| - | Language Models are Unsupervised Multitask Learners | N/A | TF  Pytorch, TF2.0  Keras  | GPT-2(117M, 124M, 345M, 355M, 774M, 1558M) |
| 2017/08 | Learned in Translation: Contextualized Word Vectors | 524 | Pytorch  Keras  | CoVe |
| 2018/01 | Universal Language Model Fine-tuning for Text Classification | 167 | Pytorch  | ULMFit(English, Zoo) |
| 2018/02 | Deep contextualized word representations | 999+ | Pytorch  TF  | ELMO(AllenNLP, TF-Hub) |
| 2018/04 | Efficient Contextualized Representation:Language Model Pruning for Sequence Labeling | 26 | Pytorch  | LD-Net |
| 2018/07 | Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation | 120 | Pytorch  | ELMo |
| 2018/08 | Direct Output Connection for a High-Rank Language Model | 24 | Pytorch  | DOC |
| 2018/10 | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | 999+ | TF  Keras  Pytorch, TF2.0  MXNet  PaddlePaddle  TF  Keras  | BERT(BERT, ERNIE, KoBERT) |
| 2018/?? | Contextual String Embeddings for Sequence Labeling | 486 | Pytorch  | Flair |
| 2018/?? | Improving Language Understanding by Generative Pre-Training | 999+ | TF  Keras  Pytorch, TF2.0  | GPT |
| 2019/01 | Multi-Task Deep Neural Networks for Natural Language Understanding | 364 | Pytorch  | MT-DNN |
| 2019/01 | BioBERT: pre-trained biomedical language representation model for biomedical text mining | 634 | TF  | BioBERT |
| 2019/01 | Cross-lingual Language Model Pretraining | 639 | Pytorch  Pytorch, TF2.0  | XLM |
| 2019/01 | Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context | 754 | TF  Pytorch  Pytorch, TF2.0  | Transformer-XL |
| 2019/02 | Efficient Contextual Representation Learning Without Softmax Layer | 2 | Pytorch  | - |
| 2019/03 | SciBERT: Pretrained Contextualized Embeddings for Scientific Text | 124 | Pytorch, TF  | SciBERT |
| 2019/04 | Publicly Available Clinical BERT Embeddings | 229 | Text  | clinicalBERT |
| 2019/04 | ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission | 84 | Pytorch  | ClinicalBERT |
| 2019/05 | ERNIE: Enhanced Language Representation with Informative Entities | 210 | Pytorch  | ERNIE |
| 2019/05 | Unified Language Model Pre-training for Natural Language Understanding and Generation | 278 | Pytorch  | UniLMv1(unilm1-large-cased, unilm1-base-cased) |
| 2019/05 | HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization | 81 | | - |
| 2019/06 | Pre-Training with Whole Word Masking for Chinese BERT | 98 | Pytorch, TF  | BERT-wwm |
| 2019/06 | XLNet: Generalized Autoregressive Pretraining for Language Understanding | 999+ | TF  Pytorch, TF2.0  | XLNet |
| 2019/07 | ERNIE 2.0: A Continual Pre-training Framework for Language Understanding | 107 | PaddlePaddle  | ERNIE 2.0 |
| 2019/07 | SpanBERT: Improving Pre-training by Representing and Predicting Spans | 282 | Pytorch  | SpanBERT |
| 2019/07 | RoBERTa: A Robustly Optimized BERT Pretraining Approach | 999+ | Pytorch  Pytorch, TF2.0  | RoBERTa |
| 2019/09 | Subword ELMo | 1 | Pytorch  | - |
| 2019/09 | Knowledge Enhanced Contextual Word Representations | 115 | | - |
| 2019/09 | TinyBERT: Distilling BERT for Natural Language Understanding | 129 | | - |
| 2019/09 | Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism | 136 | Pytorch  | Megatron-LM(BERT-345M, GPT-2-345M) |
| 2019/09 | MultiFiT: Efficient Multi-lingual Language Model Fine-tuning | 29 | Pytorch  | - |
| 2019/09 | Extreme Language Model Compression with Optimal Subwords and Shared Projections | 32 | | - |
| 2019/09 | MULE: Multimodal Universal Language Embedding | 5 | | - |
| 2019/09 | Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks | 51 | | - |
| 2019/09 | K-BERT: Enabling Language Representation with Knowledge Graph | 59 | | - |
| 2019/09 | UNITER: Learning UNiversal Image-TExt Representations | 60 | | - |
| 2019/09 | ALBERT: A Lite BERT for Self-supervised Learning of Language Representations | 803 | TF  | - |
| 2019/10 | BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension | 349 | Pytorch  | BART(bart.base, bart.large, bart.large.mnli, bart.large.cnn, bart.large.xsum) |
| 2019/10 | DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter | 481 | Pytorch, TF2.0  | DistilBERT |
| 2019/10 | Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer | 696 | TF  | T5 |
| 2019/11 | CamemBERT: a Tasty French Language Model | 102 | - | CamemBERT |
| 2019/11 | ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations | 15 | Pytorch  | - |
| 2019/11 | Unsupervised Cross-lingual Representation Learning at Scale | 319 | Pytorch  | XLM-R (XLM-RoBERTa)(xlmr.large, xlmr.base) |
| 2020/01 | ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training | 35 | Pytorch  | ProphetNet(ProphetNet-large-16GB, ProphetNet-large-160GB) |
| 2020/02 | CodeBERT: A Pre-Trained Model for Programming and Natural Languages | 25 | Pytorch  | CodeBERT |
| 2020/02 | UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training | 33 | Pytorch  | - |
| 2020/03 | ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators | 203 | TF  | ELECTRA(ELECTRA-Small, ELECTRA-Base, ELECTRA-Large) |
| 2020/04 | MPNet: Masked and Permuted Pre-training for Language Understanding | 5 | Pytorch  | MPNet |
| 2020/05 | ParsBERT: Transformer-based Model for Persian Language Understanding | 1 | Pytorch  | ParsBERT |
| 2020/05 | Language Models are Few-Shot Learners | 382 | - | - |
| 2020/07 | InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training | 12 | Pytorch  | - |