ValueError: too many values to unpack (expected 2) or not enough values to unpack (expected 2, got 1). T5ForConditionalGeneration

🤗Transformers
1

I am trying to train T5 model binary classification. When I am trying to
define data class, I am getting

batch_size, seq_length = input_shape
1017 # required mask seq length can be calculated via length of past
1018 mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length

If I am using unsqueeze(0) for input_id and attention_mask I am getting following error

ValueError: too many values to unpack (expected 2)

if I am using squeeze(0), flatten() or none of them I am getting

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

The data class is below
…

  def __init__(self,
               df: pd.DataFrame,
               tokenizer:T5Tokenizer,
               source_max_token_length: int=1000,
               target_max_token_length: int=400):
    self.tokenizer=tokenizer
    self.df=df
    self.source_max_token_length=source_max_token_length
    self.target_max_token_length=target_max_token_length

  def __len__(self):
    return len(self.df)
  def __getitem__(self,index: int):
    data_row=self.df.iloc[index]
    source_encoding=tokenizer.batch_encode_plus(
      str(data_row['question']),
      # str(data_row['context']),
      max_length=self.source_max_token_length,
      padding='max_length',
      truncation='only_second',
      return_attention_mask=True,
      add_special_tokens=True,
      return_tensors='pt'
    )
    target_encoding=tokenizer.batch_encode_plus(
      str(data_row['answers']),
      max_length=self.target_max_token_length,
      padding='max_length',
      truncation=True,
      return_attention_mask=True,
      add_special_tokens=True,
      return_tensors='pt'
    )

    labels=target_encoding['input_ids']
    labels[labels==0]=-100
    return dict(
        question=data_row['question'],
        # context=data_row['context'],
        answer_text=data_row['answers'],
        # input_ids=np.array([source_encoding['input_ids']]),#.flatten(),np.array([d['input_ids']])
        # attention_mask=np.array([source_encoding['attention_mask']]),#.flatten(),#.flatten(),
        # labels=labels
        input_ids=source_encoding['input_ids'].unsqueeze(0),#.flatten(),#.flatten(),np.array([d['input_ids']])
        attention_mask=source_encoding['attention_mask'].unsqueeze(0),#.flatten(),#.flatten(),#.flatten(),
        labels=labels.flatten()
    )

I tried ever debugging like:

      str(data_row['question']),
      # str(data_row['context']),
      max_length=1000,
      padding='max_length',
      truncation='only_second',
      return_attention_mask=True,
      add_special_tokens=True,return_tensors='pt'
      )

The size of example is example[‘input_ids’].shape

example['input_ids'].shape
torch.Size([61, 1000])

I think I do not need unsqueeze(0) or flatten(), but none of them works

sagemaker: 2.221.0
transformers: 4.41.0
pytorch_lightning: 1.1.3