RuntimeError: The size of tensor a (4000) must match the size of tensor b (512) at non-singleton dimension 1

RuntimeError: The size of tensor a (4000) must match the size of tensor b (512) at non-singleton dimension 1 error occurs when you “exceed the maximum input length limitation, usually 512 tokens.”

To fix the RuntimeError: The size of tensor a (4000) must match the size of tensor b (512) at non-singleton dimension 1 error; you need to truncate, split or use a different strategy to process your text.

Solution 1: Truncate your input text

You can truncate your input text to fit within the model’s maximum sequence length. However, remember that this might result in losing some information from your text.

from transformers import BertTokenizer, BertForSequenceClassification

tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
input_text = "your_long_text_here"
tokens = tokenizer.tokenize(input_text)
truncated_tokens = tokens[:510]

Solution 2: Splitting the input text

You can split your input text into smaller chunks and process each individually. Then, you can aggregate the results or use the most relevant chunk based on your specific use case.

def split_text_to_chunks(text, chunk_size):
  tokens = tokenizer.tokenize(text)
  chunks = [tokens[i:i + chunk_size] for i in range(0, len(tokens), chunk_size)]
  return chunks

chunks = split_text_to_chunks(input_text, 510)

Solution 3: Sliding window

You can use a sliding window approach to process overlapping segments of your input text. Combining the results can help retain more context but may require additional processing.

def sliding_window(text, window_size, stride):
 tokens = tokenizer.tokenize(text)
 windowed_tokens = [tokens[i:i + window_size] for i in range(0, len(tokens) - window_size + 1, stride)]
 return windowed_tokens

windowed_tokens = sliding_window(input_text, 510, 256)

Solution 3: Use a different model

If your use case requires processing long sequences, you can explore models designed for handling longer input sequences, such as Longformer or BigBird.

from transformers import LongformerTokenizer, LongformerForSequenceClassification

tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
model = LongformerForSequenceClassification.from_pretrained("allenai/longformer-base-4096")

Adapt your code and choose an approach that best fits your problem and requirements.

Other reasons for the error

Another reason for the error is when you try to operate on two tensors with incompatible shapes. In our case, the error message states that tensor a has a size of 4000, while tensor b has a size of 512 at dimension 1.

To fix the error, you must ensure that the shapes of the tensors are compatible with the operation you’re trying to perform.

To identify the source of the error, you can try printing the shapes of the tensors before the operation that triggers the error.

print("Tensor a shape:", a.shape)
print("Tensor b shape:", b.shape)

Once you have identified the source of the error, you can take the appropriate steps to ensure the tensors have compatible shapes.

Incorrect input size

You need to ensure that passing input tensors with the correct size. For BERT models, the input size is typically fixed at 512. If your input text has more tokens than the model’s maximum sequence length, you must truncate or split the text to fit the model’s constraints.

Mismatch in model architecture and input size

Make sure that your model architecture matches the input size you’re providing. If you have modified the model architecture, you may need to adjust the input dimensions accordingly.

Incorrect reshaping or slicing

You can check the parts of your code where you are reshaping or slicing tensors and ensure the output shapes are as expected.

That’s it.

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