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0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;security&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;security_link_product_navbar&quot;}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" 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data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;actions&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;actions_link_product_navbar&quot;}" href="https://github.com/features/actions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-workflow color-fg-subtle mr-3"> <path d="M1 3a2 2 0 0 1 2-2h6.5a2 2 0 0 1 2 2v6.5a2 2 0 0 1-2 2H7v4.063C7 16.355 7.644 17 8.438 17H12.5v-2.5a2 2 0 0 1 2-2H21a2 2 0 0 1 2 2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any workflow </div> </a></li> <li> <a class="HeaderMenu-dropdown-link 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1ZM2.5 12a9.5 9.5 0 0 0 9.5 9.5 9.5 9.5 0 0 0 9.5-9.5A9.5 9.5 0 0 0 12 2.5 9.5 9.5 0 0 0 2.5 12Zm9.5 2a2 2 0 1 1-.001-3.999A2 2 0 0 1 12 14Z"></path> </svg> <div> <div class="color-fg-default h4">Issues</div> Plan and track work </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;code_review&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;code_review_link_product_navbar&quot;}" href="https://github.com/features/code-review"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-code-review color-fg-subtle mr-3"> <path d="M10.3 6.74a.75.75 0 0 1-.04 1.06l-2.908 2.7 2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M1.5 4.25c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v12.5a1.75 1.75 0 0 1-1.75 1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 .75.75v3.19l3.72-3.72a.749.749 0 0 1 .53-.22h10a.25.25 0 0 0 .25-.25V4.25a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Review</div> Manage code changes </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;discussions&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;discussions_link_product_navbar&quot;}" 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href=\"https://github.com/huggingface/transformers/blob/main/i18n/README_ur.md\"\u003eاردو\u003c/a\u003e |\n \u003c/p\u003e\n\u003c/h4\u003e\u003ca id=\"user-content-------------english---------简体中文---------繁體中文---------한국어---------español---------日本語---------हिन्दी---------русский---------рortuguês---------తెలుగు---------français---------deutsch---------tiếng-việt---------العربية---------اردو-----\" class=\"anchor\" aria-label=\"Permalink: English |\n 简体中文 |\n 繁體中文 |\n 한국어 |\n Español |\n 日本語 |\n हिन्दी |\n Русский |\n Рortuguês |\n తెలుగు |\n Français |\n Deutsch |\n Tiếng Việt |\n العربية |\n اردو |\n \" href=\"#------------english---------简体中文---------繁體中文---------한국어---------español---------日本語---------हिन्दी---------русский---------рortuguês---------తెలుగు---------français---------deutsch---------tiếng-việt---------العربية---------اردو-----\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 align=\"center\" tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\n \u003cp dir=\"auto\"\u003eState-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\u003c/p\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-----state-of-the-art-machine-learning-for-jax-pytorch-and-tensorflow\" class=\"anchor\" aria-label=\"Permalink: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\" href=\"#----state-of-the-art-machine-learning-for-jax-pytorch-and-tensorflow\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 align=\"center\" tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\n \u003ca href=\"https://hf.co/course\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2f1e787c37f1f947eb675e8974e7efa67c37ec114457a48b15a5d8223741863a/68747470733a2f2f68756767696e67666163652e636f2f64617461736574732f68756767696e67666163652f646f63756d656e746174696f6e2d696d616765732f7265736f6c76652f6d61696e2f636f757273655f62616e6e65722e706e67\" data-canonical-src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content-----\" class=\"anchor\" aria-label=\"Permalink: \" href=\"#----\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThese models can be applied on:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, and text generation, in over 100 languages.\u003c/li\u003e\n\u003cli\u003e🖼️ Images, for tasks like image classification, object detection, and segmentation.\u003c/li\u003e\n\u003cli\u003e🗣️ Audio, for tasks like speech recognition and audio classification.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eTransformer models can also perform tasks on \u003cstrong\u003eseveral modalities combined\u003c/strong\u003e, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our \u003ca href=\"https://huggingface.co/models\" rel=\"nofollow\"\u003emodel hub\u003c/a\u003e. At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e🤗 Transformers is backed by the three most popular deep learning libraries — \u003ca href=\"https://jax.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eJax\u003c/a\u003e, \u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ePyTorch\u003c/a\u003e and \u003ca href=\"https://www.tensorflow.org/\" rel=\"nofollow\"\u003eTensorFlow\u003c/a\u003e — with a seamless integration between them. It's straightforward to train your models with one before loading them for inference with the other.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOnline demos\u003c/h2\u003e\u003ca id=\"user-content-online-demos\" class=\"anchor\" aria-label=\"Permalink: Online demos\" href=\"#online-demos\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can test most of our models directly on their pages from the \u003ca href=\"https://huggingface.co/models\" rel=\"nofollow\"\u003emodel hub\u003c/a\u003e. We also offer \u003ca href=\"https://huggingface.co/pricing\" rel=\"nofollow\"\u003eprivate model hosting, versioning, \u0026amp; an inference API\u003c/a\u003e for public and private models.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eHere are a few examples:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn Natural Language Processing:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France\" rel=\"nofollow\"\u003eMasked word completion with BERT\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city\" rel=\"nofollow\"\u003eNamed Entity Recognition with Electra\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2\" rel=\"nofollow\"\u003eText generation with Mistral\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal\" rel=\"nofollow\"\u003eNatural Language Inference with RoBERTa\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct\" rel=\"nofollow\"\u003eSummarization with BART\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F\u0026amp;context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species\" rel=\"nofollow\"\u003eQuestion answering with DistilBERT\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin\" rel=\"nofollow\"\u003eTranslation with T5\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eIn Computer Vision:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/google/vit-base-patch16-224\" rel=\"nofollow\"\u003eImage classification with ViT\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/facebook/detr-resnet-50\" rel=\"nofollow\"\u003eObject Detection with DETR\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512\" rel=\"nofollow\"\u003eSemantic Segmentation with SegFormer\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic\" rel=\"nofollow\"\u003ePanoptic Segmentation with Mask2Former\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/docs/transformers/main/model_doc/depth_anything\" rel=\"nofollow\"\u003eDepth Estimation with Depth Anything\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/docs/transformers/model_doc/videomae\" rel=\"nofollow\"\u003eVideo Classification with VideoMAE\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large\" rel=\"nofollow\"\u003eUniversal Segmentation with OneFormer\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eIn Audio:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/openai/whisper-large-v3\" rel=\"nofollow\"\u003eAutomatic Speech Recognition with Whisper\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/superb/wav2vec2-base-superb-ks\" rel=\"nofollow\"\u003eKeyword Spotting with Wav2Vec2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593\" rel=\"nofollow\"\u003eAudio Classification with Audio Spectrogram Transformer\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eIn Multimodal tasks:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/google/tapas-base-finetuned-wtq\" rel=\"nofollow\"\u003eTable Question Answering with TAPAS\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/dandelin/vilt-b32-finetuned-vqa\" rel=\"nofollow\"\u003eVisual Question Answering with ViLT\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/llava-hf/llava-1.5-7b-hf\" rel=\"nofollow\"\u003eImage captioning with LLaVa\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/google/siglip-so400m-patch14-384\" rel=\"nofollow\"\u003eZero-shot Image Classification with SigLIP\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/impira/layoutlm-document-qa\" rel=\"nofollow\"\u003eDocument Question Answering with LayoutLM\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/docs/transformers/model_doc/xclip\" rel=\"nofollow\"\u003eZero-shot Video Classification with X-CLIP\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/docs/transformers/en/model_doc/owlv2\" rel=\"nofollow\"\u003eZero-shot Object Detection with OWLv2\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/docs/transformers/model_doc/clipseg\" rel=\"nofollow\"\u003eZero-shot Image Segmentation with CLIPSeg\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/docs/transformers/model_doc/sam\" rel=\"nofollow\"\u003eAutomatic Mask Generation with SAM\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e100 projects using Transformers\u003c/h2\u003e\u003ca id=\"user-content-100-projects-using-transformers\" class=\"anchor\" aria-label=\"Permalink: 100 projects using Transformers\" href=\"#100-projects-using-transformers\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTransformers is more than a toolkit to use pretrained models: it's a community of projects built around it and the\nHugging Face Hub. We want Transformers to enable developers, researchers, students, professors, engineers, and anyone\nelse to build their dream projects.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn order to celebrate the 100,000 stars of transformers, we have decided to put the spotlight on the\ncommunity, and we have created the \u003ca href=\"/huggingface/transformers/blob/main/awesome-transformers.md\"\u003eawesome-transformers\u003c/a\u003e page which lists 100\nincredible projects built in the vicinity of transformers.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you own or use a project that you believe should be part of the list, please open a PR to add it!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSerious about AI in your organisation? Build faster with the Hugging Face Enterprise Hub.\u003c/h2\u003e\u003ca id=\"user-content-serious-about-ai-in-your-organisation-build-faster-with-the-hugging-face-enterprise-hub\" class=\"anchor\" aria-label=\"Permalink: Serious about AI in your organisation? Build faster with the Hugging Face Enterprise Hub.