usama3627/tokenizer

Implementation of BPE Tokenizer in Rust

Tokenizer

Tokenizer is a Rust project inspired by OpenAI, aimed at providing a basic implementation of the Byte Pair Encoding (BPE) algorithm. This project serves as a learning opportunity for Rust enthusiasts, particularly those interested in the field of artificial intelligence.

Introduction

Byte Pair Encoding (BPE) is a popular technique used in natural language processing (NLP) tasks, particularly in tokenization. It involves iteratively merging the most frequent pair of symbols in a corpus, effectively learning subword units that are useful for various NLP tasks.

Tokenizer implements the BPE algorithm in Rust, providing a foundation for further exploration and experimentation in tokenization and NLP.

Features

• BPE Implementation: Provides a basic implementation of the Byte Pair Encoding algorithm in Rust.
• Extensible: Designed with modularity in mind, allowing for easy expansion with additional modules for different tokenization techniques, such as GPT-2 tokenization.
• MIT License: Released under the MIT License, enabling anyone to use, modify, and distribute the project freely.

Usage

1. Clone the Repository:

   git clone https://github.com/usama3627/tokenizer.git

2. Install Rust: Ensure that you have Rust installed on your system. You can install it using rustup.

3. Build and Run:

   cd tokenizer
   cargo build
   cargo run

4. Training Data: To train the tokenizer, download the training dataset from the provided Link (huggingface dataset) and rename it to myresponse.json. Place the dataset in the project directory. For testing, I am using 100 rows of tweets.

Dependencies

• serde = { version = "1.0.104", features = ["derive"] }
• serde_json = "1.0.48"

Ensure these dependencies are specified in your Cargo.toml file.

Future Work

In future iterations of the project, the following enhancements can be considered:

• Modularization: Refactor the code into modules, separating concerns such as BPE implementation and GPT-2 tokenization.
• Optimizations: Explore optimizations to improve the performance of the tokenization process.
• Documentation: Enhance documentation to provide detailed explanations of the algorithms and codebase.
• Additional Tokenization Techniques: Integrate additional tokenization techniques to provide a comprehensive toolkit for NLP tasks.

License

Tokenizer is released under the MIT License. Feel free to use, modify, and distribute the project according to the terms of the license.

Contributors

• Usama Mehmood

Contributions to the project are welcome. Fork the repository, make your changes, and submit a pull request.

Acknowledgements

Tokenizer was inspired by the work of OpenAI and Andrej Karpathy and aims to contribute to the Rust and AI communities.