files.")
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=50,
+        help="The number of examples in one run for sequence generation.")
+    parser.add_argument(
+        "--pool_size",
+        type=int,
+        default=10000,
+        help="The buffer size to pool data.")
+    parser.add_argument(
+        "--special_token",
+        type=str,
+        default=["", "", ""],
+        nargs=3,
+        help="The ,  and  tokens in the dictionary.")
+    parser.add_argument(
+        "--token_delimiter",
+        type=lambda x: str(x.encode().decode("unicode-escape")),
+        default=" ",
+        help="The delimiter used to split tokens in source or target sentences. "
+        "For EN-DE BPE data we provided, use spaces as token delimiter. ")
+    parser.add_argument(
+        "--use_mem_opt",
+        type=ast.literal_eval,
+        default=True,
+        help="The flag indicating whether to use memory optimization.")
+    parser.add_argument(
+        "--use_py_reader",
+        type=ast.literal_eval,
+        default=True,
+        help="The flag indicating whether to use py_reader.")
+    parser.add_argument(
+        "--use_parallel_exe",
+        type=ast.literal_eval,
+        default=False,
+        help="The flag indicating whether to use ParallelExecutor.")
+    parser.add_argument(
+        'opts',
+        help='See config.py for all options',
+        default=None,
+        nargs=argparse.REMAINDER)
+    args = parser.parse_args()
+    # Append args related to dict
+    src_dict = reader.DataReader.load_dict(args.src_vocab_fpath)
+    trg_dict = reader.DataReader.load_dict(args.trg_vocab_fpath)
+    dict_args = [
+        "src_vocab_size", str(len(src_dict)), "trg_vocab_size",
+        str(len(trg_dict)), "bos_idx", str(src_dict[args.special_token[0]]),
+        "eos_idx", str(src_dict[args.special_token[1]]), "unk_idx",
+        str(src_dict[args.special_token[2]])
+    ]
+    merge_cfg_from_list(args.opts + dict_args,
+                        [InferTaskConfig, ModelHyperParams])
+    return args
+
+
+def post_process_seq(seq,
+                     bos_idx=ModelHyperParams.bos_idx,
+                     eos_idx=ModelHyperParams.eos_idx,
+                     output_bos=InferTaskConfig.output_bos,
+                     output_eos=InferTaskConfig.output_eos):
+    """
+    Post-process the beam-search decoded sequence. Truncate from the first
+     and remove the  and  tokens currently.
+    """
+    eos_pos = len(seq) - 1
+    for i, idx in enumerate(seq):
+        if idx == eos_idx:
+            eos_pos = i
+            break
+    seq = [
+        idx for idx in seq[:eos_pos + 1]
+        if (output_bos or idx != bos_idx) and (output_eos or idx != eos_idx)
+    ]
+    return seq
+
+
+def prepare_batch_input(insts, data_input_names, src_pad_idx, bos_idx, n_head,
+                        d_model, place):
+    """
+    Put all padded data needed by beam search decoder into a dict.
+    """
+    src_word, src_pos, src_slf_attn_bias, src_max_len = pad_batch_data(
+        [inst[0] for inst in insts], src_pad_idx, n_head, is_target=False)
+    # start tokens
+    trg_word = np.asarray([[bos_idx]] * len(insts), dtype="int64")
+    trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :],
+                                [1, 1, 1, 1]).astype("float32")
+    trg_word = trg_word.reshape(-1, 1, 1)
+    src_word = src_word.reshape(-1, src_max_len, 1)
+    src_pos = src_pos.reshape(-1, src_max_len, 1)
+
+    def to_lodtensor(data, place, lod=None):
+        data_tensor = fluid.LoDTensor()
+        data_tensor.set(data, place)
+        if lod is not None:
+            data_tensor.set_lod(lod)
+        return data_tensor
+
+    # beamsearch_op must use tensors with lod
+    init_score = to_lodtensor(
+        np.zeros_like(
+            trg_word, dtype="float32").reshape(-1, 1),
+        place, [range(trg_word.shape[0] + 1)] * 2)
+    trg_word = to_lodtensor(trg_word, place, [range(trg_word.shape[0] + 1)] * 2)
+    init_idx = np.asarray(range(len(insts)), dtype="int32")
+
+    data_input_dict = dict(
+        zip(data_input_names, [
+            src_word, src_pos, src_slf_attn_bias, trg_word, init_score,
+            init_idx, trg_src_attn_bias
+        ]))
+    return data_input_dict
+
+
+def prepare_feed_dict_list(data_generator, count, place):
+    """
+    Prepare the list of feed dict for multi-devices.
+    """
+    feed_dict_list = []
+    if data_generator is not None:  # use_py_reader == False
+        data_input_names = encoder_data_input_fields + fast_decoder_data_input_fields
+        data = next(data_generator)
+        for idx, data_buffer in enumerate(data):
+            data_input_dict = prepare_batch_input(
+                data_buffer, data_input_names, ModelHyperParams.eos_idx,
+                ModelHyperParams.bos_idx, ModelHyperParams.n_head,
+                ModelHyperParams.d_model, place)
+            feed_dict_list.append(data_input_dict)
+    return feed_dict_list if len(feed_dict_list) == count else None
+
+
+def py_reader_provider_wrapper(data_reader, place):
+    """
+    Data provider needed by fluid.layers.py_reader.
+    """
+
+    def py_reader_provider():
+        data_input_names = encoder_data_input_fields + fast_decoder_data_input_fields
+        for batch_id, data in enumerate(data_reader()):
+            data_input_dict = prepare_batch_input(
+                data, data_input_names, ModelHyperParams.eos_idx,
+                ModelHyperParams.bos_idx, ModelHyperParams.n_head,
+                ModelHyperParams.d_model, place)
+            yield [data_input_dict[item] for item in data_input_names]
+
+    return py_reader_provider
+
+
+def fast_infer(args):
+    """
+    Inference by beam search decoder based solely on Fluid operators.
+    """
+    out_ids, out_scores, pyreader = fast_decoder(
+        ModelHyperParams.src_vocab_size,
+        ModelHyperParams.trg_vocab_size,
+        ModelHyperParams.max_length + 1,
+        ModelHyperParams.n_layer,
+        ModelHyperParams.n_head,
+        ModelHyperParams.d_key,
+        ModelHyperParams.d_value,
+        ModelHyperParams.d_model,
+        ModelHyperParams.d_inner_hid,
+        ModelHyperParams.prepostprocess_dropout,
+        ModelHyperParams.attention_dropout,
+        ModelHyperParams.relu_dropout,
+        ModelHyperParams.preprocess_cmd,
+        ModelHyperParams.postprocess_cmd,
+        ModelHyperParams.weight_sharing,
+        InferTaskConfig.beam_size,
+        InferTaskConfig.max_out_len,
+        ModelHyperParams.eos_idx,
+        use_py_reader=args.use_py_reader)
+
+    # This is used here to set dropout to the test mode.
+    infer_program = fluid.default_main_program().clone(for_test=True)
+
+    if args.use_mem_opt:
+        fluid.memory_optimize(infer_program)
+
+    if InferTaskConfig.use_gpu:
+        place = fluid.CUDAPlace(0)
+        dev_count = fluid.core.get_cuda_device_count()
+    else:
+        place = fluid.CPUPlace()
+        dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+    exe = fluid.Executor(place)
+    exe.run(fluid.default_startup_program())
+
+    fluid.io.load_vars(
+        exe,
+        InferTaskConfig.model_path,
+        vars=[
+            var for var in infer_program.list_vars()
+            if isinstance(var, fluid.framework.Parameter)
+        ])
+
+    exec_strategy = fluid.ExecutionStrategy()
+    # For faster executor
+    exec_strategy.use_experimental_executor = True
+    exec_strategy.num_threads = 1
+    build_strategy = fluid.BuildStrategy()
+    infer_exe = fluid.ParallelExecutor(
+        use_cuda=TrainTaskConfig.use_gpu,
+        main_program=infer_program,
+        build_strategy=build_strategy,
+        exec_strategy=exec_strategy)
+
+    # data reader settings for inference
+    args.train_file_pattern = args.test_file_pattern
+    args.use_token_batch = False
+    args.sort_type = reader.SortType.NONE
+    args.shuffle = False
+    args.shuffle_batch = False
+    test_data = prepare_data_generator(
+        args,
+        is_test=False,
+        count=dev_count,
+        pyreader=pyreader,
+        py_reader_provider_wrapper=py_reader_provider_wrapper,
+        place=place)
+    if args.use_py_reader:
+        pyreader.start()
+        data_generator = None
+    else:
+        data_generator = test_data()
+    trg_idx2word = reader.DataReader.load_dict(
+        dict_path=args.trg_vocab_fpath, reverse=True)
+
+    while True:
+        try:
+            feed_dict_list = prepare_feed_dict_list(data_generator, dev_count,
+                                                    place)
+            if args.use_parallel_exe:
+                seq_ids, seq_scores = infer_exe.run(
+                    fetch_list=[out_ids.name, out_scores.name],
+                    feed=feed_dict_list,
+                    return_numpy=False)
+            else:
+                seq_ids, seq_scores = exe.run(
+                    program=infer_program,
+                    fetch_list=[out_ids.name, out_scores.name],
+                    feed=feed_dict_list[0]
+                    if feed_dict_list is not None else None,
+                    return_numpy=False,
+                    use_program_cache=True)
+            seq_ids_list, seq_scores_list = [
+                seq_ids
+            ], [seq_scores] if isinstance(
+                seq_ids, paddle.fluid.LoDTensor) else (seq_ids, seq_scores)
+            for seq_ids, seq_scores in zip(seq_ids_list, seq_scores_list):
+                # How to parse the results:
+                #   Suppose the lod of seq_ids is:
+                #     [[0, 3, 6], [0, 12, 24, 40, 54, 67, 82]]
+                #   then from lod[0]:
+                #     there are 2 source sentences, beam width is 3.
+                #   from lod[1]:
+                #     the first source sentence has 3 hyps; the lengths are 12, 12, 16
+                #     the second source sentence has 3 hyps; the lengths are 14, 13, 15
+                hyps = [[] for i in range(len(seq_ids.lod()[0]) - 1)]
+                scores = [[] for i in range(len(seq_scores.lod()[0]) - 1)]
+                for i in range(len(seq_ids.lod()[0]) -
+                               1):  # for each source sentence
+                    start = seq_ids.lod()[0][i]
+                    end = seq_ids.lod()[0][i + 1]
+                    for j in range(end - start):  # for each candidate
+                        sub_start = seq_ids.lod()[1][start + j]
+                        sub_end = seq_ids.lod()[1][start + j + 1]
+                        hyps[i].append(" ".join([
+                            trg_idx2word[idx]
+                            for idx in post_process_seq(
+                                np.array(seq_ids)[sub_start:sub_end])
+                        ]))
+                        scores[i].append(np.array(seq_scores)[sub_end - 1])
+                        print(hyps[i][-1])
+                        if len(hyps[i]) >= InferTaskConfig.n_best:
+                            break
+        except (StopIteration, fluid.core.EOFException):
+            # The data pass is over.
+            if args.use_py_reader:
+                pyreader.reset()
+            break
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    fast_infer(args)
diff --git a/PaddleNLP/neural_machine_translation/transformer/local_dist.sh b/PaddleNLP/unarchived/neural_machine_translation/transformer/local_dist.sh
similarity index 100%
rename from PaddleNLP/neural_machine_translation/transformer/local_dist.sh
rename to PaddleNLP/unarchived/neural_machine_translation/transformer/local_dist.sh
diff --git a/PaddleNLP/neural_machine_translation/transformer/model.py b/PaddleNLP/unarchived/neural_machine_translation/transformer/model.py
similarity index 100%
rename from PaddleNLP/neural_machine_translation/transformer/model.py
rename to PaddleNLP/unarchived/neural_machine_translation/transformer/model.py
diff --git a/PaddleNLP/neural_machine_translation/transformer/optim.py b/PaddleNLP/unarchived/neural_machine_translation/transformer/optim.py
similarity index 100%
rename from PaddleNLP/neural_machine_translation/transformer/optim.py
rename to PaddleNLP/unarchived/neural_machine_translation/transformer/optim.py
diff --git a/PaddleNLP/neural_machine_translation/transformer/profile.py b/PaddleNLP/unarchived/neural_machine_translation/transformer/profile.py
similarity index 100%
rename from PaddleNLP/neural_machine_translation/transformer/profile.py
rename to PaddleNLP/unarchived/neural_machine_translation/transformer/profile.py
diff --git a/PaddleNLP/unarchived/neural_machine_translation/transformer/reader.py b/PaddleNLP/unarchived/neural_machine_translation/transformer/reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..10f44ade6768edf9536cad27bcbcd0b08d16e668
--- /dev/null
+++ b/PaddleNLP/unarchived/neural_machine_translation/transformer/reader.py
@@ -0,0 +1,335 @@
+import glob
+import six
+import os
+import tarfile
+
+import numpy as np
+
+
+class SortType(object):
+    GLOBAL = 'global'
+    POOL = 'pool'
+    NONE = "none"
+
+
+class Converter(object):
+    def __init__(self, vocab, beg, end, unk, delimiter, add_beg):
+        self._vocab = vocab
+        self._beg = beg
+        self._end = end
+        self._unk = unk
+        self._delimiter = delimiter
+        self._add_beg = add_beg
+
+    def __call__(self, sentence):
+        return ([self._beg] if self._add_beg else []) + [
+            self._vocab.get(w, self._unk)
+            for w in sentence.split(self._delimiter)
+        ] + [self._end]
+
+
+class ComposedConverter(object):
+    def __init__(self, converters):
+        self._converters = converters
+
+    def __call__(self, parallel_sentence):
+        return [
+            self._converters[i](parallel_sentence[i])
+            for i in range(len(self._converters))
+        ]
+
+
+class SentenceBatchCreator(object):
+    def __init__(self, batch_size):
+        self.batch = []
+        self._batch_size = batch_size
+
+    def append(self, info):
+        self.batch.append(info)
+        if len(self.batch) == self._batch_size:
+            tmp = self.batch
+            self.batch = []
+            return tmp
+
+
+class TokenBatchCreator(object):
+    def __init__(self, batch_size):
+        self.batch = []
+        self.max_len = -1
+        self._batch_size = batch_size
+
+    def append(self, info):
+        cur_len = info.max_len
+        max_len = max(self.max_len, cur_len)
+        if max_len * (len(self.batch) + 1) > self._batch_size:
+            result = self.batch
+            self.batch = [info]
+            self.max_len = cur_len
+            return result
+        else:
+            self.max_len = max_len
+            self.batch.append(info)
+
+
+class SampleInfo(object):
+    def __init__(self, i, max_len, min_len):
+        self.i = i
+        self.min_len = min_len
+        self.max_len = max_len
+
+
+class MinMaxFilter(object):
+    def __init__(self, max_len, min_len, underlying_creator):
+        self._min_len = min_len
+        self._max_len = max_len
+        self._creator = underlying_creator
+
+    def append(self, info):
+        if info.max_len > self._max_len or info.min_len < self._min_len:
+            return
+        else:
+            return self._creator.append(info)
+
+    @property
+    def batch(self):
+        return self._creator.batch
+
+
+class DataReader(object):
+    """
+    The data reader loads all data from files and produces batches of data
+    in the way corresponding to settings.
+
+    An example of returning a generator producing data batches whose data
+    is shuffled in each pass and sorted in each pool:
+
+    ```
+    train_data = DataReader(
+        src_vocab_fpath='data/src_vocab_file',
+        trg_vocab_fpath='data/trg_vocab_file',
+        fpattern='data/part-*',
+        use_token_batch=True,
+        batch_size=2000,
+        pool_size=10000,
+        sort_type=SortType.POOL,
+        shuffle=True,
+        shuffle_batch=True,
+        start_mark='',
+        end_mark='',
+        unk_mark='',
+        clip_last_batch=False).batch_generator
+    ```
+
+    :param src_vocab_fpath: The path of vocabulary file of source language.
+    :type src_vocab_fpath: basestring
+    :param trg_vocab_fpath: The path of vocabulary file of target language.
+    :type trg_vocab_fpath: basestring
+    :param fpattern: The pattern to match data files.
+    :type fpattern: basestring
+    :param batch_size: The number of sequences contained in a mini-batch.
+        or the maximum number of tokens (include paddings) contained in a
+        mini-batch.
+    :type batch_size: int
+    :param pool_size: The size of pool buffer.
+    :type pool_size: int
+    :param sort_type: The grain to sort by length: 'global' for all
+        instances; 'pool' for instances in pool; 'none' for no sort.
+    :type sort_type: basestring
+    :param clip_last_batch: Whether to clip the last uncompleted batch.
+    :type clip_last_batch: bool
+    :param tar_fname: The data file in tar if fpattern matches a tar file.
+    :type tar_fname: basestring
+    :param min_length: The minimum length used to filt sequences.
+    :type min_length: int
+    :param max_length: The maximum length used to filt sequences.
+    :type max_length: int
+    :param shuffle: Whether to shuffle all instances.
+    :type shuffle: bool
+    :param shuffle_batch: Whether to shuffle the generated batches.
+    :type shuffle_batch: bool
+    :param use_token_batch: Whether to produce batch data according to
+        token number.
+    :type use_token_batch: bool
+    :param field_delimiter: The delimiter used to split source and target in
+        each line of data file.
+    :type field_delimiter: basestring
+    :param token_delimiter: The delimiter used to split tokens in source or
+        target sentences.
+    :type token_delimiter: basestring
+    :param start_mark: The token representing for the beginning of
+        sentences in dictionary.
+    :type start_mark: basestring
+    :param end_mark: The token representing for the end of sentences
+        in dictionary.
+    :type end_mark: basestring
+    :param unk_mark: The token representing for unknown word in dictionary.
+    :type unk_mark: basestring
+    :param seed: The seed for random.
+    :type seed: int
+    """
+
+    def __init__(self,
+                 src_vocab_fpath,
+                 trg_vocab_fpath,
+                 fpattern,
+                 batch_size,
+                 pool_size,
+                 sort_type=SortType.GLOBAL,
+                 clip_last_batch=True,
+                 tar_fname=None,
+                 min_length=0,
+                 max_length=100,
+                 shuffle=True,
+                 shuffle_batch=False,
+                 use_token_batch=False,
+                 field_delimiter="\t",
+                 token_delimiter=" ",
+                 start_mark="",
+                 end_mark="",
+                 unk_mark="",
+                 seed=0):
+        self._src_vocab = self.load_dict(src_vocab_fpath)
+        self._only_src = True
+        if trg_vocab_fpath is not None:
+            self._trg_vocab = self.load_dict(trg_vocab_fpath)
+            self._only_src = False
+        self._pool_size = pool_size
+        self._batch_size = batch_size
+        self._use_token_batch = use_token_batch
+        self._sort_type = sort_type
+        self._clip_last_batch = clip_last_batch
+        self._shuffle = shuffle
+        self._shuffle_batch = shuffle_batch
+        self._min_length = min_length
+        self._max_length = max_length
+        self._field_delimiter = field_delimiter
+        self._token_delimiter = token_delimiter
+        self.load_src_trg_ids(end_mark, fpattern, start_mark, tar_fname,
+                              unk_mark)
+        self._random = np.random
+        self._random.seed(seed)
+
+    def load_src_trg_ids(self, end_mark, fpattern, start_mark, tar_fname,
+                         unk_mark):
+        converters = [
+            Converter(
+                vocab=self._src_vocab,
+                beg=self._src_vocab[start_mark],
+                end=self._src_vocab[end_mark],
+                unk=self._src_vocab[unk_mark],
+                delimiter=self._token_delimiter,
+                add_beg=False)
+        ]
+        if not self._only_src:
+            converters.append(
+                Converter(
+                    vocab=self._trg_vocab,
+                    beg=self._trg_vocab[start_mark],
+                    end=self._trg_vocab[end_mark],
+                    unk=self._trg_vocab[unk_mark],
+                    delimiter=self._token_delimiter,
+                    add_beg=True))
+
+        converters = ComposedConverter(converters)
+
+        self._src_seq_ids = []
+        self._trg_seq_ids = None if self._only_src else []
+        self._sample_infos = []
+
+        for i, line in enumerate(self._load_lines(fpattern, tar_fname)):
+            src_trg_ids = converters(line)
+            self._src_seq_ids.append(src_trg_ids[0])
+            lens = [len(src_trg_ids[0])]
+            if not self._only_src:
+                self._trg_seq_ids.append(src_trg_ids[1])
+                lens.append(len(src_trg_ids[1]))
+            self._sample_infos.append(SampleInfo(i, max(lens), min(lens)))
+
+    def _load_lines(self, fpattern, tar_fname):
+        fpaths = glob.glob(fpattern)
+
+        if len(fpaths) == 1 and tarfile.is_tarfile(fpaths[0]):
+            if tar_fname is None:
+                raise Exception("If tar file provided, please set tar_fname.")
+
+            f = tarfile.open(fpaths[0], "r")
+            for line in f.extractfile(tar_fname):
+                fields = line.strip("\n").split(self._field_delimiter)
+                if (not self._only_src and len(fields) == 2) or (
+                        self._only_src and len(fields) == 1):
+                    yield fields
+        else:
+            for fpath in fpaths:
+                if not os.path.isfile(fpath):
+                    raise IOError("Invalid file: %s" % fpath)
+
+                with open(fpath, "rb") as f:
+                    for line in f:
+                        if six.PY3:
+                            line = line.decode()
+                        fields = line.strip("\n").split(self._field_delimiter)
+                        if (not self._only_src and len(fields) == 2) or (
+                                self._only_src and len(fields) == 1):
+                            yield fields
