add license for nlp models (#3390)

add license for nlp models

PaddleNLP/Research/ACL2018-DAM/config.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Deep Attention Matching Network
 """
@@ -5,6 +18,7 @@ Deep Attention Matching Network
 import argparse
 import six
 
+
 def parse_args():
     """
     Deep Attention Matching Network Config
@@ -12,14 +26,14 @@ def parse_args():
     parser = argparse.ArgumentParser("DAM Config")
 
     parser.add_argument(
-        '--do_train', 
-        type=bool, 
-        default=False, 
+        '--do_train',
+        type=bool,
+        default=False,
         help='Whether to perform training.')
     parser.add_argument(
-        '--do_test', 
-        type=bool, 
-        default=False, 
+        '--do_test',
+        type=bool,
+        default=False,
         help='Whether to perform training.')
 
     parser.add_argument(

PaddleNLP/Research/ACL2018-DAM/evaluation.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Evaluation
 """

PaddleNLP/Research/ACL2018-DAM/layers.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Layers
 """
@@ -77,7 +90,7 @@ def dot_product_attention(query,
     """
 
     logits = fluid.layers.matmul(
-        x=query, y=key, transpose_y=True, alpha=d_key ** (-0.5))
+        x=query, y=key, transpose_y=True, alpha=d_key**(-0.5))
 
     if (q_mask is not None) and (k_mask is not None):
         if mask_cache is not None and q_mask.name in mask_cache and k_mask.name in mask_cache[
@@ -87,7 +100,7 @@ def dot_product_attention(query,
             mask = fluid.layers.matmul(x=q_mask, y=k_mask, transpose_y=True)
             another_mask = fluid.layers.scale(
                 mask,
-                scale=float(2 ** 32 - 1),
+                scale=float(2**32 - 1),
                 bias=float(-1),
                 bias_after_scale=False)
             if mask_cache is not None:

PaddleNLP/Research/ACL2018-DAM/main.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Deep Attention Matching Network
 """
@@ -174,9 +187,8 @@ def train(args):
 
     print("device count %d" % dev_count)
     print("theoretical memory usage: ")
-    print(
-        fluid.contrib.memory_usage(
-            program=train_program, batch_size=args.batch_size))
+    print(fluid.contrib.memory_usage(
+        program=train_program, batch_size=args.batch_size))
 
     exe = fluid.Executor(place)
     exe.run(train_startup)
@@ -247,9 +259,8 @@ def train(args):
             if (args.save_path is not None) and (step % save_step == 0):
                 save_path = os.path.join(args.save_path, "step_" + str(step))
                 print("Save model at step %d ... " % step)
-                print(
-                    time.strftime('%Y-%m-%d %H:%M:%S',
-                                  time.localtime(time.time())))
+                print(time.strftime('%Y-%m-%d %H:%M:%S',
+                                    time.localtime(time.time())))
                 fluid.io.save_persistables(exe, save_path, train_program)
 
                 score_path = os.path.join(args.save_path, 'score.' + str(step))
@@ -294,9 +305,8 @@ def train(args):
                     save_path = os.path.join(args.save_path,
                                              "step_" + str(step))
                     print("Save model at step %d ... " % step)
-                    print(
-                        time.strftime('%Y-%m-%d %H:%M:%S',
-                                      time.localtime(time.time())))
+                    print(time.strftime('%Y-%m-%d %H:%M:%S',
+                                        time.localtime(time.time())))
                     fluid.io.save_persistables(exe, save_path, train_program)
 
                     score_path = os.path.join(args.save_path,

PaddleNLP/Research/ACL2018-DAM/net.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Deep Attention Matching Network
 """
@@ -12,6 +25,7 @@ class Net(object):
     """
     Deep attention matching network
     """
+
     def __init__(self, max_turn_num, max_turn_len, vocab_size, emb_size,
                  stack_num, channel1_num, channel2_num):
         """

PaddleNLP/Research/ACL2018-DAM/reader.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Reader for deep attention matching network
 """

PaddleNLP/Research/ACL2018-DAM/util.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Utils
 """
@@ -20,7 +33,7 @@ def mkdir(path):
     """
     Mkdir
     """
-    if not os.path.isdir(path): 
+    if not os.path.isdir(path):
         if os.path.split(path)[0]:
             mkdir(os.path.split(path)[0])
     else:

PaddleNLP/Research/ACL2018-DuReader/src/paragraph_extraction.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 #!/usr/bin/python
 #-*- coding:utf-8 -*-
 
@@ -25,9 +38,8 @@ def compute_paragraph_score(sample):
         doc['segmented_paragraphs_scores'] = []
         for p_idx, para_tokens in enumerate(doc['segmented_paragraphs']):
             if len(question) > 0:
-                related_score = metric_max_over_ground_truths(f1_score,
-                        para_tokens,
-                        [question])
+                related_score = metric_max_over_ground_truths(
+                    f1_score, para_tokens, [question])
             else:
                 related_score = 0.0
             doc['segmented_paragraphs_scores'].append(related_score)
@@ -63,7 +75,7 @@ def dup_remove(doc):
     prev_del_num = 0
     del_num = 0
     for p_idx in del_ids:
-        if p_idx < para_id: 
+        if p_idx < para_id:
             prev_del_num += 1
         del doc["segmented_paragraphs"][p_idx - del_num]
         del doc["segmented_paragraphs_scores"][p_idx - del_num]
@@ -142,7 +154,8 @@ def paragraph_selection(sample, mode):
         para_infos = []
         for p_idx, (para_tokens, para_scores) in \
                 enumerate(zip(doc['segmented_paragraphs'], doc['segmented_paragraphs_scores'])):
-            para_infos.append((para_tokens, para_scores, len(para_tokens), p_idx))
+            para_infos.append(
+                (para_tokens, para_scores, len(para_tokens), p_idx))
         para_infos.sort(key=lambda x: (-x[1], x[2]))
         topN_idx = []
         for para_info in para_infos[:topN]:
@@ -158,7 +171,7 @@ def paragraph_selection(sample, mode):
                 break
             if doc_id == d_idx and id == para_id and mode == "train":
                 continue
-            total_len += 1 + doc['paragraphs_length'][id] 
+            total_len += 1 + doc['paragraphs_length'][id]
             final_idx.append(id)
         total_segmented_content = copy.deepcopy(segmented_title)
         final_idx.sort()
@@ -168,7 +181,8 @@ def paragraph_selection(sample, mode):
                 incre_len += 1 + doc['paragraphs_length'][id]
             if doc_id == d_idx and id == para_id:
                 incre_len += 1
-            total_segmented_content += [splitter] + doc['segmented_paragraphs'][id]
+            total_segmented_content += [splitter] + doc['segmented_paragraphs'][
+                id]
         if doc_id == d_idx:
             answer_start = incre_len + sample['answer_spans'][0][0]
             answer_end = incre_len + sample['answer_spans'][0][1]
@@ -191,9 +205,9 @@ if __name__ == "__main__":
         try:
             sample = json.loads(line, encoding='utf8')
         except:
-            print >>sys.stderr, "Invalid input json format - '{}' will be ignored".format(line)
+            print >> sys.stderr, "Invalid input json format - '{}' will be ignored".format(
+                line)
             continue
         compute_paragraph_score(sample)
         paragraph_selection(sample, mode)
         print(json.dumps(sample, encoding='utf8', ensure_ascii=False))
-

PaddleNLP/Research/ACL2018-DuReader/utils/marco_tokenize_data.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 #coding=utf8
 
 import os, sys, json
 import nltk
 
+
 def _nltk_tokenize(sequence):
     tokens = nltk.word_tokenize(sequence)
 
@@ -11,10 +25,12 @@ def _nltk_tokenize(sequence):
     token_words = []
     for token in tokens:
         cur_char_offset = sequence.find(token, cur_char_offset)
-        token_offsets.append([cur_char_offset, cur_char_offset + len(token) - 1])
+        token_offsets.append(
+            [cur_char_offset, cur_char_offset + len(token) - 1])
         token_words.append(token)
     return token_offsets, token_words
 
+
 def segment(input_js):
     _, input_js['segmented_question'] = _nltk_tokenize(input_js['question'])
     for doc_id, doc in enumerate(input_js['documents']):
@@ -36,7 +52,7 @@ if __name__ == '__main__':
         exit()
 
     nltk.download('punkt')
-    
+
     for line in open(sys.argv[1]):
         dureader_js = json.loads(line.strip())
         segment(dureader_js)

PaddleNLP/Research/ACL2018-DuReader/utils/marcov1_to_dureader.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 #coding=utf8
 
 import sys

PaddleNLP/Research/ACL2018-DuReader/utils/marcov2_to_v1_tojsonl.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import sys
 import json
-import pandas as pd 
+import pandas as pd
 
 if __name__ == '__main__':
     if len(sys.argv) != 3:
@@ -11,4 +24,4 @@ if __name__ == '__main__':
     df = pd.read_json(infile)
     with open(outfile, 'w') as f:
         for row in df.iterrows():
-            f.write(row[1].to_json() + '\n')
\ No newline at end of file
+            f.write(row[1].to_json() + '\n')

PaddleNLP/Research/ACL2019-JEMT/config.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 class TrainTaskConfig(object):
     # support both CPU and GPU now.
     use_gpu = True

PaddleNLP/Research/ACL2019-JEMT/desc.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 # The placeholder for batch_size in compile time. Must be -1 currently to be
 # consistent with some ops' infer-shape output in compile time, such as the
 # sequence_expand op used in beamsearch decoder.
@@ -65,43 +78,37 @@ input_descs = {
 # Names of word embedding table which might be reused for weight sharing.
 word_emb_param_names = (
     "src_word_emb_table",
-    "trg_word_emb_table",
-)
+    "trg_word_emb_table", )
 
 phone_emb_param_name = "phone_emb_table"
 
 # Names of position encoding table which will be initialized externally.
 pos_enc_param_names = (
     "src_pos_enc_table",
-    "trg_pos_enc_table",
-)
+    "trg_pos_enc_table", )
 # separated inputs for different usages.
 encoder_data_input_fields = (
     "src_word",
     "src_pos",
     "src_slf_attn_bias",
     "src_phone",
-    "src_phone_mask",
-)
+    "src_phone_mask", )
 decoder_data_input_fields = (
     "trg_word",
     "trg_pos",
     "trg_slf_attn_bias",
     "trg_src_attn_bias",
-    "enc_output",
-)
+    "enc_output", )
 label_data_input_fields = (
     "lbl_word",
-    "lbl_weight",
-)
+    "lbl_weight", )
 # In fast decoder, trg_pos (only containing the current time step) is generated
 # by ops and trg_slf_attn_bias is not needed.
 fast_decoder_data_input_fields = (
     "trg_word",
     "init_score",
     "init_idx",
-    "trg_src_attn_bias",
-)
+    "trg_src_attn_bias", )
 
 # Set seed for CE
 dropout_seed = None

PaddleNLP/Research/ACL2019-JEMT/infer.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import argparse
 import ast
 import multiprocessing
@@ -86,10 +99,8 @@ def parse_args():
     trg_dict = reader.DataReader.load_dict(args.trg_vocab_fpath)
     phone_dict = reader.DataReader.load_dict(args.phoneme_vocab_fpath)
     dict_args = [
-        "src_vocab_size",
-        str(len(src_dict)), "trg_vocab_size",
-        str(len(trg_dict)), "phone_vocab_size",
-        str(len(phone_dict)), "bos_idx",
+        "src_vocab_size", str(len(src_dict)), "trg_vocab_size",
+        str(len(trg_dict)), "phone_vocab_size", str(len(phone_dict)), "bos_idx",
         str(src_dict[args.special_token[0]]), "eos_idx",
         str(src_dict[args.special_token[1]]), "unk_idx",
         str(src_dict[args.special_token[2]])
@@ -147,10 +158,10 @@ def prepare_batch_input(insts, data_input_names, src_pad_idx, phone_pad_idx,
 
