#
🤗 Transformers

PyTorch, TensorFlow, JAX를 위한 최첨단 머신러닝

🤗 Transformers는 사전학습된 최첨단 모델들을 쉽게 다운로드하고 훈련시킬 수 있는 API와 도구를 제공합니다. 사전학습된 모델을 쓰면 컴퓨팅 비용과 탄소 배출량이 줄고, 모델을 처음부터 훈련시키는 데 필요한 시간과 리소스를 절약할 수 있습니다. 저희 모델들은 다양한 분야의 태스크를 지원합니다.

📝 자연어 처리: 텍스트 분류, 개체명 인식, 질의응답, 언어 모델링, 요약, 번역, 객관식 질의응답, 텍스트 생성
🖼️ 컴퓨터 비전: 이미지 분류, 객체 탐지, 객체 분할
🗣️ 오디오: 자동음성인식, 오디오 분류
🐙 멀티모달: 표 질의응답, 광학 문자 인식 (OCR), 스캔한 문서에서 정보 추출, 비디오 분류, 시각 질의응답

🤗 Transformers는 PyTorch, TensorFlow와 JAX 간의 상호운용성을 지원합니다. 유연하게 모델의 각 단계마다 다른 프레임워크를 사용할 수도 있습니다. 예를 들어 코드 3줄만 써서 모델을 훈련시킨 다음, 다른 프레임워크 상에서 추론할 수 있습니다. 모델을 운영 환경에 배포하기 위해 ONNX나 TorchScript 형식으로 내보낼 수도 있습니다.

커뮤니티에 참여하시려면 Hub, 포럼, 디스코드를 방문해주세요!

##
Hugging Face 팀과 직접 대화하고 싶으신가요?

##
콘텐츠

저희 기술문서는 크게 5개 섹션으로 나눌 수 있습니다:

- 시작하기에서 라이브러리를 간단히 훑어보고, 본격적으로 뛰어들 수 있게 설치 방법을 안내합니다.

- 튜토리얼에서 라이브러리에 익숙해질 수 있도록 자세하고도 쉽게 기본적인 부분을 안내합니다.

- How-to 가이드에서 언어 모델링을 위해 사전학습된 모델을 파인 튜닝하는 방법이나, 직접 모델을 작성하고 공유하는 방법과 같이 특정 목표를 달성하는 방법을 안내합니다.

- 개념 가이드에서 🤗 Transformers의 설계 철학과 함께 모델이나 태스크 뒤에 숨겨진 개념들과 아이디어를 탐구하고 설명을 덧붙입니다.

- API에서 모든 클래스와 함수를 설명합니다.

  - 메인 클래스에서 configuration, model, tokenizer, pipeline과 같이 제일 중요한 클래스들을 자세히 설명합니다.
  - 모델에서 라이브러리 속 구현된 각 모델과 연관된 클래스와 함수를 자세히 설명합니다.
  - 내부 유틸리티에서 내부적으로 사용되는 유틸리티 클래스와 함수를 자세히 설명합니다.