+
+    @staticmethod
+    def load_dict(dict_path, reverse=False):
+        word_dict = {}
+        with open(dict_path, "rb") as fdict:
+            for idx, line in enumerate(fdict):
+                if six.PY3:
+                    line = line.decode()
+                if reverse:
+                    word_dict[idx] = line.strip("\n")
+                else:
+                    word_dict[line.strip("\n")] = idx
+        return word_dict
+
+    def batch_generator(self):
+        # global sort or global shuffle
+        if self._sort_type == SortType.GLOBAL:
+            infos = sorted(self._sample_infos, key=lambda x: x.max_len)
+        else:
+            if self._shuffle:
+                infos = self._sample_infos
+                self._random.shuffle(infos)
+            else:
+                infos = self._sample_infos
+
+            if self._sort_type == SortType.POOL:
+                reverse = True
+                for i in range(0, len(infos), self._pool_size):
+                    # to avoid placing short next to long sentences
+                    reverse = not reverse
+                    infos[i:i + self._pool_size] = sorted(
+                        infos[i:i + self._pool_size],
+                        key=lambda x: x.max_len,
+                        reverse=reverse)
+
+        # concat batch
+        batches = []
+        batch_creator = TokenBatchCreator(
+            self._batch_size
+        ) if self._use_token_batch else SentenceBatchCreator(self._batch_size)
+        batch_creator = MinMaxFilter(self._max_length, self._min_length,
+                                     batch_creator)
+
+        for info in infos:
+            batch = batch_creator.append(info)
+            if batch is not None:
+                batches.append(batch)
+
+        if not self._clip_last_batch and len(batch_creator.batch) != 0:
+            batches.append(batch_creator.batch)
+
+        if self._shuffle_batch:
+            self._random.shuffle(batches)
+
+        for batch in batches:
+            batch_ids = [info.i for info in batch]
+
+            if self._only_src:
+                yield [[self._src_seq_ids[idx]] for idx in batch_ids]
+            else:
+                yield [(self._src_seq_ids[idx], self._trg_seq_ids[idx][:-1],
+                        self._trg_seq_ids[idx][1:]) for idx in batch_ids]
diff --git a/PaddleNLP/unarchived/neural_machine_translation/transformer/train.py b/PaddleNLP/unarchived/neural_machine_translation/transformer/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..4313f8b441ee194935c7c47abc52271589c7765d
--- /dev/null
+++ b/PaddleNLP/unarchived/neural_machine_translation/transformer/train.py
@@ -0,0 +1,771 @@
+import argparse
+import ast
+import copy
+import logging
+import multiprocessing
+import os
+import six
+import sys
+import time
+
+import numpy as np
+import paddle.fluid as fluid
+
+import reader
+from config import *
+from model import transformer, position_encoding_init
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("Training for Transformer.")
+    parser.add_argument(
+        "--src_vocab_fpath",
+        type=str,
+        required=True,
+        help="The path of vocabulary file of source language.")
+    parser.add_argument(
+        "--trg_vocab_fpath",
+        type=str,
+        required=True,
+        help="The path of vocabulary file of target language.")
+    parser.add_argument(
+        "--train_file_pattern",
+        type=str,
+        required=True,
+        help="The pattern to match training data files.")
+    parser.add_argument(
+        "--val_file_pattern",
+        type=str,
+        help="The pattern to match validation data files.")
+    parser.add_argument(
+        "--use_token_batch",
+        type=ast.literal_eval,
+        default=True,
+        help="The flag indicating whether to "
+        "produce batch data according to token number.")
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=4096,
+        help="The number of sequences contained in a mini-batch, or the maximum "
+        "number of tokens (include paddings) contained in a mini-batch. Note "
+        "that this represents the number on single device and the actual batch "
+        "size for multi-devices will multiply the device number.")
+    parser.add_argument(
+        "--pool_size",
+        type=int,
+        default=200000,
+        help="The buffer size to pool data.")
+    parser.add_argument(
+        "--sort_type",
+        default="pool",
+        choices=("global", "pool", "none"),
+        help="The grain to sort by length: global for all instances; pool for "
+        "instances in pool; none for no sort.")
+    parser.add_argument(
+        "--shuffle",
+        type=ast.literal_eval,
+        default=True,
+        help="The flag indicating whether to shuffle instances in each pass.")
+    parser.add_argument(
+        "--shuffle_batch",
+        type=ast.literal_eval,
+        default=True,
+        help="The flag indicating whether to shuffle the data batches.")
+    parser.add_argument(
+        "--special_token",
+        type=str,
+        default=["", "", ""],
+        nargs=3,
+        help="The ,  and  tokens in the dictionary.")
+    parser.add_argument(
+        "--token_delimiter",
+        type=lambda x: str(x.encode().decode("unicode-escape")),
+        default=" ",
+        help="The delimiter used to split tokens in source or target sentences. "
+        "For EN-DE BPE data we provided, use spaces as token delimiter. ")
+    parser.add_argument(
+        'opts',
+        help='See config.py for all options',
+        default=None,
+        nargs=argparse.REMAINDER)
+    parser.add_argument(
+        '--local',
+        type=ast.literal_eval,
+        default=True,
+        help='Whether to run as local mode.')
+    parser.add_argument(
+        '--device',
+        type=str,
+        default='GPU',
+        choices=['CPU', 'GPU'],
+        help="The device type.")
+    parser.add_argument(
+        '--update_method',
+        choices=("pserver", "nccl2"),
+        default="pserver",
+        help='Update method.')
+    parser.add_argument(
+        '--sync', type=ast.literal_eval, default=True, help="sync mode.")
+    parser.add_argument(
+        "--enable_ce",
+        type=ast.literal_eval,
+        default=False,
+        help="The flag indicating whether to run the task "
+        "for continuous evaluation.")
+    parser.add_argument(
+        "--use_mem_opt",
+        type=ast.literal_eval,
+        default=True,
+        help="The flag indicating whether to use memory optimization.")
+    parser.add_argument(
+        "--use_py_reader",
+        type=ast.literal_eval,
+        default=True,
+        help="The flag indicating whether to use py_reader.")
+    parser.add_argument(
+        "--fetch_steps",
+        type=int,
+        default=100,
+        help="The frequency to fetch and print output.")
+
+    args = parser.parse_args()
+    # Append args related to dict
+    src_dict = reader.DataReader.load_dict(args.src_vocab_fpath)
+    trg_dict = reader.DataReader.load_dict(args.trg_vocab_fpath)
+    dict_args = [
+        "src_vocab_size", str(len(src_dict)), "trg_vocab_size",
+        str(len(trg_dict)), "bos_idx", str(src_dict[args.special_token[0]]),
+        "eos_idx", str(src_dict[args.special_token[1]]), "unk_idx",
+        str(src_dict[args.special_token[2]])
+    ]
+    merge_cfg_from_list(args.opts + dict_args,
+                        [TrainTaskConfig, ModelHyperParams])
+    return args
+
+
+def append_nccl2_prepare(startup_prog, trainer_id, worker_endpoints,
+                         current_endpoint):
+    assert (trainer_id >= 0 and len(worker_endpoints) > 1 and
+            current_endpoint in worker_endpoints)
+    eps = copy.deepcopy(worker_endpoints)
+    eps.remove(current_endpoint)
+    nccl_id_var = startup_prog.global_block().create_var(
+        name="NCCLID", persistable=True, type=fluid.core.VarDesc.VarType.RAW)
+    startup_prog.global_block().append_op(
+        type="gen_nccl_id",
+        inputs={},
+        outputs={"NCCLID": nccl_id_var},
+        attrs={
+            "endpoint": current_endpoint,
+            "endpoint_list": eps,
+            "trainer_id": trainer_id
+        })
+    return nccl_id_var
+
+
+def pad_batch_data(insts,
+                   pad_idx,
+                   n_head,
+                   is_target=False,
+                   is_label=False,
+                   return_attn_bias=True,
+                   return_max_len=True,
+                   return_num_token=False):
+    """
+    Pad the instances to the max sequence length in batch, and generate the
+    corresponding position data and attention bias.
+    """
+    return_list = []
+    max_len = max(len(inst) for inst in insts)
+    # Any token included in dict can be used to pad, since the paddings' loss
+    # will be masked out by weights and make no effect on parameter gradients.
+    inst_data = np.array(
+        [inst + [pad_idx] * (max_len - len(inst)) for inst in insts])
+    return_list += [inst_data.astype("int64").reshape([-1, 1])]
+    if is_label:  # label weight
+        inst_weight = np.array(
+            [[1.] * len(inst) + [0.] * (max_len - len(inst)) for inst in insts])
+        return_list += [inst_weight.astype("float32").reshape([-1, 1])]
+    else:  # position data
+        inst_pos = np.array([
+            list(range(0, len(inst))) + [0] * (max_len - len(inst))
+            for inst in insts
+        ])
+        return_list += [inst_pos.astype("int64").reshape([-1, 1])]
+    if return_attn_bias:
+        if is_target:
+            # This is used to avoid attention on paddings and subsequent
+            # words.
+            slf_attn_bias_data = np.ones((inst_data.shape[0], max_len, max_len))
+            slf_attn_bias_data = np.triu(slf_attn_bias_data,
+                                         1).reshape([-1, 1, max_len, max_len])
+            slf_attn_bias_data = np.tile(slf_attn_bias_data,
+                                         [1, n_head, 1, 1]) * [-1e9]
+        else:
+            # This is used to avoid attention on paddings.
+            slf_attn_bias_data = np.array([[0] * len(inst) + [-1e9] *
+                                           (max_len - len(inst))
+                                           for inst in insts])
+            slf_attn_bias_data = np.tile(
+                slf_attn_bias_data.reshape([-1, 1, 1, max_len]),
+                [1, n_head, max_len, 1])
+        return_list += [slf_attn_bias_data.astype("float32")]
+    if return_max_len:
+        return_list += [max_len]
+    if return_num_token:
+        num_token = 0
+        for inst in insts:
+            num_token += len(inst)
+        return_list += [num_token]
+    return return_list if len(return_list) > 1 else return_list[0]
+
+
+def prepare_batch_input(insts, data_input_names, src_pad_idx, trg_pad_idx,
+                        n_head, d_model):
+    """
+    Put all padded data needed by training into a dict.
+    """
+    src_word, src_pos, src_slf_attn_bias, src_max_len = pad_batch_data(
+        [inst[0] for inst in insts], src_pad_idx, n_head, is_target=False)
+    src_word = src_word.reshape(-1, src_max_len, 1)
+    src_pos = src_pos.reshape(-1, src_max_len, 1)
+    trg_word, trg_pos, trg_slf_attn_bias, trg_max_len = pad_batch_data(
+        [inst[1] for inst in insts], trg_pad_idx, n_head, is_target=True)
+    trg_word = trg_word.reshape(-1, trg_max_len, 1)
+    trg_pos = trg_pos.reshape(-1, trg_max_len, 1)
+
+    trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :],
+                                [1, 1, trg_max_len, 1]).astype("float32")
+
+    lbl_word, lbl_weight, num_token = pad_batch_data(
+        [inst[2] for inst in insts],
+        trg_pad_idx,
+        n_head,
+        is_target=False,
+        is_label=True,
+        return_attn_bias=False,
+        return_max_len=False,
+        return_num_token=True)
+
+    data_input_dict = dict(
+        zip(data_input_names, [
+            src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
+            trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
+        ]))
+
+    return data_input_dict, np.asarray([num_token], dtype="float32")
+
+
+def prepare_data_generator(args,
+                           is_test,
+                           count,
+                           pyreader,
+                           py_reader_provider_wrapper,
+                           place=None):
+    """
+    Data generator wrapper for DataReader. If use py_reader, set the data
+    provider for py_reader
+    """
+    data_reader = reader.DataReader(
+        fpattern=args.val_file_pattern if is_test else args.train_file_pattern,
+        src_vocab_fpath=args.src_vocab_fpath,
+        trg_vocab_fpath=args.trg_vocab_fpath,
+        token_delimiter=args.token_delimiter,
+        use_token_batch=args.use_token_batch,
+        batch_size=args.batch_size * (1 if args.use_token_batch else count),
+        pool_size=args.pool_size,
+        sort_type=args.sort_type,
+        shuffle=args.shuffle,
+        shuffle_batch=args.shuffle_batch,
+        start_mark=args.special_token[0],
+        end_mark=args.special_token[1],
+        unk_mark=args.special_token[2],
+        # count start and end tokens out
+        max_length=ModelHyperParams.max_length - 2,
+        clip_last_batch=False).batch_generator
+
+    def stack(data_reader, count, clip_last=True):
+        def __impl__():
+            res = []
+            for item in data_reader():
+                res.append(item)
+                if len(res) == count:
+                    yield res
+                    res = []
+            if len(res) == count:
+                yield res
+            elif not clip_last:
+                data = []
+                for item in res:
+                    data += item
+                if len(data) > count:
+                    inst_num_per_part = len(data) // count
+                    yield [
+                        data[inst_num_per_part * i:inst_num_per_part * (i + 1)]
+                        for i in range(count)
+                    ]
+
+        return __impl__
+
+    def split(data_reader, count):
+        def __impl__():
+            for item in data_reader():
+                inst_num_per_part = len(item) // count
+                for i in range(count):
+                    yield item[inst_num_per_part * i:inst_num_per_part * (i + 1
+                                                                          )]
+
+        return __impl__
+
+    if not args.use_token_batch:
+        # to make data on each device have similar token number
+        data_reader = split(data_reader, count)
+    if args.use_py_reader:
+        pyreader.decorate_tensor_provider(
+            py_reader_provider_wrapper(data_reader, place))
+        data_reader = None
+    else:  # Data generator for multi-devices
+        data_reader = stack(data_reader, count)
+    return data_reader
+
+
+def prepare_feed_dict_list(data_generator, init_flag, count):
+    """
+    Prepare the list of feed dict for multi-devices.
+    """
+    feed_dict_list = []
+    if data_generator is not None:  # use_py_reader == False
+        data_input_names = encoder_data_input_fields + \
+                    decoder_data_input_fields[:-1] + label_data_input_fields
+        data = next(data_generator)
+        for idx, data_buffer in enumerate(data):
+            data_input_dict, num_token = prepare_batch_input(
+                data_buffer, data_input_names, ModelHyperParams.eos_idx,
+                ModelHyperParams.eos_idx, ModelHyperParams.n_head,
+                ModelHyperParams.d_model)
+            feed_dict_list.append(data_input_dict)
+    if init_flag:
+        for idx in range(count):
+            pos_enc_tables = dict()
+            for pos_enc_param_name in pos_enc_param_names:
+                pos_enc_tables[pos_enc_param_name] = position_encoding_init(
+                    ModelHyperParams.max_length + 1, ModelHyperParams.d_model)
+            if len(feed_dict_list) <= idx:
+                feed_dict_list.append(pos_enc_tables)
+            else:
+                feed_dict_list[idx] = dict(
+                    list(pos_enc_tables.items()) + list(feed_dict_list[idx]
+                                                        .items()))
+
+    return feed_dict_list if len(feed_dict_list) == count else None
+
+
+def py_reader_provider_wrapper(data_reader, place):
+    """
+    Data provider needed by fluid.layers.py_reader.
+    """
+
+    def py_reader_provider():
+        data_input_names = encoder_data_input_fields + \
+                    decoder_data_input_fields[:-1] + label_data_input_fields
+        for batch_id, data in enumerate(data_reader()):
+            data_input_dict, num_token = prepare_batch_input(
+                data, data_input_names, ModelHyperParams.eos_idx,
+                ModelHyperParams.eos_idx, ModelHyperParams.n_head,
+                ModelHyperParams.d_model)
+            yield [data_input_dict[item] for item in data_input_names]
+
+    return py_reader_provider
+
+
+def test_context(exe, train_exe, dev_count):
+    # Context to do validation.
+    test_prog = fluid.Program()
+    startup_prog = fluid.Program()
+    if args.enable_ce:
+        test_prog.random_seed = 1000
+        startup_prog.random_seed = 1000
+    with fluid.program_guard(test_prog, startup_prog):
+        with fluid.unique_name.guard():
+            sum_cost, avg_cost, predict, token_num, pyreader = transformer(
+                ModelHyperParams.src_vocab_size,
+                ModelHyperParams.trg_vocab_size,
+                ModelHyperParams.max_length + 1,
+                ModelHyperParams.n_layer,
+                ModelHyperParams.n_head,
+                ModelHyperParams.d_key,
+                ModelHyperParams.d_value,
+                ModelHyperParams.d_model,
+                ModelHyperParams.d_inner_hid,
+                ModelHyperParams.prepostprocess_dropout,
+                ModelHyperParams.attention_dropout,
+                ModelHyperParams.relu_dropout,
+                ModelHyperParams.preprocess_cmd,
+                ModelHyperParams.postprocess_cmd,
+                ModelHyperParams.weight_sharing,
+                TrainTaskConfig.label_smooth_eps,
+                use_py_reader=args.use_py_reader,
+                is_test=True)
+    test_prog = test_prog.clone(for_test=True)
+    test_data = prepare_data_generator(
+        args,
+        is_test=True,
+        count=dev_count,
+        pyreader=pyreader,
+        py_reader_provider_wrapper=py_reader_provider_wrapper)
+
+    exe.run(startup_prog)  # to init pyreader for testing
+    if TrainTaskConfig.ckpt_path:
+        fluid.io.load_persistables(
+            exe, TrainTaskConfig.ckpt_path, main_program=test_prog)
+
+    exec_strategy = fluid.ExecutionStrategy()
+    exec_strategy.use_experimental_executor = True
+    build_strategy = fluid.BuildStrategy()
+    test_exe = fluid.ParallelExecutor(
+        use_cuda=TrainTaskConfig.use_gpu,
+        main_program=test_prog,
+        build_strategy=build_strategy,
+        exec_strategy=exec_strategy,
+        share_vars_from=train_exe)
+
+    def test(exe=test_exe, pyreader=pyreader):
+        test_total_cost = 0
+        test_total_token = 0
+
+        if args.use_py_reader:
+            pyreader.start()
+            data_generator = None
+        else:
+            data_generator = test_data()
+        while True:
+            try:
+                feed_dict_list = prepare_feed_dict_list(data_generator, False,
+                                                        dev_count)
+                outs = test_exe.run(fetch_list=[sum_cost.name, token_num.name],
+                                    feed=feed_dict_list)
+            except (StopIteration, fluid.core.EOFException):
+                # The current pass is over.
+                if args.use_py_reader:
+                    pyreader.reset()
+                break
+            sum_cost_val, token_num_val = np.array(outs[0]), np.array(outs[1])
+            test_total_cost += sum_cost_val.sum()
+            test_total_token += token_num_val.sum()
+        test_avg_cost = test_total_cost / test_total_token
+        test_ppl = np.exp([min(test_avg_cost, 100)])
+        return test_avg_cost, test_ppl
+
+    return test
+
+
+def train_loop(exe,
+               train_prog,
+               startup_prog,
+               dev_count,
+               sum_cost,
+               avg_cost,
+               token_num,
+               predict,
+               pyreader,
+               nccl2_num_trainers=1,
+               nccl2_trainer_id=0):
+    # Initialize the parameters.
+    if TrainTaskConfig.ckpt_path:
+        exe.run(startup_prog)  # to init pyreader for training
+        logging.info("load checkpoint from {}".format(
+            TrainTaskConfig.ckpt_path))
+        fluid.io.load_persistables(
+            exe, TrainTaskConfig.ckpt_path, main_program=train_prog)
+    else:
+        logging.info("init fluid.framework.default_startup_program")
+        exe.run(startup_prog)
+
+    logging.info("begin reader")
+    train_data = prepare_data_generator(
+        args,
+        is_test=False,
+        count=dev_count,
+        pyreader=pyreader,
+        py_reader_provider_wrapper=py_reader_provider_wrapper)
+
+    # For faster executor
+    exec_strategy = fluid.ExecutionStrategy()
+    exec_strategy.use_experimental_executor = True
+    exec_strategy.num_iteration_per_drop_scope = int(args.fetch_steps)
+    build_strategy = fluid.BuildStrategy()
+    # Since the token number differs among devices, customize gradient scale to
+    # use token average cost among multi-devices. and the gradient scale is
+    # `1 / token_number` for average cost.
+    # build_strategy.gradient_scale_strategy = fluid.BuildStrategy.GradientScaleStrategy.Customized
+
+    logging.info("begin executor")
+    train_exe = fluid.ParallelExecutor(
+        use_cuda=TrainTaskConfig.use_gpu,
+        loss_name=avg_cost.name,
+        main_program=train_prog,
+        build_strategy=build_strategy,
+        exec_strategy=exec_strategy,
+        num_trainers=nccl2_num_trainers,
+        trainer_id=nccl2_trainer_id)
+
+    if args.val_file_pattern is not None:
+        test = test_context(exe, train_exe, dev_count)
+
+    # the best cross-entropy value with label smoothing
+    loss_normalizer = -((1. - TrainTaskConfig.label_smooth_eps) * np.log(
