tensorflow框架下BERT实体关系联合抽取Python代码，bert4keras

以下是使用bert4keras实现BERT实体关系联合抽取的Python代码示例：

import json
import numpy as np
from bert4keras.backend import keras, K
from bert4keras.layers import Loss
from bert4keras.models import build_transformer_model
from bert4keras.optimizers import Adam
from bert4keras.tokenizers import Tokenizer
from keras.layers import Dense
from keras.models import Model

# 模型参数
maxlen = 128
epochs = 10
batch_size = 16
learning_rate = 2e-5
categories = ["疾病和诊断", "影像检查", "实验室检验", "药物"]
num_classes = len(categories)

# BERT配置
config_path = '/path/to/bert_config.json'
checkpoint_path = '/path/to/bert_model.ckpt'
dict_path = '/path/to/vocab.txt'

# 加载数据
def load_data(filename):
    D = []
    with open(filename, encoding='utf-8') as f:
        for l in f:
            l = json.loads(l)
            d = {'text': l['text'], 'spo_list': []}
            for spo in l['spo_list']:
                for o in spo['object']:
                    d['spo_list'].append((spo['subject'], spo['predicate'], o))
            D.append(d)
    return D

# 加载数据集
train_data = load_data('/path/to/train_data.json')
valid_data = load_data('/path/to/valid_data.json')
test_data = load_data('/path/to/test_data.json')

# 建立分词器
tokenizer = Tokenizer(dict_path, do_lower_case=True)

class data_generator:
    """数据生成器
    """
    def __init__(self, data, batch_size=32, shuffle=True):
        self.data = data
        self.batch_size = batch_size
        self.shuffle = shuffle
        self.steps = len(self.data) // self.batch_size
        if len(self.data) % self.batch_size != 0:
            self.steps += 1

    def __len__(self):
        return self.steps

    def __iter__(self):
        while True:
            idxs = list(range(len(self.data)))
            if self.shuffle:
                np.random.shuffle(idxs)
            X1, X2, S, Y = [], [], [], []
            for i in idxs:
                d = self.data[i]
                text = d['text'][:maxlen]
                x1, x2 = tokenizer.encode(text)
                s = np.zeros(len(text))
                for spo in d['spo_list']:
                    subject = spo[0][:maxlen]
                    object = spo[2][:maxlen]
                    start = text.find(subject)
                    if start != -1:
                        end = start + len(subject) - 1
                        s[start:end+1] = 1
                        # 构建标注数据
                        predicate = spo[1]
                        y = np.zeros(num_classes)
                        y[categories.index(predicate)] = 1
                        X1.append(x1)
                        X2.append(x2)
                        S.append(s)
                        Y.append(y)
            if len(X1) == 0:
                continue
            X1 = keras.preprocessing.sequence.pad_sequences(X1, maxlen=maxlen)
            X2 = keras.preprocessing.sequence.pad_sequences(X2, maxlen=maxlen)
            S = keras.preprocessing.sequence.pad_sequences(S, maxlen=maxlen)
            Y = np.array(Y)
            yield [X1, X2, S], Y

# 构建模型
bert_model = build_transformer_model(
    config_path,
    checkpoint_path,
    model='bert',
    return_keras_model=False,
)

output_layer = 'Transformer-%s-FeedForward-Norm' % (bert_model.num_hidden_layers - 1)
output = bert_model.get_layer(output_layer).output
output = Dense(num_classes, activation='sigmoid')(output)
model = Model(bert_model.input, output)
model.summary()

# 损失函数
class MultiLoss(Loss):
    """多任务学习的损失函数
    """
    def compute_loss(self, inputs, mask=None):
        y_true, y_pred = inputs
        y_true = K.cast(y_true, y_pred.dtype)
        loss = K.binary_crossentropy(y_true, y_pred)
        return loss

loss = MultiLoss().compute_loss

# 优化器
optimizer = Adam(learning_rate)

# 编译模型
model.compile(loss=loss, optimizer=optimizer)

# 训练模型
train_generator = data_generator(train_data, batch_size)
valid_generator = data_generator(valid_data, batch_size)
test_generator = data_generator(test_data, batch_size)
model.fit_generator(
    train_generator.forfit(),
    steps_per_epoch=len(train_generator),
    epochs=epochs,
    validation_data=valid_generator.forfit(),
    validation_steps=len(valid_generator)
)

# 评估模型
model.evaluate_generator(test_generator.forfit(), steps=len(test_generator))

# 保存模型
model.save_weights('/path/to/model.weights')