Transformer Model for Machine Translation

This repository contains a Transformer model implementation from scratch for building a machine translation system. The implementation follows the architecture described in the paper "Attention Is All You Need" by Vaswani et al.

Table of Contents

• Introduction
• Architecture
  • Multi-Head Self-Attention
  • Encoder Block
  • Decoder Block
• Usage
• Requirements
• Installation

Introduction

This project implements a Transformer model for machine translation tasks. Transformers have proven to be highly effective for various natural language processing tasks due to their ability to handle long-range dependencies and parallelize training.

Architecture

Multi-Head Self-Attention

The Transformer model relies heavily on the self-attention mechanism, which allows the model to weigh the importance of different words in a sequence relative to each other. The multi-head self-attention mechanism helps capture various aspects of the relationships between words.

Multi-Head Self-Attention

Encoder Block

The encoder block in the Transformer model consists of a multi-head self-attention mechanism followed by a feedforward neural network. Both layers have skip connections and layer normalization.

Decoder Block

The decoder block is similar to the encoder block but includes an additional multi-head cross-attention mechanism that attends to the encoder's output.

Usage

To use this model for machine translation, follow the instructions below.

Requirements

• Python 3.x
• TensorFlow or PyTorch
• NumPy
• Matplotlib

Installation

Clone the repository and install the required dependencies:

git clone https://github.com/SaYanZz0/Transformers-Attention-is-all-you-need.git
cd Transformers-Attention-is-all-you-need
pip install -r requirements.txt