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未验证 提交 f492ae4f 编写于 作者: P pkpk 提交者： GitHub
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refactor the PaddleNLP (#4351) 

* Update README.md (#4267)

* test=develop (#4269)

* 3d use new api (#4275)

* PointNet++ and PointRCNN use new API

* Update Readme of Dygraph BERT (#4277)

Fix some typos.

* Update run_classifier_multi_gpu.sh (#4279)

remove the CUDA_VISIBLE_DEVICES

* Update README.md (#4280)

* 17 update api (#4294)

* update1.7 save/load & fluid.data

* update datafeed to dataloader

* Update resnet_acnet.py (#4297)

Bias attr of square conv should be "False" rather than None during training mode.

* test=develop

* test=develop

* test=develop

* test=develop

* test
Co-authored-by: NKaipeng Deng <dengkaipeng@baidu.com>
Co-authored-by: Nzhang wenhui <frankwhzhang@126.com>
Co-authored-by: Nparap1uie-s <parap1uie-s@users.noreply.github.com>
上级 8dc42c73
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.gitmodules
浏览文件 @ f492ae4f
......@@ -13,6 +13,3 @@
[submodule "PaddleSpeech/DeepSpeech"]
path = PaddleSpeech/DeepSpeech
url = https://github.com/PaddlePaddle/DeepSpeech.git
[submodule "PaddleNLP/PALM"]
path = PaddleNLP/PALM
url = https://github.com/PaddlePaddle/PALM
PALM @ 5426f750
比较 5426f750...5426f750
Subproject commit 5426f75073cf5bd416622dbe71b146d3dc8fffb6
ERNIE @ 30b892e3
比较 30b892e3...30b892e3
Subproject commit 30b892e3c029bff706337f269e6c158b0a223f60
PaddleNLP/README.md
浏览文件 @ f492ae4f
......@@ -10,7 +10,7 @@
- **丰富而全面的NLP任务支持：**
- PaddleNLP为您提供了多粒度，多场景的应用支持。涵盖了从[分词](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/lexical_analysis)，[词性标注](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/lexical_analysis)，[命名实体识别](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/lexical_analysis)等NLP基础技术，到[文本分类](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/sentiment_classification)，[文本相似度计算](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/similarity_net)，[语义表示](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleLARK)，[文本生成](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleTextGEN)等NLP核心技术。同时，PaddleNLP还提供了针对常见NLP大型应用系统（如[阅读理解](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleMRC)，[对话系统](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleDialogue)，[机器翻译系统](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleMT)等）的特定核心技术和工具组件，模型和预训练参数等，让您在NLP领域畅通无阻。
- PaddleNLP为您提供了多粒度，多场景的应用支持。涵盖了从[分词](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/lexical_analysis)，[词性标注](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/lexical_analysis)，[命名实体识别](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/lexical_analysis)等NLP基础技术，到[文本分类](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/sentiment_classification)，[文本相似度计算](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/similarity_net)，[语义表示](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/pretrain_langauge_models)，[文本生成](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/seq2seq)等NLP核心技术。同时，PaddleNLP还提供了针对常见NLP大型应用系统（如[阅读理解](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/machine_reading_comprehension)，[对话系统](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/dialogue_system)，[机器翻译系统](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/machine_translation)等）的特定核心技术和工具组件，模型和预训练参数等，让您在NLP领域畅通无阻。
- **稳定可靠的NLP模型和强大的预训练参数：**
......@@ -50,17 +50,17 @@ cd models/PaddleNLP/sentiment_classification
| 任务场景 | 对应项目/目录 | 简介 |
| :------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| **中文分词**，**词性标注**，**命名实体识别**:fire: | [LAC](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/lexical_analysis) | LAC，全称为Lexical Analysis of Chinese，是百度内部广泛使用的中文处理工具，功能涵盖从中文分词，词性标注，命名实体识别等常见中文处理任务。 |
| **词向量（word2vec）** | [word2vec](https://github.com/PaddlePaddle/models/tree/develop/PaddleRec/word2vec) | 提供单机多卡，多机等分布式训练中文词向量能力，支持主流词向量模型（skip-gram，cbow等），可以快速使用自定义数据训练词向量模型。 |
| **语言模型** | [Language_model](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/language_model) | 基于循环神经网络（RNN）的经典神经语言模型（neural language model）。 |
| **情感分类**:fire: | [Senta](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/sentiment_classification)，[EmotionDetection](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/emotion_detection) | Senta（Sentiment Classification，简称Senta）和EmotionDetection两个项目分别提供了面向*通用场景*和*人机对话场景专用*的情感倾向性分析模型。 |
| **文本相似度计算**:fire: | [SimNet](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/similarity_net) | SimNet，又称为Similarity Net，为您提供高效可靠的文本相似度计算工具和预训练模型。 |
| **语义表示**:fire: | [PaddleLARK](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleLARK) | PaddleLARK，全称为Paddle LAngauge Representation Toolkit，集成了ELMO，BERT，ERNIE 1.0，ERNIE 2.0，XLNet等热门中英文预训练模型。 |
| **文本生成** | [PaddleTextGEN](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleTextGEN) | Paddle Text Generation为您提供了一些列经典文本生成模型案例，如vanilla seq2seq，seq2seq with attention，variational seq2seq模型等。 |
| **阅读理解** | [PaddleMRC](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleMRC) | PaddleMRC，全称为Paddle Machine Reading Comprehension，集合了百度在阅读理解领域相关的模型，工具，开源数据等一系列工作。包括DuReader (百度开源的基于真实搜索用户行为的中文大规模阅读理解数据集)，KT-Net (结合知识的阅读理解模型，SQuAD以及ReCoRD曾排名第一), D-Net (预训练-微调框架，在EMNLP2019 MRQA国际阅读理解评测获得第一)，等。 |
| **对话系统** | [PaddleDialogue](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleDialogue) | 包括：1）DGU（Dialogue General Understanding，通用对话理解模型）覆盖了包括**检索式聊天系统**中context-response matching任务和**任务完成型对话系统**中**意图识别**，**槽位解析**，**状态追踪**等常见对话系统任务，在6项国际公开数据集中都获得了最佳效果。<br/> 2) knowledge-driven dialogue：百度开源的知识驱动的开放领域对话数据集，发表于ACL2019。<br/>3）ADEM（Auto Dialogue Evaluation Model）：对话自动评估模型，可用于自动评估不同对话生成模型的回复质量。 |
| **机器翻译** | [PaddleMT](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleMT) | 全称为Paddle Machine Translation，基于Transformer的经典机器翻译模型。 |
| **其他前沿工作** | [Research](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research) | 百度最新前沿工作开源。 |
| **中文分词**，**词性标注**，**命名实体识别**:fire: | [LAC](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/lexical_analysis) | LAC，全称为Lexical Analysis of Chinese，是百度内部广泛使用的中文处理工具，功能涵盖从中文分词，词性标注，命名实体识别等常见中文处理任务。 |
| **词向量（word2vec）** | [word2vec](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleRec/word2vec) | 提供单机多卡，多机等分布式训练中文词向量能力，支持主流词向量模型（skip-gram，cbow等），可以快速使用自定义数据训练词向量模型。 |
| **语言模型** | [Language_model](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/language_model) | 基于循环神经网络（RNN）的经典神经语言模型（neural language model）。 |
| **情感分类**:fire: | [Senta](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/sentiment_classification)，[EmotionDetection](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/emotion_detection) | Senta（Sentiment Classification，简称Senta）和EmotionDetection两个项目分别提供了面向*通用场景*和*人机对话场景专用*的情感倾向性分析模型。 |
| **文本相似度计算**:fire: | [SimNet](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/similarity_net) | SimNet，又称为Similarity Net，为您提供高效可靠的文本相似度计算工具和预训练模型。 |
| **语义表示**:fire: | [pretrain_langauge_models](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/pretrain_langauge_models) | 集成了ELMO，BERT，ERNIE 1.0，ERNIE 2.0，XLNet等热门中英文预训练模型。 |
| **文本生成** | [seq2seq](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/PaddleTextGEN) | seq2seq为您提供了一些列经典文本生成模型案例，如vanilla seq2seq，seq2seq with attention，variational seq2seq模型等。 |
| **阅读理解** | [machine_reading_comprehension](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/machine_reading_comprehension) | Paddle Machine Reading Comprehension，集合了百度在阅读理解领域相关的模型，工具，开源数据等一系列工作。包括DuReader (百度开源的基于真实搜索用户行为的中文大规模阅读理解数据集)，KT-Net (结合知识的阅读理解模型，SQuAD以及ReCoRD曾排名第一), D-Net (预训练-微调框架，在EMNLP2019 MRQA国际阅读理解评测获得第一)，等。 |
| **对话系统** | [dialogue_system](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/dialogue_system) | 包括：1）DGU（Dialogue General Understanding，通用对话理解模型）覆盖了包括**检索式聊天系统**中context-response matching任务和**任务完成型对话系统**中**意图识别**，**槽位解析**，**状态追踪**等常见对话系统任务，在6项国际公开数据集中都获得了最佳效果。<br/> 2) knowledge-driven dialogue：百度开源的知识驱动的开放领域对话数据集，发表于ACL2019。<br/>3）ADEM（Auto Dialogue Evaluation Model）：对话自动评估模型，可用于自动评估不同对话生成模型的回复质量。 |
| **机器翻译** | [machine_translation](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/machine_translation) | 全称为Paddle Machine Translation，基于Transformer的经典机器翻译模型。 |
| **其他前沿工作** | [Research](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/Research) | 百度最新前沿工作开源。 |
......@@ -70,13 +70,13 @@ cd models/PaddleNLP/sentiment_classification
```text
.
├── Research # 百度NLP在research方面的工作集合
├── PaddleMT # 机器翻译相关代码，数据，预训练模型
├── PaddleDialogue # 对话系统相关代码，数据，预训练模型
├── PaddleMRC # 阅读理解相关代码，数据，预训练模型
├── PaddleLARK # 语言表示工具箱
├── machine_translation # 机器翻译相关代码，数据，预训练模型
├── dialogue_system # 对话系统相关代码，数据，预训练模型
├── machcine_reading_comprehension # 阅读理解相关代码，数据，预训练模型
├── pretrain_langauge_models # 语言表示工具箱
├── language_model # 语言模型
├── lexical_analysis # LAC词法分析
├── models # 共享网络
├── shared_modules/models # 共享网络
│ ├── __init__.py
│ ├── classification
│ ├── dialogue_model_toolkit
......@@ -87,7 +87,7 @@ cd models/PaddleNLP/sentiment_classification
│ ├── representation
│ ├── sequence_labeling
│ └── transformer_encoder.py
├── preprocess # 共享文本预处理工具
├── shared_modules/preprocess # 共享文本预处理工具
│ ├── __init__.py
│ ├── ernie
│ ├── padding.py
......
PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/_ce.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/_ce.py
浏览文件 @ f492ae4f
......@@ -21,8 +21,10 @@ from kpi import DurationKpi
train_loss_card1 = CostKpi('train_loss_card1', 0.03, 0, actived=True)
train_loss_card4 = CostKpi('train_loss_card4', 0.03, 0, actived=True)
train_duration_card1 = DurationKpi('train_duration_card1', 0.01, 0, actived=True)
train_duration_card4 = DurationKpi('train_duration_card4', 0.01, 0, actived=True)
train_duration_card1 = DurationKpi(
'train_duration_card1', 0.01, 0, actived=True)
train_duration_card4 = DurationKpi(
'train_duration_card4', 0.01, 0, actived=True)
tracking_kpis = [
train_loss_card1,
......
PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/ade/prepare_data_and_model.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/ade/prepare_data_and_model.py
浏览文件 @ f492ae4f
......@@ -20,22 +20,25 @@ import sys
import io
import os
URLLIB=urllib
URLLIB = urllib
if sys.version_info >= (3, 0):
import urllib.request
URLLIB=urllib.request
URLLIB = urllib.request
DATA_MODEL_PATH = {"DATA_PATH": "https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_dataset-1.0.0.tar.gz",
"TRAINED_MODEL": "https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_models.2.0.0.tar.gz"}
DATA_MODEL_PATH = {
"DATA_PATH":
"https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_dataset-1.0.0.tar.gz",
"TRAINED_MODEL":
"https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_models.2.0.0.tar.gz"
}
PATH_MAP = {'DATA_PATH': "./data/input",
'TRAINED_MODEL': './data/saved_models'}
PATH_MAP = {'DATA_PATH': "./data/input", 'TRAINED_MODEL': './data/saved_models'}
def un_tar(tar_name, dir_name):
try:
t = tarfile.open(tar_name)
t.extractall(path = dir_name)
t.extractall(path=dir_name)
return True
except Exception as e:
print(e)
......@@ -51,7 +54,8 @@ def download_model_and_data():
shutil.rmtree(path)
for path_key in DATA_MODEL_PATH:
filename = os.path.basename(DATA_MODEL_PATH[path_key])
URLLIB.urlretrieve(DATA_MODEL_PATH[path_key], os.path.join("./", filename))
URLLIB.urlretrieve(DATA_MODEL_PATH[path_key],
os.path.join("./", filename))
state = un_tar(filename, PATH_MAP[path_key])
if not state:
print("Tar %s error....." % path_key)
......
PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/ade/utils/save_load_io.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/ade/utils/save_load_io.py
浏览文件 @ f492ae4f
......@@ -122,5 +122,3 @@ def save_param(args, exe, program, dirname):
