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apply Whole Word Masking technique.
referred to [create_pretraining_data.py](https://github.com/google-research/bert/blob/master/create_pretraining_data.py)

fix issue of bert-base-multilingual and add support for uncased multilingual

Indentation change so that the output "nbest_predictions.json" is not empty.

Reason for issue was that optimzation steps where computed from example size, which is different from actual size of dataloader when an example is chunked into multiple instances.

Solution in this pull request is to compute num_optimization_steps directly from len(data_loader).