Byte Pair Encoding (BPE) Tokenizer

Project Overview

This is a web application that demonstrates Byte Pair Encoding (BPE) tokenization, a powerful technique used in natural language processing and machine learning, particularly in modern language models like GPT.

Features

• Tokenization: Convert text into tokens using Byte Pair Encoding
• Decoding: Reconstruct text from tokens
• Flexible Input: Support for different input formats
• Modern, Minimalist UI
• Dark Mode Design

Prerequisites

• Python 3.8+
• Flask
• NumPy

Installation

1. Clone the repository:

git clone https://github.com/yourusername/bpe-tokenizer.git
cd bpe-tokenizer

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install dependencies:

pip install flask numpy

4. Run the application:

python app.py

5. Open your browser and navigate to http://localhost:5000

Project Structure

• app.py: Flask backend application
• tokernizer.py: Custom tokenization implementation
• templates/index.html: Web application frontend
• read.txt: Sample training text for tokenizer (optional)

How It Works

Tokenization Process

1. Input text is analyzed
2. Tokens are generated using Byte Pair Encoding
3. Tokens can be decoded back to original text

Decoding Process

1. Input tokens
2. Reconstruct original text

Customization

• Modify vocab_size in app.py to change token vocabulary
• Update read.txt with your training corpus

Technologies Used

• Python
• Flask
• JavaScript
• Axios
• HTML5
• CSS3

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

• Your Name
• Email: [email protected]
• Project Link: https://github.com/yourusername/bpe-tokenizer