     # beamsearch_op must use tensors with lod
     init_score = to_lodtensor(
-        np.zeros_like(trg_word, dtype="float32").reshape(-1, 1), place,
-        [range(trg_word.shape[0] + 1)] * 2)
-    trg_word = to_lodtensor(trg_word, place,
-                            [range(trg_word.shape[0] + 1)] * 2)
+        np.zeros_like(
+            trg_word, dtype="float32").reshape(-1, 1),
+        place, [range(trg_word.shape[0] + 1)] * 2)
+    trg_word = to_lodtensor(trg_word, place, [range(trg_word.shape[0] + 1)] * 2)
     init_idx = np.asarray(range(len(insts)), dtype="int32")
 
     data_input_dict = dict(
@@ -315,7 +326,8 @@ def fast_infer(args):
                         sub_start = seq_ids.lod()[1][start + j]
                         sub_end = seq_ids.lod()[1][start + j + 1]
                         hyps[i].append(" ".join([
-                            trg_idx2word[idx] for idx in post_process_seq(
+                            trg_idx2word[idx]
+                            for idx in post_process_seq(
                                 np.array(seq_ids)[sub_start:sub_end])
                         ]))
                         scores[i].append(np.array(seq_scores)[sub_end - 1])

PaddleNLP/Research/ACL2019-JEMT/model.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 from functools import partial
 import numpy as np
 
@@ -51,12 +64,12 @@ def position_encoding_init(n_position, d_pos_vec):
     channels = d_pos_vec
     position = np.arange(n_position)
     num_timescales = channels // 2
-    log_timescale_increment = (
-        np.log(float(1e4) / float(1)) / (num_timescales - 1))
-    inv_timescales = np.exp(
-        np.arange(num_timescales)) * -log_timescale_increment
-    scaled_time = np.expand_dims(position, 1) * np.expand_dims(
-        inv_timescales, 0)
+    log_timescale_increment = (np.log(float(1e4) / float(1)) /
+                               (num_timescales - 1))
+    inv_timescales = np.exp(np.arange(
+        num_timescales)) * -log_timescale_increment
+    scaled_time = np.expand_dims(position, 1) * np.expand_dims(inv_timescales,
+                                                               0)
     signal = np.concatenate([np.sin(scaled_time), np.cos(scaled_time)], axis=1)
     signal = np.pad(signal, [[0, 0], [0, np.mod(channels, 2)]], 'constant')
     position_enc = signal
@@ -91,17 +104,15 @@ def multi_head_attention(queries,
         """
         Add linear projection to queries, keys, and values.
         """
-        q = layers.fc(
-            input=queries,
-            size=d_key * n_head,
-            bias_attr=False,
-            num_flatten_dims=2)
+        q = layers.fc(input=queries,
+                      size=d_key * n_head,
+                      bias_attr=False,
+                      num_flatten_dims=2)
         # For encoder-decoder attention in inference, insert the ops and vars
         # into global block to use as cache among beam search.
         fc_layer = wrap_layer_with_block(
-            layers.fc,
-            fluid.default_main_program().current_block().
-            parent_idx) if cache is not None and static_kv else layers.fc
+            layers.fc, fluid.default_main_program().current_block(
+            ).parent_idx) if cache is not None and static_kv else layers.fc
         k = fc_layer(
             input=keys,
             size=d_key * n_head,
@@ -132,12 +143,12 @@ def multi_head_attention(queries,
         # into global block to use as cache among beam search.
         reshape_layer = wrap_layer_with_block(
             layers.reshape,
-            fluid.default_main_program().current_block().
-            parent_idx) if cache is not None and static_kv else layers.reshape
+            fluid.default_main_program().current_block(
+            ).parent_idx) if cache is not None and static_kv else layers.reshape
         transpose_layer = wrap_layer_with_block(
             layers.transpose,
-            fluid.default_main_program().current_block().parent_idx
-        ) if cache is not None and static_kv else layers.transpose
+            fluid.default_main_program().current_block().
+            parent_idx) if cache is not None and static_kv else layers.transpose
         reshaped_k = reshape_layer(
             x=keys, shape=[0, 0, n_head, d_key], inplace=True)
         k = transpose_layer(x=reshaped_k, perm=[0, 2, 1, 3])
@@ -214,8 +225,10 @@ def multi_head_attention(queries,
     out = __combine_heads(ctx_multiheads)
 
     # Project back to the model size.
-    proj_out = layers.fc(
-        input=out, size=d_model, bias_attr=False, num_flatten_dims=2)
+    proj_out = layers.fc(input=out,
+                         size=d_model,
+                         bias_attr=False,
+                         num_flatten_dims=2)
     return proj_out
 
 
@@ -225,14 +238,13 @@ def positionwise_feed_forward(x, d_inner_hid, d_hid, dropout_rate):
     This module consists of two linear transformations with a ReLU activation
     in between, which is applied to each position separately and identically.
     """
-    hidden = layers.fc(
-        input=x, size=d_inner_hid, num_flatten_dims=2, act="relu")
+    hidden = layers.fc(input=x,
+                       size=d_inner_hid,
+                       num_flatten_dims=2,
+                       act="relu")
     if dropout_rate:
         hidden = layers.dropout(
-            hidden,
-            dropout_prob=dropout_rate,
-            seed=dropout_seed,
-            is_test=False)
+            hidden, dropout_prob=dropout_rate, seed=dropout_seed, is_test=False)
     out = layers.fc(input=hidden, size=d_hid, num_flatten_dims=2)
     return out
 
@@ -313,8 +325,7 @@ def prepare_encoder(src_word,
         param_attr=fluid.ParamAttr(
             name=pos_enc_param_names[0], trainable=False))
     src_pos_enc.stop_gradient = True
-    enc_input = (
-        1 - beta) * src_word_emb + beta * mean_phone_emb + src_pos_enc
+    enc_input = (1 - beta) * src_word_emb + beta * mean_phone_emb + src_pos_enc
     return layers.dropout(
         enc_input, dropout_prob=dropout_rate, seed=dropout_seed,
         is_test=False) if dropout_rate else enc_input
@@ -374,8 +385,8 @@ def encoder_layer(enc_input,
     """
     attn_output = multi_head_attention(
         pre_process_layer(enc_input, preprocess_cmd,
-                          prepostprocess_dropout), None, None, attn_bias,
-        d_key, d_value, d_model, n_head, attention_dropout)
+                          prepostprocess_dropout), None, None, attn_bias, d_key,
+        d_value, d_model, n_head, attention_dropout)
     attn_output = post_process_layer(enc_input, attn_output, postprocess_cmd,
                                      prepostprocess_dropout)
     ffd_output = positionwise_feed_forward(
@@ -415,8 +426,7 @@ def encoder(enc_input,
             attention_dropout,
             relu_dropout,
             preprocess_cmd,
-            postprocess_cmd,
-        )
+            postprocess_cmd, )
         enc_input = enc_output
     enc_output = pre_process_layer(enc_output, preprocess_cmd,
                                    prepostprocess_dropout)
@@ -459,8 +469,7 @@ def decoder_layer(dec_input,
         dec_input,
         slf_attn_output,
         postprocess_cmd,
-        prepostprocess_dropout,
-    )
+        prepostprocess_dropout, )
     enc_attn_output = multi_head_attention(
         pre_process_layer(slf_attn_output, preprocess_cmd,
                           prepostprocess_dropout),
@@ -479,21 +488,18 @@ def decoder_layer(dec_input,
         slf_attn_output,
         enc_attn_output,
         postprocess_cmd,
-        prepostprocess_dropout,
-    )
+        prepostprocess_dropout, )
     ffd_output = positionwise_feed_forward(
         pre_process_layer(enc_attn_output, preprocess_cmd,
                           prepostprocess_dropout),
         d_inner_hid,
         d_model,
-        relu_dropout,
-    )
+        relu_dropout, )
     dec_output = post_process_layer(
         enc_attn_output,
         ffd_output,
         postprocess_cmd,
-        prepostprocess_dropout,
-    )
+        prepostprocess_dropout, )
     return dec_output
 
 
@@ -632,8 +638,7 @@ def transformer(src_vocab_size,
         postprocess_cmd,
         weight_sharing,
         beta,
-        enc_inputs,
-    )
+        enc_inputs, )
 
     predict = wrap_decoder(
         trg_vocab_size,
@@ -651,14 +656,14 @@ def transformer(src_vocab_size,
         postprocess_cmd,
         weight_sharing,
         dec_inputs,
-        enc_output,
-    )
+        enc_output, )
 
     # Padding index do not contribute to the total loss. The weights is used to
     # cancel padding index in calculating the loss.
     if label_smooth_eps:
         label = layers.label_smooth(
-            label=layers.one_hot(input=label, depth=trg_vocab_size),
+            label=layers.one_hot(
+                input=label, depth=trg_vocab_size),
             epsilon=label_smooth_eps)
 
     cost = layers.softmax_with_cross_entropy(
@@ -730,8 +735,7 @@ def wrap_encoder(src_vocab_size,
         attention_dropout,
         relu_dropout,
         preprocess_cmd,
-        postprocess_cmd,
-    )
+        postprocess_cmd, )
     return enc_output
 
 
@@ -803,8 +807,9 @@ def wrap_decoder(trg_vocab_size,
                 word_emb_param_names[0]),
             transpose_y=True)
     else:
-        predict = layers.fc(
-            input=dec_output, size=trg_vocab_size, bias_attr=False)
+        predict = layers.fc(input=dec_output,
+                            size=trg_vocab_size,
+                            bias_attr=False)
     if dec_inputs is None:
         # Return probs for independent decoder program.
         predict = layers.softmax(predict)
@@ -879,8 +884,7 @@ def fast_decode(src_vocab_size,
             force_cpu=True)
         step_idx = layers.fill_constant(
             shape=[1], dtype=start_tokens.dtype, value=0, force_cpu=True)
-        cond = layers.less_than(
-            x=step_idx, y=max_len)  # default force_cpu=True
+        cond = layers.less_than(x=step_idx, y=max_len)  # default force_cpu=True
         while_op = layers.While(cond)
         # array states will be stored for each step.
         ids = layers.array_write(

PaddleNLP/Research/ACL2019-JEMT/reader.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import glob
 import six
 import os
@@ -302,9 +315,8 @@ class DataReader(object):
             f = tarfile.open(fpaths[0], "r")
             for line in f.extractfile(tar_fname):
                 fields = line.strip("\n").split(self._field_delimiter)
-                if (not self._only_src
-                        and len(fields) == 2) or (self._only_src
-                                                  and len(fields) == 1):
+                if (not self._only_src and len(fields) == 2) or (
+                        self._only_src and len(fields) == 1):
                     yield fields
         else:
             for fpath in fpaths:
@@ -381,5 +393,5 @@ class DataReader(object):
                        for idx in batch_ids]
             else:
                 yield [(self._src_seq_ids[idx], self._src_phone_ids[idx],
-                        self._trg_seq_ids[idx][:-1],
-                        self._trg_seq_ids[idx][1:]) for idx in batch_ids]
+                        self._trg_seq_ids[idx][:-1], self._trg_seq_ids[idx][1:])
+                       for idx in batch_ids]

