在Huggingbert模型顶部添加稠密层

Question

我想在输出原始隐藏状态的裸BERT模型转换器的顶部添加一个密集层，然后微调生成的模型。具体来说，我使用的是this基本模型。这就是模型应该做的：

1. 对句子进行编码（一个向量，每个句子标记包含768个元素）
2. 仅保留第一个向量（与第一个标记相关）
3. 在该向量的顶部添加一个密集层，以获得所需的变换

到目前为止，我已经成功地对以下句子进行了编码：

from sklearn.neural_network import MLPRegressor

import torch

from transformers import AutoModel, AutoTokenizer

# List of strings
sentences = [...]
# List of numbers
labels = [...]

tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
model = AutoModel.from_pretrained("dbmdz/bert-base-italian-xxl-cased")

# 2D array, one line per sentence containing the embedding of the first token
encoded_sentences = torch.stack([model(**tokenizer(s, return_tensors='pt'))[0][0][0]
                                 for s in sentences]).detach().numpy()

regr = MLPRegressor()
regr.fit(encoded_sentences, labels)

通过这种方式，我可以通过输入编码的句子来训练神经网络。然而，这种方法显然不能微调基本的伯特模型。有人能帮我吗？我如何构建一个可以完全微调的模型（可能在pytorch中或使用Huggingface库）

Answer 1

有两种方法：由于您希望为类似于分类的下游任务微调模型，因此可以直接使用：

BertForSequenceClassification类。在768的输出维度上执行逻辑回归层的微调

或者，您可以定义一个自定义模块，该模块基于预先训练的权重创建一个bert模型，并在其上添加层

from transformers import BertModel
class CustomBERTModel(nn.Module):
    def __init__(self):
          super(CustomBERTModel, self).__init__()
          self.bert = BertModel.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
          ### New layers:
          self.linear1 = nn.Linear(768, 256)
          self.linear2 = nn.Linear(256, 3) ## 3 is the number of classes in this example

    def forward(self, ids, mask):
          sequence_output, pooled_output = self.bert(
               ids, 
               attention_mask=mask)

          # sequence_output has the following shape: (batch_size, sequence_length, 768)
          linear1_output = self.linear1(sequence_output[:,0,:].view(-1,768)) ## extract the 1st token's embeddings

          linear2_output = self.linear2(linear2_output)

          return linear2_output

tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
model = CustomBERTModel() # You can pass the parameters if required to have more flexible model
model.to(torch.device("cpu")) ## can be gpu
criterion = nn.CrossEntropyLoss() ## If required define your own criterion
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()))

for epoch in epochs:
    for batch in data_loader: ## If you have a DataLoader()  object to get the data.

        data = batch[0]
        targets = batch[1] ## assuming that data loader returns a tuple of data and its targets
        
        optimizer.zero_grad()   
        encoding = tokenizer.batch_encode_plus(data, return_tensors='pt', padding=True, truncation=True,max_length=50, add_special_tokens = True)
        outputs = model(input_ids, attention_mask=attention_mask)
        outputs = F.log_softmax(outputs, dim=1)
        input_ids = encoding['input_ids']
        attention_mask = encoding['attention_mask']
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()