Pytorch 在collate_批次（自身、特征）——>；ValueError：只能将一个元素张量转换为Python标量

我试图在commonsense_qa数据集上使用transformers。我想使用数据作为

问题，选择1 问题、选择2等。 对于每一个选项，我也会为每一个输入传递一个问题。对于输入标识、注意标识、解码器输入标识、解码器注意标识，我执行以下操作。 最大长度=128

数据加载器是

class NLPDataCollator(DataCollator):
    """
    Extending the existing DataCollator to work with NLP dataset batches
    """
    def collate_batch(self, features: List[Union[InputDataClass, Dict]]) -> Dict[str, torch.Tensor]:
        first = features[0]
        if isinstance(first, dict):
          # NLP data sets current works presents features as lists of dictionary
          # (one per example), so we  will adapt the collate_batch logic for that
          if "labels" in first and first["labels"] is not None:
              if first["labels"].dtype == torch.int64:
                  labels = torch.tensor([f["labels"] for f in features], dtype=torch.long)
              else:
                  labels = torch.tensor([f["labels"] for f in features], dtype=torch.float)
              batch = {"labels": labels}
          for k, v in first.items():
              if k != "labels" and v is not None and not isinstance(v, str):
                  batch[k] = torch.stack([f[k] for f in features])
          return batch
        else:
          # otherwise, revert to using the default collate_batch
          return DefaultDataCollator().collate_batch(features)

培训师是

trainer = MultitaskTrainer(
    model=model,
    args=transformers.TrainingArguments(
        output_dir="./models/",
        overwrite_output_dir=True,
        learning_rate=1e-5,
        do_train=True,
        do_eval=True,
        num_train_epochs=3,
        # Adjust batch size if this doesn't fit on the Colab GPU
        per_device_train_batch_size=8,  
        save_steps=3000,
    ),
    data_collator=NLPDataCollator(),
    train_dataset=train_dataset,
    eval_dataset = valid_dataset,

)
trainer.train()

我得到以下错误：

ValueError                                Traceback (most recent call last)

<ipython-input-49-68181c1063c6> in <module>()
     26 
     27 )
---> 28 trainer.train()

8 frames

<ipython-input-39-4359c3725689> in collate_batch(self, features)
     20           if "labels" in first and first["labels"] is not None:
     21               if first["labels"].dtype == torch.int64:
---> 22                   labels = torch.tensor([f["labels"] for f in features], dtype=torch.long)
     23               else:
     24                   labels = torch.tensor([f["labels"] for f in features], dtype=torch.float)

ValueError: only one element tensors can be converted to Python scalars

ValueError回溯（最近一次调用）
在（）
26
27 )
--->28.培训师
8帧
在collate_批次中（自身、特征）
20如果第一个和第一个[“标签”]中的“标签”不是无：
21如果第一个[“标签”].dtype==torch.int64：
--->22标签=torch.tensor（[f[“标签”]表示特征中的f]，dtype=torch.long）
23其他：
24个标签=torch.tensor（[f[“标签”]表示特征中的f]，dtype=torch.float）
ValueError：只能将一个元素张量转换为Python标量

ValueError                                Traceback (most recent call last)

<ipython-input-49-68181c1063c6> in <module>()
     26 
     27 )
---> 28 trainer.train()

8 frames

<ipython-input-39-4359c3725689> in collate_batch(self, features)
     20           if "labels" in first and first["labels"] is not None:
     21               if first["labels"].dtype == torch.int64:
---> 22                   labels = torch.tensor([f["labels"] for f in features], dtype=torch.long)
     23               else:
     24                   labels = torch.tensor([f["labels"] for f in features], dtype=torch.float)

ValueError: only one element tensors can be converted to Python scalars