###
지원 모델

1. ALBERT (from Google Research and the Toyota Technological Institute at Chicago) released with the paper ALBERT: A Lite BERT for Self-supervised Learning of Language Representations, by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
2. BART (from Facebook) released with the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
3. BARThez (from École polytechnique) released with the paper BARThez: a Skilled Pretrained French Sequence-to-Sequence Model by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
4. BARTpho (from VinAI Research) released with the paper BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
5. BEiT (from Microsoft) released with the paper BEiT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong, Furu Wei.
6. BERT (from Google) released with the paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
7. BERT For Sequence Generation (from Google) released with the paper Leveraging Pre-trained Checkpoints for Sequence Generation Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
8. BERTweet (from VinAI Research) released with the paper BERTweet: A pre-trained language model for English Tweets by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
9. BigBird-Pegasus (from Google Research) released with the paper Big Bird: Transformers for Longer Sequences by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
10. BigBird-RoBERTa (from Google Research) released with the paper Big Bird: Transformers for Longer Sequences by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
11. Blenderbot (from Facebook) released with the paper Recipes for building an open-domain chatbot by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
12. BlenderbotSmall (from Facebook) released with the paper Recipes for building an open-domain chatbot by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
13. BLOOM (from BigScience workshop) released by the BigScience Workshop.
14. BORT (from Alexa) released with the paper Optimal Subarchitecture Extraction For BERT by Adrian de Wynter and Daniel J. Perry.
15. ByT5 (from Google Research) released with the paper ByT5: Towards a token-free future with pre-trained byte-to-byte models by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
16. CamemBERT (from Inria/Facebook/Sorbonne) released with the paper CamemBERT: a Tasty French Language Model by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
17. CANINE (from Google Research) released with the paper CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
18. CLIP (from OpenAI) released with the paper Learning Transferable Visual Models From Natural Language Supervision by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
19. CLIPSeg (from University of Göttingen) released with the paper Image Segmentation Using Text and Image Prompts by Timo Lüddecke and Alexander Ecker.
20. CodeGen (from Salesforce) released with the paper A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.
21. Conditional DETR (from Microsoft Research Asia) released with the paper Conditional DETR for Fast Training Convergence by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang.
22. ConvBERT (from YituTech) released with the paper ConvBERT: Improving BERT with Span-based Dynamic Convolution by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
23. ConvNeXT (from Facebook AI) released with the paper A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
24. ConvNeXTV2 (from Facebook AI) released with the paper ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie.
25. CPM (from Tsinghua University) released with the paper CPM: A Large-scale Generative Chinese Pre-trained Language Model by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
26. CTRL (from Salesforce) released with the paper CTRL: A Conditional Transformer Language Model for Controllable Generation by Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong and Richard Socher.
27. CvT (from Microsoft) released with the paper CvT: Introducing Convolutions to Vision Transformers by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, Lei Zhang.
28. Data2Vec (from Facebook) released with the paper Data2Vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.
29. DeBERTa (from Microsoft) released with the paper DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
30. DeBERTa-v2 (from Microsoft) released with the paper DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
31. Decision Transformer (from Berkeley/Facebook/Google) released with the paper Decision Transformer: Reinforcement Learning via Sequence Modeling by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
32. Deformable DETR (from SenseTime Research) released with the paper Deformable DETR: Deformable Transformers for End-to-End Object Detection by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.
33. DeiT (from Facebook) released with the paper Training data-efficient image transformers & distillation through attention by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
34. DETR (from Facebook) released with the paper End-to-End Object Detection with Transformers by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
35. DialoGPT (from Microsoft Research) released with the paper DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
36. DistilBERT (from HuggingFace), released together with the paper DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into DistilGPT2, RoBERTa into DistilRoBERTa, Multilingual BERT into DistilmBERT and a German version of DistilBERT.
37. DiT (from Microsoft Research) released with the paper DiT: Self-supervised Pre-training for Document Image Transformer by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
38. Donut (from NAVER), released together with the paper OCR-free Document Understanding Transformer by Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park.
