Whole Word Masking Implementation
# 🚀 Feature request

Currently, training the models from scratch like Roberta do not support whole word masking (e.g., language modeling examples). Only pre-trained models are available. Is it possible to include whole word masking in the input layers?

## Motivation

Whole word masking leads to performance boosts. So, adding this feature would be useful if someone wants to train the models from scratch.
Would be a great improvement :+1: 

Here's btw. the commit that introduced WWM in BERT:

https://github.com/google-research/bert/commit/0fce551b55caabcfba52c61e18f34b541aef186a<|||||>BERT using wordpiece tokenizer, however, roberta uses byte-piece tokenizer. I think the implementations may be slightly different, if not starkly different (due to different start token indicators).<|||||>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.
