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我正在尝试实现 RoBERTa 模型进行情绪分析。首先，我声明了 GPReviewDataset 来创建一个 PyTorch 数据集。

MAX_LEN = 160
class GPReviewDataset(Dataset):
  def __init__(self, reviews, targets, tokenizer, max_len):
    self.reviews = reviews
    self.targets = targets
    self.tokenizer = tokenizer
    self.max_len = max_len
  def __len__(self):
    return len(self.reviews)
  def __getitem__(self, item):
    review = str(self.reviews[item])
    target = self.targets[item]
    encoding = self.tokenizer.encode_plus(
      review,
      add_special_tokens=True,
      max_length=self.max_len,
      return_token_type_ids=False,
      pad_to_max_length=True,
      return_attention_mask=True,
      return_tensors='pt',
    )
    return {
      'review_text': review,
      'input_ids': encoding['input_ids'].flatten(),
      'attention_mask': encoding['attention_mask'].flatten(),
      'targets': torch.tensor(target, dtype=torch.long)
    }

接下来，我实现create_data_loader创建几个数据加载器。这是一个辅助函数：

def create_data_loader(df, tokenizer, max_len, batch_size):
  ds = GPReviewDataset(
    reviews=df.text.to_numpy(),
    targets=df.sentiment.to_numpy(),
    tokenizer=tokenizer,
    max_len=max_len
  )
  return DataLoader(
    ds,
    batch_size=batch_size,
    num_workers=4
  )

BATCH_SIZE = 16
train_data_loader = create_data_loader(df_train, tokenizer, MAX_LEN, BATCH_SIZE)
val_data_loader = create_data_loader(df_val, tokenizer, MAX_LEN, BATCH_SIZE)
test_data_loader = create_data_loader(df_test, tokenizer, MAX_LEN, BATCH_SIZE)

dt = next(iter(train_data_loader))

但是，当我运行此代码时，它会停止并给出以下错误：

TypeError                                 Traceback (most recent call last)
<ipython-input-35-a673c0794f60> in <module>()
----> 1 dt = next(iter(train_data_loader))

3 frames
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __next__(self)
    433         if self._sampler_iter is None:
    434             self._reset()
--> 435         data = self._next_data()
    436         self._num_yielded += 1
    437         if self._dataset_kind == _DatasetKind.Iterable and \

/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in _next_data(self)
   1083             else:
   1084                 del self._task_info[idx]
-> 1085                 return self._process_data(data)
   1086 
   1087     def _try_put_index(self):

/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in _process_data(self, data)
   1109         self._try_put_index()
   1110         if isinstance(data, ExceptionWrapper):
-> 1111             data.reraise()
   1112         return data
   1113 

/usr/local/lib/python3.6/dist-packages/torch/_utils.py in reraise(self)
    426             # have message field
    427             raise self.exc_type(message=msg)
--> 428         raise self.exc_type(msg)
    429 
    430 

TypeError: Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/worker.py", line 198, in _worker_loop
    data = fetcher.fetch(index)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "<ipython-input-18-1e537ce5a428>", line 25, in __getitem__
    'targets': torch.tensor(target, dtype=torch.long)
TypeError: new(): invalid data type 'str'

我不明白为什么会这样，谁能帮我解释一下。

4

1 回答 1

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您需要将您的类定义为整数。我假设您正在处理分类问题。但看起来您已将类定义为字符串。您需要将您的类从字符串转换为整数。例如，如果 df.sentiment 对应于正数，则必须用 0 表示，或者 df.sentiment 对应于负数，则需要在新列中用 1 表示。

def to_int_sentiment(label):
  if label == "positive":
    return 0
  elif label == "negative":
    return 1

df['int_sentiment'] = df.sentiment.apply(to_int_sentiment)

然后你应该使用列 df.int_sentiment 而不是 df.sentiment。所以你必须改变 create_data_loader 函数如下。

def create_data_loader(df, tokenizer, max_len, batch_size):
  ds = GPReviewDataset(
    reviews=df.text.to_numpy(),
    targets=df.int_sentiment.to_numpy(),
    tokenizer=tokenizer,
    max_len=max_len
  )
  return DataLoader(
    ds,
    batch_size=batch_size,
    num_workers=4
  )
于 2021-03-31T13:30:44.927 回答