study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and
training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every
model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results
highlight the importance of previously overlooked design choices, and raise questions about the source of recently
reported improvements. We release our models and code.
Tips:
This implementation is the same as [BertModel] with a tiny embeddings tweak as well as a setup
  for Roberta pretrained models.
RoBERTa has the same architecture as BERT, but uses a byte-level BPE as a tokenizer (same as GPT-2) and uses a
  different pretraining scheme.
RoBERTa doesn't have token_type_ids, you don't need to indicate which token belongs to which segment. Just
