BERT tokenizer - set special tokens 
Hi,

I was wondering whether the team could expand BERT so that fine-tuning with newly defined special tokens would be possible - just like the GPT allows.

@thomwolf Could you share your thought with me on that? 

Regards,
Adrian.
Hi Adrian, BERT already has a few unused tokens that can be used similarly to the `special_tokens` of GPT/GPT-2.
For more details see https://github.com/google-research/bert/issues/9#issuecomment-434796704 and issue #405 for instance.<|||||>In case we use an unused special token from the vocabulary, is it enough to finetune a classification task or do we need to train an embedding from scratch? Did anyone already do this?

Two different and somehow related questions I had when looking into the implementation:

1) The Bert paper mentions a (learned) positional embedding. How is this implemented here? examples/extract_features/convert_examples_to_features() defines tokens (representation), input_type_ids (the difference between the first and second sequence) and an input_mask (distinguishing padding/real tokens) but no positional embedding. Is this done internally?

2) Can I use a special token as input_type_ids for Bert? In the classification example, only values of [0,1] are possible and I'm wondering what would happen if I would choose a special token instead? Is this possible with a pretrained embedding or do i need to retrain the whole embedding as a consequence?
<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