+        (1. - TrainTaskConfig.label_smooth_eps
+         )) + TrainTaskConfig.label_smooth_eps *
+                        np.log(TrainTaskConfig.label_smooth_eps / (
+                            ModelHyperParams.trg_vocab_size - 1) + 1e-20))
+
+    step_idx = 0
+    init_flag = True
+
+    logging.info("begin train")
+    for pass_id in six.moves.xrange(TrainTaskConfig.pass_num):
+        pass_start_time = time.time()
+
+        if args.use_py_reader:
+            pyreader.start()
+            data_generator = None
+        else:
+            data_generator = train_data()
+
+        batch_id = 0
+        while True:
+            try:
+                feed_dict_list = prepare_feed_dict_list(data_generator,
+                                                        init_flag, dev_count)
+                outs = train_exe.run(
+                    fetch_list=[sum_cost.name, token_num.name]
+                    if step_idx % args.fetch_steps == 0 else [],
+                    feed=feed_dict_list)
+
+                if step_idx % args.fetch_steps == 0:
+                    sum_cost_val, token_num_val = np.array(outs[0]), np.array(
+                        outs[1])
+                    # sum the cost from multi-devices
+                    total_sum_cost = sum_cost_val.sum()
+                    total_token_num = token_num_val.sum()
+                    total_avg_cost = total_sum_cost / total_token_num
+
+                    if step_idx == 0:
+                        logging.info(
+                            "step_idx: %d, epoch: %d, batch: %d, avg loss: %f, "
+                            "normalized loss: %f, ppl: %f" %
+                            (step_idx, pass_id, batch_id, total_avg_cost,
+                             total_avg_cost - loss_normalizer,
+                             np.exp([min(total_avg_cost, 100)])))
+                        avg_batch_time = time.time()
+                    else:
+                        logging.info(
+                            "step_idx: %d, epoch: %d, batch: %d, avg loss: %f, "
+                            "normalized loss: %f, ppl: %f, speed: %.2f step/s" %
+                            (step_idx, pass_id, batch_id, total_avg_cost,
+                             total_avg_cost - loss_normalizer,
+                             np.exp([min(total_avg_cost, 100)]),
+                             args.fetch_steps / (time.time() - avg_batch_time)))
+                        avg_batch_time = time.time()
+
+                if step_idx % TrainTaskConfig.save_freq == 0 and step_idx > 0:
+                    fluid.io.save_persistables(
+                        exe,
+                        os.path.join(TrainTaskConfig.ckpt_dir,
+                                     "latest.checkpoint"), train_prog)
+                    fluid.io.save_params(
+                        exe,
+                        os.path.join(TrainTaskConfig.model_dir,
+                                     "iter_" + str(step_idx) + ".infer.model"),
+                        train_prog)
+
+                init_flag = False
+                batch_id += 1
+                step_idx += 1
+            except (StopIteration, fluid.core.EOFException):
+                # The current pass is over.
+                if args.use_py_reader:
+                    pyreader.reset()
+                break
+
+        time_consumed = time.time() - pass_start_time
+        # Validate and save the persistable.
+        if args.val_file_pattern is not None:
+            val_avg_cost, val_ppl = test()
+            logging.info(
+                "epoch: %d, val avg loss: %f, val normalized loss: %f, val ppl: %f,"
+                " consumed %fs" % (pass_id, val_avg_cost,
+                                   val_avg_cost - loss_normalizer, val_ppl,
+                                   time_consumed))
+        else:
+            logging.info("epoch: %d, consumed %fs" % (pass_id, time_consumed))
+        if not args.enable_ce:
+            fluid.io.save_persistables(
+                exe,
+                os.path.join(TrainTaskConfig.ckpt_dir,
+                             "pass_" + str(pass_id) + ".checkpoint"),
+                train_prog)
+
+    if args.enable_ce:  # For CE
+        print("kpis\ttrain_cost_card%d\t%f" % (dev_count, total_avg_cost))
+        if args.val_file_pattern is not None:
+            print("kpis\ttest_cost_card%d\t%f" % (dev_count, val_avg_cost))
+        print("kpis\ttrain_duration_card%d\t%f" % (dev_count, time_consumed))
+
+
+def train(args):
+    # priority: ENV > args > config
+    is_local = os.getenv("PADDLE_IS_LOCAL", "1")
+    if is_local == '0':
+        args.local = False
+    logging.info(args)
+
+    if args.device == 'CPU':
+        TrainTaskConfig.use_gpu = False
+
+    training_role = os.getenv("TRAINING_ROLE", "TRAINER")
+
+    if training_role == "PSERVER" or (not TrainTaskConfig.use_gpu):
+        place = fluid.CPUPlace()
+        dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+    else:
+        place = fluid.CUDAPlace(0)
+        dev_count = fluid.core.get_cuda_device_count()
+
+    exe = fluid.Executor(place)
+
+    train_prog = fluid.Program()
+    startup_prog = fluid.Program()
+
+    if args.enable_ce:
+        train_prog.random_seed = 1000
+        startup_prog.random_seed = 1000
+
+    with fluid.program_guard(train_prog, startup_prog):
+        with fluid.unique_name.guard():
+            sum_cost, avg_cost, predict, token_num, pyreader = transformer(
+                ModelHyperParams.src_vocab_size,
+                ModelHyperParams.trg_vocab_size,
+                ModelHyperParams.max_length + 1,
+                ModelHyperParams.n_layer,
+                ModelHyperParams.n_head,
+                ModelHyperParams.d_key,
+                ModelHyperParams.d_value,
+                ModelHyperParams.d_model,
+                ModelHyperParams.d_inner_hid,
+                ModelHyperParams.prepostprocess_dropout,
+                ModelHyperParams.attention_dropout,
+                ModelHyperParams.relu_dropout,
+                ModelHyperParams.preprocess_cmd,
+                ModelHyperParams.postprocess_cmd,
+                ModelHyperParams.weight_sharing,
+                TrainTaskConfig.label_smooth_eps,
+                use_py_reader=args.use_py_reader,
+                is_test=False)
+
+            optimizer = None
+            if args.sync:
+                lr_decay = fluid.layers.learning_rate_scheduler.noam_decay(
+                    ModelHyperParams.d_model, TrainTaskConfig.warmup_steps)
+                logging.info("before adam")
+
+                with fluid.default_main_program()._lr_schedule_guard():
+                    learning_rate = lr_decay * TrainTaskConfig.learning_rate
+
+                optimizer = fluid.optimizer.Adam(
+                    learning_rate=learning_rate,
+                    beta1=TrainTaskConfig.beta1,
+                    beta2=TrainTaskConfig.beta2,
+                    epsilon=TrainTaskConfig.eps)
+            else:
+                optimizer = fluid.optimizer.SGD(0.003)
+            optimizer.minimize(avg_cost)
+
+    if args.use_mem_opt:
+        fluid.memory_optimize(train_prog)
+
+    if args.local:
+        logging.info("local start_up:")
+        train_loop(exe, train_prog, startup_prog, dev_count, sum_cost, avg_cost,
+                   token_num, predict, pyreader)
+    else:
+        if args.update_method == "nccl2":
+            trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
+            port = os.getenv("PADDLE_PORT")
+            worker_ips = os.getenv("PADDLE_TRAINERS")
+            worker_endpoints = []
+            for ip in worker_ips.split(","):
+                worker_endpoints.append(':'.join([ip, port]))
+            trainers_num = len(worker_endpoints)
+            current_endpoint = os.getenv("POD_IP") + ":" + port
+            if trainer_id == 0:
+                logging.info("train_id == 0, sleep 60s")
+                time.sleep(60)
+            logging.info("trainers_num:{}".format(trainers_num))
+            logging.info("worker_endpoints:{}".format(worker_endpoints))
+            logging.info("current_endpoint:{}".format(current_endpoint))
+            append_nccl2_prepare(startup_prog, trainer_id, worker_endpoints,
+                                 current_endpoint)
+            train_loop(exe, train_prog, startup_prog, dev_count, sum_cost,
+                       avg_cost, token_num, predict, pyreader, trainers_num,
+                       trainer_id)
+            return
+
+        port = os.getenv("PADDLE_PORT", "6174")
+        pserver_ips = os.getenv("PADDLE_PSERVERS")  # ip,ip...
+        eplist = []
+        for ip in pserver_ips.split(","):
+            eplist.append(':'.join([ip, port]))
+        pserver_endpoints = ",".join(eplist)  # ip:port,ip:port...
+        trainers = int(os.getenv("PADDLE_TRAINERS_NUM", "0"))
+        current_endpoint = os.getenv("POD_IP") + ":" + port
+        trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
+
+        logging.info("pserver_endpoints:{}".format(pserver_endpoints))
+        logging.info("current_endpoint:{}".format(current_endpoint))
+        logging.info("trainer_id:{}".format(trainer_id))
+        logging.info("pserver_ips:{}".format(pserver_ips))
+        logging.info("port:{}".format(port))
+
+        t = fluid.DistributeTranspiler()
+        t.transpile(
+            trainer_id,
+            pservers=pserver_endpoints,
+            trainers=trainers,
+            program=train_prog,
+            startup_program=startup_prog)
+
+        if training_role == "PSERVER":
+            logging.info("distributed: pserver started")
+            current_endpoint = os.getenv("POD_IP") + ":" + os.getenv(
+                "PADDLE_PORT")
+            if not current_endpoint:
+                logging.critical("need env SERVER_ENDPOINT")
+                exit(1)
+            pserver_prog = t.get_pserver_program(current_endpoint)
+            pserver_startup = t.get_startup_program(current_endpoint,
+                                                    pserver_prog)
+
+            exe.run(pserver_startup)
+            exe.run(pserver_prog)
+        elif training_role == "TRAINER":
+            logging.info("distributed: trainer started")
+            trainer_prog = t.get_trainer_program()
+
+            train_loop(exe, train_prog, startup_prog, dev_count, sum_cost,
+                       avg_cost, token_num, predict, pyreader)
+        else:
+            logging.critical(
+                "environment var TRAINER_ROLE should be TRAINER os PSERVER")
+            exit(1)
+
+
+if __name__ == "__main__":
+    LOG_FORMAT = "[%(asctime)s %(levelname)s %(filename)s:%(lineno)d] %(message)s"
+    logging.basicConfig(
+        stream=sys.stdout, level=logging.DEBUG, format=LOG_FORMAT)
+    logging.getLogger().setLevel(logging.INFO)
+
+    args = parse_args()
+    train(args)
diff --git a/PaddleNLP/sequence_tagging_for_ner/.run_ce.sh b/PaddleNLP/unarchived/sequence_tagging_for_ner/.run_ce.sh
similarity index 100%
rename from PaddleNLP/sequence_tagging_for_ner/.run_ce.sh
rename to PaddleNLP/unarchived/sequence_tagging_for_ner/.run_ce.sh
diff --git a/PaddleNLP/sequence_tagging_for_ner/README.md b/PaddleNLP/unarchived/sequence_tagging_for_ner/README.md
similarity index 100%
rename from PaddleNLP/sequence_tagging_for_ner/README.md
rename to PaddleNLP/unarchived/sequence_tagging_for_ner/README.md
diff --git a/PaddleNLP/sequence_tagging_for_ner/_ce.py b/PaddleNLP/unarchived/sequence_tagging_for_ner/_ce.py
similarity index 100%
rename from PaddleNLP/sequence_tagging_for_ner/_ce.py
rename to PaddleNLP/unarchived/sequence_tagging_for_ner/_ce.py
diff --git a/PaddleNLP/sequence_tagging_for_ner/data/download.sh b/PaddleNLP/unarchived/sequence_tagging_for_ner/data/download.sh
similarity index 100%
rename from PaddleNLP/sequence_tagging_for_ner/data/download.sh
rename to PaddleNLP/unarchived/sequence_tagging_for_ner/data/download.sh
diff --git a/PaddleNLP/sequence_tagging_for_ner/data/target.txt b/PaddleNLP/unarchived/sequence_tagging_for_ner/data/target.txt
similarity index 100%
rename from PaddleNLP/sequence_tagging_for_ner/data/target.txt
rename to PaddleNLP/unarchived/sequence_tagging_for_ner/data/target.txt
diff --git a/PaddleNLP/sequence_tagging_for_ner/data/test b/PaddleNLP/unarchived/sequence_tagging_for_ner/data/test
similarity index 100%
rename from PaddleNLP/sequence_tagging_for_ner/data/test
rename to PaddleNLP/unarchived/sequence_tagging_for_ner/data/test
diff --git a/PaddleNLP/sequence_tagging_for_ner/data/train b/PaddleNLP/unarchived/sequence_tagging_for_ner/data/train
similarity index 100%
rename from PaddleNLP/sequence_tagging_for_ner/data/train
rename to PaddleNLP/unarchived/sequence_tagging_for_ner/data/train
diff --git a/PaddleNLP/sequence_tagging_for_ner/imgs/convergence_curve.png b/PaddleNLP/unarchived/sequence_tagging_for_ner/imgs/convergence_curve.png
similarity index 100%
rename from PaddleNLP/sequence_tagging_for_ner/imgs/convergence_curve.png
rename to PaddleNLP/unarchived/sequence_tagging_for_ner/imgs/convergence_curve.png
diff --git a/PaddleNLP/sequence_tagging_for_ner/infer.py b/PaddleNLP/unarchived/sequence_tagging_for_ner/infer.py
similarity index 86%
rename from PaddleNLP/sequence_tagging_for_ner/infer.py
rename to PaddleNLP/unarchived/sequence_tagging_for_ner/infer.py
index 20f00ccda5dcaafeccf5435ae32db0d5d2dcbd6a..8041b8942e26a2e9c83426a2e1250be1969ae284 100644
--- a/PaddleNLP/sequence_tagging_for_ner/infer.py
+++ b/PaddleNLP/unarchived/sequence_tagging_for_ner/infer.py
@@ -38,12 +38,11 @@ def infer(model_path, batch_size, test_data_file, vocab_file, target_file,
         for data in test_data():
             word = to_lodtensor([x[0] for x in data], place)
             mark = to_lodtensor([x[1] for x in data], place)
-            crf_decode = exe.run(
-                inference_program,
-                feed={"word": word,
-                      "mark": mark},
-                fetch_list=fetch_targets,
-                return_numpy=False)
+            crf_decode = exe.run(inference_program,
+                                 feed={"word": word,
+                                       "mark": mark},
+                                 fetch_list=fetch_targets,
+                                 return_numpy=False)
             lod_info = (crf_decode[0].lod())[0]
             np_data = np.array(crf_decode[0])
             assert len(data) == len(lod_info) - 1
@@ -52,7 +51,7 @@ def infer(model_path, batch_size, test_data_file, vocab_file, target_file,
                     sen_index + 1] - lod_info[sen_index]
                 word_index = 0
                 for tag_index in six.moves.xrange(lod_info[sen_index],
-                                        lod_info[sen_index + 1]):
+                                                  lod_info[sen_index + 1]):
                     word = word_reverse_dict[data[sen_index][0][word_index]]
                     gold_tag = label_reverse_dict[data[sen_index][2][
                         word_index]]
diff --git a/PaddleNLP/sequence_tagging_for_ner/network_conf.py b/PaddleNLP/unarchived/sequence_tagging_for_ner/network_conf.py
similarity index 100%
rename from PaddleNLP/sequence_tagging_for_ner/network_conf.py
rename to PaddleNLP/unarchived/sequence_tagging_for_ner/network_conf.py
diff --git a/PaddleNLP/sequence_tagging_for_ner/reader.py b/PaddleNLP/unarchived/sequence_tagging_for_ner/reader.py
similarity index 100%
rename from PaddleNLP/sequence_tagging_for_ner/reader.py
rename to PaddleNLP/unarchived/sequence_tagging_for_ner/reader.py
diff --git a/PaddleNLP/sequence_tagging_for_ner/train.py b/PaddleNLP/unarchived/sequence_tagging_for_ner/train.py
similarity index 100%
rename from PaddleNLP/sequence_tagging_for_ner/train.py
rename to PaddleNLP/unarchived/sequence_tagging_for_ner/train.py
diff --git a/PaddleNLP/sequence_tagging_for_ner/utils.py b/PaddleNLP/unarchived/sequence_tagging_for_ner/utils.py
similarity index 100%
rename from PaddleNLP/sequence_tagging_for_ner/utils.py
rename to PaddleNLP/unarchived/sequence_tagging_for_ner/utils.py
diff --git a/PaddleNLP/sequence_tagging_for_ner/utils_extend.py b/PaddleNLP/unarchived/sequence_tagging_for_ner/utils_extend.py
similarity index 100%
rename from PaddleNLP/sequence_tagging_for_ner/utils_extend.py
rename to PaddleNLP/unarchived/sequence_tagging_for_ner/utils_extend.py
diff --git a/PaddleNLP/text_classification/.run_ce.sh b/PaddleNLP/unarchived/text_classification/.run_ce.sh
similarity index 100%
rename from PaddleNLP/text_classification/.run_ce.sh
rename to PaddleNLP/unarchived/text_classification/.run_ce.sh
diff --git a/PaddleNLP/text_classification/README.md b/PaddleNLP/unarchived/text_classification/README.md
similarity index 100%
rename from PaddleNLP/text_classification/README.md
rename to PaddleNLP/unarchived/text_classification/README.md
diff --git a/PaddleNLP/text_classification/_ce.py b/PaddleNLP/unarchived/text_classification/_ce.py
similarity index 100%
rename from PaddleNLP/text_classification/_ce.py
rename to PaddleNLP/unarchived/text_classification/_ce.py
diff --git a/PaddleNLP/text_classification/async_executor/README.md b/PaddleNLP/unarchived/text_classification/async_executor/README.md
similarity index 100%
rename from PaddleNLP/text_classification/async_executor/README.md
rename to PaddleNLP/unarchived/text_classification/async_executor/README.md
diff --git a/PaddleNLP/text_classification/async_executor/data_generator.sh b/PaddleNLP/unarchived/text_classification/async_executor/data_generator.sh
similarity index 100%
rename from PaddleNLP/text_classification/async_executor/data_generator.sh
rename to PaddleNLP/unarchived/text_classification/async_executor/data_generator.sh
diff --git a/PaddleNLP/text_classification/async_executor/data_generator/IMDB.py b/PaddleNLP/unarchived/text_classification/async_executor/data_generator/IMDB.py
similarity index 100%
rename from PaddleNLP/text_classification/async_executor/data_generator/IMDB.py
rename to PaddleNLP/unarchived/text_classification/async_executor/data_generator/IMDB.py
diff --git a/PaddleNLP/text_classification/async_executor/data_generator/build_raw_data.py b/PaddleNLP/unarchived/text_classification/async_executor/data_generator/build_raw_data.py
similarity index 100%
rename from PaddleNLP/text_classification/async_executor/data_generator/build_raw_data.py
rename to PaddleNLP/unarchived/text_classification/async_executor/data_generator/build_raw_data.py
diff --git a/PaddleNLP/text_classification/async_executor/data_generator/data_generator.py b/PaddleNLP/unarchived/text_classification/async_executor/data_generator/data_generator.py
similarity index 100%
rename from PaddleNLP/text_classification/async_executor/data_generator/data_generator.py
rename to PaddleNLP/unarchived/text_classification/async_executor/data_generator/data_generator.py
diff --git a/PaddleNLP/text_classification/async_executor/data_generator/splitfile.py b/PaddleNLP/unarchived/text_classification/async_executor/data_generator/splitfile.py
similarity index 100%
rename from PaddleNLP/text_classification/async_executor/data_generator/splitfile.py
rename to PaddleNLP/unarchived/text_classification/async_executor/data_generator/splitfile.py
diff --git a/PaddleNLP/text_classification/async_executor/data_reader.py b/PaddleNLP/unarchived/text_classification/async_executor/data_reader.py
similarity index 100%
rename from PaddleNLP/text_classification/async_executor/data_reader.py
rename to PaddleNLP/unarchived/text_classification/async_executor/data_reader.py
diff --git a/PaddleNLP/text_classification/async_executor/infer.py b/PaddleNLP/unarchived/text_classification/async_executor/infer.py
similarity index 100%
rename from PaddleNLP/text_classification/async_executor/infer.py
rename to PaddleNLP/unarchived/text_classification/async_executor/infer.py
diff --git a/PaddleNLP/text_classification/async_executor/train.py b/PaddleNLP/unarchived/text_classification/async_executor/train.py
similarity index 100%
rename from PaddleNLP/text_classification/async_executor/train.py
rename to PaddleNLP/unarchived/text_classification/async_executor/train.py
diff --git a/PaddleNLP/text_classification/clouds/scdb_parallel_executor.py b/PaddleNLP/unarchived/text_classification/clouds/scdb_parallel_executor.py
similarity index 100%
rename from PaddleNLP/text_classification/clouds/scdb_parallel_executor.py
rename to PaddleNLP/unarchived/text_classification/clouds/scdb_parallel_executor.py
diff --git a/PaddleNLP/text_classification/clouds/scdb_single_card.py b/PaddleNLP/unarchived/text_classification/clouds/scdb_single_card.py
similarity index 100%
rename from PaddleNLP/text_classification/clouds/scdb_single_card.py
rename to PaddleNLP/unarchived/text_classification/clouds/scdb_single_card.py
diff --git a/PaddleNLP/text_classification/infer.py b/PaddleNLP/unarchived/text_classification/infer.py
similarity index 100%
rename from PaddleNLP/text_classification/infer.py
rename to PaddleNLP/unarchived/text_classification/infer.py
diff --git a/PaddleNLP/text_classification/nets.py b/PaddleNLP/unarchived/text_classification/nets.py
similarity index 100%
rename from PaddleNLP/text_classification/nets.py
rename to PaddleNLP/unarchived/text_classification/nets.py
diff --git a/PaddleNLP/text_classification/train.py b/PaddleNLP/unarchived/text_classification/train.py
similarity index 100%
rename from PaddleNLP/text_classification/train.py
rename to PaddleNLP/unarchived/text_classification/train.py
diff --git a/PaddleNLP/text_classification/utils.py b/PaddleNLP/unarchived/text_classification/utils.py
similarity index 100%
rename from PaddleNLP/text_classification/utils.py
rename to PaddleNLP/unarchived/text_classification/utils.py
diff --git a/PaddleNLP/text_matching_on_quora/.run_ce.sh b/PaddleNLP/unarchived/text_matching_on_quora/.run_ce.sh
similarity index 100%
rename from PaddleNLP/text_matching_on_quora/.run_ce.sh
rename to PaddleNLP/unarchived/text_matching_on_quora/.run_ce.sh
diff --git a/PaddleNLP/text_matching_on_quora/README.md b/PaddleNLP/unarchived/text_matching_on_quora/README.md
similarity index 97%
rename from PaddleNLP/text_matching_on_quora/README.md
rename to PaddleNLP/unarchived/text_matching_on_quora/README.md
index 77d93943ae7dcbe775e60307b74430f320dbaab1..eb1346b671e7873918d171cfb2c4993367c9d781 100644
--- a/PaddleNLP/text_matching_on_quora/README.md
+++ b/PaddleNLP/unarchived/text_matching_on_quora/README.md
@@ -20,7 +20,7 @@
 
