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State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!

Transformers.js is designed to be functionally equivalent to Hugging Face'stransformers python library, meaning you can run the same pretrained models using a very similar API. These models support common tasks in different modalities, such as:

• 📝Natural Language Processing: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation.
• 🖼️Computer Vision: image classification, object detection, and segmentation.
• 🗣️Audio: automatic speech recognition and audio classification.
• 🐙Multimodal: zero-shot image classification.

Transformers.js usesONNX Runtime to run models in the browser. The best part about it, is that you can easilyconvert your pretrained PyTorch, TensorFlow, or JAX models to ONNX using🤗 Optimum.

For more information, check out the fulldocumentation.

It's super simple to translate from existing code! Just like the python library, we support thepipeline API. Pipelines group together a pretrained model with preprocessing of inputs and postprocessing of outputs, making it the easiest way to run models with the library.

fromtransformersimportpipeline# Allocate a pipeline for sentiment-analysispipe=pipeline('sentiment-analysis')out=pipe('I love transformers!')# [{'label': 'POSITIVE', 'score': 0.999806941}]

import{pipeline}from'@xenova/transformers';// Allocate a pipeline for sentiment-analysisletpipe=awaitpipeline('sentiment-analysis');letout=awaitpipe('I love transformers!');// [{'label': 'POSITIVE', 'score': 0.999817686}]

You can also use a different model by specifying the model id or path as the second argument to thepipeline function. For example:

// Use a different model for sentiment-analysisletpipe=awaitpipeline('sentiment-analysis','nlptown/bert-base-multilingual-uncased-sentiment');

To install viaNPM, run:

npm i @xenova/transformers

Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, usingES Modules, you can import the library with:

<scripttype="module">import{pipeline}from'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.7.0';</script>

Want to jump straight in? Get started with one of our sample applications/templates:

By default, Transformers.js useshosted pretrained models andprecompiled WASM binaries, which should work out-of-the-box. You can customize this as follows:

import{env}from'@xenova/transformers';// Specify a custom location for models (defaults to '/models/').env.localModelPath='/path/to/models/';// Disable the loading of remote models from the Hugging Face Hub:env.allowRemoteModels=false;// Set location of .wasm files. Defaults to use a CDN.env.backends.onnx.wasm.wasmPaths='/path/to/files/';

For a full list of available settings, check out theAPI Reference.

We recommend using ourconversion script to convert your PyTorch, TensorFlow, or JAX models to ONNX in a single command. Behind the scenes, it uses🤗 Optimum to perform conversion and quantization of your model.

python -m scripts.convert --quantize --model_id<model_name_or_path>

For example, convert and quantizebert-base-uncased using:

python -m scripts.convert --quantize --model_id bert-base-uncased

This will save the following files to./models/:

bert-base-uncased/├── config.json├── tokenizer.json├── tokenizer_config.json└── onnx/    ├── model.onnx    └── model_quantized.onnx

Here is the list of all tasks and architectures currently supported by Transformers.js.If you don't see your task/model listed here or it is not yet supported, feel freeto open up a feature requesthere.

To find compatible models on the Hub, select the "transformers.js" library tag in the filter menu (or visitthis link).You can refine your search by selecting the task you're interested in (e.g.,text-classification).