\" href=\"#serious-about-ai-in-your-organisation-build-faster-with-the-hugging-face-enterprise-hub\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003ca href=\"https://huggingface.co/enterprise\" rel=\"nofollow\"\u003e\n \u003cimg alt=\"Hugging Face Enterprise Hub\" src=\"https://private-user-images.githubusercontent.com/3841370/376175414-247fb16d-d251-4583-96c4-d3d76dda4925.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.2mKiBPrbOq55oj0JudVSIUE70w1HjQRV5bMPEQescfM\" secured-asset-link=\"\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eQuick tour\u003c/h2\u003e\u003ca id=\"user-content-quick-tour\" class=\"anchor\" aria-label=\"Permalink: Quick tour\" href=\"#quick-tour\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo immediately use a model on a given input (text, image, audio, ...), we provide the \u003ccode\u003epipeline\u003c/code\u003e API. Pipelines group together a pretrained model with the preprocessing that was used during that model's training. Here is how to quickly use a pipeline to classify positive versus negative texts:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; from transformers import pipeline\n\n# Allocate a pipeline for sentiment-analysis\n\u0026gt;\u0026gt;\u0026gt; classifier = pipeline('sentiment-analysis')\n\u0026gt;\u0026gt;\u0026gt; classifier('We are very happy to introduce pipeline to the transformers repository.')\n[{'label': 'POSITIVE', 'score': 0.9996980428695679}]\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransformers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epipeline\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Allocate a pipeline for sentiment-analysis\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eclassifier\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003epipeline\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'sentiment-analysis'\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eclassifier\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'We are very happy to introduce pipeline to the transformers repository.'\u003c/span\u003e)\n[{\u003cspan class=\"pl-s\"\u003e'label'\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'POSITIVE'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'score'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e0.9996980428695679\u003c/span\u003e}]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe second line of code downloads and caches the pretrained model used by the pipeline, while the third evaluates it on the given text. Here, the answer is \"positive\" with a confidence of 99.97%.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eMany tasks have a pre-trained \u003ccode\u003epipeline\u003c/code\u003e ready to go, in NLP but also in computer vision and speech. For example, we can easily extract detected objects in an image:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; import requests\n\u0026gt;\u0026gt;\u0026gt; from PIL import Image\n\u0026gt;\u0026gt;\u0026gt; from transformers import pipeline\n\n# Download an image with cute cats\n\u0026gt;\u0026gt;\u0026gt; url = \u0026quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png\u0026quot;\n\u0026gt;\u0026gt;\u0026gt; image_data = requests.get(url, stream=True).raw\n\u0026gt;\u0026gt;\u0026gt; image = Image.open(image_data)\n\n# Allocate a pipeline for object detection\n\u0026gt;\u0026gt;\u0026gt; object_detector = pipeline('object-detection')\n\u0026gt;\u0026gt;\u0026gt; object_detector(image)\n[{'score': 0.9982201457023621,\n 'label': 'remote',\n 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},\n {'score': 0.9960021376609802,\n 'label': 'remote',\n 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},\n {'score': 0.9954745173454285,\n 'label': 'couch',\n 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},\n {'score': 0.9988006353378296,\n 'label': 'cat',\n 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},\n {'score': 0.9986783862113953,\n 'label': 'cat',\n 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}]\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erequests\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-v\"\u003ePIL\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eImage\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransformers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epipeline\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Download an image with cute cats\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eurl\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png\"\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eimage_data\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erequests\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eget\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eurl\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003estream\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e).\u003cspan class=\"pl-s1\"\u003eraw\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eimage\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eImage\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eopen\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eimage_data\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Allocate a pipeline for object detection\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eobject_detector\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003epipeline\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'object-detection'\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eobject_detector\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eimage\u003c/span\u003e)\n[{\u003cspan class=\"pl-s\"\u003e'score'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e0.9982201457023621\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e'label'\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'remote'\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e'box'\u003c/span\u003e: {\u003cspan class=\"pl-s\"\u003e'xmin'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e40\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'ymin'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e70\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'xmax'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e175\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'ymax'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e117\u003c/span\u003e}},\n {\u003cspan class=\"pl-s\"\u003e'score'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e0.9960021376609802\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e'label'\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'remote'\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e'box'\u003c/span\u003e: {\u003cspan class=\"pl-s\"\u003e'xmin'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e333\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'ymin'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e72\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'xmax'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e368\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'ymax'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e187\u003c/span\u003e}},\n {\u003cspan class=\"pl-s\"\u003e'score'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e0.9954745173454285\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e'label'\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'couch'\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e'box'\u003c/span\u003e: {\u003cspan class=\"pl-s\"\u003e'xmin'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'ymin'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'xmax'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e639\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'ymax'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e473\u003c/span\u003e}},\n {\u003cspan class=\"pl-s\"\u003e'score'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e0.9988006353378296\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e'label'\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'cat'\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e'box'\u003c/span\u003e: {\u003cspan class=\"pl-s\"\u003e'xmin'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e13\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'ymin'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e52\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'xmax'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e314\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'ymax'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e470\u003c/span\u003e}},\n {\u003cspan class=\"pl-s\"\u003e'score'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e0.9986783862113953\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e'label'\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'cat'\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e'box'\u003c/span\u003e: {\u003cspan class=\"pl-s\"\u003e'xmin'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e345\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'ymin'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e23\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'xmax'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e640\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'ymax'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e368\u003c/span\u003e}}]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eHere, we get a list of objects detected in the image, with a box surrounding the object and a confidence score. Here is the original image on the left, with the predictions displayed on the right:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 align=\"center\" tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/6877546cf122bc77923ec1797846ccf72db736080dcfd5d5d75fdfaa2a852603/68747470733a2f2f68756767696e67666163652e636f2f64617461736574732f68756767696e67666163652f646f63756d656e746174696f6e2d696d616765732f7265736f6c76652f6d61696e2f636f636f5f73616d706c652e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6877546cf122bc77923ec1797846ccf72db736080dcfd5d5d75fdfaa2a852603/68747470733a2f2f68756767696e67666163652e636f2f64617461736574732f68756767696e67666163652f646f63756d656e746174696f6e2d696d616765732f7265736f6c76652f6d61696e2f636f636f5f73616d706c652e706e67\" width=\"400\" data-canonical-src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/b2a59afd2a2478b7c605ad7d07b9d08536e7f3e02551b553a287aad5cc0fd920/68747470733a2f2f68756767696e67666163652e636f2f64617461736574732f68756767696e67666163652f646f63756d656e746174696f6e2d696d616765732f7265736f6c76652f6d61696e2f636f636f5f73616d706c655f706f73745f70726f6365737365642e706e67\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/b2a59afd2a2478b7c605ad7d07b9d08536e7f3e02551b553a287aad5cc0fd920/68747470733a2f2f68756767696e67666163652e636f2f64617461736574732f68756767696e67666163652f646f63756d656e746174696f6e2d696d616765732f7265736f6c76652f6d61696e2f636f636f5f73616d706c655f706f73745f70726f6365737365642e706e67\" width=\"400\" data-canonical-src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/h3\u003e\u003ca id=\"user-content---------\" class=\"anchor\" aria-label=\"Permalink: \" href=\"#--------\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can learn more about the tasks supported by the \u003ccode\u003epipeline\u003c/code\u003e API in \u003ca href=\"https://huggingface.co/docs/transformers/task_summary\" rel=\"nofollow\"\u003ethis tutorial\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn addition to \u003ccode\u003epipeline\u003c/code\u003e, to download and use any of the pretrained models on your given task, all it takes is three lines of code. Here is the PyTorch version:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; from transformers import AutoTokenizer, AutoModel\n\n\u0026gt;\u0026gt;\u0026gt; tokenizer = AutoTokenizer.from_pretrained(\u0026quot;google-bert/bert-base-uncased\u0026quot;)\n\u0026gt;\u0026gt;\u0026gt; model = AutoModel.from_pretrained(\u0026quot;google-bert/bert-base-uncased\u0026quot;)\n\n\u0026gt;\u0026gt;\u0026gt; inputs = tokenizer(\u0026quot;Hello world!