 ### a brief review of the Quora Question Pair (QQP) Task
 
-The [Quora Question Pair](https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs) dataset contains 400,000 question pairs from [Quora](https://www.quora.com/), where people ask and answer questions related to specific areas. Each sample in the dataset consists of two questions (both English) and a label that represents whether the questions are duplicate. The dataset is well annotated by human. 
+The [Quora Question Pair](https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs) dataset contains 400,000 question pairs from [Quora](https://www.quora.com/), where people ask and answer questions related to specific areas. Each sample in the dataset consists of two questions (both English) and a label that represents whether the questions are duplicate. The dataset is well annotated by human.
 
 Below are two samples from the dataset. The last column indicates whether the two questions are duplicate (1) or not (0).
 
@@ -33,7 +33,7 @@ Below are two samples from the dataset. The last column indicates whether the tw
 
 The kaggle competition has inspired much effective work. However, most of these models are rule-based and difficult to be transferred to new tasks. Researchers are seeking for more general models that work well on this task and other natual language processing (NLP) tasks.
 
-[Wang _et al._](https://arxiv.org/abs/1702.03814) proposed a bilateral multi-perspective matching (BIMPM) model based on the Quora Question Pair dataset. They splitted the original dataset to [3 parts](https://drive.google.com/file/d/0B0PlTAo--BnaQWlsZl9FZ3l1c28/view?usp=sharing): _train.tsv_ (384,348 samples), _dev.tsv_ (10,000 samples) and _test.tsv_ (10,000 samples). The class distribution of _train.tsv_ is unbalanced (37% positive and 63% negative), while those of _dev.tsv_ and _test.tsv_ are balanced(50% positive and 50% negetive). We used the same splitting method in our experiments. 
+[Wang _et al._](https://arxiv.org/abs/1702.03814) proposed a bilateral multi-perspective matching (BIMPM) model based on the Quora Question Pair dataset. They splitted the original dataset to [3 parts](https://drive.google.com/file/d/0B0PlTAo--BnaQWlsZl9FZ3l1c28/view?usp=sharing): _train.tsv_ (384,348 samples), _dev.tsv_ (10,000 samples) and _test.tsv_ (10,000 samples). The class distribution of _train.tsv_ is unbalanced (37% positive and 63% negative), while those of _dev.tsv_ and _test.tsv_ are balanced(50% positive and 50% negetive). We used the same splitting method in our experiments.
 
 ### Our Work
 
@@ -43,7 +43,7 @@ Based on the Quora Question Pair Dataset, we implemented some classic models in
 
 ### Install Fluid release 1.0
 
-Please follow the [official document in English](http://www.paddlepaddle.org/documentation/docs/en/1.0/build_and_install/pip_install_en.html) or [official document in Chinese](http://www.paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/install/Start.html) to install the Fluid deep learning framework. 
+Please follow the [official document in English](http://www.paddlepaddle.org/documentation/docs/en/1.0/build_and_install/pip_install_en.html) or [official document in Chinese](http://www.paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/install/Start.html) to install the Fluid deep learning framework.
 
 #### Have I installed Fluid successfully?
 
@@ -62,7 +62,7 @@ Please download the Quora dataset from [Google drive](https://drive.google.com/f
 Then run _data/prepare_quora_data.sh_ to download the pre-trained _word2vec_ embedding file -- _glove.840B.300d.zip_:
 
 ```shell
-sh data/prepare_quora_data.sh   
+sh data/prepare_quora_data.sh  
 ```
 
 At this point the dataset directory ($HOME/.cache/paddle/dataset) structure should be:
@@ -170,8 +170,8 @@ We implemeted 4 models for now: the convolutional deep-structured semantic model
 
 In our experiment, we found that LSTM-based models outperformed convolution-based models. The DecAtt model has fewer parameters than LSTM-based models, but is sensitive to hyper-parameters.
 
-
 
+

 
- test_acc 
+ test_acc
 
 

diff --git a/PaddleNLP/unarchived/text_matching_on_quora/__init__.py b/PaddleNLP/unarchived/text_matching_on_quora/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/PaddleNLP/text_matching_on_quora/_ce.py b/PaddleNLP/unarchived/text_matching_on_quora/_ce.py
similarity index 76%
rename from PaddleNLP/text_matching_on_quora/_ce.py
rename to PaddleNLP/unarchived/text_matching_on_quora/_ce.py
index eadeb821da6f7049d1916a65a1ae4eb995c5cb6d..930d7142dff933c8cd6f9139d5b3c5847931e43d 100644
--- a/PaddleNLP/text_matching_on_quora/_ce.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/_ce.py
@@ -6,22 +6,23 @@ sys.path.append(os.environ['ceroot'])
 from kpi import CostKpi
 from kpi import DurationKpi
 
-
-each_pass_duration_card1_kpi = DurationKpi('each_pass_duration_card1', 0.08, 0, actived=True)
+each_pass_duration_card1_kpi = DurationKpi(
+    'each_pass_duration_card1', 0.08, 0, actived=True)
 train_avg_cost_card1_kpi = CostKpi('train_avg_cost_card1', 0.08, 0)
 train_avg_acc_card1_kpi = CostKpi('train_avg_acc_card1', 0.02, 0)
-each_pass_duration_card4_kpi = DurationKpi('each_pass_duration_card4', 0.08, 0, actived=True)
+each_pass_duration_card4_kpi = DurationKpi(
+    'each_pass_duration_card4', 0.08, 0, actived=True)
 train_avg_cost_card4_kpi = CostKpi('train_avg_cost_card4', 0.08, 0)
 train_avg_acc_card4_kpi = CostKpi('train_avg_acc_card4', 0.02, 0)
 
 tracking_kpis = [
-        each_pass_duration_card1_kpi,
-        train_avg_cost_card1_kpi,
-        train_avg_acc_card1_kpi,
-        each_pass_duration_card4_kpi,
-        train_avg_cost_card4_kpi,
-        train_avg_acc_card4_kpi,
-        ]
+    each_pass_duration_card1_kpi,
+    train_avg_cost_card1_kpi,
+    train_avg_acc_card1_kpi,
+    each_pass_duration_card4_kpi,
+    train_avg_cost_card4_kpi,
+    train_avg_acc_card4_kpi,
+]
 
 
 def parse_log(log):
diff --git a/PaddleNLP/text_matching_on_quora/cdssm_base.log b/PaddleNLP/unarchived/text_matching_on_quora/cdssm_base.log
similarity index 100%
rename from PaddleNLP/text_matching_on_quora/cdssm_base.log
rename to PaddleNLP/unarchived/text_matching_on_quora/cdssm_base.log
diff --git a/PaddleNLP/text_matching_on_quora/configs/__init__.py b/PaddleNLP/unarchived/text_matching_on_quora/configs/__init__.py
similarity index 100%
rename from PaddleNLP/text_matching_on_quora/configs/__init__.py
rename to PaddleNLP/unarchived/text_matching_on_quora/configs/__init__.py
diff --git a/PaddleNLP/text_matching_on_quora/configs/basic_config.py b/PaddleNLP/unarchived/text_matching_on_quora/configs/basic_config.py
similarity index 94%
rename from PaddleNLP/text_matching_on_quora/configs/basic_config.py
rename to PaddleNLP/unarchived/text_matching_on_quora/configs/basic_config.py
index c6de4093e30b0e36784b53aa90cc5ef1bb027051..ccc37926b76c70b39e9ef0d28e168b1e56a61721 100755
--- a/PaddleNLP/text_matching_on_quora/configs/basic_config.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/configs/basic_config.py
@@ -1,13 +1,13 @@
 from __future__ import print_function
 
