Transformers.js - Machine Learning for JavaScript

Transformers.js enables running state-of-the-art machine learning models directly in JavaScript, both in browsers and Node.js environments, with no 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 classifier.dispose();

⚠️ Memory Management: All pipelines must be disposed with pipe.dispose() when finished to prevent memory leaks. See examples in Code Examples 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 - experimental)
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 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

```ja