ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length

🤗Transformers
1

Hello everyone!

I’m trying to execute the code from the summarization task of chapter 7 in the Hugging Face Course. I’m running it on Jupyter Notebooks. But when I execute these lines:

features = [tokenized_datasets[“train”][i] for i in range(2)]
data_collator(features)

I get this error:

You’re using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the __call__ method is faster than using a method to encode the text followed by a call to the pad method to get a padded encoding.

ValueError Traceback (most recent call last)
/opt/conda/lib/python3.9/site-packages/transformers/tokenization_utils_base.py in convert_to_tensors(self, tensor_type, prepend_batch_axis)
715 if not is_tensor(value):
→ 716 tensor = as_tensor(value)
717

ValueError: expected sequence of length 7 at dim 1 (got 6)

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last)
/tmp/ipykernel_615/1644402533.py in
1 features = [tokenized_datasets[“train”][i] for i in range(2)]
----> 2 data_collator(features)

/opt/conda/lib/python3.9/site-packages/transformers/data/data_collator.py in call(self, features, return_tensors)
584 feature[“labels”] = np.concatenate([remainder, feature[“labels”]]).astype(np.int64)
585
→ 586 features = self.tokenizer.pad(
587 features,
588 padding=self.padding,

/opt/conda/lib/python3.9/site-packages/transformers/tokenization_utils_base.py in pad(self, encoded_inputs, padding, max_length, pad_to_multiple_of, return_attention_mask, return_tensors, verbose)
2979 batch_outputs[key].append(value)
2980
→ 2981 return BatchEncoding(batch_outputs, tensor_type=return_tensors)
2982
2983 def create_token_type_ids_from_sequences(

/opt/conda/lib/python3.9/site-packages/transformers/tokenization_utils_base.py in init(self, data, encoding, tensor_type, prepend_batch_axis, n_sequences)
204 self._n_sequences = n_sequences
205
→ 206 self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
207
208 @property

/opt/conda/lib/python3.9/site-packages/transformers/tokenization_utils_base.py in convert_to_tensors(self, tensor_type, prepend_batch_axis)
730 “Please see if a fast version of this tokenizer is available to have this feature available.”
731 )
→ 732 raise ValueError(
733 “Unable to create tensor, you should probably activate truncation and/or padding with”
734 " ‘padding=True’ ‘truncation=True’ to have batched tensors with the same length. Perhaps your"

ValueError: Unable to create tensor, you should probably activate truncation and/or padding with ‘padding=True’ ‘truncation=True’ to have batched tensors with the same length. Perhaps your features (labels_mask in this case) have excessive nesting (inputs type list where type int is expected).

Unfortunately, I do not know where to look further for a solution. Does anybody know how to solve this? Thank you very much in advance!

2 Likes
3

You have this preprocess function, right?

def preprocess_function(examples):
    model_inputs = tokenizer(
        examples["review_body"],
        max_length=max_input_length,
        truncation=True,
    )
    labels = tokenizer(examples["review_title"], max_length=max_target_length, truncation=True)
    model_inputs["labels"] = labels["input_ids"]
    model_inputs["labels_mask"] = labels["attention_mask"]
    return model_inputs

All you have to do is to remove the line:
model_inputs["labels_mask"] = labels["attention_mask"]

So at the end you end up with:

def preprocess_function(examples):
    model_inputs = tokenizer(
        text = examples["review_body"],
        max_length=max_input_length,
        truncation=True,
    )
    labels = tokenizer(examples["review_title"], max_length=max_target_length, truncation=True)
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs
4

This actually it is the problem, but can we have an explanation why is this the issue? There are two masks, the one coming from running tokenizer on the examples["review_body"], and one coming from running the tokenizer on examples["review_title"]. The first one is not an issue, but the second is and I cannot understand why.

3 Likes
5

There is a solution here : ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. ¡ Issue #15505 ¡ huggingface/transformers ¡ GitHub

1 Like