\u0026quot;, return_tensors=\u0026quot;pt\u0026quot;)\n\u0026gt;\u0026gt;\u0026gt; outputs = model(**inputs)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransformers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoTokenizer\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eAutoModel\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etokenizer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoTokenizer\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efrom_pretrained\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"google-bert/bert-base-uncased\"\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoModel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efrom_pretrained\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"google-bert/bert-base-uncased\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003etokenizer\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"Hello world!\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ereturn_tensors\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"pt\"\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003emodel\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAnd here is the equivalent code for TensorFlow:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; from transformers import AutoTokenizer, TFAutoModel\n\n\u0026gt;\u0026gt;\u0026gt; tokenizer = AutoTokenizer.from_pretrained(\u0026quot;google-bert/bert-base-uncased\u0026quot;)\n\u0026gt;\u0026gt;\u0026gt; model = TFAutoModel.from_pretrained(\u0026quot;google-bert/bert-base-uncased\u0026quot;)\n\n\u0026gt;\u0026gt;\u0026gt; inputs = tokenizer(\u0026quot;Hello world!\u0026quot;, return_tensors=\u0026quot;tf\u0026quot;)\n\u0026gt;\u0026gt;\u0026gt; outputs = model(**inputs)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransformers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoTokenizer\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eTFAutoModel\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etokenizer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoTokenizer\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efrom_pretrained\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"google-bert/bert-base-uncased\"\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTFAutoModel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efrom_pretrained\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"google-bert/bert-base-uncased\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003etokenizer\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"Hello world!\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ereturn_tensors\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"tf\"\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003emodel\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e**\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe tokenizer is responsible for all the preprocessing the pretrained model expects and can be called directly on a single string (as in the above examples) or a list. It will output a dictionary that you can use in downstream code or simply directly pass to your model using the ** argument unpacking operator.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe model itself is a regular \u003ca href=\"https://pytorch.org/docs/stable/nn.html#torch.nn.Module\" rel=\"nofollow\"\u003ePytorch \u003ccode\u003enn.Module\u003c/code\u003e\u003c/a\u003e or a \u003ca href=\"https://www.tensorflow.org/api_docs/python/tf/keras/Model\" rel=\"nofollow\"\u003eTensorFlow \u003ccode\u003etf.keras.Model\u003c/code\u003e\u003c/a\u003e (depending on your backend) which you can use as usual. \u003ca href=\"https://huggingface.co/docs/transformers/training\" rel=\"nofollow\"\u003eThis tutorial\u003c/a\u003e explains how to integrate such a model into a classic PyTorch or TensorFlow training loop, or how to use our \u003ccode\u003eTrainer\u003c/code\u003e API to quickly fine-tune on a new dataset.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWhy should I use transformers?\u003c/h2\u003e\u003ca id=\"user-content-why-should-i-use-transformers\" class=\"anchor\" aria-label=\"Permalink: Why should I use transformers?\" href=\"#why-should-i-use-transformers\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eEasy-to-use state-of-the-art models:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHigh performance on natural language understanding \u0026amp; generation, computer vision, and audio tasks.\u003c/li\u003e\n\u003cli\u003eLow barrier to entry for educators and practitioners.\u003c/li\u003e\n\u003cli\u003eFew user-facing abstractions with just three classes to learn.\u003c/li\u003e\n\u003cli\u003eA unified API for using all our pretrained models.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eLower compute costs, smaller carbon footprint:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eResearchers can share trained models instead of always retraining.\u003c/li\u003e\n\u003cli\u003ePractitioners can reduce compute time and production costs.\u003c/li\u003e\n\u003cli\u003eDozens of architectures with over 400,000 pretrained models across all modalities.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eChoose the right framework for every part of a model's lifetime:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTrain state-of-the-art models in 3 lines of code.\u003c/li\u003e\n\u003cli\u003eMove a single model between TF2.0/PyTorch/JAX frameworks at will.\u003c/li\u003e\n\u003cli\u003eSeamlessly pick the right framework for training, evaluation, and production.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eEasily customize a model or an example to your needs:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWe provide examples for each architecture to reproduce the results published by its original authors.\u003c/li\u003e\n\u003cli\u003eModel internals are exposed as consistently as possible.\u003c/li\u003e\n\u003cli\u003eModel files can be used independently of the library for quick experiments.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWhy shouldn't I use transformers?\u003c/h2\u003e\u003ca id=\"user-content-why-shouldnt-i-use-transformers\" class=\"anchor\" aria-label=\"Permalink: Why shouldn't I use transformers?\" href=\"#why-shouldnt-i-use-transformers\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThis library is not a modular toolbox of building blocks for neural nets. The code in the model files is not refactored with additional abstractions on purpose, so that researchers can quickly iterate on each of the models without diving into additional abstractions/files.\u003c/li\u003e\n\u003cli\u003eThe training API is not intended to work on any model but is optimized to work with the models provided by the library. For generic machine learning loops, you should use another library (possibly, \u003ca href=\"https://huggingface.co/docs/accelerate\" rel=\"nofollow\"\u003eAccelerate\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003eWhile we strive to present as many use cases as possible, the scripts in our \u003ca href=\"https://github.com/huggingface/transformers/tree/main/examples\"\u003eexamples folder\u003c/a\u003e are just that: examples. It is expected that they won't work out-of-the-box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWith pip\u003c/h3\u003e\u003ca id=\"user-content-with-pip\" class=\"anchor\" aria-label=\"Permalink: With pip\" href=\"#with-pip\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis repository is tested on Python 3.9+, Flax 0.4.1+, PyTorch 1.11+, and TensorFlow 2.6+.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou should install 🤗 Transformers in a \u003ca href=\"https://docs.python.org/3/library/venv.html\" rel=\"nofollow\"\u003evirtual environment\u003c/a\u003e. If you're unfamiliar with Python virtual environments, check out the \u003ca href=\"https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/\" rel=\"nofollow\"\u003euser guide\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFirst, create a virtual environment with the version of Python you're going to use and activate it.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThen, you will need to install at least one of Flax, PyTorch, or TensorFlow.\nPlease refer to \u003ca href=\"https://www.tensorflow.org/install/\" rel=\"nofollow\"\u003eTensorFlow installation page\u003c/a\u003e, \u003ca href=\"https://pytorch.org/get-started/locally/#start-locally\" rel=\"nofollow\"\u003ePyTorch installation page\u003c/a\u003e and/or \u003ca href=\"https://github.com/google/flax#quick-install\"\u003eFlax\u003c/a\u003e and \u003ca href=\"https://github.com/google/jax#installation\"\u003eJax\u003c/a\u003e installation pages regarding the specific installation command for your platform.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWhen one of those backends has been installed, 🤗 Transformers can be installed using pip as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install transformers\"\u003e\u003cpre\u003epip install transformers\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you'd like to play with the examples or need the bleeding edge of the code and can't wait for a new release, you must \u003ca href=\"https://huggingface.co/docs/transformers/installation#installing-from-source\" rel=\"nofollow\"\u003einstall the library from source\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWith conda\u003c/h3\u003e\u003ca id=\"user-content-with-conda\" class=\"anchor\" aria-label=\"Permalink: With conda\" href=\"#with-conda\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e🤗 Transformers can be installed using conda as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda install conda-forge::transformers\"\u003e\u003cpre\u003econda install conda-forge::transformers\u003c/pre\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e\u003cem\u003eNOTE:\u003c/em\u003e\u003c/strong\u003e Installing \u003ccode\u003etransformers\u003c/code\u003e from the \u003ccode\u003ehuggingface\u003c/code\u003e channel is deprecated.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eFollow the installation pages of Flax, PyTorch or TensorFlow to see how to install them with conda.