给我一段bert微调阶段的代码示例

下面是一个使用Hugging Face的Transformers库进行BERT微调的代码示例：

import torch
from transformers import BertTokenizer, BertForSequenceClassification
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from transformers import AdamW

# 加载预训练的BERT模型和tokenizer
model_name = 'bert-base-uncased'
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)

# 准备数据集
train_texts = ['Example sentence 1', 'Example sentence 2', ...]
train_labels = [0, 1, ...]

# 对文本进行分词和编码
train_encodings = tokenizer(train_texts, truncation=True, padding=True)

# 创建数据集对象
class CustomDataset(torch.utils.data.Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels
        
    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        item['labels'] = torch.tensor(self.labels[idx])
        return item
    
    def __len__(self):
        return len(self.labels)

train_dataset = CustomDataset(train_encodings, train_labels)

# 定义训练参数
batch_size = 16
epochs = 3
learning_rate = 2e-5

# 创建数据加载器
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=batch_size)

# 定义优化器和损失函数
optimizer = AdamW(model.parameters(), lr=learning_rate)
loss_fn = torch.nn.CrossEntropyLoss()

# 开始微调
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model.to(device)
model.train()

for epoch in range(epochs):
    total_loss = 0
    
    for batch in train_dataloader:
        optimizer.zero_grad()
        
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        labels = batch['labels'].to(device)
        
        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
        loss = outputs.loss
        total_loss += loss.item()
        
        loss.backward()
        optimizer.step()
    
    average_loss = total_loss / len(train_dataloader)
    print(f'Epoch {epoch + 1}: Average Loss = {average_loss}')

# 保存微调后的模型
model.save_pretrained('path/to/save/model')
tokenizer.save_pretrained('path/to/save/tokenizer')

请注意，此代码仅为示例，实际使用时可能需要根据您的数据集和任务进行适当的调整和修改。另外，此示例仅展示了训练过程，如果需要在验证集或测试集上进行评估，请自行添加相应的代码。