Transformers Explained

Welcome to the "Transformers Explained" repository! This project aims to demystify the Transformer architecture, a groundbreaking model that has revolutionized the field of Natural Language Processing (NLP) and beyond.

Whether you're a curious beginner or an experienced practitioner looking to deepen your knowledge, this repository offers a comprehensive and hands-on approach to understanding Transformers. We'll explore fundamental concepts, the attention mechanism, and even build a Transformer from scratch.

Table of Contents

• Introduction to Transformers
• The Attention Mechanism
• Building a Transformer from Scratch
• Fine-Tuning Transformers
• Installation
• Usage
• Project Structure
• Contributing
• License

Introduction to Transformers

This section provides an overview of the Transformer architecture, its history, and its significance in the NLP domain. We'll cover key concepts such as positional encoding, encoder and decoder blocks, and how Transformers have overcome the limitations of recurrent and convolutional models.

The Attention Mechanism

At the core of Transformers lies the attention mechanism, especially self-attention. This section will explain in detail how attention allows the model to weigh different parts of the input sequence during prediction, thereby capturing long-range dependencies.

Building a Transformer from Scratch

For a deeper understanding, we will build a simplified Transformer using popular deep learning libraries. This hands-on approach will help you grasp the intricacies of each component and how they interact.

Fine-Tuning Transformers

Pre-trained Transformer models have become the standard for many NLP tasks. This section will cover fine-tuning techniques, showing how to adapt these powerful models to specific tasks with smaller datasets.

Installation

Detailed instructions on setting up your development environment and installing the necessary dependencies.

git clone https://github.com/your-username/transformers-explained.git
cd transformers-explained
pip install -r requirements.txt

Usage

How to run the Jupyter notebooks and explore the code examples. jupyter notebook Then navigate through the notebooks/ directory to explore the tutorials.

Project Structure

transformers-explained/
├── notebooks/
│   ├── 01_introduction_to_transformers.ipynb
│   ├── 02_attention_mechanism_explained.ipynb
│   ├── 03_building_a_transformer_from_scratch.ipynb
│   └── 04_fine_tuning_transformers.ipynb
├── assets/
│   └── # Images et autres médias
├── data/
│   └── # Jeux de données
├── README.md
├── requirements.txt
├── LICENSE
└── .gitignore

Contributing

We welcome contributions! Please refer to the CONTRIBUTING.md file (coming soon) for more details.

License

This project is licensed under the MIT License. See the LICENSE file for more details.