这种错误通常是由于目标大小与模型的输出不匹配导致的。可以在处理标签时对其进行one-hot编码，以确保匹配模型的输出大小。下面是对标签进行one-hot编码以解决此问题的示例代码：

import torch
from torch.utils.data import Dataset, DataLoader
from transformers import BertModel, BertTokenizer

class MyDataset(Dataset):
    def __init__(self, texts, labels, tokenizer, max_len):
        self.texts = texts
        self.labels = labels
        self.tokenizer = tokenizer
        self.max_len = max_len
        
    def __len__(self):
        return len(self.texts)
    
    def __getitem__(self, index):
        text = self.texts[index]
        label = self.labels[index]
        
        # 对标签进行one-hot编码
        label = torch.eye(2)[label].squeeze(0)
        
        encoding = self.tokenizer.encode_plus(
            text, 
            add_special_tokens=True, 
            max_length=self.max_len,
            padding='max_length',
            truncation=True,
            return_attention_mask=True,
            return_tensors='pt'
        )
        
        return { 
            'text': text,
            'input_ids': encoding['input_ids'].flatten(),
            'attention_mask': encoding['attention_mask'].flatten(),
            'label': label
        }

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')

texts = ['This is the first sentence', 'This is the second sentence']
labels = [0, 1]

dataset = MyDataset(texts, labels, tokenizer, max_len=10)
dataloader = DataLoader(dataset, batch_size=2)

for batch in dataloader:
    input_ids = batch['input_ids']
    attention_mask = batch['attention_mask']
    label = batch['label']
    
    outputs = model(input_ids=input_ids, attention_mask=attention_mask)
    last_hidden_state = outputs.last_hidden_state
    
    # 注：这里假设模型输出的是二分类问题的结果，因此使用sigmoid函数处理
    logits = torch.sigmoid(last_hidden_state[:, 0, :])
    
    loss_fn = torch.nn.BCELoss()
    loss = loss_fn(logits, label)
    
    print(loss)

在上面的示例中，我们将标签进行one-hot编码，并使用BCELoss作为损失函数计算总