BERT+CRF实现中文命名体识别的pytorch算法代码

以下是BERT+CRF实现中文命名实体识别的PyTorch算法代码，其中使用了Hugging Face的Transformers库：

import torch
import torch.nn as nn
import torch.optim as optim
from torchcrf import CRF
from transformers import BertTokenizer, BertModel


class BERTCRF(nn.Module):
    def __init__(self, num_labels):
        super(BERTCRF, self).__init__()
        self.num_labels = num_labels
        self.bert = BertModel.from_pretrained('bert-base-chinese')
        self.dropout = nn.Dropout(0.1)
        self.hidden2label = nn.Linear(768, self.num_labels)
        self.crf = CRF(self.num_labels)

    def forward(self, input_ids, attention_mask, labels=None):
        bert_output = self.bert(input_ids, attention_mask=attention_mask)
        sequence_output = bert_output.last_hidden_state
        sequence_output = self.dropout(sequence_output)
        emissions = self.hidden2label(sequence_output)
        if labels is not None:
            loss = -self.crf(emissions, labels, mask=attention_mask.byte(), reduction='token_mean')
            return loss
        else:
            return self.crf.decode(emissions, mask=attention_mask.byte())

在训练过程中，可以使用类似以下的代码对模型进行训练：

model = BERTCRF(num_labels=4)
optimizer = optim.AdamW(model.parameters(), lr=5e-5)

for epoch in range(epochs):
    model.train()
    for batch in train_dataloader:
        input_ids, attention_mask, labels = batch
        optimizer.zero_grad()
        loss = model(input_ids, attention_mask, labels)
        loss.backward()
        optimizer.step()
        
    model.eval()
    with torch.no_grad():
        for batch in dev_dataloader:
            input_ids, attention_mask, labels = batch
            loss = model(input_ids, attention_mask, labels)
            # do evaluation
            

其中，`train_dataloader`和`dev_dataloader`是训练和验证数据的`DataLoader`对象，`num_labels`表示标签数量，这里默认为4，可以根据需要进行修改。