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antigravity-skills-reference/skills/transformers-js/references/EXAMPLES.md
sickn33 bdcfbb9625 feat(hugging-face): Add official ecosystem skills 
Import the official Hugging Face ecosystem skills and sync the\nexisting local coverage with upstream metadata and assets.\n\nRegenerate the canonical catalog, plugin mirrors, docs, and release\nnotes after the maintainer merge batch so main stays in sync.\n\nFixes #417
2026-03-29 18:31:46 +02:00

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Transformers.js Code Examples

Working examples showing how to use Transformers.js across different runtimes and frameworks.

All examples use the same task and model for consistency:

  • Task: feature-extraction
  • Model: onnx-community/all-MiniLM-L6-v2-ONNX

Table of Contents

  1. Browser (Vanilla JS)
  2. Node.js
  3. React
  4. Express API

Browser (Vanilla JS)

Basic Usage

<!DOCTYPE html>
<html>
<head>
  <title>Feature Extraction</title>
</head>
<body>
  <h1>Text Embedding Generator</h1>
  <textarea id="input" placeholder="Enter text to embed..."></textarea>
  <button onclick="generateEmbedding()">Generate Embedding</button>
  <div id="result"></div>
  <div id="loading" style="display:none;">Loading model...</div>

  <script type="module">
    import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.8.1';
    
    let extractor;
    
    // Initialize model on page load
    document.getElementById('loading').style.display = 'block';
    extractor = await pipeline(
      'feature-extraction',
      'onnx-community/all-MiniLM-L6-v2-ONNX'
    );
    document.getElementById('loading').style.display = 'none';
    
    window.generateEmbedding = async function() {
      const text = document.getElementById('input').value;
      const output = await extractor(text, { pooling: 'mean', normalize: true });
      
      document.getElementById('result').innerHTML = `
        <h3>Embedding Generated:</h3>
        <p>Dimensions: ${output.data.length}</p>
        <p>First 5 values: ${Array.from(output.data).slice(0, 5).join(', ')}</p>
      `;
    };
    
    // Cleanup on page unload
    window.addEventListener('beforeunload', () => {
      if (extractor) extractor.dispose();
    });
  </script>
</body>
</html>

With Progress Tracking

<!DOCTYPE html>
<html>
<head>
  <title>Feature Extraction with Progress</title>
  <style>
    .file-progress {
      margin: 10px 0;
    }
    .file-name {
      font-size: 12px;
      margin-bottom: 5px;
    }
    .progress-bar {
      width: 100%;
      height: 20px;
      background: #f0f0f0;
      border-radius: 5px;
      overflow: hidden;
    }
    .progress-fill {
      height: 100%;
      background: #4CAF50;
      transition: width 0.3s;
    }
  </style>
</head>
<body>
  <h1>Text Embedding Generator</h1>
  <div id="loading">
    <p id="status">Loading model...</p>
    <div id="progress-container"></div>
  </div>
  <div id="app" style="display:none;">
    <textarea id="input" placeholder="Enter text..."></textarea>
    <button onclick="generateEmbedding()">Generate</button>
    <div id="result"></div>
  </div>

  <script type="module">
    import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.8.1';
    
    let extractor;
    const fileProgressBars = {};
    const progressContainer = document.getElementById('progress-container');
    
    extractor = await pipeline(
      'feature-extraction',
      'onnx-community/all-MiniLM-L6-v2-ONNX',
      {
        progress_callback: (info) => {
          document.getElementById('status').textContent = `${info.status}: ${info.file}`;
          
          if (info.status === 'progress') {
            // Create progress bar for each file
            if (!fileProgressBars[info.file]) {
              const fileDiv = document.createElement('div');
              fileDiv.className = 'file-progress';
              fileDiv.innerHTML = `
                <div class="file-name">${info.file}</div>
                <div class="progress-bar">
                  <div class="progress-fill"></div>
                </div>
              `;
              progressContainer.appendChild(fileDiv);
              fileProgressBars[info.file] = fileDiv.querySelector('.progress-fill');
            }
            
            // Update progress
            fileProgressBars[info.file].style.width = `${info.progress}%`;
          }
          
          if (info.status === 'ready') {
            document.getElementById('loading').style.display = 'none';
            document.getElementById('app').style.display = 'block';
          }
        }
      }
    );
    
    window.generateEmbedding = async function() {
      const text = document.getElementById('input').value;
      const output = await extractor(text, { pooling: 'mean', normalize: true });
      
      document.getElementById('result').innerHTML = `
        <p>Embedding: ${output.data.length} dimensions</p>
      `;
    };
    
    // Cleanup on page unload
    window.addEventListener('beforeunload', () => {
      if (extractor) extractor.dispose();
    });
  </script>
</body>
</html>

