Welcome to the Transformer Tutorials repository! This collection is dedicated to explaining the intricacies of transformer models in deep learning, from their foundational concepts to advanced applications and research topics. Designed for beginners and advanced practitioners alike, our tutorials aim to demystify transformers and highlight their potential across various domains.

• 🌱 Basics and Introduction
• 🚀 Intermediate Topics
• 🔬 Advanced Topics
• 🌐 Specialized Applications and Exploration
• 🛠 Setting Up the Local Environment
• 🚀 How to Use
• 🤝 Contributions
• 📜 License

• Introduction to Transformers
• Understanding the Transformer Architecture
• Working with HuggingFace's Transformers Library
• Tokenization Deep Dive
• Embeddings in Transformers

• Fine-tuning Transformers for Text Classification
• Sequence-to-Sequence Tasks with Transformers
• Named Entity Recognition with Transformers
• Question Answering Systems with Transformers
• Transformers for Text Generation
• Sentiment Analysis with Transformers
• Transformers in Computer Vision
• Handling Long Sequences with Transformers
• Transfer Learning and Transformers

• Customizing Transformer Architectures
• Efficient Training of Transformers
• Optimizing Transformers for Production
• Transformers for Non-NLP Tasks
• Attention Mechanisms Explored
• Positional Encodings and Variants

To run the tutorials and notebooks on your local machine, follow these steps:

First, clone the repository to your local machine:

git clone https://github.com/YOUR_USERNAME/transformer-tutorials.git
cd transformer-tutorials

Replace YOUR_USERNAME with your actual GitHub username.

Using a virtual environment helps manage dependencies and ensures that the packages installed don't interfere with packages for other projects.

If you don't have venv module installed, you can do so using:

pip install virtualenv

Now, create and activate the virtual environment:

For macOS and Linux:

python -m venv venv
source venv/bin/activate

For Windows:

python -m venv venv
.\venv\Scripts\activate

With the virtual environment activated, install the required packages:

pip install -r requirements.txt

You can now launch Jupyter Notebook to access and run the tutorials:

jupyter notebook

This will open a tab in your web browser where you can navigate to the desired notebook and run it.

Once you're done, you can deactivate the virtual environment and return to your global Python environment by simply running:

deactivate

Follow the steps in "Setting Up the Local Environment" to set up your machine. Navigate to the desired notebook and run it using Jupyter Notebook.

Feel free to submit pull requests or raise issues if you find any problems or have suggestions.

MIT License