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e\u003cem\u003eNOTE:\u003c/em\u003e\u003c/strong\u003e On Windows, you may be prompted to activate Developer Mode in order to benefit from caching. If this is not an option for you, please let us know in \u003ca href=\"https://github.com/huggingface/huggingface_hub/issues/1062\" data-hovercard-type=\"issue\" data-hovercard-url=\"/huggingface/huggingface_hub/issues/1062/hovercard\"\u003ethis issue\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModel architectures\u003c/h2\u003e\u003ca id=\"user-content-model-architectures\" class=\"anchor\" aria-label=\"Permalink: Model architectures\" href=\"#model-architectures\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e\u003ca href=\"https://huggingface.co/models\" rel=\"nofollow\"\u003eAll the model checkpoints\u003c/a\u003e\u003c/strong\u003e provided by 🤗 Transformers are seamlessly integrated from the huggingface.co \u003ca href=\"https://huggingface.co/models\" rel=\"nofollow\"\u003emodel hub\u003c/a\u003e, where they are uploaded directly by \u003ca href=\"https://huggingface.co/users\" rel=\"nofollow\"\u003eusers\u003c/a\u003e and \u003ca href=\"https://huggingface.co/organizations\" rel=\"nofollow\"\u003eorganizations\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCurrent number of checkpoints: \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/9b5c7f5815b91e9ab3d501a52038a2cdf570a5aa96d837d4fb7500435f4ae5c4/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d68747470733a2f2f68756767696e67666163652e636f2f6170692f736869656c64732f6d6f64656c7326636f6c6f723d627269676874677265656e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/9b5c7f5815b91e9ab3d501a52038a2cdf570a5aa96d837d4fb7500435f4ae5c4/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d68747470733a2f2f68756767696e67666163652e636f2f6170692f736869656c64732f6d6f64656c7326636f6c6f723d627269676874677265656e\" alt=\"\" data-canonical-src=\"https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models\u0026amp;color=brightgreen\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e🤗 Transformers currently provides the following architectures: see \u003ca href=\"https://huggingface.co/docs/transformers/model_summary\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for a high-level summary of each them.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo check if each model has an implementation in Flax, PyTorch or TensorFlow, or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to \u003ca href=\"https://huggingface.co/docs/transformers/index#supported-frameworks\" rel=\"nofollow\"\u003ethis table\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThese implementations have been tested on several datasets (see the example scripts) and should match the performance of the original implementations. You can find more details on performance in the Examples section of the \u003ca href=\"https://github.com/huggingface/transformers/tree/main/examples\"\u003edocumentation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLearn more\u003c/h2\u003e\u003ca id=\"user-content-learn-more\" class=\"anchor\" aria-label=\"Permalink: Learn more\" href=\"#learn-more\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSection\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/docs/transformers/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFull API documentation and tutorials\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/docs/transformers/task_summary\" rel=\"nofollow\"\u003eTask summary\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eTasks supported by 🤗 Transformers\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/docs/transformers/preprocessing\" rel=\"nofollow\"\u003ePreprocessing tutorial\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eUsing the \u003ccode\u003eTokenizer\u003c/code\u003e class to prepare data for the models\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/docs/transformers/training\" rel=\"nofollow\"\u003eTraining and fine-tuning\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eUsing the models provided by 🤗 Transformers in a PyTorch/TensorFlow training loop and the \u003ccode\u003eTrainer\u003c/code\u003e API\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/huggingface/transformers/tree/main/examples\"\u003eQuick tour: Fine-tuning/usage scripts\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eExample scripts for fine-tuning models on a wide range of tasks\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/docs/transformers/model_sharing\" rel=\"nofollow\"\u003eModel sharing and uploading\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eUpload and share your fine-tuned models with the community\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe now have a \u003ca href=\"https://www.aclweb.org/anthology/2020.emnlp-demos.6/\" rel=\"nofollow\"\u003epaper\u003c/a\u003e you can cite for the 🤗 Transformers library:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"@inproceedings{wolf-etal-2020-transformers,\n title = \u0026quot;Transformers: State-of-the-Art Natural Language Processing\u0026quot;,\n author = \u0026quot;Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush\u0026quot;,\n booktitle = \u0026quot;Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations\u0026quot;,\n month = oct,\n year = \u0026quot;2020\u0026quot;,\n address = \u0026quot;Online\u0026quot;,\n publisher = \u0026quot;Association for Computational Linguistics\u0026quot;,\n url = \u0026quot;https://www.aclweb.org/anthology/2020.emnlp-demos.6\u0026quot;,\n pages = \u0026quot;38--45\u0026quot;\n}\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@inproceedings\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003ewolf-etal-2020-transformers\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eTransformers: State-of-the-Art Natural Language Processing\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eThomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ebooktitle\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eProceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003emonth\u003c/span\u003e = oct,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e2020\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eaddress\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eOnline\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003epublisher\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eAssociation for Computational Linguistics\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eurl\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehttps://www.aclweb.org/anthology/2020.emnlp-demos.6\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003epages\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e38--45\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n}\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":4,"text":"English |\n 简体中文 |\n 繁體中文 |\n 한국어 |\n Español |\n 日本語 |\n हिन्दी |\n Русский |\n Рortuguês |\n తెలుగు |\n Français |\n Deutsch |\n Tiếng Việt |\n العربية |\n اردو |\n ","anchor":"------------english---------简体中文---------繁體中文---------한국어---------español---------日本語---------हिन्दी---------русский---------рortuguês---------తెలుగు---------français---------deutsch---------tiếng-việt---------العربية---------اردو-----","htmlText":"English |\n 简体中文 |\n 繁體中文 |\n 한국어 |\n Español |\n 日本語 |\n हिन्दी |\n Русский |\n Рortuguês |\n తెలుగు |\n Français |\n Deutsch |\n Tiếng Việt |\n العربية |\n اردو |\n "},{"level":3,"text":"State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow","anchor":"----state-of-the-art-machine-learning-for-jax-pytorch-and-tensorflow","htmlText":"State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"},{"level":3,"text":"","anchor":"----","htmlText":""},{"level":2,"text":"Online demos","anchor":"online-demos","htmlText":"Online demos"},{"level":2,"text":"100 projects using Transformers","anchor":"100-projects-using-transformers","htmlText":"100 projects using Transformers"},{"level":2,"text":"Serious about AI in your organisation? Build faster with the Hugging Face Enterprise Hub.","anchor":"serious-about-ai-in-your-organisation-build-faster-with-the-hugging-face-enterprise-hub","htmlText":"Serious about AI in your organisation? Build faster with the Hugging Face Enterprise Hub."},{"level":2,"text":"Quick tour","anchor":"quick-tour","htmlText":"Quick tour"},{"level":3,"text":"","anchor":"--------","htmlText":""},{"level":2,"text":"Why should I use transformers?","anchor":"why-should-i-use-transformers","htmlText":"Why should I use transformers?"},{"level":2,"text":"Why shouldn't I use transformers?","anchor":"why-shouldnt-i-use-transformers","htmlText":"Why shouldn't I use transformers?"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":3,"text":"With pip","anchor":"with-pip","htmlText":"With pip"},{"level":3,"text":"With conda","anchor":"with-conda","htmlText":"With conda"},{"level":2,"text":"Model architectures","anchor":"model-architectures","htmlText":"Model architectures"},{"level":2,"text":"Learn more","anchor":"learn-more","htmlText":"Learn more"},{"level":2,"text":"Citation","anchor":"citation","htmlText":"Citation"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fhuggingface%2Ftransformers"}},{"displayName":"CODE_OF_CONDUCT.md","repoName":"transformers","refName":"main","path":"CODE_OF_CONDUCT.md","preferredFileType":"code_of_conduct","tabName":"Code of conduct","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fhuggingface%2Ftransformers"}},{"displayName":"LICENSE","repoName":"transformers","refName":"main","path":"LICENSE","preferredFileType":"license","tabName":"Apache-2.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fhuggingface%2Ftransformers"}},{"displayName":"SECURITY.md","repoName":"transformers","refName":"main","path":"SECURITY.md","preferredFileType":"security","tabName":"Security","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fhuggingface%2Ftransformers"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"accessible_code_button":true}}}}</script> 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href="#------------english---------简体中文---------繁體中文---------한국어---------español---------日本語---------हिन्दी---------русский---------рortuguês---------తెలుగు---------français---------deutsch---------tiếng-việt---------العربية---------اردو-----"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 align="center" tabindex="-1" class="heading-element" dir="auto"> <p dir="auto">State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow</p> </h3><a id="user-content-----state-of-the-art-machine-learning-for-jax-pytorch-and-tensorflow" class="anchor" aria-label="Permalink: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow" href="#----state-of-the-art-machine-learning-for-jax-pytorch-and-tensorflow"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 align="center" tabindex="-1" class="heading-element" dir="auto"> <a href="https://hf.co/course" rel="nofollow"><img src="https://camo.githubusercontent.com/2f1e787c37f1f947eb675e8974e7efa67c37ec114457a48b15a5d8223741863a/68747470733a2f2f68756767696e67666163652e636f2f64617461736574732f68756767696e67666163652f646f63756d656e746174696f6e2d696d616765732f7265736f6c76652f6d61696e2f636f757273655f62616e6e65722e706e67" data-canonical-src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png" style="max-width: 100%;"></a> </h3><a id="user-content-----" class="anchor" aria-label="Permalink: " href="#----"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.</p> <p dir="auto">These models can be applied on:</p> <ul dir="auto"> <li>📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, and text generation, in over 100 languages.</li> <li>🖼️ Images, for tasks like image classification, object detection, and segmentation.</li> <li>🗣️ Audio, for tasks like speech recognition and audio classification.</li> </ul> <p dir="auto">Transformer models can also perform tasks on <strong>several modalities combined</strong>, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.</p> <p dir="auto">🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our <a href="https://huggingface.co/models" rel="nofollow">model hub</a>. At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments.</p> <p dir="auto">🤗 Transformers is backed by the three most popular deep learning libraries — <a href="https://jax.readthedocs.io/en/latest/" rel="nofollow">Jax</a>, <a href="https://pytorch.org/" rel="nofollow">PyTorch</a> and <a href="https://www.tensorflow.org/" rel="nofollow">TensorFlow</a> — with a seamless integration between them. It's straightforward to train your models with one before loading them for inference with the other.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Online demos</h2><a id="user-content-online-demos" class="anchor" aria-label="Permalink: Online demos" href="#online-demos"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can test most of our models directly on their pages from the <a href="https://huggingface.co/models" rel="nofollow">model hub</a>. We also offer <a href="https://huggingface.co/pricing" rel="nofollow">private model hosting, versioning, &amp; an inference API</a> for public and private models.</p> <p dir="auto">Here are a few examples:</p> <p dir="auto">In Natural Language Processing:</p> <ul dir="auto"> <li><a href="https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France" rel="nofollow">Masked word completion with BERT</a></li> <li><a href="https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city" rel="nofollow">Named Entity Recognition with Electra</a></li> <li><a href="https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2" rel="nofollow">Text generation with Mistral</a></li> <li><a href="https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal" rel="nofollow">Natural Language Inference with RoBERTa</a></li> <li><a href="https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct" rel="nofollow">Summarization with BART</a></li> <li><a href="https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&amp;context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species" rel="nofollow">Question answering with DistilBERT</a></li> <li><a href="https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin" rel="nofollow">Translation with T5</a></li> </ul> <p dir="auto">In Computer Vision:</p> <ul dir="auto"> <li><a href="https://huggingface.co/google/vit-base-patch16-224" rel="nofollow">Image classification with ViT</a></li> <li><a href="https://huggingface.co/facebook/detr-resnet-50" rel="nofollow">Object Detection with DETR</a></li> <li><a href="https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512" rel="nofollow">Semantic Segmentation with SegFormer</a></li> <li><a href="https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic" rel="nofollow">Panoptic Segmentation with Mask2Former</a></li> <li><a href="https://huggingface.co/docs/transformers/main/model_doc/depth_anything" rel="nofollow">Depth Estimation with Depth Anything</a></li> <li><a href="https://huggingface.co/docs/transformers/model_doc/videomae" rel="nofollow">Video Classification with VideoMAE</a></li> <li><a href="https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large" rel="nofollow">Universal Segmentation with OneFormer</a></li> </ul> <p dir="auto">In Audio:</p> <ul dir="auto"> <li><a href="https://huggingface.co/openai/whisper-large-v3" rel="nofollow">Automatic Speech Recognition with Whisper</a></li> <li><a href="https://huggingface.co/superb/wav2vec2-base-superb-ks" rel="nofollow">Keyword Spotting with Wav2Vec2</a></li> <li><a href="https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593" rel="nofollow">Audio Classification with Audio Spectrogram Transformer</a></li> </ul> <p dir="auto">In Multimodal tasks:</p> <ul dir="auto"> <li><a href="https://huggingface.co/google/tapas-base-finetuned-wtq" rel="nofollow">Table Question Answering with TAPAS</a></li> <li><a href="https://huggingface.co/dandelin/vilt-b32-finetuned-vqa" rel="nofollow">Visual Question Answering with ViLT</a></li> <li><a href="https://huggingface.co/llava-hf/llava-1.5-7b-hf" rel="nofollow">Image captioning with LLaVa</a></li> <li><a href="https://huggingface.co/google/siglip-so400m-patch14-384" rel="nofollow">Zero-shot Image Classification with SigLIP</a></li> <li><a href="https://huggingface.co/impira/layoutlm-document-qa" rel="nofollow">Document Question Answering with LayoutLM</a></li> <li><a href="https://huggingface.co/docs/transformers/model_doc/xclip" rel="nofollow">Zero-shot Video Classification with X-CLIP</a></li> <li><a href="https://huggingface.co/docs/transformers/en/model_doc/owlv2" rel="nofollow">Zero-shot Object Detection with OWLv2</a></li> <li><a href="https://huggingface.co/docs/transformers/model_doc/clipseg" rel="nofollow">Zero-shot Image Segmentation with CLIPSeg</a></li> <li><a href="https://huggingface.co/docs/transformers/model_doc/sam" rel="nofollow">Automatic Mask Generation with SAM</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">100 projects using Transformers</h2><a id="user-content-100-projects-using-transformers" class="anchor" aria-label="Permalink: 100 projects using Transformers" href="#100-projects-using-transformers"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Transformers is more than a toolkit to use pretrained models: it's a community of projects built around it and the Hugging Face Hub. We want Transformers to enable developers, researchers, students, professors, engineers, and anyone else to build their dream projects.</p> <p dir="auto">In order to celebrate the 100,000 stars of transformers, we have decided to put the spotlight on the community, and we have created the <a href="/huggingface/transformers/blob/main/awesome-transformers.md">awesome-transformers</a> page which lists 100 incredible projects built in the vicinity of transformers.</p> <p dir="auto">If you own or use a project that you believe should be part of the list, please open a PR to add it!</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Serious about AI in your organisation? Build faster with the Hugging Face Enterprise Hub.</h2><a id="user-content-serious-about-ai-in-your-organisation-build-faster-with-the-hugging-face-enterprise-hub" class="anchor" aria-label="Permalink: Serious about AI in your organisation? Build faster with the Hugging Face Enterprise Hub." href="#serious-about-ai-in-your-organisation-build-faster-with-the-hugging-face-enterprise-hub"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <a href="https://huggingface.co/enterprise" rel="nofollow"> <img alt="Hugging Face Enterprise Hub" src="https://private-user-images.githubusercontent.com/3841370/376175414-247fb16d-d251-4583-96c4-d3d76dda4925.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.2mKiBPrbOq55oj0JudVSIUE70w1HjQRV5bMPEQescfM" secured-asset-link="" style="max-width: 100%;"> </a><br> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Quick tour</h2><a id="user-content-quick-tour" class="anchor" aria-label="Permalink: Quick tour" href="#quick-tour"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To immediately use a model on a given input (text, image, audio, ...), we provide the <code>pipeline</code> API. Pipelines group together a pretrained model with the preprocessing that was used during that model's training. Here is how to quickly use a pipeline to classify positive versus negative texts:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="&gt;&gt;&gt; from transformers import pipeline # Allocate a pipeline for sentiment-analysis &gt;&gt;&gt; classifier = pipeline('sentiment-analysis') &gt;&gt;&gt; classifier('We are very happy to introduce pipeline to the transformers repository.') [{'label': 'POSITIVE', 'score': 0.9996980428695679}]"><pre><span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-k">from</span> <span class="pl-s1">transformers</span> <span class="pl-k">import</span> <span class="pl-s1">pipeline</span> <span class="pl-c"># Allocate a pipeline for sentiment-analysis</span> <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">classifier</span> <span class="pl-c1">=</span> <span class="pl-en">pipeline</span>(<span class="pl-s">'sentiment-analysis'</span>) <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-en">classifier</span>(<span class="pl-s">'We are very happy to introduce pipeline to the transformers repository.'</span>) [{<span class="pl-s">'label'</span>: <span class="pl-s">'POSITIVE'</span>, <span class="pl-s">'score'</span>: <span class="pl-c1">0.9996980428695679</span>}]</pre></div> <p dir="auto">The second line of code downloads and caches the pretrained model used by the pipeline, while the third evaluates it on the given text. Here, the answer is "positive" with a confidence of 99.97%.</p> <p dir="auto">Many tasks have a pre-trained <code>pipeline</code> ready to go, in NLP but also in computer vision and speech. For example, we can easily extract detected objects in an image:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="&gt;&gt;&gt; import requests &gt;&gt;&gt; from PIL import Image &gt;&gt;&gt; from transformers import pipeline # Download an image with cute cats &gt;&gt;&gt; url = &quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png&quot; &gt;&gt;&gt; image_data = requests.get(url, stream=True).raw &gt;&gt;&gt; image = Image.open(image_data) # Allocate a pipeline for object detection &gt;&gt;&gt; object_detector = pipeline('object-detection') &gt;&gt;&gt; object_detector(image) [{'score': 0.9982201457023621, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, {'score': 0.9960021376609802, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, {'score': 0.9954745173454285, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, {'score': 0.9988006353378296, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, {'score': 0.9986783862113953, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}]"><pre><span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-k">import</span> <span class="pl-s1">requests</span> <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-k">from</span> <span class="pl-v">PIL</span> <span class="pl-k">import</span> <span class="pl-v">Image</span> <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-k">from</span> <span class="pl-s1">transformers</span> <span class="pl-k">import</span> <span class="pl-s1">pipeline</span> <span class="pl-c"># Download an image with cute cats</span> <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">url</span> <span class="pl-c1">=</span> <span class="pl-s">"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png"</span> <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">image_data</span> <span class="pl-c1">=</span> <span class="pl-s1">requests</span>.<span class="pl-en">get</span>(<span class="pl-s1">url</span>, <span class="pl-s1">stream</span><span class="pl-c1">=</span><span class="pl-c1">True</span>).<span class="pl-s1">raw</span> <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">image</span> <span class="pl-c1">=</span> <span class="pl-v">Image</span>.