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Pi=new rR({}),Wi=new rR({}),Fi=new rR({}),Yi=new rR({}),Ch=new rR({}),{c(){On=r("meta"),aR=d(),Wn=r("h1"),Kn=r("a"),qp=r("span"),qL(Pi.$$.fragment),MR=d(),jp=r("span"),GR=l("\u{1F917} Transformers"),nR=d(),jt=r("p"),kR=l("State-of-the-art Machine Learning for "),Ni=r("a"),PR=l("PyTorch"),NR=l(", "),Ci=r("a"),CR=l("TensorFlow"),IR=l(", and "),Ii=r("a"),BR=l("JAX"),xR=l("."),lR=d(),Jh=r("p"),OR=l("\u{1F917} Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. These models support common tasks in different modalities, such as:"),oR=d(),T=r("p"),WR=l("\u{1F4DD} "),Qp=r("strong"),FR=l("Natural Language Processing"),JR=l(": text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation."),YR=r("br"),VR=l(`
\u{1F5BC}\uFE0F `),$p=r("strong"),HR=l("Computer Vision"),XR=l(": image classification, object detection, and segmentation."),ZR=r("br"),KR=l(`
\u{1F5E3}\uFE0F `),e8=r("strong"),zR=l("Audio"),UR=l(": automatic speech recognition and audio classification."),qR=r("br"),jR=l(`
\u{1F419} `),t8=r("strong"),QR=l("Multimodal"),$R=l(": table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering."),iR=d(),Yh=r("p"),eb=l("\u{1F917} Transformers support framework interoperability between PyTorch, TensorFlow, and JAX. This provides the flexibility to use a different framework at each stage of a model\u2019s life; train a model in three lines of code in one framework, and load it for inference in another. Models can also be exported to a format like ONNX and TorchScript for deployment in production environments."),dR=d(),Qt=r("p"),tb=l("Join the growing community on the "),Bi=r("a"),rb=l("Hub"),ab=l(", "),xi=r("a"),nb=l("forum"),lb=l(", or "),Oi=r("a"),ob=l("Discord"),ib=l(" today!"),sR=d(),Fn=r("h2"),zn=r("a"),r8=r("span"),qL(Wi.$$.fragment),db=d(),a8=r("span"),sb=l("If you are looking for custom support from the Hugging Face team"),hR=d(),Jn=r("a"),Gn=r("img"),hb=d(),gR=r("br"),cR=d(),Yn=r("h2"),Un=r("a"),n8=r("span"),qL(Fi.$$.fragment),gb=d(),l8=r("span"),cb=l("Contents"),fR=d(),Vh=r("p"),fb=l("The documentation is organized into five sections:"),uR=d(),Ut=r("ul"),o8=r("li"),Hh=r("p"),i8=r("strong"),ub=l("GET STARTED"),vb=l(" provides a quick tour of the library and installation instructions to get up and running."),Eb=d(),d8=r("li"),Xh=r("p"),s8=r("strong"),Tb=l("TUTORIALS"),_b=l(" are a great place to start if you\u2019re a beginner. This section will help you gain the basic skills you need to start using the library."),mb=d(),h8=r("li"),Zh=r("p"),g8=r("strong"),pb=l("HOW-TO GUIDES"),Db=l(" show you how to achieve a specific goal, like finetuning a pretrained model for language modeling or how to write and share a custom model."),yb=d(),c8=r("li"),Kh=r("p"),f8=r("strong"),Ab=l("CONCEPTUAL GUIDES"),Lb=l(" offers more discussion and explanation of the underlying concepts and ideas behind models, tasks, and the design philosophy of \u{1F917} Transformers."),Rb=d(),Ji=r("li"),zh=r("p"),u8=r("strong"),bb=l("API"),Sb=l(" describes all classes and functions:"),wb=d(),Vn=r("ul"),Uh=r("li"),v8=r("strong"),Mb=l("MAIN CLASSES"),Gb=l(" details the most important classes like configuration, model, tokenizer, and pipeline."),kb=d(),qh=r("li"),E8=r("strong"),Pb=l("MODELS"),Nb=l(" details the classes and functions related to each model implemented in the library."),Cb=d(),jh=r("li"),T8=r("strong"),Ib=l("INTERNAL HELPERS"),Bb=l(" details utility classes and functions used internally."),vR=d(),Hn=r("h3"),qn=r("a"),_8=r("span"),qL(Yi.