✨ What if you could classify anything without training models?

import { classify } from 'zerolabel';

// Classify single or multiple texts at once
const results = await classify({
  texts: [
    'I love this product!',
    'This is terrible quality',
    'Not bad, could be better'
  ],
  labels: ['positive', 'negative', 'neutral'],
  apiKey: process.env.INFERENCE_API_KEY
});

// Get results for each text
results.forEach((result, i) => {
  console.log(`Text ${i + 1}: ${result.predicted_label} (${result.confidence}%)`);
});

That's it. Text, images, or both. Single items or batches. Any labels you want. Results in milliseconds.

🤔 The Problem

Building classification usually means:

• ❌ Collecting thousands of labeled examples
• ❌ Training models for hours/days
• ❌ Managing ML infrastructure
• ❌ Retraining when you need new categories

zerolabel solves this in one line of code.

🤔 The Solution

import { classify } from 'zerolabel';

// Classify single or multiple texts at once
const results = await classify({
  texts: [
    'I love this product!',
    'This is terrible quality',
    'Not bad, could be better'
  ],
  labels: ['positive', 'negative', 'neutral'],
  apiKey: process.env.INFERENCE_API_KEY
});

// Get results for each text
results.forEach((result, i) => {
  console.log(`Text ${i + 1}: ${result.predicted_label} (${result.confidence}%)`);
});

That's it. No training, no infrastructure, no complexity.

⚡ Installation

npm install zerolabel

🚀 Examples

Text Classification (Single or Batch)

// Process multiple texts efficiently
await classify({
  texts: [
    'Amazing product!', 
    'Worst purchase ever', 
    'It\'s okay', 
    'Best value for money',
    'Would not recommend'
  ],
  labels: ['positive', 'negative', 'neutral'],
  apiKey: process.env.INFERENCE_API_KEY
});

// Or just one text
await classify({
  texts: ['Single text to classify'],
  labels: ['positive', 'negative', 'neutral'],
  apiKey: process.env.INFERENCE_API_KEY
});

Image Classification

await classify({
  images: ['data:image/jpeg;base64,...'],
  labels: ['cat', 'dog', 'bird'],
  apiKey: process.env.INFERENCE_API_KEY
});

Both Together (Multimodal)

await classify({
  texts: ['Check out this cute animal!'],
  images: ['data:image/jpeg;base64,...'],
  labels: ['cute cat', 'cute dog', 'not cute'],
  apiKey: process.env.INFERENCE_API_KEY
});

Custom Categories

await classify({
  texts: ['Fix login bug', 'Add dark mode', 'Server is down!'],
  labels: ['bug_report', 'feature_request', 'incident'],
  apiKey: process.env.INFERENCE_API_KEY
});

Batch Processing Made Easy

Process thousands of texts efficiently in a single API call:

import { classify } from 'zerolabel';

// Classify entire datasets at once
const reviews = [
  "Amazing product, highly recommend!",
  "Terrible quality, waste of money",
  "It's okay, nothing special",
  "Best purchase I've made this year",
  "Would not buy again",
  // ... thousands more
];

const results = await classify({
  texts: reviews,
  labels: ['positive', 'negative', 'neutral'],
  apiKey: process.env.INFERENCE_API_KEY
});

// Process results
results.forEach((result, index) => {
  console.log(`Review ${index + 1}: ${result.predicted_label} (${result.confidence}%)`);
});

// Or analyze by label distribution
const distribution = results.reduce((acc, result) => {
  acc[result.predicted_label] = (acc[result.predicted_label] || 0) + 1;
  return acc;
}, {});

console.log('Sentiment distribution:', distribution);

Benefits of batch processing:

• ✅ Faster: Single API call vs. hundreds of individual requests
• ✅ Cost-effective: Reduced API overhead and latency
• ✅ Simple: Same API, just pass an array
• ✅ Scalable: Handle datasets of any size

🎯 Real-World Use Cases

| Use Case | Labels | Input | |----------|--------|-------| | Email Triage | ['urgent', 'normal', 'spam'] | Single email or batch of emails | | Content Moderation | ['safe', 'nsfw', 'spam'] | User posts + images (single or batch) | | Support Tickets | ['bug', 'feature', 'question'] | Ticket descriptions (process entire queue) | | Document Classification | ['invoice', 'receipt', 'contract'] | Document images (single or batch) | | Sentiment Analysis | ['positive', 'negative', 'neutral'] | Reviews/feedback (analyze all at once) |

🏗️ How It Works

1. You provide: Text/images and your custom labels
2. We handle: The AI model (Google Gemma 3-27B), prompting, and inference
3. You get: Instant predictions with confidence scores

📊 Response Format

[
  {
    "text": "I love this product!",
    "predicted_label": "positive", 
    "confidence": 95.2,
    "probabilities": {
      "positive": 0.952,
      "negative": 0.048
    }
  }
]

🔧 Configuration

import { ZeroLabelClient } from 'zerolabel';

const client = new ZeroLabelClient({
  apiKey: process.env.INFERENCE_API_KEY,
  maxRetries: 3
});

const results = await client.classify({
  texts: ['Hello world'],
  labels: ['greeting', 'question']
});

🔑 Getting Your API Key

1. Sign up at inference.net
2. Get your API key from the dashboard
3. Set it as INFERENCE_API_KEY environment variable

export INFERENCE_API_KEY="your-key-here"

💡 Why zerolabel?

| Traditional ML | zerolabel | |----------------|-----------| | Weeks to collect data | ✅ Instant | | Hours to train models | ✅ No training needed | | Complex infrastructure | ✅ One npm install | | Fixed categories | ✅ Any labels you want | | Expensive compute | ✅ Pay per request |

🌟 Live Demo

Try it yourself: zerolabel.dev

📚 API Reference

classify(options)

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | texts | string[] | No* | Array of texts to classify (single or multiple) | | images | string[] | No* | Array of base64 image data URIs |
| labels | string[] | ✅ | Your classification categories | | apiKey | string | ✅ | Your inference.net API key (set as INFERENCE_API_KEY) | | criteria | string | No | Additional classification criteria |

*At least one of texts or images is required

🛠️ TypeScript Support

Full TypeScript definitions included:

import type { 
  ClassificationInput, 
  ClassificationResult,
  ZeroLabelConfig 
} from 'zerolabel';

❓ FAQ

Q: What models does this use?
A: Google Gemma 3-27B, optimized for classification tasks.

Q: How accurate is it?
A: Comparable to fine-tuned models for most classification tasks, especially with descriptive labels.

Q: Can I process multiple texts at once?
A: Yes! Pass an array of texts and get results for each one in a single API call.

Q: Can I use custom models?
A: No, we use inference.net's infrastructure with optimized models for best performance.

Q: Is there a rate limit?
A: Limits depend on your inference.net plan.

🤝 Contributing

Issues and PRs welcome! See our GitHub repo.

📄 License

MIT - Use it however you want!