-
Notifications
You must be signed in to change notification settings - Fork 18
/
classification_ifytek_auxiliary_seq2seq_task.py
183 lines (148 loc) · 6.9 KB
/
classification_ifytek_auxiliary_seq2seq_task.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
# -*- coding: utf-8 -*-
# @Date : 2020/9/11
# @Author : mingming.xu
# @Email : xv44586@gmail.com
# @File : classification_ifytek_auxiliary_seq2seq_task.py
"""
### 通过构造一个附加的任务来增强模型的分类性能
借鉴UniLM增加一个自回归任务：将label信息作为需要预测的序列，增加一个seq2seq的任务，尝试增强文本分类的性能
best val acc:59.91
"""
import json
from tqdm import tqdm
from toolkit4nlp.backend import keras, K
from toolkit4nlp.tokenizers import Tokenizer, load_vocab
from toolkit4nlp.models import build_transformer_model, Model
from toolkit4nlp.optimizers import Adam
from toolkit4nlp.utils import pad_sequences, DataGenerator
from toolkit4nlp.layers import *
num_classes = 119
maxlen = 128
batch_size = 32
max_label = 7
# BERT base
config_path = '/home/mingming.xu/pretrain/NLP/chinese_L-12_H-768_A-12/bert_config.json'
checkpoint_path = '/home/mingming.xu/pretrain/NLP/chinese_L-12_H-768_A-12/bert_model.ckpt'
dict_path = '/home/mingming.xu/pretrain/NLP/chinese_L-12_H-768_A-12/vocab.txt'
def load_data(filename):
D = []
with open(filename) as f:
for i, l in enumerate(f):
l = json.loads(l)
text, label, label_des = l['sentence'], l['label'], l['label_des']
D.append((text, int(label), label_des))
return D
# 加载数据集
train_data = load_data(
'/home/mingming.xu/datasets/NLP/CLUE/iflytek/train.json'
)
valid_data = load_data(
'/home/mingming.xu/datasets/NLP/CLUE/iflytek/dev.json'
)
# 加载并精简词表，建立分词器
token_dict, keep_tokens = load_vocab(
vocab_path=dict_path,
simplified=True,
startswith=['[PAD]', '[UNK]', '[CLS]', '[SEP]'],
)
tokenizer = Tokenizer(token_dict, do_lower_case=True)
class data_generator(DataGenerator):
"""数据生成器
"""
def __init__(self, seq2seq=False, **kwargs):
super(data_generator, self).__init__(**kwargs)
self.seq2seq = seq2seq
def __iter__(self, shuffle=False):
batch_token_ids, batch_segment_ids, batch_labels = [], [], []
for is_end, (text, label, label_des) in self.get_sample(shuffle):
if not self.seq2seq:
token_ids, segment_ids = tokenizer.encode(text, maxlen=maxlen)
else:
text_token_ids = tokenizer.encode(text, maxlen=maxlen)[0]
label_token_ids = tokenizer.encode(label_des, maxlen=max_label + 2)[0][1:]
token_ids = text_token_ids + label_token_ids
segment_ids = [0] * len(text_token_ids) + [1] * len(label_token_ids)
batch_token_ids.append(token_ids)
batch_segment_ids.append(segment_ids)
batch_labels.append([label])
if len(batch_token_ids) == self.batch_size or is_end:
batch_token_ids = pad_sequences(batch_token_ids)
batch_segment_ids = pad_sequences(batch_segment_ids)
batch_labels = pad_sequences(batch_labels)
# if self.seq2seq:
# yield [batch_token_ids, batch_segment_ids], None
# else:
yield [batch_token_ids, batch_segment_ids], batch_labels
batch_token_ids, batch_segment_ids, batch_labels = [], [], []
# 转换数据集
train_generator = data_generator(data=train_data, batch_size=batch_size, seq2seq=True)
valid_generator = data_generator(data=valid_data, batch_size=batch_size)
class TotalLoss(Loss):
"计算两部分loss：seq2seq的交叉熵，probs与true label的交叉熵"
def compute_loss(self, inputs, mask=None):
seq2seq_loss = self.compute_loss_of_seq2seq(inputs, mask)
# classification_loss = self.compute_loss_of_classification(inputs, mask)
self.add_metric(seq2seq_loss, name='seq2seq_loss')
# self.add_metric(classification_loss, name='classification_loss')
# acc = self.compute_classification_acc(inputs, mask)
# self.add_metric(acc, name='acc')
return seq2seq_loss
def compute_loss_of_seq2seq(self, inputs, mask=None):
y_true, y_mask, _, y_pred = inputs
y_true = y_true[:, 1:] # 目标token
y_pred = y_pred[:, :-1] # 错开一位
y_mask = y_mask[:, 1:] # 利用segment_ids mask掉第一个segment
loss = K.sparse_categorical_crossentropy(y_true, y_pred)
loss = K.sum(loss * y_mask) / K.sum(y_mask)
return loss * 0.2
def compute_loss_of_classification(self, inputs, mask=None):
_, _, y_pred, _, y_true = inputs
return K.sparse_categorical_crossentropy(y_true, y_pred)
def compute_classification_acc(self, inputs, mask=None):
_, _, y_pred, _, y_true = inputs
equal = K.equal(K.cast(K.argmax(y_pred, axis=-1), 'int32'), K.cast(y_true, 'int32'))
return K.cast(equal, K.floatx()) / K.cast(K.shape(y_true)[0], K.floatx())
bert = build_transformer_model(checkpoint_path=checkpoint_path,
config_path=config_path,
with_pool='linear',
application='unilm',
keep_tokens=keep_tokens,
return_keras_model=False)
label_inputs = Input(shape=(None,), name='label_inputs')
pooler = bert.model.outputs[0]
classification_output = Dense(units=num_classes, activation='softmax', name='classifier')(pooler)
classifier = Model(bert.model.inputs, classification_output)
seq2seq = Model(bert.model.inputs, bert.model.outputs[1])
outputs = TotalLoss([2])(bert.model.inputs + bert.model.outputs)
# outputs = Dense(num_classes, activation='softmax')(outputs)
train_model = Model(bert.model.inputs, [classification_output, outputs])
train_model.compile(loss=['sparse_categorical_crossentropy', None], optimizer=Adam(1e-5), metrics=['acc'])
train_model.summary()
def evaluate(val_data=valid_generator):
total = 0.
right = 0.
for x, y_true in tqdm(val_data):
y_pred = classifier.predict(x).argmax(axis=-1)
y_true = y_true[:, 0]
total += len(y_true)
right += (y_true == y_pred).sum()
print(total, right)
return right / total
class Evaluator(keras.callbacks.Callback):
def __init__(self, save_path='best_model.weights'):
self.best_val_acc = 0.
self.save_path = save_path
def on_epoch_end(self, epoch, logs=None):
val_acc = evaluate()
if val_acc > self.best_val_acc:
self.best_val_acc = val_acc
self.model.save_weights(self.save_path)
print('current acc :{}, best val acc: {}'.format(val_acc, self.best_val_acc))
if __name__ == '__main__':
evaluator = Evaluator()
train_model.fit_generator(train_generator.generator(),
steps_per_epoch=len(train_generator),
epochs=5,
callbacks=[evaluator])
else:
classifier.load_weights('best_model.weights')