-class config(object):
 
+class config(object):
     def __init__(self):
         self.batch_size = 128
         self.epoch_num = 50
 
-        self.optimizer_type = 'adam' # sgd, adagrad
-        
+        self.optimizer_type = 'adam'  # sgd, adagrad
+
         # pretrained word embedding 
         self.use_pretrained_word_embedding = True
         # when employing pretrained word embedding,  
@@ -28,13 +28,14 @@ class config(object):
         self.duplicate_data = False
 
         self.metric_type = ['accuracy']
- 
+
     def list_config(self):
         print("config", self.__dict__)
 
     def has_member(self, var_name):
         return var_name in self.__dict__
 
+
 if __name__ == "__main__":
     basic = config()
     basic.list_config()
diff --git a/PaddleNLP/text_matching_on_quora/configs/cdssm.py b/PaddleNLP/unarchived/text_matching_on_quora/configs/cdssm.py
similarity index 93%
rename from PaddleNLP/text_matching_on_quora/configs/cdssm.py
rename to PaddleNLP/unarchived/text_matching_on_quora/configs/cdssm.py
index 22e7bd30e4324d184f4a04d72e4fd1e7a04aedaa..b773d4cacb2bb21468a6208fd6317ad1e9779c92 100755
--- a/PaddleNLP/text_matching_on_quora/configs/cdssm.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/configs/cdssm.py
@@ -14,6 +14,7 @@
 
 from . import basic_config
 
+
 def cdssm_base():
     """
     set configs
@@ -22,8 +23,8 @@ def cdssm_base():
     config.learning_rate = 0.001
     config.save_dirname = "model_dir"
     config.use_pretrained_word_embedding = True
-    config.dict_dim = 40000 # approx_vocab_size
-    
+    config.dict_dim = 40000  # approx_vocab_size
+
     # net config
     config.emb_dim = 300
     config.kernel_size = 5
@@ -34,4 +35,4 @@ def cdssm_base():
     config.droprate_fc = 0.1
     config.class_dim = 2
 
-    return config 
+    return config
diff --git a/PaddleNLP/text_matching_on_quora/configs/dec_att.py b/PaddleNLP/unarchived/text_matching_on_quora/configs/dec_att.py
similarity index 87%
rename from PaddleNLP/text_matching_on_quora/configs/dec_att.py
rename to PaddleNLP/unarchived/text_matching_on_quora/configs/dec_att.py
index 3df9f897e4c240554bf75108afb8bd54a5cf94fa..dbb9977e445ff3304a2dd19665d8e7760726ed4e 100755
--- a/PaddleNLP/text_matching_on_quora/configs/dec_att.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/configs/dec_att.py
@@ -14,6 +14,7 @@
 
 from . import basic_config
 
+
 def decatt_glove():
     """
     use config 'decAtt_glove' in the paper 'Neural Paraphrase Identification of Questions with Noisy Pretraining'
@@ -22,7 +23,7 @@ def decatt_glove():
     config.learning_rate = 0.05
     config.save_dirname = "model_dir"
     config.use_pretrained_word_embedding = True
-    config.dict_dim = 40000 # approx_vocab_size
+    config.dict_dim = 40000  # approx_vocab_size
     config.metric_type = ['accuracy', 'accuracy_with_threshold']
     config.optimizer_type = 'sgd'
     config.lr_decay = 1
@@ -30,18 +31,19 @@ def decatt_glove():
     config.embedding_norm = False
     config.OOV_fill = 'uniform'
     config.duplicate_data = False
-    
+
     # net config
     config.emb_dim = 300
-    config.proj_emb_dim = 200 #TODO: has project?
+    config.proj_emb_dim = 200  #TODO: has project?
     config.num_units = [400, 200]
     config.word_embedding_trainable = True
     config.droprate = 0.1
-    config.share_wight_btw_seq =  True
+    config.share_wight_btw_seq = True
     config.class_dim = 2
 
     return config
 
+
 def decatt_word():
     """
     use config 'decAtt_glove' in the paper 'Neural Paraphrase Identification of Questions with Noisy Pretraining'
@@ -50,7 +52,7 @@ def decatt_word():
     config.learning_rate = 0.05
     config.save_dirname = "model_dir"
     config.use_pretrained_word_embedding = False
-    config.dict_dim = 40000 # approx_vocab_size
+    config.dict_dim = 40000  # approx_vocab_size
     config.metric_type = ['accuracy', 'accuracy_with_threshold']
     config.optimizer_type = 'sgd'
     config.lr_decay = 1
@@ -61,11 +63,11 @@ def decatt_word():
 
     # net config
     config.emb_dim = 300
-    config.proj_emb_dim = 200 #TODO: has project?
+    config.proj_emb_dim = 200  #TODO: has project?
     config.num_units = [400, 200]
     config.word_embedding_trainable = True
     config.droprate = 0.1
-    config.share_wight_btw_seq =  True
+    config.share_wight_btw_seq = True
     config.class_dim = 2
 
-    return config 
+    return config
diff --git a/PaddleNLP/text_matching_on_quora/configs/infer_sent.py b/PaddleNLP/unarchived/text_matching_on_quora/configs/infer_sent.py
similarity index 93%
rename from PaddleNLP/text_matching_on_quora/configs/infer_sent.py
rename to PaddleNLP/unarchived/text_matching_on_quora/configs/infer_sent.py
index 1b71f7b99893a1ec3661b8e1879b4bce7b2a97f0..896672c49806ace936c20fe27a865ea6d59254bc 100755
--- a/PaddleNLP/text_matching_on_quora/configs/infer_sent.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/configs/infer_sent.py
@@ -14,6 +14,7 @@
 
 from . import basic_config
 
+
 def infer_sent_v1():
     """
     set configs
@@ -24,11 +25,11 @@ def infer_sent_v1():
     config.optimizer_type = 'sgd'
     config.save_dirname = "model_dir"
     config.use_pretrained_word_embedding = True
-    config.dict_dim = 40000 # approx_vocab_size
+    config.dict_dim = 40000  # approx_vocab_size
     config.class_dim = 2
 
     # net config
-    config.emb_dim = 300 
+    config.emb_dim = 300
     config.droprate_lstm = 0.0
     config.droprate_fc = 0.0
     config.word_embedding_trainable = False
@@ -37,6 +38,7 @@ def infer_sent_v1():
 
     return config
 
+
 def infer_sent_v2():
     """
     use our own config
@@ -47,7 +49,7 @@ def infer_sent_v2():
     config.optimizer_type = 'adam'
     config.save_dirname = "model_dir"
     config.use_pretrained_word_embedding = True
-    config.dict_dim = 40000 # approx_vocab_size
+    config.dict_dim = 40000  # approx_vocab_size
     config.class_dim = 2
 
     # net config
diff --git a/PaddleNLP/text_matching_on_quora/configs/sse.py b/PaddleNLP/unarchived/text_matching_on_quora/configs/sse.py
similarity index 96%
rename from PaddleNLP/text_matching_on_quora/configs/sse.py
rename to PaddleNLP/unarchived/text_matching_on_quora/configs/sse.py
index 45d4ab6d6c5f059170de7acc26865ea273755fe1..4966465ff2be4cb53d28cfe2be79b0e754f4f977 100755
--- a/PaddleNLP/text_matching_on_quora/configs/sse.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/configs/sse.py
@@ -14,6 +14,7 @@
 
 from . import basic_config
 
+
 def sse_base():
     """
     use config in the paper 'Shortcut-Stacked Sentence Encoders for Multi-Domain Inference'
@@ -23,14 +24,14 @@ def sse_base():
     config.lr_decay = 0.7
     config.save_dirname = "model_dir"
     config.use_pretrained_word_embedding = True
-    config.dict_dim = 40000 # approx_vocab_size
+    config.dict_dim = 40000  # approx_vocab_size
     config.metric_type = ['accuracy']
     config.optimizer_type = 'adam'
     config.use_lod_tensor = True
     config.embedding_norm = False
     config.OOV_fill = 'uniform'
     config.duplicate_data = False
-    
+
     # net config
     config.emb_dim = 300
     config.rnn_hid_dim = [512, 1024, 2048]
diff --git a/PaddleNLP/text_matching_on_quora/data/prepare_quora_data.sh b/PaddleNLP/unarchived/text_matching_on_quora/data/prepare_quora_data.sh
similarity index 100%
rename from PaddleNLP/text_matching_on_quora/data/prepare_quora_data.sh
rename to PaddleNLP/unarchived/text_matching_on_quora/data/prepare_quora_data.sh
diff --git a/PaddleNLP/text_matching_on_quora/imgs/README.md b/PaddleNLP/unarchived/text_matching_on_quora/imgs/README.md
similarity index 100%
rename from PaddleNLP/text_matching_on_quora/imgs/README.md
rename to PaddleNLP/unarchived/text_matching_on_quora/imgs/README.md
diff --git a/PaddleNLP/text_matching_on_quora/imgs/models_test_acc.png b/PaddleNLP/unarchived/text_matching_on_quora/imgs/models_test_acc.png
similarity index 100%
rename from PaddleNLP/text_matching_on_quora/imgs/models_test_acc.png
rename to PaddleNLP/unarchived/text_matching_on_quora/imgs/models_test_acc.png
diff --git a/PaddleNLP/text_matching_on_quora/metric.py b/PaddleNLP/unarchived/text_matching_on_quora/metric.py
similarity index 100%
rename from PaddleNLP/text_matching_on_quora/metric.py
rename to PaddleNLP/unarchived/text_matching_on_quora/metric.py
index fd2318bb6d96225f94e2f494e0c19e2d78e0bd89..382bce6652f08aa1aa8ff3a8b7ad3958b7a56e8a 100755
--- a/PaddleNLP/text_matching_on_quora/metric.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/metric.py
@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import numpy as np
-
 """
 This Module defines evaluate metrics for classification tasks
 """
@@ -27,6 +26,7 @@ def accuracy(y_pred, label):
     y_pred_idx = np.argmax(y_pred, axis=1)
     return 1.0 * np.sum(y_pred_idx == label) / label.shape[0]
 
+
 def accuracy_with_threshold(y_pred, label, threshold=0.5):
     """
     define correct: the y_true class's prob in y_pred is bigger than threshold
diff --git a/PaddleNLP/text_matching_on_quora/models/__init__.py b/PaddleNLP/unarchived/text_matching_on_quora/models/__init__.py
similarity index 100%
rename from PaddleNLP/text_matching_on_quora/models/__init__.py
rename to PaddleNLP/unarchived/text_matching_on_quora/models/__init__.py
diff --git a/PaddleNLP/text_matching_on_quora/models/cdssm.py b/PaddleNLP/unarchived/text_matching_on_quora/models/cdssm.py
similarity index 63%
rename from PaddleNLP/text_matching_on_quora/models/cdssm.py
rename to PaddleNLP/unarchived/text_matching_on_quora/models/cdssm.py
index 2ff0cb5f3d9eaac872651362f17168c0878cb77b..334cfebb092e9d691ac7fbdd54f1f5429551cdb2 100755
--- a/PaddleNLP/text_matching_on_quora/models/cdssm.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/models/cdssm.py
@@ -14,39 +14,44 @@
 
 import paddle.fluid as fluid
 
+
 class cdssmNet():
     """cdssm net"""
 
     def __init__(self, config):
-         self._config = config
+        self._config = config
 
     def __call__(self, seq1, seq2, label):
         return self.body(seq1, seq2, label, self._config)
 
     def body(self, seq1, seq2, label, config):
         """Body function"""
-        
+
         def conv_model(seq):
-            embed = fluid.layers.embedding(input=seq, size=[config.dict_dim, config.emb_dim], param_attr='emb.w')
-            conv = fluid.layers.sequence_conv(embed,
-                                        num_filters=config.kernel_count,
-                                        filter_size=config.kernel_size,
-                                        filter_stride=1,
-                                        padding=True, # TODO: what is padding
-                                        bias_attr=False,
-                                        param_attr='conv1d.w',
-                                        act='relu')
+            embed = fluid.layers.embedding(
+                input=seq,
+                size=[config.dict_dim, config.emb_dim],
+                param_attr='emb.w')
+            conv = fluid.layers.sequence_conv(
+                embed,
+                num_filters=config.kernel_count,
+                filter_size=config.kernel_size,
+                filter_stride=1,
+                padding=True,  # TODO: what is padding
+                bias_attr=False,
+                param_attr='conv1d.w',
+                act='relu')
             #print paddle.parameters.get('conv1d.w').shape
 
-            conv = fluid.layers.dropout(conv, dropout_prob = config.droprate_conv)
+            conv = fluid.layers.dropout(conv, dropout_prob=config.droprate_conv)
             pool = fluid.layers.sequence_pool(conv, pool_type="max")
             fc = fluid.layers.fc(pool,
-                             size=config.fc_dim,
-                             param_attr='fc1.w',
-                             bias_attr='fc1.b',
-                             act='relu')
+                                 size=config.fc_dim,
+                                 param_attr='fc1.w',
+                                 bias_attr='fc1.b',
+                                 act='relu')
             return fc
- 
+
         def MLP(vec):
             for dim in config.mlp_hid_dim:
                 vec = fluid.layers.fc(vec, size=dim, act='relu')
@@ -57,9 +62,10 @@ class cdssmNet():
         seq2_fc = conv_model(seq2)
         concated_seq = fluid.layers.concat(input=[seq1_fc, seq2_fc], axis=1)
         mlp_res = MLP(concated_seq)
-        prediction = fluid.layers.fc(mlp_res, size=config.class_dim, act='softmax')
+        prediction = fluid.layers.fc(mlp_res,
+                                     size=config.class_dim,
+                                     act='softmax')
         loss = fluid.layers.cross_entropy(input=prediction, label=label)
         avg_cost = fluid.layers.mean(x=loss)
         acc = fluid.layers.accuracy(input=prediction, label=label)
         return avg_cost, acc, prediction
-
diff --git a/PaddleNLP/text_matching_on_quora/models/dec_att.py b/PaddleNLP/unarchived/text_matching_on_quora/models/dec_att.py
similarity index 52%
rename from PaddleNLP/text_matching_on_quora/models/dec_att.py
rename to PaddleNLP/unarchived/text_matching_on_quora/models/dec_att.py
index 68bde4e117d71b09e6faff12b6175984c30a9f7a..4c3fecbe2b6fbea766dc353688e64780be967b97 100755
--- a/PaddleNLP/text_matching_on_quora/models/dec_att.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/models/dec_att.py
@@ -14,12 +14,13 @@
 
 import paddle.fluid as fluid
 
+
 class DecAttNet():
     """decompose attention net"""
 
     def __init__(self, config):
-         self._config = config
-         self.initializer = fluid.initializer.Xavier(uniform=False)
+        self._config = config
+        self.initializer = fluid.initializer.Xavier(uniform=False)
 
     def __call__(self, seq1, seq2, mask1, mask2, label):
         return self.body(seq1, seq2, mask1, mask2, label)
@@ -33,41 +34,54 @@ class DecAttNet():
         alpha, beta = self.attend(masked_q1, masked_q2)
         if self._config.share_wight_btw_seq:
             seq1_compare = self.compare(masked_q1, beta, param_prefix='compare')
-            seq2_compare = self.compare(masked_q2, alpha, param_prefix='compare')
+            seq2_compare = self.compare(
+                masked_q2, alpha, param_prefix='compare')
         else:
-            seq1_compare = self.compare(masked_q1, beta, param_prefix='compare_1')
-            seq2_compare = self.compare(masked_q2, alpha, param_prefix='compare_2')
+            seq1_compare = self.compare(
+                masked_q1, beta, param_prefix='compare_1')
+            seq2_compare = self.compare(
+                masked_q2, alpha, param_prefix='compare_2')
         aggregate_res = self.aggregate(seq1_compare, seq2_compare)
-        prediction = fluid.layers.fc(aggregate_res, size=self._config.class_dim, act='softmax')
+        prediction = fluid.layers.fc(aggregate_res,
+                                     size=self._config.class_dim,
+                                     act='softmax')
         loss = fluid.layers.cross_entropy(input=prediction, label=label)
         avg_cost = fluid.layers.mean(x=loss)
         acc = fluid.layers.accuracy(input=prediction, label=label)
         return avg_cost, acc, prediction
 
     def apply_mask(self, seq, mask):
-       """
+        """
        apply mask on seq
        Input: seq in shape [batch_size, seq_len, embedding_size]
        Input: mask in shape [batch_size, seq_len]
        Output: masked seq in shape [batch_size, seq_len, embedding_size]
        """
-       return fluid.layers.elementwise_mul(x=seq, y=mask, axis=0)
+        return fluid.layers.elementwise_mul(x=seq, y=mask, axis=0)
 
     def feed_forward_2d(self, vec, param_prefix):
         """
         Input: vec in shape [batch_size, seq_len, vec_dim]
         Output: fc2 in shape [batch_size, seq_len, num_units[1]]
         """
-        fc1 = fluid.layers.fc(vec, size=self._config.num_units[0], num_flatten_dims=2,
-                        param_attr=fluid.ParamAttr(name=param_prefix+'_fc1.w',
-                                                   initializer=self.initializer),
-                        bias_attr=param_prefix + '_fc1.b', act='relu')
-        fc1 = fluid.layers.dropout(fc1, dropout_prob = self._config.droprate)
-        fc2 = fluid.layers.fc(fc1, size=self._config.num_units[1], num_flatten_dims=2,
-                        param_attr=fluid.ParamAttr(name=param_prefix+'_fc2.w',
-                                                   initializer=self.initializer),
-                        bias_attr=param_prefix + '_fc2.b', act='relu')
-        fc2 = fluid.layers.dropout(fc2, dropout_prob = self._config.droprate)
+        fc1 = fluid.layers.fc(vec,
+                              size=self._config.num_units[0],
+                              num_flatten_dims=2,
+                              param_attr=fluid.ParamAttr(
+                                  name=param_prefix + '_fc1.w',
+                                  initializer=self.initializer),
+                              bias_attr=param_prefix + '_fc1.b',
+                              act='relu')
+        fc1 = fluid.layers.dropout(fc1, dropout_prob=self._config.droprate)
+        fc2 = fluid.layers.fc(fc1,
+                              size=self._config.num_units[1],
+                              num_flatten_dims=2,
+                              param_attr=fluid.ParamAttr(
+                                  name=param_prefix + '_fc2.w',
+                                  initializer=self.initializer),
+                              bias_attr=param_prefix + '_fc2.b',
+                              act='relu')
+        fc2 = fluid.layers.dropout(fc2, dropout_prob=self._config.droprate)
         return fc2
 
