ai-zero-shot-classifier

Checkout the demo for a quick start!

🚀 Introduction

ai-zero-shot-classifier is a powerful, flexible JavaScript library designed to perform zero-shot text classification using pre-trained AI embeddings. The library supports multiple providers and models, enabling you to choose the best AI tools for your project, whether it's OpenAI's models or alternative providers like Groq.

🧐 Why ai-zero-shot-classifier?

The Problem

Traditional text classification requires extensive labeled data and retraining models to adapt to new categories. This process can be costly, time-consuming, and impractical when dealing with constantly evolving datasets or dynamic categories.

The Innovation

ai-zero-shot-classifier eliminates the need for labeled datasets by leveraging pre-trained AI embeddings. It allows for dynamic and task-specific labels, enabling real-time classification across various domains without retraining models. It supports multiple providers and their respective models, making it adaptable to diverse use cases.

✨ Features

• Multi-Provider Support: Works with providers like OpenAI and Groq, enabling integration with models such as GPT, Llama, and others.
• Dynamic Labels: Define your labels dynamically for each classification task.
• Multiple Similarity Functions: Supports cosine similarity, dot product, and Euclidean distance for flexible classification needs.
• Batch Processing: Efficiently handles large datasets with customizable batch sizes and concurrency.
• Highly Configurable: Adjustable settings for embeddings, similarity calculations, and more.
• Seamless Integration: Simple API designed for easy use in Node.js and browser environments.

📦 Installation

npm install ai-zero-shot-classifier

or

yarn add ai-zero-shot-classifier

🚀 Usage

Basic Example with classify Function

import { classify } from 'ai-zero-shot-classifier';

const labels = ['Technology', 'Health', 'Finance'];
const data = [
  'Artificial Intelligence is transforming industries.',
  'The stock market has seen unprecedented growth.',
  'Healthcare advancements are improving lives.'
];

classify({ labels, data, config: { similarity: 'cosine' } })
  .then((results) => {
    console.log(results);
  })
  .catch((error) => {
    console.error(error);
  });

Example with ZeroShotClassifier Class

import ZeroShotClassifier from 'ai-zero-shot-classifier';

const labels = ['Technology', 'Health', 'Finance'];
const data = [
  'Artificial Intelligence is transforming industries.',
  'The stock market has seen unprecedented growth.',
  'Healthcare advancements are improving lives.'
];

// Create an instance of the classifier
const classifier = new ZeroShotClassifier({
  provider: 'openai', // Specify the provider
  model: 'text-embedding-3-small', // Specify the model
  apiKey: 'your-api-key', // API key for authentication
  labels, // Provide labels for classification
  dimensions: undefined, // Pass dimensions as a number here to configure vector dimensions
});

(async () => {
  try {
    const results = await classifier.classify(data, {
      similarity: 'cosine', // Choose the similarity metric
    });

    // perform more classification

    console.log('Classification Results:', results);
  } catch (error) {
    console.error('Error during classification:', error);
  }
})();

⚙️ Configuration Options

| Option | Description | Default | |----------------------------|---------------------------------------------------|------------------| | similarity | Similarity function to use (cosine, dot, euclidean) | cosine | | embeddingBatchSizeData | Batch size for data embeddings | 50 | | embeddingBatchSizeLabels | Batch size for label embeddings | 50 | | embeddingConcurrencyData | Concurrency for data embeddings | 5 | | embeddingConcurrencyLabels | Concurrency for label embeddings | 5 | | comparingConcurrencyTop | Concurrency for top-level comparisons | 10 | | comparingConcurrencyBottom | Concurrency for bottom-level comparisons | 10 |

🛠️ Development

Clone the repository:

git clone https://github.com/a-tokyo/ai-zero-shot-classifier.git

Install dependencies:

yarn install

Run the development server:

yarn start

Run tests:

yarn test

🤝 Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.