$$.fragment),xb=d(),m8=r("span"),Ob=l("Supported models"),ER=d(),h=r("ol"),jn=r("li"),p8=r("strong"),Qh=r("a"),Wb=l("ALBERT"),Fb=l(" (from Google Research and the Toyota Technological Institute at Chicago) released with the paper "),Vi=r("a"),Jb=l("ALBERT: A Lite BERT for Self-supervised Learning of Language Representations"),Yb=l(", by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."),Vb=d(),Qn=r("li"),D8=r("strong"),$h=r("a"),Hb=l("BART"),Xb=l(" (from Facebook) released with the paper "),Hi=r("a"),Zb=l("BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension"),Kb=l(" by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer."),zb=d(),$n=r("li"),y8=r("strong"),eg=r("a"),Ub=l("BARThez"),qb=l(" (from \xC9cole polytechnique) released with the paper "),Xi=r("a"),jb=l("BARThez: a Skilled Pretrained French Sequence-to-Sequence Model"),Qb=l(" by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis."),$b=d(),el=r("li"),A8=r("strong"),tg=r("a"),eS=l("BARTpho"),tS=l(" (from VinAI Research) released with the paper "),Zi=r("a"),rS=l("BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese"),aS=l(" by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen."),nS=d(),tl=r("li"),L8=r("strong"),rg=r("a"),lS=l("BEiT"),oS=l(" (from Microsoft) released with the paper "),Ki=r("a"),iS=l("BEiT: BERT Pre-Training of Image Transformers"),dS=l(" by Hangbo Bao, Li Dong, Furu Wei."),sS=d(),rl=r("li"),R8=r("strong"),ag=r("a"),hS=l("BERT"),gS=l(" (from Google) released with the paper "),zi=r("a"),cS=l("BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"),fS=l(" by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova."),uS=d(),al=r("li"),b8=r("strong"),ng=r("a"),vS=l("BERT For Sequence Generation"),ES=l(" (from Google) released with the paper "),Ui=r("a"),TS=l("Leveraging Pre-trained Checkpoints for Sequence Generation Tasks"),_S=l(" by Sascha Rothe, Shashi Narayan, Aliaksei Severyn."),mS=d(),nl=r("li"),S8=r("strong"),lg=r("a"),pS=l("BERTweet"),DS=l(" (from VinAI Research) released with the paper "),qi=r("a"),yS=l("BERTweet: A pre-trained language model for English Tweets"),AS=l(" by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen."),LS=d(),ll=r("li"),w8=r("strong"),og=r("a"),RS=l("BigBird-Pegasus"),bS=l(" (from Google Research) released with the paper "),ji=r("a"),SS=l("Big Bird: Transformers for Longer Sequences"),wS=l(" by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed."),MS=d(),ol=r("li"),M8=r("strong"),ig=r("a"),GS=l("BigBird-RoBERTa"),kS=l(" (from Google Research) released with the paper "),Qi=r("a"),PS=l("Big Bird: Transformers for Longer Sequences"),NS=l(" by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed."),CS=d(),il=r("li"),G8=r("strong"),dg=r("a"),IS=l("Blenderbot"),BS=l(" (from Facebook) released with the paper "),$i=r("a"),xS=l("Recipes for building an open-domain chatbot"),OS=l(" by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. 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Varshney, Caiming Xiong and Richard Socher."),eM=d(),ml=r("li"),Z8=r("strong"),yg=r("a"),tM=l("CvT"),rM=l(" (from Microsoft) released with the paper "),fd=r("a"),aM=l("CvT: Introducing Convolutions to Vision Transformers"),nM=l(" by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, Lei Zhang."),lM=d(),pl=r("li"),K8=r("strong"),Ag=r("a"),oM=l("Data2Vec"),iM=l(" (from Facebook) released with the paper "),ud=r("a"),dM=l("Data2Vec:  A General Framework for Self-supervised Learning in Speech, Vision and Language"),sM=l(" by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli."),hM=d(),Dl=r("li"),z8=r("strong"),Lg=r("a"),gM=l("DeBERTa"),cM=l(" (from Microsoft) released with the paper "),vd=r("a"),fM=l("DeBERTa: Decoding-enhanced