39. DPR (from Facebook) released with the paper Dense Passage Retrieval for Open-Domain Question Answering by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
40. DPT (from Intel Labs) released with the paper Vision Transformers for Dense Prediction by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
41. EfficientNet (from Google Research) released with the paper EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks by Mingxing Tan and Quoc V. Le.
42. ELECTRA (from Google Research/Stanford University) released with the paper ELECTRA: Pre-training text encoders as discriminators rather than generators by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
43. EncoderDecoder (from Google Research) released with the paper Leveraging Pre-trained Checkpoints for Sequence Generation Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
44. ERNIE (from Baidu) released with the paper ERNIE: Enhanced Representation through Knowledge Integration by Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, Hua Wu.
45. ESM (from Meta AI) are transformer protein language models. ESM-1b was released with the paper Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences by Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. ESM-1v was released with the paper Language models enable zero-shot prediction of the effects of mutations on protein function by Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu and Alexander Rives. ESM-2 and ESMFold were released with the paper Language models of protein sequences at the scale of evolution enable accurate structure prediction by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives.
46. FLAN-T5 (from Google AI) released in the repository google-research/t5x by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
47. FlauBERT (from CNRS) released with the paper FlauBERT: Unsupervised Language Model Pre-training for French by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
48. FLAVA (from Facebook AI) released with the paper FLAVA: A Foundational Language And Vision Alignment Model by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.
49. FNet (from Google Research) released with the paper FNet: Mixing Tokens with Fourier Transforms by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
50. Funnel Transformer (from CMU/Google Brain) released with the paper Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
51. GLPN (from KAIST) released with the paper Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
52. GPT (from OpenAI) released with the paper Improving Language Understanding by Generative Pre-Training by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
53. GPT Neo (from EleutherAI) released in the repository EleutherAI/gpt-neo by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
54. GPT NeoX (from EleutherAI) released with the paper GPT-NeoX-20B: An Open-Source Autoregressive Language Model by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
55. GPT NeoX Japanese (from ABEJA) released by Shinya Otani, Takayoshi Makabe, Anuj Arora, and Kyo Hattori.
56. GPT-2 (from OpenAI) released with the paper Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.
57. GPT-J (from EleutherAI) released in the repository kingoflolz/mesh-transformer-jax by Ben Wang and Aran Komatsuzaki.
58. GPTSAN-japanese released in the repository tanreinama/GPTSAN by Toshiyuki Sakamoto(tanreinama).
59. GroupViT (from UCSD, NVIDIA) released with the paper GroupViT: Semantic Segmentation Emerges from Text Supervision by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
60. Hubert (from Facebook) released with the paper HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
61. I-BERT (from Berkeley) released with the paper I-BERT: Integer-only BERT Quantization by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
62. ImageGPT (from OpenAI) released with the paper Generative Pretraining from Pixels by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
63. Jukebox (from OpenAI) released with the paper Jukebox: A Generative Model for Music by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever.
64. LayoutLM (from Microsoft Research Asia) released with the paper LayoutLM: Pre-training of Text and Layout for Document Image Understanding by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
65. LayoutLMv2 (from Microsoft Research Asia) released with the paper LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
66. LayoutLMv3 (from Microsoft Research Asia) released with the paper LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei.
67. LayoutXLM (from Microsoft Research Asia) released with the paper LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
68. LED (from AllenAI) released with the paper Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan.
69. LeViT (from Meta AI) released with the paper LeViT: A Vision Transformer in ConvNet’s Clothing for Faster Inference by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.
70. LiLT (from South China University of Technology) released with the paper LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding by Jiapeng Wang, Lianwen Jin, Kai Ding.
71. Longformer (from AllenAI) released with the paper Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan.
72. LongT5 (from Google AI) released with the paper LongT5: Efficient Text-To-Text Transformer for Long Sequences by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
73. LUKE (from Studio Ousia) released with the paper LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
74. LXMERT (from UNC Chapel Hill) released with the paper LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering by Hao Tan and Mohit Bansal.
75. M-CTC-T (from Facebook) released with the paper Pseudo-Labeling For Massively Multilingual Speech Recognition by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert.
76. M2M100 (from Facebook) released with the paper Beyond English-Centric Multilingual Machine Translation by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
