# Transformers Js

Use Transformers.js to run state-of-the-art machine learning models directly in...

- Repository: https://github.com/huggingface/skills (11249 stars)
- Category: Data and ML
- License: Apache-2.0
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add huggingface/skills --skill transformers-js -g -a claude-code -y`
Codex: `npx skills add huggingface/skills --skill transformers-js -g -a codex -y`
Cursor: `npx skills add huggingface/skills --skill transformers-js -g -a cursor -y`
Gemini CLI: `npx skills add huggingface/skills --skill transformers-js -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add huggingface/skills`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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
```bash
npm install @huggingface/transformers
```

### Browser Usage (CDN)
```javascript
<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:

```javascript
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:

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

**Finding Models:**

The rest: https://github.com/huggingface/skills/blob/HEAD/skills/transformers-js/SKILL.md