PaddleNLP/Research/ACL2019-JEMT/train.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import argparse
 import ast
 import copy
@@ -141,10 +154,8 @@ def parse_args():
     trg_dict = reader.DataReader.load_dict(args.trg_vocab_fpath)
     phone_dict = reader.DataReader.load_dict(args.phoneme_vocab_fpath)
     dict_args = [
-        "src_vocab_size",
-        str(len(src_dict)), "trg_vocab_size",
-        str(len(trg_dict)), "phone_vocab_size",
-        str(len(phone_dict)), "bos_idx",
+        "src_vocab_size", str(len(src_dict)), "trg_vocab_size",
+        str(len(trg_dict)), "phone_vocab_size", str(len(phone_dict)), "bos_idx",
         str(src_dict[args.special_token[0]]), "eos_idx",
         str(src_dict[args.special_token[1]]), "unk_idx",
         str(src_dict[args.special_token[2]])
@@ -157,8 +168,8 @@ def parse_args():
 
 def append_nccl2_prepare(startup_prog, trainer_id, worker_endpoints,
                          current_endpoint):
-    assert (trainer_id >= 0 and len(worker_endpoints) > 1
-            and current_endpoint in worker_endpoints)
+    assert (trainer_id >= 0 and len(worker_endpoints) > 1 and
+            current_endpoint in worker_endpoints)
     eps = copy.deepcopy(worker_endpoints)
     eps.remove(current_endpoint)
     nccl_id_var = startup_prog.global_block().create_var(
@@ -189,8 +200,8 @@ def pad_phoneme_data(phoneme_seqs, pad_idx, max_seq_len):
     batch_size = len(phoneme_seqs)
     phoneme_data = pad_idx * np.ones(
         (batch_size, max_seq_len, max_ph_seq_len), dtype=np.int64)
-    phoneme_mask = np.zeros((batch_size, max_seq_len, max_ph_seq_len),
-                            dtype=np.int64)
+    phoneme_mask = np.zeros(
+        (batch_size, max_seq_len, max_ph_seq_len), dtype=np.int64)
 
     for i in range(batch_size):
         cur_ph_seq = phoneme_seqs[i]
@@ -237,17 +248,16 @@ def pad_batch_data(insts,
         if is_target:
             # This is used to avoid attention on paddings and subsequent
             # words.
-            slf_attn_bias_data = np.ones((inst_data.shape[0], max_len,
-                                          max_len))
+            slf_attn_bias_data = np.ones((inst_data.shape[0], max_len, max_len))
             slf_attn_bias_data = np.triu(slf_attn_bias_data,
                                          1).reshape([-1, 1, max_len, max_len])
             slf_attn_bias_data = np.tile(slf_attn_bias_data,
                                          [1, n_head, 1, 1]) * [-1e9]
         else:
             # This is used to avoid attention on paddings.
-            slf_attn_bias_data = np.array(
-                [[0] * len(inst) + [-1e9] * (max_len - len(inst))
-                 for inst in insts])
+            slf_attn_bias_data = np.array([[0] * len(inst) + [-1e9] *
+                                           (max_len - len(inst))
+                                           for inst in insts])
             slf_attn_bias_data = np.tile(
                 slf_attn_bias_data.reshape([-1, 1, 1, max_len]),
                 [1, n_head, max_len, 1])
@@ -359,8 +369,8 @@ def prepare_data_generator(args,
             for item in data_reader():
                 inst_num_per_part = len(item) // count
                 for i in range(count):
-                    yield item[inst_num_per_part * i:inst_num_per_part *
-                               (i + 1)]
+                    yield item[inst_num_per_part * i:inst_num_per_part * (i + 1
+                                                                          )]
 
         return __impl__
 
@@ -401,8 +411,8 @@ def prepare_feed_dict_list(data_generator, init_flag, count):
                 feed_dict_list.append(pos_enc_tables)
             else:
                 feed_dict_list[idx] = dict(
-                    list(pos_enc_tables.items()) +
-                    list(feed_dict_list[idx].items()))
+                    list(pos_enc_tables.items()) + list(feed_dict_list[idx]
+                                                        .items()))
     return feed_dict_list if len(feed_dict_list) == count else None
 
 
@@ -487,11 +497,10 @@ def test_context(exe, train_exe, dev_count):
             data_generator = test_data()
         while True:
             try:
-                feed_dict_list = prepare_feed_dict_list(
-                    data_generator, False, dev_count)
-                outs = test_exe.run(
-                    fetch_list=[sum_cost.name, token_num.name],
-                    feed=feed_dict_list)
+                feed_dict_list = prepare_feed_dict_list(data_generator, False,
+                                                        dev_count)
+                outs = test_exe.run(fetch_list=[sum_cost.name, token_num.name],
+                                    feed=feed_dict_list)
             except (StopIteration, fluid.core.EOFException):
                 # The current pass is over.
                 if args.use_py_reader:
@@ -562,10 +571,10 @@ def train_loop(exe,
 
     # the best cross-entropy value with label smoothing
     loss_normalizer = -((1. - TrainTaskConfig.label_smooth_eps) * np.log(
-        (1. - TrainTaskConfig.label_smooth_eps)) +
-                        TrainTaskConfig.label_smooth_eps *
-                        np.log(TrainTaskConfig.label_smooth_eps /
-                               (ModelHyperParams.trg_vocab_size - 1) + 1e-20))
+        (1. - TrainTaskConfig.label_smooth_eps
+         )) + TrainTaskConfig.label_smooth_eps *
+                        np.log(TrainTaskConfig.label_smooth_eps / (
+                            ModelHyperParams.trg_vocab_size - 1) + 1e-20))
 
     step_idx = 0
     init_flag = True
@@ -583,8 +592,8 @@ def train_loop(exe,
         batch_id = 0
         while True:
             try:
-                feed_dict_list = prepare_feed_dict_list(
-                    data_generator, init_flag, dev_count)
+                feed_dict_list = prepare_feed_dict_list(data_generator,
+                                                        init_flag, dev_count)
                 outs = train_exe.run(
                     fetch_list=[sum_cost.name, token_num.name]
                     if step_idx % args.fetch_steps == 0 else [],
@@ -609,12 +618,11 @@ def train_loop(exe,
                     else:
                         logging.info(
                             "step_idx: %d, epoch: %d, batch: %d, avg loss: %f, "
-                            "normalized loss: %f, ppl: %f, speed: %.2f step/s"
-                            % (step_idx, pass_id, batch_id, total_avg_cost,
-                               total_avg_cost - loss_normalizer,
-                               np.exp([min(total_avg_cost, 100)
-                                       ]), args.fetch_steps /
-                               (time.time() - avg_batch_time)))
+                            "normalized loss: %f, ppl: %f, speed: %.2f step/s" %
+                            (step_idx, pass_id, batch_id, total_avg_cost,
+                             total_avg_cost - loss_normalizer, np.exp(
+                                 [min(total_avg_cost, 100)]),
+                             args.fetch_steps / (time.time() - avg_batch_time)))
                         avg_batch_time = time.time()
 
                 if step_idx % TrainTaskConfig.save_freq == 0 and step_idx > 0:
@@ -643,8 +651,9 @@ def train_loop(exe,
             val_avg_cost, val_ppl = test()
             logging.info(
                 "epoch: %d, val avg loss: %f, val normalized loss: %f, val ppl: %f,"
-                " consumed %fs" % (pass_id, val_avg_cost, val_avg_cost -
-                                   loss_normalizer, val_ppl, time_consumed))
+                " consumed %fs" % (pass_id, val_avg_cost,
+                                   val_avg_cost - loss_normalizer, val_ppl,
+                                   time_consumed))
         else:
             logging.info("epoch: %d, consumed %fs" % (pass_id, time_consumed))
         if not args.enable_ce:
@@ -734,8 +743,8 @@ def train(args):
 
     if args.local:
         logging.info("local start_up:")
-        train_loop(exe, train_prog, startup_prog, dev_count, sum_cost,
-                   avg_cost, token_num, predict, pyreader)
+        train_loop(exe, train_prog, startup_prog, dev_count, sum_cost, avg_cost,
+                   token_num, predict, pyreader)
     else:
         if args.update_method == "nccl2":
             trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))

PaddleNLP/add_license.py

+import os
+
+filePath = os.getcwd()
+
+
+def get_all_files(dir):
+    fileDirList = []
+
+    for root, dirs, files in os.walk(dir):
+        for file in files:
+            file_path = os.path.join(root, file)
+            fileDirList.append(file_path)
+        for dir in dirs:
+            dir_path = os.path.join(root, dir)
+            get_all_files(dir_path)
+
+    return fileDirList
+
+
+fileDirList = get_all_files(filePath)
+for code in fileDirList:
+    split = os.path.splitext(code)
+    if (split[1] == '.py' and not '__init__' in split[0] and
+            not '_ce' in split[0]):
+
+        with open(code, 'r') as fz:
+            content = fz.read()
+        if content.find('Copyright') >= 0:
+            fz.close()
+            continue
+        else:
+            string = "#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.\n" \
+                     "#\n" \
+                     "# Licensed under the Apache License, Version 2.0 (the \"License\");\n" \
+                     "# you may not use this file except in compliance with the License.\n" \
+                     "# You may obtain a copy of the License at\n" \
+                     "#\n" \
+                     "#     http://www.apache.org/licenses/LICENSE-2.0\n" \
+                     "#\n" \
+                     "# Unless required by applicable law or agreed to in writing, software\n" \
+                     "# distributed under the License is distributed on an \"AS IS\" BASIS,\n" \
+                     "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n" \
+                     "# See the License for the specific language governing permissions and\n" \
+                     "# limitations under the License.\n"+content
+            fz.close()
+            with open(code, 'w') as f:
+                f.write(string)
+                print "file %s write success!" % code
+            f.close()
+print "read and write success!"

PaddleNLP/emotion_detection/config.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 EmoTect config
 """
@@ -9,10 +22,12 @@ from __future__ import print_function
 import six
 import json
 
+
 class EmoTectConfig(object):
     """
     EmoTect Config
     """
+
     def __init__(self, config_path):
         self._config_dict = self._parse(config_path)
 
@@ -21,7 +36,8 @@ class EmoTectConfig(object):
             with open(config_path) as json_file:
                 config_dict = json.load(json_file)
         except Exception:
-            raise IOError("Error in parsing emotect model config file '%s'" % config_path)
+            raise IOError("Error in parsing emotect model config file '%s'" %
+                          config_path)
         else:
             return config_dict
 

PaddleNLP/emotion_detection/reader.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 EmoTect Reader, data converters for classification data.
 """
@@ -10,14 +23,13 @@ import numpy as np
 from utils import load_vocab
 from utils import data_reader
 
