基于torch框架的bert+bilstm+crf的实体识别实战_# 加载bert模型和tokenizer

首先，我们需要导入所需的库：

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

然后定义一些超参数和模型结构：

# 超参数
MAX_LEN = 128
BATCH_SIZE = 32
EPOCHS = 10
LEARNING_RATE = 0.001
 
# 加载BERT模型和tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
bert_model = BertModel.from_pretrained('bert-base-chinese')
 
class EntityModel(nn.Module):
    def __init__(self, bert_model, hidden_size, num_tags):
        super(EntityModel, self).__init__()
        self.bert = bert_model
        self.dropout = nn.Dropout(0.1)
        self.bilstm = nn.LSTM(bidirectional=True, input_size=hidden_size, hidden_size=hidden_size // 2, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_tags)
        self.crf = CRF(num_tags)
 
    def forward(self, input_ids, attention_mask, labels=None):
        outputs = self.bert(input_ids, attention_mask=attention_mask)
        sequence_output = outputs[0]
        sequence_output = self.dropout(sequence_output)
        lstm_output, _ = self.bilstm(sequence_output)
        logits = self.fc(lstm_output)
        if labels is not None:
            loss = -self.crf(logits, labels, mask=attention_mask.byte())
            return loss
        else:
            tags = self.crf.decode(logits, mask=attention_mask.byte())
            return tags

在这里，我们使用了BERT模型和BiLSTM层来提取句子的特征，然后通过全连接层将其映射到标签空间，并使用CRF层来对标签序列进行建模。

接下来，我们需要定义一些辅助函数：

def tokenize_and_preserve_labels(text, labels):
    tokenized_text = []
    token_labels = []
    for word, label in zip(text, labels):
        tokenized_word = tokenizer.tokenize(word)
        n_subwords = len(tokenized_word)
 
        tokenized_text.extend(tokenized_word)
        token_labels.extend([label] * n_subwords)
 
    return tokenized_text, token_labels
 
def pad_sequences(sequences, max_len, padding_value=0):
    padded_sequences = torch.zeros((len(sequences), max_len)).long()
    for i, seq in enumerate(sequences):
        seq_len = len(seq)
        if seq_len <= max_len:
            padded_sequences[i, :seq_len] = torch.tensor(seq)
        else:
            padded_sequences[i, :] = torch.tensor(seq[:max_len])
    return padded_sequences
 
def train(model, optimizer, train_dataloader):
    model.train()
    total_loss = 0
    for step, batch in enumerate(train_dataloader):
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        labels = batch['labels'].to(device)
 
        loss = model(input_ids, attention_mask, labels)
        total_loss += loss.item()
 
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
 
    avg_train_loss = total_loss / len(train_dataloader)
    return avg_train_loss
 
def evaluate(model, eval_dataloader):
    model.eval()
    total_loss = 0
    with torch.no_grad():
        for step, batch in enumerate(eval_dataloader):
            input_ids = batch['input_ids'].to(device)
            attention_mask = batch['attention_mask'].to(device)
            labels = batch['labels'].to(device)
 
            loss = model(input_ids, attention_mask, labels)
            total_loss += loss.item()
 
    avg_eval_loss = total_loss / len(eval_dataloader)
    return avg_eval_loss
 
def predict(model, text):
    model.eval()
    tokenized_text = tokenizer.tokenize(text)
    tokenized_text_with_labels = [(token, 'O') for token in tokenized_text]
    input_ids = torch.tensor([tokenizer.convert_tokens_to_ids(tokenized_text)])
    attention_mask = torch.ones_like(input_ids)
 
    with torch.no_grad():
        tags = model(input_ids.to(device), attention_mask.to(device))
 
    tag_labels = [id2label[tag] for tag in tags[0]]
    return list(zip(tokenized_text, tag_labels))

在这里，我们定义了一个标记化函数，用于将原始文本和标签转换为标记化的文本和标签序列。我们还定义了一个填充函数，用于对序列进行填充，以便它们可以被批处理。然后我们定义了训练、评估和预测函数。

接下来，我们需要加载数据集并将其转换为模型所需的格式：

# 加载数据集
train_data = []
with open('train.txt', 'r', encoding='utf-8') as f:
    words = []
    labels = []
    for line in f:
        line = line.strip()
        if line == '':
            train_data.append((words, labels))
            words = []
            labels = []
        else:
            word, label = line.split()
            words.append(word)
            labels.append(label)
 
if len(words) > 0:
    train_data.append((words, labels))
 
# 将数据集转换为模型所需的格式
train_input_ids = []
train_attention_masks = []
train_labels = []
 
for words, labels in train_data:
    tokenized_text, token_labels = tokenize_and_preserve_labels(words, labels)
    input_ids = tokenizer.convert_tokens_to_ids(tokenized_text)
    attention_mask = [1] * len(input_ids)
 
    train_input_ids.append(input_ids)
    train_attention_masks.append(attention_mask)
    train_labels.append([label2id[label] for label in token_labels])
 
train_input_ids = pad_sequences(train_input_ids, MAX_LEN)
train_attention_masks = pad_sequences(train_attention_masks, MAX_LEN)
train_labels = pad_sequences(train_labels, MAX_LEN, padding_value=-1)
 
train_dataset = torch.utils.data.TensorDataset(train_input_ids, train_attention_masks, train_labels)
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
 
# 同样地，我们还需要加载验证集和测试集，并将它们转换为模型所需的格式

在这里，我们加载了一个包含训练数据的文件，并将其转换为模型所需的格式。我们使用了标记化函数和填充函数来实现这一点。

最后，我们可以使用上述辅助函数和数据集来训练、评估和测试模型：

# 训练模型
model = EntityModel(bert_model, hidden_size=768, num_tags=len(label2id))
model.to(device)
 
optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)
 
for epoch in range(EPOCHS):
    avg_train_loss = train(model, optimizer, train_dataloader)
    avg_eval_loss = evaluate(model, eval_dataloader)
    print(f'Epoch {epoch + 1}: train_loss={avg_train_loss:.4f}, eval_loss={avg_eval_loss:.4f}')
 
# 测试模型
test_sentences = ['今天是个好日子', '我喜欢中国菜', '巴黎是一座美丽的城市']
for sentence in test_sentences:
    tags = predict(model, sentence)
    print(tags)

在这里，我们使用Adam优化器和交叉熵损失函数来训练模型。然后，我们使用测试集来评估模型的性能，并使用模型来预测一些新句子中的实体。