print("save parameters at %s" % (os.path.join(param_dir, dirname)))
return True
PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/ade_net.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/ade_net.py
浏览文件 @ f492ae4f
......@@ -21,8 +21,7 @@ import paddle
import paddle.fluid as fluid
def create_net(
is_training,
def create_net(is_training,
model_input,
args,
clip_value=10.0,
......@@ -52,14 +51,12 @@ def create_net(
initializer=fluid.initializer.Normal(scale=0.1)))
#fc to fit dynamic LSTM
context_fc = fluid.layers.fc(
input=context_emb,
context_fc = fluid.layers.fc(input=context_emb,
size=args.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,
response_fc = fluid.layers.fc(input=response_emb,
size=args.hidden_size * 4,
param_attr=fluid.ParamAttr(name='fc_weight'),
bias_attr=fluid.ParamAttr(name='fc_bias'))
......@@ -106,7 +103,5 @@ def set_word_embedding(word_emb, place, word_emb_name="shared_word_emb"):
"""
Set word embedding
"""
word_emb_param = fluid.global_scope().find_var(
word_emb_name).get_tensor()
word_emb_param = fluid.global_scope().find_var(word_emb_name).get_tensor()
word_emb_param.set(word_emb, place)
PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/inference_model.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/inference_model.py
浏览文件 @ f492ae4f
......@@ -42,22 +42,24 @@ def do_save_inference_model(args):
with fluid.unique_name.guard():
context_wordseq = fluid.data(
name='context_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
name='context_wordseq',
shape=[-1, 1],
dtype='int64',
lod_level=1)
response_wordseq = fluid.data(
name='response_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
labels = fluid.data(
name='labels', shape=[-1, 1], dtype='int64')
name='response_wordseq',
shape=[-1, 1],
dtype='int64',
lod_level=1)
labels = fluid.data(name='labels', shape=[-1, 1], dtype='int64')
input_inst = [context_wordseq, response_wordseq, labels]
input_field = InputField(input_inst)
data_reader = fluid.io.PyReader(feed_list=input_inst,
capacity=4, iterable=False)
data_reader = fluid.io.PyReader(
feed_list=input_inst, capacity=4, iterable=False)
logits = create_net(
is_training=False,
model_input=input_field,
args=args
)
is_training=False, model_input=input_field, args=args)
if args.use_cuda:
place = fluid.CUDAPlace(0)
......@@ -81,9 +83,7 @@ def do_save_inference_model(args):
input_field.context_wordseq.name,
input_field.response_wordseq.name,
],
target_vars=[
logits,
],
target_vars=[logits, ],
executor=exe,
main_program=test_prog,
model_filename="model.pdmodel",
......
PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/main.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/main.py
浏览文件 @ f492ae4f
......@@ -26,7 +26,6 @@ from inference_model import do_save_inference_model
from ade.utils.configure import PDConfig
if __name__ == "__main__":
args = PDConfig(yaml_file="./data/config/ade.yaml")
......
PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/predict.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/predict.py
浏览文件 @ f492ae4f
......@@ -46,22 +46,24 @@ def do_predict(args):
with fluid.unique_name.guard():
context_wordseq = fluid.data(
name='context_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
name='context_wordseq',
shape=[-1, 1],
dtype='int64',
lod_level=1)
response_wordseq = fluid.data(
name='response_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
labels = fluid.data(
name='labels', shape=[-1, 1], dtype='int64')
name='response_wordseq',
shape=[-1, 1],
dtype='int64',
lod_level=1)
labels = fluid.data(name='labels', shape=[-1, 1], dtype='int64')
input_inst = [context_wordseq, response_wordseq, labels]
input_field = InputField(input_inst)
data_reader = fluid.io.PyReader(feed_list=input_inst,
capacity=4, iterable=False)
data_reader = fluid.io.PyReader(
feed_list=input_inst, capacity=4, iterable=False)
logits = create_net(
is_training=False,
model_input=input_field,
args=args
)
is_training=False, model_input=input_field, args=args)
logits.persistable = True
fetch_list = [logits.name]
......@@ -89,10 +91,7 @@ def do_predict(args):
batch_size=args.batch_size)
batch_generator = processor.data_generator(
place=place,
phase="test",
shuffle=False,
sample_pro=1)
place=place, phase="test", shuffle=False, sample_pro=1)
num_test_examples = processor.get_num_examples(phase='test')
data_reader.decorate_batch_generator(batch_generator)
......@@ -107,7 +106,7 @@ def do_predict(args):
data_reader.reset()
break
scores = scores[: num_test_examples]
scores = scores[:num_test_examples]
print("Write the predicted results into the output_prediction_file")
fw = io.open(args.output_prediction_file, 'w', encoding="utf8")
for index, score in enumerate(scores):
......
PaddleNLP/PaddleDialogue/auto_dialogue_evaluation/train.py → PaddleNLP/dialogue_system/auto_dialogue_evaluation/train.py
浏览文件 @ f492ae4f
......@@ -49,22 +49,24 @@ def do_train(args):
with fluid.unique_name.guard():
context_wordseq = fluid.data(
name='context_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
name='context_wordseq',
shape=[-1, 1],
dtype='int64',
lod_level=1)
response_wordseq = fluid.data(
name='response_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
labels = fluid.data(
name='labels', shape=[-1, 1], dtype='int64')
name='response_wordseq',
shape=[-1, 1],
dtype='int64',
lod_level=1)
labels = fluid.data(name='labels', shape=[-1, 1], dtype='int64')
input_inst = [context_wordseq, response_wordseq, labels]
input_field = InputField(input_inst)
data_reader = fluid.io.PyReader(feed_list=input_inst,
capacity=4, iterable=False)
data_reader = fluid.io.PyReader(
feed_list=input_inst, capacity=4, iterable=False)
loss = create_net(
is_training=True,
model_input=input_field,
args=args
)
is_training=True, model_input=input_field, args=args)
loss.persistable = True
# gradient clipping
fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByValue(
......@@ -74,7 +76,8 @@ def do_train(args):
if args.use_cuda:
dev_count = fluid.core.get_cuda_device_count()
place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0')))
place = fluid.CUDAPlace(
int(os.getenv('FLAGS_selected_gpus', '0')))
else:
dev_count = int(os.environ.get('CPU_NUM', 1))
place = fluid.CPUPlace()
......@@ -114,9 +117,14 @@ def do_train(args):
if args.word_emb_init:
print("start loading word embedding init ...")
if six.PY2:
word_emb = np.array(pickle.load(io.open(args.word_emb_init, 'rb'))).astype('float32')
word_emb = np.array(
pickle.load(io.open(args.word_emb_init, 'rb'))).astype(
'float32')
else:
word_emb = np.array(pickle.load(io.open(args.word_emb_init, 'rb'), encoding="bytes")).astype('float32')
word_emb = np.array(
pickle.load(
io.open(args.word_emb_init, 'rb'),
encoding="bytes")).astype('float32')
set_word_embedding(word_emb, place)
print("finish init word embedding ...")
......@@ -147,15 +155,20 @@ def do_train(args):
used_time = time_end - time_begin
current_time = time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time()))
print('%s epoch: %d, step: %s, avg loss %s, speed: %f steps/s' % (current_time, epoch_step, steps, sum_loss / args.print_steps, args.print_steps / used_time))
print(
'%s epoch: %d, step: %s, avg loss %s, speed: %f steps/s'
% (current_time, epoch_step, steps, sum_loss /
args.print_steps, args.print_steps / used_time))
sum_loss = 0.0
time_begin = time.time()
if steps % args.save_steps == 0:
if args.save_checkpoint:
save_load_io.save_checkpoint(args, exe, train_prog, "step_" + str(steps))
save_load_io.save_checkpoint(args, exe, train_prog,
"step_" + str(steps))
if args.save_param:
save_load_io.save_param(args, exe, train_prog, "step_" + str(steps))
save_load_io.save_param(args, exe, train_prog,
"step_" + str(steps))
steps += 1
except fluid.core.EOFException:
data_reader.reset()
......
PaddleNLP/PaddleDialogue/dialogue_general_understanding/_ce.py → PaddleNLP/dialogue_system/dialogue_general_understanding/_ce.py
浏览文件 @ f492ae4f
......@@ -20,12 +20,18 @@ from kpi import CostKpi
from kpi import DurationKpi
from kpi import AccKpi
each_step_duration_atis_slot_card1 = DurationKpi('each_step_duration_atis_slot_card1', 0.01, 0, actived=True)
train_loss_atis_slot_card1 = CostKpi('train_loss_atis_slot_card1', 0.08, 0, actived=True)
train_acc_atis_slot_card1 = CostKpi('train_acc_atis_slot_card1', 0.01, 0, actived=True)
each_step_duration_atis_slot_card4 = DurationKpi('each_step_duration_atis_slot_card4', 0.06, 0, actived=True)
train_loss_atis_slot_card4 = CostKpi('train_loss_atis_slot_card4', 0.03, 0, actived=True)
train_acc_atis_slot_card4 = CostKpi('train_acc_atis_slot_card4', 0.01, 0, actived=True)
each_step_duration_atis_slot_card1 = DurationKpi(
'each_step_duration_atis_slot_card1', 0.01, 0, actived=True)
train_loss_atis_slot_card1 = CostKpi(
'train_loss_atis_slot_card1', 0.08, 0, actived=True)
train_acc_atis_slot_card1 = CostKpi(
'train_acc_atis_slot_card1', 0.01, 0, actived=True)
each_step_duration_atis_slot_card4 = DurationKpi(
'each_step_duration_atis_slot_card4', 0.06, 0, actived=True)
train_loss_atis_slot_card4 = CostKpi(
'train_loss_atis_slot_card4', 0.03, 0, actived=True)
train_acc_atis_slot_card4 = CostKpi(
'train_acc_atis_slot_card4', 0.01, 0, actived=True)
tracking_kpis = [
each_step_duration_atis_slot_card1,
......
PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/batching.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/batching.py
浏览文件 @ f492ae4f
......@@ -100,8 +100,12 @@ def prepare_batch_data(task_name,
if isinstance(insts[0][3], list):
if task_name == "atis_slot":
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])]
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])
]
elif task_name == "dstc2":
labels_list = [inst[3] for inst in insts]
labels_list = [np.array(labels_list).astype("int64")]
......@@ -124,10 +128,7 @@ def prepare_batch_data(task_name,
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)
out, max_len, pad_idx=pad_id, return_input_mask=True)
pos_id = pad_batch_data(
batch_pos_ids,
max_len,
......@@ -163,13 +164,13 @@ def pad_batch_data(insts,
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)
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
])
[inst + list([pad_idx] * (max_len - len(inst))) for inst in insts])
return_list += [inst_data.astype("int64").reshape([-1, max_len])]
# position data
......
PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/define_predict_pack.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/define_predict_pack.py
浏览文件 @ f492ae4f
......@@ -25,18 +25,21 @@ class DefinePredict(object):
"""
Packaging Prediction Results
"""
def __init__(self):
"""
init
"""
self.task_map = {'udc': 'get_matching_res',
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'}
'multi-woz': 'get_multi_cls_res'
}
def get_matching_res(self, probs, params=None):
"""
......@@ -79,7 +82,3 @@ class DefinePredict(object):
label_str = " ".join([str(l) for l in sorted(labels)])
return label_str
PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/prepare_data_and_model.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/prepare_data_and_model.py
浏览文件 @ f492ae4f
......@@ -20,25 +20,29 @@ import sys
import io
import os
URLLIB=urllib
URLLIB = urllib
if sys.version_info >= (3, 0):
import urllib.request
URLLIB=urllib.request
URLLIB = urllib.request
DATA_MODEL_PATH = {"DATA_PATH": "https://baidu-nlp.bj.bcebos.com/dmtk_data_1.0.0.tar.gz",
"PRETRAIN_MODEL": "https://bert-models.bj.bcebos.com/uncased_L-12_H-768_A-12.tar.gz",
"TRAINED_MODEL": "https://baidu-nlp.bj.bcebos.com/dgu_models_2.0.0.tar.gz"}
DATA_MODEL_PATH = {
"DATA_PATH": "https://baidu-nlp.bj.bcebos.com/dmtk_data_1.0.0.tar.gz",
"PRETRAIN_MODEL":
"https://bert-models.bj.bcebos.com/uncased_L-12_H-768_A-12.tar.gz",