+
 class EmoTectProcessor(object):
     """
     Processor class for data convertors for EmoTect.
     """
-    def __init__(self,
-                 data_dir,
-                 vocab_path,
-                 random_seed=None):
+
+    def __init__(self, data_dir, vocab_path, random_seed=None):
         self.data_dir = data_dir
         self.vocab = load_vocab(vocab_path)
         self.num_examples = {"train": -1, "dev": -1, "test": -1, "infer": -1}
@@ -27,29 +39,33 @@ class EmoTectProcessor(object):
         """
         Load training examples
         """
-        return data_reader(os.path.join(self.data_dir, "train.tsv"),
-                    self.vocab, self.num_examples, "train", epoch)
+        return data_reader(
+            os.path.join(self.data_dir, "train.tsv"), self.vocab,
+            self.num_examples, "train", epoch)
 
     def get_dev_examples(self, data_dir):
         """
         Load dev examples
         """
-        return data_reader(os.path.join(self.data_dir, "dev.tsv"),
-                    self.vocab, self.num_examples, "dev")
+        return data_reader(
+            os.path.join(self.data_dir, "dev.tsv"), self.vocab,
+            self.num_examples, "dev")
 
     def get_test_examples(self, data_dir):
         """
         Load test examples
         """
-        return data_reader(os.path.join(self.data_dir, "test.tsv"),
-                    self.vocab, self.num_examples, "test")
+        return data_reader(
+            os.path.join(self.data_dir, "test.tsv"), self.vocab,
+            self.num_examples, "test")
 
     def get_infer_examples(self, data_dir):
         """
         Load infer querys
         """
-        return data_reader(os.path.join(self.data_dir, "infer.tsv"),
-                    self.vocab, self.num_examples, "infer")
+        return data_reader(
+            os.path.join(self.data_dir, "infer.tsv"), self.vocab,
+            self.num_examples, "infer")
 
     def get_labels(self):
         """
@@ -63,7 +79,8 @@ class EmoTectProcessor(object):
         """
         if phase not in ['train', 'dev', 'test', 'infer']:
             raise ValueError(
-                "Unknown phase, which should be in ['train', 'dev', 'test', 'infer'].")
+                "Unknown phase, which should be in ['train', 'dev', 'test', 'infer']."
+            )
         return self.num_examples[phase]
 
     def get_train_progress(self):
@@ -77,14 +94,18 @@ class EmoTectProcessor(object):
         Generate data for train, dev or test
         """
         if phase == "train":
-            return paddle.batch(self.get_train_examples(self.data_dir, epoch), batch_size)
+            return paddle.batch(
+                self.get_train_examples(self.data_dir, epoch), batch_size)
         elif phase == "dev":
-            return paddle.batch(self.get_dev_examples(self.data_dir), batch_size)
+            return paddle.batch(
+                self.get_dev_examples(self.data_dir), batch_size)
         elif phase == "test":
-            return paddle.batch(self.get_test_examples(self.data_dir), batch_size)
+            return paddle.batch(
+                self.get_test_examples(self.data_dir), batch_size)
         elif phase == "infer":
-            return paddle.batch(self.get_infer_examples(self.data_dir), batch_size)
+            return paddle.batch(
+                self.get_infer_examples(self.data_dir), batch_size)
         else:
             raise ValueError(
-                "Unknown phase, which should be in ['train', 'dev', 'test', 'infer'].")
-
+                "Unknown phase, which should be in ['train', 'dev', 'test', 'infer']."
+            )

PaddleNLP/emotion_detection/run_classifier.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Emotion Detection Task
 """
@@ -25,37 +38,48 @@ import utils
 
 parser = argparse.ArgumentParser(__doc__)
 model_g = utils.ArgumentGroup(parser, "model", "model configuration and paths.")
-model_g.add_arg("config_path", str, None, "Path to the json file for EmoTect model config.")
-model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.")
+model_g.add_arg("config_path", str, None,
+                "Path to the json file for EmoTect model config.")
+model_g.add_arg("init_checkpoint", str, None,
+                "Init checkpoint to resume training from.")
 model_g.add_arg("output_dir", str, None, "Directory path to save checkpoints")
 
 train_g = utils.ArgumentGroup(parser, "training", "training options.")
 train_g.add_arg("epoch", int, 10, "Number of epoches for training.")
-train_g.add_arg("save_steps", int, 10000, "The steps interval to save checkpoints.")
-train_g.add_arg("validation_steps", int, 1000, "The steps interval to evaluate model performance.")
+train_g.add_arg("save_steps", int, 10000,
+                "The steps interval to save checkpoints.")
+train_g.add_arg("validation_steps", int, 1000,
+                "The steps interval to evaluate model performance.")
 train_g.add_arg("lr", float, 0.002, "The Learning rate value for training.")
 
 log_g = utils.ArgumentGroup(parser, "logging", "logging related")
 log_g.add_arg("skip_steps", int, 10, "The steps interval to print loss.")
 log_g.add_arg("verbose", bool, False, "Whether to output verbose log")
 
-data_g = utils.ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options")
+data_g = utils.ArgumentGroup(
+    parser, "data", "Data paths, vocab paths and data processing options")
 data_g.add_arg("data_dir", str, None, "Directory path to training data.")
 data_g.add_arg("vocab_path", str, None, "Vocabulary path.")
-data_g.add_arg("batch_size", int, 256, "Total examples' number in batch for training.")
+data_g.add_arg("batch_size", int, 256,
+               "Total examples' number in batch for training.")
 data_g.add_arg("random_seed", int, 0, "Random seed.")
 
 run_type_g = utils.ArgumentGroup(parser, "run_type", "running type options.")
 run_type_g.add_arg("use_cuda", bool, False, "If set, use GPU for training.")
-run_type_g.add_arg("task_name", str, None, "The name of task to perform sentiment classification.")
+run_type_g.add_arg("task_name", str, None,
+                   "The name of task to perform sentiment classification.")
 run_type_g.add_arg("do_train", bool, False, "Whether to perform training.")
 run_type_g.add_arg("do_val", bool, False, "Whether to perform evaluation.")
 run_type_g.add_arg("do_infer", bool, False, "Whether to perform inference.")
 
-parser.add_argument('--enable_ce', action='store_true', help='If set, run the task with continuous evaluation logs.')
+parser.add_argument(
+    '--enable_ce',
+    action='store_true',
+    help='If set, run the task with continuous evaluation logs.')
 
 args = parser.parse_args()
 
+
 def create_model(args,
                  pyreader_name,
                  emotect_config,
@@ -98,11 +122,17 @@ def create_model(args,
 
     if is_infer:
         data = fluid.layers.read_file(pyreader)
-        probs = network(data, None, emotect_config["vocab_size"], class_dim=num_labels, is_infer=True)
+        probs = network(
+            data,
+            None,
+            emotect_config["vocab_size"],
+            class_dim=num_labels,
+            is_infer=True)
         return pyreader, probs
 
     data, label = fluid.layers.read_file(pyreader)
-    avg_loss, probs = network(data, label, emotect_config["vocab_size"], class_dim=num_labels)
+    avg_loss, probs = network(
+        data, label, emotect_config["vocab_size"], class_dim=num_labels)
     num_seqs = fluid.layers.create_tensor(dtype='int64')
     accuracy = fluid.layers.accuracy(input=probs, label=label, total=num_seqs)
     return pyreader, avg_loss, accuracy, num_seqs
@@ -118,8 +148,8 @@ def evaluate(exe, test_program, test_pyreader, fetch_list, eval_phase):
     while True:
         try:
             np_loss, np_acc, np_num_seqs = exe.run(program=test_program,
-                                                fetch_list=fetch_list,
-                                                return_numpy=False)
+                                                   fetch_list=fetch_list,
+                                                   return_numpy=False)
             np_loss = np.array(np_loss)
             np_acc = np.array(np_acc)
             np_num_seqs = np.array(np_num_seqs)
@@ -131,8 +161,8 @@ def evaluate(exe, test_program, test_pyreader, fetch_list, eval_phase):
             break
     time_end = time.time()
     print("[%s evaluation] avg loss: %f, avg acc: %f, elapsed time: %f s" %
-        (eval_phase, np.sum(total_cost) / np.sum(total_num_seqs),
-        np.sum(total_acc) / np.sum(total_num_seqs), time_end - time_begin))
+          (eval_phase, np.sum(total_cost) / np.sum(total_num_seqs),
+           np.sum(total_acc) / np.sum(total_num_seqs), time_end - time_begin))
 
 
 def infer(exe, infer_program, infer_pyreader, fetch_list, infer_phase):
@@ -141,10 +171,11 @@ def infer(exe, infer_program, infer_pyreader, fetch_list, infer_phase):
     while True:
         try:
             batch_probs = exe.run(program=infer_program,
-                            fetch_list=fetch_list,
-                            return_numpy=True)
+                                  fetch_list=fetch_list,
+                                  return_numpy=True)
             for probs in batch_probs[0]:
-                print("%d\t%f\t%f\t%f" % (np.argmax(probs), probs[0], probs[1], probs[2]))
+                print("%d\t%f\t%f\t%f" %
+                      (np.argmax(probs), probs[0], probs[1], probs[2]))
         except fluid.core.EOFException as e:
             infer_pyreader.reset()
             break
@@ -165,9 +196,10 @@ def main(args):
     exe = fluid.Executor(place)
 
     task_name = args.task_name.lower()
-    processor = reader.EmoTectProcessor(data_dir=args.data_dir,
-                                      vocab_path=args.vocab_path,
-                                      random_seed=args.random_seed)
+    processor = reader.EmoTectProcessor(
+        data_dir=args.data_dir,
+        vocab_path=args.vocab_path,
+        random_seed=args.random_seed)
     num_labels = len(processor.get_labels())
 
     if not (args.do_train or args.do_val or args.do_infer):
@@ -180,9 +212,7 @@ def main(args):
 
     if args.do_train:
         train_data_generator = processor.data_generator(
-            batch_size=args.batch_size,
-            phase='train',
-            epoch=args.epoch)
+            batch_size=args.batch_size, phase='train', epoch=args.epoch)
 
         num_train_examples = processor.get_num_examples(phase="train")
         max_train_steps = args.epoch * num_train_examples // args.batch_size + 1
@@ -210,7 +240,7 @@ def main(args):
             lower_mem, upper_mem, unit = fluid.contrib.memory_usage(
                 program=train_program, batch_size=args.batch_size)
             print("Theoretical memory usage in training: %.3f - %.3f %s" %
-                (lower_mem, upper_mem, unit))
+                  (lower_mem, upper_mem, unit))
 
     if args.do_val:
         test_prog = fluid.Program()
@@ -241,17 +271,12 @@ def main(args):
     if args.do_train:
         if args.init_checkpoint:
             utils.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 infer!")
-        utils.init_checkpoint(
-            exe,
-            args.init_checkpoint,
-            main_program=test_prog)
+        utils.init_checkpoint(exe, args.init_checkpoint, main_program=test_prog)
 
     if args.do_train:
         train_exe = exe
@@ -288,22 +313,27 @@ 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()
                     used_time = time_end - time_begin
                     print("step: %d, avg loss: %f, "
-                        "avg acc: %f, speed: %f steps/s" %
-                        (steps, np.sum(total_cost) / np.sum(total_num_seqs),
-                        np.sum(total_acc) / np.sum(total_num_seqs),
-                        args.skip_steps / used_time))
-                    ce_info.append([np.sum(total_cost) / np.sum(total_num_seqs), np.sum(total_acc) / np.sum(total_num_seqs), used_time])
+                          "avg acc: %f, speed: %f steps/s" %
+                          (steps, np.sum(total_cost) / np.sum(total_num_seqs),
+                           np.sum(total_acc) / np.sum(total_num_seqs),
+                           args.skip_steps / used_time))
+                    ce_info.append([
+                        np.sum(total_cost) / np.sum(total_num_seqs),
+                        np.sum(total_acc) / np.sum(total_num_seqs), used_time
+                    ])
                     total_cost, total_acc, total_num_seqs = [], [], []
                     time_begin = time.time()
 
                 if steps % args.save_steps == 0:
-                    save_path = os.path.join(args.output_dir, "step_" + str(steps))
+                    save_path = os.path.join(args.output_dir,
+                                             "step_" + str(steps))
                     fluid.io.save_persistables(exe, save_path, train_program)
 
                 if steps % args.validation_steps == 0:
@@ -315,8 +345,8 @@ def main(args):
                                 phase='dev',
                                 epoch=1))
                         evaluate(test_exe, test_prog, test_pyreader,
-                                [loss.name, accuracy.name, num_seqs.name],
-                                "dev")
+                                 [loss.name, accuracy.name, num_seqs.name],
+                                 "dev")
 
             except fluid.core.EOFException:
                 save_path = os.path.join(args.output_dir, "step_" + str(steps))
@@ -336,33 +366,25 @@ def main(args):
         except:
             print("ce info error")
         print("kpis\teach_step_duration_%s_card%s\t%s" %
-                (task_name, card_num, ce_time))
-        print("kpis\ttrain_loss_%s_card%s\t%f" %
-            (task_name, card_num, ce_loss))
-        print("kpis\ttrain_acc_%s_card%s\t%f" %
-            (task_name, card_num, ce_acc))
+              (task_name, card_num, ce_time))
+        print("kpis\ttrain_loss_%s_card%s\t%f" % (task_name, card_num, ce_loss))
+        print("kpis\ttrain_acc_%s_card%s\t%f" % (task_name, card_num, ce_acc))
 