     def feed_forward(self, vec, param_prefix):
@@ -75,27 +89,41 @@ class DecAttNet():
         Input: vec in shape [batch_size, vec_dim]
         Output: fc2 in shape [batch_size, num_units[1]]
         """
-        fc1 = fluid.layers.fc(vec, size=self._config.num_units[0], num_flatten_dims=1,
-                        param_attr=fluid.ParamAttr(name=param_prefix+'_fc1.w',
-                                                   initializer=self.initializer),
-                        bias_attr=param_prefix + '_fc1.b', act='relu')
-        fc1 = fluid.layers.dropout(fc1, dropout_prob = self._config.droprate)
-        fc2 = fluid.layers.fc(fc1, size=self._config.num_units[1], num_flatten_dims=1,
-                        param_attr=fluid.ParamAttr(name=param_prefix+'_fc2.w',
-                                                   initializer=self.initializer),
-                        bias_attr=param_prefix + '_fc2.b', act='relu')
-        fc2 = fluid.layers.dropout(fc2, dropout_prob = self._config.droprate)
+        fc1 = fluid.layers.fc(vec,
+                              size=self._config.num_units[0],
+                              num_flatten_dims=1,
+                              param_attr=fluid.ParamAttr(
+                                  name=param_prefix + '_fc1.w',
+                                  initializer=self.initializer),
+                              bias_attr=param_prefix + '_fc1.b',
+                              act='relu')
+        fc1 = fluid.layers.dropout(fc1, dropout_prob=self._config.droprate)
+        fc2 = fluid.layers.fc(fc1,
+                              size=self._config.num_units[1],
+                              num_flatten_dims=1,
+                              param_attr=fluid.ParamAttr(
+                                  name=param_prefix + '_fc2.w',
+                                  initializer=self.initializer),
+                              bias_attr=param_prefix + '_fc2.b',
+                              act='relu')
+        fc2 = fluid.layers.dropout(fc2, dropout_prob=self._config.droprate)
         return fc2
 
     def transformation(self, seq):
-        embed = fluid.layers.embedding(input=seq, size=[self._config.dict_dim, self._config.emb_dim],
-                                       param_attr=fluid.ParamAttr(name='emb.w', trainable=self._config.word_embedding_trainable))
+        embed = fluid.layers.embedding(
+            input=seq,
+            size=[self._config.dict_dim, self._config.emb_dim],
+            param_attr=fluid.ParamAttr(
+                name='emb.w', trainable=self._config.word_embedding_trainable))
         if self._config.proj_emb_dim is not None:
-            return fluid.layers.fc(embed, size=self._config.proj_emb_dim, num_flatten_dims=2,
-                        param_attr=fluid.ParamAttr(name='project' + '_fc1.w',
-                                                   initializer=self.initializer),
-                        bias_attr=False,
-                        act=None)
+            return fluid.layers.fc(embed,
+                                   size=self._config.proj_emb_dim,
+                                   num_flatten_dims=2,
+                                   param_attr=fluid.ParamAttr(
+                                       name='project' + '_fc1.w',
+                                       initializer=self.initializer),
+                                   bias_attr=False,
+                                   act=None)
         return embed
 
     def attend(self, seq1, seq2):
@@ -114,7 +142,8 @@ class DecAttNet():
         attention_weight = fluid.layers.matmul(seq1, seq2, transpose_y=True)
         normalized_attention_weight = fluid.layers.softmax(attention_weight)
         beta = fluid.layers.matmul(normalized_attention_weight, seq2)
-        attention_weight_t = fluid.layers.transpose(attention_weight, perm=[0, 2, 1])
+        attention_weight_t = fluid.layers.transpose(
+            attention_weight, perm=[0, 2, 1])
         normalized_attention_weight_t = fluid.layers.softmax(attention_weight_t)
         alpha = fluid.layers.matmul(normalized_attention_weight_t, seq1)
         return alpha, beta
@@ -128,4 +157,3 @@ class DecAttNet():
         vec2 = fluid.layers.reduce_sum(vec2, dim=1)
         concat_vec = fluid.layers.concat(input=[vec1, vec2], axis=1)
         return self.feed_forward(concat_vec, param_prefix='aggregate')
-
diff --git a/PaddleNLP/text_matching_on_quora/models/infer_sent.py b/PaddleNLP/unarchived/text_matching_on_quora/models/infer_sent.py
similarity index 70%
rename from PaddleNLP/text_matching_on_quora/models/infer_sent.py
rename to PaddleNLP/unarchived/text_matching_on_quora/models/infer_sent.py
index 08679cb9fbf489e63390a52ba0f842d5832023d1..67de901bdd4d5f8ce1bcc911c2de87fe89f58033 100644
--- a/PaddleNLP/text_matching_on_quora/models/infer_sent.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/models/infer_sent.py
@@ -16,6 +16,7 @@ import paddle.fluid as fluid
 from .my_layers import bi_lstm_layer
 from .match_layers import ElementwiseMatching
 
+
 class InferSentNet():
     """
     Base on the paper: Supervised Learning of Universal Sentence Representations from Natural Language Inference Data:
@@ -23,7 +24,7 @@ class InferSentNet():
     """
 
     def __init__(self, config):
-         self._config = config
+        self._config = config
 
     def __call__(self, seq1, seq2, label):
         return self.body(seq1, seq2, label, self._config)
@@ -36,7 +37,9 @@ class InferSentNet():
         seq_match = ElementwiseMatching(seq1_rnn, seq2_rnn)
 
         mlp_res = self.MLP(seq_match)
-        prediction = fluid.layers.fc(mlp_res, size=self._config.class_dim, act='softmax')
+        prediction = fluid.layers.fc(mlp_res,
+                                     size=self._config.class_dim,
+                                     act='softmax')
         loss = fluid.layers.cross_entropy(input=prediction, label=label)
         avg_cost = fluid.layers.mean(x=loss)
         acc = fluid.layers.accuracy(input=prediction, label=label)
@@ -46,26 +49,29 @@ class InferSentNet():
         """encoder"""
 
         embed = fluid.layers.embedding(
-                    input=seq,
-                    size=[self._config.dict_dim, self._config.emb_dim],
-                    param_attr=fluid.ParamAttr(name='emb.w', trainable=self._config.word_embedding_trainable))
+            input=seq,
+            size=[self._config.dict_dim, self._config.emb_dim],
+            param_attr=fluid.ParamAttr(
+                name='emb.w', trainable=self._config.word_embedding_trainable))
 
         bi_lstm_h = bi_lstm_layer(
-                        embed,
-                        rnn_hid_dim = self._config.rnn_hid_dim, 
-                        name='encoder')
+            embed, rnn_hid_dim=self._config.rnn_hid_dim, name='encoder')
 
-        bi_lstm_h = fluid.layers.dropout(bi_lstm_h, dropout_prob=self._config.droprate_lstm)
+        bi_lstm_h = fluid.layers.dropout(
+            bi_lstm_h, dropout_prob=self._config.droprate_lstm)
         pool = fluid.layers.sequence_pool(input=bi_lstm_h, pool_type='max')
         return pool
 
     def MLP(self, vec):
         if self._config.mlp_non_linear:
-            drop1 = fluid.layers.dropout(vec, dropout_prob=self._config.droprate_fc)
+            drop1 = fluid.layers.dropout(
+                vec, dropout_prob=self._config.droprate_fc)
             fc1 = fluid.layers.fc(drop1, size=512, act='tanh')
-            drop2 = fluid.layers.dropout(fc1, dropout_prob=self._config.droprate_fc)
+            drop2 = fluid.layers.dropout(
+                fc1, dropout_prob=self._config.droprate_fc)
             fc2 = fluid.layers.fc(drop2, size=512, act='tanh')
-            res = fluid.layers.dropout(fc2, dropout_prob=self._config.droprate_fc)
+            res = fluid.layers.dropout(
+                fc2, dropout_prob=self._config.droprate_fc)
         else:
             fc1 = fluid.layers.fc(vec, size=512, act=None)
             res = fluid.layers.fc(fc1, size=512, act=None)
diff --git a/PaddleNLP/text_matching_on_quora/models/match_layers.py b/PaddleNLP/unarchived/text_matching_on_quora/models/match_layers.py
similarity index 84%
rename from PaddleNLP/text_matching_on_quora/models/match_layers.py
rename to PaddleNLP/unarchived/text_matching_on_quora/models/match_layers.py
index b913ece03dfa7113bafcb209afba5d3405f4c277..314d5b2c290d5befb7db12a1d31a6e33fc70e3b8 100755
--- a/PaddleNLP/text_matching_on_quora/models/match_layers.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/models/match_layers.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """
 This Module provide different kinds of Match layers
 """
@@ -26,11 +25,9 @@ def MultiPerspectiveMatching(vec1, vec2, perspective_num):
     sim_res = None
     for i in range(perspective_num):
         vec1_res = fluid.layers.elementwise_add_with_weight(
-                       vec1,
-                       param_attr="elementwise_add_with_weight." + str(i))
+            vec1, param_attr="elementwise_add_with_weight." + str(i))
         vec2_res = fluid.layers.elementwise_add_with_weight(
-                       vec2,
-                       param_attr="elementwise_add_with_weight." + str(i))
+            vec2, param_attr="elementwise_add_with_weight." + str(i))
         m = fluid.layers.cos_sim(vec1_res, vec2_res)
         if sim_res is None:
             sim_res = m
@@ -52,8 +49,7 @@ def ElementwiseMatching(vec1, vec2):
     reference: [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://arxiv.org/abs/1705.02364)
     """
     elementwise_mul = fluid.layers.elementwise_mul(x=vec1, y=vec2)
-    elementwise_sub = fluid.layers.elementwise_sub(x=vec1, y=vec2) 
+    elementwise_sub = fluid.layers.elementwise_sub(x=vec1, y=vec2)
     elementwise_abs_sub = fluid.layers.abs(elementwise_sub)
-    return fluid.layers.concat(input=[vec1, vec2, elementwise_mul, elementwise_abs_sub], axis=1)
-
-
+    return fluid.layers.concat(
+        input=[vec1, vec2, elementwise_mul, elementwise_abs_sub], axis=1)
diff --git a/PaddleNLP/text_matching_on_quora/models/my_layers.py b/PaddleNLP/unarchived/text_matching_on_quora/models/my_layers.py
similarity index 59%
rename from PaddleNLP/text_matching_on_quora/models/my_layers.py
rename to PaddleNLP/unarchived/text_matching_on_quora/models/my_layers.py
index 842d76cabc88c9b8aa5c57fd23e0e6abbb1283fc..374d798209920dbd20f447269a62e57f38523d0a 100755
--- a/PaddleNLP/text_matching_on_quora/models/my_layers.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/models/my_layers.py
@@ -11,34 +11,35 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """
 This module defines some Frequently-used DNN layers
 """
 
 import paddle.fluid as fluid
 
+
 def bi_lstm_layer(input, rnn_hid_dim, name):
     """
     This is a Bi-directional LSTM(long short term memory) Module
     """
-    fc0 = fluid.layers.fc(input=input,              # fc for lstm
-                                size=rnn_hid_dim * 4,
-                                param_attr=name + '.fc0.w',
-                                bias_attr=False,
-                                act=None)
-        
+    fc0 = fluid.layers.fc(
+        input=input,  # fc for lstm
+        size=rnn_hid_dim * 4,
+        param_attr=name + '.fc0.w',
+        bias_attr=False,
+        act=None)
+
     lstm_h, c = fluid.layers.dynamic_lstm(
-                 input=fc0,
-                 size=rnn_hid_dim * 4,
-                 is_reverse=False,
-                 param_attr=name + '.lstm_w',
-                 bias_attr=name + '.lstm_b')
+        input=fc0,
+        size=rnn_hid_dim * 4,
+        is_reverse=False,
+        param_attr=name + '.lstm_w',
+        bias_attr=name + '.lstm_b')
 
     reversed_lstm_h, reversed_c = fluid.layers.dynamic_lstm(
-                 input=fc0,
-                 size=rnn_hid_dim * 4,
-                 is_reverse=True,
-                 param_attr=name + '.reversed_lstm_w',
-                 bias_attr=name + '.reversed_lstm_b')
-    return fluid.layers.concat(input=[lstm_h, reversed_lstm_h], axis=1) 
+        input=fc0,
+        size=rnn_hid_dim * 4,
+        is_reverse=True,
+        param_attr=name + '.reversed_lstm_w',
+        bias_attr=name + '.reversed_lstm_b')
+    return fluid.layers.concat(input=[lstm_h, reversed_lstm_h], axis=1)
diff --git a/PaddleNLP/text_matching_on_quora/models/pwim.py b/PaddleNLP/unarchived/text_matching_on_quora/models/pwim.py
similarity index 100%
rename from PaddleNLP/text_matching_on_quora/models/pwim.py
rename to PaddleNLP/unarchived/text_matching_on_quora/models/pwim.py
diff --git a/PaddleNLP/text_matching_on_quora/models/sse.py b/PaddleNLP/unarchived/text_matching_on_quora/models/sse.py
similarity index 67%
rename from PaddleNLP/text_matching_on_quora/models/sse.py
rename to PaddleNLP/unarchived/text_matching_on_quora/models/sse.py
index d75727fe515329612a9fc0d362af5efa860e9346..621f442528eb038457c4f4d99ef47c676a11ad6e 100644
--- a/PaddleNLP/text_matching_on_quora/models/sse.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/models/sse.py
@@ -16,6 +16,7 @@ import paddle.fluid as fluid
 from .my_layers import bi_lstm_layer
 from .match_layers import ElementwiseMatching
 
+
 class SSENet():
     """
     SSE net: Shortcut-Stacked Sentence Encoders for Multi-Domain Inference
@@ -23,7 +24,7 @@ class SSENet():
     """
 
     def __init__(self, config):
-         self._config = config
+        self._config = config
 
     def __call__(self, seq1, seq2, label):
         return self.body(seq1, seq2, label, self._config)
@@ -32,29 +33,38 @@ class SSENet():
         """Body function"""
 
         def stacked_bi_rnn_model(seq):
-            embed = fluid.layers.embedding(input=seq, size=[self._config.dict_dim, self._config.emb_dim], param_attr='emb.w')
+            embed = fluid.layers.embedding(
+                input=seq,
+                size=[self._config.dict_dim, self._config.emb_dim],
+                param_attr='emb.w')
             stacked_lstm_out = [embed]
             for i in range(len(self._config.rnn_hid_dim)):
                 if i == 0:
                     feature = embed
                 else:
-                    feature = fluid.layers.concat(input = stacked_lstm_out, axis=1)
-                bi_lstm_h = bi_lstm_layer(feature,
-                                          rnn_hid_dim=self._config.rnn_hid_dim[i],
-                                          name="lstm_" + str(i))
+                    feature = fluid.layers.concat(
+                        input=stacked_lstm_out, axis=1)
+                bi_lstm_h = bi_lstm_layer(
+                    feature,
+                    rnn_hid_dim=self._config.rnn_hid_dim[i],
+                    name="lstm_" + str(i))
 
                 # add dropout except for the last stacked lstm layer
                 if i != len(self._config.rnn_hid_dim) - 1:
-                    bi_lstm_h = fluid.layers.dropout(bi_lstm_h, dropout_prob=self._config.droprate_lstm)
+                    bi_lstm_h = fluid.layers.dropout(
+                        bi_lstm_h, dropout_prob=self._config.droprate_lstm)
                 stacked_lstm_out.append(bi_lstm_h)
             pool = fluid.layers.sequence_pool(input=bi_lstm_h, pool_type='max')
             return pool
 
         def MLP(vec):
             for i in range(len(self._config.fc_dim)):
-                vec = fluid.layers.fc(vec, size=self._config.fc_dim[i], act='relu')
+                vec = fluid.layers.fc(vec,
+                                      size=self._config.fc_dim[i],
+                                      act='relu')
                 # add dropout after every layer of MLP
-                vec = fluid.layers.dropout(vec, dropout_prob=self._config.droprate_fc)
+                vec = fluid.layers.dropout(
+                    vec, dropout_prob=self._config.droprate_fc)
             return vec
 
         seq1_rnn = stacked_bi_rnn_model(seq1)
@@ -62,7 +72,9 @@ class SSENet():
         seq_match = ElementwiseMatching(seq1_rnn, seq2_rnn)
 
         mlp_res = MLP(seq_match)
-        prediction = fluid.layers.fc(mlp_res, size=self._config.class_dim, act='softmax')
+        prediction = fluid.layers.fc(mlp_res,
+                                     size=self._config.class_dim,
+                                     act='softmax')
         loss = fluid.layers.cross_entropy(input=prediction, label=label)
         avg_cost = fluid.layers.mean(x=loss)
         acc = fluid.layers.accuracy(input=prediction, label=label)
diff --git a/PaddleNLP/unarchived/text_matching_on_quora/models/test.py b/PaddleNLP/unarchived/text_matching_on_quora/models/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/PaddleNLP/text_matching_on_quora/pretrained_word2vec.py b/PaddleNLP/unarchived/text_matching_on_quora/pretrained_word2vec.py
similarity index 78%
rename from PaddleNLP/text_matching_on_quora/pretrained_word2vec.py
rename to PaddleNLP/unarchived/text_matching_on_quora/pretrained_word2vec.py
index cda934d3402d1fd05432e75e3b7bfd0a1bd4ad2c..a6df805533bdb141af5c6665a4fb95762bf659c8 100755
--- a/PaddleNLP/text_matching_on_quora/pretrained_word2vec.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/pretrained_word2vec.py
@@ -11,51 +11,57 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """
 This Module provide pretrained word-embeddings 
 """
 
-from __future__ import print_function, unicode_literals 
+from __future__ import print_function, unicode_literals
 import numpy as np
 import time, datetime
 import os, sys
 
