Fine tuning XLMRoberta for Question Answering
# ❓ Questions & Help

<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
     new models and benchmarks, and migration questions. For all other questions,
     we direct you to Stack Overflow (SO) where a whole community of PyTorch and
     Tensorflow enthusiast can help you out. Make sure to tag your question with the
     right deep learning framework as well as the huggingface-transformers tag: 
     https://stackoverflow.com/questions/tagged/huggingface-transformers 
     
     If your question wasn't answered after a period of time on Stack Overflow, you
     can always open a question on GitHub. You should then link to the SO question 
     that you posted.
     -->

## Details
<!-- Description of your issue -->
I am trying to fine-tune XLM Roberta for SQuAD using the run_squad.py script available in examples. I have simply written an `XLMRobertaForQuestionAnswering` class as suggested at https://github.com/huggingface/transformers/issues/3694. However, the performance is extremely poor. I am wondering, do I need to perform anything special during preprocessing the SQuAD dataset when I use XLMRoberta?

I am using all the default hyper-parameters provided in the example script.