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 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: