在Huggingface BERT模型顶部添加密集层

问题描述

我想在输出原始隐藏状态的裸BERT模型转换器之上添加一个密集层，然后微调生成的模型。具体来说，我正在使用this基本模型。这就是模型应该做的：

对句子进行编码（该句子的每个标记含768个元素的向量）
仅保留第一个向量（与第一个令牌相关）
在此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，我可以通过向神经网络提供编码后的句子来训练它。但是，这种方法显然不能对基本BERT模型进行微调。有谁能够帮助我？如何建立可以完全微调的模型（可能是在pytorch或使用Huggingface库）？

解决方法

有两种方法可以执行此操作：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的输出维度上对逻辑回归层进行微调。

或者，您可以定义一个自定义模块，该模块基于预训练的权重创建bert模型，并在其之上添加层。

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[:,:].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()

如果要调整BERT模型本身，则需要修改模型的参数。为此，您很可能希望使用PyTorch。这是一些粗略的伪代码来说明：

from torch.optim import SGD

model = ... # whatever model you are using
parameters = model.parameters() # or some more specific set of parameters
optimizer = SGD(parameters,lr=.01) # or whatever optimizer you want
optimizer.zero_grad() # boiler-platy pytorch function

input = ... # whatever the appropriate input for your task is
label = ... # whatever the appropriate label for your task is
loss = model(**input,label) # usuall loss is the first item returned
loss.backward() # calculates gradient
optim.step() # runs optimization algorithm

我遗漏了所有相关细节，因为它们非常繁琐且特定于您的特定任务。 Huggingface有一篇不错的文章，详细介绍了here，在使用任何pytorch内容时，您一定会参考一些pytorch文档。我强烈建议您pytorch blitz，然后再尝试对其进行任何认真的处理。

huggingface-transformers neural-network python python-3.x pytorch