77. MarianMT Machine translation models trained using OPUS data by Jörg Tiedemann. The Marian Framework is being developed by the Microsoft Translator Team.
78. MarkupLM (from Microsoft Research Asia) released with the paper MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei.
79. Mask2Former (from FAIR and UIUC) released with the paper Masked-attention Mask Transformer for Universal Image Segmentation by Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar.
80. MaskFormer (from Meta and UIUC) released with the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.
81. mBART (from Facebook) released with the paper Multilingual Denoising Pre-training for Neural Machine Translation by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
82. mBART-50 (from Facebook) released with the paper Multilingual Translation with Extensible Multilingual Pretraining and Finetuning by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
83. Megatron-BERT (from NVIDIA) released with the paper Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
84. Megatron-GPT2 (from NVIDIA) released with the paper Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
85. mLUKE (from Studio Ousia) released with the paper mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.
86. MobileBERT (from CMU/Google Brain) released with the paper MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou.
87. MobileViT (from Apple) released with the paper MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer by Sachin Mehta and Mohammad Rastegari.
88. MPNet (from Microsoft Research) released with the paper MPNet: Masked and Permuted Pre-training for Language Understanding by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
89. MT5 (from Google AI) released with the paper mT5: A massively multilingual pre-trained text-to-text transformer by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
90. MVP (from RUC AI Box) released with the paper MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
91. Nezha (from Huawei Noah’s Ark Lab) released with the paper NEZHA: Neural Contextualized Representation for Chinese Language Understanding by Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.
92. NLLB (from Meta) released with the paper No Language Left Behind: Scaling Human-Centered Machine Translation by the NLLB team.
93. Nyströmformer (from the University of Wisconsin - Madison) released with the paper Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
94. OneFormer (from SHI Labs) released with the paper OneFormer: One Transformer to Rule Universal Image Segmentation by Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi.
95. OPT (from Meta AI) released with the paper OPT: Open Pre-trained Transformer Language Models by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
96. OWL-ViT (from Google AI) released with the paper Simple Open-Vocabulary Object Detection with Vision Transformers by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
97. Pegasus (from Google) released with the paper PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
98. PEGASUS-X (from Google) released with the paper Investigating Efficiently Extending Transformers for Long Input Summarization by Jason Phang, Yao Zhao, and Peter J. Liu.
99. Perceiver IO (from Deepmind) released with the paper Perceiver IO: A General Architecture for Structured Inputs & Outputs by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
100. PhoBERT (from VinAI Research) released with the paper PhoBERT: Pre-trained language models for Vietnamese by Dat Quoc Nguyen and Anh Tuan Nguyen.
101. PLBart (from UCLA NLP) released with the paper Unified Pre-training for Program Understanding and Generation by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.
102. PoolFormer (from Sea AI Labs) released with the paper MetaFormer is Actually What You Need for Vision by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.
103. ProphetNet (from Microsoft Research) released with the paper ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
104. QDQBert (from NVIDIA) released with the paper Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
105. RAG (from Facebook) released with the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
106. REALM (from Google Research) released with the paper REALM: Retrieval-Augmented Language Model Pre-Training by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
107. Reformer (from Google Research) released with the paper Reformer: The Efficient Transformer by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
108. RegNet (from META Platforms) released with the paper Designing Network Design Space by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
109. RemBERT (from Google Research) released with the paper Rethinking embedding coupling in pre-trained language models by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
110. ResNet (from Microsoft Research) released with the paper Deep Residual Learning for Image Recognition by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
111. RoBERTa (from Facebook), released together with the paper RoBERTa: A Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
112. RoCBert (from WeChatAI) released with the paper RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou.
113. RoFormer (from ZhuiyiTechnology), released together with the paper RoFormer: Enhanced Transformer with Rotary Position Embedding by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
114. SegFormer (from NVIDIA) released with the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
115. SEW (from ASAPP) released with the paper Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
116. SEW-D (from ASAPP) released with the paper Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
117. SpeechToTextTransformer (from Facebook), released together with the paper fairseq S2T: Fast Speech-to-Text Modeling with fairseq by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
118. SpeechToTextTransformer2 (from Facebook), released together with the paper Large-Scale Self- and Semi-Supervised Learning for Speech Translation by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
119. Splinter (from Tel Aviv University), released together with the paper Few-Shot Question Answering by Pretraining Span Selection by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
120. SqueezeBERT (from Berkeley) released with the paper SqueezeBERT: What can computer vision teach NLP about efficient neural networks? by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
121. Swin Transformer (from Microsoft) released with the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
122. Swin Transformer V2 (from Microsoft) released with the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
123. T5 (from Google AI) released with the paper Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
124. T5v1.1 (from Google AI) released in the repository google-research/text-to-text-transfer-transformer by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
125. Table Transformer (from Microsoft Research) released with the paper PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents by Brandon Smock, Rohith Pesala, Robin Abraham.
126. TAPAS (from Google AI) released with the paper TAPAS: Weakly Supervised Table Parsing via Pre-training by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
127. TAPEX (from Microsoft Research) released with the paper TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
128. Time Series Transformer (from HuggingFace).
129. Trajectory Transformer (from the University of California at Berkeley) released with the paper Offline Reinforcement Learning as One Big Sequence Modeling Problem by Michael Janner, Qiyang Li, Sergey Levine
130. Transformer-XL (from Google/CMU) released with the paper Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
131. TrOCR (from Microsoft), released together with the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
132. UL2 (from Google Research) released with the paper Unifying Language Learning Paradigms by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler
133. UniSpeech (from Microsoft Research) released with the paper UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
134. UniSpeechSat (from Microsoft Research) released with the paper UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
135. VAN (from Tsinghua University and Nankai University) released with the paper Visual Attention Network by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
136. VideoMAE (from Multimedia Computing Group, Nanjing University) released with the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Zhan Tong, Yibing Song, Jue Wang, Limin Wang.
137. ViLT (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision by Wonjae Kim, Bokyung Son, Ildoo Kim.
138. Vision Transformer (ViT) (from Google AI) released with the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
139. VisualBERT (from UCLA NLP) released with the paper VisualBERT: A Simple and Performant Baseline for Vision and Language by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
140. ViTMAE (from Meta AI) released with the paper Masked Autoencoders Are Scalable Vision Learners by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
141. ViTMSN (from Meta AI) released with the paper Masked Siamese Networks for Label-Efficient Learning by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas.
142. Wav2Vec2 (from Facebook AI) released with the paper wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
143. Wav2Vec2-Conformer (from Facebook AI) released with the paper FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
144. Wav2Vec2Phoneme (from Facebook AI) released with the paper Simple and Effective Zero-shot Cross-lingual Phoneme Recognition by Qiantong Xu, Alexei Baevski, Michael Auli.
145. WavLM (from Microsoft Research) released with the paper WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
146. Whisper (from OpenAI) released with the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.
147. X-CLIP (from Microsoft Research) released with the paper Expanding Language-Image Pretrained Models for General Video Recognition by Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, Haibin Ling.
148. XGLM (From Facebook AI) released with the paper Few-shot Learning with Multilingual Language Models by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
149. XLM (from Facebook) released together with the paper Cross-lingual Language Model Pretraining by Guillaume Lample and Alexis Conneau.
150. XLM-ProphetNet (from Microsoft Research) released with the paper ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
151. XLM-RoBERTa (from Facebook AI), released together with the paper Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
152. XLM-RoBERTa-XL (from Facebook AI), released together with the paper Larger-Scale Transformers for Multilingual Masked Language Modeling by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
153. XLNet (from Google/CMU) released with the paper ​XLNet: Generalized Autoregressive Pretraining for Language Understanding by Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
154. XLS-R (from Facebook AI) released with the paper XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
155. XLSR-Wav2Vec2 (from Facebook AI) released with the paper Unsupervised Cross-Lingual Representation Learning For Speech Recognition by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
156. YOLOS (from Huazhong University of Science & Technology) released with the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
157. YOSO (from the University of Wisconsin - Madison) released with the paper You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.

###
지원 프레임워크

아래 표는 라이브러리 속 각 모델의 지원 현황을 나타냅니다. 토큰화를 파이썬 (별칭 “slow”) 또는 🤗 Tokenizers (별칭 “fast”) 라이브러리로 하는지; (Flax를 통한) Jax, PyTorch, TensorFlow 중 어떤 프레임워크를 지원하는지 표시되어 있습니다.

Model | Tokenizer slow | Tokenizer fast | PyTorch support | TensorFlow support | Flax Support
--- | --- | --- | --- | --- | ---
ALBERT | ✅ | ✅ | ✅ | ✅ | ✅
BART | ✅ | ✅ | ✅ | ✅ | ✅
BEiT | ❌ | ❌ | ✅ | ❌ | ✅
BERT | ✅ | ✅ | ✅ | ✅ | ✅
Bert Generation | ✅ | ❌ | ✅ | ❌ | ❌
BigBird | ✅ | ✅ | ✅ | ❌ | ✅
BigBird-Pegasus | ❌ | ❌ | ✅ | ❌ | ❌
Blenderbot | ✅ | ✅ | ✅ | ✅ | ✅
BlenderbotSmall | ✅ | ✅ | ✅ | ✅ | ✅
BLOOM | ❌ | ✅ | ✅ | ❌ | ❌
CamemBERT | ✅ | ✅ | ✅ | ✅ | ❌
CANINE | ✅ | ❌ | ✅ | ❌ | ❌
CLIP | ✅ | ✅ | ✅ | ✅ | ✅
CLIPSeg | ❌ | ❌ | ✅ | ❌ | ❌
CodeGen | ✅ | ✅ | ✅ | ❌ | ❌
Conditional DETR | ❌ | ❌ | ✅ | ❌ | ❌
ConvBERT | ✅ | ✅ | ✅ | ✅ | ❌
ConvNeXT | ❌ | ❌ | ✅ | ✅ | ❌
CTRL | ✅ | ❌ | ✅ | ✅ | ❌
CvT | ❌ | ❌ | ✅ | ✅ | ❌
Data2VecAudio | ❌ | ❌ | ✅ | ❌ | ❌
Data2VecText | ❌ | ❌ | ✅ | ❌ | ❌
Data2VecVision | ❌ | ❌ | ✅ | ✅ | ❌
DeBERTa | ✅ | ✅ | ✅ | ✅ | ❌
DeBERTa-v2 | ✅ | ✅ | ✅ | ✅ | ❌
Decision Transformer | ❌ | ❌ | ✅ | ❌ | ❌
Deformable DETR | ❌ | ❌ | ✅ | ❌ | ❌
DeiT | ❌ | ❌ | ✅ | ✅ | ❌
DETR | ❌ | ❌ | ✅ | ❌ | ❌
DistilBERT | ✅ | ✅ | ✅ | ✅ | ✅
DonutSwin | ❌ | ❌ | ✅ | ❌ | ❌
DPR | ✅ | ✅ | ✅ | ✅ | ❌
DPT | ❌ | ❌ | ✅ | ❌ | ❌
ELECTRA | ✅ | ✅ | ✅ | ✅ | ✅
Encoder decoder | ❌ | ❌ | ✅ | ✅ | ✅
ERNIE | ❌ | ❌ | ✅ | ❌ | ❌
ESM | ✅ | ❌ | ✅ | ✅ | ❌
FairSeq Machine-Translation | ✅ | ❌ | ✅ | ❌ | ❌
FlauBERT | ✅ | ❌ | ✅ | ✅ | ❌
FLAVA | ❌ | ❌ | ✅ | ❌ | ❌
FNet | ✅ | ✅ | ✅ | ❌ | ❌
Funnel Transformer | ✅ | ✅ | ✅ | ✅ | ❌
GLPN | ❌ | ❌ | ✅ | ❌ | ❌
GPT Neo | ❌ | ❌ | ✅ | ❌ | ✅
GPT NeoX | ❌ | ✅ | ✅ | ❌ | ❌
GPT NeoX Japanese | ✅ | ❌ | ✅ | ❌ | ❌
GPT-J | ❌ | ❌ | ✅ | ✅ | ✅
GroupViT | ❌ | ❌ | ✅ | ✅ | ❌
Hubert | ❌ | ❌ | ✅ | ✅ | ❌
I-BERT | ❌ | ❌ | ✅ | ❌ | ❌
ImageGPT | ❌ | ❌ | ✅ | ❌ | ❌
Jukebox | ✅ | ❌ | ✅ | ❌ | ❌
LayoutLM | ✅ | ✅ | ✅ | ✅ | ❌
LayoutLMv2 | ✅ | ✅ | ✅ | ❌ | ❌
LayoutLMv3 | ✅ | ✅ | ✅ | ✅ | ❌
LED | ✅ | ✅ | ✅ | ✅ | ❌
LeViT | ❌ | ❌ | ✅ | ❌ | ❌
LiLT | ❌ | ❌ | ✅ | ❌ | ❌
Longformer | ✅ | ✅ | ✅ | ✅ | ❌
LongT5 | ❌ | ❌ | ✅ | ❌ | ✅
LUKE | ✅ | ❌ | ✅ | ❌ | ❌
LXMERT | ✅ | ✅ | ✅ | ✅ | ❌
M-CTC-T | ❌ | ❌ | ✅ | ❌ | ❌
M2M100 | ✅ | ❌ | ✅ | ❌ | ❌
Marian | ✅ | ❌ | ✅ | ✅ | ✅
