Hi, I am interested in using the DeBERTa model that was recently implemented here and incorporating it into FARM so that it can also be used in open-domain QA settings through Haystack.

Just wondering why there's only a Slow Tokenizer implemented for DeBERTa and wondering if there are plans to create the Fast Tokeni

The Split class accepts SplitDelimiterBehavior which is really useful. The Punctuation however always uses SplitDelimiterBehavior::Isolated (and Whitespace on the other hand behaves like SplitDelimiterBehavior::Removed).

impl PreTokenizer for Punctuation {
    fn pre_tokenize(&self, pretokenized: &mut PreTokenizedString) -> Result<()> {
        pretokenized.split(|_, s| s.spl

I'm playing around with this wonderful code but I'm running into a curious issue when I try to train the model with my own data.

I replicated the personachat_self_original.json file structure and added my own data. I deleted dataset_cache_OpenAIGPTTokenizer file but when I try to train, I get this error:

INFO:train.py:Pad inputs and convert to Tensor
Traceback (most recent call last)