"TRAINED_MODEL": "https://baidu-nlp.bj.bcebos.com/dgu_models_2.0.0.tar.gz"
}
PATH_MAP = {'DATA_PATH': "./data/input",
PATH_MAP = {
'DATA_PATH': "./data/input",
'PRETRAIN_MODEL': './data/pretrain_model',
'TRAINED_MODEL': './data/saved_models'}
'TRAINED_MODEL': './data/saved_models'
}
def un_tar(tar_name, dir_name):
try:
t = tarfile.open(tar_name)
t.extractall(path = dir_name)
t.extractall(path=dir_name)
return True
except Exception as e:
print(e)
......@@ -48,13 +52,18 @@ def un_tar(tar_name, dir_name):
def download_model_and_data():
print("Downloading dgu data, pretrain model and trained models......")
print("This process is quite long, please wait patiently............")
for path in ['./data/input/data', './data/pretrain_model/uncased_L-12_H-768_A-12', './data/saved_models/trained_models']:
for path in [
'./data/input/data',
'./data/pretrain_model/uncased_L-12_H-768_A-12',
'./data/saved_models/trained_models'
]:
if not os.path.exists(path):
continue
shutil.rmtree(path)
for path_key in DATA_MODEL_PATH:
filename = os.path.basename(DATA_MODEL_PATH[path_key])
URLLIB.urlretrieve(DATA_MODEL_PATH[path_key], os.path.join("./", filename))
URLLIB.urlretrieve(DATA_MODEL_PATH[path_key],
os.path.join("./", filename))
state = un_tar(filename, PATH_MAP[path_key])
if not state:
print("Tar %s error....." % path_key)
......
PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/scripts/README.md → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/scripts/README.md
浏览文件 @ f492ae4f
......@@ -19,6 +19,3 @@ python run_build_data.py udc
python run_build_data.py atis
生成槽位识别数据在dialogue_general_understanding/data/input/data/atis/atis_slot
生成意图识别数据在dialogue_general_understanding/data/input/data/atis/atis_intent
PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/scripts/build_atis_dataset.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/scripts/build_atis_dataset.py
浏览文件 @ f492ae4f
......@@ -12,7 +12,6 @@
# 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.
"""build swda train dev test dataset"""
import json
......@@ -27,6 +26,7 @@ class ATIS(object):
"""
nlu dataset atis data process
"""
def __init__(self):
"""
init instance
......@@ -73,7 +73,8 @@ class ATIS(object):
if example[1] not in self.intent_dict:
self.intent_dict[example[1]] = self.intent_id
self.intent_id += 1
fw.write(u"%s\t%s\n" % (self.intent_dict[example[1]], example[0].lower()))
fw.write(u"%s\t%s\n" %
(self.intent_dict[example[1]], example[0].lower()))
fw = io.open(self.map_tag_intent, 'w', encoding="utf8")
for tag in self.intent_dict:
......@@ -109,17 +110,19 @@ class ATIS(object):
tags_slot.append(str(self.slot_dict[tag]))
if i == 0:
if start not in [0, 1]:
prefix_num = len(text[: start].strip().split())
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())
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(u"%s\t%s\n" % (text.encode('utf8'), " ".join(tags).encode('utf8')))
fw.write(u"%s\t%s\n" %
(text.encode('utf8'), " ".join(tags).encode('utf8')))
fw = io.open(self.map_tag_slot, 'w', encoding="utf8")
for slot in self.slot_dict:
......@@ -152,7 +155,3 @@ class ATIS(object):
if __name__ == "__main__":
atis_inst = ATIS()
atis_inst.main()
PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/scripts/build_dstc2_dataset.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/scripts/build_dstc2_dataset.py
浏览文件 @ f492ae4f
......@@ -28,6 +28,7 @@ class DSTC2(object):
"""
dialogue state tracking dstc2 data process
"""
def __init__(self):
"""
init instance
......@@ -49,7 +50,8 @@ class DSTC2(object):
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])
self.data_dict[data_type][i] = os.path.join(
self.src_dir, self.data_dict[data_type][i])
def _load_ontology(self):
"""
......@@ -97,15 +99,25 @@ class DSTC2(object):
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])
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)
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(u"%s\n" % out.encode('utf8'))
fw_asr.write(u"%s\n" % out_asr.encode('utf8'))
......@@ -144,10 +156,7 @@ class DSTC2(object):
self.get_test_dataset()
self.get_labels()
if __name__ == "__main__":
dstc_inst = DSTC2()
dstc_inst.main()
PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/scripts/build_mrda_dataset.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/scripts/build_mrda_dataset.py
浏览文件 @ f492ae4f
......@@ -27,6 +27,7 @@ class MRDA(object):
"""
dialogue act dataset mrda data process
"""
def __init__(self):
"""
init instance
......@@ -67,7 +68,7 @@ class MRDA(object):
for dadb_key in dadb_list:
dadb_file = self.dadb_dict[dadb_key]
fr = io.open(dadb_file, 'r', encoding="utf8")
row = csv.reader(fr, delimiter = ',')
row = csv.reader(fr, delimiter=',')
for line in row:
elems = line
conv_id = elems[2]
......@@ -87,7 +88,7 @@ class MRDA(object):
for trans_key in trans_list:
trans_file = self.trans_dict[trans_key]
fr = io.open(trans_file, 'r', encoding="utf8")
row = csv.reader(fr, delimiter = ',')
row = csv.reader(fr, delimiter=',')
for line in row:
elems = line
if len(elems) != 3:
......@@ -120,7 +121,8 @@ class MRDA(object):
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])
out = "%s\t%s\t%s\t%s" % (conv_no, self.map_tag_dict[tag], caller,
v_trans[0])
fw.write(u"%s\n" % out)
def get_train_dataset(self):
......@@ -158,10 +160,7 @@ class MRDA(object):
self.get_test_dataset()
self.get_labels()
if __name__ == "__main__":
mrda_inst = MRDA()
mrda_inst.main()
PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/scripts/build_swda_dataset.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/scripts/build_swda_dataset.py
浏览文件 @ f492ae4f
......@@ -27,6 +27,7 @@ class SWDA(object):
"""
dialogue act dataset swda data process
"""
def __init__(self):
"""
init instance
......@@ -63,7 +64,7 @@ class SWDA(object):
file_path = self.file_dict[name]
fr = io.open(file_path, 'r', encoding="utf8")
idx = 0
row = csv.reader(fr, delimiter = ',')
row = csv.reader(fr, delimiter=',')
for r in row:
if idx == 0:
idx += 1
......@@ -224,10 +225,7 @@ class SWDA(object):
self.get_test_dataset()
self.get_labels()
if __name__ == "__main__":
swda_inst = SWDA()
swda_inst.main()
PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/scripts/run_build_data.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/scripts/run_build_data.py
浏览文件 @ f492ae4f
......@@ -20,7 +20,6 @@ from build_dstc2_dataset import DSTC2
from build_mrda_dataset import MRDA
from build_swda_dataset import SWDA
if __name__ == "__main__":
task_name = sys.argv[1]
task_name = task_name.lower()
......@@ -38,11 +37,12 @@ if __name__ == "__main__":
elif task_name == 'atis':
atis_inst = ATIS()
atis_inst.main()
shutil.copyfile("../../data/input/data/atis/atis_slot/test.txt", "../../data/input/data/atis/atis_slot/dev.txt")
shutil.copyfile("../../data/input/data/atis/atis_intent/test.txt", "../../data/input/data/atis/atis_intent/dev.txt")
shutil.copyfile("../../data/input/data/atis/atis_slot/test.txt",
"../../data/input/data/atis/atis_slot/dev.txt")
shutil.copyfile("../../data/input/data/atis/atis_intent/test.txt",
"../../data/input/data/atis/atis_intent/dev.txt")
elif task_name == 'dstc2':
dstc_inst = DSTC2()
dstc_inst.main()
else:
exit(0)
PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/tokenization.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/tokenization.py
浏览文件 @ f492ae4f
......@@ -12,7 +12,6 @@
# 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.
"""Tokenization classes."""
from __future__ import absolute_import
......
PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/transformer_encoder.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/transformer_encoder.py
浏览文件 @ f492ae4f
......@@ -113,7 +113,7 @@ def multi_head_attention(queries,
"""
Scaled Dot-Product Attention
"""
scaled_q = layers.scale(x=q, scale=d_key ** -0.5)
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
......
PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu/utils/save_load_io.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/utils/save_load_io.py
浏览文件 @ f492ae4f
......@@ -122,5 +122,3 @@ def save_param(args, exe, program, dirname):
print("save parameters at %s" % (os.path.join(param_dir, dirname)))
return True
PaddleNLP/PaddleDialogue/dialogue_general_understanding/dgu_net.py → PaddleNLP/dialogue_system/dialogue_general_understanding/dgu_net.py
浏览文件 @ f492ae4f
......@@ -23,12 +23,7 @@ from dgu.bert import BertModel
from dgu.utils.configure import JsonConfig
def create_net(
is_training,
model_input,
num_labels,
paradigm_inst,
args):
def create_net(is_training, model_input, num_labels, paradigm_inst, args):
"""create dialogue task model"""
src_ids = model_input.src_ids
......@@ -48,14 +43,15 @@ def create_net(
config=bert_conf,
use_fp16=False)
params = {'num_labels': num_labels,
params = {
'num_labels': num_labels,
'src_ids': src_ids,
'pos_ids': pos_ids,
'sent_ids': sent_ids,
'input_mask': input_mask,
'labels': labels,
'is_training': is_training}
'is_training': is_training
}
results = paradigm_inst.paradigm(bert, params)
return results
PaddleNLP/PaddleDialogue/dialogue_general_understanding/inference_model.py → PaddleNLP/dialogue_system/dialogue_general_understanding/inference_model.py
浏览文件 @ f492ae4f
......@@ -66,7 +66,9 @@ def do_save_inference_model(args):
sent_ids = fluid.data(
name='sent_ids', shape=[-1, args.max_seq_len], dtype='int64')
input_mask = fluid.data(
name='input_mask', shape=[-1, args.max_seq_len], dtype='float32')
name='input_mask',
shape=[-1, args.max_seq_len],
dtype='float32')
if args.task_name == 'atis_slot':
labels = fluid.data(
name='labels', shape=[-1, args.max_seq_len], dtype='int64')
......@@ -74,8 +76,7 @@ def do_save_inference_model(args):
labels = fluid.data(
name='labels', shape=[-1, num_labels], dtype='int64')
else:
labels = fluid.data(
name='labels', shape=[-1, 1], dtype='int64')
labels = fluid.data(name='labels', shape=[-1, 1], dtype='int64')
input_inst = [src_ids, pos_ids, sent_ids, input_mask, labels]
input_field = InputField(input_inst)
......@@ -107,14 +108,10 @@ def do_save_inference_model(args):
fluid.io.save_inference_model(
args.inference_model_dir,
feeded_var_names=[
input_field.src_ids.name,
input_field.pos_ids.name,
input_field.sent_ids.name,
input_field.input_mask.name
],
target_vars=[
probs
input_field.src_ids.name, input_field.pos_ids.name,
input_field.sent_ids.name, input_field.input_mask.name
],
target_vars=[probs],
executor=exe,
main_program=test_prog,
model_filename="model.pdmodel",
......
PaddleNLP/PaddleDialogue/dialogue_general_understanding/main.py → PaddleNLP/dialogue_system/dialogue_general_understanding/main.py
浏览文件 @ f492ae4f
......@@ -26,7 +26,6 @@ from inference_model import do_save_inference_model
from dgu.utils.configure import PDConfig
if __name__ == "__main__":
args = PDConfig(yaml_file="./data/config/dgu.yaml")
......
PaddleNLP/PaddleDialogue/dialogue_general_understanding/train.py → PaddleNLP/dialogue_system/dialogue_general_understanding/train.py
浏览文件 @ f492ae4f
......@@ -66,7 +66,9 @@ def do_train(args):
sent_ids = fluid.data(
name='sent_ids', shape=[-1, args.max_seq_len], dtype='int64')
input_mask = fluid.data(
name='input_mask', shape=[-1, args.max_seq_len], dtype='float32')
name='input_mask',
shape=[-1, args.max_seq_len],
dtype='float32')
if args.task_name == 'atis_slot':
labels = fluid.data(
name='labels', shape=[-1, args.max_seq_len], dtype='int64')
......@@ -74,13 +76,12 @@ def do_train(args):
labels = fluid.data(
name='labels', shape=[-1, num_labels], dtype='int64')
else:
labels = fluid.data(
name='labels', shape=[-1, 1], dtype='int64')
labels = fluid.data(name='labels', shape=[-1, 1], dtype='int64')
input_inst = [src_ids, pos_ids, sent_ids, input_mask, labels]