Node.js

Basic Script

// embed.js
import { pipeline } from '@huggingface/transformers';

async function generateEmbedding(text) {
  const extractor = await pipeline(
    'feature-extraction',
    'onnx-community/all-MiniLM-L6-v2-ONNX'
  );
  
  const output = await extractor(text, { pooling: 'mean', normalize: true });
  
  console.log('Text:', text);
  console.log('Embedding dimensions:', output.data.length);
  console.log('First 5 values:', Array.from(output.data).slice(0, 5));
  
  await extractor.dispose();
}

generateEmbedding('Hello, world!');

Batch Processing

// batch-embed.js
import { pipeline } from '@huggingface/transformers';
import fs from 'fs/promises';

async function embedDocuments(documents) {
  const extractor = await pipeline(
    'feature-extraction',
    'onnx-community/all-MiniLM-L6-v2-ONNX'
  );
  
  console.log(`Processing ${documents.length} documents...`);
  
  const embeddings = [];
  
  for (let i = 0; i < documents.length; i++) {
    const output = await extractor(documents[i], { 
      pooling: 'mean', 
      normalize: true 
    });
    
    embeddings.push({
      text: documents[i],
      embedding: Array.from(output.data)
    });
    
    console.log(`Processed ${i + 1}/${documents.length}`);
  }
  
  await fs.writeFile(
    'embeddings.json',
    JSON.stringify(embeddings, null, 2)
  );
  
  console.log('Saved to embeddings.json');
  
  await extractor.dispose();
}

const documents = [
  'The cat sat on the mat',
  'A dog played in the park',
  'Machine learning is fascinating'
];

embedDocuments(documents);

CLI with Progress

// cli-embed.js
import { pipeline } from '@huggingface/transformers';

async function main() {
  const text = process.argv[2] || 'Hello, world!';
  
  console.log('Loading model...');
  
  const fileProgress = {};
  
  const extractor = await pipeline(
    'feature-extraction',
    'onnx-community/all-MiniLM-L6-v2-ONNX',
    {
      progress_callback: (info) => {
        if (info.status === 'progress') {
          fileProgress[info.file] = info.progress;
          
          // Show all files progress
          const progressLines = Object.entries(fileProgress)
            .map(([file, progress]) => `  ${file}: ${progress.toFixed(1)}%`)
            .join('\n');
          
          process.stdout.write(`\r\x1b[K${progressLines}`);
        }
        
        if (info.status === 'done') {
          console.log(`\n✓ ${info.file} complete`);
        }
        
        if (info.status === 'ready') {
          console.log('\nModel ready!');
        }
      }
    }
  );
  
  console.log('Generating embedding...');
  const output = await extractor(text, { pooling: 'mean', normalize: true });
  
  console.log(`\nText: "${text}"`);
  console.log(`Dimensions: ${output.data.length}`);
  console.log(`First 5 values: ${Array.from(output.data).slice(0, 5).join(', ')}`);
  
  await extractor.dispose();
}

main();

React

Basic Component

// EmbeddingGenerator.jsx
import { useState, useRef, useEffect } from 'react';
import { pipeline } from '@huggingface/transformers';

export function EmbeddingGenerator() {
  const extractorRef = useRef(null);
  const [text, setText] = useState('');
  const [embedding, setEmbedding] = useState(null);
  const [loading, setLoading] = useState(false);

  const generate = async () => {
    if (!text) return;
    
    setLoading(true);
    
    // Load model on first generate
    if (!extractorRef.current) {
      extractorRef.current = await pipeline(
        'feature-extraction',
        'onnx-community/all-MiniLM-L6-v2-ONNX'
      );
    }
    
    const output = await extractorRef.current(text, { 
      pooling: 'mean', 
      normalize: true 
    });
    setEmbedding(Array.from(output.data));
    setLoading(false);
  };

  // Cleanup on unmount
  useEffect(() => {
    return () => {
      if (extractorRef.current) {
        extractorRef.current.dispose();
      }
    };
  }, []);

  return (
    <div>
      <h2>Text Embedding Generator</h2>
      
      <textarea
        value={text}
        onChange={(e) => setText(e.target.value)}
        placeholder="Enter text"
        disabled={loading}
      />
      
      <button onClick={generate} disabled={loading || !text}>
        {loading ? 'Processing...' : 'Generate Embedding'}
      </button>
      
      {embedding && (
        <div>
          <h3>Result:</h3>
          <p>Dimensions: {embedding.length}</p>
          <p>First 5 values: {embedding.slice(0, 5).join(', ')}</p>
        </div>
      )}
    </div>
  );
}

With Progress Tracking

// EmbeddingGeneratorWithProgress.jsx
import { useState, useRef, useEffect } from 'react';
import { pipeline } from '@huggingface/transformers';

export function EmbeddingGeneratorWithProgress() {
  const extractorRef = useRef(null);
  const [text, setText] = useState('');
  const [embedding, setEmbedding] = useState(null);
  const [fileProgress, setFileProgress] = useState({});
  const [status, setStatus] = useState('');
  const [loading, setLoading] = useState(false);

  const generate = async () => {
    if (!text) return;
    
    setLoading(true);
    