     # evaluate on test set
     if not args.do_train and args.do_val:
         test_pyreader.decorate_paddle_reader(
             processor.data_generator(
-                batch_size=args.batch_size,
-                phase='test',
-                epoch=1))
+                batch_size=args.batch_size, phase='test', epoch=1))
         print("Final test result:")
         evaluate(test_exe, test_prog, test_pyreader,
-                [loss.name, accuracy.name, num_seqs.name],
-                "test")
+                 [loss.name, accuracy.name, num_seqs.name], "test")
 
     # infer
     if args.do_infer:
         infer_pyreader.decorate_paddle_reader(
             processor.data_generator(
-                batch_size=args.batch_size,
-                phase='infer',
-                epoch=1))
-        infer(test_exe, test_prog, infer_pyreader,
-             [probs.name], "infer")
+                batch_size=args.batch_size, phase='infer', epoch=1))
+        infer(test_exe, test_prog, infer_pyreader, [probs.name], "infer")
 
 
 def get_cards():

PaddleNLP/emotion_detection/run_ernie_classifier.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Emotion Detection Task, based on ERNIE
 """

PaddleNLP/emotion_detection/utils.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 EmoTect utilities.
 """
@@ -16,6 +29,7 @@ import paddle
 import paddle.fluid as fluid
 import numpy as np
 
+
 def str2bool(value):
     """
     String to Boolean
@@ -29,6 +43,7 @@ class ArgumentGroup(object):
     """
     Argument Class
     """
+
     def __init__(self, parser, title, des):
         self._group = parser.add_argument_group(title=title, description=des)
 
@@ -92,27 +107,33 @@ def data_reader(file_path, word_dict, num_examples, phrase, epoch=1):
                 cols = line.strip().split("\t")
                 if len(cols) != 1:
                     query = cols[-1]
-                wids = [word_dict[x] if x in word_dict else unk_id
-                        for x in query.strip().split(" ")]
-                all_data.append((wids,))
+                wids = [
+                    word_dict[x] if x in word_dict else unk_id
+                    for x in query.strip().split(" ")
+                ]
+                all_data.append((wids, ))
             else:
                 cols = line.strip().split("\t")
                 if len(cols) != 2:
                     sys.stderr.write("[NOTICE] Error Format Line!")
                     continue
                 label = int(cols[0])
-                wids = [word_dict[x] if x in word_dict else unk_id
-                        for x in cols[1].split(" ")]
+                wids = [
+                    word_dict[x] if x in word_dict else unk_id
+                    for x in cols[1].split(" ")
+                ]
                 all_data.append((wids, label))
     num_examples[phrase] = len(all_data)
 
     if phrase == "infer":
+
         def reader():
             """
             Infer reader function
             """
             for wids in all_data:
                 yield wids
+
         return reader
 
     def reader():
@@ -124,6 +145,7 @@ def data_reader(file_path, word_dict, num_examples, phrase, epoch=1):
                 random.shuffle(all_data)
             for wids, label in all_data:
                 yield wids, label
+
     return reader
 
 

PaddleNLP/language_representations_kit/ELMo/LAC_demo/network.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 The function lex_net(args) define the lexical analysis network structure
 """

PaddleNLP/language_representations_kit/ELMo/LAC_demo/reader.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 #coding: utf-8
 """
 The file_reader converts raw corpus to input.

PaddleNLP/language_representations_kit/ELMo/LAC_demo/train.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 This file is used to train the model.
 """

PaddleNLP/lexical_analysis/evaluate.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 #coding=utf-8
 """
 evaluate wordseg for LAC and other open-source wordseg tools
@@ -20,7 +33,7 @@ def to_unicode(string):
 def to_set(words):
     """ cut list to set of (string, off) """
     off = 0
-    s= set()
+    s = set()
     for w in words:
         if w:
             s.add((off, w))
@@ -145,7 +158,7 @@ def get_pkuseg_result(sentences):
     seg = pkuseg.pkuseg()
     preds = []
     for sentence in sentences:
-        sent_seg  = " ".join(seg.cut(sentence))
+        sent_seg = " ".join(seg.cut(sentence))
         sent_seg = to_unicode(sent_seg)
         preds.append(sent_seg)
     return preds
@@ -161,7 +174,8 @@ def get_hanlp_result(sentences):
     preds = []
     for sentence in sentences:
         arraylist = HanLP.segment(sentence)
-        sent_seg = " ".join([term.toString().split("/")[0] for term in arraylist])
+        sent_seg = " ".join(
+            [term.toString().split("/")[0] for term in arraylist])
         sent_seg = to_unicode(sent_seg)
         preds.append(sent_seg)
     return preds

PaddleNLP/lexical_analysis/reader.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 The file_reader converts raw corpus to input.
 """
@@ -9,7 +22,10 @@ import glob
 
 
 def load_kv_dict(dict_path,
-        reverse=False, delimiter="\t", key_func=None, value_func=None):
+                 reverse=False,
+                 delimiter="\t",
+                 key_func=None,
+                 value_func=None):
     """
     Load key-value dict from file
     """
@@ -34,11 +50,14 @@ def load_kv_dict(dict_path,
 
 class Dataset(object):
     """data reader"""
+
     def __init__(self, args, mode="train"):
         # read dict
-        self.word2id_dict = load_kv_dict(args.word_dict_path, reverse=True, value_func=int)
+        self.word2id_dict = load_kv_dict(
+            args.word_dict_path, reverse=True, value_func=int)
         self.id2word_dict = load_kv_dict(args.word_dict_path)
-        self.label2id_dict = load_kv_dict(args.label_dict_path, reverse=True, value_func=int)
+        self.label2id_dict = load_kv_dict(
+            args.label_dict_path, reverse=True, value_func=int)
         self.id2label_dict = load_kv_dict(args.label_dict_path)
         self.word_replace_dict = load_kv_dict(args.word_rep_dict_path)
 
@@ -78,12 +97,12 @@ class Dataset(object):
             label_ids.append(label_id)
         return label_ids
 
-
     def file_reader(self, filename, max_seq_len=64, mode="train"):
         """
         yield (word_idx, target_idx) one by one from file,
             or yield (word_idx, ) in `infer` mode
         """
+
         def wrapper():
             fread = io.open(filename, "r", encoding="utf-8")
             headline = next(fread)
@@ -93,9 +112,11 @@ class Dataset(object):
                 for line in fread:
                     words = line.strip("\n").split("\002")
                     word_ids = self.word_to_ids(words)
-                    yield word_ids[0:max_seq_len], [0 for _ in word_ids][0: max_seq_len]
+                    yield word_ids[0:max_seq_len], [0 for _ in word_ids][
+                        0:max_seq_len]
             else:
-                assert len(headline) == 2 and headline[0] == "text_a" and headline[1] == "label"
+                assert len(headline) == 2 and headline[
+                    0] == "text_a" and headline[1] == "label"
                 for line in fread:
                     words, labels = line.strip("\n").split("\t")
                     word_ids = self.word_to_ids(words.split("\002"))
@@ -109,9 +130,21 @@ class Dataset(object):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(__doc__)
-    parser.add_argument("--word_dict_path", type=str, default="./conf/word.dic", help="word dict")
-    parser.add_argument("--label_dict_path", type=str, default="./conf/tag.dic", help="label dict")
-    parser.add_argument("--word_rep_dict_path", type=str, default="./conf/q2b.dic", help="word replace dict")
+    parser.add_argument(
+        "--word_dict_path",
+        type=str,
+        default="./conf/word.dic",
+        help="word dict")
+    parser.add_argument(
+        "--label_dict_path",
+        type=str,
+        default="./conf/tag.dic",
+        help="label dict")
+    parser.add_argument(
+        "--word_rep_dict_path",
+        type=str,
+        default="./conf/q2b.dic",
+        help="word replace dict")
     args = parser.parse_args()
     dataset = Dataset(args)
     data_generator = dataset.file_reader("data/train.tsv")

PaddleNLP/lexical_analysis/run_ernie_sequence_labeling.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Sentiment Classification Task
 """
@@ -28,7 +41,6 @@ from models.representation.ernie import ernie_encoder
 from models.sequence_labeling import nets
 import utils
 
-
 # yapf: disable
 parser = argparse.ArgumentParser(__doc__)
 model_g = utils.ArgumentGroup(parser, "model", "model configuration and paths.")

PaddleNLP/lexical_analysis/run_sequence_labeling.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Baidu's open-source Lexical Analysis tool for Chinese, including:
     1. Word Segmentation,

PaddleNLP/lexical_analysis/utils.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 util tools
 """
@@ -19,6 +32,7 @@ class ArgumentGroup(object):
     """
     Put arguments to one group
     """
+
     def __init__(self, parser, title, des):
         """none"""
         self._group = parser.add_argument_group(title=title, description=des)
@@ -86,7 +100,7 @@ def parse_result(words, crf_decode, dataset):
         sent_len = offset_list[sent_index + 1] - offset_list[sent_index]
         last_word = ""
         last_tag = ""
-        for tag_index in range(sent_len): # iterate every word in sent
+        for tag_index in range(sent_len):  # iterate every word in sent
             index = tag_index + offset_list[sent_index]
             cur_word_id = str(words[index][0])
             cur_tag_id = str(crf_decode[index][0])

PaddleNLP/models/classification/nets.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 This module provide nets for text classification
 """
 
 import paddle.fluid as fluid
 
+
 def bow_net(data,
             label,
             dict_dim,
@@ -192,14 +206,14 @@ def gru_net(data,
 
 
 def textcnn_net(data,
-            label,
-            dict_dim,
-            emb_dim=128,
-            hid_dim=128,
-            hid_dim2=96,
-            class_dim=2,
-            win_sizes=None,
-            is_infer=False):
+                label,
+                dict_dim,
+                emb_dim=128,
+                hid_dim=128,
+                hid_dim2=96,
+                class_dim=2,
+                win_sizes=None,
+                is_infer=False):
     """
     Textcnn_net
     """

PaddleNLP/models/matching/bow.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 bow class
 """

PaddleNLP/models/matching/cnn.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 cnn class
 """
@@ -30,8 +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/matching/gru.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 gru class
 """

PaddleNLP/models/matching/losses/hinge_loss.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 hinge loss
 """

PaddleNLP/models/matching/losses/log_loss.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 log loss
 """

PaddleNLP/models/matching/losses/softmax_cross_entropy_loss.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 softmax loss
 """

PaddleNLP/models/matching/lstm.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 lstm class
 """

PaddleNLP/models/matching/mm_dnn.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 MMDNN class
 """

PaddleNLP/models/matching/optimizers/paddle_optimizers.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 optimizer calss
 """
@@ -43,5 +56,8 @@ class AdamOptimizer(object):
         Adam optimizer operation
         """
         adam = fluid.optimizer.AdamOptimizer(
-            self.learning_rate, beta1=self.beta1, beta2=self.beta2, epsilon=self.epsilon)
+            self.learning_rate,
+            beta1=self.beta1,
+            beta2=self.beta2,
+            epsilon=self.epsilon)
         adam.minimize(loss)

PaddleNLP/models/matching/paddle_layers.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 network layers
 """
@@ -23,9 +36,11 @@ class EmbeddingLayer(object):
         """
         operation
         """
-        emb = fluid.layers.embedding(input=input, size=[
-            self.dict_size, self.emb_dim], is_sparse=True,
-                                     param_attr=attr.ParamAttr(name=self.name))
+        emb = fluid.layers.embedding(
+            input=input,
+            size=[self.dict_size, self.emb_dim],
+            is_sparse=True,
+            param_attr=attr.ParamAttr(name=self.name))
         return emb
 