input_field = InputField(input_inst)
data_reader = fluid.io.PyReader(feed_list=input_inst,
capacity=4, iterable=False)
data_reader = fluid.io.PyReader(
feed_list=input_inst, capacity=4, iterable=False)
processor = processors[task_name](data_dir=args.data_dir,
vocab_path=args.vocab_path,
max_seq_len=args.max_seq_len,
......@@ -113,9 +114,7 @@ def do_train(args):
dev_count = int(os.environ.get('CPU_NUM', 1))
batch_generator = processor.data_generator(
batch_size=args.batch_size,
phase='train',
shuffle=True)
batch_size=args.batch_size, phase='train', shuffle=True)
num_train_examples = processor.get_num_examples(phase='train')
if args.in_tokens:
......@@ -217,37 +216,32 @@ def do_train(args):
current_time = time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time()))
if accuracy is not None:
print(
"%s epoch: %d, step: %d, ave loss: %f, "
print("%s epoch: %d, step: %d, ave loss: %f, "
"ave acc: %f, speed: %f steps/s" %
(current_time, epoch_step, steps,
np.mean(np_loss),
np.mean(np_acc),
np.mean(np_loss), np.mean(np_acc),
args.print_steps / used_time))
ce_info.append([
np.mean(np_loss),
np.mean(np_acc),
np.mean(np_loss), np.mean(np_acc),
args.print_steps / used_time
])
else:
print(
"%s epoch: %d, step: %d, ave loss: %f, "
print("%s epoch: %d, step: %d, ave loss: %f, "
"speed: %f steps/s" %
(current_time, epoch_step, steps,
np.mean(np_loss),
args.print_steps / used_time))
ce_info.append([
np.mean(np_loss),
args.print_steps / used_time
])
np.mean(np_loss), args.print_steps / used_time))
ce_info.append(
[np.mean(np_loss), args.print_steps / used_time])
time_begin = time.time()
if steps % args.save_steps == 0:
save_path = "step_" + str(steps)
if args.save_checkpoint:
save_load_io.save_checkpoint(args, exe, train_prog, save_path)
save_load_io.save_checkpoint(args, exe, train_prog,
save_path)
if args.save_param:
save_load_io.save_param(args, exe, train_prog, save_path)
save_load_io.save_param(args, exe, train_prog,
save_path)
except fluid.core.EOFException:
data_reader.reset()
......
PaddleNLP/emotion_detection/inference_model.py
浏览文件 @ f492ae4f
......@@ -19,8 +19,7 @@ from __future__ import print_function
import os
import sys
sys.path.append("../")
sys.path.append("../shared_modules/")
import paddle
import paddle.fluid as fluid
import numpy as np
......
PaddleNLP/emotion_detection/run_classifier.py
浏览文件 @ f492ae4f
......@@ -23,7 +23,7 @@ import os
import time
import multiprocessing
import sys
sys.path.append("../")
sys.path.append("../shared_modules/")
import paddle
import paddle.fluid as fluid
......
PaddleNLP/emotion_detection/run_ernie_classifier.py
浏览文件 @ f492ae4f
......@@ -24,7 +24,7 @@ import time
import argparse
import multiprocessing
import sys
sys.path.append("../")
sys.path.append("../shared_modules/")
import paddle
import paddle.fluid as fluid
......
PaddleNLP/language_model/train.py
浏览文件 @ f492ae4f
......@@ -36,7 +36,7 @@ import sys
if sys.version[0] == '2':
reload(sys)
sys.setdefaultencoding("utf-8")
sys.path.append('../')
sys.path.append('../shared_modules/')
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
......
PaddleNLP/lexical_analysis/creator.py
浏览文件 @ f492ae4f
......@@ -26,7 +26,7 @@ from paddle.fluid.initializer import NormalInitializer
from reader import Dataset
from ernie_reader import SequenceLabelReader
sys.path.append("..")
sys.path.append("../shared_modules/")
from models.sequence_labeling import nets
from models.representation.ernie import ernie_encoder, ernie_pyreader
......@@ -35,7 +35,8 @@ def create_model(args, vocab_size, num_labels, mode='train'):
"""create lac model"""
# model's input data
words = fluid.data(name='words', shape=[None, 1], dtype='int64', lod_level=1)
words = fluid.data(
name='words', shape=[None, 1], dtype='int64', lod_level=1)
targets = fluid.data(
name='targets', shape=[None, 1], dtype='int64', lod_level=1)
......@@ -88,7 +89,8 @@ def create_pyreader(args,
return_reader=False,
mode='train'):
# init reader
device_count = len(fluid.cuda_places()) if args.use_cuda else len(fluid.cpu_places())
device_count = len(fluid.cuda_places()) if args.use_cuda else len(
fluid.cpu_places())
if model == 'lac':
pyreader = fluid.io.DataLoader.from_generator(
......@@ -107,14 +109,14 @@ def create_pyreader(args,
fluid.io.shuffle(
reader.file_reader(file_name),
buf_size=args.traindata_shuffle_buffer),
batch_size=args.batch_size/device_count),
batch_size=args.batch_size / device_count),
places=place)
else:
pyreader.set_sample_list_generator(
fluid.io.batch(
reader.file_reader(
file_name, mode=mode),
batch_size=args.batch_size/device_count),
batch_size=args.batch_size / device_count),
places=place)
elif model == 'ernie':
......
PaddleNLP/lexical_analysis/ernie_reader.py
浏览文件 @ f492ae4f
......@@ -20,7 +20,7 @@ import sys
from collections import namedtuple
import numpy as np
sys.path.append("..")
sys.path.append("../shared_modules/")
from preprocess.ernie.task_reader import BaseReader, tokenization
......
PaddleNLP/lexical_analysis/eval.py
浏览文件 @ f492ae4f
......@@ -24,7 +24,7 @@ import paddle
import utils
import reader
import creator
sys.path.append('../models/')
sys.path.append('../shared_modules/models/')
from model_check import check_cuda
from model_check import check_version
......
PaddleNLP/lexical_analysis/inference_model.py
浏览文件 @ f492ae4f
......@@ -10,7 +10,7 @@ import paddle.fluid as fluid
import creator
import reader
import utils
sys.path.append('../models/')
sys.path.append('../shared_modules/models/')
from model_check import check_cuda
from model_check import check_version
......
PaddleNLP/lexical_analysis/predict.py
浏览文件 @ f492ae4f
......@@ -24,7 +24,7 @@ import paddle
import utils
import reader
import creator
sys.path.append('../models/')
sys.path.append('../shared_modules/models/')
from model_check import check_cuda
from model_check import check_version
......
PaddleNLP/lexical_analysis/run_ernie_sequence_labeling.py
浏览文件 @ f492ae4f
......@@ -34,7 +34,7 @@ import paddle.fluid as fluid
import creator
import utils
sys.path.append("..")
sys.path.append("../shared_modules/")
from models.representation.ernie import ErnieConfig
from models.model_check import check_cuda
from models.model_check import check_version
......@@ -187,8 +187,8 @@ def do_train(args):
end_time - start_time, train_pyreader.queue.size()))
if steps % args.save_steps == 0:
save_path = os.path.join(args.model_save_dir, "step_" + str(steps),
"checkpoint")
save_path = os.path.join(args.model_save_dir,
"step_" + str(steps), "checkpoint")
print("\tsaving model as %s" % (save_path))
fluid.save(train_program, save_path)
......@@ -199,6 +199,7 @@ def do_train(args):
"checkpoint")
fluid.save(train_program, save_path)
def do_eval(args):
# init executor
if args.use_cuda:
......
PaddleNLP/lexical_analysis/train.py
浏览文件 @ f492ae4f
......@@ -29,7 +29,7 @@ import reader
import utils
import creator
from eval import test_process
sys.path.append('../models/')
sys.path.append('../shared_modules/models/')
from model_check import check_cuda
from model_check import check_version
......@@ -151,8 +151,7 @@ def do_train(args):
# save checkpoints
if step % args.save_steps == 0 and step != 0:
save_path = os.path.join(args.model_save_dir,
"step_" + str(step),
"checkpoint")
"step_" + str(step), "checkpoint")
fluid.save(train_program, save_path)
step += 1
......
PaddleNLP/PaddleMRC/README.md → PaddleNLP/machine_reading_comprehension/README.md
浏览文件 @ f492ae4f
......@@ -39,4 +39,3 @@ D-NET是一个以提升**阅读理解模型泛化能力**为目标的“预训
- 在微调阶段引入多任务、多领域的学习策略 (基于[PALM](https://github.com/PaddlePaddle/PALM)多任务学习框架)，有效的提升了模型在不同领域的泛化能力
百度利用D-NET框架在EMNLP 2019 [MRQA](https://mrqa.github.io/shared)国际阅读理解评测中以超过第二名近两个百分点的成绩夺得冠军，同时，在全部12个测试数据集中的10个排名第一。
PaddleNLP/PaddleMT/transformer/desc.py → PaddleNLP/machine_translation/transformer/desc.py
浏览文件 @ f492ae4f
......@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
def get_input_descs(args):
"""
Generate a dict mapping data fields to the corresponding data shapes and
......@@ -42,7 +43,8 @@ def get_input_descs(args):
# 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"],
"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), "int64",
......@@ -54,12 +56,14 @@ def get_input_descs(args):
# 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"],
"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"],
"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]
......@@ -80,6 +84,7 @@ def get_input_descs(args):
return input_descs
# Names of word embedding table which might be reused for weight sharing.
word_emb_param_names = (
"src_word_emb_table",
......
PaddleNLP/PaddleMT/transformer/inference_model.py → PaddleNLP/machine_translation/transformer/inference_model.py
浏览文件 @ f492ae4f
......@@ -87,7 +87,8 @@ def do_save_inference_model(args):
# saving inference model
fluid.io.save_inference_model(args.inference_model_dir,
fluid.io.save_inference_model(
args.inference_model_dir,
feeded_var_names=list(input_field_names),
target_vars=[out_ids, out_scores],
executor=exe,
......
PaddleNLP/PaddleMT/transformer/main.py → PaddleNLP/machine_translation/transformer/main.py
浏览文件 @ f492ae4f
......@@ -25,7 +25,6 @@ from train import do_train
from predict import do_predict
from inference_model import do_save_inference_model
if __name__ == "__main__":
LOG_FORMAT = "[%(asctime)s %(levelname)s %(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(
......
PaddleNLP/PaddleMT/transformer/train.py → PaddleNLP/machine_translation/transformer/train.py
浏览文件 @ f492ae4f
......@@ -142,8 +142,8 @@ def do_train(args):
## init from some checkpoint, to resume the previous training
if args.init_from_checkpoint:
load(train_prog, os.path.join(args.init_from_checkpoint, "transformer"),
exe)
load(train_prog,
os.path.join(args.init_from_checkpoint, "transformer"), exe)
print("finish initing model from checkpoint from %s" %
(args.init_from_checkpoint))
......@@ -221,7 +221,6 @@ def do_train(args):
"transformer")
fluid.save(train_prog, model_path)
batch_id += 1
step_idx += 1
total_batch_num = total_batch_num + 1 # this is for benchmark
......
PaddleNLP/PaddleMT/transformer/transformer.py → PaddleNLP/machine_translation/transformer/transformer.py
浏览文件 @ f492ae4f
......@@ -25,7 +25,6 @@ from desc import *
dropout_seed = None
def wrap_layer_with_block(layer, block_idx):
"""
Make layer define support indicating block, by which we can add layers
......@@ -300,12 +299,13 @@ def prepare_encoder_decoder(src_word,
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)))
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 = fluid.embedding(src_pos,
src_pos_enc = fluid.embedding(
src_pos,
size=[src_max_len, src_emb_dim],
param_attr=fluid.ParamAttr(
name=pos_enc_param_name, trainable=False))
......@@ -477,7 +477,8 @@ def decoder(dec_input,
The decoder is composed of a stack of identical decoder_layer layers.
"""
for i in range(n_layer):
dec_output = decoder_layer(dec_input,
dec_output = decoder_layer(
dec_input,
enc_output,
dec_slf_attn_bias,
dec_enc_attn_bias,
......@@ -491,8 +492,7 @@ def decoder(dec_input,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
cache=None if caches is None else
(caches[i], i))
cache=None if caches is None else (caches[i], i))
dec_input = dec_output
dec_output = pre_process_layer(dec_output, preprocess_cmd,
prepostprocess_dropout)
......@@ -530,7 +530,8 @@ def transformer(model_input,
label = model_input.lbl_word
weights = model_input.lbl_weight
enc_output = wrap_encoder(enc_inputs,
enc_output = wrap_encoder(
enc_inputs,
src_vocab_size,
max_length,
n_layer,
......@@ -547,7 +548,8 @@ def transformer(model_input,
weight_sharing,
bos_idx=bos_idx)
predict = wrap_decoder(dec_inputs,
predict = wrap_decoder(
