IndexError：张量的索引太多2：在huggingface模型上添加自定义层时

Question

我尝试在二进制分类任务上将自定义层添加到HuggingFace Transformer模型中。作为一个绝对的初学者，我试图跟随此教程

不幸的是，这是一个自定义模型

class CustomModel(nn.Module):
  def __init__(self,checkpoint,num_labels): 
    super(CustomModel,self).__init__() 
    self.num_labels = num_labels 

    #Load Model with given checkpoint and extract its body
    self.model =  AutoModelForSequenceClassification.from_pretrained(checkpoint,config=AutoConfig.from_pretrained(checkpoint, output_attentions=True,output_hidden_states=True))
    self.dropout = nn.Dropout(0.1) 
    self.classifier = nn.Linear(768,num_labels) # load and initialize weights

  def forward(self, input_ids=None, attention_mask=None,labels=None):
    #Extract outputs from the body
    outputs = self.model(input_ids=input_ids, attention_mask=attention_mask)

    #Add custom layers
    sequence_output = self.dropout(outputs[0]) #outputs[0]=last hidden state

    logits = self.classifier(sequence_output[:,0,:].view(-1,768)) # calculate losses
    
    loss = None
    if labels is not None:
      loss_fct = nn.CrossEntropyLoss()
      loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
    
    return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states,attentions=outputs.attentions)

，它产生了一个错误：

<ipython-input-32-5d5e07952b71> in forward(self, input_ids, attention_mask, labels)
     19     sequence_output = self.dropout(outputs[0]) #outputs[0]=last hidden state
     20 
---> 21     logits = self.classifier(sequence_output[:,0,:].view(-1,768)) # calculate losses
     22 
     23     loss = None

IndexError: too many indices for tensor of dimension 2

另外，您可以找到所有代码此处。