 
@@ -44,8 +59,7 @@ class SequencePoolLayer(object):
         """
         operation
         """
-        pool = fluid.layers.sequence_pool(
-            input=input, pool_type=self.pool_type)
+        pool = fluid.layers.sequence_pool(input=input, pool_type=self.pool_type)
         return pool
 
 
@@ -66,9 +80,12 @@ class FCLayer(object):
         """
         operation
         """
-        fc = fluid.layers.fc(input=input, size=self.fc_dim, param_attr=attr.ParamAttr(
-            name="%s.w" % self.name),
-                             bias_attr=attr.ParamAttr(name="%s.b" % self.name), act=self.act, name=self.name)
+        fc = fluid.layers.fc(input=input,
+                             size=self.fc_dim,
+                             param_attr=attr.ParamAttr(name="%s.w" % self.name),
+                             bias_attr=attr.ParamAttr(name="%s.b" % self.name),
+                             act=self.act,
+                             name=self.name)
         return fc
 
 
@@ -88,12 +105,16 @@ class DynamicGRULayer(object):
         """
         operation
         """
-        proj = fluid.layers.fc(input=input, size=self.gru_dim * 3,
-                               param_attr=attr.ParamAttr(name="%s_fc.w" % self.name),
-                               bias_attr=attr.ParamAttr(name="%s_fc.b" % self.name))
-        gru = fluid.layers.dynamic_gru(input=proj, size=self.gru_dim,
-                                       param_attr=attr.ParamAttr(name="%s.w" % self.name),
-                                       bias_attr=attr.ParamAttr(name="%s.b" % self.name))
+        proj = fluid.layers.fc(
+            input=input,
+            size=self.gru_dim * 3,
+            param_attr=attr.ParamAttr(name="%s_fc.w" % self.name),
+            bias_attr=attr.ParamAttr(name="%s_fc.b" % self.name))
+        gru = fluid.layers.dynamic_gru(
+            input=proj,
+            size=self.gru_dim,
+            param_attr=attr.ParamAttr(name="%s.w" % self.name),
+            bias_attr=attr.ParamAttr(name="%s.b" % self.name))
         return gru
 
 
@@ -113,12 +134,16 @@ class DynamicLSTMLayer(object):
         """
         operation
         """
-        proj = fluid.layers.fc(input=input, size=self.lstm_dim * 4,
-                               param_attr=attr.ParamAttr(name="%s_fc.w" % self.name),
-                               bias_attr=attr.ParamAttr(name="%s_fc.b" % self.name))
-        lstm, _ = fluid.layers.dynamic_lstm(input=proj, size=self.lstm_dim * 4,
-                                            param_attr=attr.ParamAttr(name="%s.w" % self.name),
-                                            bias_attr=attr.ParamAttr(name="%s.b" % self.name))
+        proj = fluid.layers.fc(
+            input=input,
+            size=self.lstm_dim * 4,
+            param_attr=attr.ParamAttr(name="%s_fc.w" % self.name),
+            bias_attr=attr.ParamAttr(name="%s_fc.b" % self.name))
+        lstm, _ = fluid.layers.dynamic_lstm(
+            input=proj,
+            size=self.lstm_dim * 4,
+            param_attr=attr.ParamAttr(name="%s.w" % self.name),
+            bias_attr=attr.ParamAttr(name="%s.b" % self.name))
         return lstm
 
 
@@ -161,9 +186,12 @@ class SequenceConvPoolLayer(object):
         """
         operation
         """
-        conv = fluid.nets.sequence_conv_pool(input=input, filter_size=self.filter_size,
-                                             num_filters=self.num_filters,
-                                             param_attr=attr.ParamAttr(name=self.name), act="relu")
+        conv = fluid.nets.sequence_conv_pool(
+            input=input,
+            filter_size=self.filter_size,
+            num_filters=self.num_filters,
+            param_attr=attr.ParamAttr(name=self.name),
+            act="relu")
         return conv
 
 
@@ -259,7 +287,8 @@ class SoftmaxWithCrossEntropyLayer(object):
         """
         operation
         """
-        loss = fluid.layers.softmax_with_cross_entropy(logits=input, label=label)
+        loss = fluid.layers.softmax_with_cross_entropy(
+            logits=input, label=label)
         return loss
 
 
@@ -354,8 +383,8 @@ class ConstantLayer(object):
         """
         operation
         """
-        constant = fluid.layers.fill_constant_batch_size_like(
-            input, shape, dtype, value)
+        constant = fluid.layers.fill_constant_batch_size_like(input, shape,
+                                                              dtype, value)
         return constant
 
 
@@ -396,6 +425,7 @@ class SoftsignLayer(object):
         softsign = fluid.layers.softsign(input)
         return softsign
 
+
 # class MatmulLayer(object):
 #     def __init__(self, transpose_x, transpose_y):
 #         self.transpose_x = transpose_x
@@ -405,7 +435,6 @@ class SoftsignLayer(object):
 #         matmul = fluid.layers.matmul(x, y, self.transpose_x, self.transpose_y)
 #         return matmul
 
-
 # class Conv2dLayer(object):
 #     def __init__(self, num_filters, filter_size, act, name):
 #         self.num_filters = num_filters
@@ -417,7 +446,6 @@ class SoftsignLayer(object):
 #         conv = fluid.layers.conv2d(input, self.num_filters, self.filter_size, param_attr=attr.ParamAttr(name="%s.w" % self.name), bias_attr=attr.ParamAttr(name="%s.b" % self.name), act=self.act)
 #         return conv
 
-
 # class Pool2dLayer(object):
 #     def __init__(self, pool_size, pool_type):
 #         self.pool_size = pool_size

PaddleNLP/models/neural_machine_translation/transformer/desc.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 # The placeholder for batch_size in compile time. Must be -1 currently to be
 # consistent with some ops' infer-shape output in compile time, such as the
 # sequence_expand op used in beamsearch decoder.

PaddleNLP/models/neural_machine_translation/transformer/model.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 from functools import partial
 import numpy as np
 

PaddleNLP/models/representation/ernie.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 This module provides ErnieModel and ErnieConfig
 """

PaddleNLP/models/sequence_labeling/nets.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 The function lex_net(args) define the lexical analysis network structure
 """
@@ -96,8 +109,7 @@ def lex_net(word, target, args, vocab_size, num_labels):
             input=emission,
             label=target,
             param_attr=fluid.ParamAttr(
-                name='crfw',
-                learning_rate=crf_lr))
+                name='crfw', learning_rate=crf_lr))
         crf_decode = fluid.layers.crf_decoding(
             input=emission, param_attr=fluid.ParamAttr(name='crfw'))
         avg_cost = fluid.layers.mean(x=crf_cost)

PaddleNLP/models/transformer_encoder.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """Transformer encoder."""
 
 from __future__ import absolute_import
@@ -100,7 +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/preprocess/ernie/task_reader.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 This module provides reader for classification and sequence labing
 """
@@ -18,6 +31,7 @@ from preprocess.padding import pad_batch_data
 
 class BaseReader(object):
     """BaseReader for classify and sequence labeling task"""
+
     def __init__(self,
                  vocab_path,
                  label_map_config=None,
@@ -211,6 +225,7 @@ class BaseReader(object):
 
 class ClassifyReader(BaseReader):
     """ClassifyReader"""
+
     def _read_tsv(self, input_file, quotechar=None):
         """Reads a tab separated value file."""
         with open(input_file, "r") as f:
@@ -239,7 +254,10 @@ class ClassifyReader(BaseReader):
 
         # padding
         padded_token_ids, input_mask, seq_lens = pad_batch_data(
-            batch_token_ids, pad_idx=self.pad_id, return_input_mask=True, return_seq_lens=True)
+            batch_token_ids,
+            pad_idx=self.pad_id,
+            return_input_mask=True,
+            return_seq_lens=True)
         padded_text_type_ids = pad_batch_data(
             batch_text_type_ids, pad_idx=self.pad_id)
         padded_position_ids = pad_batch_data(
@@ -255,6 +273,7 @@ class ClassifyReader(BaseReader):
 
 class SequenceLabelReader(BaseReader):
     """SequenceLabelReader"""
+
     def _pad_batch_records(self, batch_records):
         batch_token_ids = [record.token_ids for record in batch_records]
         batch_text_type_ids = [record.text_type_ids for record in batch_records]
@@ -314,7 +333,9 @@ class SequenceLabelReader(BaseReader):
         position_ids = list(range(len(token_ids)))
         text_type_ids = [0] * len(token_ids)
         no_entity_id = len(self.label_map) - 1
-        labels = [label if label in self.label_map else u"O" for label in labels]
+        labels = [
+            label if label in self.label_map else u"O" for label in labels
+        ]
         label_ids = [no_entity_id] + [
             self.label_map[label] for label in labels
         ] + [no_entity_id]
@@ -332,6 +353,7 @@ class SequenceLabelReader(BaseReader):
 
 class ExtractEmbeddingReader(BaseReader):
     """ExtractEmbeddingReader"""
+
     def _pad_batch_records(self, batch_records):
         batch_token_ids = [record.token_ids for record in batch_records]
         batch_text_type_ids = [record.text_type_ids for record in batch_records]

PaddleNLP/preprocess/padding.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Mask, padding and batching.
 """

PaddleNLP/preprocess/tokenizer/reader.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 The file_reader converts raw corpus to input.
 """
@@ -5,6 +18,7 @@ import os
 import __future__
 import io
 
+
 def file_reader(file_dir,
                 word2id_dict,
                 label2id_dict,
@@ -15,6 +29,7 @@ def file_reader(file_dir,
     """
     word_dict_len = max(map(int, word2id_dict.values())) + 1
     label_dict_len = max(map(int, label2id_dict.values())) + 1
+
     def reader():
         """
         the data generator
@@ -24,7 +39,8 @@ def file_reader(file_dir,
             for filename in files:
                 if not filename.startswith(filename_feature):
                     continue
-                for line in io.open(os.path.join(root, filename), 'r', encoding='utf8'):
+                for line in io.open(
+                        os.path.join(root, filename), 'r', encoding='utf8'):
                     index += 1
                     bad_line = False
                     line = line.strip("\n")
@@ -52,8 +68,9 @@ def file_reader(file_dir,
                         else:
                             target_idx.append(int(label2id_dict["O"]))
                     if len(word_idx) != len(target_idx):
-                        continue             
+                        continue
                     yield word_idx, target_idx
+
     return reader
 
 
@@ -68,6 +85,7 @@ def test_reader(file_dir,
     #print (word2id_dict)
     word_dict_len = max(map(int, word2id_dict.values())) + 1
     label_dict_len = max(map(int, label2id_dict.values())) + 1
+
     #print word_dict_len
     #print label_dict_len
     def reader():
@@ -94,6 +112,7 @@ def test_reader(file_dir,
                 else:
                     word_idx.append(int(word2id_dict["OOV"]))
             yield word_idx, words
+
     return reader
 
 

PaddleNLP/preprocess/tokenizer/tokenizer.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 This module provides wordseg tools
 """
@@ -11,12 +24,13 @@ import time
 import sys
 import io
 
-if sys.version_info > (3,):
+if sys.version_info > (3, ):
     sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
 else:
     reload(sys)
     sys.setdefaultencoding("utf8")
 