+
 def maybe_open(filepath):
-    if sys.version_info <= (3, 0): # for python2
+    if sys.version_info <= (3, 0):  # for python2
         return open(filepath, 'r')
     else:
         return open(filepath, 'r', encoding="utf-8")
-    
+
+
 def Glove840B_300D(filepath, keys=None):
     """
     input: the "glove.840B.300d.txt" file path
     return: a dict, key: word (unicode), value: a numpy array with shape [300]
     """
     if keys is not None:
-        assert(isinstance(keys, set))
+        assert (isinstance(keys, set))
     print("loading word2vec from ", filepath)
     print("please wait for a minute.")
     start = time.time()
     word2vec = {}
     with maybe_open(filepath) as f:
         for line in f:
-            if sys.version_info <= (3, 0): # for python2
+            if sys.version_info <= (3, 0):  # for python2
                 line = line.decode('utf-8')
             info = line.strip("\n").split(" ")
             word = info[0]
             if (keys is not None) and (word not in keys):
                 continue
             vector = info[1:]
-            assert(len(vector) == 300)
+            assert (len(vector) == 300)
             word2vec[word] = np.asarray(vector, dtype='float32')
 
     end = time.time()
-    print("Spent ", str(datetime.timedelta(seconds=end-start)), " on loading word2vec.")
+    print(
+        "Spent ",
+        str(datetime.timedelta(seconds=end - start)),
+        " on loading word2vec.")
     return word2vec
 
+
 if __name__ == '__main__':
     from os.path import expanduser
     home = expanduser("~")
-    embed_dict = Glove840B_300D(os.path.join(home, "./.cache/paddle/dataset/glove.840B.300d.txt"))
+    embed_dict = Glove840B_300D(
+        os.path.join(home, "./.cache/paddle/dataset/glove.840B.300d.txt"))
     exit(0)
diff --git a/PaddleNLP/text_matching_on_quora/quora_question_pairs.py b/PaddleNLP/unarchived/text_matching_on_quora/quora_question_pairs.py
similarity index 93%
rename from PaddleNLP/text_matching_on_quora/quora_question_pairs.py
rename to PaddleNLP/unarchived/text_matching_on_quora/quora_question_pairs.py
index 4a1694929dc9a5a1d78bce2f99be04de0f1ba8e5..e21742aedb4bb0a5211ac00e78904a9d856cb74c 100755
--- a/PaddleNLP/text_matching_on_quora/quora_question_pairs.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/quora_question_pairs.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """
 """
 
@@ -25,7 +24,6 @@ import os, sys
 import nltk
 from os.path import expanduser
 
-
 __all__ = ['word_dict', 'train', 'dev', 'test']
 
 URL = "https://drive.google.com/file/d/0B0PlTAo--BnaQWlsZl9FZ3l1c28/view"
@@ -38,18 +36,19 @@ QUORA_DEV_FILE_NAME = os.path.join(DATA_HOME, DATA_DIR, 'dev.tsv')
 QUORA_TEST_FILE_NAME = os.path.join(DATA_HOME, DATA_DIR, 'test.tsv')
 
 # punctuation or nltk or space
-TOKENIZE_METHOD='space'
+TOKENIZE_METHOD = 'space'
 
 COLUMN_COUNT = 4
 
 
 def tokenize(s):
-    if sys.version_info <= (3, 0): # for python2
+    if sys.version_info <= (3, 0):  # for python2
         s = s.decode('utf-8')
     if TOKENIZE_METHOD == "nltk":
         return nltk.tokenize.word_tokenize(s)
     elif TOKENIZE_METHOD == "punctuation":
-        return s.translate({ord(char): None for char in string.punctuation}).lower().split()
+        return s.translate({ord(char): None
+                            for char in string.punctuation}).lower().split()
     elif TOKENIZE_METHOD == "space":
         return s.split()
     else:
@@ -68,7 +67,7 @@ def maybe_open(file_name):
                 "     |- readme.txt\n"
                 "     |- wordvec.txt\n")
         raise RuntimeError(msg)
-    if sys.version_info <= (3, 0): # for python2
+    if sys.version_info <= (3, 0):  # for python2
         return open(file_name, 'r')
     else:
         return open(file_name, 'r', encoding="utf-8")
@@ -133,10 +132,10 @@ def reader_creator(file_name, word_idx):
         for (q1, q2, label) in tokenized_question_pairs(file_name):
             q1_ids = [word_idx.get(w, UNK_ID) for w in q1]
             q2_ids = [word_idx.get(w, UNK_ID) for w in q2]
-            if q1_ids != [] and q2_ids != []: # [] is not allowed in fluid
-                assert(label in [0, 1])
+            if q1_ids != [] and q2_ids != []:  # [] is not allowed in fluid
+                assert (label in [0, 1])
                 yield q1_ids, q2_ids, label
-    
+
     return reader
 
 
@@ -151,7 +150,7 @@ def train(word_idx):
     :type word_idx: dict
     :return: Training reader creator
     :rtype: callable
-    """   
+    """
     return reader_creator(QUORA_TRAIN_FILE_NAME, word_idx)
 
 
@@ -170,6 +169,7 @@ def dev(word_idx):
     """
     return reader_creator(QUORA_DEV_FILE_NAME, word_idx)
 
+
 def test(word_idx):
     """
     Quora test set creator.
@@ -193,4 +193,3 @@ def word_dict():
     :rtype: dict
     """
     return build_dict(file_name=QUORA_TRAIN_FILE_NAME, cutoff=4)
-
diff --git a/PaddleNLP/text_matching_on_quora/train_and_evaluate.py b/PaddleNLP/unarchived/text_matching_on_quora/train_and_evaluate.py
similarity index 63%
rename from PaddleNLP/text_matching_on_quora/train_and_evaluate.py
rename to PaddleNLP/unarchived/text_matching_on_quora/train_and_evaluate.py
index 0f88c6b6ef13aec25e08527b7efabe8638a3af25..303dd4156a874fa1debaf4cfbe6295c736954ba3 100755
--- a/PaddleNLP/text_matching_on_quora/train_and_evaluate.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/train_and_evaluate.py
@@ -27,21 +27,32 @@ import paddle.fluid as fluid
 import utils, metric, configs
 import models
 
-from pretrained_word2vec import Glove840B_300D 
+from pretrained_word2vec import Glove840B_300D
 
 parser = argparse.ArgumentParser(description=__doc__)
 
-parser.add_argument('--model_name',       type=str,   default='cdssmNet',                  help="Which model to train")
-parser.add_argument('--config',           type=str,   default='cdssm_base',       help="The global config setting")
-parser.add_argument('--enable_ce', action='store_true', help='If set, run the task with continuous evaluation logs.')
+parser.add_argument(
+    '--model_name', type=str, default='cdssmNet', help="Which model to train")
+parser.add_argument(
+    '--config',
+    type=str,
+    default='cdssm_base',
+    help="The global config setting")
+parser.add_argument(
+    '--enable_ce',
+    action='store_true',
+    help='If set, run the task with continuous evaluation logs.')
 parser.add_argument('--epoch_num', type=int, help='Number of epoch')
 
 DATA_DIR = os.path.join(os.path.expanduser('~'), '.cache/paddle/dataset')
 
-def evaluate(epoch_id, exe, inference_program, dev_reader, test_reader, fetch_list, feeder, metric_type):
+
+def evaluate(epoch_id, exe, inference_program, dev_reader, test_reader,
+             fetch_list, feeder, metric_type):
     """
     evaluate on test/dev dataset
     """
+
     def infer(test_reader):
         """
         do inference function
@@ -50,10 +61,11 @@ def evaluate(epoch_id, exe, inference_program, dev_reader, test_reader, fetch_li
         total_count = 0
         preds, labels = [], []
         for data in test_reader():
-            avg_cost, avg_acc, batch_prediction = exe.run(inference_program,
-                          feed=feeder.feed(data),
-                          fetch_list=fetch_list,
-                          return_numpy=True)
+            avg_cost, avg_acc, batch_prediction = exe.run(
+                inference_program,
+                feed=feeder.feed(data),
+                fetch_list=fetch_list,
+                return_numpy=True)
             total_cost += avg_cost * len(data)
             total_count += len(data)
             preds.append(batch_prediction)
@@ -64,52 +76,44 @@ def evaluate(epoch_id, exe, inference_program, dev_reader, test_reader, fetch_li
         metric_res = []
         for metric_name in metric_type:
             if metric_name == 'accuracy_with_threshold':
-                metric_res.append((metric_name, metric.accuracy_with_threshold(y_pred, y_label, threshold=0.3)))
+                metric_res.append((metric_name, metric.accuracy_with_threshold(
+                    y_pred, y_label, threshold=0.3)))
             elif metric_name == 'accuracy':
-                metric_res.append((metric_name, metric.accuracy(y_pred, y_label)))
+                metric_res.append(
+                    (metric_name, metric.accuracy(y_pred, y_label)))
             else:
                 print("Unknown metric type: ", metric_name)
                 exit()
         return total_cost / (total_count * 1.0), metric_res
 
     dev_cost, dev_metric_res = infer(dev_reader)
-    print("[%s] epoch_id: %d, dev_cost: %f, " % (
-                 time.asctime( time.localtime(time.time()) ),
-                 epoch_id,
-                 dev_cost)
-               + ', '.join([str(x[0]) + ": " + str(x[1]) for x in dev_metric_res]))
+    print("[%s] epoch_id: %d, dev_cost: %f, " % (time.asctime(
+        time.localtime(time.time())), epoch_id, dev_cost) + ', '.join(
+            [str(x[0]) + ": " + str(x[1]) for x in dev_metric_res]))
 
     test_cost, test_metric_res = infer(test_reader)
-    print("[%s] epoch_id: %d, test_cost: %f, " % (
-                time.asctime( time.localtime(time.time()) ),
-                epoch_id,
-                test_cost)
-              + ', '.join([str(x[0]) + ": " + str(x[1]) for x in test_metric_res]))
+    print("[%s] epoch_id: %d, test_cost: %f, " % (time.asctime(
+        time.localtime(time.time())), epoch_id, test_cost) + ', '.join(
+            [str(x[0]) + ": " + str(x[1]) for x in test_metric_res]))
     print("")
 
 
-def train_and_evaluate(train_reader,
-          dev_reader,
-          test_reader, 
-          network,
-          optimizer,
-          global_config,
-          pretrained_word_embedding,
-          use_cuda,
-          parallel):
+def train_and_evaluate(train_reader, dev_reader, test_reader, network,
+                       optimizer, global_config, pretrained_word_embedding,
+                       use_cuda, parallel):
     """
     train network
     """
-    
+
     # define the net
-    if global_config.use_lod_tensor: 
+    if global_config.use_lod_tensor:
         # automatic add batch dim
         q1 = fluid.layers.data(
             name="question1", shape=[1], dtype="int64", lod_level=1)
         q2 = fluid.layers.data(
             name="question2", shape=[1], dtype="int64", lod_level=1)
         label = fluid.layers.data(name="label", shape=[1], dtype="int64")
-        cost, acc, prediction = network(q1, q2, label)  
+        cost, acc, prediction = network(q1, q2, label)
     else:
         # shape: [batch_size, max_seq_len_in_batch, 1]
         q1 = fluid.layers.data(
@@ -139,7 +143,8 @@ def train_and_evaluate(train_reader,
     if global_config.use_lod_tensor:
         feeder = fluid.DataFeeder(feed_list=[q1, q2, label], place=place)
     else:
-        feeder = fluid.DataFeeder(feed_list=[q1, q2, mask1, mask2, label], place=place)
+        feeder = fluid.DataFeeder(
+            feed_list=[q1, q2, mask1, mask2, label], place=place)
 
     # only for ce
     args = parser.parse_args()
@@ -151,29 +156,31 @@ def train_and_evaluate(train_reader,
     # logging param info
     for param in fluid.default_main_program().global_block().all_parameters():
         print("param name: %s; param shape: %s" % (param.name, param.shape))
-    
+
     # define inference_program
     inference_program = fluid.default_main_program().clone(for_test=True)
 
     optimizer.minimize(cost)
 
     exe.run(fluid.default_startup_program())
-    
+
     # load emb from a numpy erray
     if pretrained_word_embedding is not None:
         print("loading pretrained word embedding to param")
         embedding_name = "emb.w"
-        embedding_param = fluid.global_scope().find_var(embedding_name).get_tensor()
+        embedding_param = fluid.global_scope().find_var(
+            embedding_name).get_tensor()
         embedding_param.set(pretrained_word_embedding, place)
-   
-    evaluate(-1,
-             exe,
-             inference_program,
-             dev_reader,
-             test_reader,
-             fetch_list=[cost, acc, prediction],
-             feeder=feeder,
-             metric_type=global_config.metric_type)
+
+    evaluate(
+        -1,
+        exe,
+        inference_program,
+        dev_reader,
+        test_reader,
+        fetch_list=[cost, acc, prediction],
+        feeder=feeder,
+        metric_type=global_config.metric_type)
 
     # start training
     total_time = 0.0
@@ -192,12 +199,9 @@ def train_and_evaluate(train_reader,
             total_cost += data_size * avg_cost_np[0]
             data_count += data_size
             if batch_id % 100 == 0:
-                print("[%s] epoch_id: %d, batch_id: %d, cost: %f, acc: %f" % (
-                    time.asctime(time.localtime(time.time())),
-                    epoch_id, 
-                    batch_id, 
-                    avg_cost_np,
-                    avg_acc_np))
+                print("[%s] epoch_id: %d, batch_id: %d, cost: %f, acc: %f" %
+                      (time.asctime(time.localtime(time.time())), epoch_id,
+                       batch_id, avg_cost_np, avg_acc_np))
             batch_id += 1
         avg_cost = total_cost / data_count
         avg_acc = total_acc / data_count
@@ -205,10 +209,10 @@ def train_and_evaluate(train_reader,
         total_time += epoch_end_time - epoch_begin_time
 
         print("")
-        print("[%s] epoch_id: %d, train_avg_cost: %f, train_avg_acc: %f, epoch_time_cost: %f" % (
-            time.asctime( time.localtime(time.time())),
-            epoch_id, avg_cost, avg_acc,
-            time.time() - epoch_begin_time))
+        print(
+            "[%s] epoch_id: %d, train_avg_cost: %f, train_avg_acc: %f, epoch_time_cost: %f"
+            % (time.asctime(time.localtime(time.time())), epoch_id, avg_cost,
+               avg_acc, time.time() - epoch_begin_time))
 
         # only for ce
         if epoch_id == global_config.epoch_num - 1 and args.enable_ce:
@@ -217,23 +221,23 @@ def train_and_evaluate(train_reader,
             gpu_num = get_cards(args)
             print("kpis\teach_pass_duration_card%s\t%s" % \
                   (gpu_num, total_time / (global_config.epoch_num)))
-            print("kpis\ttrain_avg_cost_card%s\t%s" %
-                  (gpu_num, avg_cost))
-            print("kpis\ttrain_avg_acc_card%s\t%s" %
-                  (gpu_num, avg_acc))
-
+            print("kpis\ttrain_avg_cost_card%s\t%s" % (gpu_num, avg_cost))
+            print("kpis\ttrain_avg_acc_card%s\t%s" % (gpu_num, avg_acc))
 
         epoch_model = global_config.save_dirname + "/" + "epoch" + str(epoch_id)
-        fluid.io.save_inference_model(epoch_model, ["question1", "question2", "label"], acc, exe)    
-        
-        evaluate(epoch_id, 
-                 exe, 
-                 inference_program,
-                 dev_reader,
-                 test_reader, 
-                 fetch_list=[cost, acc, prediction], 
-                 feeder=feeder, 
-                 metric_type=global_config.metric_type)
+        fluid.io.save_inference_model(
+            epoch_model, ["question1", "question2", "label"], acc, exe)
+
+        evaluate(
+            epoch_id,
+            exe,
+            inference_program,
+            dev_reader,
+            test_reader,
+            fetch_list=[cost, acc, prediction],
+            feeder=feeder,
+            metric_type=global_config.metric_type)
+
 
 def main():
     """
@@ -254,30 +258,31 @@ def main():
     # get reader
     train_reader, dev_reader, test_reader = utils.prepare_data(
         "quora_question_pairs",
-         word_dict=word_dict,
-         batch_size = global_config.batch_size,
-         buf_size=800000,
-         duplicate_data=global_config.duplicate_data,
-         use_pad=(not global_config.use_lod_tensor))
- 
+        word_dict=word_dict,
+        batch_size=global_config.batch_size,
+        buf_size=800000,
+        duplicate_data=global_config.duplicate_data,
+        use_pad=(not global_config.use_lod_tensor))
+
     # load pretrained_word_embedding
     if global_config.use_pretrained_word_embedding:
-        word2vec = Glove840B_300D(filepath=os.path.join(DATA_DIR, "glove.840B.300d.txt"),
-                                  keys=set(word_dict.keys()))
+        word2vec = Glove840B_300D(
+            filepath=os.path.join(DATA_DIR, "glove.840B.300d.txt"),
+            keys=set(word_dict.keys()))
         pretrained_word_embedding = utils.get_pretrained_word_embedding(
-                                        word2vec=word2vec,
-                                        word2id=word_dict,
-                                        config=global_config)
-        print("pretrained_word_embedding to be load:", pretrained_word_embedding)
+            word2vec=word2vec, word2id=word_dict, config=global_config)
+        print("pretrained_word_embedding to be load:",
+              pretrained_word_embedding)
     else:
         pretrained_word_embedding = None
 