dec_inputs,
trg_vocab_size,
max_length,
n_layer,
......@@ -569,8 +571,9 @@ def transformer(model_input,
if label_smooth_eps:
# TODO: use fluid.input.one_hot after softmax_with_cross_entropy removing
# the enforcement that the last dimension of label must be 1.
label = layers.label_smooth(label=layers.one_hot(input=label,
depth=trg_vocab_size),
label = layers.label_smooth(
label=layers.one_hot(
input=label, depth=trg_vocab_size),
epsilon=label_smooth_eps)
cost = layers.softmax_with_cross_entropy(
......@@ -714,7 +717,8 @@ def fast_decode(model_input, src_vocab_size, trg_vocab_size, max_in_len,
dec_inputs = (model_input.trg_word, model_input.init_score,
model_input.init_idx, model_input.trg_src_attn_bias)
enc_output = wrap_encoder(enc_inputs,
enc_output = wrap_encoder(
enc_inputs,
src_vocab_size,
max_in_len,
n_layer,
......@@ -763,9 +767,15 @@ def fast_decode(model_input, src_vocab_size, trg_vocab_size, max_in_len,
dtype=enc_output.dtype,
value=0),
"static_k": # for encoder-decoder attention
fluid.data(shape=[None, n_head, 0, d_key], dtype=enc_output.dtype, name=("static_k_%d"%i)),
fluid.data(
shape=[None, n_head, 0, d_key],
dtype=enc_output.dtype,
name=("static_k_%d" % i)),
"static_v": # for encoder-decoder attention
fluid.data(shape=[None, n_head, 0, d_value], dtype=enc_output.dtype, name=("static_v_%d"%i)),
fluid.data(
shape=[None, n_head, 0, d_value],
dtype=enc_output.dtype,
name=("static_v_%d" % i)),
} for i in range(n_layer)
]
......@@ -780,8 +790,8 @@ def fast_decode(model_input, src_vocab_size, trg_vocab_size, max_in_len,
# gather cell states corresponding to selected parent
pre_caches = map_structure(
lambda x: layers.gather(x, index=gather_idx), caches)
pre_src_attn_bias = layers.gather(trg_src_attn_bias,
index=gather_idx)
pre_src_attn_bias = layers.gather(
trg_src_attn_bias, index=gather_idx)
pre_pos = layers.elementwise_mul(
x=layers.fill_constant_batch_size_like(
input=pre_src_attn_bias, # cann't use lod tensor here
......@@ -790,7 +800,8 @@ def fast_decode(model_input, src_vocab_size, trg_vocab_size, max_in_len,
dtype=pre_ids.dtype),
y=step_idx,
axis=0)
logits = wrap_decoder((pre_ids, pre_pos, None, pre_src_attn_bias),
logits = wrap_decoder(
(pre_ids, pre_pos, None, pre_src_attn_bias),
trg_vocab_size,
max_in_len,
n_layer,
......@@ -811,9 +822,8 @@ def fast_decode(model_input, src_vocab_size, trg_vocab_size, max_in_len,
# 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)
accu_scores = layers.elementwise_add(
x=layers.log(topk_scores), y=pre_scores, axis=0)
# beam_search op uses lod to differentiate branches.
accu_scores = layers.lod_reset(accu_scores, pre_ids)
# topK reduction across beams, also contain special handle of
......@@ -832,11 +842,12 @@ def fast_decode(model_input, src_vocab_size, trg_vocab_size, max_in_len,
return (step_idx, selected_ids, selected_scores, gather_idx,
pre_caches, pre_src_attn_bias)
_ = layers.while_loop(cond=cond_func,
_ = layers.while_loop(
cond=cond_func,
body=body_func,
loop_vars=[
step_idx, start_tokens, init_scores,
parent_idx, caches, trg_src_attn_bias
step_idx, start_tokens, init_scores, parent_idx, caches,
trg_src_attn_bias
],
is_test=True)
......
PaddleNLP/PaddleMT/transformer/utils/configure.py → PaddleNLP/machine_translation/transformer/utils/configure.py
浏览文件 @ f492ae4f
......@@ -199,9 +199,14 @@ class PDConfig(object):
"Whether to perform model saving for inference.")
# NOTE: args for profiler
self.default_g.add_arg("is_profiler", int, 0, "the switch of profiler tools. (used for benchmark)")
self.default_g.add_arg("profiler_path", str, './', "the profiler output file path. (used for benchmark)")
self.default_g.add_arg("max_iter", int, 0, "the max train batch num.(used for benchmark)")
self.default_g.add_arg(
"is_profiler", int, 0,
"the switch of profiler tools. (used for benchmark)")
self.default_g.add_arg(
"profiler_path", str, './',
"the profiler output file path. (used for benchmark)")
self.default_g.add_arg("max_iter", int, 0,
"the max train batch num.(used for benchmark)")
self.parser = parser
......
PaddleNLP/PaddleLARK/BERT/README.md → PaddleNLP/pretrain_langauge_models/BERT/README.md
浏览文件 @ f492ae4f
......@@ -415,5 +415,3 @@ for (size_t i = 0; i < output.front().data.length() / sizeof(float); i += 3) {
<< static_cast<float *>(output.front().data.data())[i + 2] << std::endl;
}
```
PaddleNLP/PaddleLARK/ELMo/README.md → PaddleNLP/pretrain_langauge_models/ELMo/README.md
浏览文件 @ f492ae4f
......@@ -90,5 +90,3 @@ word_embedding=fluid.layers.concat(input=[elmo_embedding, word_embedding], axis=
### 参考论文
[Deep contextualized word representations](https://arxiv.org/abs/1802.05365)
PaddleNLP/PaddleLARK/XLNet/_ce.py → PaddleNLP/pretrain_langauge_models/XLNet/_ce.py
浏览文件 @ f492ae4f
......@@ -7,7 +7,6 @@ from kpi import CostKpi, DurationKpi, AccKpi
#### NOTE kpi.py should shared in models in some way!!!!
train_duration_sts_b_card1 = DurationKpi(
'train_duration_sts_b_card1', 0.01, 0, actived=True)
train_cost_sts_b_card1 = CostKpi(
......
PaddleNLP/sentiment_classification/inference_model_ernie.py
浏览文件 @ f492ae4f
# -*- coding: utf_8 -*-
import os
import sys
sys.path.append("../")
sys.path.append("../models/classification")
sys.path.append("../shared_modules/")
sys.path.append("../shared_modules/models/classification")
import paddle
import paddle.fluid as fluid
import numpy as np
......@@ -17,6 +17,7 @@ from models.representation.ernie import ErnieConfig
from models.representation.ernie import ernie_encoder, ernie_encoder_with_paddle_hub
from preprocess.ernie import task_reader
def do_save_inference_model(args):
ernie_config = ErnieConfig(args.ernie_config_path)
......@@ -37,18 +38,17 @@ def do_save_inference_model(args):
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
infer_pyreader, ernie_inputs, labels = ernie_pyreader(
args,
pyreader_name="infer_reader")
args, pyreader_name="infer_reader")
if args.use_paddle_hub:
embeddings = ernie_encoder_with_paddle_hub(ernie_inputs, args.max_seq_len)
embeddings = ernie_encoder_with_paddle_hub(ernie_inputs,
args.max_seq_len)
else:
embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config)
embeddings = ernie_encoder(
ernie_inputs, ernie_config=ernie_config)
probs = create_model(args,
embeddings,
labels=labels,
is_prediction=True)
probs = create_model(
args, embeddings, labels=labels, is_prediction=True)
test_prog = test_prog.clone(for_test=True)
exe.run(startup_prog)
......@@ -59,11 +59,11 @@ def do_save_inference_model(args):
fluid.io.save_inference_model(
args.inference_model_dir,
feeded_var_names=[ernie_inputs["src_ids"].name,
ernie_inputs["sent_ids"].name,
ernie_inputs["pos_ids"].name,
ernie_inputs["input_mask"].name,
ernie_inputs["seq_lens"].name],
feeded_var_names=[
ernie_inputs["src_ids"].name, ernie_inputs["sent_ids"].name,
ernie_inputs["pos_ids"].name, ernie_inputs["input_mask"].name,
ernie_inputs["seq_lens"].name
],
target_vars=[probs],
executor=exe,
main_program=test_prog,
......@@ -72,6 +72,7 @@ def do_save_inference_model(args):
print("save inference model at %s" % (args.inference_model_dir))
def inference(exe, test_program, test_pyreader, fetch_list, infer_phrase):
"""
Inference Function
......@@ -80,13 +81,16 @@ def inference(exe, test_program, test_pyreader, fetch_list, infer_phrase):
test_pyreader.start()
while True:
try:
np_props = exe.run(program=test_program, fetch_list=fetch_list, return_numpy=True)
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
def test_inference_model(args):
ernie_config = ErnieConfig(args.ernie_config_path)
ernie_config.print_config()
......@@ -113,15 +117,11 @@ def test_inference_model(args):
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
infer_pyreader, ernie_inputs, labels = ernie_pyreader(
args,
pyreader_name="infer_pyreader")
args, pyreader_name="infer_pyreader")
embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config)
probs = create_model(
args,
embeddings,
labels=labels,
is_prediction=True)
args, embeddings, labels=labels, is_prediction=True)
test_prog = test_prog.clone(for_test=True)
exe.run(startup_prog)
......@@ -129,7 +129,7 @@ def test_inference_model(args):
assert (args.inference_model_dir)
infer_data_generator = reader.data_generator(
input_file=args.test_set,
batch_size=args.batch_size/dev_count,
batch_size=args.batch_size / dev_count,
phase="infer",
epoch=1,
shuffle=False)
......@@ -141,8 +141,8 @@ def test_inference_model(args):
params_filename="params.pdparams")
infer_pyreader.set_batch_generator(infer_data_generator)
inference(exe, test_prog, infer_pyreader,
[probs.name], "infer")
inference(exe, test_prog, infer_pyreader, [probs.name], "infer")
if __name__ == "__main__":
args = PDConfig()
......
PaddleNLP/sentiment_classification/run_classifier.py
浏览文件 @ f492ae4f
......@@ -12,8 +12,8 @@ import argparse
import numpy as np
import multiprocessing
import sys
sys.path.append("../models/classification/")
sys.path.append("../")
sys.path.append("../shared_modules/models/classification/")
sys.path.append("../shared_modules/")
from nets import bow_net
from nets import lstm_net
......@@ -30,24 +30,19 @@ import paddle.fluid as fluid
import reader
from utils import init_checkpoint
def create_model(args,
pyreader_name,
num_labels,
is_prediction=False):
def create_model(args, pyreader_name, num_labels, is_prediction=False):
"""
Create Model for sentiment classification
"""
data = fluid.data(
name="src_ids", shape=[None, args.max_seq_len], dtype='int64')
label = fluid.data(
name="label", shape=[None, 1], dtype="int64")
seq_len = fluid.data(
name="seq_len", shape=[None], dtype="int64")
label = fluid.data(name="label", shape=[None, 1], dtype="int64")
seq_len = fluid.data(name="seq_len", shape=[None], dtype="int64")
data_reader = fluid.io.DataLoader.from_generator(feed_list=[data, label, seq_len],
capacity=4, iterable=False)
data_reader = fluid.io.DataLoader.from_generator(
feed_list=[data, label, seq_len], capacity=4, iterable=False)
if args.model_type == "bilstm_net":
network = bilstm_net
......@@ -63,18 +58,19 @@ def create_model(args,
raise ValueError("Unknown network type!")
if is_prediction:
probs = network(data, seq_len, None, args.vocab_size, is_prediction=is_prediction)
probs = network(
data, seq_len, None, args.vocab_size, is_prediction=is_prediction)
print("create inference model...")
return data_reader, probs, [data.name, seq_len.name]
ce_loss, probs = network(data, seq_len, label, args.vocab_size, is_prediction=is_prediction)
ce_loss, probs = network(
data, seq_len, label, args.vocab_size, is_prediction=is_prediction)
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 data_reader, loss, accuracy, num_seqs
def evaluate(exe, test_program, test_pyreader, fetch_list, eval_phase):
"""
Evaluation Function
......@@ -111,7 +107,8 @@ def inference(exe, test_program, test_pyreader, fetch_list, infer_phrase):
time_begin = time.time()
while True:
try:
np_props = exe.run(program=test_program, fetch_list=fetch_list,
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]))
......@@ -135,7 +132,8 @@ def main(args):
exe = fluid.Executor(place)
task_name = args.task_name.lower()
processor = reader.SentaProcessor(data_dir=args.data_dir,
processor = reader.SentaProcessor(
data_dir=args.data_dir,
vocab_path=args.vocab_path,
random_seed=args.random_seed,
max_seq_len=args.max_seq_len)
......@@ -151,7 +149,7 @@ def main(args):
if args.do_train:
train_data_generator = processor.data_generator(
batch_size=args.batch_size/dev_count,
batch_size=args.batch_size / dev_count,
phase='train',
epoch=args.epoch,
shuffle=True)
......@@ -187,7 +185,7 @@ def main(args):
if args.do_val:
test_data_generator = processor.data_generator(
batch_size=args.batch_size/dev_count,
