Hi @miOmiO  - text length limitation could be an issue here. Note that the length refers to the number of tokens, not the number of words. Because BERT models generally use subword tokenization it can happen that one word is split into 2 or more tokens. That is why even reducing the number of words to 460 sometimes might throw an error.
To test this you could try to use the model row by row and see if the number of examples that fail correspond to the same ones in your batch job. If it is indeed the number of tokens that cause the model to fail you should be seeing an error message like "... sequence length is longer than the specified maximum sequence length for this model ..."
If this indeed the source of error then it might be easiest to truncate the input sequence of tokens after the tokenization  (rather than the number of words before tokenization).
Hope that helps.