     # define optimizer
     optimizer = utils.getOptimizer(global_config)
-   
+
     # use cuda or not
     if not global_config.has_member('use_cuda'):
-        if 'CUDA_VISIBLE_DEVICES' in os.environ and os.environ['CUDA_VISIBLE_DEVICES'] != '':
+        if 'CUDA_VISIBLE_DEVICES' in os.environ and os.environ[
+                'CUDA_VISIBLE_DEVICES'] != '':
             global_config.use_cuda = True
         else:
             global_config.use_cuda = False
@@ -285,15 +290,15 @@ def main():
     global_config.list_config()
 
     train_and_evaluate(
-                   train_reader,
-                   dev_reader,
-                   test_reader,
-                   net,
-                   optimizer,
-                   global_config,
-                   pretrained_word_embedding,
-                   use_cuda=global_config.use_cuda,
-                   parallel=False)
+        train_reader,
+        dev_reader,
+        test_reader,
+        net,
+        optimizer,
+        global_config,
+        pretrained_word_embedding,
+        use_cuda=global_config.use_cuda,
+        parallel=False)
 
 
 def get_cards(args):
diff --git a/PaddleNLP/text_matching_on_quora/utils.py b/PaddleNLP/unarchived/text_matching_on_quora/utils.py
similarity index 65%
rename from PaddleNLP/text_matching_on_quora/utils.py
rename to PaddleNLP/unarchived/text_matching_on_quora/utils.py
index 572b32e749b4969c00670237a5d5ca9045ffb5d4..71df50039b7d20091643211f19b3dbdc9421d746 100755
--- a/PaddleNLP/text_matching_on_quora/utils.py
+++ b/PaddleNLP/unarchived/text_matching_on_quora/utils.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """
 This module provides utilities for data generator and optimizer definition 
 """
@@ -48,21 +47,27 @@ def getOptimizer(global_config):
     get Optimizer by config
     """
     if global_config.optimizer_type == "adam":
-        optimizer = fluid.optimizer.Adam(learning_rate=fluid.layers.exponential_decay(
-                                                      learning_rate=global_config.learning_rate,
-                                                      decay_steps=global_config.train_samples_num // global_config.batch_size,
-                                                      decay_rate=global_config.lr_decay))
+        optimizer = fluid.optimizer.Adam(
+            learning_rate=fluid.layers.exponential_decay(
+                learning_rate=global_config.learning_rate,
+                decay_steps=global_config.train_samples_num //
+                global_config.batch_size,
+                decay_rate=global_config.lr_decay))
     elif global_config.optimizer_type == "sgd":
-        optimizer = fluid.optimizer.SGD(learning_rate=fluid.layers.exponential_decay(
-                                                      learning_rate=global_config.learning_rate,
-                                                      decay_steps=global_config.train_samples_num // global_config.batch_size,
-                                                      decay_rate=global_config.lr_decay))
+        optimizer = fluid.optimizer.SGD(
+            learning_rate=fluid.layers.exponential_decay(
+                learning_rate=global_config.learning_rate,
+                decay_steps=global_config.train_samples_num //
+                global_config.batch_size,
+                decay_rate=global_config.lr_decay))
 
     elif global_config.optimizer_type == "adagrad":
-        optimizer = fluid.optimizer.Adagrad(learning_rate=fluid.layers.exponential_decay(
-                                                      learning_rate=global_config.learning_rate,
-                                                      decay_steps=global_config.train_samples_num // global_config.batch_size,
-                                                      decay_rate=global_config.lr_decay))
+        optimizer = fluid.optimizer.Adagrad(
+            learning_rate=fluid.layers.exponential_decay(
+                learning_rate=global_config.learning_rate,
+                decay_steps=global_config.train_samples_num //
+                global_config.batch_size,
+                decay_rate=global_config.lr_decay))
 
     return optimizer
 
@@ -70,22 +75,28 @@ def getOptimizer(global_config):
 def get_pretrained_word_embedding(word2vec, word2id, config):
     """get pretrained embedding in shape [config.dict_dim, config.emb_dim]"""
     print("preparing pretrained word embedding ...")
-    assert(config.dict_dim >= len(word2id))
-    word2id = sorted(word2id.items(), key = lambda x : x[1])
+    assert (config.dict_dim >= len(word2id))
+    word2id = sorted(word2id.items(), key=lambda x: x[1])
     words = [x[0] for x in word2id]
     words = words + [''] * (config.dict_dim - len(words))
     pretrained_emb = []
     for _, word in enumerate(words):
         if word in word2vec:
-            assert(len(word2vec[word] == config.emb_dim))
+            assert (len(word2vec[word] == config.emb_dim))
             if config.embedding_norm:
-                pretrained_emb.append(word2vec[word] / np.linalg.norm(word2vec[word]))
+                pretrained_emb.append(word2vec[word] /
+                                      np.linalg.norm(word2vec[word]))
             else:
                 pretrained_emb.append(word2vec[word])
         elif config.OOV_fill == 'uniform':
-            pretrained_emb.append(np.random.uniform(-0.05, 0.05, size=[config.emb_dim]).astype(np.float32))
+            pretrained_emb.append(
+                np.random.uniform(
+                    -0.05, 0.05, size=[config.emb_dim]).astype(np.float32))
         elif config.OOV_fill == 'normal':
-            pretrained_emb.append(np.random.normal(loc=0.0, scale=0.1, size=[config.emb_dim]).astype(np.float32))
+            pretrained_emb.append(
+                np.random.normal(
+                    loc=0.0, scale=0.1, size=[config.emb_dim]).astype(
+                        np.float32))
         else:
             print("Unkown OOV fill method: ", OOV_fill)
             exit()
@@ -128,7 +139,7 @@ def pad(reader, PAD_ID):
     Output: padded_reader, yield batches of [(padded_question1, padded_question2, mask1, mask2, label), ... ]
     """
 
-    assert(isinstance(PAD_ID, int))
+    assert (isinstance(PAD_ID, int))
 
     def padded_reader():
         for batch in reader():
@@ -144,14 +155,18 @@ def pad(reader, PAD_ID):
                 mask2 = [1] * seq_len2 + [0] * (max_len2 - seq_len2)
                 padded_question1 = question1 + [PAD_ID] * (max_len1 - seq_len1)
                 padded_question2 = question2 + [PAD_ID] * (max_len2 - seq_len2)
-                padded_question1 = [[x] for x in padded_question1] # last dim of questions must be 1, according to fluid's request
+                padded_question1 = [
+                    [x] for x in padded_question1
+                ]  # last dim of questions must be 1, according to fluid's request
                 padded_question2 = [[x] for x in padded_question2]
-                assert(len(mask1) == max_len1)
-                assert(len(mask2) == max_len2)
-                assert(len(padded_question1) == max_len1)
-                assert(len(padded_question2) == max_len2)
-                padded_batch.append((padded_question1, padded_question2, mask1, mask2, label))
+                assert (len(mask1) == max_len1)
+                assert (len(mask2) == max_len2)
+                assert (len(padded_question1) == max_len1)
+                assert (len(padded_question2) == max_len2)
+                padded_batch.append(
+                    (padded_question1, padded_question2, mask1, mask2, label))
             yield padded_batch
+
     return padded_reader
 
 
@@ -165,25 +180,26 @@ def prepare_data(data_type,
     prepare data
     """
 
-    PAD_ID=word_dict['']
+    PAD_ID = word_dict['']
 
     if data_type == "quora_question_pairs":
-	# train/dev/test reader are batched iters which yield a batch of (question1, question2, label) each time
-	# qestion1 and question2 are lists of word ID
-	# label is 0 or 1
-	# for example: ([1, 3, 2], [7, 5, 4, 99], 1)
-        
+        # train/dev/test reader are batched iters which yield a batch of (question1, question2, label) each time
+        # qestion1 and question2 are lists of word ID
+        # label is 0 or 1
+        # for example: ([1, 3, 2], [7, 5, 4, 99], 1)
+
         def prepare_reader(reader):
             if duplicate_data:
                 reader = duplicate(reader)
             reader = paddle.batch(
-                       paddle.reader.shuffle(reader, buf_size=buf_size),
-                       batch_size=batch_size, 
-                       drop_last=False)
+                paddle.reader.shuffle(
+                    reader, buf_size=buf_size),
+                batch_size=batch_size,
+                drop_last=False)
             if use_pad:
                 reader = pad(reader, PAD_ID=PAD_ID)
             return reader
-        
+
         train_reader = prepare_reader(quora_question_pairs.train(word_dict))
         dev_reader = prepare_reader(quora_question_pairs.dev(word_dict))
         test_reader = prepare_reader(quora_question_pairs.test(word_dict))
@@ -192,4 +208,3 @@ def prepare_data(data_type,
         raise RuntimeError("no such dataset")
 
     return train_reader, dev_reader, test_reader
-
diff --git a/README.md b/README.md
index 6ce65ed3c1d8ecd730234cd396256d287d42ae37..f14dd579fbebb985f7a74f852e207fa627dd89b0 100644
--- a/README.md
+++ b/README.md
@@ -43,14 +43,13 @@ PaddlePaddle 提供了丰富的计算单元，使得用户可以采用模块化
 ## PaddleNLP
 模型|简介|模型优势|参考论文
 --|:--:|:--:|:--:
-[Transformer](./PaddleNLP/neural_machine_translation/transformer/README_cn.md)|机器翻译模型|基于self-attention，计算复杂度小，并行度高，容易学习长程依赖，翻译效果更好|[Attention Is All You Need](https://arxiv.org/abs/1706.03762)
+[Transformer](./PaddleNLP/neural_machine_translation/transformer/README.md)|机器翻译模型|基于self-attention，计算复杂度小，并行度高，容易学习长程依赖，翻译效果更好|[Attention Is All You Need](https://arxiv.org/abs/1706.03762)
 [BERT](https://github.com/PaddlePaddle/LARK/tree/develop/BERT)|语义表示模型|在多个 NLP 任务上取得 SOTA 效果，支持多卡多机训练，支持混合精度训练|[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)
 [LAC](https://github.com/baidu/lac/blob/master/README.md)|联合的词法分析模型|能够整体性地完成中文分词、词性标注、专名识别任务|[Chinese Lexical Analysis with Deep Bi-GRU-CRF Network](https://arxiv.org/abs/1807.01882)
 [Senta](https://github.com/baidu/Senta/blob/master/README.md)|情感倾向分析模型集|百度AI开放平台中情感倾向分析模型|-
-[DAM](./PaddleNLP/deep_attention_matching_net)|语义匹配模型|百度自然语言处理部发表于ACL-2018的工作,用于检索式聊天机器人多轮对话中应答的选择|[Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network](http://aclweb.org/anthology/P18-1103)
+[DAM](./PaddleNLP/dialogue_model_toolkit/deep_attention_matching)|语义匹配模型|百度自然语言处理部发表于ACL-2018的工作,用于检索式聊天机器人多轮对话中应答的选择|[Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network](http://aclweb.org/anthology/P18-1103)
 [SimNet](https://github.com/baidu/AnyQ/blob/master/tools/simnet/train/paddle/README.md)|语义匹配框架|使用SimNet构建出的模型可以便捷的加入AnyQ系统中，增强AnyQ系统的语义匹配能力|-
-[DuReader](./PaddleNLP/machine_reading_comprehension/README.md)|阅读理解模型|百度MRC数据集上的机器阅读理解模型|-
-[Bi-GRU-CRF](./PaddleNLP/sequence_tagging_for_ner/README.md)|命名实体识别|结合了CRF和双向GRU的命名实体识别模型|-
+[DuReader](./PaddleNLP/reading_comprehension/README.md)|阅读理解模型|百度MRC数据集上的机器阅读理解模型|-
 [dialogue model](https://github.com/baidu/knowledge-driven-dialogue/tree/master/generative_paddle/README.md)|知识驱动的对话模型|基于双向RNN和attention实现的生成式对话系统|-
 
 
diff --git a/fluid/PaddleNLP/chinese_ner/README.md b/fluid/PaddleNLP/chinese_ner/README.md
index d4497b248da56eb8936147c2d1d7c8444f98cb5c..06398c9164e5a39dbd444c78ebddb3ae46093574 100644
--- a/fluid/PaddleNLP/chinese_ner/README.md
+++ b/fluid/PaddleNLP/chinese_ner/README.md
@@ -1,3 +1,3 @@
 
 
-您好，该项目已被迁移，请移步到 [PaddleNLP/chinese_ner](../../../PaddleNLP/chinese_ner/) 目录下浏览本项目。
+您好，该项目已被迁移，请移步到 [PaddleNLP/unarchived/chinese_ner](../../../PaddleNLP/unarchived/chinese_ner/) 目录下浏览本项目。
diff --git a/fluid/PaddleNLP/deep_attention_matching_net/README.md b/fluid/PaddleNLP/deep_attention_matching_net/README.md
index 5208812ce239a901d6acef714cfaed56ccfff628..7f4995ff102baadd095d31560c274ba9d57eea9c 100644
--- a/fluid/PaddleNLP/deep_attention_matching_net/README.md
+++ b/fluid/PaddleNLP/deep_attention_matching_net/README.md
@@ -3,4 +3,4 @@ Hi!
 
 This directory has been deprecated. 
 
-Please visit the project at [PaddleNLP/deep_attention_matching_net](../../../PaddleNLP/deep_attention_matching_net).
+Please visit the project at [PaddleNLP/unarchived/deep_attention_matching_net](../../../PaddleNLP/unarchived/deep_attention_matching_net).
diff --git a/fluid/PaddleNLP/language_model/gru/README.md b/fluid/PaddleNLP/language_model/gru/README.md
index f508e7061c12d6a7f053fa7c533f8350144d67cc..15176770b1e2df48790386ab0137fcbe7c5d4200 100644
--- a/fluid/PaddleNLP/language_model/gru/README.md
+++ b/fluid/PaddleNLP/language_model/gru/README.md
@@ -1,2 +1,2 @@
 
-您好，该项目已被迁移，请移步到 [PaddleNLP/language_model/gru](../../../../PaddleNLP/language_model/gru) 目录下浏览本项目。
+您好，该项目已被迁移，请移步到 [PaddleNLP/unarchived/language_model/gru](../../../../PaddleNLP/unarchived/language_model/gru) 目录下浏览本项目。
diff --git a/fluid/PaddleNLP/language_model/lstm/README.md b/fluid/PaddleNLP/language_model/lstm/README.md
index 9aa66dd7eb87b16ab503342014c4dabdfbe4906d..8358bea7d81494c81c14a833e653a36d5eceadfb 100644
--- a/fluid/PaddleNLP/language_model/lstm/README.md
+++ b/fluid/PaddleNLP/language_model/lstm/README.md
@@ -1,2 +1,2 @@
 
-您好，该项目已被迁移，请移步到 [PaddleNLP/language_model/lstm](../../../../PaddleNLP/language_model/lstm) 目录下浏览本项目。
+您好，该项目已被迁移，请移步到 [PaddleNLP/unarchived/language_model/lstm](../../../../PaddleNLP/unarchived/language_model/lstm) 目录下浏览本项目。
diff --git a/fluid/PaddleNLP/machine_reading_comprehension/README.md b/fluid/PaddleNLP/machine_reading_comprehension/README.md
index eb527349298e0b804ccc11d179cc9b78f1715b49..e9642bc36abc0cfa9b3f0fed27c044b706eb0074 100644
--- a/fluid/PaddleNLP/machine_reading_comprehension/README.md
+++ b/fluid/PaddleNLP/machine_reading_comprehension/README.md
@@ -3,4 +3,4 @@ Hi!
 
 This directory has been deprecated. 
 
-Please visit the project at [PaddleNLP/machine_reading_comprehension](../../../PaddleNLP/machine_reading_comprehension).
+Please visit the project at [PaddleNLP/unarchived/machine_reading_comprehension](../../../PaddleNLP/unarchived/machine_reading_comprehension).
diff --git a/fluid/PaddleNLP/neural_machine_translation/README.md b/fluid/PaddleNLP/neural_machine_translation/README.md
index 08446669df7617a2364c455d5093d086f1fd1a6e..0117e6214f596b87baf097724526b23db23820f8 100644
--- a/fluid/PaddleNLP/neural_machine_translation/README.md
+++ b/fluid/PaddleNLP/neural_machine_translation/README.md
@@ -3,4 +3,4 @@ Hi!
 
 This directory has been deprecated. 
 
-Please visit the project at [PaddleNLP/neural_machine_translation](../../../PaddleNLP/neural_machine_translation).
+Please visit the project at [PaddleNLP/unarchived/neural_machine_translation](../../../PaddleNLP/unarchived/neural_machine_translation).
diff --git a/fluid/PaddleNLP/sequence_tagging_for_ner/README.md b/fluid/PaddleNLP/sequence_tagging_for_ner/README.md
index d9650941eb9469a557dac879cd5cc52a3b0a03d3..772c4249c2c635ed9a6070b72028ce3a78a6d548 100644
--- a/fluid/PaddleNLP/sequence_tagging_for_ner/README.md
+++ b/fluid/PaddleNLP/sequence_tagging_for_ner/README.md
@@ -1,2 +1,2 @@
 
-您好，该项目已被迁移，请移步到 [PaddleNLP/sequence_tagging_for_ner](../../../PaddleNLP/sequence_tagging_for_ner) 目录下浏览本项目。
+您好，该项目已被迁移，请移步到 [PaddleNLP/unarchived/sequence_tagging_for_ner](../../../PaddleNLP/unarchived/sequence_tagging_for_ner) 目录下浏览本项目。
diff --git a/fluid/PaddleNLP/text_classification/README.md b/fluid/PaddleNLP/text_classification/README.md
index 28351d6a035babea1ee1ff6a8e4b0c69780657de..48b09ed2c4245a2efe8f97a08a3c80f573e94336 100644
--- a/fluid/PaddleNLP/text_classification/README.md
+++ b/fluid/PaddleNLP/text_classification/README.md
@@ -1,2 +1,2 @@
 
-您好，该项目已被迁移，请移步到 [PaddleNLP/text_classification](../../../PaddleNLP/text_classification) 目录下浏览本项目。
+您好，该项目已被迁移，请移步到 [PaddleNLP/unarchived/text_classification](../../../PaddleNLP/unarchived/text_classification) 目录下浏览本项目。
diff --git a/fluid/PaddleNLP/text_matching_on_quora/README.md b/fluid/PaddleNLP/text_matching_on_quora/README.md
index 2b268a809b14a9d88a13a12ec4b73d02b7bccf78..b735660f56cb775a582f393cb06eb725b8ad36e7 100644
--- a/fluid/PaddleNLP/text_matching_on_quora/README.md
+++ b/fluid/PaddleNLP/text_matching_on_quora/README.md
@@ -3,4 +3,4 @@ Hi!
 
 This directory has been deprecated. 
 
-Please visit the project at [PaddleNLP/text_matching_on_quora](../../../PaddleNLP/text_matching_on_quora).
+Please visit the project at [PaddleNLP/unarchived/text_matching_on_quora](../../../PaddleNLP/unarchived/text_matching_on_quora).