/transformers-js

name: transformers-js
description: Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.
license: Apache-2.0
metadata:
  author: huggingface
  version: "4.x"
  category: machine-learning
  repository: https://github.com/huggingface/transformers.js
compatibility: Requires Node.js 18+ (or compatible Bun/Deno runtime) or modern browser with ES modules support. WebGPU requires runtime and hardware support; WASM is the broad fallback. Internet access is needed for downloading models from Hugging Face Hub (optional if using local models).

Transformers.js - Machine Learning for JavaScript

Transformers.js enables running state-of-the-art machine learning models directly in JavaScript across browsers and server-side runtimes (Node.js, Bun, Deno), with no Python server required.

When to Use This Skill

Use this skill when you need to:

• Run ML models for text analysis, generation, or translation in JavaScript
• Perform image classification, object detection, or segmentation
• Implement speech recognition or audio processing
• Build multimodal AI applications (text-to-image, image-to-text, etc.)
• Run models client-side in the browser without a backend

Installation

NPM Installation

npm install @huggingface/transformers

Browser Usage (CDN)

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

Core Concepts

1. Pipeline API

The pipeline API is the easiest way to use models. It groups together preprocessing, model inference, and postprocessing:

import { pipeline } from '@huggingface/transformers';

// Create a pipeline for a specific task
const pipe = await pipeline('sentiment-analysis');

// Use the pipeline
const result = await pipe('I love transformers!');
// Output: [{ label: 'POSITIVE', score: 0.999817686 }]

// IMPORTANT: Always dispose when done to free memory
await pipe.dispose();

**⚠️ Memory Management:** All pipelines must be disposed with `pipe.dispose()` when finished to prevent memory leaks. See examples in [Code Examples](./references/EXAMPLES.md) for cleanup patterns across different environments.

2. Model Selection

You can specify a custom model as the second argument:

const pipe = await pipeline(
  'sentiment-analysis',
  'Xenova/bert-base-multilingual-uncased-sentiment'
);

**Finding Models:**

Browse available Transformers.js models on Hugging Face Hub:

• **All models**: https://huggingface.co/models?library=transformers.js&sort=trending
• **By task**: Add `pipeline_tag` parameter
• Text generation: https://huggingface.co/models?pipeline_tag=text-generation&library=transformers.js&sort=trending
• Image classification: https://huggingface.co/models?pipeline_tag=image-classification&library=transformers.js&sort=trending
• Speech recognition: https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&library=transformers.js&sort=trending

**Tip:** Filter by task type, sort by trending/downloads, and check model cards for performance metrics and usage examples.

3. Device Selection

Choose where to run the model:

// Run on CPU (default for WASM)
const pipe = await pipeline('sentiment-analysis', 'model-id');

// Run on GPU (WebGPU)
const pipe = await pipeline('sentiment-analysis', 'model-id', {
  device: 'webgpu',
});

4. Quantization Options

Control model precision vs. performance:

// Use quantized model (faster, smaller)
const pipe = await pipeline('sentiment-analysis', 'model-id', {
  dtype: 'q4',  // Options: 'fp32', 'fp16', 'q8', 'q4'
});

Supported Tasks

**Note:** All examples below show basic usage.

Natural Language Processing

Text Classification

const classifier = await pipeline('text-classification');
const result = await classifier('This movie was amazing!');

Named Entity Recognition (NER)

const ner = await pipeline('token-classification');
const entities = await ner('My name is John and I live in New York.');

Question Answering

const qa = await pipeline('question-answering');
const answer = await qa({
  question: 'What is the capital of France?',
  context: 'Paris is the capital and largest city of France.'
});

Text Generation

const generator = await pipeline('text-generation', 'onnx-community/gemma-3-270m-it-ONNX');
const text = await generator('Once upon a time', {
  max_new_tokens: 100,
  temperature: 0.7
});

**For streaming and chat:** See **[Text Generation Guide](./references/TEXT_GENERATION.md)** for:

• Streaming token-by-token output with `TextStreamer`
• Chat/conversation format with system/user/assistant roles
• Generation parameters (temperature, top_k, top_p)
• Browser and Node.js examples
• React components and API endpoints

Translation

const translator = await pipeline('translation', 'Xenova/nllb-200-distilled-600M');
const output = await translator('Hello, how are you?', {
  src_lang: 'eng_Latn',
  tgt_lang: 'fra_Latn'
});

Summarization

const summarizer = await pipeline('summarization');
const summary = await summarizer(longText, {
  max_length: 100,
  min_length: 30
});

Zero-Shot Classification

const classifier = await pipeline('zero-shot-classification');
const result = await classifier('This is a story about sports.', ['politics', 'sports', 'technology']);

Computer Vision

Image Classification

const classifier = await pipeline('image-classification');
const res