BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Code

• github.com/google-research/bert
  OfficialIn papertf★ 39,935
• github.com/PaddlePaddle/PaddleNLP
  paddle★ 12,937
• github.com/shreyashankar/gpt3-sandbox
  none★ 2,880
• github.com/lukemelas/PyTorch-Pretrained-ViT
  pytorch★ 853
• github.com/Y1ran/NLP-BERT--ChineseVersion
  pytorch★ 842
• github.com/google-research/bigbird
  tf★ 633
• github.com/google/seqio
  tf★ 594
• github.com/re-search/DocProduct
  tf★ 571
• github.com/sberbank-ai/ner-bert
  pytorch★ 408
• github.com/thu-keg/omnievent
  pytorch★ 405

Abstract

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models 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).

Tasks

Benchmark Results

Reproductions