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Yo=new RD({}),Ho=new RD({}),Xo=new RD({}),Uo=new RD({}),Rs=new RD({}),{c(){ln=r("meta"),SD=d(),on=r("h1"),vn=r("a"),j6=r("span"),pD(Yo.$$.fragment),eA=d(),q6=r("span"),tA=l("\u{1F917} Transformers"),wD=d(),Ns=r("p"),rA=l("Maschinelles Lernen auf dem neuesten Stand der Technik f\xFCr PyTorch, TensorFlow und JAX."),MD=d(),Bs=r("p"),aA=l("\u{1F917} Transformers bietet APIs zum einfachen Herunterladen und Trainieren von vortrainierten Modellen auf dem neuesten Stand der Technik. Die Verwendung von vortrainierten Modellen kann Rechenkosten sparen und den CO2-Fu\xDFabdruck reduzieren und Zeit sparen, die f\xFCr das Training eines Modells von Grund auf ben\xF6tigt wird. Die Modelle k\xF6nnen f\xFCr verschiedene Modalit\xE4ten verwendet werden, wie z. B.:"),kD=d(),Nt=r("ul"),Q6=r("li"),nA=l("\u{1F4DD} Text: Textklassifizierung, Informationsextrahierung, Beantwortung von Fragen, Zusammenfassung, \xDCbersetzung und Texterstellung in \xFCber 100 Sprachen."),lA=d(),$6=r("li"),oA=l("\u{1F5BC}\uFE0F Bilder: Bildklassifizierung, Objekterkennung und Segmentierung."),iA=d(),em=r("li"),dA=l("\u{1F5E3}\uFE0F Audio: Spracherkennung und Audioklassifizierung."),sA=d(),tm=r("li"),hA=l("\u{1F419} Multimodal: Beantwortung von Tabellenfragen, optische Zeichenerkennung, Informationsextraktion aus gescannten Dokumenten, Videoklassifizierung und Beantwortung visueller Fragen."),GD=d(),Bt=r("p"),gA=l("Unsere Bibliothek unterst\xFCtzt die nahtlose Integration von drei der beliebtesten Deep-Learning-Bibliotheken: "),zo=r("a"),cA=l("PyTorch"),fA=l(", "),Jo=r("a"),uA=l("TensorFlow"),vA=l(" und "),Zo=r("a"),EA=l("JAX"),TA=l(". Trainieren Sie Ihr Modell in drei Codezeilen in einem Framework und laden Sie es zur Inferenz mit einem anderen."),PD=d(),Cs=r("p"),_A=l("Jede \u{1F917} Transformers-Architektur ist in einem eigenst\xE4ndigen Python-Modul definiert, so dass sie leicht f\xFCr Forschung und Experimente angepasst werden kann."),ND=d(),dn=r("h2"),En=r("a"),rm=r("span"),pD(Ho.$$.fragment),mA=d(),am=r("span"),pA=l("Wenn Sie auf der Suche nach individueller Unterst\xFCtzung durch das Hugging Face-Team sind"),BD=d(),sn=r("a"),qa=r("img"),DA=d(),CD=r("br"),ID=d(),hn=r("h2"),Tn=r("a"),nm=r("span"),pD(Xo.$$.fragment),AA=d(),lm=r("span"),LA=l("Inhalt"),xD=d(),Is=r("p"),yA=l("Die Dokumentation ist in f\xFCnf Teile gegliedert:"),OD=d(),Gt=r("ul"),om=r("li"),xs=r("p"),im=r("strong"),bA=l("GET STARTED"),RA=l(" enth\xE4lt eine kurze Tour und Installationsanweisungen, um mit \u{1F917} Transformers loszulegen."),SA=d(),dm=r("li"),Os=r("p"),sm=r("strong"),wA=l("TUTORIALS"),MA=l(" sind ein hervorragender Ausgangspunkt, wenn Sie neu in unserer Bibliothek sind. Dieser Abschnitt hilft Ihnen, die grundlegenden F\xE4higkeiten zu erlangen, die Sie ben\xF6tigen, um mit \u{1F917} Transformers zu arbeiten."),kA=d(),hm=r("li"),Fs=r("p"),gm=r("strong"),GA=l("HOW-TO GUIDES"),PA=l(" zeigen Ihnen, wie Sie ein bestimmtes Ziel erreichen k\xF6nnen, z. B. die Feinabstimmung eines vortrainierten Modells f\xFCr die Sprachmodellierung oder die Erstellung eines benutzerdefinierten