<span class="pl-en">open</span>(<span class="pl-s1">image_data</span>) <span class="pl-c"># Allocate a pipeline for object detection</span> <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">object_detector</span> <span class="pl-c1">=</span> <span class="pl-en">pipeline</span>(<span class="pl-s">'object-detection'</span>) <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-en">object_detector</span>(<span class="pl-s1">image</span>) [{<span class="pl-s">'score'</span>: <span class="pl-c1">0.9982201457023621</span>, <span class="pl-s">'label'</span>: <span class="pl-s">'remote'</span>, <span class="pl-s">'box'</span>: {<span class="pl-s">'xmin'</span>: <span class="pl-c1">40</span>, <span class="pl-s">'ymin'</span>: <span class="pl-c1">70</span>, <span class="pl-s">'xmax'</span>: <span class="pl-c1">175</span>, <span class="pl-s">'ymax'</span>: <span class="pl-c1">117</span>}}, {<span class="pl-s">'score'</span>: <span class="pl-c1">0.9960021376609802</span>, <span class="pl-s">'label'</span>: <span class="pl-s">'remote'</span>, <span class="pl-s">'box'</span>: {<span class="pl-s">'xmin'</span>: <span class="pl-c1">333</span>, <span class="pl-s">'ymin'</span>: <span class="pl-c1">72</span>, <span class="pl-s">'xmax'</span>: <span class="pl-c1">368</span>, <span class="pl-s">'ymax'</span>: <span class="pl-c1">187</span>}}, {<span class="pl-s">'score'</span>: <span class="pl-c1">0.9954745173454285</span>, <span class="pl-s">'label'</span>: <span class="pl-s">'couch'</span>, <span class="pl-s">'box'</span>: {<span class="pl-s">'xmin'</span>: <span class="pl-c1">0</span>, <span class="pl-s">'ymin'</span>: <span class="pl-c1">1</span>, <span class="pl-s">'xmax'</span>: <span class="pl-c1">639</span>, <span class="pl-s">'ymax'</span>: <span class="pl-c1">473</span>}}, {<span class="pl-s">'score'</span>: <span class="pl-c1">0.9988006353378296</span>, <span class="pl-s">'label'</span>: <span class="pl-s">'cat'</span>, <span class="pl-s">'box'</span>: {<span class="pl-s">'xmin'</span>: <span class="pl-c1">13</span>, <span class="pl-s">'ymin'</span>: <span class="pl-c1">52</span>, <span class="pl-s">'xmax'</span>: <span class="pl-c1">314</span>, <span class="pl-s">'ymax'</span>: <span class="pl-c1">470</span>}}, {<span class="pl-s">'score'</span>: <span class="pl-c1">0.9986783862113953</span>, <span class="pl-s">'label'</span>: <span class="pl-s">'cat'</span>, <span class="pl-s">'box'</span>: {<span class="pl-s">'xmin'</span>: <span class="pl-c1">345</span>, <span class="pl-s">'ymin'</span>: <span class="pl-c1">23</span>, <span class="pl-s">'xmax'</span>: <span class="pl-c1">640</span>, <span class="pl-s">'ymax'</span>: <span class="pl-c1">368</span>}}]</pre></div> <p dir="auto">Here, we get a list of objects detected in the image, with a box surrounding the object and a confidence score. Here is the original image on the left, with the predictions displayed on the right:</p> <div class="markdown-heading" dir="auto"><h3 align="center" tabindex="-1" class="heading-element" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/6877546cf122bc77923ec1797846ccf72db736080dcfd5d5d75fdfaa2a852603/68747470733a2f2f68756767696e67666163652e636f2f64617461736574732f68756767696e67666163652f646f63756d656e746174696f6e2d696d616765732f7265736f6c76652f6d61696e2f636f636f5f73616d706c652e706e67"><img src="https://camo.githubusercontent.com/6877546cf122bc77923ec1797846ccf72db736080dcfd5d5d75fdfaa2a852603/68747470733a2f2f68756767696e67666163652e636f2f64617461736574732f68756767696e67666163652f646f63756d656e746174696f6e2d696d616765732f7265736f6c76652f6d61696e2f636f636f5f73616d706c652e706e67" width="400" data-canonical-src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" style="max-width: 100%;"></a> <a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/b2a59afd2a2478b7c605ad7d07b9d08536e7f3e02551b553a287aad5cc0fd920/68747470733a2f2f68756767696e67666163652e636f2f64617461736574732f68756767696e67666163652f646f63756d656e746174696f6e2d696d616765732f7265736f6c76652f6d61696e2f636f636f5f73616d706c655f706f73745f70726f6365737365642e706e67"><img src="https://camo.githubusercontent.com/b2a59afd2a2478b7c605ad7d07b9d08536e7f3e02551b553a287aad5cc0fd920/68747470733a2f2f68756767696e67666163652e636f2f64617461736574732f68756767696e67666163652f646f63756d656e746174696f6e2d696d616765732f7265736f6c76652f6d61696e2f636f636f5f73616d706c655f706f73745f70726f6365737365642e706e67" width="400" data-canonical-src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png" style="max-width: 100%;"></a> </h3><a id="user-content---------" class="anchor" aria-label="Permalink: " href="#--------"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can learn more about the tasks supported by the <code>pipeline</code> API in <a href="https://huggingface.co/docs/transformers/task_summary" rel="nofollow">this tutorial</a>.</p> <p dir="auto">In addition to <code>pipeline</code>, to download and use any of the pretrained models on your given task, all it takes is three lines of code. Here is the PyTorch version:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="&gt;&gt;&gt; from transformers import AutoTokenizer, AutoModel &gt;&gt;&gt; tokenizer = AutoTokenizer.from_pretrained(&quot;google-bert/bert-base-uncased&quot;) &gt;&gt;&gt; model = AutoModel.from_pretrained(&quot;google-bert/bert-base-uncased&quot;) &gt;&gt;&gt; inputs = tokenizer(&quot;Hello world!&quot;, return_tensors=&quot;pt&quot;) &gt;&gt;&gt; outputs = model(**inputs)"><pre><span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-k">from</span> <span class="pl-s1">transformers</span> <span class="pl-k">import</span> <span class="pl-v">AutoTokenizer</span>, <span class="pl-v">AutoModel</span> <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">tokenizer</span> <span class="pl-c1">=</span> <span class="pl-v">AutoTokenizer</span>.<span class="pl-en">from_pretrained</span>(<span class="pl-s">"google-bert/bert-base-uncased"</span>) <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">model</span> <span class="pl-c1">=</span> <span class="pl-v">AutoModel</span>.<span class="pl-en">from_pretrained</span>(<span class="pl-s">"google-bert/bert-base-uncased"</span>) <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">inputs</span> <span class="pl-c1">=</span> <span class="pl-en">tokenizer</span>(<span class="pl-s">"Hello world!"</span>, <span class="pl-s1">return_tensors</span><span class="pl-c1">=</span><span class="pl-s">"pt"</span>) <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">outputs</span> <span class="pl-c1">=</span> <span class="pl-en">model</span>(<span class="pl-c1">**</span><span class="pl-s1">inputs</span>)</pre></div> <p dir="auto">And here is the equivalent code for TensorFlow:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="&gt;&gt;&gt; from transformers import AutoTokenizer, TFAutoModel &gt;&gt;&gt; tokenizer = AutoTokenizer.from_pretrained(&quot;google-bert/bert-base-uncased&quot;) &gt;&gt;&gt; model = TFAutoModel.from_pretrained(&quot;google-bert/bert-base-uncased&quot;) &gt;&gt;&gt; inputs = tokenizer(&quot;Hello world!&quot;, return_tensors=&quot;tf&quot;) &gt;&gt;&gt; outputs = model(**inputs)"><pre><span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-k">from</span> <span class="pl-s1">transformers</span> <span class="pl-k">import</span> <span class="pl-v">AutoTokenizer</span>, <span class="pl-v">TFAutoModel</span> <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">tokenizer</span> <span class="pl-c1">=</span> <span class="pl-v">AutoTokenizer</span>.<span class="pl-en">from_pretrained</span>(<span class="pl-s">"google-bert/bert-base-uncased"</span>) <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">model</span> <span class="pl-c1">=</span> <span class="pl-v">TFAutoModel</span>.<span class="pl-en">from_pretrained</span>(<span class="pl-s">"google-bert/bert-base-uncased"</span>) <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">inputs</span> <span class="pl-c1">=</span> <span class="pl-en">tokenizer</span>(<span class="pl-s">"Hello world!"</span>, <span class="pl-s1">return_tensors</span><span class="pl-c1">=</span><span class="pl-s">"tf"</span>) <span class="pl-c1">&gt;&gt;</span><span class="pl-c1">&gt;</span> <span class="pl-s1">outputs</span> <span class="pl-c1">=</span> <span class="pl-en">model</span>(<span class="pl-c1">**</span><span class="pl-s1">inputs</span>)</pre></div> <p dir="auto">The tokenizer is responsible for all the preprocessing the pretrained model expects and can be called directly on a single string (as in the above examples) or a list. It will output a dictionary that you can use in downstream code or simply directly pass to your model using the ** argument unpacking operator.</p> <p dir="auto">The model itself is a regular <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">Pytorch <code>nn.Module</code></a> or a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">TensorFlow <code>tf.keras.Model</code></a> (depending on your backend) which you can use as usual. <a href="https://huggingface.co/docs/transformers/training" rel="nofollow">This tutorial</a> explains how to integrate such a model into a classic PyTorch or TensorFlow training loop, or how to use our <code>Trainer</code> API to quickly fine-tune on a new dataset.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Why should I use transformers?</h2><a id="user-content-why-should-i-use-transformers" class="anchor" aria-label="Permalink: Why should I use transformers?" href="#why-should-i-use-transformers"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto">Easy-to-use state-of-the-art models:</p> <ul dir="auto"> <li>High performance on natural language understanding &amp; generation, computer vision, and audio tasks.</li> <li>Low barrier to entry for educators and practitioners.</li> <li>Few user-facing abstractions with just three classes to learn.</li> <li>A unified API for using all our pretrained models.</li> </ul> </li> <li> <p dir="auto">Lower compute costs, smaller carbon footprint:</p> <ul dir="auto"> <li>Researchers can share trained models instead of always retraining.</li> <li>Practitioners can reduce compute time and production costs.</li> <li>Dozens of architectures with over 400,000 pretrained models across all modalities.</li> </ul> </li> <li> <p dir="auto">Choose the right framework for every part of a model's lifetime:</p> <ul dir="auto"> <li>Train state-of-the-art models in 3 lines of code.</li> <li>Move a single model between TF2.0/PyTorch/JAX frameworks at will.</li> <li>Seamlessly pick the right framework for training, evaluation, and production.</li> </ul> </li> <li> <p dir="auto">Easily customize a model or an example to your needs:</p> <ul dir="auto"> <li>We provide examples for each architecture to reproduce the results published by its original authors.</li> <li>Model internals are exposed as consistently as possible.</li> <li>Model files can be used independently of the library for quick experiments.</li> </ul> </li> </ol> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Why shouldn't I use transformers?</h2><a id="user-content-why-shouldnt-i-use-transformers" class="anchor" aria-label="Permalink: Why shouldn't I use transformers?" href="#why-shouldnt-i-use-transformers"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>This library is not a modular toolbox of building blocks for neural nets. The code in the model files is not refactored with additional abstractions on purpose, so that researchers can quickly iterate on each of the models without diving into additional abstractions/files.</li> <li>The training API is not intended to work on any model but is optimized to work with the models provided by the library. For generic machine learning loops, you should use another library (possibly, <a href="https://huggingface.co/docs/accelerate" rel="nofollow">Accelerate</a>).</li> <li>While we strive to present as many use cases as possible, the scripts in our <a href="https://github.com/huggingface/transformers/tree/main/examples">examples folder</a> are just that: examples. It is expected that they won't work out-of-the-box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">With pip</h3><a id="user-content-with-pip" class="anchor" aria-label="Permalink: With pip" href="#with-pip"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This repository is tested on Python 3.9+, Flax 0.4.1+, PyTorch 1.11+, and TensorFlow 2.6+.