I got the problem ValueError: not enough values to unpack (expected 2, got 1)

Question

I used a pretrained Roberta model,and this is my roberta model.the pretrained model is https://huggingface.co/rinna/japanese-roberta-base And I install the sentencepiece.

!pip install sentencepiece

class RoBERTaClass(torch.nn.Module):
  def __init__(self, pretrained, drop_rate, otuput_size):
    super().__init__()
    self.roberta = RobertaForMaskedLM.from_pretrained(pretrained)
    self.drop = torch.nn.Dropout(drop_rate)
    self.fc = torch.nn.Linear(768, otuput_size)
  
  def forward(self, ids, mask):
    _, out = self.roberta(ids, attention_mask=mask,return_dict=False)
    out = self.fc(self.drop(out))
    return out

/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2269: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).
  FutureWarning,
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-20-49ec9d027d5c> in <module>()
     12 
     13 # モデルの学習
---> 14 log = train_model(dataset_train, dataset_valid, BATCH_SIZE, model, criterion, optimizer, NUM_EPOCHS, device=device)
     15 
     16 # 正解率の算出

2 frames
<ipython-input-16-7edee8da91ec> in forward(self, ids, mask)
      7 
      8   def forward(self, ids, mask):
----> 9     _, out = self.roberta(ids, attention_mask=mask,return_dict=False)
     10     out = self.fc(self.drop(out))
     11     return out

ValueError: not enough values to unpack (expected 2, got 1)

what should i do? i dont know where should i fix.

and i fix the model another problem i have.

class RoBERTaClass(torch.nn.Module):
  def __init__(self, pretrained, drop_rate, otuput_size):
    super().__init__()
    self.roberta = RobertaForMaskedLM.from_pretrained(pretrained)
    self.drop = torch.nn.Dropout(drop_rate)
    self.fc = torch.nn.Linear(768, otuput_size)  # ROBERTAの出力に合わせて768次元を指定
  
  def forward(self, ids, mask):
    out = self.roberta(ids, attention_mask=mask,return_dict=False)
    out = self.fc(self.drop(out))
    return out

TypeError                                 Traceback (most recent call last)
<ipython-input-24-49ec9d027d5c> in <module>()
     12 
     13 # モデルの学習
---> 14 log = train_model(dataset_train, dataset_valid, BATCH_SIZE, model, criterion, optimizer, NUM_EPOCHS, device=device)
     15 
     16 # 正解率の算出

5 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in dropout(input, p, training, inplace)
   1277     if p < 0.0 or p > 1.0:
   1278         raise ValueError("dropout probability has to be between 0 and 1, " "but got {}".format(p))
-> 1279     return _VF.dropout_(input, p, training) if inplace else _VF.dropout(input, p, training)
   1280 
   1281 

TypeError: dropout(): argument 'input' (position 1) must be Tensor, not tuple

and this is my creatdataset class.Where do i need to fix.thank you for answer.

class CreateDataset(Dataset):
  def __init__(self, X, y, tokenizer, max_len):
    self.X = X
    self.y = y
    self.tokenizer = tokenizer
    self.max_len = max_len

  def __len__(self):  # len(Dataset)で返す値を指定
    return len(self.y)

  def __getitem__(self, index):  # Dataset[index]で返す値を指定
    text = self.X[index]
    inputs = self.tokenizer.encode_plus(
      text,
      add_special_tokens=True,
      max_length=self.max_len,
      pad_to_max_length=True
    )
    ids = inputs['input_ids']
    mask = inputs['attention_mask']

    return {
      'ids': torch.LongTensor(ids),
      'mask': torch.LongTensor(mask),
      'labels': torch.Tensor(self.y[index])
    }