MarkupLM | ✅ | ✅ | ✅ | ❌ | ❌
MaskFormer | ❌ | ❌ | ✅ | ❌ | ❌
mBART | ✅ | ✅ | ✅ | ✅ | ✅
Megatron-BERT | ❌ | ❌ | ✅ | ❌ | ❌
MobileBERT | ✅ | ✅ | ✅ | ✅ | ❌
MobileViT | ❌ | ❌ | ✅ | ✅ | ❌
MPNet | ✅ | ✅ | ✅ | ✅ | ❌
MT5 | ✅ | ✅ | ✅ | ✅ | ✅
MVP | ✅ | ✅ | ✅ | ❌ | ❌
Nezha | ❌ | ❌ | ✅ | ❌ | ❌
Nyströmformer | ❌ | ❌ | ✅ | ❌ | ❌
OpenAI GPT | ✅ | ✅ | ✅ | ✅ | ❌
OpenAI GPT-2 | ✅ | ✅ | ✅ | ✅ | ✅
OPT | ❌ | ❌ | ✅ | ✅ | ✅
OWL-ViT | ❌ | ❌ | ✅ | ❌ | ❌
Pegasus | ✅ | ✅ | ✅ | ✅ | ✅
PEGASUS-X | ❌ | ❌ | ✅ | ❌ | ❌
Perceiver | ✅ | ❌ | ✅ | ❌ | ❌
PLBart | ✅ | ❌ | ✅ | ❌ | ❌
PoolFormer | ❌ | ❌ | ✅ | ❌ | ❌
ProphetNet | ✅ | ❌ | ✅ | ❌ | ❌
QDQBert | ❌ | ❌ | ✅ | ❌ | ❌
RAG | ✅ | ❌ | ✅ | ✅ | ❌
REALM | ✅ | ✅ | ✅ | ❌ | ❌
Reformer | ✅ | ✅ | ✅ | ❌ | ❌
RegNet | ❌ | ❌ | ✅ | ✅ | ✅
RemBERT | ✅ | ✅ | ✅ | ✅ | ❌
ResNet | ❌ | ❌ | ✅ | ✅ | ✅
RetriBERT | ✅ | ✅ | ✅ | ❌ | ❌
RoBERTa | ✅ | ✅ | ✅ | ✅ | ✅
RoCBert | ✅ | ❌ | ✅ | ❌ | ❌
RoFormer | ✅ | ✅ | ✅ | ✅ | ✅
SegFormer | ❌ | ❌ | ✅ | ✅ | ❌
SEW | ❌ | ❌ | ✅ | ❌ | ❌
SEW-D | ❌ | ❌ | ✅ | ❌ | ❌
Speech Encoder decoder | ❌ | ❌ | ✅ | ❌ | ✅
Speech2Text | ✅ | ❌ | ✅ | ✅ | ❌
Speech2Text2 | ✅ | ❌ | ❌ | ❌ | ❌
Splinter | ✅ | ✅ | ✅ | ❌ | ❌
SqueezeBERT | ✅ | ✅ | ✅ | ❌ | ❌
Swin Transformer | ❌ | ❌ | ✅ | ✅ | ❌
Swin Transformer V2 | ❌ | ❌ | ✅ | ❌ | ❌
T5 | ✅ | ✅ | ✅ | ✅ | ✅
Table Transformer | ❌ | ❌ | ✅ | ❌ | ❌
TAPAS | ✅ | ❌ | ✅ | ✅ | ❌
Time Series Transformer | ❌ | ❌ | ✅ | ❌ | ❌
Trajectory Transformer | ❌ | ❌ | ✅ | ❌ | ❌
Transformer-XL | ✅ | ❌ | ✅ | ✅ | ❌
TrOCR | ❌ | ❌ | ✅ | ❌ | ❌
UniSpeech | ❌ | ❌ | ✅ | ❌ | ❌
UniSpeechSat | ❌ | ❌ | ✅ | ❌ | ❌
VAN | ❌ | ❌ | ✅ | ❌ | ❌
VideoMAE | ❌ | ❌ | ✅ | ❌ | ❌
ViLT | ❌ | ❌ | ✅ | ❌ | ❌
Vision Encoder decoder | ❌ | ❌ | ✅ | ✅ | ✅
VisionTextDualEncoder | ❌ | ❌ | ✅ | ❌ | ✅
VisualBERT | ❌ | ❌ | ✅ | ❌ | ❌
ViT | ❌ | ❌ | ✅ | ✅ | ✅
ViTMAE | ❌ | ❌ | ✅ | ✅ | ❌
ViTMSN | ❌ | ❌ | ✅ | ❌ | ❌
Wav2Vec2 | ✅ | ❌ | ✅ | ✅ | ✅
Wav2Vec2-Conformer | ❌ | ❌ | ✅ | ❌ | ❌
WavLM | ❌ | ❌ | ✅ | ❌ | ❌
Whisper | ✅ | ❌ | ✅ | ✅ | ❌
X-CLIP | ❌ | ❌ | ✅ | ❌ | ❌
XGLM | ✅ | ✅ | ✅ | ✅ | ✅
XLM | ✅ | ❌ | ✅ | ✅ | ❌
XLM-ProphetNet | ✅ | ❌ | ✅ | ❌ | ❌
XLM-RoBERTa | ✅ | ✅ | ✅ | ✅ | ✅
XLM-RoBERTa-XL | ❌ | ❌ | ✅ | ❌ | ❌
XLNet | ✅ | ✅ | ✅ | ✅ | ❌
YOLOS | ❌ | ❌ | ✅ | ❌ | ❌
YOSO | ❌ | ❌ | ✅ | ❌ | ❌

< > Update on GitHub