Transformer Implementation 🚀

This repository contains an implementation of a Transformer model from scratch using PyTorch. The project explores the architecture, key components, and training process of transformers, focusing on natural language processing (NLP) tasks.

📌 Project Overview

• Implemented a Transformer model from scratch using PyTorch.
• Incorporated key components such as:
  • Multi-head attention
  • Positional encoding
  • Feedforward neural networks
  • Layer normalization
• Trained the model on the Opus Books dataset for translation tasks.
• Visualized attention weights to interpret how the model focuses on different input sequences during translation.

🔥 Implementation Details

✅ 1. Transformer Model

• Inspired by the Attention Is All You Need paper.
• Implements encoder-decoder architecture with:
  • Self-attention and cross-attention layers.
  • Positional encodings for handling word order.
  • Layer normalization and residual connections.

✅ 2. Training Process

• Dataset: Opus Books dataset for translation tasks.
• Optimizer: Adam with learning rate scheduling.
• Loss Function: Cross-entropy loss.

📊 Results & Observations

• Successfully trained a Transformer model for translation.
• Attention visualization shows how the model aligns words during translation.
• Demonstrated the impact of multi-head attention and positional encoding.

📌 Future Enhancements

• ✅ Implement Transformer XL for handling longer sequences.
• ✅ Experiment with pre-trained models like BERT or GPT for comparison.

💡 References

• Attention Is All You Need
• PyTorch Transformer Documentation
• opus books Dataset