+
 def parse_args():
     """
     Arguments Parse
@@ -26,32 +40,27 @@ def parse_args():
         '--batch_size',
         type=int,
         default=5,
-        help='The size of a batch. (default: %(default)d)'
-    )
+        help='The size of a batch. (default: %(default)d)')
     parser.add_argument(
         '--model_path',
         type=str,
         default='./conf/model',
-        help='A path to the model. (default: %(default)s)'
-    )
+        help='A path to the model. (default: %(default)s)')
     parser.add_argument(
         '--test_data_dir',
         type=str,
         default='./data/test_data',
-        help='A directory with test data files. (default: %(default)s)'
-    )
+        help='A directory with test data files. (default: %(default)s)')
     parser.add_argument(
         "--word_dict_path",
         type=str,
         default="./conf/word.dic",
-        help="The path of the word dictionary. (default: %(default)s)"
-    )
+        help="The path of the word dictionary. (default: %(default)s)")
     parser.add_argument(
         "--label_dict_path",
         type=str,
         default="./conf/tag.dic",
-        help="The path of the label dictionary. (default: %(default)s)"
-    )
+        help="The path of the label dictionary. (default: %(default)s)")
     parser.add_argument(
         "--word_rep_dict_path",
         type=str,
@@ -104,17 +113,15 @@ def infer(args):
     Tokenize
     """
     id2word_dict = reader.load_dict(args.word_dict_path)
-    word2id_dict = reader.load_reverse_dict(args.word_dict_path) 
+    word2id_dict = reader.load_reverse_dict(args.word_dict_path)
 
     id2label_dict = reader.load_dict(args.label_dict_path)
     label2id_dict = reader.load_reverse_dict(args.label_dict_path)
     q2b_dict = reader.load_dict(args.word_rep_dict_path)
     test_data = paddle.batch(
-                    reader.test_reader(args.test_data_dir,
-                        word2id_dict,
-                        label2id_dict,
-                        q2b_dict),
-                    batch_size = args.batch_size)
+        reader.test_reader(args.test_data_dir, word2id_dict, label2id_dict,
+                           q2b_dict),
+        batch_size=args.batch_size)
     place = fluid.CPUPlace()
     #place = fluid.CUDAPlace(0)
     exe = fluid.Executor(place)
@@ -130,9 +137,9 @@ def infer(args):
             #print(word_idx)
             word_list = [x[1] for x in data]
             (crf_decode, ) = exe.run(inference_program,
-                                 feed={"word":word_idx},
-                                 fetch_list=fetch_targets,
-                                 return_numpy=False)
+                                     feed={"word": word_idx},
+                                     fetch_list=fetch_targets,
+                                     return_numpy=False)
             lod_info = (crf_decode.lod())[0]
             np_data = np.array(crf_decode)
             assert len(data) == len(lod_info) - 1
@@ -145,7 +152,7 @@ def infer(args):
                 cur_full_tag = ""
                 words = word_list[sen_index]
                 for tag_index in range(lod_info[sen_index],
-                                        lod_info[sen_index + 1]):
+                                       lod_info[sen_index + 1]):
                     cur_word = words[word_index]
                     cur_tag = id2label_dict[str(np_data[tag_index][0])]
                     if cur_tag.endswith("-B") or cur_tag.endswith("O"):

PaddleNLP/sentiment_classification/config.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Senta model.
 """
@@ -11,10 +24,12 @@ import json
 import numpy as np
 import paddle.fluid as fluid
 
+
 class SentaConfig(object):
     """
     Senta Config
     """
+
     def __init__(self, config_path):
         self._config_dict = self._parse(config_path)
 
@@ -24,7 +39,7 @@ class SentaConfig(object):
                 config_dict = json.load(json_file)
         except Exception:
             raise IOError("Error in parsing bert model config file '%s'" %
-                config_path)
+                          config_path)
 
         else:
             return config_dict

PaddleNLP/sentiment_classification/reader.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Senta Reader
 """
@@ -12,15 +25,13 @@ from utils import data_reader
 import paddle
 import paddle.fluid as fluid
 
+
 class SentaProcessor(object):
     """
     Processor class for data convertors for senta
     """
 
-    def __init__(self,
-                 data_dir,
-                 vocab_path,
-                 random_seed=None):
+    def __init__(self, data_dir, vocab_path, random_seed=None):
         self.data_dir = data_dir
         self.vocab = load_vocab(vocab_path)
         self.num_examples = {"train": -1, "dev": -1, "infer": -1}
@@ -30,19 +41,22 @@ class SentaProcessor(object):
         """
         Load training examples
         """
-        return data_reader((self.data_dir + "/train.tsv"), self.vocab, self.num_examples, "train", epoch)
+        return data_reader((self.data_dir + "/train.tsv"), self.vocab,
+                           self.num_examples, "train", epoch)
 
     def get_dev_examples(self, data_dir, epoch):
         """
         Load dev examples
         """
-        return data_reader((self.data_dir + "/dev.tsv"), self.vocab, self.num_examples, "dev", epoch)
+        return data_reader((self.data_dir + "/dev.tsv"), self.vocab,
+                           self.num_examples, "dev", epoch)
 
     def get_test_examples(self, data_dir, epoch):
         """
         Load test examples
         """
-        return data_reader((self.data_dir + "/test.tsv"), self.vocab, self.num_examples, "infer", epoch)
+        return data_reader((self.data_dir + "/test.tsv"), self.vocab,
+                           self.num_examples, "infer", epoch)
 
     def get_labels(self):
         """
@@ -70,11 +84,14 @@ class SentaProcessor(object):
         Generate data for train, dev or infer
         """
         if phase == "train":
-            return paddle.batch(self.get_train_examples(self.data_dir, epoch), batch_size)
+            return paddle.batch(
+                self.get_train_examples(self.data_dir, epoch), batch_size)
         elif phase == "dev":
-            return paddle.batch(self.get_dev_examples(self.data_dir, epoch), batch_size)
+            return paddle.batch(
+                self.get_dev_examples(self.data_dir, epoch), batch_size)
         elif phase == "infer":
-            return paddle.batch(self.get_test_examples(self.data_dir, epoch), batch_size)
+            return paddle.batch(
+                self.get_test_examples(self.data_dir, epoch), batch_size)
         else:
             raise ValueError(
                 "Unknown phase, which should be in ['train', 'dev', 'infer'].")

PaddleNLP/sentiment_classification/run_classifier.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Sentiment Classification Task
 """

PaddleNLP/sentiment_classification/run_ernie_classifier.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Sentiment Classification Task
 """

PaddleNLP/sentiment_classification/utils.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Arguments for configuration
 """
@@ -31,6 +44,7 @@ class ArgumentGroup(object):
     """
     Argument Class
     """
+
     def __init__(self, parser, title, des):
         self._group = parser.add_argument_group(title=title, description=des)
 
@@ -79,7 +93,7 @@ def init_checkpoint(exe, init_checkpoint_path, main_program):
         predicate=existed_persitables)
     print("Load model from {}".format(init_checkpoint_path))
 
-    
+
 def data_reader(file_path, word_dict, num_examples, phrase, epoch):
     """
     Convert word sequence into slot
@@ -95,15 +109,17 @@ def data_reader(file_path, word_dict, num_examples, phrase, epoch):
                 sys.stderr.write("[NOTICE] Error Format Line!")
                 continue
             label = int(cols[1])
-            wids = [word_dict[x] if x in word_dict else unk_id
-                    for x in cols[0].split(" ")]
+            wids = [
+                word_dict[x] if x in word_dict else unk_id
+                for x in cols[0].split(" ")
+            ]
             all_data.append((wids, label))
 
     if phrase == "train":
         random.shuffle(all_data)
 
     num_examples[phrase] = len(all_data)
-        
+
     def reader():
         """
         Reader Function
@@ -111,8 +127,10 @@ def data_reader(file_path, word_dict, num_examples, phrase, epoch):
         for epoch_index in range(epoch):
             for doc, label in all_data:
                 yield doc, label
+
     return reader
 
+
 def load_vocab(file_path):
     """
     load the given vocabulary

PaddleNLP/similarity_net/config.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 SimNet config
 """
@@ -21,12 +34,14 @@ class SimNetConfig(object):
             with open(config_path) as json_file:
                 config_dict = json.load(json_file)
         except Exception:
-            raise IOError("Error in parsing simnet model config file '%s'" % config_path)
+            raise IOError("Error in parsing simnet model config file '%s'" %
+                          config_path)
 
         else:
             if config_dict["task_mode"] != self.task_mode:
                 raise ValueError(
-                    "the config '{}' does not match the task_mode '{}'".format(self.config_path, self.task_mode))
+                    "the config '{}' does not match the task_mode '{}'".format(
+                        self.config_path, self.task_mode))
             return config_dict
 
     def __getitem__(self, key):

PaddleNLP/similarity_net/evaluate/unicom_compute_pos_neg.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 comput unicom
 """

PaddleNLP/similarity_net/evaluate/unicom_split.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 split unicom file
 """

