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huggingface/transformers.js

State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server! observed · 2026-08-28

github.com/huggingface/transformers.js · homepage · JavaScript · Apache-2.0 (permissive) observed · 2026-08-28

Health v2 · maintenance only

91/100

  • Activity 99
  • Release rhythm 80
  • Longevity 92
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: 9
  • age_days: 1297
  • days_rel: 132
  • days_push: 7
  • n_releases_24m: 38

Full methodology

Adoption not part of the score

16270 stars · 1184 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Transformers.js is a JavaScript library that lets you run Hugging Face Transformers pretrained models directly in the browser (or Node.js) using ONNX Runtime, with no server required. It offers a pipeline API functionally equivalent to the Python transformers library, covering NLP, computer vision, audio, and multimodal tasks.

Use cases

  • run sentiment analysis in the browser without a server
  • do object detection on uploaded images client-side
  • transcribe speech to text in a web app
  • translate text multilingually in a React app
  • generate text embeddings for semantic search in JavaScript
  • classify images or audio with zero-shot models in the browser
  • run text generation or summarization locally in the user's browser

When to choose

  • you want privacy-preserving, client-side ML inference with no backend or API costs
  • you want to use Hugging Face pretrained models from JavaScript with a familiar pipeline API
  • you're building a web app (vanilla JS, React, Next.js) that needs NLP, vision, or audio ML features
  • you want models cached in the browser for fast offline-capable inference

When to avoid

  • you need to train or fine-tune models — this is inference only
  • you need maximum performance or very large models better suited to GPU servers
  • your model architecture isn't supported by ONNX Runtime / Optimum conversion
  • you're working in Python — use the transformers library instead

Facets

library · maturity active

machine-learning llm-inference nlp computer-vision speech-recognition tts image-processing audio-processing machine-learning deep-learning computer-vision web-development artificial-intelligence browser wasm onnx-runtime transformers client-side-inference hugging-face webml pretrained-models natural-language-processing nodejs javascript

10 sources

Member repositories

RepositoryRoleHealth v2
huggingface/transformers.jsmain91

For agents

markdown · JSON · MCP: product_card(name="huggingface/transformers.js")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem