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# 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**: ```bash 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](https://rustup.rs/). 3. **Build and Run**: ```bash cd tokenizer cargo build cargo run ``` 4. **Training Data**: To train the tokenizer, download the training dataset from the provided [Link (huggingface dataset)](https://datasets-server.huggingface.co/rows?dataset=tweet_eval&config=emoji&split=train&offset=0&length=100) 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](LICENSE). Feel free to use, modify, and distribute the project according to the terms of the license. ## Contributors - [Usama Mehmood](https://github.com/usama3627) 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.

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