This repository is to collect BERT related resources.

Papers:

arXiv:1810.04805, https://arxiv.org/abs/1810.04805,

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

Authors: Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova

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 by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT representations 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 benchmark to 80.4% (7.6% absolute improvement), MultiNLI accuracy to 86.7 (5.6% absolute improvement) and the SQuAD v1.1 question answering Test F1 to 93.2 (1.5% absolute improvement), outperforming human performance by 2.0%.

Github Repositories:

official implement:

1. google-research/bert , https://github.com/google-research/bert, **officical** TensorFlow code and pre-trained models for BERT

implement of BERT besides tensorflow:

1. codertimo/BERT-pytorch, https://github.com/codertimo/BERT-pytorch, Google AI 2018 BERT pytorch implementation

2. huggingface/pytorch-pretrained-BERT , https://github.com/huggingface/pytorch-pretrained-BERT, A PyTorch implementation of Google AI's BERT model with script to load Google's pre-trained models

3. Separius/BERT-keras, https://github.com/Separius/BERT-keras, Keras implementation of BERT with pre-trained weights

4. soskek/bert-chainer, https://github.com/soskek/bert-chainer, Chainer implementation of "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"

5. innodatalabs/tbert, https://github.com/innodatalabs/tbert, PyTorch port of BERT ML model

6. guotong1988/BERT-tensorflow, https://github.com/guotong1988/BERT-tensorflow, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

7. dreamgonfly/BERT-pytorch, https://github.com/dreamgonfly/BERT-pytorch, PyTorch implementation of BERT in "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"

other resources for BERT:

1. hanxiao/bert-as-service, https://github.com/hanxiao/bert-as-service, Mapping a variable-length sentence to a fixed-length vector using pretrained BERT model

2. brightmart/bert_language_understanding, https://github.com/brightmart/bert_language_understanding, Pre-training of Deep Bidirectional Transformers for Language Understanding: pre-train TextCNN

3. Y1ran/NLP-BERT--ChineseVersion, https://github.com/Y1ran/NLP-BERT--ChineseVersion, this is the code copy from google's BERT model

4. algteam/bert-examples, https://github.com/algteam/bert-examples

5. yuanxiaosc/Deep_dynamic_word_representation, https://github.com/yuanxiaosc/Deep_dynamic_word_representation, TensorFlow code and pre-trained models for deep dynamic word representation (DDWR). It combines the BERT model and ELMo's deep context word representation.

6. yangbisheng2009/cn-bert, https://github.com/yangbisheng2009/cn-bert, BERT在中文NLP的应用

BERT QA task:

1. benywon/ChineseBert, https://github.com/benywon/ChineseBert, This is a chinese Bert model specific for question answering

2. vliu15/BERT, https://github.com/vliu15/BERT, Tensorflow implementation of BERT for QA

3. benywon/ChineseBert, https://github.com/benywon/ChineseBert, This is a chinese Bert model specific for question answering

BERT classification task:

1. zhpmatrix/Kaggle-Quora-Insincere-Questions-Classification, https://github.com/zhpmatrix/Kaggle-Quora-Insincere-Questions-Classification, Kaggle新赛(baseline)-基于BERT的fine-tuning方案+基于tensor2tensor的Transformer Encoder方案

2. maksna/bert-fine-tuning-for-chinese-multiclass-classification, https://github.com/maksna/bert-fine-tuning-for-chinese-multiclass-classification, use google pre-training model bert to fine-tuning for the chinese multiclass classification

3. NLPScott/bert-Chinese-classification-task, https://github.com/NLPScott/bert-Chinese-classification-task, bert中文分类实践

4. Socialbird-AILab/BERT-Classification-Tutorial, https://github.com/Socialbird-AILab/BERT-Classification-Tutorial,

BERT NER task:

1. JamesGu14/BERT-NER-CLI, https://github.com/JamesGu14/BERT-NER-CLI, Bert NER command line tester with step by step setup guide

2. zhpmatrix/bert-sequence-tagging, https://github.com/zhpmatrix/bert-sequence-tagging, 基于BERT的中文序列标注

3. kyzhouhzau/BERT-NER, https://github.com/kyzhouhzau/BERT-NER, Use google BERT to do CoNLL-2003 NER !

4. king-menin/ner-bert, https://github.com/king-menin/ner-bert, NER task solution (bert-Bi-LSTM-CRF) with google bert https://github.com/google-research.

5. macanv/BERT-BiLSMT-CRF-NER, https://github.com/macanv/BERT-BiLSMT-CRF-NER, Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning