PyTorch BERT TypeError: forward() got an

Question

使用 PyTorch 转换器训练 BERT 模型（按照此处的教程进行操作）。教程中的以下声明loss = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask, labels=b_labels)导致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。这里有一个类似的问题，但我仍然不明白解决方案是什么。

Answer 1

据我所知，BertModel 在函数中不带标签forward()。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，并且 API 为BertForSequenceClassification提供了一个类。如您所见，它的 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！