T5-PyTorch-DeepDive-Building-and-Exploring-Text-to-Text-Transfer-Transformer-from-Scratch

Implementation for the Paper Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (T5) Alongside a Comprehensive Demonstration.

Goals and Scope

• Educational Purpose: This project is intended as a learning resource. It aims to offer a clear understanding of the T5 architecture through a hands-on approach by implementing the model from scratch using PyTorch.
• Model Understanding: Users can delve into each part of the model's architecture, including the encoder, decoder, and the self-attention mechanism, to understand how they contribute to the model's performance.
• Project Customization: While the primary focus is educational, users are encouraged to modify and adapt the provided components to fit the requirements of their projects.

Limitations

• Dataset Handling: The current implementation of the dataset loading and processing is optimized for simplicity and small-scale data. Users intending to train models on larger datasets may need to modify the dataset handling components for efficiency.
• Pre-training Only: This codebase is set up for model pre-training. To use this implementation for fine-tuning on specific tasks, further modifications may be required.

Getting Started

Prerequisites

Ensure you have Python 3.7+ and PyTorch installed. Additionally, this project uses the Transformers library for tokenization.

Installation

Clone the repository and install the required Python packages:

git clone https://github.com/AliHaiderAhmad001/T5-PyTorch-DeepDive-Building-and-Exploring-Text-to-Text-Transfer-Transformer-from-Scratch.git
cd T5-PyTorch-DeepDive-Building-and-Exploring-Text-to-Text-Transfer-Transformer-from-Scratch
pip install -r requirements.txt

Usage

To train the model with the default settings, simply run:

python run.py

You can customize the training by passing additional arguments. For example, to change the number of training epochs and batch size, you can run:

python run.py --epochs 3 --batch_size 32

Contributing

This project is open to contributions and corrections. If you find any inconsistencies with the original T5 paper or have suggestions for improving the implementation, please feel free to open an issue or submit a pull request.

Acknowledgments

This project is inspired by the original T5 paper and the PyTorch community. Special thanks to the authors of the T5 model for their groundbreaking work in NLP.

License

This project is licensed under the MIT License.