Stuck on an issue?

Lightrun Answers was designed to reduce the constant googling that comes with debugging 3rd party libraries. It collects links to all the places you might be looking at while hunting down a tough bug.

And, if you’re still stuck at the end, we’re happy to hop on a call to see how we can help out.

Tokenizers bug: version 2.10 doesn't honor `max_len` when instantiating a pretrained model

See original GitHub issue

🐛 Bug

Information

Hello! I’ve just upgraded from Transformers 2.8 to Transformers 2.10, and noticed that parameter max_len is not properly honored when instantiating a pretrained model. For example, in Transformer 2.8.0, I was able to limit the length of a tokenized sequence as follows:

import transformers

>>> tok = transformers.RobertaTokenizer.from_pretrained('roberta-base', max_len=16)
>>> tok.encode('This is a sentence', pad_to_max_length=True)

[0, 152, 16, 10, 3645, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

>>>print(tok.max_len)

16

However, on version 2.10, max_len is ignored when loading a pretrained tokenizer:

import transformers

>>> tok = transformers.RobertaTokenizer.from_pretrained('roberta-base', max_len=16)
>>> tok.encode('This is a sentence', pad_to_max_length=True)

[0, 152, 16, 10, 3645, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...] # 512 tokens

>>>print(tok.max_len)

512

This bug can be temporary solved by using model_max_length instead of max_len, but it broke the all my scripts that relied on that attribute. It seems that this issue was introduced in a recent change in tokenization_utils.py (line 825):

# For backward compatibility we fallback to set model_max_length from max_len if provided
model_max_length = model_max_length if model_max_length is not None else kwargs.pop("max_len", None)

This compatibility is not guaranteed if the pretrained model contains model_max_length among its parameters, but max_len is specified in from_pretrained.

Model I am using (Bert, XLNet …): As far as I can tell, this affects all pretrained models. Observed on BERT, RoBERTa, and DistilBERT.

Language I am using the model on (English, Chinese …): As far as I can tell, this affects all pretrained models. Observed on English.

The problem arises when using: