# Transfer-related Papers a list of BERT-related papers. Any feedback is welcome. ## Table of Contents - [Downstream task](#downstream-task) - [Generation](#generation) - [Modification (multi-task, masking strategy, etc.)](#modification-multi-task-masking-strategy-etc) - [Probe](#probe) - [Inside BERT](#inside-bert) - [Multi-lingual](#multi-lingual) - [Other than English models](#other-than-english-models) - [Domain specific](#domain-specific) - [Multi-modal](#multi-modal) - [Model compression](#model-compression) - [Misc.](#misc) ## Downstream task ### QA, MC, Dialogue - [A BERT Baseline for the Natural Questions](https://arxiv.org/abs/1901.08634) - [MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension](https://arxiv.org/abs/1905.13453) (ACL2019) - [Unsupervised Domain Adaptation on Reading Comprehension](https://arxiv.org/abs/1911.06137) - [BERTQA -- Attention on Steroids](https://arxiv.org/abs/1912.10435) - [A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning](https://arxiv.org/abs/1908.05514) (EMNLP2019) - [SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering](https://arxiv.org/abs/1812.03593) - [Multi-hop Question Answering via Reasoning Chains](https://arxiv.org/abs/1910.02610) - [Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents](https://arxiv.org/abs/1911.00484) - [Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering](https://arxiv.org/abs/1909.07598) (EMNLP2019 WS) - [End-to-End Open-Domain Question Answering with BERTserini](https://arxiv.org/abs/1902.01718) (NAALC2019) - [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://arxiv.org/abs/1906.00300) (ACL2019) - [Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering](https://arxiv.org/abs/1908.08167) (EMNLP2019) - [Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering](https://arxiv.org/abs/1911.10470) (ICLR2020) - [Learning to Ask Unanswerable Questions for Machine Reading Comprehension](https://arxiv.org/abs/1906.06045) (ACL2019) - [Unsupervised Question Answering by Cloze Translation](https://arxiv.org/abs/1906.04980) (ACL2019) - [Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation](https://arxiv.org/abs/1908.04942) - [A Recurrent BERT-based Model for Question Generation](https://www.aclweb.org/anthology/D19-5821/) (EMNLP2019 WS) - [Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds](https://arxiv.org/abs/1911.02365) - [Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension](https://www.aclweb.org/anthology/papers/P/P19/P19-1226/) (ACL2019) - [Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning](https://arxiv.org/abs/1908.04530) (CIKM2019) - [SG-Net: Syntax-Guided Machine Reading Comprehension](https://arxiv.org/abs/1908.05147) - [MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension](https://arxiv.org/abs/1910.00458) - [Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://arxiv.org/abs/1909.00277) (EMNLP2019) - [ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning](https://arxiv.org/abs/2002.04326) (ICLR2020) - [Robust Reading Comprehension with Linguistic Constraints via Posterior Regularization](https://arxiv.org/abs/1911.06948) - [BAS: An Answer Selection Method Using BERT Language Model](https://arxiv.org/abs/1911.01528) - [Beat the AI: Investigating Adversarial Human Annotations for Reading Comprehension](https://arxiv.org/abs/2002.00293) - [A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension](https://arxiv.org/abs/1905.12848) (ACL2019 WS) - [FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension](https://arxiv.org/abs/1908.05117) (ACL2019 WS) - [BERT with History Answer Embedding for Conversational Question Answering](https://arxiv.org/abs/1905.05412) (SIGIR2019) - [GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension](https://arxiv.org/abs/1908.00059) (ICML2019 WS) - [Beyond English-only Reading Comprehension: Experiments in Zero-Shot Multilingual Transfer for Bulgarian](https://arxiv.org/abs/1908.01519) (RANLP2019) - [XQA: A Cross-lingual Open-domain Question Answering Dataset](https://www.aclweb.org/anthology/P19-1227/) (ACL2019) - [Cross-Lingual Machine Reading Comprehension](https://arxiv.org/abs/1909.00361) (EMNLP2019) - [Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model](https://arxiv.org/abs/1909.09587) - [Multilingual Question Answering from Formatted Text applied to Conversational Agents](https://arxiv.org/abs/1910.04659) - [BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels](https://arxiv.org/abs/1910.05040) (EMNLP2019) - [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://arxiv.org/abs/1910.07475) - [Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension](https://arxiv.org/abs/1904.09679) (TACL) - [SberQuAD - Russian Reading Comprehension Dataset: Description and Analysis](https://arxiv.org/abs/1912.09723) - [Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension](https://arxiv.org/abs/1909.00109) (EMNLP2019) - [BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer](https://arxiv.org/abs/1907.03040) (Interspeech2019) - [Dialog State Tracking: A Neural Reading Comprehension Approach](https://arxiv.org/abs/1908.01946) - [A Simple but Effective BERT Model for Dialog State Tracking on Resource-Limited Systems](https://arxiv.org/abs/1910.12995) - [Fine-Tuning BERT for Schema-Guided Zero-Shot Dialogue State Tracking](https://arxiv.org/abs/2002.00181) - [Goal-Oriented Multi-Task BERT-Based Dialogue State Tracker](https://arxiv.org/abs/2002.02450) - [Domain Adaptive Training BERT for Response Selection](https://arxiv.org/abs/1908.04812) - [BERT Goes to Law School: Quantifying the Competitive Advantage of Access to Large Legal Corpora in Contract Understanding](https://arxiv.org/abs/1911.00473) ### Slot filling - [BERT for Joint Intent Classification and Slot Filling](https://arxiv.org/abs/1902.10909) - [Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model](https://arxiv.org/abs/1907.02884) - [A Comparison of Deep Learning Methods for Language Understanding](https://www.isca-speech.org/archive/Interspeech_2019/abstracts/1262.html) (Interspeech2019) ### Analysis - [Fine-grained Information Status Classification Using Discourse Context-Aware Self-Attention](https://arxiv.org/abs/1908.04755) - [GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://arxiv.org/abs/1908.07245) (EMNLP2019) - [Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations](https://arxiv.org/abs/1910.00194) (EMNLP2019) - [Using BERT for Word Sense Disambiguation](https://arxiv.org/abs/1909.08358) - [Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation](https://www.aclweb.org/anthology/P19-1569.pdf) (ACL2019) - [Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision](https://arxiv.org/abs/1907.03750) (ACL2019) - [Assessing BERT’s Syntactic Abilities](https://arxiv.org/abs/1901.05287) - [Does BERT agree? Evaluating knowledge of structure dependence through agreement relations](https://arxiv.org/abs/1908.09892) - [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://arxiv.org/abs/1904.05255) - [LIMIT-BERT : Linguistic Informed Multi-Task BERT](https://arxiv.org/abs/1910.14296) - [A Simple BERT-Based Approach for Lexical Simplification](https://arxiv.org/abs/1907.06226) - [Multi-headed Architecture Based on BERT for Grammatical Errors Correction](https://www.aclweb.org/anthology/papers/W/W19/W19-4426/) (ACL2019 WS) - [Towards Minimal Supervision BERT-based Grammar Error Correction](https://arxiv.org/abs/2001.03521) - [BERT-Based Arabic Social Media Author Profiling](https://arxiv.org/abs/1909.04181) - [Sentence-Level BERT and Multi-Task Learning of Age and Gender in Social Media](https://arxiv.org/abs/1911.00637) - [Evaluating the Factual Consistency of Abstractive Text Summarization](https://arxiv.org/abs/1910.12840) - [NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution](https://arxiv.org/abs/1911.04211) - [xSLUE: A Benchmark and Analysis Platform for Cross-Style Language Understanding and Evaluation](https://arxiv.org/abs/1911.03663) - [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://arxiv.org/abs/1909.02164) - [Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents](https://arxiv.org/abs/2002.01861) - [LAMBERT: Layout-Aware language Modeling using BERT for information extraction](https://arxiv.org/abs/2002.08087) ### Word segmentation, parsing, NER - [BERT Meets Chinese Word Segmentation](https://arxiv.org/abs/1909.09292) - [Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning](https://arxiv.org/abs/1903.04190) - [Establishing Strong Baselines for the New Decade: Sequence Tagging, Syntactic and Semantic Parsing with BERT](https://arxiv.org/abs/1908.04943) - [Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing](https://arxiv.org/abs/1908.07448) - [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204) - [Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing -- A Tale of Two Parsers Revisited](https://arxiv.org/abs/1908.07397) (EMNLP2019) - [Parsing as Pretraining](https://arxiv.org/abs/2002.01685) (AAAI2020) - [Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing](https://arxiv.org/abs/1909.06775) - [Named Entity Recognition -- Is there a glass ceiling?](https://arxiv.org/abs/1910.02403) (CoNLL2019) - [A Unified MRC Framework for Named Entity Recognition](https://arxiv.org/abs/1910.11476) - [Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models](https://arxiv.org/abs/1910.06294) - [Robust Named Entity Recognition with Truecasing Pretraining](https://arxiv.org/abs/1912.07095) (AAAI2020) - [LTP: A New Active Learning Strategy for Bert-CRF Based Named Entity Recognition](https://arxiv.org/abs/2001.02524) - [MT-BioNER: Multi-task Learning for Biomedical Named Entity Recognition using Deep Bidirectional Transformers](https://arxiv.org/abs/2001.08904) - [Portuguese Named Entity Recognition using BERT-CRF](https://arxiv.org/abs/1909.10649) - [Towards Lingua Franca Named Entity Recognition with BERT](https://arxiv.org/abs/1912.01389) ### Pronoun/coreference resolution - [Resolving Gendered Ambiguous Pronouns with BERT](https://arxiv.org/abs/1906.01161) (ACL2019 WS) - [Anonymized BERT: An Augmentation Approach to the Gendered Pronoun Resolution Challenge](https://arxiv.org/abs/1905.01780) (ACL2019 WS) - [Gendered Pronoun Resolution using BERT and an extractive question answering formulation](https://arxiv.org/abs/1906.03695) (ACL2019 WS) - [MSnet: A BERT-based Network for Gendered Pronoun Resolution](https://arxiv.org/abs/1908.00308) (ACL2019 WS) - [Fill the GAP: Exploiting BERT for Pronoun Resolution](https://www.aclweb.org/anthology/papers/W/W19/W19-3815/) (ACL2019 WS) - [On GAP Coreference Resolution Shared Task: Insights from the 3rd Place Solution](https://www.aclweb.org/anthology/W19-3816/) (ACL2019 WS) - [Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution](https://arxiv.org/abs/1905.08868) (ACL2019 WS) - [BERT Masked Language Modeling for Co-reference Resolution](https://www.aclweb.org/anthology/papers/W/W19/W19-3811/) (ACL2019 WS) - [Coreference Resolution with Entity Equalization](https://www.aclweb.org/anthology/P19-1066/) (ACL2019) - [BERT for Coreference Resolution: Baselines and Analysis](https://arxiv.org/abs/1908.09091) (EMNLP2019) [[github](https://github.com/mandarjoshi90/coref)] - [WikiCREM: A Large Unsupervised Corpus for Coreference Resolution](https://arxiv.org/abs/1908.08025) (EMNLP2019) - [Ellipsis and Coreference Resolution as Question Answering](https://arxiv.org/abs/1908.11141) - [Coreference Resolution as Query-based Span Prediction](https://arxiv.org/abs/1911.01746) ### Sentiment analysis - [Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence](https://arxiv.org/abs/1903.09588) (NAACL2019) - [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://arxiv.org/abs/1904.02232) (NAACL2019) - [Exploiting BERT for End-to-End Aspect-based Sentiment Analysis](https://arxiv.org/abs/1910.00883) (EMNLP2019 WS) - [Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification](https://arxiv.org/abs/1908.11860) - [An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese](https://arxiv.org/abs/1905.09642) (ACL2019) - ["Mask and Infill" : Applying Masked Language Model to Sentiment Transfer](https://arxiv.org/abs/1908.08039) - [Adversarial Training for Aspect-Based Sentiment Analysis with BERT](https://arxiv.org/abs/2001.11316) - [Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference](https://arxiv.org/abs/2002.04815) ### Relation extraction - [Matching the Blanks: Distributional Similarity for Relation Learning](https://arxiv.org/abs/1906.03158) (ACL2019) - [BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction](https://arxiv.org/abs/1908.05908) (NLPCC2019) - [Enriching Pre-trained Language Model with Entity Information for Relation Classification](https://arxiv.org/abs/1905.08284) - [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://arxiv.org/abs/1909.07755) - [Fine-tune Bert for DocRED with Two-step Process](https://arxiv.org/abs/1909.11898) - [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://arxiv.org/abs/1909.03546) (EMNLP2019) - [Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text](https://arxiv.org/abs/1908.07721) ### Knowledge base - [KG-BERT: BERT for Knowledge Graph Completion](https://arxiv.org/abs/1909.03193) - [Language Models as Knowledge Bases?](https://arxiv.org/abs/1909.01066) (EMNLP2019) [[github](https://github.com/facebookresearch/LAMA)] - [BERT is Not a Knowledge Base (Yet): Factual Knowledge vs. Name-Based Reasoning in Unsupervised QA](https://arxiv.org/abs/1911.03681) - [Inducing Relational Knowledge from BERT](https://arxiv.org/abs/1911.12753) (AAAI2020) - [Latent Relation Language Models](https://arxiv.org/abs/1908.07690) (AAAI2020) - [Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model](https://openreview.net/forum?id=BJlzm64tDH) (ICLR2020) - [Zero-shot Entity Linking with Dense Entity Retrieval](https://arxiv.org/abs/1911.03814) - [Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking](https://www.aclweb.org/anthology/K19-1063/) (CoNLL2019) - [Improving Entity Linking by Modeling Latent Entity Type Information](https://arxiv.org/abs/2001.01447) (AAAI2020) - [How Can We Know What Language Models Know?](https://arxiv.org/abs/1911.12543) - [REALM: Retrieval-Augmented Language Model Pre-Training](https://kentonl.com/pub/gltpc.2020.pdf) ### Text classification - [How to Fine-Tune BERT for Text Classification?](https://arxiv.org/abs/1905.05583) - [X-BERT: eXtreme Multi-label Text Classification with BERT](https://arxiv.org/abs/1905.02331) - [DocBERT: BERT for Document Classification](https://arxiv.org/abs/1904.08398) - [Enriching BERT with Knowledge Graph Embeddings for Document Classification](https://arxiv.org/abs/1909.08402) - [Classification and Clustering of Arguments with Contextualized Word Embeddings](https://arxiv.org/abs/1906.09821) (ACL2019) - [BERT for Evidence Retrieval and Claim Verification](https://arxiv.org/abs/1910.02655) - [Conditional BERT Contextual Augmentation](https://arxiv.org/abs/1812.06705) - [Stacked DeBERT: All Attention in Incomplete Data for Text Classification](https://arxiv.org/abs/2001.00137) ### WSC, WNLI, NLI - [Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge](https://arxiv.org/abs/1904.09705) - [A Surprisingly Robust Trick for the Winograd Schema Challenge](https://arxiv.org/abs/1905.06290) - [WinoGrande: An Adversarial Winograd Schema Challenge at Scale](https://arxiv.org/abs/1907.10641) (AAAI2020) - [Improving Natural Language Inference with a Pretrained Parser](https://arxiv.org/abs/1909.08217) - [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://arxiv.org/abs/1910.14599) - [Adversarial Analysis of Natural Language Inference Systems](https://arxiv.org/abs/1912.03441) (ICSC2020) - [Evaluating BERT for natural language inference: A case study on the CommitmentBank](https://www.aclweb.org/anthology/D19-1630/) (EMNLP2019) ### Commonsense - [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://arxiv.org/abs/1811.00937) (NAACL2019) - [HellaSwag: Can a Machine Really Finish Your Sentence?](https://arxiv.org/abs/1905.07830) (ACL2019) [[website](https://rowanzellers.com/hellaswag/)] - [Story Ending Prediction by Transferable BERT](https://arxiv.org/abs/1905.07504) (IJCAI2019) - [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://arxiv.org/abs/1906.02361) (ACL2019) - [Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models](https://arxiv.org/abs/1908.06725) - [Informing Unsupervised Pretraining with External Linguistic Knowledge](https://arxiv.org/abs/1909.02339) - [Commonsense Knowledge + BERT for Level 2 Reading Comprehension Ability Test](https://arxiv.org/abs/1909.03415) - [BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge](https://arxiv.org/abs/1910.07713) - [Commonsense Knowledge Mining from Pretrained Models](https://arxiv.org/abs/1909.00505) (EMNLP2019) - [Do Massively Pretrained Language Models Make Better Storytellers?](https://arxiv.org/abs/1909.10705) (CoNLL2019) - [PIQA: Reasoning about Physical Commonsense in Natural Language](https://arxiv.org/abs/1911.11641v1) (AAAI2020) - [Why Do Masked Neural Language Models Still Need Common Sense Knowledge?](https://arxiv.org/abs/1911.03024) ### Extractive summarization - [HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization](https://arxiv.org/abs/1905.06566) (ACL2019) - [Deleter: Leveraging BERT to Perform Unsupervised Successive Text Compression](https://arxiv.org/abs/1909.03223) - [Discourse-Aware Neural Extractive Model for Text Summarization](https://arxiv.org/abs/1910.14142) ### IR - [Passage Re-ranking with BERT](https://arxiv.org/abs/1901.04085) - [Investigating the Successes and Failures of BERT for Passage Re-Ranking](https://arxiv.org/abs/1905.01758) - [Understanding the Behaviors of BERT in Ranking](https://arxiv.org/abs/1904.07531) - [Document Expansion by Query Prediction](https://arxiv.org/abs/1904.08375) - [CEDR: Contextualized Embeddings for Document Ranking](https://arxiv.org/abs/1904.07094) (SIGIR2019) - [Deeper Text Understanding for IR with Contextual Neural Language Modeling](https://arxiv.org/abs/1905.09217) (SIGIR2019) - [FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance](https://arxiv.org/abs/1905.02851) (SIGIR2019) - [Multi-Stage Document Ranking with BERT](https://arxiv.org/abs/1910.14424) ## Generation - [BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model](https://arxiv.org/abs/1902.04094) (NAACL2019 WS) - [Pretraining-Based Natural Language Generation for Text Summarization](https://arxiv.org/abs/1902.09243) - [Text Summarization with Pretrained Encoders](https://arxiv.org/abs/1908.08345) (EMNLP2019) [[github (original)](https://github.com/nlpyang/PreSumm)] [[github (huggingface)](https://github.com/huggingface/transformers/tree/master/examples/summarization)] - [Multi-stage Pretraining for Abstractive Summarization](https://arxiv.org/abs/1909.10599) - [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) - [MASS: Masked Sequence to Sequence Pre-training for Language Generation](https://arxiv.org/abs/1905.02450) (ICML2019) [[github](https://github.com/microsoft/MASS)], [[github](https://github.com/microsoft/MASS/tree/master/MASS-fairseq)] - [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://arxiv.org/abs/1905.03197) (NeurIPS2019) - [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) - [Towards Making the Most of BERT in Neural Machine Translation](https://arxiv.org/abs/1908.05672) - [Improving Neural Machine Translation with Pre-trained Representation](https://arxiv.org/abs/1908.07688) - [On the use of BERT for Neural Machine Translation](https://arxiv.org/abs/1909.12744) - [Incorporating BERT into Neural Machine Translation](https://openreview.net/forum?id=Hyl7ygStwB) (ICLR2020) - [Recycling a Pre-trained BERT Encoder for Neural Machine Translation](https://www.aclweb.org/anthology/D19-5603/) - [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) - [Mask-Predict: Parallel Decoding of Conditional Masked Language Models](https://arxiv.org/abs/1904.09324) (EMNLP2019) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) - [ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation](https://arxiv.org/abs/2001.11314) - [Cross-Lingual Natural Language Generation via Pre-Training](https://arxiv.org/abs/1909.10481) (AAAI2020) [[github](https://github.com/CZWin32768/XNLG)] - [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) - [PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable](https://arxiv.org/abs/1910.07931) - [Unsupervised Pre-training for Natural Language Generation: A Literature Review](https://arxiv.org/abs/1911.06171) ## Modification (multi-task, masking strategy, etc.) - [Multi-Task Deep Neural Networks for Natural Language Understanding](https://arxiv.org/abs/1901.11504) (ACL2019) - [The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding](https://arxiv.org/abs/2002.07972) - [BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning](https://arxiv.org/abs/1902.02671) (ICML2019) - [Unifying Question Answering and Text Classification via Span Extraction](https://arxiv.org/abs/1904.09286) - [ERNIE: Enhanced Language Representation with Informative Entities](https://arxiv.org/abs/1905.07129) (ACL2019) - [ERNIE: Enhanced Representation through Knowledge Integration](https://arxiv.org/abs/1904.09223) - [ERNIE 2.0: A Continual Pre-training Framework for Language Understanding](https://arxiv.org/abs/1907.12412) (AAAI2020) - [Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101) - [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://arxiv.org/abs/1907.10529) [[github](https://github.com/facebookresearch/SpanBERT)] - [Blank Language Models](https://arxiv.org/abs/2002.03079) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) [[github](https://github.com/pytorch/fairseq/tree/master/examples/roberta)] - [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942) (ICLR2020) - [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/forum?id=r1xMH1BtvB) (ICLR2020) - [FreeLB: Enhanced Adversarial Training for Language Understanding](https://openreview.net/forum?id=BygzbyHFvB) (ICLR2020) - [KERMIT: Generative Insertion-Based Modeling for Sequences](https://arxiv.org/abs/1906.01604) - [DisSent: Sentence Representation Learning from Explicit Discourse Relations](https://arxiv.org/abs/1710.04334) (ACL2019) - [StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding](https://arxiv.org/abs/1908.04577) (ICLR2020) - [Syntax-Infused Transformer and BERT models for Machine Translation and Natural Language Understanding](https://arxiv.org/abs/1911.06156) - [SenseBERT: Driving Some Sense into BERT](https://arxiv.org/abs/1908.05646) - [Semantics-aware BERT for Language Understanding](https://arxiv.org/abs/1909.02209) (AAAI2020) - [K-BERT: Enabling Language Representation with Knowledge Graph](https://arxiv.org/abs/1909.07606) - [Knowledge Enhanced Contextual Word Representations](https://arxiv.org/abs/1909.04164) (EMNLP2019) - [KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation](https://arxiv.org/abs/1911.06136) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084) (EMNLP2019) - [SBERT-WK: A Sentence Embedding Method By Dissecting BERT-based Word Models](https://arxiv.org/abs/2002.06652) - [Universal Text Representation from BERT: An Empirical Study](https://arxiv.org/abs/1910.07973) - [Symmetric Regularization based BERT for Pair-wise Semantic Reasoning](https://arxiv.org/abs/1909.03405) - [Transfer Fine-Tuning: A BERT Case Study](https://arxiv.org/abs/1909.00931) (EMNLP2019) - [Improving Pre-Trained Multilingual Models with Vocabulary Expansion](https://arxiv.org/abs/1909.12440) (CoNLL2019) - [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/forum?id=r1xMH1BtvB) - [SesameBERT: Attention for Anywhere](https://arxiv.org/abs/1910.03176) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) [[github](https://github.com/google-research/text-to-text-transfer-transformer)] - [SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://arxiv.org/abs/1911.03437) ## Transformer variants - [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) (ACL2019) [[github](https://github.com/kimiyoung/transformer-xl)] - [The Evolved Transformer](https://arxiv.org/abs/1901.11117) (ICML2019) - [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) (ICLR2020) [[github](https://github.com/google/trax/tree/master/trax/models/reformer)] - [Transformer on a Diet](https://arxiv.org/abs/2002.06170) ## Probe - [A Structural Probe for Finding Syntax in Word Representations](https://aclweb.org/anthology/papers/N/N19/N19-1419/) (NAACL2019) - [Linguistic Knowledge and Transferability of Contextual Representations](https://arxiv.org/abs/1903.08855) (NAACL2019) [[github](https://github.com/nelson-liu/contextual-repr-analysis)] - [Probing What Different NLP Tasks Teach Machines about Function Word Comprehension](https://arxiv.org/abs/1904.11544) (*SEM2019) - [BERT Rediscovers the Classical NLP Pipeline](https://arxiv.org/abs/1905.05950) (ACL2019) - [Probing Neural Network Comprehension of Natural Language Arguments](https://arxiv.org/abs/1907.07355) (ACL2019) - [Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations](https://arxiv.org/abs/1910.01157) (EMNLP2019 WS) - [What do you mean, BERT? Assessing BERT as a Distributional Semantics Model](https://arxiv.org/abs/1911.05758) - [Quantity doesn't buy quality syntax with neural language models](https://arxiv.org/abs/1909.00111) (EMNLP2019) - [Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction](https://openreview.net/forum?id=H1xPR3NtPB) (ICLR2020) - [oLMpics -- On what Language Model Pre-training Captures](https://arxiv.org/abs/1912.13283) - [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](http://colinraffel.com/publications/arxiv2020how.pdf) ## Inside BERT - [What does BERT learn about the structure of language?](https://hal.inria.fr/hal-02131630/document) (ACL2019) - [Open Sesame: Getting Inside BERT's Linguistic Knowledge](https://arxiv.org/abs/1906.01698) (ACL2019 WS) - [Analyzing the Structure of Attention in a Transformer Language Model](https://arxiv.org/abs/1906.04284) (ACL2019 WS) - [What Does BERT Look At? An Analysis of BERT's Attention](https://arxiv.org/abs/1906.04341) (ACL2019 WS) - [Do Attention Heads in BERT Track Syntactic Dependencies?](https://arxiv.org/abs/1911.12246) - [Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains](https://arxiv.org/abs/1906.01539) (ACL2019 WS) - [Inducing Syntactic Trees from BERT Representations](https://arxiv.org/abs/1906.11511) (ACL2019 WS) - [A Multiscale Visualization of Attention in the Transformer Model](https://arxiv.org/abs/1906.05714) (ACL2019 Demo) - [Visualizing and Measuring the Geometry of BERT](https://arxiv.org/abs/1906.02715) - [How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings](https://arxiv.org/abs/1909.00512) (EMNLP2019) - [Are Sixteen Heads Really Better than One?](https://arxiv.org/abs/1905.10650) (NeurIPS2019) - [On the Validity of Self-Attention as Explanation in Transformer Models](https://arxiv.org/abs/1908.04211) - [Visualizing and Understanding the Effectiveness of BERT](https://arxiv.org/abs/1908.05620) (EMNLP2019) - [Attention Interpretability Across NLP Tasks](https://arxiv.org/abs/1909.11218) - [Revealing the Dark Secrets of BERT](https://arxiv.org/abs/1908.08593) (EMNLP2019) - [Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs](https://arxiv.org/abs/1909.02597) (EMNLP2019) - [The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives](https://arxiv.org/abs/1909.01380) (EMNLP2019) - [A Primer in BERTology: What we know about how BERT works](https://arxiv.org/abs/2002.12327) - [Do NLP Models Know Numbers? Probing Numeracy in Embeddings](https://arxiv.org/abs/1909.07940) (EMNLP2019) - [How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations](https://arxiv.org/abs/1909.04925) (CIKM2019) - [Whatcha lookin' at? DeepLIFTing BERT's Attention in Question Answering](https://arxiv.org/abs/1910.06431) - [What does BERT Learn from Multiple-Choice Reading Comprehension Datasets?](https://arxiv.org/abs/1910.12391) - [exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models](https://arxiv.org/abs/1910.05276) [[github](https://github.com/bhoov/exbert)] ## Multi-lingual - [Multilingual Constituency Parsing with Self-Attention and Pre-Training](https://arxiv.org/abs/1812.11760) (ACL2019) - [Language Model Pretraining](https://arxiv.org/abs/1901.07291) (NeurIPS2019) [[github](https://github.com/facebookresearch/XLM)] - [75 Languages, 1 Model: Parsing Universal Dependencies Universally](https://arxiv.org/abs/1904.02099) (EMNLP2019) [[github](https://github.com/hyperparticle/udify)] - [Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations](https://arxiv.org/abs/1910.05479) (EMNLP2019 WS) - [Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT](https://arxiv.org/abs/1904.09077) (EMNLP2019) - [How multilingual is Multilingual BERT?](https://arxiv.org/abs/1906.01502) (ACL2019) - [How Language-Neutral is Multilingual BERT?](https://arxiv.org/abs/1911.03310) - [Is Multilingual BERT Fluent in Language Generation?](https://arxiv.org/abs/1910.03806) - [BERT is Not an Interlingua and the Bias of Tokenization](https://www.aclweb.org/anthology/D19-6106/) (EMNLP2019 WS) - [Cross-Lingual Ability of Multilingual BERT: An Empirical Study](https://openreview.net/forum?id=HJeT3yrtDr) (ICLR2020) - [Multilingual Alignment of Contextual Word Representations](https://arxiv.org/abs/2002.03518) (ICLR2020) - [On the Cross-lingual Transferability of Monolingual Representations](https://arxiv.org/abs/1910.11856) - [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) - [Emerging Cross-lingual Structure in Pretrained Language Models](https://arxiv.org/abs/1911.01464) - [Can Monolingual Pretrained Models Help Cross-Lingual Classification?](https://arxiv.org/abs/1911.03913) - [Fully Unsupervised Crosslingual Semantic Textual Similarity Metric Based on BERT for Identifying Parallel Data](https://www.aclweb.org/anthology/K19-1020/) (CoNLL2019) ## Other than English models - [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) - [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) - [Multilingual is not enough: BERT for Finnish](https://arxiv.org/abs/1912.07076) - [BERTje: A Dutch BERT Model](https://arxiv.org/abs/1912.09582) - [RobBERT: a Dutch RoBERTa-based Language Model](https://arxiv.org/abs/2001.06286) - [Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language](https://arxiv.org/abs/1905.07213) ## Domain specific - [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://arxiv.org/abs/1901.08746) - [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://arxiv.org/abs/1906.05474) (ACL2019 WS) - [BERT-based Ranking for Biomedical Entity Normalization](https://arxiv.org/abs/1908.03548) - [PubMedQA: A Dataset for Biomedical Research Question Answering](https://arxiv.org/abs/1909.06146) (EMNLP2019) - [Pre-trained Language Model for Biomedical Question Answering](https://arxiv.org/abs/1909.08229) - [How to Pre-Train Your Model? Comparison of Different Pre-Training Models for Biomedical Question Answering](https://arxiv.org/abs/1911.00712) - [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://arxiv.org/abs/1904.05342) - [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) (NAACL2019 WS) - [Progress Notes Classification and Keyword Extraction using Attention-based Deep Learning Models with BERT](https://arxiv.org/abs/1910.05786) - [SciBERT: Pretrained Contextualized Embeddings for Scientific Text](https://arxiv.org/abs/1903.10676) [[github](https://github.com/allenai/scibert)] - [PatentBERT: Patent Classification with Fine-Tuning a pre-trained BERT Model](https://arxiv.org/abs/1906.02124) ## Multi-modal - [VideoBERT: A Joint Model for Video and Language Representation Learning](https://arxiv.org/abs/1904.01766) (ICCV2019) - [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://arxiv.org/abs/1908.02265) (NeurIPS2019) - [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/abs/1908.03557) - [Selfie: Self-supervised Pretraining for Image Embedding](https://arxiv.org/abs/1906.02940) - [ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data](https://arxiv.org/abs/2001.07966) - [Contrastive Bidirectional Transformer for Temporal Representation Learning](https://arxiv.org/abs/1906.05743) - [M-BERT: Injecting Multimodal Information in the BERT Structure](https://arxiv.org/abs/1908.05787) - [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://arxiv.org/abs/1908.07490) (EMNLP2019) - [Fusion of Detected Objects in Text for Visual Question Answering](https://arxiv.org/abs/1908.05054) (EMNLP2019) - [Unified Vision-Language Pre-Training for Image Captioning and VQA](https://arxiv.org/abs/1909.11059) [[github](https://github.com/LuoweiZhou/VLP)] - [Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline](https://arxiv.org/abs/1912.02379) - [VL-BERT: Pre-training of Generic Visual-Linguistic Representations](https://arxiv.org/abs/1908.08530) (ICLR2020) - [Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training](https://arxiv.org/abs/1908.06066) - [UNITER: Learning UNiversal Image-TExt Representations](https://arxiv.org/abs/1909.11740) - [Supervised Multimodal Bitransformers for Classifying Images and Text](https://arxiv.org/abs/1909.02950) - [Weak Supervision helps Emergence of Word-Object Alignment and improves Vision-Language Tasks](https://arxiv.org/abs/1912.03063) - [BERT Can See Out of the Box: On the Cross-modal Transferability of Text Representations](https://arxiv.org/abs/2002.10832) - [BERT for Large-scale Video Segment Classification with Test-time Augmentation](https://arxiv.org/abs/1912.01127) (ICCV2019WS) - [SpeechBERT: Cross-Modal Pre-trained Language Model for End-to-end Spoken Question Answering](https://arxiv.org/abs/1910.11559) - [vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations](https://arxiv.org/abs/1910.05453) - [Effectiveness of self-supervised pre-training for speech recognition](https://arxiv.org/abs/1911.03912) - [Understanding Semantics from Speech Through Pre-training](https://arxiv.org/abs/1909.10924) - [Towards Transfer Learning for End-to-End Speech Synthesis from Deep Pre-Trained Language Models](https://arxiv.org/abs/1906.07307) ## Model compression - [Distilling Task-Specific Knowledge from BERT into Simple Neural Networks](https://arxiv.org/abs/1903.12136) - [Patient Knowledge Distillation for BERT Model Compression](https://arxiv.org/abs/1908.09355) (EMNLP2019) - [Small and Practical BERT Models for Sequence Labeling](https://arxiv.org/abs/1909.00100) (EMNLP2019) - [Pruning a BERT-based Question Answering Model](https://arxiv.org/abs/1910.06360) - [TinyBERT: Distilling BERT for Natural Language Understanding](https://arxiv.org/abs/1909.10351) [[github](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/TinyBERT)] - [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) (NeurIPS2019 WS) [[github](https://github.com/huggingface/transformers/tree/master/examples/distillation)] - [PoWER-BERT: Accelerating BERT inference for Classification Tasks](https://arxiv.org/abs/2001.08950) - [WaLDORf: Wasteless Language-model Distillation On Reading-comprehension](https://arxiv.org/abs/1912.06638) - [Extreme Language Model Compression with Optimal Subwords and Shared Projections](https://arxiv.org/abs/1909.11687) - [BERT-of-Theseus: Compressing BERT by Progressive Module Replacing](https://arxiv.org/abs/2002.02925) - [Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning](https://arxiv.org/abs/2002.08307) - [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://arxiv.org/abs/2002.10957) - [Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT](https://arxiv.org/abs/1909.05840) - [Q8BERT: Quantized 8Bit BERT](https://arxiv.org/abs/1910.06188) (NeurIPS2019 WS) ## Misc. - [Cloze-driven Pretraining of Self-attention Networks](https://arxiv.org/abs/1903.07785) - [Learning and Evaluating General Linguistic Intelligence](https://arxiv.org/abs/1901.11373) - [To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks](https://arxiv.org/abs/1903.05987) (ACL2019 WS) - [BERTScore: Evaluating Text Generation with BERT](https://arxiv.org/abs/1904.09675) (ICLR2020) - [Machine Translation Evaluation with BERT Regressor](https://arxiv.org/abs/1907.12679) - [SumQE: a BERT-based Summary Quality Estimation Model](https://arxiv.org/abs/1909.00578) (EMNLP2019) - [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://arxiv.org/abs/1904.00962) (ICLR2020) - [Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models](https://openreview.net/forum?id=HkgaETNtDB) (ICLR2020) - [A Mutual Information Maximization Perspective of Language Representation Learning](https://openreview.net/forum?id=Syx79eBKwr) (ICLR2020) - [Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment](https://arxiv.org/abs/1907.11932) (AAAI2020) - [Thieves on Sesame Street! Model Extraction of BERT-based APIs](https://arxiv.org/abs/1910.12366) (ICLR2020) - [Graph-Bert: Only Attention is Needed for Learning Graph Representations](https://arxiv.org/abs/2001.05140) - [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://arxiv.org/abs/2002.08155) - [Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping](https://arxiv.org/abs/2002.06305) - [Extending Machine Language Models toward Human-Level Language Understanding](https://arxiv.org/abs/1912.05877) - [Glyce: Glyph-vectors for Chinese Character Representations](https://arxiv.org/abs/1901.10125) - [Back to the Future -- Sequential Alignment of Text Representations](https://arxiv.org/abs/1909.03464) - [Improving Cuneiform Language Identification with BERT](https://www.aclweb.org/anthology/papers/W/W19/W19-1402/) (NAACL2019 WS) - [BERT has a Moral Compass: Improvements of ethical and moral values of machines](https://arxiv.org/abs/1912.05238) - [SMILES-BERT: Large Scale Unsupervised Pre-Training for Molecular Property Prediction](https://dl.acm.org/citation.cfm?id=3342186) (ACM-BCB2019) - [On the comparability of Pre-trained Language Models](https://arxiv.org/abs/2001.00781) - [Transformers: State-of-the-art Natural Language Processing](https://arxiv.org/abs/1910.03771) - [Evolution of transfer learning in natural language processing](https://arxiv.org/abs/1910.07370) # collect BERT related resources. ## 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 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)] # Github Repositories: ## official implement: 1. [google-research/bert](https://github.com/google-research/bert), **officical** TensorFlow code and pre-trained models for BERT , ![](https://img.shields.io/github/stars/google-research/bert.svg) ## implement of BERT besides tensorflow: 1. [codertimo/BERT-pytorch](https://github.com/codertimo/BERT-pytorch), Google AI 2018 BERT pytorch implementation, ![](https://img.shields.io/github/stars/codertimo/BERT-pytorch.svg) 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, ![](https://img.shields.io/github/stars/huggingface/pytorch-pretrained-BERT.svg) 3. [Separius/BERT-keras](https://github.com/Separius/BERT-keras), Keras implementation of BERT with pre-trained weights, ![](https://img.shields.io/github/stars/Separius/BERT-keras.svg) 4. [soskek/bert-chainer](https://github.com/soskek/bert-chainer), Chainer implementation of "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding", ![](https://img.shields.io/github/stars/soskek/bert-chainer.svg) 5. [innodatalabs/tbert](https://github.com/innodatalabs/tbert), PyTorch port of BERT ML model ![](https://img.shields.io/github/stars/innodatalabs/tbert.svg) 6. [guotong1988/BERT-tensorflow](https://github.com/guotong1988/BERT-tensorflow), BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding ![](https://img.shields.io/github/stars/guotong1988/BERT-tensorflow.svg) 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" ![](https://img.shields.io/github/stars/dreamgonfly/BERT-pytorch.svg) 8. [CyberZHG/keras-bert](https://github.com/CyberZHG/keras-bert), Implementation of BERT that could load official pre-trained models for feature extraction and prediction ![](https://img.shields.io/github/stars/CyberZHG/keras-bert.svg) 9. [soskek/bert-chainer](https://github.com/soskek/bert-chainer), Chainer implementation of "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" ![](https://img.shields.io/github/stars/soskek/bert-chainer.svg) 10. [MaZhiyuanBUAA/bert-tf1.4.0](https://github.com/MaZhiyuanBUAA/bert-tf1.4.0), bert-tf1.4.0 ![](https://img.shields.io/github/stars/MaZhiyuanBUAA/bert-tf1.4.0.svg) 11. [dhlee347/pytorchic-bert](https://github.com/dhlee347/pytorchic-bert), Pytorch Implementation of Google BERT, ![](https://img.shields.io/github/stars/dhlee347/pytorchic-bert.svg) 12. [kpot/keras-transformer](https://github.com/kpot/keras-transformer), Keras library for building (Universal) Transformers, facilitating BERT and GPT models, ![](https://img.shields.io/github/stars/kpot/keras-transformer.svg) 13. [miroozyx/BERT_with_keras](https://github.com/miroozyx/BERT_with_keras), A Keras version of Google's BERT model, ![](https://img.shields.io/github/stars/miroozyx/BERT_with_keras.svg) 14. [conda-forge/pytorch-pretrained-bert-feedstock](https://github.com/conda-forge/pytorch-pretrained-bert-feedstock), A conda-smithy repository for pytorch-pretrained-bert. , ![](https://img.shields.io/github/stars/conda-forge/pytorch-pretrained-bert-feedstock.svg) 15. [Rshcaroline/BERT_Pytorch_fastNLP](https://github.com/Rshcaroline/BERT_Pytorch_fastNLP), A PyTorch & fastNLP implementation of Google AI's BERT model. ![](https://img.shields.io/github/stars/Rshcaroline/BERT_Pytorch_fastNLP.svg) 17. [nghuyong/ERNIE-Pytorch](https://github.com/nghuyong/ERNIE-Pytorch), ERNIE Pytorch Version, ![](https://img.shields.io/github/stars/nghuyong/ERNIE-Pytorch.svg) 18. [dmlc/gluon-nlp](https://github.com/dmlc/gluon-nlp), Gluon + MXNet implementation that reproduces BERT pretraining and finetuning on GLUE benchmark, SQuAD, etc, ![](https://img.shields.io/github/stars/dmlc/gluon-nlp.svg) 19. [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. ![](https://img.shields.io/github/stars/dbiir/UER-py.svg) ## improvement over BERT: 1. [thunlp/ERNIE](https://github.com/https://github.com/thunlp/ERNIE), Source code and dataset for ACL 2019 paper "ERNIE: Enhanced Language Representation with Informative Entities", imporove bert with heterogeneous information fusion. ![](https://img.shields.io/github/stars/thunlp/ERNIE.svg) 2. [PaddlePaddle/LARK](https://github.com/PaddlePaddle/LARK), LAnguage Representations Kit, PaddlePaddle implementation of BERT. It also contains an improved version of BERT, ERNIE, for chinese NLP tasks. ![](https://img.shields.io/github/stars/PaddlePaddle/LARK.svg) 3. [ymcui/Chinese-BERT-wwm](https://github.com/ymcui/Chinese-BERT-wwm), Pre-Training with Whole Word Masking for Chinese BERT https://arxiv.org/abs/1906.08101, ![](https://img.shields.io/github/stars/ymcui/Chinese-BERT-wwm.svg) 4. [zihangdai/xlnet](https://github.com/zihangdai/xlnet), XLNet: Generalized Autoregressive Pretraining for Language Understanding, ![](https://img.shields.io/github/stars/zihangdai/xlnet.svg) 5. [kimiyoung/transformer-xl](https://github.com/kimiyoung/transformer-xl), Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context, This repository contains the code in both PyTorch and TensorFlow for our paper. ![](https://img.shields.io/github/stars/kimiyoung/transformer-xl.svg) 6. [GaoPeng97/transformer-xl-chinese](https://github.com/GaoPeng97/transformer-xl-chinese), （transformer xl for text generation of chinese）, ![](https://img.shields.io/github/stars/GaoPeng97/transformer-xl-chinese.svg) ## other resources for BERT: 1. [brightmart/bert_language_understanding](https://github.com/brightmart/bert_language_understanding), Pre-training of Deep Bidirectional Transformers for Language Understanding: pre-train TextCNN, ![](https://img.shields.io/github/stars/brightmart/bert_language_understanding.svg) 2. [Y1ran/NLP-BERT--ChineseVersion](https://github.com/Y1ran/NLP-BERT--ChineseVersion), ![](https://img.shields.io/github/stars/Y1ran/NLP-BERT--ChineseVersion.svg) 3. [yangbisheng2009/cn-bert](https://github.com/yangbisheng2009/cn-bert), ![](https://img.shields.io/github/stars/yangbisheng2009/cn-bert.svg) 4. [JayYip/bert-multiple-gpu](https://github.com/JayYip/bert-multiple-gpu), A multiple GPU support version of BERT, ![](https://img.shields.io/github/stars/JayYip/bert-multiple-gpu.svg) 5. [HighCWu/keras-bert-tpu](https://github.com/HighCWu/keras-bert-tpu), Implementation of BERT that could load official pre-trained models for feature extraction and prediction on TPU, ![](https://img.shields.io/github/stars/HighCWu/keras-bert-tpu.svg) 6. [Willyoung2017/Bert_Attempt](https://github.com/Willyoung2017/Bert_Attempt), PyTorch Pretrained Bert, ![](https://img.shields.io/github/stars/Willyoung2017/Bert_Attempt.svg) 7. [Pydataman/bert_examples](https://github.com/Pydataman/bert_examples), some examples of bert, run_classifier.py ![](https://img.shields.io/github/stars/Pydataman/bert_examples.svg) 8. [guotong1988/BERT-chinese](https://github.com/guotong1988/BERT-chinese), BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding ![](https://img.shields.io/github/stars/guotong1988/BERT-chinese.svg) 9. [zhongyunuestc/bert_multitask](https://github.com/zhongyunuestc/bert_multitask), 多任务task ![](https://img.shields.io/github/stars/zhongyunuestc/bert_multitask.svg) 10. [Microsoft/AzureML-BERT](https://github.com/Microsoft/AzureML-BERT), End-to-end walk through for fine-tuning BERT using Azure Machine Learning , ![](https://img.shields.io/github/stars/Microsoft/AzureML-BERT.svg) 11. [bigboNed3/bert_serving](https://github.com/bigboNed3/bert_serving), export bert model for serving, ![](https://img.shields.io/github/stars/nghuyong/ERNIE-Pytorch.svg) 12. [yoheikikuta/bert-japanese](https://github.com/yoheikikuta/bert-japanese), BERT with SentencePiece for Japanese text. ![](https://img.shields.io/github/stars/bigboNed3/bert_serving.svg) 13. [whqwill/seq2seq-keyphrase-bert](https://github.com/whqwill/seq2seq-keyphrase-bert), add BERT to encoder part for https://github.com/memray/seq2seq-keyphrase-pytorch, ![](https://img.shields.io/github/stars/whqwill/seq2seq-keyphrase-bert.svg) 14. [algteam/bert-examples](https://github.com/algteam/bert-examples), bert-demo, ![](https://img.shields.io/github/stars/algteam/bert-examples.svg) 15. [cedrickchee/awesome-bert-nlp](https://github.com/cedrickchee/awesome-bert-nlp), A curated list of NLP resources focused on BERT, attention mechanism, Transformer networks, and transfer learning. ![](https://img.shields.io/github/stars/cedrickchee/awesome-bert-nlp.svg) 16. [cnfive/cnbert](https://github.com/cnfive/cnbert), ![](https://img.shields.io/github/stars/cnfive/cnbert.svg) 17. [brightmart/bert_customized](https://github.com/brightmart/bert_customized), bert with customized features, ![](https://img.shields.io/github/stars/brightmart/bert_customized.svg) 19. [JayYip/bert-multitask-learning](https://github.com/JayYip/bert-multitask-learning), BERT for Multitask Learning, ![](https://img.shields.io/github/stars/JayYip/bert-multitask-learning.svg) 20. [yuanxiaosc/BERT_Paper_Chinese_Translation](https://github.com/yuanxiaosc/BERT_Paper_Chinese_Translation), BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding 。Chinese Translation! https://yuanxiaosc.github.io/2018/12/…, ![](https://img.shields.io/github/stars/yuanxiaosc/BERT_Paper_Chinese_Translation.svg) 21. [yaserkl/BERTvsULMFIT](https://github.com/yaserkl/BERTvsULMFIT), Comparing Text Classification results using BERT embedding and ULMFIT embedding, ![](https://img.shields.io/github/stars/yaserkl/BERTvsULMFIT.svg) 22. [kpot/keras-transformer](https://github.com/kpot/keras-transformer), Keras library for building (Universal) Transformers, facilitating BERT and GPT models, ![](https://img.shields.io/github/stars/kpot/keras-transformer.svg) 23. [1234560o/Bert-model-code-interpretation](https://github.com/1234560o/Bert-model-code-interpretation), ![](https://img.shields.io/github/stars/1234560o/Bert-model-code-interpretation.svg) 24. [cdathuraliya/bert-inference](https://github.com/cdathuraliya/bert-inference), A helper class for Google BERT (Devlin et al., 2018) to support online prediction and model pipelining. ![](https://img.shields.io/github/stars/cdathuraliya/bert-inference.svg) 26. [gameofdimension/java-bert-predict](https://github.com/gameofdimension/java-bert-predict), turn bert pretrain checkpoint into saved model for a feature extracting demo in java ![](https://img.shields.io/github/stars/gameofdimension/java-bert-predict.svg) 27. [1234560o/Bert-model-code-interpretation](https://github.com/1234560o/Bert-model-code-interpretation), ![](https://img.shields.io/github/stars/1234560o/Bert-model-code-interpretation.svg) ## domain specific BERT: 1. [allenai/scibert](https://github.com/allenai/scibert), A BERT model for scientific text. https://arxiv.org/abs/1903.10676, ![](https://img.shields.io/github/stars/allenai/scibert.svg) 2. [MeRajat/SolvingAlmostAnythingWithBert](https://github.com/MeRajat/SolvingAlmostAnythingWithBert), BioBert Pytorch ![](https://img.shields.io/github/stars/MeRajat/SolvingAlmostAnythingWithBert.svg) 3. [kexinhuang12345/clinicalBERT](https://github.com/kexinhuang12345/clinicalBERT), ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission https://arxiv.org/abs/1904.05342 ![](https://img.shields.io/github/stars/kexinhuang12345/clinicalBERT.svg) 4. [EmilyAlsentzer/clinicalBERT](https://github.com/EmilyAlsentzer/clinicalBERT), repository for Publicly Available Clinical BERT Embeddings ![](https://img.shields.io/github/stars/EmilyAlsentzer/clinicalBERT.svg) ## BERT Deploy Tricks: 1. [zhihu/cuBERT](https://github.com/zhihu/cuBERT), Fast implementation of BERT inference directly on NVIDIA (CUDA, CUBLAS) and Intel MKL ![](https://img.shields.io/github/stars/zhihu/cuBERT.svg) 2. [xmxoxo/BERT-train2deploy](https://github.com/xmxoxo/BERT-train2deploy), Bert Model training and deploy, ![](https://img.shields.io/github/stars/xmxoxo/BERT-train2deploy.svg) ## BERT QA & RC task: 1. [sogou/SMRCToolkit](https://github.com/sogou/SMRCToolkit), This toolkit was designed for the fast and efficient development of modern machine comprehension models, including both published models and original prototypes., ![](https://img.shields.io/github/stars/sogou/SMRCToolkit.svg) 1. [benywon/ChineseBert](https://github.com/benywon/ChineseBert), This is a chinese Bert model specific for question answering, ![](https://img.shields.io/github/stars/benywon/ChineseBert.svg) 3. [matthew-z/R-net](https://github.com/matthew-z/R-net), R-net in PyTorch, with BERT and ELMo, ![](https://img.shields.io/github/stars/matthew-z/R-net.svg) 4. [nyu-dl/dl4marco-bert](https://github.com/nyu-dl/dl4marco-bert), Passage Re-ranking with BERT, ![](https://img.shields.io/github/stars/nyu-dl/dl4marco-bert.svg) 5. [xzp27/BERT-for-Chinese-Question-Answering](https://github.com/xzp27/BERT-for-Chinese-Question-Answering), ![](https://img.shields.io/github/stars/xzp27/BERT-for-Chinese-Question-Answering.svg) 6. [chiayewken/bert-qa](https://github.com/chiayewken/bert-qa), BERT for question answering starting with HotpotQA, ![](https://img.shields.io/github/stars/chiayewken/bert-qa.svg) 8. [ankit-ai/BertQA-Attention-on-Steroids](https://github.com/ankit-ai/BertQA-Attention-on-Steroids), BertQA - Attention on Steroids, ![](https://img.shields.io/github/stars/ankit-ai/BertQA-Attention-on-Steroids.svg) 10. [NoviScl/BERT-RACE](https://github.com/NoviScl/BERT-RACE), This work is based on Pytorch implementation of BERT (https://github.com/huggingface/pytorch-pretrained-BERT). I adapted the original BERT model to work on multiple choice machine comprehension. ![](https://img.shields.io/github/stars/NoviScl/BERT-RACE.svg) 11. [eva-n27/BERT-for-Chinese-Question-Answering](https://github.com/eva-n27/BERT-for-Chinese-Question-Answering), ![](https://img.shields.io/github/stars/eva-n27/BERT-for-Chinese-Question-Answering.svg) 12. [allenai/allennlp-bert-qa-wrapper](https://github.com/allenai/allennlp-bert-qa-wrapper), This is a simple wrapper on top of pretrained BERT based QA models from pytorch-pretrained-bert to make AllenNLP model archives, so that you can serve demos from AllenNLP. ![](https://img.shields.io/github/stars/allenai/allennlp-bert-qa-wrapper.svg) 13. [edmondchensj/ChineseQA-with-BERT](https://github.com/edmondchensj/ChineseQA-with-BERT), EECS 496: Advanced Topics in Deep Learning Final Project: Chinese Question Answering with BERT (Baidu DuReader Dataset) ![](https://img.shields.io/github/stars/edmondchensj/ChineseQA-with-BERT.svg) 14. [graykode/toeicbert](https://github.com/graykode/toeicbert), TOEIC(Test of English for International Communication) solving using pytorch-pretrained-BERT model., ![](https://img.shields.io/github/starsgraykode/toeicbert.svg) 15. [graykode/KorQuAD-beginner](https://github.com/graykode/KorQuAD-beginner), https://github.com/graykode/KorQuAD-beginner ![](https://img.shields.io/github/stars/graykode/KorQuAD-beginner.svg) 16. [krishna-sharma19/SBU-QA](https://github.com/krishna-sharma19/SBU-QA), This repository uses pretrain BERT embeddings for transfer learning in QA domain ![](https://img.shields.io/github/stars/krishna-sharma19/SBU-QA.svg) ## BERT classification task: 1. [zhpmatrix/Kaggle-Quora-Insincere-Questions-Classification](https://github.com/zhpmatrix/Kaggle-Quora-Insincere-Questions-Classification), ![](https://img.shields.io/github/stars/zhpmatrix/Kaggle-Quora-Insincere-Questions-Classification.svg) 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 ![](https://img.shields.io/github/stars/maksna/bert-fine-tuning-for-chinese-multiclass-classification.svg) 3. [NLPScott/bert-Chinese-classification-task](https://github.com/NLPScott/bert-Chinese-classification-task), ![](https://img.shields.io/github/stars/NLPScott/bert-Chinese-classification-task.svg) 4. [Socialbird-AILab/BERT-Classification-Tutorial](https://github.com/Socialbird-AILab/BERT-Classification-Tutorial), ![](https://img.shields.io/github/stars/Socialbird-AILab/BERT-Classification-Tutorial.svg) 5. [fooSynaptic/BERT_classifer_trial](https://github.com/fooSynaptic/BERT_classifer_trial), BERT trial for chinese corpus classfication ![](https://img.shields.io/github/stars/fooSynaptic/BERT_classifer_trial.svg) 6. [xiaopingzhong/bert-finetune-for-classfier](https://github.com/xiaopingzhong/bert-finetune-for-classfier), ![](https://img.shields.io/github/stars/xiaopingzhong/bert-finetune-for-classfier.svg) 8. [pengming617/bert_classification](https://github.com/pengming617/bert_classification), , ![](https://img.shields.io/github/stars/pengming617/bert_classification.svg) 9. [xieyufei1993/Bert-Pytorch-Chinese-TextClassification](https://github.com/xieyufei1993/Bert-Pytorch-Chinese-TextClassification), Pytorch Bert Finetune in Chinese Text Classification, ![](https://img.shields.io/github/stars/xieyufei1993/Bert-Pytorch-Chinese-TextClassification.svg) 10. [liyibo/text-classification-demos](https://github.com/liyibo/text-classification-demos), Neural models for Text Classification in Tensorflow, such as cnn, dpcnn, fasttext, bert ..., ![](https://img.shields.io/github/stars/liyibo/text-classification-demos.svg) 11. [circlePi/BERT_Chinese_Text_Class_By_pytorch](https://github.com/circlePi/BERT_Chinese_Text_Class_By_pytorch), A Pytorch implements of Chinese text class based on BERT_Pretrained_Model, ![](https://img.shields.io/github/stars/circlePi/BERT_Chinese_Text_Class_By_pytorch.svg) 12. [kaushaltrivedi/bert-toxic-comments-multilabel](https://github.com/kaushaltrivedi/bert-toxic-comments-multilabel), Multilabel classification for Toxic comments challenge using Bert, ![](https://img.shields.io/github/stars/kaushaltrivedi/bert-toxic-comments-multilabel.svg) 13. [lonePatient/BERT-chinese-text-classification-pytorch](https://github.com/lonePatient/BERT-chinese-text-classification-pytorch), This repo contains a PyTorch implementation of a pretrained BERT model for text classification., ![](https://img.shields.io/github/stars/lonePatient/BERT-chinese-text-classification-pytorch.svg) ## BERT Sentiment Analysis 1. [Chung-I/Douban-Sentiment-Analysis](https://github.com/Chung-I/Douban-Sentiment-Analysis), Sentiment Analysis on Douban Movie Short Comments Dataset using BERT. ![](https://img.shields.io/github/stars/Chung-I/Douban-Sentiment-Analysis.svg) 14. [lynnna-xu/bert_sa](https://github.com/lynnna-xu/bert_sa), bert sentiment analysis tensorflow serving with RESTful API ![](https://img.shields.io/github/stars/lynnna-xu/bert_sa.svg) 15. [HSLCY/ABSA-BERT-pair](https://github.com/HSLCY/ABSA-BERT-pair), Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (NAACL 2019) https://arxiv.org/abs/1903.09588, ![](https://img.shields.io/github/stars/HSLCY/ABSA-BERT-pair.svg) 16. [songyouwei/ABSA-PyTorch](https://github.com/songyouwei/ABSA-PyTorch), Aspect Based Sentiment Analysis, PyTorch Implementations., ![](https://img.shields.io/github/stars/songyouwei/ABSA-PyTorch.svg) 17. [howardhsu/BERT-for-RRC-ABSA](https://github.com/howardhsu/BERT-for-RRC-ABSA), code for our NAACL 2019 paper: "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis", ![](https://img.shields.io/github/stars/howardhsu/BERT-for-RRC-ABSA.svg) 7. [brightmart/sentiment_analysis_fine_grain](https://github.com/brightmart/sentiment_analysis_fine_grain), Multi-label Classification with BERT; Fine Grained Sentiment Analysis from AI challenger, ![](https://img.shields.io/github/stars/brightmart/sentiment_analysis_fine_grain.svg) ## BERT NER task: 2. [zhpmatrix/bert-sequence-tagging](https://github.com/zhpmatrix/bert-sequence-tagging), ![](https://img.shields.io/github/stars/zhpmatrix/bert-sequence-tagging.svg) 3. [kyzhouhzau/BERT-NER](https://github.com/kyzhouhzau/BERT-NER), Use google BERT to do CoNLL-2003 NER ! , ![](https://img.shields.io/github/stars/kyzhouhzau/BERT-NER.svg) 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. ![](https://img.shields.io/github/stars/king-menin/ner-bert.svg) 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 , ![](https://img.shields.io/github/stars/macanv/BERT-BiLSMT-CRF-NER.svg) 6. [FuYanzhe2/Name-Entity-Recognition](https://github.com/FuYanzhe2/Name-Entity-Recognition), Lstm-crf,Lattice-CRF,bert-ner ![](https://img.shields.io/github/stars/FuYanzhe2/Name-Entity-Recognition.svg) 7. [mhcao916/NER_Based_on_BERT](https://github.com/mhcao916/NER_Based_on_BERT), this project is based on google bert model, which is a Chinese NER ![](https://img.shields.io/github/stars/mhcao916/NER_Based_on_BERT.svg) 8. [ProHiryu/bert-chinese-ner](https://github.com/ProHiryu/bert-chinese-ner), ![](https://img.shields.io/github/stars/ProHiryu/bert-chinese-ner.svg) 9. [sberbank-ai/ner-bert](https://github.com/sberbank-ai/ner-bert), BERT-NER (nert-bert) with google bert, ![](https://img.shields.io/github/stars/sberbank-ai/ner-bert.svg) 10. [kyzhouhzau/Bert-BiLSTM-CRF](https://github.com/kyzhouhzau/Bert-BiLSTM-CRF), This model base on bert-as-service. Model structure : bert-embedding bilstm crf. , ![](https://img.shields.io/github/stars/kyzhouhzau/Bert-BiLSTM-CRF.svg) 11. [Hoiy/berserker](https://github.com/Hoiy/berserker), Berserker - BERt chineSE woRd toKenizER, Berserker (BERt chineSE woRd toKenizER) is a Chinese tokenizer built on top of Google's BERT model. , ![](https://img.shields.io/github/stars/Hoiy/berserker.svg) 12. [Kyubyong/bert_ner](https://github.com/Kyubyong/bert_ner), Ner with Bert, ![](https://img.shields.io/github/stars/Kyubyong/bert_ner.svg) 13. [jiangpinglei/BERT_ChineseWordSegment](https://github.com/jiangpinglei/BERT_ChineseWordSegment), A Chinese word segment model based on BERT, F1-Score 97%, ![](https://img.shields.io/github/stars/jiangpinglei/BERT_ChineseWordSegment.svg) 14. [yanwii/ChineseNER](https://github.com/yanwii/ChineseNER), ![](https://img.shields.io/github/stars/yanwii/ChineseNER.svg) 15. [lemonhu/NER-BERT-pytorch](https://github.com/lemonhu/NER-BERT-pytorch), PyTorch solution of NER task Using Google AI's pre-trained BERT model. ![](https://img.shields.io/github/stars/lemonhu/NER-BERT-pytorch.svg) ## BERT Text Summarization Task: 1. [nlpyang/BertSum](https://github.com/nlpyang/BertSum), Code for paper Fine-tune BERT for Extractive Summarization, ![](https://img.shields.io/github/stars/nlpyang/BertSum.svg) 2. [santhoshkolloju/Abstractive-Summarization-With-Transfer-Learning](https://github.com/santhoshkolloju/Abstractive-Summarization-With-Transfer-Learning), Abstractive summarisation using Bert as encoder and Transformer Decoder, ![](https://img.shields.io/github/stars/santhoshkolloju/Abstractive-Summarization-With-Transfer-Learning.svg) 3. [nayeon7lee/bert-summarization](https://github.com/nayeon7lee/bert-summarization), Implementation of 'Pretraining-Based Natural Language Generation for Text Summarization', Paper: https://arxiv.org/pdf/1902.09243.pdf ![](https://img.shields.io/github/stars/nayeon7lee/bert-summarization.svg) 4. [dmmiller612/lecture-summarizer](https://github.com/dmmiller612/lecture-summarizer), Lecture summarizer with BERT ![](https://img.shields.io/github/stars/dmmiller612/lecture-summarizer.svg) ## BERT Text Generation Task: 1. [asyml/texar](https://github.com/asyml/texar), Toolkit for Text Generation and Beyond https://texar.io, Texar is a general-purpose text generation toolkit, has also implemented BERT here for classification, and text generation applications by combining with Texar's other modules. ![](https://img.shields.io/github/stars/asyml/texar.svg) 2. [voidful/BertGenerate](https://github.com/voidful/BertGenerate), Fine tuning bert for text generation, ![](https://img.shields.io/github/stars/voidful/BertGenerate.svg) 3. [Tiiiger/bert_score](https://github.com/Tiiiger/bert_score), BERT score for language generation, ![](https://img.shields.io/github/stars/Tiiiger/bert_score.svg) ## BERT Knowledge Graph Task : 1. [lvjianxin/Knowledge-extraction](https://github.com/lvjianxin/Knowledge-extraction), ![](https://img.shields.io/github/stars/lvjianxin/Knowledge-extraction.svg) 2. [sakuranew/BERT-AttributeExtraction](https://github.com/sakuranew/BERT-AttributeExtraction), USING BERT FOR Attribute Extraction in KnowledgeGraph. fine-tuning and feature extraction., ![](https://img.shields.io/github/stars/sakuranew/BERT-AttributeExtraction.svg) 3. [aditya-AI/Information-Retrieval-System-using-BERT](https://github.com/aditya-AI/Information-Retrieval-System-using-BERT), ![](https://img.shields.io/github/stars/aditya-AI/Information-Retrieval-System-using-BERT.svg) 4. [jkszw2014/bert-kbqa-NLPCC2017](https://github.com/jkszw2014/bert-kbqa-NLPCC2017), A trial of kbqa based on bert for NLPCC2016/2017 Task 5, https://blog.csdn.net/ai_1046067944/article/details/86707784 , ![](https://img.shields.io/github/stars/jkszw2014/bert-kbqa-NLPCC2017.svg) 5. [yuanxiaosc/Schema-based-Knowledge-Extraction](https://github.com/yuanxiaosc/Schema-based-Knowledge-Extraction), Code for http://lic2019.ccf.org.cn/kg, ![](https://img.shields.io/github/stars/yuanxiaosc/Schema-based-Knowledge-Extraction.svg) 6. [yuanxiaosc/Entity-Relation-Extraction](https://github.com/yuanxiaosc/Entity-Relation-Extraction), Entity and Relation Extraction Based on TensorFlow.Schema based Knowledge Extraction, SKE 2019 http://lic2019.ccf.org.cn, ![](https://img.shields.io/github/stars/yuanxiaosc/Entity-Relation-Extraction.svg) 7. [WenRichard/KBQA-BERT](https://github.com/WenRichard/KBQA-BERT), https://zhuanlan.zhihu.com/p/62946533 , ![](https://img.shields.io/github/stars/WenRichard/KBQA-BERT.svg) ## BERT Coreference Resolution 1. [ianycxu/RGCN-with-BERT](https://github.com/ianycxu/RGCN-with-BERT), Gated-Relational Graph Convolutional Networks (RGCN) with BERT for Coreference Resolution Task ![](https://img.shields.io/github/stars/ianycxu/RGCN-with-BERT.svg) 2. [isabellebouchard/BERT_for_GAP-coreference](https://github.com/isabellebouchard/BERT_for_GAP-coreference), BERT finetuning for GAP unbiased pronoun resolution ![](https://img.shields.io/github/stars/isabellebouchard/BERT_for_GAP-coreference.svg) ## BERT visualization toolkit: 1. [jessevig/bertviz](https://github.com/jessevig/bertviz), Tool for visualizing BERT's attention, ![](https://img.shields.io/github/stars/jessevig/bertviz.svg) ## BERT chatbot : 1. [GaoQ1/rasa_nlu_gq](https://github.com/GaoQ1/rasa_nlu_gq), turn natural language into structured data, ![](https://img.shields.io/github/stars/GaoQ1/rasa_nlu_gq.svg) 2. [GaoQ1/rasa_chatbot_cn](https://github.com/GaoQ1/rasa_chatbot_cn), ![](https://img.shields.io/github/stars/GaoQ1/rasa_chatbot_cn.svg) 3. [GaoQ1/rasa-bert-finetune](https://github.com/GaoQ1/rasa-bert-finetune), ![](https://img.shields.io/github/stars/GaoQ1/rasa-bert-finetune.svg) 5. [geodge831012/bert_robot](https://github.com/geodge831012/bert_robot) ![](https://img.shields.io/github/stars/geodge831012/bert_robot.svg) 6. [yuanxiaosc/BERT-for-Sequence-Labeling-and-Text-Classification](https://github.com/yuanxiaosc/BERT-for-Sequence-Labeling-and-Text-Classification), This is the template code to use BERT for sequence lableing and text classification, in order to facilitate BERT for more tasks. Currently, the template code has included conll-2003 named entity identification, Snips Slot Filling and Intent Prediction. ![](https://img.shields.io/github/stars/yuanxiaosc/BERT-for-Sequence-Labeling-and-Text-Classification.svg) 7. [guillaume-chevalier/ReuBERT](https://github.com/guillaume-chevalier/ReuBERT), A question-answering chatbot, simply. ![](https://img.shields.io/github/stars/guillaume-chevalier/ReuBERT.svg) ## BERT language model and embedding: 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, ![](https://img.shields.io/github/stars/hanxiao/bert-as-service.svg) 2. [YC-wind/embedding_study](https://github.com/YC-wind/embedding_study), ![](https://img.shields.io/github/stars/YC-wind/embedding_study.svg) 3. [Kyubyong/bert-token-embeddings](https://github.com/Kyubyong/bert-token-embeddings), Bert Pretrained Token Embeddings, ![](https://img.shields.io/github/stars/Kyubyong/bert-token-embeddings.svg) 4. [xu-song/bert_as_language_model](https://github.com/xu-song/bert_as_language_model), bert as language model, fork from https://github.com/google-research/bert, ![](https://img.shields.io/github/stars/xu-song/bert_as_language_model.svg) 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., ![](https://img.shields.io/github/stars/yuanxiaosc/Deep_dynamic_word_representation.svg) 6. [imgarylai/bert-embedding](https://github.com/imgarylai/bert-embedding), Token level embeddings from BERT model on mxnet and gluonnlp http://bert-embedding.readthedocs.io/, ![](https://img.shields.io/github/stars/imgarylai/bert-embedding.svg) 7. [terrifyzhao/bert-utils](https://github.com/terrifyzhao/bert-utils), ![](https://img.shields.io/github/stars/terrifyzhao/bert-utils.svg) 8. [fennuDetudou/BERT_implement](https://github.com/fennuDetudou/BERT_implement), ![](https://img.shields.io/github/stars/fennuDetudou/BERT_implement.svg) 9. [whqwill/seq2seq-keyphrase-bert](https://github.com/whqwill/seq2seq-keyphrase-bert), add BERT to encoder part for https://github.com/memray/seq2seq-keyphrase-pytorch, ![](https://img.shields.io/github/stars/whqwill/seq2seq-keyphrase-bert.svg) 10. [charles9n/bert-sklearn](https://github.com/charles9n/bert-sklearn), a sklearn wrapper for Google's BERT model, ![](https://img.shields.io/github/stars/charles9n/bert-sklearn.svg) 12. [NVIDIA/Megatron-LM](https://github.com/NVIDIA/Megatron-LM), Ongoing research training transformer language models at scale, including: BERT, ![](https://img.shields.io/github/stars/NVIDIA/Megatron-LM.svg) 13. [hankcs/BERT-token-level-embedding](https://github.com/hankcs/BERT-token-level-embedding), Generate BERT token level embedding without pain ![](https://img.shields.io/github/stars/hankcs/BERT-token-level-embedding.svg) ## BERT Text Match: 1. [pengming617/bert_textMatching](https://github.com/pengming617/bert_textMatching), 利用预训练的中文模型实现基于bert的语义匹配模型 数据集为LCQMC官方数据 ![](https://img.shields.io/github/stars/pengming617/bert_textMatching.svg) 14. [Brokenwind/BertSimilarity](https://github.com/Brokenwind/BertSimilarity), Computing similarity of two sentences with google's BERT algorithm ![](https://img.shields.io/github/stars/Brokenwind/BertSimilarity.svg) 25. [policeme/chinese_bert_similarity](https://github.com/policeme/chinese_bert_similarity), bert chinese similarity ![](https://img.shields.io/github/stars/policeme/chinese_bert_similarity.svg) 26. [lonePatient/bert-sentence-similarity-pytorch](https://github.com/lonePatient/bert-sentence-similarity-pytorch), This repo contains a PyTorch implementation of a pretrained BERT model for sentence similarity task. ![](https://img.shields.io/github/stars/lonePatient/bert-sentence-similarity-pytorch.svg) 27. [nouhadziri/DialogEntailment](https://github.com/nouhadziri/DialogEntailment), The implementation of the paper "Evaluating Coherence in Dialogue Systems using Entailment" https://arxiv.org/abs/1904.03371 ![](https://img.shields.io/github/stars/nouhadziri/DialogEntailment.svg) ## ko bert https://github.com/jeongukjae/KR-BERT-SimCSE ## BERT tutorials: 1. [graykode/nlp-tutorial](https://github.com/graykode/nlp-tutorial), Natural Language Processing Tutorial for Deep Learning Researchers https://www.reddit.com/r/MachineLearn…, ![](https://img.shields.io/github/stars/graykode/nlp-tutorial.svg) 2. [dragen1860/TensorFlow-2.x-Tutorials](https://github.com/dragen1860/TensorFlow-2.x-Tutorials), TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0。, ![](https://img.shields.io/github/stars/dragen1860/TensorFlow-2.x-Tutorials.svg) ## 한국어 sentence bert 모델 - 깃허브에 sentence-transformers 다국어 모델과의 벤치마크 성능 비교를 기재해두었습니다) ko-sentence-transformers 라이브러리를 설치하시면 허깅페이스 허브에서 바로 다운받아 사용 가능합니다. - 허깅페이스 모델: https://huggingface.co/jhgan/ko-sbert-multitask - 깃허브 저장소: https://github.com/jhgan00/ko-sentence-transformers - ## 논문 요약: https://www.marktechpost.com/.../researchers-from-china.../ 종이: https://arxiv.org/pdf/2207.07116v1.pdf Github: https://github.com/lightdxy/bootmae ## Time Series Related Survey * Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects, in *arXiv* 2023. [\[paper\]](https://arxiv.org/abs/2306.10125) [\[Website\]](https://github.com/qingsongedu/Awesome-SSL4TS) * A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection, in *arXiv* 2023. [\[paper\]](https://arxiv.org/abs/2307.03759) [\[Website\]](https://github.com/KimMeen/Awesome-GNN4TS) * Time series data augmentation for deep learning: a survey, in *IJCAI* 2021. [\[paper\]](https://arxiv.org/abs/2002.12478) * Neural temporal point processes: a review, in *IJCAI* 2021. [\[paper\]](https://arxiv.org/abs/2104.03528v5) * Time-series forecasting with deep learning: a survey, in *Philosophical Transactions of the Royal Society A* 2021. [\[paper\]](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2020.0209) * Deep learning for time series forecasting: a survey, in *Big Data* 2021. [\[paper\]](https://www.liebertpub.com/doi/abs/10.1089/big.2020.0159) * Neural forecasting: Introduction and literature overview, in *arXiv* 2020. [\[paper\]](https://arxiv.org/abs/2004.10240) * Deep learning for anomaly detection in time-series data: review, analysis, and guidelines, in *Access* 2021. [\[paper\]](https://ieeexplore.ieee.org/abstract/document/9523565) * A review on outlier/anomaly detection in time series data, in *ACM Computing Surveys* 2021. [\[paper\]](https://arxiv.org/abs/2002.04236) * A unifying review of deep and shallow anomaly detection, in *Proceedings of the IEEE* 2021. [\[paper\]](http://128.84.4.34/abs/2009.11732) * Deep learning for time series classification: a review, in *Data Mining and Knowledge Discovery* 2019. [\[paper\]](https://link.springer.com/article/10.1007/s10618-019-00619-1?sap-outbound-id=11FC28E054C1A9EB6F54F987D4B526A6EE3495FD&mkt-key=005056A5C6311EE999A3A1E864CDA986) * More related time series surveys, tutorials, and papers can be found at this [repo](https://github.com/qingsongedu/awesome-AI-for-time-series-papers). ## Application Domains of Time Series Transformers ### Transformers in Forecasting #### Time Series Forecasting * Make Transformer Great Again for Time Series Forecasting: Channel Aligned Robust Dual Transformer, in *arXiv* 2023. [\[paper\]](https://arxiv.org/abs/2305.12095) * A Time Series is Worth 64 Words: Long-term Forecasting with Transformers, in *ICLR* 2023. [\[paper\]](https://openreview.net/forum?id=Jbdc0vTOcol) [\[code\]](https://github.com/yuqinie98/PatchTST) * Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting, in *ICLR* 2023. [\[paper\]](https://openreview.net/forum?id=vSVLM2j9eie) * Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting, in *ICLR* 2023. [\[paper\]](https://openreview.net/forum?id=sCrnllCtjoE) * Non-stationary Transformers: Rethinking the Stationarity in Time Series Forecasting, in *NeurIPS* 2022. [\[paper\]](https://arxiv.org/abs/2205.14415) * Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting”, in *KDD* 2022. [\[paper\]](https://dl.acm.org/doi/10.1145/3534678.3539234) * FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting, in *ICML* 2022. [\[paper\]](https://arxiv.org/abs/2201.12740) [\[official code\]](https://github.com/MAZiqing/FEDformer) * TACTiS: Transformer-Attentional Copulas for Time Series, in *ICML* 2022. [\[paper\]](https://arxiv.org/abs/2202.03528) * Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting, in *ICLR* 2022. [\[paper\]](https://openreview.net/forum?id=0EXmFzUn5I) [\[official code\]](https://github.com/alipay/Pyraformer) * Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting, in *NeurIPS* 2021. [\[paper\]](https://proceedings.neurips.cc/paper/2021/hash/bcc0d400288793e8bdcd7c19a8ac0c2b-Abstract.html) [\[official code\]](https://github.com/thuml/autoformer) * Informer: Beyond efficient transformer for long sequence time-series forecasting, in *AAAI* 2021. [\[paper\]](https://www.aaai.org/AAAI21Papers/AAAI-7346.ZhouHaoyi.pdf) [\[official code\]](https://github.com/zhouhaoyi/Informer2020) [\[dataset\]](https://github.com/zhouhaoyi/ETDataset) * Temporal fusion transformers for interpretable multi-horizon time series forecasting, in *International Journal of Forecasting* 2021. [\[paper\]](https://www.sciencedirect.com/science/article/pii/S0169207021000637) [\[code\]](https://github.com/mattsherar/Temporal_Fusion_Transform) * Probabilistic Transformer For Time Series Analysis, in *NeurIPS* 2021. [\[paper\]](https://proceedings.neurips.cc/paper/2021/hash/c68bd9055776bf38d8fc43c0ed283678-Abstract.html) * Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case, in *arXiv* 2020. [\[paper\]](https://arxiv.org/abs/2001.08317) * Adversarial sparse transformer for time series forecasting, in *NeurIPS* 2020. [\[paper\]](https://proceedings.neurips.cc/paper/2020/hash/c6b8c8d762da15fa8dbbdfb6baf9e260-Abstract.html) [\[code\]](https://github.com/hihihihiwsf/AST) * Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting, in *NeurIPS* 2019. [\[paper\]](https://proceedings.neurips.cc/paper/2019/hash/6775a0635c302542da2c32aa19d86be0-Abstract.html) [\[code\]](https://github.com/mlpotter/Transformer_Time_Series) * SSDNet: State Space Decomposition Neural Network for Time Series Forecasting, in *ICDM* 2021, [\[paper\]](https://arxiv.org/abs/2112.10251) * From Known to Unknown: Knowledge-guided Transformer for Time-Series Sales Forecasting in Alibaba, in *arXiv* 2021. [\[paper\]](https://arxiv.org/abs/2109.08381) * TCCT: Tightly-coupled convolutional transformer on time series forecasting, in *Neurocomputing* 2022. [\[paper\]](https://www.sciencedirect.com/science/article/pii/S0925231222000571) * Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting, in *IJCAI* 2022. [\[paper\]](https://arxiv.org/abs/2204.13767) #### Spatio-Temporal Forecasting * AirFormer: Predicting Nationwide Air Quality in China with Transformers, in *AAAI* 2023. [\[paper\]](https://arxiv.org/abs/2211.15979) [\[official code\]](https://github.com/yoshall/AirFormer) * Earthformer: Exploring Space-Time Transformers for Earth System Forecasting, in *NeurIPS* 2022. [\[paper\]](https://arxiv.org/abs/2207.05833) [\[official code\]](https://github.com/amazon-science/earth-forecasting-transformer) * Bidirectional Spatial-Temporal Adaptive Transformer for Urban Traffic Flow Forecasting, in *TNNLS* 2022. [\[paper\]](https://ieeexplore.ieee.org/abstract/document/9810964) * Spatio-temporal graph transformer networks for pedestrian trajectory prediction, in *ECCV* 2020. [\[paper\]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1636_ECCV_2020_paper.php) [\[official code\]](https://github.com/Majiker/STAR) * Spatial-temporal transformer networks for traffic flow forecasting, in *arXiv* 2020. [\[paper\]](https://arxiv.org/abs/2001.02908) [\[official code\]](https://github.com/xumingxingsjtu/STTN) * Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting, in *Transactions in GIS* 2022. [\[paper\]](https://coolgiserz.github.io/publication/traffic-transformer-capturing-the-continuity-and-periodicity-of-time-series-for-traffic-forecasting/traffic-transformer-capturing-the-continuity-and-periodicity-of-time-series-for-traffic-forecasting.pdf) #### Event Forecasting * HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences，in *NeurIPS* 2022. [\[paper\]](https://arxiv.org/abs/2210.01753) [\[official code\]](https://github.com/ant-research/hypro_tpp) * Transformer Embeddings of Irregularly Spaced Events and Their Participants, in *ICLR* 2022. [\[paper\]](https://openreview.net/forum?id=Rty5g9imm7H) [\[official code\]](https://github.com/yangalan123/anhp-andtt) * Self-attentive Hawkes process, in *ICML* 2020. [\[paper\]](http://proceedings.mlr.press/v119/zhang20q.html) [\[official code\]](https://github.com/QiangAIResearcher/sahp_repo) * Transformer Hawkes process, in *ICML* 2020. [\[paper\]](https://proceedings.mlr.press/v119/zuo20a.html) [\[official code\]](https://github.com/SimiaoZuo/Transformer-Hawkes-Process) ### Transformers in Anomaly Detection * CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences, in *KDD* 2022. [\[paper\]](https://dl.acm.org/doi/abs/10.1145/3534678.3539155) [\[official code\]](https://github.com/mmichaelzhang/CAT) * DCT-GAN: Dilated Convolutional Transformer-based GAN for Time Series Anomaly Detection, in *TKDE* 2022. [\[paper\]](https://ieeexplore.ieee.org/abstract/document/9626552) * Concept Drift Adaptation for Time Series Anomaly Detection via Transformer, in *Neural Processing Letters* 2022. [\[paper\]](https://link.springer.com/article/10.1007/s11063-022-11015-0) * Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy, in *ICLR* 2022. [\[paper\]](https://arxiv.org/abs/2110.02642) [\[official code\]](https://github.com/thuml/Anomaly-Transformer) * TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data, in *VLDB* 2022. [\[paper\]](https://arxiv.org/abs/2201.07284) [\[official code\]](https://github.com/imperial-qore/tranad) * Learning graph structures with transformer for multivariate time series anomaly detection in IoT, in *IEEE Internet of Things Journal* 2021. [\[paper\]](https://arxiv.org/abs/2104.03466) [\[official code\]](https://github.com/ZEKAICHEN/GTA) * Spacecraft Anomaly Detection via Transformer Reconstruction Error, in *ICASSE* 2019. [\[paper\]](http://www.utias.utoronto.ca/wp-content/uploads/2019/07/88-Spacecraft-anomaly-detection-via-transformer-reconstruction-error.pdf) * Unsupervised Anomaly Detection in Multivariate Time Series through Transformer-based Variational Autoencoder, in *CCDC* 2021. [\[paper\]](https://ieeexplore.ieee.org/abstract/document/9601669) * Variational Transformer-based anomaly detection approach for multivariate time series, in *Measurement* 2022. [\[paper\]](https://www.sciencedirect.com/science/article/abs/pii/S0263224122000914) ### Transformers in Classification * TrajFormer: Efficient Trajectory Classification with Transformers, in *CIKM* 2022. [\[paper\]](https://dl.acm.org/doi/pdf/10.1145/3511808.3557481) * TARNet : Task-Aware Reconstruction for Time-Series Transformer, in *KDD* 2022. [\[paper\]](https://dl.acm.org/doi/pdf/10.1145/3534678.3539329) [\[official code\]](https://github.com/ranakroychowdhury/TARNet) * A transformer-based framework for multivariate time series representation learning, in *KDD* 2021. [\[paper\]](https://arxiv.org/abs/2010.02803) [\[official code\]](https://github.com/gzerveas/mvts_transformer) * Voice2series: Reprogramming acoustic models for time series classification, in *ICML* 2021. [\[paper\]](https://arxiv.org/abs/2106.09296) [\[official code\]](https://github.com/huckiyang/Voice2Series-Reprogramming) * Gated Transformer Networks for Multivariate Time Series Classification, in *arXiv* 2021. [\[paper\]](https://arxiv.org/abs/2103.14438) [\[official code\]](https://github.com/ZZUFaceBookDL/GTN) * Self-attention for raw optical satellite time series classification, in *ISPRS Journal of Photogrammetry and Remote Sensing* 2020. [\[paper\]](https://arxiv.org/abs/1910.10536) [\[official code\]](https://github.com/marccoru/crop-type-mapping) * Self-supervised pretraining of transformers for satellite image time series classification, in *IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing* 2020. [\[paper\]](https://ieeexplore.ieee.org/abstract/document/9252123) * Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series, in *ACM TKDD* 2022. [\[paper\]](https://arxiv.org/abs/2107.14293) [\[official code\]](https://github.com/sindhura97/STraTS) ### canvers-ko2en 840만 번역쌍으로 bart 기반으로 튜닝이 되어 있습니다. GPU가 있다면, transformers 에서 flash attention 2 와 사용하실 수도 있고 ctranslate2 버전도 있어 cpu에서도 충분히 빠르게 모델을 사용할 수도 있습니다. 저희 내부적인 전략이 영문 모델을 한글 튜닝하기 보다는 영문 모델을 기본으로 빠르게 follow-up 하되 앞뒤로 번역을 붙여서 쓰는 방식을 취하고 있는데, 관리차원에서 올린 모델중에 번역모델들이 꾸준하게 다운로드가 되고 있긴 하더라구요. https://huggingface.co/circulus/canvers-ko2en-v2 https://huggingface.co/circulus/canvers-en2ko-v2 https://huggingface.co/circulus/canvers-ko2en-ct2-v2 https://huggingface.co/circulus/canvers-en2ko-ct2-v2