PaddleNLP/similarity_net/reader.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 SimNet reader
 """
@@ -25,15 +38,24 @@ class SimNetProcessor(object):
                 Reader with Pairwise
             """
             if mode == "valid":
-                with codecs.open(self.args.valid_data_dir, "r", "utf-8") as file:
+                with codecs.open(self.args.valid_data_dir, "r",
+                                 "utf-8") as file:
                     for line in file:
                         query, title, label = line.strip().split("\t")
-                        if len(query) == 0 or len(title) == 0 or len(label) == 0 or not label.isdigit() or int(
-                                label) not in [0, 1]:
-                            logging.warning("line not match format in test file")
+                        if len(query) == 0 or len(title) == 0 or len(
+                                label) == 0 or not label.isdigit() or int(
+                                    label) not in [0, 1]:
+                            logging.warning(
+                                "line not match format in test file")
                             continue
-                        query = [self.vocab[word] for word in query.split(" ") if word in self.vocab]
-                        title = [self.vocab[word] for word in title.split(" ") if word in self.vocab]
+                        query = [
+                            self.vocab[word] for word in query.split(" ")
+                            if word in self.vocab
+                        ]
+                        title = [
+                            self.vocab[word] for word in title.split(" ")
+                            if word in self.vocab
+                        ]
                         if len(query) == 0:
                             query = [0]
                         if len(title) == 0:
@@ -43,27 +65,47 @@ class SimNetProcessor(object):
                 with codecs.open(self.args.test_data_dir, "r", "utf-8") as file:
                     for line in file:
                         query, title, label = line.strip().split("\t")
-                        if len(query) == 0 or len(title) == 0 or len(label) == 0 or not label.isdigit() or int(
-                                label) not in [0, 1]:
-                            logging.warning("line not match format in test file")
+                        if len(query) == 0 or len(title) == 0 or len(
+                                label) == 0 or not label.isdigit() or int(
+                                    label) not in [0, 1]:
+                            logging.warning(
+                                "line not match format in test file")
                             continue
-                        query = [self.vocab[word] for word in query.split(" ") if word in self.vocab]
-                        title = [self.vocab[word] for word in title.split(" ") if word in self.vocab]
+                        query = [
+                            self.vocab[word] for word in query.split(" ")
+                            if word in self.vocab
+                        ]
+                        title = [
+                            self.vocab[word] for word in title.split(" ")
+                            if word in self.vocab
+                        ]
                         if len(query) == 0:
                             query = [0]
                         if len(title) == 0:
                             title = [0]
                         yield [query, title]
             else:
-                with codecs.open(self.args.train_data_dir, "r", "utf-8") as file:
+                with codecs.open(self.args.train_data_dir, "r",
+                                 "utf-8") as file:
                     for line in file:
                         query, pos_title, neg_title = line.strip().split("\t")
-                        if len(query) == 0 or len(pos_title) == 0 or len(neg_title) == 0:
-                            logging.warning("line not match format in test file")
+                        if len(query) == 0 or len(pos_title) == 0 or len(
+                                neg_title) == 0:
+                            logging.warning(
+                                "line not match format in test file")
                             continue
-                        query = [self.vocab[word] for word in query.split(" ") if word in self.vocab]
-                        pos_title = [self.vocab[word] for word in pos_title.split(" ") if word in self.vocab]
-                        neg_title = [self.vocab[word] for word in neg_title.split(" ") if word in self.vocab]
+                        query = [
+                            self.vocab[word] for word in query.split(" ")
+                            if word in self.vocab
+                        ]
+                        pos_title = [
+                            self.vocab[word] for word in pos_title.split(" ")
+                            if word in self.vocab
+                        ]
+                        neg_title = [
+                            self.vocab[word] for word in neg_title.split(" ")
+                            if word in self.vocab
+                        ]
                         if len(query) == 0:
                             query = [0]
                         if len(pos_title) == 0:
@@ -77,15 +119,24 @@ class SimNetProcessor(object):
             Reader with Pointwise
             """
             if mode == "valid":
-                with codecs.open(self.args.valid_data_dir, "r", "utf-8") as file:
+                with codecs.open(self.args.valid_data_dir, "r",
+                                 "utf-8") as file:
                     for line in file:
                         query, title, label = line.strip().split("\t")
-                        if len(query) == 0 or len(title) == 0 or len(label) == 0 or not label.isdigit() or int(
-                                label) not in [0, 1]:
-                            logging.warning("line not match format in test file")
+                        if len(query) == 0 or len(title) == 0 or len(
+                                label) == 0 or not label.isdigit() or int(
+                                    label) not in [0, 1]:
+                            logging.warning(
+                                "line not match format in test file")
                             continue
-                        query = [self.vocab[word] for word in query.split(" ") if word in self.vocab]
-                        title = [self.vocab[word] for word in title.split(" ") if word in self.vocab]
+                        query = [
+                            self.vocab[word] for word in query.split(" ")
+                            if word in self.vocab
+                        ]
+                        title = [
+                            self.vocab[word] for word in title.split(" ")
+                            if word in self.vocab
+                        ]
                         if len(query) == 0:
                             query = [0]
                         if len(title) == 0:
@@ -95,27 +146,44 @@ class SimNetProcessor(object):
                 with codecs.open(self.args.test_data_dir, "r", "utf-8") as file:
                     for line in file:
                         query, title, label = line.strip().split("\t")
-                        if len(query) == 0 or len(title) == 0 or len(label) == 0 or not label.isdigit() or int(
-                                label) not in [0, 1]:
-                            logging.warning("line not match format in test file")
+                        if len(query) == 0 or len(title) == 0 or len(
+                                label) == 0 or not label.isdigit() or int(
+                                    label) not in [0, 1]:
+                            logging.warning(
+                                "line not match format in test file")
                             continue
-                        query = [self.vocab[word] for word in query.split(" ") if word in self.vocab]
-                        title = [self.vocab[word] for word in title.split(" ") if word in self.vocab]
+                        query = [
+                            self.vocab[word] for word in query.split(" ")
+                            if word in self.vocab
+                        ]
+                        title = [
+                            self.vocab[word] for word in title.split(" ")
+                            if word in self.vocab
+                        ]
                         if len(query) == 0:
                             query = [0]
                         if len(title) == 0:
                             title = [0]
                         yield [query, title]
             else:
-                with codecs.open(self.args.train_data_dir, "r", "utf-8") as file:
+                with codecs.open(self.args.train_data_dir, "r",
+                                 "utf-8") as file:
                     for line in file:
                         query, title, label = line.strip().split("\t")
-                        if len(query) == 0 or len(title) == 0 or len(label) == 0 or not label.isdigit() or int(
-                                label) not in [0, 1]:
-                            logging.warning("line not match format in test file")
+                        if len(query) == 0 or len(title) == 0 or len(
+                                label) == 0 or not label.isdigit() or int(
+                                    label) not in [0, 1]:
+                            logging.warning(
+                                "line not match format in test file")
                             continue
-                        query = [self.vocab[word] for word in query.split(" ") if word in self.vocab]
-                        title = [self.vocab[word] for word in title.split(" ") if word in self.vocab]
+                        query = [
+                            self.vocab[word] for word in query.split(" ")
+                            if word in self.vocab
+                        ]
+                        title = [
+                            self.vocab[word] for word in title.split(" ")
+                            if word in self.vocab
+                        ]
                         label = int(label)
                         if len(query) == 0:
                             query = [0]
@@ -138,8 +206,14 @@ class SimNetProcessor(object):
                 if len(query) == 0 or len(title) == 0:
                     logging.warning("line not match format in test file")
                     continue
-                query = [self.vocab[word] for word in query.split(" ") if word in self.vocab]
-                title = [self.vocab[word] for word in title.split(" ") if word in self.vocab]
+                query = [
+                    self.vocab[word] for word in query.split(" ")
+                    if word in self.vocab
+                ]
+                title = [
+                    self.vocab[word] for word in title.split(" ")
+                    if word in self.vocab
+                ]
                 if len(query) == 0:
                     query = [0]
                 if len(title) == 0:

PaddleNLP/similarity_net/run_classifier.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 SimNet Task
 """
@@ -164,16 +177,16 @@ def train(conf_dict, args):
         infer_program = fluid.default_main_program().clone(for_test=True)
         avg_cost = loss.compute(pred, label)
         avg_cost.persistable = True
-    
+
     # operate Optimization
     optimizer.ops(avg_cost)
     executor = fluid.Executor(place)
     executor.run(fluid.default_startup_program())
 
     if args.init_checkpoint is not None:
-        utils.init_checkpoint(executor, args.init_checkpoint, 
-                fluid.default_startup_program())
-    
+        utils.init_checkpoint(executor, args.init_checkpoint,
+                              fluid.default_startup_program())
+
     # Get and run executor
     parallel_executor = fluid.ParallelExecutor(
         use_cuda=args.use_cuda,

PaddleNLP/similarity_net/utils.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 # --coding=utf-8
 """
 SimNet utilities.
@@ -17,6 +30,7 @@ import paddle.fluid as fluid
 ******functions for file processing******
 """
 
+
 def load_vocab(file_path):
     """
     load the given vocabulary
@@ -47,7 +61,8 @@ def get_result_file(args):
     """
     with codecs.open(args.test_data_dir, "r", "utf-8") as test_file:
         with codecs.open("predictions.txt", "r", "utf-8") as predictions_file:
-            with codecs.open(args.test_result_path, "w", "utf-8") as test_result_file:
+            with codecs.open(args.test_result_path, "w",
+                             "utf-8") as test_result_file:
                 test_datas = [line.strip("\n") for line in test_file]
                 predictions = [line.strip("\n") for line in predictions_file]
                 for test_data, prediction in zip(test_datas, predictions):
@@ -287,7 +302,7 @@ def init_checkpoint(exe, init_checkpoint_path, main_program):
     """
     assert os.path.exists(
         init_checkpoint_path), "[%s] cann't be found." % init_checkpoint_path
-    
+
     def existed_persitables(var):
         if not fluid.io.is_persistable(var):
             return False
@@ -299,4 +314,3 @@ def init_checkpoint(exe, init_checkpoint_path, main_program):
         main_program=main_program,
         predicate=existed_persitables)
     print("Load model from {}".format(init_checkpoint_path))
-

PaddleNLP/unarchived/chinese_ner/infer.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import numpy as np
 import argparse
 import time

PaddleNLP/unarchived/chinese_ner/reader.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import os
 
 

PaddleNLP/unarchived/chinese_ner/train.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import os
 import math
 import time

PaddleNLP/unarchived/deep_attention_matching_net/model.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import six
 import numpy as np
 import paddle.fluid as fluid

PaddleNLP/unarchived/deep_attention_matching_net/test_and_evaluate.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import os
 import six
 import numpy as np

PaddleNLP/unarchived/deep_attention_matching_net/train_and_evaluate.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import os
 import six
 import numpy as np

PaddleNLP/unarchived/deep_attention_matching_net/utils/douban_evaluation.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import sys
 import six
 import numpy as np

PaddleNLP/unarchived/deep_attention_matching_net/utils/evaluation.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import sys
 import six
 

PaddleNLP/unarchived/deep_attention_matching_net/utils/layers.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import paddle.fluid as fluid
 
 

PaddleNLP/unarchived/deep_attention_matching_net/utils/reader.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import six
 import numpy as np
 
@@ -190,7 +203,8 @@ def make_one_batch_input(data_batches, index):
 
     turns = np.array(data_batches["turns"][index]).astype('int64')
     tt_turns_len = np.array(data_batches["tt_turns_len"][index]).astype('int64')
-    every_turn_len = np.array(data_batches["every_turn_len"][index]).astype('int64')
+    every_turn_len = np.array(data_batches["every_turn_len"][index]).astype(
+        'int64')
     response = np.array(data_batches["response"][index]).astype('int64')
     response_len = np.array(data_batches["response_len"][index]).astype('int64')
 

PaddleNLP/unarchived/deep_attention_matching_net/utils/util.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import six
 import os
 

PaddleNLP/unarchived/language_model/gru/infer.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import sys
 import time
 import math

PaddleNLP/unarchived/language_model/gru/train.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import os
 import sys
 import time

PaddleNLP/unarchived/language_model/gru/train_on_cloud.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import os
 import sys
 import time

PaddleNLP/unarchived/language_model/gru/utils.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import sys
 import time
 import numpy as np

PaddleNLP/unarchived/machine_reading_comprehension/paragraph_extraction.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 #!/usr/bin/python
 #-*- coding:utf-8 -*-
 

PaddleNLP/unarchived/machine_reading_comprehension/utils/marco_tokenize_data.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 #coding=utf8
 
 import os, sys, json

PaddleNLP/unarchived/machine_reading_comprehension/utils/marcov1_to_dureader.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 #coding=utf8
 
 import sys

PaddleNLP/unarchived/machine_reading_comprehension/utils/marcov2_to_v1_tojsonl.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import sys
 import json
 import pandas as pd

PaddleNLP/unarchived/neural_machine_translation/rnn_search/attention_model.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

PaddleNLP/unarchived/neural_machine_translation/transformer/config.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 class TrainTaskConfig(object):
     # support both CPU and GPU now.
     use_gpu = True

PaddleNLP/unarchived/neural_machine_translation/transformer/infer.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import argparse
 import ast
 import multiprocessing

PaddleNLP/unarchived/neural_machine_translation/transformer/model.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 from functools import partial
 import numpy as np
 

PaddleNLP/unarchived/neural_machine_translation/transformer/optim.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import numpy as np
 
 import paddle.fluid as fluid

PaddleNLP/unarchived/neural_machine_translation/transformer/profile.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import argparse
 import ast
 import contextlib

PaddleNLP/unarchived/neural_machine_translation/transformer/reader.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import glob
 import six
 import os

PaddleNLP/unarchived/neural_machine_translation/transformer/train.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import argparse
 import ast
 import copy

PaddleNLP/unarchived/sequence_tagging_for_ner/infer.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 from __future__ import print_function
 
 import numpy as np

PaddleNLP/unarchived/sequence_tagging_for_ner/network_conf.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import math
 
 import paddle.fluid as fluid

PaddleNLP/unarchived/sequence_tagging_for_ner/reader.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 """
 Conll03 dataset.
 """

PaddleNLP/unarchived/sequence_tagging_for_ner/train.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 from __future__ import print_function
 
 import os

PaddleNLP/unarchived/sequence_tagging_for_ner/utils.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 import logging

PaddleNLP/unarchived/sequence_tagging_for_ner/utils_extend.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import numpy as np
 
 import paddle.fluid as fluid

PaddleNLP/unarchived/text_classification/clouds/scdb_parallel_executor.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import unittest
 import contextlib
 import paddle

PaddleNLP/unarchived/text_classification/clouds/scdb_single_card.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import unittest
 import contextlib
 import paddle

PaddleNLP/unarchived/text_classification/infer.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import sys
 import time
 import unittest

PaddleNLP/unarchived/text_classification/nets.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import sys
 import time
 import numpy as np

PaddleNLP/unarchived/text_classification/train.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import os
 import six
 import sys