1. ALBERT (from Google Research and the Toyota Technological Institute at Chicago) released with the paperALBERT: A Lite BERT for Self-supervised Learning of Language Representations, by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
2. BART (from Facebook) released with the paperBART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
3. BEiT (from Microsoft) released with the paperBEiT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong, Furu Wei.
4. BERT (from Google) released with the paperBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
5. Blenderbot (from Facebook) released with the paperRecipes for building an open-domain chatbot by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
6. BlenderbotSmall (from Facebook) released with the paperRecipes for building an open-domain chatbot by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
7. BLOOM (from BigScience workshop) released by theBigScience Workshop.
8. CamemBERT (from Inria/Facebook/Sorbonne) released with the paperCamemBERT: a Tasty French Language Model by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
9. CLIP (from OpenAI) released with the paperLearning Transferable Visual Models From Natural Language Supervision by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
10. CodeGen (from Salesforce) released with the paperA Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.
11. CodeLlama (from MetaAI) released with the paperCode Llama: Open Foundation Models for Code by Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, Gabriel Synnaeve.
12. DeBERTa (from Microsoft) released with the paperDeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
13. DeBERTa-v2 (from Microsoft) released with the paperDeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
14. DeiT (from Facebook) released with the paperTraining data-efficient image transformers & distillation through attention by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
15. DETR (from Facebook) released with the paperEnd-to-End Object Detection with Transformers by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
16. DistilBERT (from HuggingFace), released together with the paperDistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 intoDistilGPT2, RoBERTa intoDistilRoBERTa, Multilingual BERT intoDistilmBERT and a German version of DistilBERT.
17. Donut (from NAVER), released together with the paperOCR-free Document Understanding Transformer by Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park.
18. FLAN-T5 (from Google AI) released in the repositorygoogle-research/t5x by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
19. GPT Neo (from EleutherAI) released in the repositoryEleutherAI/gpt-neo by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
20. GPT NeoX (from EleutherAI) released with the paperGPT-NeoX-20B: An Open-Source Autoregressive Language Model by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
21. GPT-2 (from OpenAI) released with the paperLanguage Models are Unsupervised Multitask Learners by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
22. GPT-J (from EleutherAI) released in the repositorykingoflolz/mesh-transformer-jax by Ben Wang and Aran Komatsuzaki.
23. GPTBigCode (from BigCode) released with the paperSantaCoder: don't reach for the stars! by Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo García del Río, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra.
24. HerBERT (from Allegro.pl, AGH University of Science and Technology) released with the paperKLEJ: Comprehensive Benchmark for Polish Language Understanding by Piotr Rybak, Robert Mroczkowski, Janusz Tracz, Ireneusz Gawlik.
25. LongT5 (from Google AI) released with the paperLongT5: Efficient Text-To-Text Transformer for Long Sequences by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
26. LLaMA (from The FAIR team of Meta AI) released with the paperLLaMA: Open and Efficient Foundation Language Models by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample.
27. Llama2 (from The FAIR team of Meta AI) released with the paperLlama2: Open Foundation and Fine-Tuned Chat Models by Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom.
28. M2M100 (from Facebook) released with the paperBeyond English-Centric Multilingual Machine Translation by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
29. MarianMT Machine translation models trained usingOPUS data by Jörg Tiedemann. TheMarian Framework is being developed by the Microsoft Translator Team.
30. mBART (from Facebook) released with the paperMultilingual Denoising Pre-training for Neural Machine Translation by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
31. mBART-50 (from Facebook) released with the paperMultilingual Translation with Extensible Multilingual Pretraining and Finetuning by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
32. MMS (from Facebook) released with the paperScaling Speech Technology to 1,000+ Languages by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli.
33. MobileBERT (from CMU/Google Brain) released with the paperMobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou.
34. MobileViT (from Apple) released with the paperMobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer by Sachin Mehta and Mohammad Rastegari.
35. MPNet (from Microsoft Research) released with the paperMPNet: Masked and Permuted Pre-training for Language Understanding by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
36. MPT (from MosaiML) released with the repositoryllm-foundry by the MosaicML NLP Team.
37. MT5 (from Google AI) released with the papermT5: A massively multilingual pre-trained text-to-text transformer by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
38. NLLB (from Meta) released with the paperNo Language Left Behind: Scaling Human-Centered Machine Translation by the NLLB team.
39. OPT (from Meta AI) released with the paperOPT: Open Pre-trained Transformer Language Models by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
40. ResNet (from Microsoft Research) released with the paperDeep Residual Learning for Image Recognition by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
41. RoBERTa (from Facebook), released together with the paperRoBERTa: A Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
42. SpeechT5 (from Microsoft Research) released with the paperSpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
43. SqueezeBERT (from Berkeley) released with the paperSqueezeBERT: What can computer vision teach NLP about efficient neural networks? by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
44. Swin Transformer (from Microsoft) released with the paperSwin Transformer: Hierarchical Vision Transformer using Shifted Windows by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
45. T5 (from Google AI) released with the paperExploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
46. T5v1.1 (from Google AI) released in the repositorygoogle-research/text-to-text-transfer-transformer by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
47. Vision Transformer (ViT) (from Google AI) released with the paperAn Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
48. Wav2Vec2 (from Facebook AI) released with the paperwav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
49. WavLM (from Microsoft Research) released with the paperWavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
50. Whisper (from OpenAI) released with the paperRobust Speech Recognition via Large-Scale Weak Supervision by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.
51. XLM (from Facebook) released together with the paperCross-lingual Language Model Pretraining by Guillaume Lample and Alexis Conneau.
52. XLM-RoBERTa (from Facebook AI), released together with the paperUnsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
53. YOLOS (from Huazhong University of Science & Technology) released with the paperYou Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.