python - PyTorch BERT 类型错误 : forward() got an unexpected keyword argument 'labels'

Question

使用 PyTorch 转换器训练 BERT 模型(遵循教程 here)。

教程中的以下语句

loss = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask, labels=b_labels)

造成

TypeError: forward() got an unexpected keyword argument 'labels'

这是完整的错误，

TypeError                                 Traceback (most recent call last)
<ipython-input-53-56aa2f57dcaf> in <module>
     26         optimizer.zero_grad()
     27         # Forward pass
---> 28         loss = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask, labels=b_labels)
     29         train_loss_set.append(loss.item())
     30         # Backward pass

~/anaconda3/envs/systreviewclassifi/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    539             result = self._slow_forward(*input, **kwargs)
    540         else:
--> 541             result = self.forward(*input, **kwargs)
    542         for hook in self._forward_hooks.values():
    543             hook_result = hook(self, input, result)

TypeError: forward() got an unexpected keyword argument 'labels'

我似乎无法弄清楚 forward() 函数期望什么样的参数。

有类似问题 here ，但我仍然不明白解决方案是什么。

系统信息:

操作系统:Ubuntu 16.04 LTS

Python 版本:3.6.x

火炬版本:1.3.0

Torch Vision 版本:0.4.1

PyTorch 转换器版本:1.2.0

Answer 1

据我所知，BertModel 在 forward() 中不带标签。功能。查看 forward函数参数。

我怀疑您正在尝试为序列分类任务微调 BertModel，而 API 为该任务提供了一个类 BertForSequenceClassification .如您所见，其 forward() 函数定义:

def forward(self, input_ids, attention_mask=None, token_type_ids=None,
            position_ids=None, head_mask=None, labels=None):

请注意，forward() 方法返回以下内容。

Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
        **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
            Classification (or regression if config.num_labels==1) loss.
        **logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
            Classification (or regression if config.num_labels==1) scores (before SoftMax).
        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
            of shape ``(batch_size, sequence_length, hidden_size)``:
            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. 

希望这可以帮助!