Modellkopfs."),NA=d(),cm=r("li"),Ws=r("p"),fm=r("strong"),BA=l("KONZEPTUELLE ANLEITUNGEN"),CA=l(" bietet weitere Diskussionen und Erkl\xE4rungen zu den zugrunde liegenden Konzepten und Ideen hinter Modellen, Aufgaben und der Designphilosophie von \u{1F917} Transformers."),IA=d(),Ko=r("li"),Vs=r("p"),um=r("strong"),xA=l("API"),OA=l(" beschreibt jede Klasse und Funktion, gruppiert in:"),FA=d(),gn=r("ul"),Ys=r("li"),vm=r("strong"),WA=l("MAIN CLASSES"),VA=l(" f\xFCr die Hauptklassen, die die wichtigsten APIs der Bibliothek darstellen."),YA=d(),Em=r("li"),zA=l("MODELLE** f\xFCr die Klassen und Funktionen, die zu jedem in der Bibliothek implementierten Modell geh\xF6ren."),JA=d(),zs=r("li"),Tm=r("strong"),ZA=l("INTERNAL HELPERS"),HA=l(" f\xFCr die Klassen und Funktionen, die wir intern verwenden."),FD=d(),Js=r("p"),XA=l("Die Bibliothek enth\xE4lt derzeit JAX-, PyTorch- und TensorFlow-Implementierungen, vortrainierte Modellgewichte, Nutzungsskripte und Konvertierungsprogramme f\xFCr die folgenden Modelle."),WD=d(),cn=r("h3"),_n=r("a"),_m=r("span"),pD(Uo.$$.fragment),KA=d(),mm=r("span"),UA=l("Unterst\xFCtze Modelle"),VD=d(),h=r("ol"),mn=r("li"),pm=r("strong"),Zs=r("a"),jA=l("ALBERT"),qA=l(" (from Google Research and the Toyota Technological Institute at Chicago) released with the paper "),jo=r("a"),QA=l("ALBERT: A Lite BERT for Self-supervised Learning of Language Representations"),$A=l(", by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."),eL=d(),pn=r("li"),Dm=r("strong"),Hs=r("a"),tL=l("BART"),rL=l(" (from Facebook) released with the paper "),qo=r("a"),aL=l("BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension"),nL=l(" by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer."),lL=d(),Dn=r("li"),Am=r("strong"),Xs=r("a"),oL=l("BARThez"),iL=l(" (from \xC9cole polytechnique) released with the paper "),Qo=r("a"),dL=l("BARThez: a Skilled Pretrained French Sequence-to-Sequence Model"),sL=l(" by Moussa Kamal Eddine, Antoine J.-P. 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Peters, Arman Cohan."),Mw=d(),ml=r("li"),w7=r("strong"),Uh=r("a"),kw=l("LongT5"),Gw=l(" (from Google AI) released with the paper "),ad=r("a"),Pw=l("LongT5: Efficient Text-To-Text Transformer for Long Sequences"),Nw=l(" by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang."),Bw=d(),pl=r("li"),M7=r("strong"),jh=r("a"),Cw=l("LUKE"),Iw=l(" (from Studio Ousia) released with the paper "),nd=r("a"),xw=l("LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention"),Ow=l(" by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto."),Fw=d(),Dl=r("li"),k7=r("strong"),qh=r("a"),Ww=l("LXMERT"),Vw=l(" (from UNC Chapel Hill) released with the paper "),ld=r("a"),Yw=l("LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering"),zw=l(" by Hao Tan and Mohit Bansal."),Jw=d(),Al=r("li"),G7=r("strong"),Qh=r("a"),Zw=l("M-CTC-T"),Hw=l(" (from Facebook) released with the paper "),od=r("a"),Xw=l("Pseudo-Labeling For Massively Multilingual Speech Recognition"),Kw=l(" by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert."),Uw=d(),Ll=r("li"),P7=r("strong"),$h=r("a"),jw=l("M2M100"),qw=l(" (from Facebook) released