batch_size=args.batch_size / dev_count,
phase='dev',
epoch=1,
shuffle=False)
......@@ -204,7 +202,7 @@ def main(args):
if args.do_infer:
infer_data_generator = processor.data_generator(
batch_size=args.batch_size/dev_count,
batch_size=args.batch_size / dev_count,
phase='infer',
epoch=1,
shuffle=False)
......@@ -223,18 +221,13 @@ def main(args):
if args.do_train:
if args.init_checkpoint:
init_checkpoint(
exe,
args.init_checkpoint,
main_program=startup_prog)
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)
init_checkpoint(exe, args.init_checkpoint, main_program=startup_prog)
if args.do_train:
train_exe = exe
......@@ -262,7 +255,9 @@ def main(args):
else:
fetch_list = []
outputs = train_exe.run(program=train_program, fetch_list=fetch_list, return_numpy=False)
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
......@@ -274,7 +269,8 @@ def main(args):
total_num_seqs.extend(np_num_seqs)
if args.verbose:
verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size()
verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size(
)
print(verbose)
time_end = time.time()
......@@ -289,8 +285,7 @@ def main(args):
if steps % args.save_steps == 0:
save_path = os.path.join(args.checkpoints,
"step_" + str(steps),
"checkpoint")
"step_" + str(steps), "checkpoint")
fluid.save(train_program, save_path)
if steps % args.validation_steps == 0:
......@@ -317,8 +312,7 @@ def main(args):
# final eval on test set
if args.do_infer:
print("Final test result:")
inference(exe, infer_prog, infer_reader,
[prop.name], "infer")
inference(exe, infer_prog, infer_reader, [prop.name], "infer")
if __name__ == "__main__":
......
PaddleNLP/sentiment_classification/run_ernie_classifier.py
浏览文件 @ f492ae4f
......@@ -16,8 +16,8 @@ import sys
import paddle
import paddle.fluid as fluid
sys.path.append("../models/classification/")
sys.path.append("..")
sys.path.append("../shared_modules/models/classification/")
sys.path.append("../shared_modules/")
print(sys.path)
from nets import bow_net
......@@ -36,6 +36,7 @@ from config import PDConfig
from utils import init_checkpoint
def ernie_pyreader(args, pyreader_name):
src_ids = fluid.data(
name="src_ids", shape=[None, args.max_seq_len, 1], dtype="int64")
......@@ -45,10 +46,8 @@ def ernie_pyreader(args, pyreader_name):
name="pos_ids", shape=[None, args.max_seq_len, 1], dtype="int64")
input_mask = fluid.data(
name="input_mask", shape=[None, args.max_seq_len, 1], dtype="float32")
labels = fluid.data(
name="labels", shape=[None, 1], dtype="int64")
seq_lens = fluid.data(
name="seq_lens", shape=[None], dtype="int64")
labels = fluid.data(name="labels", shape=[None, 1], dtype="int64")
seq_lens = fluid.data(name="seq_lens", shape=[None], dtype="int64")
pyreader = fluid.io.DataLoader.from_generator(
feed_list=[src_ids, sent_ids, pos_ids, input_mask, labels, seq_lens],
......@@ -61,15 +60,13 @@ def ernie_pyreader(args, pyreader_name):
"sent_ids": sent_ids,
"pos_ids": pos_ids,
"input_mask": input_mask,
"seq_lens": seq_lens}
"seq_lens": seq_lens
}
return pyreader, ernie_inputs, labels
def create_model(args,
embeddings,
labels,
is_prediction=False):
def create_model(args, embeddings, labels, is_prediction=False):
"""
Create Model for sentiment classification based on ERNIE encoder
"""
......@@ -132,7 +129,8 @@ def infer(exe, infer_program, infer_pyreader, fetch_list, infer_phase):
time_begin = time.time()
while True:
try:
batch_probs = exe.run(program=infer_program, fetch_list=fetch_list,
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]))
......@@ -195,21 +193,19 @@ def main(args):
with fluid.unique_name.guard():
# create ernie_pyreader
train_pyreader, ernie_inputs, labels = ernie_pyreader(
args,
pyreader_name='train_pyreader')
args, pyreader_name='train_pyreader')
# get ernie_embeddings
if args.use_paddle_hub:
embeddings = ernie_encoder_with_paddle_hub(ernie_inputs, args.max_seq_len)
embeddings = ernie_encoder_with_paddle_hub(ernie_inputs,
args.max_seq_len)
else:
embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config)
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)
args, embeddings, labels=labels, is_prediction=False)
optimizer = fluid.optimizer.Adam(learning_rate=args.lr)
optimizer.minimize(loss)
......@@ -232,21 +228,19 @@ def main(args):
with fluid.unique_name.guard():
# create ernie_pyreader
test_pyreader, ernie_inputs, labels = ernie_pyreader(
args,
pyreader_name='eval_reader')
args, pyreader_name='eval_reader')
# get ernie_embeddings
if args.use_paddle_hub:
embeddings = ernie_encoder_with_paddle_hub(ernie_inputs, args.max_seq_len)
embeddings = ernie_encoder_with_paddle_hub(ernie_inputs,
args.max_seq_len)
else:
embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config)
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)
args, embeddings, labels=labels, is_prediction=False)
test_prog = test_prog.clone(for_test=True)
......@@ -261,19 +255,18 @@ def main(args):
with fluid.program_guard(infer_prog, startup_prog):
with fluid.unique_name.guard():
infer_pyreader, ernie_inputs, labels = ernie_pyreader(
args,
pyreader_name="infer_pyreader")
args, pyreader_name="infer_pyreader")
# get ernie_embeddings
if args.use_paddle_hub:
embeddings = ernie_encoder_with_paddle_hub(ernie_inputs, args.max_seq_len)
embeddings = ernie_encoder_with_paddle_hub(ernie_inputs,
args.max_seq_len)
else:
embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config)
embeddings = ernie_encoder(
ernie_inputs, ernie_config=ernie_config)
probs = create_model(args,
embeddings,
labels=labels,
is_prediction=True)
probs = create_model(
args, embeddings, labels=labels, is_prediction=True)
infer_prog = infer_prog.clone(for_test=True)
......@@ -282,25 +275,17 @@ def main(args):
if args.do_train:
if args.init_checkpoint:
init_checkpoint(
exe,
args.init_checkpoint,
main_program=train_program)
exe, args.init_checkpoint, main_program=train_program)
elif args.do_val:
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=test_prog)
init_checkpoint(exe, args.init_checkpoint, main_program=test_prog)
elif 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=infer_prog)
init_checkpoint(exe, args.init_checkpoint, main_program=infer_prog)
if args.do_train:
train_exe = exe
......@@ -327,7 +312,9 @@ def main(args):
else:
fetch_list = []
outputs = train_exe.run(program=train_program, fetch_list=fetch_list, return_numpy=False)
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)
......@@ -338,7 +325,8 @@ def main(args):
total_num_seqs.extend(np_num_seqs)
if args.verbose:
verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size()
verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size(
)
print(verbose)
time_end = time.time()
......@@ -353,8 +341,7 @@ def main(args):
if steps % args.save_steps == 0:
save_path = os.path.join(args.checkpoints,
"step_" + str(steps),
"checkpoint")
"step_" + str(steps), "checkpoint")
fluid.save(train_program, save_path)
if steps % args.validation_steps == 0:
......@@ -380,8 +367,8 @@ def main(args):
# final eval on test set
if args.do_infer:
print("Final test result:")
infer(exe, infer_prog, infer_pyreader,
[probs.name], "infer")
infer(exe, infer_prog, infer_pyreader, [probs.name], "infer")
if __name__ == "__main__":
args = PDConfig()
......
PaddleNLP/PaddleTextGEN/seq2seq/train.py → PaddleNLP/seq2seq/seq2seq/train.py
浏览文件 @ f492ae4f
......@@ -230,8 +230,7 @@ def main():
if not args.profile:
save_path = os.path.join(args.model_path,
"epoch_" + str(epoch_id),
"checkpoint")
"epoch_" + str(epoch_id), "checkpoint")
print("begin to save", save_path)
fluid.save(train_program, save_path)
print("save finished")
......
PaddleNLP/models/classification/nets.py → PaddleNLP/shared_modules/models/classification/nets.py
浏览文件 @ f492ae4f
......@@ -4,6 +4,7 @@ This module provide nets for text classification
import paddle.fluid as fluid
def bow_net(data,
seq_len,
label,
......
PaddleNLP/models/matching/cnn.py → PaddleNLP/shared_modules/models/matching/cnn.py
浏览文件 @ f492ae4f
......@@ -43,8 +43,8 @@ class CNN(object):
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")
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
......
PaddleNLP/models/model_check.py → PaddleNLP/shared_modules/models/model_check.py
浏览文件 @ f492ae4f
......@@ -33,6 +33,7 @@ def check_cuda(use_cuda, err = \
except Exception as e:
pass
def check_version():
"""
Log error and exit when the installed version of paddlepaddle is
......
PaddleNLP/models/representation/ernie.py → PaddleNLP/shared_modules/models/representation/ernie.py
浏览文件 @ f492ae4f
......@@ -30,10 +30,14 @@ from models.transformer_encoder import encoder, pre_process_layer
def ernie_pyreader(args, pyreader_name):
"""define standard ernie pyreader"""
src_ids = fluid.data(name='1', shape=[-1, args.max_seq_len, 1], dtype='int64')
sent_ids = fluid.data(name='2', shape=[-1, args.max_seq_len, 1], dtype='int64')
pos_ids = fluid.data(name='3', shape=[-1, args.max_seq_len, 1], dtype='int64')
input_mask = fluid.data(name='4', shape=[-1, args.max_seq_len, 1], dtype='float32')
src_ids = fluid.data(
name='1', shape=[-1, args.max_seq_len, 1], dtype='int64')
sent_ids = fluid.data(
name='2', shape=[-1, args.max_seq_len, 1], dtype='int64')
pos_ids = fluid.data(
name='3', shape=[-1, args.max_seq_len, 1], dtype='int64')
input_mask = fluid.data(
name='4', shape=[-1, args.max_seq_len, 1], dtype='float32')
labels = fluid.data(name='5', shape=[-1, 1], dtype='int64')
seq_lens = fluid.data(name='6', shape=[-1], dtype='int64')
......
PaddleNLP/preprocess/ernie/task_reader.py → PaddleNLP/shared_modules/preprocess/ernie/task_reader.py
浏览文件 @ f492ae4f
......@@ -29,6 +29,7 @@ from preprocess.ernie import tokenization
from preprocess.padding import pad_batch_data
import io
def csv_reader(fd, delimiter='\t'):
def gen():
for i in fd:
......@@ -37,8 +38,10 @@ def csv_reader(fd, delimiter='\t'):
yield slots,
else:
yield slots
return gen()
class BaseReader(object):
"""BaseReader for classify and sequence labeling task"""
......
PaddleNLP/preprocess/ernie/tokenization.py → PaddleNLP/shared_modules/preprocess/ernie/tokenization.py
浏览文件 @ f492ae4f
......@@ -23,6 +23,7 @@ import unicodedata
import six
import io
def convert_to_unicode(text):
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
if six.PY3:
......
PaddleNLP/similarity_net/run_classifier.py
浏览文件 @ f492ae4f
......@@ -30,7 +30,7 @@ if sys.getdefaultencoding() != defaultencoding:
reload(sys)
sys.setdefaultencoding(defaultencoding)
sys.path.append("..")
sys.path.append("../shared_modules/")
import paddle
import paddle.fluid as fluid
......@@ -47,14 +47,14 @@ from models.model_check import check_version
from models.model_check import check_cuda
def create_model(args, pyreader_name, is_inference = False, is_pointwise = False):
def create_model(args, pyreader_name, is_inference=False, is_pointwise=False):
"""
Create Model for simnet
"""
if is_inference:
inf_pyreader = fluid.layers.py_reader(
capacity=16,
shapes=([-1,1], [-1,1]),
shapes=([-1, 1], [-1, 1]),
dtypes=('int64', 'int64'),
lod_levels=(1, 1),
name=pyreader_name,
......@@ -67,7 +67,7 @@ def create_model(args, pyreader_name, is_inference = False, is_pointwise = False
if is_pointwise:
pointwise_pyreader = fluid.layers.py_reader(
capacity=16,
shapes=([-1,1], [-1,1], [-1,1]),
shapes=([-1, 1], [-1, 1], [-1, 1]),
dtypes=('int64', 'int64', 'int64'),
lod_levels=(1, 1, 0),
name=pyreader_name,
......@@ -79,15 +79,17 @@ def create_model(args, pyreader_name, is_inference = False, is_pointwise = False
else:
pairwise_pyreader = fluid.layers.py_reader(
capacity=16,
shapes=([-1,1], [-1,1], [-1,1]),
shapes=([-1, 1], [-1, 1], [-1, 1]),
dtypes=('int64', 'int64', 'int64'),
lod_levels=(1, 1, 1),
name=pyreader_name,