    // Load model on first generate
    if (!extractorRef.current) {
      setStatus('Loading model...');
      
      extractorRef.current = await pipeline(
        'feature-extraction',
        'onnx-community/all-MiniLM-L6-v2-ONNX',
        {
          progress_callback: (info) => {
            setStatus(`${info.status}: ${info.file}`);
            
            if (info.status === 'progress') {
              setFileProgress(prev => ({
                ...prev,
                [info.file]: info.progress
              }));
            }
            
            if (info.status === 'ready') {
              setStatus('Model ready!');
            }
          }
        }
      );
    }
    
    setStatus('Generating embedding...');
    const output = await extractorRef.current(text, { 
      pooling: 'mean', 
      normalize: true 
    });
    setEmbedding(Array.from(output.data));
    setStatus('Complete!');
    setLoading(false);
  };

  // Cleanup on unmount
  useEffect(() => {
    return () => {
      if (extractorRef.current) {
        extractorRef.current.dispose();
      }
    };
  }, []);

  return (
    <div>
      <h2>Text Embedding Generator</h2>
      
      {loading && Object.keys(fileProgress).length > 0 && (
        <div>
          <p>{status}</p>
          {Object.entries(fileProgress).map(([file, progress]) => (
            <div key={file} style={{ margin: '10px 0' }}>
              <div style={{ fontSize: '12px', marginBottom: '5px' }}>{file}</div>
              <div style={{ width: '100%', height: '20px', background: '#f0f0f0', borderRadius: '5px', overflow: 'hidden' }}>
                <div 
                  style={{ 
                    width: `${progress}%`, 
                    height: '100%', 
                    background: '#4CAF50',
                    transition: 'width 0.3s'
                  }} 
                />
              </div>
            </div>
          ))}
        </div>
      )}
      
      <textarea
        value={text}
        onChange={(e) => setText(e.target.value)}
        placeholder="Enter text"
        disabled={loading}
      />
      
      <button onClick={generate} disabled={loading || !text}>
        {loading ? 'Processing...' : 'Generate Embedding'}
      </button>
      
      {embedding && (
        <div>
          <h3>Result:</h3>
          <p>Dimensions: {embedding.length}</p>
          <p>First 5 values: {embedding.slice(0, 5).join(', ')}</p>
        </div>
      )}
    </div>
  );
}

Express API

Basic API Server

// server.js
import express from 'express';
import { pipeline } from '@huggingface/transformers';

const app = express();
app.use(express.json());

// Initialize model once at startup
let extractor;
(async () => {
  console.log('Loading model...');
  extractor = await pipeline(
    'feature-extraction',
    'onnx-community/all-MiniLM-L6-v2-ONNX'
  );
  console.log('Model ready!');
})();

app.post('/embed', async (req, res) => {
  try {
    const { text } = req.body;
    
    if (!text) {
      return res.status(400).json({ error: 'Text is required' });
    }
    
    const output = await extractor(text, { 
      pooling: 'mean', 
      normalize: true 
    });
    
    res.json({
      text,
      embedding: Array.from(output.data),
      dimensions: output.data.length
    });
  } catch (error) {
    console.error('Error:', error);
    res.status(500).json({ error: 'Failed to generate embedding' });
  }
});

app.listen(3000, () => {
  console.log('Server running on http://localhost:3000');
});

API with Graceful Shutdown

// server-with-shutdown.js
import express from 'express';
import { pipeline } from '@huggingface/transformers';

const app = express();
app.use(express.json());

let extractor;
let server;

async function initialize() {
  console.log('Loading model...');
  extractor = await pipeline(
    'feature-extraction',
    'onnx-community/all-MiniLM-L6-v2-ONNX'
  );
  console.log('Model ready!');
}

app.post('/embed', async (req, res) => {
  try {
    const { text } = req.body;
    
    if (!text) {
      return res.status(400).json({ error: 'Text is required' });
    }
    
    const output = await extractor(text, { 
      pooling: 'mean', 
      normalize: true 
    });
    
    res.json({
      embedding: Array.from(output.data),
      dimensions: output.data.length
    });
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

async function shutdown(signal) {
  console.log(`\n${signal} received. Shutting down...`);
  
  if (server) {
    server.close(() => {
      console.log('HTTP server closed');
    });
  }
  
  if (extractor) {
    console.log('Disposing model...');
    await extractor.dispose();
    console.log('Model disposed');
  }
  
  process.exit(0);
}

process.on('SIGTERM', () => shutdown('SIGTERM'));
process.on('SIGINT', () => shutdown('SIGINT'));

initialize().then(() => {
  server = app.listen(3000, () => {
    console.log('Server running on http://localhost:3000');
  });
});

These examples demonstrate the same functionality across different runtimes and frameworks, making it easy to adapt to your specific use case. All examples include proper cleanup with .dispose() to free memory.

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