with the paper "),id=r("a"),Qw=l("Beyond English-Centric Multilingual Machine Translation"),$w=l(" 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."),eM=d(),en=r("li"),N7=r("strong"),eg=r("a"),tM=l("MarianMT"),rM=l(" Machine translation models trained using "),dd=r("a"),aM=l("OPUS"),nM=l(" data by J\xF6rg Tiedemann. The "),sd=r("a"),lM=l("Marian Framework"),oM=l(" is being developed by the Microsoft Translator Team."),iM=d(),yl=r("li"),B7=r("strong"),tg=r("a"),dM=l("MaskFormer"),sM=l(" (from Meta and UIUC) released with the paper "),hd=r("a"),hM=l("Per-Pixel Classification is Not All You Need for Semantic Segmentation"),gM=l(" by Bowen Cheng, Alexander G. 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Liu."),Ok=d(),Vl=r("li"),q7=r("strong"),_g=r("a"),Fk=l("Perceiver IO"),Wk=l(" (from Deepmind) released with the paper "),Sd=r("a"),Vk=l("Perceiver IO: A General Architecture for Structured Inputs & Outputs"),Yk=l(" by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier H\xE9naff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, Jo\xE3o Carreira."),zk=d(),Yl=r("li"),Q7=r("strong"),mg=r("a"),Jk=l("PhoBERT"),Zk=l(" (from VinAI Research) released with the paper "),wd=r("a"),Hk=l("PhoBERT: Pre-trained language models for Vietnamese"),Xk=l(" by Dat Quoc Nguyen and Anh Tuan Nguyen."),Kk=d(),zl=r("li"),$7=r("strong"),pg=r("a"),Uk=l("PLBart"),jk=l(" (from UCLA NLP) released with the paper "),Md=r("a"),qk=l("Unified Pre-training for Program Understanding and Generation"),Qk=l(" by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang."),$k=d(),Jl=r("li"),ep=r("strong"),Dg=r("a"),eG=l("PoolFormer"),tG=l(" (from Sea AI Labs) released with the paper "),kd=r("a"),rG=l("MetaFormer is Actually What You Need for Vision"),aG=l(" by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng."),nG=d(),Zl=r("li"),tp=r("strong"),Ag=r("a"),lG=l("ProphetNet"),oG=l(" (from Microsoft Research) released with the paper "),Gd=r("a"),iG=l("ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training"),dG=l(" by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou."),sG=d(),Hl=r("li"),rp=r("strong"),Lg=r("a"),hG=l("QDQBert"),gG=l(" (from NVIDIA) released with the paper "),Pd=r("a"),cG=l("Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation"),fG=l(" by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius."),uG=d(),Xl=r("li"),ap=r("strong"),yg=r("a"),vG=l("RAG"),EG=l(" (from Facebook) released with the paper "),Nd=r("a"),TG=l("Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks"),_G=l(" by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich K\xFCttler, Mike Lewis, Wen-tau Yih, Tim Rockt\xE4schel, Sebastian Riedel, Douwe Kiela."),mG=d(),Kl=r("li"),np=r("strong"),bg=r("a"),pG=l("REALM"),DG=l(" (from Google Research) released with the paper "),Bd=r("a"),AG=l("REALM: Retrieval-Augmented Language Model Pre-Training"),LG=l(" by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang."),yG=d(),Ul=r("li"),lp=r("strong"),Rg=r("a"),bG=l("Reformer"),RG=l(" (from Google Research) released with the paper "),Cd=r("a"),SG=l("Reformer: The Efficient Transformer"),wG=l(" by Nikita