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<center><b><font color=#A52A2A size=5 >公众号:数据挖掘与机器学习笔记</font></b></center>
1.基于BiLSTM的命名实体识别
Embedding+BiLSTM+BiLSTM+Dense
from tensorflow.keras.layers import Embedding, LSTM, Bidirectional, Dense, Dropout, Masking
from tensorflow.keras.models import Sequential
def build_model():
"""
使用Sequential构建模型网络:双向LSTM
:return:
"""
model = Sequential()
model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim))
model.add(Masking(mask_value=0))
model.add(Bidirectional(LSTM(lstm_hidden_size, return_sequences=True)))
model.add(Dropout(dropout_rate))
model.add(Bidirectional(LSTM(lstm_hidden_size, return_sequences=True)))
model.add(Dropout(dropout_rate))
model.add(Dense(tag_size, activation="softmax"))
model.compile(loss=loss, optimizer=optimizer, metrics=[metrics])
model.summary()
return model
if __name__ == '__main__':
model = build_model()
train_x = np.array(padding_sequences_ids)
train_y = np.array(padding_tags_ids)
# model.fit(x=train_x, y=train_y, epochs=1, batch_size=batch_size, validation_split=0.2)
2. 基于BiLSTM-CRF的命名实体识别
Embedding+BiLSTM+BiLSTM+CRF
将Dense换成CRF层
from tensorflow.keras.layers import Embedding, Bidirectional, LSTM, Dense, Dropout, Input, Masking
from tensorflow.keras.models import Model
from crf import CRF
def build_model():
"""
使用Sequential构建模型网络:双向LSTM+CRF
:return:
"""
inputs = Input(shape=(None,), dtype='int32')
output = Embedding(vocab_size, embedding_dim, trainable=True)(inputs)
output = Masking(mask_value=0)(output)
output = Bidirectional(LSTM(lstm_hidden_size, return_sequences=True))(output)
output = Dropout(dropout_rate)(output)
output = Bidirectional(LSTM(lstm_hidden_size, return_sequences=True))(output)
output = Dropout(dropout_rate)(output)
output = Dense(tag_size, activation=None)(output)
crf = CRF(dtype="float32")
output = crf(output)
model = Model(inputs, output)
model.compile(loss=crf.loss, optimizer=optimizer, metrics=[crf.accuracy])
model.summary()
return model
if __name__ == '__main__':
model = build_model()
train_x = np.array(padding_sequences_ids)
train_y = np.array(padding_tags_ids)
print(train_x.shape, train_y.shape)
model.fit(x=train_x, y=train_y, epochs=1, batch_size=batch_size, validation_split=0.2)
3. 基于BiLSTM-Attention的命名实体识别
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Embedding, Bidirectional, LSTM, Dense, Input, Attention
def build_model():
"""
使用Sequential构建模型网络:双向LSTM+self-attention
:return:
"""
query_input = Input(shape=(None,), dtype="int32")
token_embedding = Embedding(input_dim=vocab_size, output_dim=embedding_dim)
query_embeddings = token_embedding(query_input)
value_embedding = token_embedding(query_input)
bilstm = Bidirectional(LSTM(lstm_hidden_size, return_sequences=True))
query_seq_encoding = bilstm(query_embeddings)
value_seq_encoding = bilstm(value_embedding)
attention = Attention()([query_seq_encoding, value_seq_encoding])
output = Dense(tag_size, activation="softmax")(attention)
model = Model(query_input, output)
model.compile(loss=loss, optimizer=optimizer, metrics=[metrics])
model.summary()
return model
if __name__ == '__main__':
model = build_model()
train_x = np.array(padding_sequences_ids)
train_y = np.array(padding_tags_ids)
print(train_x.shape, train_y.shape)
model.fit(x=train_x, y=train_y, epochs=1, batch_size=batch_size, validation_split=0.2)
4. 基于Bert-BiLSTM-CRF的命名实体识别
这列使用的是Albert,也可以换成Bert或者其它的
import os
os.environ['TF_KERAS'] = '1' # 必须放在前面,才能使用tf.keras
from bert4keras.models import build_transformer_model
from tensorflow.keras.layers import Dense, Bidirectional, LSTM, Dropout
from tensorflow.keras.models import Model
from crf import CRF
def build_model(use_bilstm=True, use_crf=True):
albert = build_transformer_model(config_path, checkpoint_path, model='albert', return_keras_model=False) # 建立模型,加载权重
output = albert.model.output
if use_bilstm:
output = Bidirectional(LSTM(lstm_hidden_size, return_sequences=True))(output)
output = Dropout(dropout_rate)(output)
if use_crf:
activation = None
else:
activation = "softmax"
output = Dense(tag_size, activation=activation, kernel_initializer=albert.initializer)(output)
if use_crf:
crf = CRF(dtype="float32")
output = crf(output)
model = Model(albert.model.inputs, output)
model.compile(optimizer=optimizer, loss=crf.loss, metrics=[crf.accuracy])
model.summary()
return model
if __name__ == '__main__':
model = build_model()
train_x1 = np.array(bert_sequence_ids)
train_x2 = np.array(bert_datatype_ids)
train_y = np.array(bert_label_ids)
print(train_x1.shape,train_x2.shape, train_y.shape)
# model.fit(x=[train_x1, train_x2], y=train_y, epochs=10, batch_size=8, validation_split=0.2)
代码:https://github.com/chongzicbo/KG_Tutorial/tree/main/ner