Rofeeah-Tijani/-Yoruba-English-Sequence-to-Sequence-Translation-Model

# 🌍 Yoruba → English Sequence-to-Sequence Translation Model This project demonstrates a **Sequence-to-Sequence (Seq2Seq) neural network** for translating Yoruba sentences into English using deep learning. It was built to understand how encoder-decoder architectures work using GRU-based recurrent neural networks. --- # 🧠 What is this project about? This project builds a model that learns: > Yoruba sentence → English sentence Instead of memorizing translations, the model learns patterns between languages using a neural network. --- # ⚙️ How it works (High-Level) The model follows the Seq2Seq architecture: Input Sentence (Yoruba) ↓ Encoder (GRU) ↓ Context Vector (compressed meaning) ↓ Decoder (GRU) ↓ Output Sentence (English) --- # 🧩 Key Concepts Used ## 1. Sequence-to-Sequence (Seq2Seq) A model that converts one sequence into another sequence. Examples: - Yoruba → English translation - Text summarization - Chatbots --- ## 2. Encoder-Decoder Architecture - **Encoder**: Reads and understands the input sentence - **Decoder**: Generates the output sentence step-by-step --- ## 3. GRU (Gated Recurrent Unit) A type of recurrent neural network that helps the model remember important information in sequences. Used in both encoder and decoder. --- ## 4. Embedding Layer Converts words into dense numerical vectors so the model can understand meaning. --- ## 5. Teacher Forcing (Training Concept) During training, the correct previous word is used to improve learning stability. --- # 🧪 Dataset A small custom dataset of Yoruba-English sentence pairs: Example: | Yoruba | English | |--------|--------| | mo n lo si ile | i am going home | | inu mi dun | i am happy | | o n jeun | he is eating | --- # 🏗️ Model Architecture ``` Encoder: Embedding → GRU → Context Vector Decoder: Embedding → GRU → Dense (Softmax) ``` --- # 📦 Requirements ```bash tensorflow numpy ``` --- # 🚀 How to run ```bash # Clone the repository git clone github.com …