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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
bert nlp papers, applications and github resources, including the newst xlnet ， BERT、XLNet 相关论文和 github 项目
| Date | Stars |
|---|---|
| 2026-07-30 | 1840 |
| 2026-08-09 | 1839 |
| 2026-09-21 | 1840 |
| 2026-09-22 | 1839 |
| 2026-09-29 | 1840 |
| 2026-10-31 | 1841 |
| 2026-11-01 | 1841 |
| 2026-11-04 | 1841 |
Today
— stars today
This week
+1 stars this week
This month
+1 stars this month
Momentum
0.0
growth rate 0.05%/day
# This repository is to collect BERT related resources. AD: a repository for graph convolutional networks at https://github.com/Jiakui/awesome-gcn (resources for graph convolutional networks （图卷积神经网络相关资源）). # Papers: 1. [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 <details> <summary><b> Click to see more </b></summary> 2. [arXiv:1812.06705](https://arxiv.org/abs/1812.06705), Conditional BERT Contextual Augmentation, Authors: Xing Wu, Shangwen Lv, Liangjun Zang, Jizhong Han, Songlin Hu 3. [arXiv:1812.03593](https://arxiv.org/pdf/1812.03593), SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering, Authors: Chenguang Zhu, Michael Zeng, Xuedong Huang 4. [arXiv:1901.02860](https://arxiv.org/abs/1901.02860), Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context, Authors: Zihang Dai, Zhilin Yang, Yiming Yang, William W. Cohen, Jaime Carbonell, Quoc V. Le and Ruslan Salakhutdinov. 5. [arXiv:1901.04085](https://arxiv.org/pdf/1901.04085.pdf), Passage Re-ranking with BERT, Authors: Rodrigo Nogueira, Kyunghyun Cho 6. [arXiv:1902.02671](https://arxiv.org/pdf/1902.02671.pdf), BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning, Authors: Asa Cooper Stickland, Iain Murray 7. [arXiv:1904.02232](https://arxiv.org/abs/1904.02232), BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis, Authors: Hu Xu, Bing Liu, Lei Shu, Philip S. Yu, [[code](https://github.com/howardhsu/BERT-for-RRC-ABSA)] </details> # 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. [dmlc/gluon-nlp](https://github.com/dmlc/gluon-nlp), Gluon + MXNet implementation that reproduces BERT pretraining and finetuning on GLUE benchmark, SQuAD, etc,  4. [dbiir/UER-py](https://github.com/dbiir/UER-py), UER-py is a toolkit for pre-training on general-domain corpus and fine-tuning on downstream task. UER-py maintains model modularity and supports research extensibility. It facilitates the use of different pre-training models (e.g. BERT), and provides interfaces for users to further extend upon.  5. [BrikerMan/Kashgari](https://github.com/BrikerMan/Kashgari), Simple, Keras-powered multilingual NLP framework, allows you to build your models in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS) and text classification tasks. Includes BERT, GPT-2 and word2vec embedding.  6. [kaushaltrivedi/fast-bert](https://github.com/kaushaltrivedi/fast-bert), Super easy library for BERT based NLP models,  <details> <summary><b> Click to see more </b></summary> 7. [Separius/BERT-keras](https://github.com/Separius/BERT-keras), Keras implementation of BERT with pre-trained weights,  8. [soskek/bert-chainer](https://github.com/soskek/bert-chainer), Chai
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0130007b7f3582d2, topic:nlp, readme:named entity recognition, readme:text classification