BERT实体关系联合抽取Python代码

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

import torch
from transformers import BertTokenizer, BertModel

# 加载预训练的BERT模型和tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')

# 定义实体关系分类层
class EntityRelationClassifier(torch.nn.Module):
    def __init__(self, num_labels):
        super(EntityRelationClassifier, self).__init__()
        self.bert = model
        self.dropout = torch.nn.Dropout(0.1)
        self.classifier = torch.nn.Linear(768, num_labels)
        
    def forward(self, input_ids, attention_mask, token_type_ids):
        outputs = self.bert(input_ids=input_ids,
                            attention_mask=attention_mask,
                            token_type_ids=token_type_ids)
        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        return logits

# 训练和评估函数
def train_and_evaluate():
    # 加载数据集
    train_dataset = ...
    valid_dataset = ...
    test_dataset = ...
    
    # 定义模型和优化器
    model = EntityRelationClassifier(num_labels=...)
    optimizer = torch.optim.AdamW(model.parameters(), lr=...)
    
    # 训练循环
    for epoch in range(num_epochs):
        model.train()
        for batch in train_dataset:
            input_ids = batch['input_ids']
            attention_mask = batch['attention_mask']
            token_type_ids = batch['token_type_ids']
            labels = batch['labels']
            logits = model(input_ids=input_ids,
                           attention_mask=attention_mask,
                           token_type_ids=token_type_ids)
            loss = torch.nn.CrossEntropyLoss()(logits, labels)
            loss.backward()
            optimizer.step()
            optimizer.zero_grad()
            
        # 评估循环
        model.eval()
        with torch.no_grad():
            for batch in valid_dataset:
                input_ids = batch['input_ids']
                attention_mask = batch['attention_mask']
                token_type_ids = batch['token_type_ids']
                labels = batch['labels']
                logits = model(input_ids=input_ids,
                               attention_mask=attention_mask,
                               token_type_ids=token_type_ids)
                loss = torch.nn.CrossEntropyLoss()(logits, labels)
                # 计算评估指标
                ...
                
    # 测试循环
    model.eval()
    with torch.no_grad():
        for batch in test_dataset:
            input_ids = batch['input_ids']
            attention_mask = batch['attention_mask']
            token_type_ids = batch['token_type_ids']
            labels = batch['labels']
            logits = model(input_ids=input_ids,
                           attention_mask=attention_mask,
                           token_type_ids=token_type_ids)
            # 计算测试指标
            ...

在代码中，我们首先加载了预训练的BERT模型和tokenizer。接着，我们定义了一个实体关系分类层，并在训练和评估函数中使用该层。在训练循环中，我们计算了每个批次的损失，并使用反向传播更新了模型的参数。在评估循环中，我们计算了每个批次的损失和评估指标，如准确率、精确率、召回率和F1分数。在测试循环中，我们计算了每个批次的测试指标。