which iterator to use for different hugging face transformer models for solving multiple choice questions?
Hello,

I have used Hugging Face GPT-2 models to do Natural Language Processing.
When I used the GPT-2 model, I used the ```BPTTIterator``` from TorchText to pre-process my text data, since GPT-2 essentially performs regular language modelling (next token prediction).

I am wondering, when I use GPT-2, BERT and XLNet for **multiple-choice solving**:

1. What type of iterator should I use for multiple-choice question solving? If there is no specific iterator that can accommodate multiple-choice question solving, how should I pre-process my text (multiple-choice questions) before feeding them into the different transformer models?

2. if hugging face transformer models (BERT, XLNet, GPT-2)  use special tokens to separate questions from multiple-choice options, what are those special tokens? I want to make the use of the pre-trained models rather than training a new model with new special tokens on my own.

3. Is the format of multiple-choice questions that can be processed with, for example, BERT different than the multiple-choice question format that can be processed by GPT-2? or can each of the transformer models process any type of multiple-choice questions? (for example, BERT could only be used to solve fill-in-the-blank multiple-choice questions, whereas multiple-choice question for GPT-2 does not have to have the fill-in-the-blank format?)

Thank you,
Do I need to build my own iterator for this?
How should the tokens be arranged for multiple choice question solving?

e.g. (question) (token1) (token2) (MCoption)(answer1)
. ----> is this the right format for all of the GPT-2, BERT and XLNet? 

Thank you<|||||>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.
