Vocab size for microsoft/layoutxlm-base about transformers-tutorials HOT 3 CLOSED

Hi,

Thanks for the kind words! Actually, I recently updated the config.json of LayoutXLM, and I probably know the reason: in a recent PR, I added a LayoutXLMProcessor, together with a new LayoutXLMTokenizer/LayoutXLMTokenizerFast.

Therefore, I removed the "tokenizer_class" attribute of LayoutXLM"s configuration, as this was still set to XLMRobertaTokenizer.

However, you've got install Transformers from master for them to use them for now: pip install git+https://github.com/huggingface/transformers.git.

Maybe it's better for me to add the tokenizer_class attribute again, and remove it once Transformers has a new version on PyPi.

Thanks for reporting!

from transformers-tutorials.

Hmm ok I see. Well I'm using the right tokenizer and everything is working fine now.

Just to update I let the model train with the previous change in the word embedding layer but it doesn't converged, the loss kept high all the training process.

Appreciate your help!

from transformers-tutorials.

Update: I've restored the tokenizer_class attribute for now, such that there are no breaking changes. So for now, the recommended way to use the tokenizer for LayoutXLM is by using the AutoTokenizer class.

However, in the new version of Transformers, it's recommended to use LayoutXLMProcessor and LayoutXLMTokenizer/LayoutXLMTokenizerFast, which support bounding boxes and labels, to be prepared for the model.

from transformers-tutorials.