use_double_buffer=False)
left, pos_right, neg_right = fluid.layers.read_file(pairwise_pyreader)
left, pos_right, neg_right = fluid.layers.read_file(
pairwise_pyreader)
return pairwise_pyreader, left, pos_right, neg_right
def train(conf_dict, args):
"""
train processic
......@@ -97,16 +99,16 @@ def train(conf_dict, args):
# get vocab size
conf_dict['dict_size'] = len(vocab)
# Load network structure dynamically
net = utils.import_class("../models/matching",
net = utils.import_class("../shared_modules/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",
loss = utils.import_class("../shared_modules/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",
"../shared_modules/models/matching/optimizers", "paddle_optimizers",
conf_dict["optimizer"]["class_name"])(conf_dict)
# load auc method
metric = fluid.metrics.Auc(name="auc")
......@@ -131,8 +133,7 @@ def train(conf_dict, args):
with fluid.program_guard(train_program, startup_prog):
with fluid.unique_name.guard():
train_pyreader, left, pos_right, neg_right = create_model(
args,
pyreader_name='train_reader')
args, pyreader_name='train_reader')
left_feat, pos_score = net.predict(left, pos_right)
pred = pos_score
_, neg_score = net.predict(left, neg_right)
......@@ -141,12 +142,14 @@ def train(conf_dict, args):
optimizer.ops(avg_cost)
# Get Reader
get_train_examples = simnet_process.get_reader("train",epoch=args.epoch)
get_train_examples = simnet_process.get_reader(
"train", epoch=args.epoch)
if args.do_valid:
test_prog = fluid.Program()
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
test_pyreader, left, pos_right= create_model(args, pyreader_name = 'test_reader',is_inference=True)
test_pyreader, left, pos_right = create_model(
args, pyreader_name='test_reader', is_inference=True)
left_feat, pos_score = net.predict(left, pos_right)
pred = pos_score
test_prog = test_prog.clone(for_test=True)
......@@ -156,40 +159,41 @@ def train(conf_dict, args):
with fluid.program_guard(train_program, startup_prog):
with fluid.unique_name.guard():
train_pyreader, left, right, label = create_model(
args,
pyreader_name='train_reader',
is_pointwise=True)
args, pyreader_name='train_reader', is_pointwise=True)
left_feat, pred = net.predict(left, right)
avg_cost = loss.compute(pred, label)
avg_cost.persistable = True
optimizer.ops(avg_cost)
# Get Feeder and Reader
get_train_examples = simnet_process.get_reader("train",epoch=args.epoch)
get_train_examples = simnet_process.get_reader(
"train", epoch=args.epoch)
if args.do_valid:
test_prog = fluid.Program()
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
test_pyreader, left, right= create_model(args, pyreader_name = 'test_reader',is_inference=True)
test_pyreader, left, right = create_model(
args, pyreader_name='test_reader', is_inference=True)
left_feat, pred = net.predict(left, right)
test_prog = test_prog.clone(for_test=True)
if args.init_checkpoint is not "":
utils.init_checkpoint(exe, args.init_checkpoint,
startup_prog)
utils.init_checkpoint(exe, args.init_checkpoint, startup_prog)
def valid_and_test(test_program, test_pyreader, get_valid_examples, process, mode, exe, fetch_list):
def valid_and_test(test_program, test_pyreader, get_valid_examples, process,
mode, exe, fetch_list):
"""
return auc and acc
"""
# Get Batch Data
batch_data = fluid.io.batch(get_valid_examples, args.batch_size, drop_last=False)
batch_data = fluid.io.batch(
get_valid_examples, args.batch_size, drop_last=False)
test_pyreader.decorate_paddle_reader(batch_data)
test_pyreader.start()
pred_list = []
while True:
try:
_pred = exe.run(program=test_program,fetch_list=[pred.name])
_pred = exe.run(program=test_program, fetch_list=[pred.name])
pred_list += list(_pred)
except fluid.core.EOFException:
test_pyreader.reset()
......@@ -222,7 +226,8 @@ def train(conf_dict, args):
#for epoch_id in range(args.epoch):
# used for continuous evaluation
if args.enable_ce:
train_batch_data = fluid.io.batch(get_train_examples, args.batch_size, drop_last=False)
train_batch_data = fluid.io.batch(
get_train_examples, args.batch_size, drop_last=False)
else:
train_batch_data = fluid.io.batch(
fluid.io.shuffle(
......@@ -238,19 +243,23 @@ def train(conf_dict, args):
try:
global_step += 1
fetch_list = [avg_cost.name]
avg_loss = train_exe.run(program=train_program, fetch_list = fetch_list)
avg_loss = train_exe.run(program=train_program,
fetch_list=fetch_list)
losses.append(np.mean(avg_loss[0]))
if args.do_valid and global_step % args.validation_steps == 0:
get_valid_examples = simnet_process.get_reader("valid")
valid_result = valid_and_test(test_prog,test_pyreader,get_valid_examples,simnet_process,"valid",exe,[pred.name])
valid_result = valid_and_test(
test_prog, test_pyreader, get_valid_examples,
simnet_process, "valid", exe, [pred.name])
if args.compute_accuracy:
valid_auc, valid_acc = valid_result
logging.info(
"global_steps: %d, valid_auc: %f, valid_acc: %f, valid_loss: %f" %
(global_step, valid_auc, valid_acc, np.mean(losses)))
"global_steps: %d, valid_auc: %f, valid_acc: %f, valid_loss: %f"
% (global_step, valid_auc, valid_acc, np.mean(losses)))
else:
valid_auc = valid_result
logging.info("global_steps: %d, valid_auc: %f, valid_loss: %f" %
logging.info(
"global_steps: %d, valid_auc: %f, valid_loss: %f" %
(global_step, valid_auc, np.mean(losses)))
if global_step % args.save_steps == 0:
model_save_dir = os.path.join(args.output_dir,
......@@ -269,8 +278,7 @@ def train(conf_dict, args):
]
target_vars = [left_feat, pred]
fluid.io.save_inference_model(model_path, feed_var_names,
target_vars, exe,
test_prog)
target_vars, exe, test_prog)
logging.info("saving infer model in %s" % model_path)
except fluid.core.EOFException:
......@@ -282,8 +290,7 @@ def train(conf_dict, args):
ce_info.append([np.mean(losses), end_time - start_time])
#final save
logging.info("the final step is %s" % global_step)
model_save_dir = os.path.join(args.output_dir,
conf_dict["model_path"])
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)
......@@ -296,8 +303,7 @@ def train(conf_dict, args):
right.name,
]
target_vars = [left_feat, pred]
fluid.io.save_inference_model(model_path, feed_var_names,
target_vars, exe,
fluid.io.save_inference_model(model_path, feed_var_names, target_vars, exe,
test_prog)
logging.info("saving infer model in %s" % model_path)
# used for continuous evaluation
......@@ -322,7 +328,9 @@ def train(conf_dict, args):
else:
# Get Feeder and Reader
get_test_examples = simnet_process.get_reader("test")
test_result = valid_and_test(test_prog,test_pyreader,get_test_examples,simnet_process,"test",exe,[pred.name])
test_result = valid_and_test(test_prog, test_pyreader,
get_test_examples, simnet_process, "test",
exe, [pred.name])
if args.compute_accuracy:
test_auc, test_acc = test_result
logging.info("AUC of test is %f, Accuracy of test is %f" %
......@@ -348,12 +356,13 @@ def test(conf_dict, args):
startup_prog = fluid.Program()
get_test_examples = simnet_process.get_reader("test")
batch_data = fluid.io.batch(get_test_examples, args.batch_size, drop_last=False)
batch_data = fluid.io.batch(
get_test_examples, args.batch_size, drop_last=False)
test_prog = fluid.Program()
conf_dict['dict_size'] = len(vocab)
net = utils.import_class("../models/matching",
net = utils.import_class("../shared_modules/models/matching",
conf_dict["net"]["module_name"],
conf_dict["net"]["class_name"])(conf_dict)
......@@ -364,9 +373,7 @@ def test(conf_dict, args):
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
test_pyreader, left, pos_right = create_model(
args,
pyreader_name = 'test_reader',
is_inference=True)
args, pyreader_name='test_reader', is_inference=True)
left_feat, pos_score = net.predict(left, pos_right)
pred = pos_score
test_prog = test_prog.clone(for_test=True)
......@@ -375,18 +382,13 @@ def test(conf_dict, args):
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
test_pyreader, left, right = create_model(
args,
pyreader_name = 'test_reader',
is_inference=True)
args, pyreader_name='test_reader', is_inference=True)
left_feat, pred = net.predict(left, right)
test_prog = test_prog.clone(for_test=True)
exe.run(startup_prog)
utils.init_checkpoint(
exe,
args.init_checkpoint,
main_program=test_prog)
utils.init_checkpoint(exe, args.init_checkpoint, main_program=test_prog)
test_exe = exe
test_pyreader.decorate_paddle_reader(batch_data)
......@@ -398,15 +400,18 @@ def test(conf_dict, args):
output = []
while True:
try:
output = test_exe.run(program=test_prog,fetch_list=fetch_list)
output = test_exe.run(program=test_prog, fetch_list=fetch_list)
if args.task_mode == "pairwise":
pred_list += list(map(lambda item: float(item[0]), output[0]))
pred_list += list(
map(lambda item: float(item[0]), output[0]))
predictions_file.write(u"\n".join(
map(lambda item: str((item[0] + 1) / 2), output[0])) + "\n")
map(lambda item: str((item[0] + 1) / 2), output[0])) +
"\n")
else:
pred_list += map(lambda item: item, output[0])
predictions_file.write(u"\n".join(
map(lambda item: str(np.argmax(item)), output[0])) + "\n")
map(lambda item: str(np.argmax(item)), output[0])) +
"\n")
except fluid.core.EOFException:
test_pyreader.reset()
break
......@@ -450,36 +455,36 @@ def infer(conf_dict, args):
startup_prog = fluid.Program()
get_infer_examples = simnet_process.get_infer_reader
batch_data = fluid.io.batch(get_infer_examples, args.batch_size, drop_last=False)
batch_data = fluid.io.batch(
get_infer_examples, args.batch_size, drop_last=False)
test_prog = fluid.Program()
conf_dict['dict_size'] = len(vocab)
net = utils.import_class("../models/matching",
net = utils.import_class("../shared_modules/models/matching",
conf_dict["net"]["module_name"],
conf_dict["net"]["class_name"])(conf_dict)
if args.task_mode == "pairwise":
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
infer_pyreader, left, pos_right = create_model(args, pyreader_name = 'infer_reader', is_inference = True)
infer_pyreader, left, pos_right = create_model(
args, pyreader_name='infer_reader', is_inference=True)
left_feat, pos_score = net.predict(left, pos_right)
pred = pos_score
test_prog = test_prog.clone(for_test=True)
else:
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
infer_pyreader, left, right = create_model(args, pyreader_name = 'infer_reader', is_inference = True)
infer_pyreader, left, right = create_model(
args, pyreader_name='infer_reader', is_inference=True)
left_feat, pred = net.predict(left, right)
test_prog = test_prog.clone(for_test=True)
exe.run(startup_prog)
utils.init_checkpoint(
exe,
args.init_checkpoint,
main_program=test_prog)
utils.init_checkpoint(exe, args.init_checkpoint, main_program=test_prog)
test_exe = exe
infer_pyreader.decorate_sample_list_generator(batch_data)
......@@ -491,7 +496,7 @@ def infer(conf_dict, args):
infer_pyreader.start()
while True:
try:
output = test_exe.run(program=test_prog,fetch_list=fetch_list)
output = test_exe.run(program=test_prog, fetch_list=fetch_list)
if args.task_mode == "pairwise":
preds_list += list(
map(lambda item: str((item[0] + 1) / 2), output[0]))
......@@ -514,6 +519,7 @@ def get_cards():
num = len(cards.split(","))
return num
if __name__ == "__main__":
args = ArgConfig()
......
README.md
浏览文件 @ f492ae4f
......@@ -146,10 +146,10 @@ PaddlePaddle 提供了丰富的计算单元，使得用户可以采用模块化
## PaddleNLP
[**PaddleNLP**](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP) 是基于 PaddlePaddle 深度学习框架开发的自然语言处理 (NLP) 工具，算法，模型和数据的开源项目。百度在 NLP 领域十几年的深厚积淀为 PaddleNLP 提供了强大的核心动力。使用 PaddleNLP，您可以得到：
[**PaddleNLP**](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP) 是基于 PaddlePaddle 深度学习框架开发的自然语言处理 (NLP) 工具，算法，模型和数据的开源项目。百度在 NLP 领域十几年的深厚积淀为 PaddleNLP 提供了强大的核心动力。使用 PaddleNLP，您可以得到：
- **丰富而全面的 NLP 任务支持：**
