for a wide range of tasks, such as question answering and language inference, without substantial task-specific
architecture modifications.
BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural
language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI
accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute
improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).
Tips:
BERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.
BERT was trained with the masked language modeling (MLM) and next sentence prediction (NSP) objectives. It is
