The ValueError: TextEncodeInput must be Union[TextInputSequence, Tuple[InputSequence, InputSequence]] – Tokenizing BERT / Distilbert Error occurs when the input format provided to the tokenizer for the BERT or DistilBERT model is incorrect.
To fix the ValueError: TextEncodeInput must be Union[TextInputSequence, Tuple[InputSequence, InputSequence]] – Tokenizing BERT / Distilbert Error, ensure that you are using the correct method for tokenization like the tokenizer.encode() or tokenizer.encode_plus() methods to tokenize your input if you are using the “transformers” library.
Example
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# Tokenize a single string
input_text = "This is an example sentence."
encoded_input = tokenizer.encode_plus(input_text, return_tensors='pt')
# Tokenize a pair of strings
input_text1 = "This is the first sentence."
input_text2 = "This is the second sentence."
encoded_input = tokenizer.encode_plus(input_text1, input_text2, return_tensors='pt')
print(encoded_input)Output
{'input_ids': tensor([[ 101, 2023, 2003, 1996, 2034, 6251, 1012, 102,
2023, 2003, 1996, 2117, 6251, 1012, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0,
0, 0, 0, 0, 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]])
}By ensuring that you are passing the correct input type and using the appropriate tokenization method, you should be able to fix the ValueError.
Ensure you pass a single string, a list of strings, or a tuple of two strings (or lists of strings) to the tokenizer. The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as a list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
The error suggests that the input might be of a different type or format. If your input is a single string, ensure it’s not mistakenly wrapped in a list or tuple.
That’s it.
