kirubel-Nigussie/Machine_Translation_amharic_to_english

# Amharic to English Machine Translation This project implements a neural machine translation system that translates text from Amharic to English using a sequence-to-sequence model with LSTM architecture. ## Project Overview The translation system uses a deep learning approach with the following components: - Encoder-Decoder architecture with LSTM layers - SentencePiece tokenization for both Amharic and English - Embedding dimension: 128 - LSTM units: 256 - Vocabulary size: 4000 tokens per language ## Dataset The model is trained on a parallel corpus of Amharic-English sentence pairs. The dataset is loaded in chunks to manage memory efficiently. amh.txt = 53313 Sentence eng.txt = 53313 Sentence ## Training Configuration - Number of epochs: 50 (with early stopping) - Batch size: 64 - Learning rate: 0.001 - Validation split: 10% - Early stopping patience: 3 epochs ## Model Performance The model's performance is evaluated using BLEU score, though it's important to note that the current implementation has room for improvement. The model's limitations include: 1. Limited training data compared to state-of-the-art systems 2. Reduced model size to accommodate available computational resources 3. Basic architecture without attention mechanisms 4. Limited vocabulary size (4000 tokens per language) ## Running the Project ### Backend Setup 1. Install the required dependencies: ```bash pip install -r requirements.txt ``` 2. Start the FastAPI backend server: ```bash uvicorn app:app --reload ``` The backend will be available at `localhost` ### Frontend Setup 1. Navigate to the frontend directory: ```bash cd frontend ``` 2. Install frontend dependencies: ```bash npm install ``` 3. Start the development server: ```bash npm run dev ``` The frontend will be available at `localhost` ## API Endpoints ### Translation Endpoint - URL: `localhost` - Method: GET - Parameter: `amharic` (string) - Response: JSON with `transl …