BERT with Disentangled Attention"),uM=l(" by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen."),vM=d(),yl=r("li"),U8=r("strong"),Rg=r("a"),EM=l("DeBERTa-v2"),TM=l(" (from Microsoft) released with the paper "),Ed=r("a"),_M=l("DeBERTa: Decoding-enhanced BERT with Disentangled Attention"),mM=l(" by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen."),pM=d(),Al=r("li"),q8=r("strong"),bg=r("a"),DM=l("Decision Transformer"),yM=l(" (from Berkeley/Facebook/Google) released with the paper "),Td=r("a"),AM=l("Decision Transformer: Reinforcement Learning via Sequence Modeling"),LM=l(" by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch."),RM=d(),Ll=r("li"),j8=r("strong"),Sg=r("a"),bM=l("Deformable DETR"),SM=l(" (from SenseTime Research) released with the paper "),_d=r("a"),wM=l("Deformable DETR: Deformable Transformers for End-to-End Object Detection"),MM=l(" by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai."),GM=d(),Rl=r("li"),Q8=r("strong"),wg=r("a"),kM=l("DeiT"),PM=l(" (from Facebook) released with the paper "),md=r("a"),NM=l("Training data-efficient image transformers & distillation through attention"),CM=l(" by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Herv\xE9 J\xE9gou."),IM=d(),bl=r("li"),$8=r("strong"),Mg=r("a"),BM=l("DETR"),xM=l(" (from Facebook) released with the paper "),pd=r("a"),OM=l("End-to-End Object Detection with Transformers"),WM=l(" by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko."),FM=d(),Sl=r("li"),e9=r("strong"),Gg=r("a"),JM=l("DialoGPT"),YM=l(" (from Microsoft Research) released with the paper "),Dd=r("a"),VM=l("DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation"),HM=l(" by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan."),XM=d(),qt=r("li"),t9=r("strong"),kg=r("a"),ZM=l("DistilBERT"),KM=l(" (from HuggingFace), released together with the paper "),yd=r("a"),zM=l("DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter"),UM=l(" by Victor Sanh, Lysandre Debut and Thomas Wolf. 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"),h9=r("strong"),FG=l("ESM-1b"),JG=l(" was released with the paper "),Nd=r("a"),YG=l("Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences"),VG=l(" by Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. "),g9=r("strong"),HG=l("ESM-1v"),XG=l(" was released with the paper "),Cd=r("a"),ZG=l("Language models enable zero-shot prediction of the effects of mutations on protein function"),KG=l(" by Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu and Alexander Rives. "),c9=r("strong"),zG=l("ESM-2"),UG=l(" was released with the paper "),Id=r("a"),qG=l("Language models of protein sequences at the scale of evolution enable accurate structure prediction"),jG=l(" by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives."),QG=d(),Il=r("li"),f9=r("strong"),Fg=r("a"),$G=l("FLAN-T5"),ek=l(" (from Google AI) released in the repository "),Bd=r("a"),tk=l("google-research/t5x"),rk=l(" 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. 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lPe={local:"transformers",sections:[{local:"if-you-are-looking-for-custom-support-from-the-hugging-face-team",title:"If you are looking for custom support from the Hugging Face team"},{local:"contents",sections:[{local:"supported-models",title:"Supported models"},{local:"supported-frameworks",title:"Supported frameworks"}],title:"Contents"}],title:"\u{1F917} Transformers"};function oPe(bve){return aPe(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class sPe extends jke{constructor(On){super();Qke(this,On,oPe,nPe,$ke,{})}}export{sPe as default,lPe as metadata};
