Error ValueError: too many values to unpack (expected 2) in model training

[felipenandz]

Hello,

I’m getting a persistente error while trying to train my model LayoutLM3.

Let me describe

I already encoded my dataset with this method here

def process(list):

for data in list:
  labels = data["labels"]
  bboxes = data["bboxes"]
  tokens = data["tokens"]
  image = Image.open(datasetRootPath + data["file_name"]).convert("RGB")

  encoding = processor(
      image,
      text=tokens,
      boxes=bboxes,
      word_labels=labels,
      padding="max_length",  
      truncation=True,       
      return_tensors="pt"    
  )
  
  yield encoding

train_dataset = list(process(dataset))

[{'input_ids': tensor([[    0,   316, 17442,  2064,   337,  3938,  5553,  6560,  4006,  6623,
          1696,  3842,  6304,  1168,  1322, 11903, 14502,   274,  3721,  3808,
           717,  8748,  8041, 22556,   673,   274, 29092,  5945,  1723,     6,
           654,   501,   910,   321,  2831, 35033,  2663,  5479,   541,  5339,
             2,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1,     1,     1,     1,     1,     1,     1,     1,     1,
             1,     1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
         0, 0, 0, 0, 0, 0, 0, 0]]), 'bbox': tensor([[[   0.0000,    0.0000,    0.0000,    0.0000],
         [ 110.0006, 1385.4795,  182.0733, 1438.7952],
         [  79.8919,  486.5594,  361.0753,  508.1014],
         ...,
         [   0.0000,    0.0000,    0.0000,    0.0000],
         [   0.0000,    0.0000,    0.0000,    0.0000],
         [   0.0000,    0.0000,    0.0000,    0.0000]]]), 'labels': tensor([[-100,    5,    0,    0, -100, -100,    0,    0, -100,    0, -100,    0,
            4, -100, -100,    1, -100,    1, -100, -100, -100,    1, -100, -100,
         -100,    1, -100, -100, -100, -100,    3,    3,    3,    2, -100, -100,
         -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
         -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
         -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
         -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
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         -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
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         -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
         -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
         -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
         -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
         -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
         -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
         -100, -100, -100, -100, -100, -100, -100, -100]]), 'pixel_values': tensor([[[[1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000],
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000],
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000],
          ...,
          [0.9373, 0.9373, 0.9373,  ..., 0.9529, 0.9529, 0.9608],
          [0.9137, 0.9137, 0.9137,  ..., 0.9373, 0.9373, 0.9451],
          [0.8980, 0.8980, 0.8980,  ..., 0.9216, 0.9294, 0.9294]],

         [[1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000],
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000],
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000],
          ...,
          [0.9373, 0.9373, 0.9373,  ..., 0.9529, 0.9529, 0.9608],
          [0.9137, 0.9137, 0.9137,  ..., 0.9373, 0.9373, 0.9451],
          [0.8980, 0.8980, 0.8980,  ..., 0.9216, 0.9294, 0.9294]],

         [[1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000],
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000],
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000],
          ...,
          [0.9373, 0.9373, 0.9373,  ..., 0.9529, 0.9529, 0.9608],
          [0.9137, 0.9137, 0.9137,  ..., 0.9373, 0.9373, 0.9451],
          [0.8980, 0.8980, 0.8980,  ..., 0.9216, 0.9294, 0.9294]]]])}]

but when I’m passing the train_dataset to the trainer I’m facing the following error

forward(self, input_ids, bbox, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, pixel_values, output_attentions, output_hidden_states, return_dict)
    885         if input_ids is not None:
    886             input_shape = input_ids.size()
--> 887             batch_size, seq_length = input_shape
    888             device = input_ids.device
    889         elif inputs_embeds is not None:

ValueError: too many values to unpack (expected 2)

Any clue about it ?

[John6666]

Maybe this?

Batch_size, seq_length = input_shape ValueError: too many values to unpack (expected 2) Transformer Sentence Similarity Classification

I encountering a problem when doing Sentence Similarity Classification Task Using Transformer. It happens when I was about to train my model, I load and process my dataset in this way, and this is what the training dataset element shape looks like: [image] but when I train the model, it says: ValueError: Caught ValueError in replica 0 on device 0. Original Traceback (most recent call last): File “/home/fanshaoqi/anaconda3/envs/tf2/lib/python3.10/site-packages/torch/nn/parallel/parallel_ap…