- PaddleNLP 为您提供了多粒度，多场景的应用支持。涵盖了从[分词](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/lexical_analysis)，[词性标注](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/lexical_analysis)，[命名实体识别](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/lexical_analysis)等 NLP 基础技术，到[文本分类](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/sentiment_classification)，[文本相似度计算](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/similarity_net)，[语义表示](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleLARK)，[文本生成](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleTextGEN)等 NLP 核心技术。同时，PaddleNLP 还提供了针对常见 NLP 大型应用系统（如[阅读理解](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleMRC)，[对话系统](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleDialogue)，[机器翻译系统](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleMT)等）的特定核心技术和工具组件，模型和预训练参数等，让您在 NLP 领域畅通无阻。
- PaddleNLP 为您提供了多粒度，多场景的应用支持。涵盖了从[分词](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/lexical_analysis)，[词性标注](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/lexical_analysis)，[命名实体识别](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/lexical_analysis)等 NLP 基础技术，到[文本分类](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/sentiment_classification)，[文本相似度计算](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/similarity_net)，[语义表示](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/pretrain_langauge_models)，[文本生成](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/seq2seq)等 NLP 核心技术。同时，PaddleNLP 还提供了针对常见 NLP 大型应用系统（如[阅读理解](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/machine_reading_comprehension)，[对话系统](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/dialogue_system)，[机器翻译系统](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/machine_translation)等）的特定核心技术和工具组件，模型和预训练参数等，让您在 NLP 领域畅通无阻。
- **稳定可靠的 NLP 模型和强大的预训练参数：**
- PaddleNLP集成了百度内部广泛使用的 NLP 工具模型，为您提供了稳定可靠的 NLP 算法解决方案。基于百亿级数据的预训练参数和丰富的预训练模型，助您轻松提高模型效果，为您的 NLP 业务注入强大动力。
- **持续改进和技术支持，零基础搭建 NLP 应用：**
......@@ -159,30 +159,30 @@ PaddlePaddle 提供了丰富的计算单元，使得用户可以采用模块化
| 任务类型 | 目录 | 简介 |
| ------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| 中文词法分析 | [LAC(Lexical Analysis of Chinese)](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/lexical_analysis) | 百度自主研发中文特色模型词法分析任务，集成了中文分词、词性标注和命名实体识别任务。输入是一个字符串，而输出是句子中的词边界和词性、实体类别。 |
| 词向量 | [Word2vec](https://github.com/PaddlePaddle/models/tree/develop/PaddleRec/word2vec) | 提供单机多卡，多机等分布式训练中文词向量能力，支持主流词向量模型（skip-gram，cbow等），可以快速使用自定义数据训练词向量模型。 |
| 语言模型 | [Language_model](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/language_model) | 给定一个输入词序列（中文需要先分词、英文需要先 tokenize），计算其生成概率。 语言模型的评价指标 PPL(困惑度)，用于表示模型生成句子的流利程度。 |
| 中文词法分析 | [LAC(Lexical Analysis of Chinese)](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/lexical_analysis) | 百度自主研发中文特色模型词法分析任务，集成了中文分词、词性标注和命名实体识别任务。输入是一个字符串，而输出是句子中的词边界和词性、实体类别。 |
| 词向量 | [Word2vec](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleRec/word2vec) | 提供单机多卡，多机等分布式训练中文词向量能力，支持主流词向量模型（skip-gram，cbow等），可以快速使用自定义数据训练词向量模型。 |
| 语言模型 | [Language_model](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/language_model) | 给定一个输入词序列（中文需要先分词、英文需要先 tokenize），计算其生成概率。 语言模型的评价指标 PPL(困惑度)，用于表示模型生成句子的流利程度。 |
### NLP 核心技术
#### 语义表示
[PaddleLARK](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleLARK) (Paddle LAngauge Representation ToolKit) 是传统语言模型的进一步发展，通过在大规模语料上训练得到的通用的语义表示模型，可以助益其他自然语言处理任务，是通用预训练 + 特定任务精调范式的体现。PaddleLARK 集成了 ELMO，BERT，ERNIE 1.0，ERNIE 2.0，XLNet 等热门中英文预训练模型。
[PaddleLARK](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/pretrain_language_models) 通过在大规模语料上训练得到的通用的语义表示模型，可以助益其他自然语言处理任务，是通用预训练 + 特定任务精调范式的体现。PaddleLARK 集成了 ELMO，BERT，ERNIE 1.0，ERNIE 2.0，XLNet 等热门中英文预训练模型。
| 模型 | 简介 |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
| [ERNIE](https://github.com/PaddlePaddle/ERNIE)(Enhanced Representation from kNowledge IntEgration) | 百度自研的语义表示模型，通过建模海量数据中的词、实体及实体关系，学习真实世界的语义知识。相较于 BERT 学习原始语言信号，ERNIE 直接对先验语义知识单元进行建模，增强了模型语义表示能力。 |
| [BERT](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleLARK/BERT)(Bidirectional Encoder Representation from Transformers) | 一个迁移能力很强的通用语义表示模型， 以 Transformer 为网络基本组件，以双向 Masked Language Model和 Next Sentence Prediction 为训练目标，通过预训练得到通用语义表示，再结合简单的输出层，应用到下游的 NLP 任务，在多个任务上取得了 SOTA 的结果。 |
| [XLNet](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleLARK/XLNet)(XLNet: Generalized Autoregressive Pretraining for Language Understanding) | 重要的语义表示模型之一，引入 Transformer-XL 为骨架，以 Permutation Language Modeling 为优化目标，在若干下游任务上优于 BERT 的性能。 |
| [ELMo](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleLARK/ELMo)(Embeddings from Language Models) | 重要的通用语义表示模型之一，以双向 LSTM 为网路基本组件，以 Language Model 为训练目标，通过预训练得到通用的语义表示，将通用的语义表示作为 Feature 迁移到下游 NLP 任务中，会显著提升下游任务的模型性能。 |
| [BERT](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/pretrain_language_models/BERT)(Bidirectional Encoder Representation from Transformers) | 一个迁移能力很强的通用语义表示模型， 以 Transformer 为网络基本组件，以双向 Masked Language Model和 Next Sentence Prediction 为训练目标，通过预训练得到通用语义表示，再结合简单的输出层，应用到下游的 NLP 任务，在多个任务上取得了 SOTA 的结果。 |
| [XLNet](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/pretrain_language_models/XLNet)(XLNet: Generalized Autoregressive Pretraining for Language Understanding) | 重要的语义表示模型之一，引入 Transformer-XL 为骨架，以 Permutation Language Modeling 为优化目标，在若干下游任务上优于 BERT 的性能。 |
| [ELMo](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/pretrain_language_models/ELMo)(Embeddings from Language Models) | 重要的通用语义表示模型之一，以双向 LSTM 为网路基本组件，以 Language Model 为训练目标，通过预训练得到通用的语义表示，将通用的语义表示作为 Feature 迁移到下游 NLP 任务中，会显著提升下游任务的模型性能。 |
#### 文本相似度计算
[SimNet](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/similarity_net) (Similarity Net) 是一个计算短文本相似度的框架，主要包括 BOW、CNN、RNN、MMDNN 等核心网络结构形式。SimNet 框架在百度各产品上广泛应用，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。
[SimNet](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/similarity_net) (Similarity Net) 是一个计算短文本相似度的框架，主要包括 BOW、CNN、RNN、MMDNN 等核心网络结构形式。SimNet 框架在百度各产品上广泛应用，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。
#### 文本生成
[PaddleTextGEN](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleTextGEN) (Paddle Text Generation) ,一个基于 PaddlePaddle 的文本生成框架，提供了一些列经典文本生成模型案例，如 vanilla seq2seq，seq2seq with attention，variational seq2seq 模型等。
[seq2seq](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/seq2seq) (Paddle Text Generation) ,一个基于 PaddlePaddle 的文本生成框架，提供了一些列经典文本生成模型案例，如 vanilla seq2seq，seq2seq with attention，variational seq2seq 模型等。
### NLP 系统应用
......@@ -190,35 +190,35 @@ PaddlePaddle 提供了丰富的计算单元，使得用户可以采用模块化
| 模型 | 简介 |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
| [Senta](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/sentiment_classification) (Sentiment Classification，简称Senta) | 面向**通用场景**的情感分类模型，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。 |
| [EmotionDetection](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/emotion_detection) (Emotion Detection，简称EmoTect) | 专注于识别**人机对话场景**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。 |
| [Senta](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/sentiment_classification) (Sentiment Classification，简称Senta) | 面向**通用场景**的情感分类模型，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。 |
| [EmotionDetection](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/emotion_detection) (Emotion Detection，简称EmoTect) | 专注于识别**人机对话场景**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。 |
#### 阅读理解
[PaddleMRC](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleMRC) (Paddle Machine Reading Comprehension)，集合了百度在阅读理解领域相关的模型，工具，开源数据集等一系列工作。
[machine_reading_comprehension](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/machine_reading_comprehension) (Paddle Machine Reading Comprehension)，集合了百度在阅读理解领域相关的模型，工具，开源数据集等一系列工作。
| 模型 | 简介 |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
| [DuReader](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/ACL2018-DuReader) | 包含百度开源的基于真实搜索用户行为的中文大规模阅读理解数据集以及基线模型。 |
| [KT-Net](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/ACL2019-KTNET) | 结合知识的阅读理解模型，Squad 曾排名第一。 |
| [D-Net](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/MRQA2019-D-NET) | 阅读理解十项全能模型，在 EMNLP2019 国际阅读理解大赛夺得 10 项冠军。 |
| [DuReader](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/Research/ACL2018-DuReader) | 包含百度开源的基于真实搜索用户行为的中文大规模阅读理解数据集以及基线模型。 |
| [KT-Net](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/Research/ACL2019-KTNET) | 结合知识的阅读理解模型，Squad 曾排名第一。 |
| [D-Net](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/Research/MRQA2019-D-NET) | 阅读理解十项全能模型，在 EMNLP2019 国际阅读理解大赛夺得 10 项冠军。 |
#### 机器翻译
[PaddleMT](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleMT) ，全称为Paddle Machine Translation，基于Transformer的经典机器翻译模型，基于论文 [Attention Is All You Need](https://arxiv.org/abs/1706.03762)。
[machine_translation](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/machine_translation) ，全称为Paddle Machine Translation，基于Transformer的经典机器翻译模型，基于论文 [Attention Is All You Need](https://arxiv.org/abs/1706.03762)。
#### 对话系统
[PaddleDialogue](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleDialogue) 包含对话系统方向的模型、数据集和工具。
[dialogue_system](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/dialogue_system) 包含对话系统方向的模型、数据集和工具。
| 模型 | 简介 |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
| [DGU](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleDialogue/dialogue_general_understanding) (Dialogue General Understanding，通用对话理解模型) | 覆盖了包括**检索式聊天系统**中 context-response matching 任务和**任务完成型对话系统**中**意图识别**，**槽位解析**，**状态追踪**等常见对话系统任务，在 6 项国际公开数据集中都获得了最佳效果。 |
| [ADEM](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/PaddleDialogue/auto_dialogue_evaluation) (Auto Dialogue Evaluation Model) | 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。 |
| [Proactive Conversation](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/ACL2019-DuConv) | 包含百度开源的知识驱动的开放领域对话数据集 [DuConv](https://ai.baidu.com/broad/subordinate?dataset=duconv)，以及基线模型。对应论文 [Proactive Human-Machine Conversation with Explicit Conversation Goals](https://arxiv.org/abs/1906.05572) 发表于 ACL2019。 |
| [DAM](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/ACL2018-DAM)（Deep Attention Matching Network，深度注意力机制模型） | 开放领域多轮对话匹配模型，对应论文 [Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network](https://aclweb.org/anthology/P18-1103/) 发表于 ACL2018。 |
| [DGU](https://github.com/PaddlePaddle/models/tree/develop/release/1.7/dialogue_system/dialogue_general_understanding) (Dialogue General Understanding，通用对话理解模型) | 覆盖了包括**检索式聊天系统**中 context-response matching 任务和**任务完成型对话系统**中**意图识别**，**槽位解析**，**状态追踪**等常见对话系统任务，在 6 项国际公开数据集中都获得了最佳效果。 |
| [ADEM](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/dialogue_system/auto_dialogue_evaluation) (Auto Dialogue Evaluation Model) | 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。 |
| [Proactive Conversation](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/Research/ACL2019-DuConv) | 包含百度开源的知识驱动的开放领域对话数据集 [DuConv](https://ai.baidu.com/broad/subordinate?dataset=duconv)，以及基线模型。对应论文 [Proactive Human-Machine Conversation with Explicit Conversation Goals](https://arxiv.org/abs/1906.05572) 发表于 ACL2019。 |
| [DAM](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/Research/ACL2018-DAM)（Deep Attention Matching Network，深度注意力机制模型） | 开放领域多轮对话匹配模型，对应论文 [Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network](https://aclweb.org/anthology/P18-1103/) 发表于 ACL2018。 |
百度最新前沿工作开源，请参考 [Research](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research)。
百度最新前沿工作开源，请参考 [Research](https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleNLP/Research)。
## PaddleRec
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