</p> <p dir="auto">You should install 🤗 Transformers in a <a href="https://docs.python.org/3/library/venv.html" rel="nofollow">virtual environment</a>. If you're unfamiliar with Python virtual environments, check out the <a href="https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/" rel="nofollow">user guide</a>.</p> <p dir="auto">First, create a virtual environment with the version of Python you're going to use and activate it.</p> <p dir="auto">Then, you will need to install at least one of Flax, PyTorch, or TensorFlow. Please refer to <a href="https://www.tensorflow.org/install/" rel="nofollow">TensorFlow installation page</a>, <a href="https://pytorch.org/get-started/locally/#start-locally" rel="nofollow">PyTorch installation page</a> and/or <a href="https://github.com/google/flax#quick-install">Flax</a> and <a href="https://github.com/google/jax#installation">Jax</a> installation pages regarding the specific installation command for your platform.</p> <p dir="auto">When one of those backends has been installed, 🤗 Transformers can be installed using pip as follows:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install transformers"><pre>pip install transformers</pre></div> <p dir="auto">If you'd like to play with the examples or need the bleeding edge of the code and can't wait for a new release, you must <a href="https://huggingface.co/docs/transformers/installation#installing-from-source" rel="nofollow">install the library from source</a>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">With conda</h3><a id="user-content-with-conda" class="anchor" aria-label="Permalink: With conda" href="#with-conda"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">🤗 Transformers can be installed using conda as follows:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda install conda-forge::transformers"><pre>conda install conda-forge::transformers</pre></div> <blockquote> <p dir="auto"><strong><em>NOTE:</em></strong> Installing <code>transformers</code> from the <code>huggingface</code> channel is deprecated.</p> </blockquote> <p dir="auto">Follow the installation pages of Flax, PyTorch or TensorFlow to see how to install them with conda.</p> <blockquote> <p dir="auto"><strong><em>NOTE:</em></strong> On Windows, you may be prompted to activate Developer Mode in order to benefit from caching. If this is not an option for you, please let us know in <a href="https://github.com/huggingface/huggingface_hub/issues/1062" data-hovercard-type="issue" data-hovercard-url="/huggingface/huggingface_hub/issues/1062/hovercard">this issue</a>.</p> </blockquote> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Model architectures</h2><a id="user-content-model-architectures" class="anchor" aria-label="Permalink: Model architectures" href="#model-architectures"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong><a href="https://huggingface.co/models" rel="nofollow">All the model checkpoints</a></strong> provided by 🤗 Transformers are seamlessly integrated from the huggingface.co <a href="https://huggingface.co/models" rel="nofollow">model hub</a>, where they are uploaded directly by <a href="https://huggingface.co/users" rel="nofollow">users</a> and <a href="https://huggingface.co/organizations" rel="nofollow">organizations</a>.</p> <p dir="auto">Current number of checkpoints: <a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/9b5c7f5815b91e9ab3d501a52038a2cdf570a5aa96d837d4fb7500435f4ae5c4/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d68747470733a2f2f68756767696e67666163652e636f2f6170692f736869656c64732f6d6f64656c7326636f6c6f723d627269676874677265656e"><img src="https://camo.githubusercontent.com/9b5c7f5815b91e9ab3d501a52038a2cdf570a5aa96d837d4fb7500435f4ae5c4/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d68747470733a2f2f68756767696e67666163652e636f2f6170692f736869656c64732f6d6f64656c7326636f6c6f723d627269676874677265656e" alt="" data-canonical-src="https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&amp;color=brightgreen" style="max-width: 100%;"></a></p> <p dir="auto">🤗 Transformers currently provides the following architectures: see <a href="https://huggingface.co/docs/transformers/model_summary" rel="nofollow">here</a> for a high-level summary of each them.</p> <p dir="auto">To check if each model has an implementation in Flax, PyTorch or TensorFlow, or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to <a href="https://huggingface.co/docs/transformers/index#supported-frameworks" rel="nofollow">this table</a>.</p> <p dir="auto">These implementations have been tested on several datasets (see the example scripts) and should match the performance of the original implementations. You can find more details on performance in the Examples section of the <a href="https://github.com/huggingface/transformers/tree/main/examples">documentation</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Learn more</h2><a id="user-content-learn-more" class="anchor" aria-label="Permalink: Learn more" href="#learn-more"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th>Section</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><a href="https://huggingface.co/docs/transformers/" rel="nofollow">Documentation</a></td> <td>Full API documentation and tutorials</td> </tr> <tr> <td><a href="https://huggingface.co/docs/transformers/task_summary" rel="nofollow">Task summary</a></td> <td>Tasks supported by 🤗 Transformers</td> </tr> <tr> <td><a href="https://huggingface.co/docs/transformers/preprocessing" rel="nofollow">Preprocessing tutorial</a></td> <td>Using the <code>Tokenizer</code> class to prepare data for the models</td> </tr> <tr> <td><a href="https://huggingface.co/docs/transformers/training" rel="nofollow">Training and fine-tuning</a></td> <td>Using the models provided by 🤗 Transformers in a PyTorch/TensorFlow training loop and the <code>Trainer</code> API</td> </tr> <tr> <td><a href="https://github.com/huggingface/transformers/tree/main/examples">Quick tour: Fine-tuning/usage scripts</a></td> <td>Example scripts for fine-tuning models on a wide range of tasks</td> </tr> <tr> <td><a href="https://huggingface.co/docs/transformers/model_sharing" rel="nofollow">Model sharing and uploading</a></td> <td>Upload and share your fine-tuned models with the community</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citation</h2><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We now have a <a href="https://www.aclweb.org/anthology/2020.emnlp-demos.6/" rel="nofollow">paper</a> you can cite for the 🤗 Transformers library:</p> <div class="highlight highlight-text-bibtex notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="@inproceedings{wolf-etal-2020-transformers, title = &quot;Transformers: State-of-the-Art Natural Language Processing&quot;, author = &quot;Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush&quot;, booktitle = &quot;Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations&quot;, month = oct, year = &quot;2020&quot;, address = &quot;Online&quot;, publisher = &quot;Association for Computational Linguistics&quot;, url = &quot;https://www.aclweb.org/anthology/2020.emnlp-demos.6&quot;, pages = &quot;38--45&quot; }"><pre><span class="pl-k">@inproceedings</span>{<span class="pl-en">wolf-etal-2020-transformers</span>, <span class="pl-s">title</span> = <span class="pl-s"><span class="pl-pds">"</span>Transformers: State-of-the-Art Natural Language Processing<span class="pl-pds">"</span></span>, <span class="pl-s">author</span> = <span class="pl-s"><span class="pl-pds">"</span>Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush<span class="pl-pds">"</span></span>, <span class="pl-s">booktitle</span> = <span class="pl-s"><span class="pl-pds">"</span>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations<span class="pl-pds">"</span></span>, <span class="pl-s">month</span> = oct, <span class="pl-s">year</span> = <span class="pl-s"><span class="pl-pds">"</span>2020<span class="pl-pds">"</span></span>, <span class="pl-s">address</span> = <span class="pl-s"><span class="pl-pds">"</span>Online<span class="pl-pds">"</span></span>, <span class="pl-s">publisher</span> = <span class="pl-s"><span class="pl-pds">"</span>Association for Computational Linguistics<span class="pl-pds">"</span></span>, <span class="pl-s">url</span> = <span class="pl-s"><span class="pl-pds">"</span>https://www.aclweb.org/anthology/2020.emnlp-demos.6<span class="pl-pds">"</span></span>, <span class="pl-s">pages</span> = <span class="pl-s"><span class="pl-pds">"</span>38--45<span class="pl-pds">"</span></span> }</pre></div> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="ztnX9F2fiZ7MDkKpB5K7DlwqFqm+iHeOxvzRRxcwPLyVSTDI8w+TZo2uHFgfEhb/YMNQAlOL9kwjjy8RhksQmg==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. </p> <div class="my-3 d-flex flex-items-center"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link flex-shrink-0 mr-2"> <path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path> </svg> <span class="flex-auto min-width-0 css-truncate css-truncate-target width-fit"> <a title="https://huggingface.co/transformers" role="link" target="_blank" rel="noopener noreferrer nofollow" class="text-bold" href="https://huggingface.co/transformers">huggingface.co/transformers</a> </span> </div> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:python" href="/topics/python" title="Topic: python" data-view-component="true" class="topic-tag topic-tag-link"> python </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:nlp" href="/topics/nlp" title="Topic: nlp" data-view-component="true" class="topic-tag topic-tag-link"> nlp </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:machine-learning" href="/topics/machine-learning" title="Topic: machine-learning" data-view-component="true" class="topic-tag topic-tag-link"> machine-learning </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:natural-language-processing" href="/topics/natural-language-processing" title="Topic: natural-language-processing" data-view-component="true" class="topic-tag topic-tag-link"> natural-language-processing </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:deep-learning" href="/topics/deep-learning" title="Topic: deep-learning" data-view-component="true" class="topic-tag topic-tag-link"> deep-learning </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:tensorflow" href="/topics/tensorflow" title="Topic: tensorflow" data-view-component="true" class="topic-tag topic-tag-link"> tensorflow </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:pytorch" href="/topics/pytorch" title="Topic: pytorch" data-view-component="true" class="topic-tag topic-tag-link"> pytorch </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:transformer" href="/topics/transformer" title="Topic: transformer" data-view-component="true" class="topic-tag topic-tag-link"> transformer </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:speech-recognition" href="/topics/speech-recognition" title="Topic: speech-recognition" data-view-component="true" class="topic-tag topic-tag-link"> speech-recognition </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:seq2seq" href="/topics/seq2seq" title="Topic: seq2seq" data-view-component="true" class="topic-tag topic-tag-link"> seq2seq </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:flax" href="/topics/flax" title="Topic: flax" data-view-component="true" class="topic-tag topic-tag-link"> flax </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:pretrained-models" href="/topics/pretrained-models" title="Topic: pretrained-models" data-view-component="true" class="topic-tag topic-tag-link"> pretrained-models </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:language-models" href="/topics/language-models" title="Topic: language-models" data-view-component="true" class="topic-tag topic-tag-link"> language-models </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:nlp-library" href="/topics/nlp-library" title="Topic: nlp-library" data-view-component="true" class="topic-tag topic-tag-link"> nlp-library </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:language-model" href="/topics/language-model" title="Topic: language-model" data-view-component="true" class="topic-tag topic-tag-link"> language-model </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:hacktoberfest" href="/topics/hacktoberfest" title="Topic: hacktoberfest" data-view-component="true" class="topic-tag topic-tag-link"> hacktoberfest </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:bert" href="/topics/bert" title="Topic: bert" data-view-component="true" class="topic-tag topic-tag-link"> bert </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:jax" href="/topics/jax" title="Topic: jax" data-view-component="true" class="topic-tag topic-tag-link"> jax </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:pytorch-transformers" href="/topics/pytorch-transformers" title="Topic: pytorch-transformers" data-view-component="true" class="topic-tag topic-tag-link"> pytorch-transformers </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:model-hub" href="/topics/model-hub" title="Topic: model-hub" data-view-component="true" class="topic-tag topic-tag-link"> model-hub </a> </div> </div> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" 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<path d="M8.048 2.241c.964-.709 2.079-1.238 3.325-1.241a4.616 4.616 0 0 1 3.282 1.355c.41.408.757.86.996 1.428.238.568.348 1.206.347 1.968 0 2.193-1.505 4.254-3.081 5.862-1.496 1.526-3.213 2.796-4.249 3.563l-.22.163a.749.749 0 0 1-.895 0l-.221-.163c-1.036-.767-2.753-2.037-4.249-3.563C1.51 10.008.007 7.952.002 5.762a4.614 4.614 0 0 1 1.353-3.407C3.123.585 6.223.537 8.048 2.24Zm-1.153.983c-1.25-1.033-3.321-.967-4.48.191a3.115 3.115 0 0 0-.913 2.335c0 1.556 1.109 3.24 2.652 4.813C5.463 11.898 6.96 13.032 8 13.805c.353-.262.758-.565 1.191-.905l-1.326-1.223a.75.75 0 0 1 1.018-1.102l1.48 1.366c.328-.281.659-.577.984-.887L9.99 9.802a.75.75 0 1 1 1.019-1.103l1.384 1.28c.295-.329.566-.661.81-.995L12.92 8.7l-1.167-1.168c-.674-.671-1.78-.664-2.474.03-.268.269-.538.537-.802.797-.893.882-2.319.843-3.185-.032-.346-.35-.693-.697-1.043-1.047a.75.75 0 0 1-.04-1.016c.162-.191.336-.401.52-.623.62-.748 1.356-1.637 2.166-2.417Zm7.112 4.442c.313-.65.491-1.293.491-1.916v-.001c0-.614-.088-1.045-.23-1.385-.143-.339-.357-.633-.673-.949a3.111 3.111 0 0 0-2.218-.915c-1.092.003-2.165.627-3.226 1.602-.823.755-1.554 1.637-2.228 2.45l-.127.154.562.566a.755.755 0 0 0 1.066.02l.794-.79c1.258-1.258 3.312-1.31 4.594-.032.396.394.792.791 1.173 1.173Z"></path> </svg> Code of conduct </a> </div> <h3 class="sr-only">Security policy</h3> <div class="mt-2"> <a href="#security-ov-file" class="Link--muted" data-analytics-event="{&quot;category&quot;:&quot;Repository Overview&quot;,&quot;action&quot;:&quot;click&quot;,&quot;label&quot;:&quot;location:sidebar;file:security policy&quot;}" > <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-law mr-2"> <path d="M8.75.75V2h.985c.304 0 .603.08.867.231l1.29.736c.038.022.08.033.124.033h2.234a.75.75 0 0 1 0 1.5h-.427l2.111 4.692a.75.75 0 0 1-.154.838l-.53-.53.529.531-.001.002-.002.002-.006.006-.006.005-.01.01-.045.04c-.21.176-.441.327-.686.45C14.556 10.78 13.88 11 13 11a4.498 4.498 0 0 1-2.023-.454 3.544 3.544 0 0 1-.686-.45l-.045-.04-.016-.015-.006-.006-.004-.004v-.001a.75.75 0 0 1-.154-.838L12.178 4.5h-.162c-.305 0-.604-.079-.868-.231l-1.29-.736a.245.245 0 0 0-.124-.033H8.75V13h2.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1 0-1.5h2.5V3.5h-.984a.245.245 0 0 0-.124.033l-1.289.737c-.265.15-.564.23-.869.23h-.162l2.112 4.692a.75.75 0 0 1-.154.838l-.53-.53.529.531-.001.002-.002.002-.006.006-.016.015-.045.04c-.21.176-.441.327-.686.45C4.556 10.78 3.88 11 3 11a4.498 4.498 0 0 1-2.023-.454 3.544 3.544 0 0 1-.686-.45l-.045-.04-.016-.015-.006-.006-.004-.004v-.001a.75.75 0 0 1-.154-.838L2.178 4.5H1.75a.75.75 0 0 1 0-1.5h2.234a.249.249 0 0 0 .125-.033l1.288-.737c.265-.15.564-.23.869-.23h.984V.75a.75.75 0 0 1 1.5 0Zm2.945 8.477c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L13 6.327Zm-10 0c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L3 6.327Z"></path> </svg> Security policy </a> </div> <include-fragment src="/huggingface/transformers/hovercards/citation/sidebar_partial?tree_name=main"> </include-fragment> <div class="mt-2"> <a href="/huggingface/transformers/activity" data-view-component="true" class="Link Link--muted"> <svg text="gray" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-pulse mr-2"> <path d="M6 2c.306 0 .582.187.696.471L10 10.731l1.304-3.26A.751.751 0 0 1 12 7h3.25a.75.75 0 0 1 0 1.5h-2.742l-1.812 4.528a.751.751 0 0 1-1.392 0L6 4.77 4.696 8.03A.75.75 0 0 1 4 8.5H.75a.75.75 0 0 1 0-1.5h2.742l1.812-4.529A.751.751 0 0 1 6 2Z"></path> </svg> <span class="color-fg-muted">Activity</span> </a> </div> <div class="mt-2"> <a href="/huggingface/transformers/custom-properties" data-view-component="true" class="Link Link--muted"> <svg text="gray" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-note mr-2"> <path d="M0 3.75C0 2.784.784 2 1.75 2h12.5c.966 0 1.75.784 1.75 1.75v8.5A1.75 1.75 0 0 1 14.25 14H1.75A1.75 1.75 0 0 1 0 12.25Zm1.75-.25a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25v-8.5a.25.25 0 0 0-.25-.25ZM3.5 6.25a.75.75 0 0 1 .75-.75h7a.75.75 0 0 1 0 1.5h-7a.75.75 0 0 1-.75-.75Zm.75 2.25h4a.75.75 0 0 1 0 1.5h-4a.75.75 0 0 1 0-1.5Z"></path> </svg> <span class="color-fg-muted">Custom properties</span> </a> </div> <h3 class="sr-only">Stars</h3> <div class="mt-2"> <a href="/huggingface/transformers/stargazers" data-view-component="true" class="Link Link--muted"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-star mr-2"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <strong>136k</strong> stars </a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/huggingface/transformers/watchers" data-view-component="true" class="Link Link--muted"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-eye mr-2"> <path d="M8 2c1.981 0 3.671.992 4.933 2.078 1.27 1.091 2.187 2.345 2.637 3.023a1.62 1.62 0 0 1 0 1.798c-.45.678-1.367 1.932-2.637 3.023C11.67 13.008 9.981 14 8 14c-1.981 0-3.671-.992-4.933-2.078C1.797 10.83.88 9.576.43 8.898a1.62 1.62 0 0 1 0-1.798c.45-.677 1.367-1.931 2.637-3.022C4.33 2.992 6.019 2 8 2ZM1.679 7.932a.12.12 0 0 0 0 .136c.411.622 1.241 1.75 2.366 2.717C5.176 11.758 6.527 12.5 8 12.5c1.473 0 2.825-.742 3.955-1.715 1.124-.967 1.954-2.096 2.366-2.717a.12.12 0 0 0 0-.136c-.412-.621-1.242-1.75-2.366-2.717C10.824 4.242 9.473 3.5 8 3.5c-1.473 0-2.825.742-3.955 1.715-1.124.967-1.954 2.096-2.366 2.717ZM8 10a2 2 0 1 1-.001-3.999A2 2 0 0 1 8 10Z"></path> </svg> <strong>1.1k</strong> watching </a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/huggingface/transformers/forks" data-view-component="true" class="Link Link--muted"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-2"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <strong>27.1k</strong> forks </a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Fhuggingface%2Ftransformers&amp;report=huggingface+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame"> <a href="/huggingface/transformers/releases" data-view-component="true" class="Link--primary no-underline Link"> Releases <span title="172" data-view-component="true" class="Counter">172</span> </a></h2> <a class="Link--primary d-flex no-underline" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/huggingface/transformers/releases/tag/v4.46.3"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag flex-shrink-0 mt-1 color-fg-success"> <path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <div class="ml-2 min-width-0"> <div class="d-flex"> <span class="css-truncate css-truncate-target text-bold mr-2" style="max-width: none;">Patch release v4.46.3</span> <span title="Label: Latest" data-view-component="true" class="Label Label--success flex-shrink-0"> Latest </span> </div> <div class="text-small color-fg-muted"><relative-time datetime="2024-11-18T22:13:14Z" class="no-wrap">Nov 18, 2024</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a text="small" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/huggingface/transformers/releases" data-view-component="true" class="Link"> + 171 releases </a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/orgs/huggingface/packages?repo_name=transformers" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center"> Packages <span title="0" hidden="hidden" data-view-component="true" class="Counter ml-1">0</span> </a></h2> <div class="text-small color-fg-muted" > No packages published <br> </div> </div> </div> <div class="BorderGrid-row" > <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/huggingface/transformers/network/dependents" data-view-component="true" class="Link--primary no-underline Link"> Used by <span title="241,214" data-view-component="true" class="Counter">241k</span> </a> </h2> <a class="d-flex flex-items-center" href="/huggingface/transformers/network/dependents"> <ul class="hx_flex-avatar-stack list-style-none min-width-0"> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/189715074?s=64&amp;v=4" width="32" height="32" alt="@jimmie-alapag" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/82608113?s=64&amp;v=4" width="32" height="32" alt="@KangPixel" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/1101482?s=64&amp;v=4" width="32" height="32" alt="@yusufsevinir" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/115070248?s=64&amp;v=4" width="32" height="32" alt="@ZhuofanWen" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/185773377?s=64&amp;v=4" width="32" height="32" alt="@Thanush656" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/66177614?s=64&amp;v=4" width="32" height="32" alt="@FishPain" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/73253354?s=64&amp;v=4" width="32" height="32" alt="@pruthvirajjadhav1" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/189704625?s=64&amp;v=4" width="32" height="32" alt="@Maqif14" /> </li> </ul> <span class="px-2 text-bold text-small no-wrap"> + 241,206 </span> </a> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/huggingface/transformers/graphs/contributors" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center"> Contributors <span title="2,849" data-view-component="true" class="Counter ml-1">2,849</span> </a></h2> <ul class="list-style-none d-flex flex-wrap mb-n2"> <li class="mb-2 mr-2" > <a href="https://github.com/sgugger" class="" data-hovercard-type="user" data-hovercard-url="/users/sgugger/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/35901082?s=64&amp;v=4" alt="@sgugger" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/thomwolf" class="" data-hovercard-type="user" data-hovercard-url="/users/thomwolf/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/7353373?s=64&amp;v=4" alt="@thomwolf" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/LysandreJik" class="" data-hovercard-type="user" 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