使用bert-bilstm进行实体抽取的代码

这里是一个使用BERT-BiLSTM-CRF模型进行中文实体抽取的代码示例：

import tensorflow as tf
from transformers import BertTokenizer
from tensorflow.keras.layers import Input, Embedding, Bidirectional, LSTM, TimeDistributed, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.utils import to_categorical
from seqeval.metrics import f1_score

# 加载BERT tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')

# 定义模型输入
input_ids = Input(shape=(None,), dtype='int32')
input_mask = Input(shape=(None,), dtype='int32')
segment_ids = Input(shape=(None,), dtype='int32')

# 加载BERT模型
bert_model = TFBertModel.from_pretrained('bert-base-chinese')
bert_output = bert_model(input_ids, attention_mask=input_mask, token_type_ids=segment_ids)[0]

# BiLSTM层
lstm = Bidirectional(LSTM(units=128, return_sequences=True))(bert_output)

# 全连接层
dense = TimeDistributed(Dense(units=128, activation='relu'))(lstm)

# CRF层
crf = CRF(num_labels + 1)
output = crf(dense)

# 定义模型输入和输出
model = Model(inputs=[input_ids, input_mask, segment_ids], outputs=[output])
model.summary()

# 编译模型
optimizer = Adam(lr=1e-4)
model.compile(optimizer=optimizer, loss=crf.loss_function, metrics=[crf.accuracy])

# 训练模型
checkpoint = ModelCheckpoint('model.h5', monitor='val_loss', save_best_only=True)
history = model.fit([train_input_ids, train_input_mask, train_segment_ids], to_categorical(train_labels, num_classes=num_labels + 1),
                    validation_data=([val_input_ids, val_input_mask, val_segment_ids], to_categorical(val_labels, num_classes=num_labels + 1)),
                    batch_size=32, epochs=10, callbacks=[checkpoint])

# 预测测试集
test_pred = model.predict([test_input_ids, test_input_mask, test_segment_ids])
test_pred = np.argmax(test_pred, axis=-1)
test_labels = np.argmax(to_categorical(test_labels, num_classes=num_labels + 1), axis=-1)

# 计算F1分数
print(f1_score(test_labels, test_pred))

在这个示例中，我们使用了BERT模型和BiLSTM-CRF模型来进行中文实体抽取。我们首先使用BERT tokenizer对中文文本进行分词，然后将分词结果输入到BERT模型中，获取BERT的输出。接着，我们将BERT的输出输入到一个BiLSTM层中，再将BiLSTM的输出输入到一个全连接层中，最后使用CRF层来进行标签预测。我们使用seqeval库中的f1_score函数来计算模型的F1分数。