Kitaev, \u0141ukasz Kaiser, Anselm Levskaya."),MG=d(),jl=r("li"),op=r("strong"),Sg=r("a"),kG=l("RegNet"),GG=l(" (from META Platforms) released with the paper "),Id=r("a"),PG=l("Designing Network Design Space"),NG=l(" by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Doll\xE1r."),BG=d(),ql=r("li"),ip=r("strong"),wg=r("a"),CG=l("RemBERT"),IG=l(" (from Google Research) released with the paper "),xd=r("a"),xG=l("Rethinking embedding coupling in pre-trained language models"),OG=l(" by Hyung Won Chung, Thibault F\xE9vry, Henry Tsai, M. Johnson, Sebastian Ruder."),FG=d(),Ql=r("li"),dp=r("strong"),Mg=r("a"),WG=l("ResNet"),VG=l(" (from Microsoft Research) released with the paper "),Od=r("a"),YG=l("Deep Residual Learning for Image Recognition"),zG=l(" by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun."),JG=d(),$l=r("li"),sp=r("strong"),kg=r("a"),ZG=l("RoBERTa"),HG=l(" (from Facebook), released together with the paper "),Fd=r("a"),XG=l("RoBERTa: A Robustly Optimized BERT Pretraining Approach"),KG=l(" by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov."),UG=d(),eo=r("li"),hp=r("strong"),Gg=r("a"),jG=l("RoFormer"),qG=l(" (from ZhuiyiTechnology), released together with the paper "),Wd=r("a"),QG=l("RoFormer: Enhanced Transformer with Rotary Position Embedding"),$G=l(" by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu."),eP=d(),to=r("li"),gp=r("strong"),Pg=r("a"),tP=l("SegFormer"),rP=l(" (from NVIDIA) released with the paper "),Vd=r("a"),aP=l("SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers"),nP=l(" by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. 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Keutzer."),CP=d(),so=r("li"),_p=r("strong"),Fg=r("a"),IP=l("Swin Transformer"),xP=l(" (from Microsoft) released with the paper "),Kd=r("a"),OP=l("Swin Transformer: Hierarchical Vision Transformer using Shifted Windows"),FP=l(" by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo."),WP=d(),ho=r("li"),mp=r("strong"),Wg=r("a"),VP=l("Swin Transformer V2"),YP=l(" (from Microsoft) released with the paper "),Ud=r("a"),zP=l("Swin Transformer V2: Scaling Up Capacity and Resolution"),JP=l(" 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."),ZP=d(),go=r("li"),pp=r("strong"),Vg=r("a"),HP=l("T5"),XP=l(" (from Google AI) released with the paper "),jd=r("a"),KP=l("Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"),UP=l(" 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. 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sDe={local:"transformers",sections:[{local:"wenn-sie-auf-der-suche-nach-individueller-untersttzung-durch-das-hugging-faceteam-sind",title:"Wenn Sie auf der Suche nach individueller Unterst\xFCtzung durch das Hugging Face-Team sind"},{local:"inhalt",sections:[{local:"untersttze-modelle",title:"Unterst\xFCtze Modelle"},{local:"untersttzte-frameworks",title:"Unterst\xFCtzte Frameworks"}],title:"Inhalt"}],title:"\u{1F917} Transformers"};function hDe(tse){return iDe(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class fDe extends tDe{constructor(ln){super();rDe(this,ln,hDe,dDe,aDe,{})}}export{fDe as default,sDe as metadata};
