bert-large-uncased-whole-word-masking

BERT large model (uncased) whole word masking is a self-supervised model . It does not make a difference between English and English . This model was trained with a new technique: Whole Word Masking . The training is identical -- each masked WordPiece token is predicted independently . You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on downstream tasks that use the whole sentence (potentially masked)to make decisions, such as sequence classification, token classification or question answering . The team releasing BERT did not write a model card for this model so this model card has been written by the Hug,