Galsenaicommunity/Wolof-NMT

# French-Wolof Translator A modular, production-ready French-Wolof translation system built on Facebook's NLLB (No Language Left Behind) model. This project provides a complete framework for training, evaluating, and using bidirectional translation models between French and Wolof. ## 📋 Table of Contents - Features - Installation - Quick Start - Project Structure - Usage - Training - Configuration - Versioning - Contributing - License > 📖 **For detailed configuration instructions, see CONFIGURATION.md** ## ✨ Features - **Bidirectional Translation**: Translate between French and Wolof in both directions - **Modular Architecture**: Clean, maintainable codebase organized into logical modules - **Easy Training**: Simple interface for fine-tuning models on custom datasets - **Evaluation Metrics**: Built-in BLEU score evaluation for model assessment - **HuggingFace Integration**: Seamless integration with HuggingFace Hub for model sharing - **Flexible Configuration**: Centralized configuration system for easy customization - **Version Control**: Semantic versioning system for tracking releases ## 🚀 Installation ### Prerequisites - Python 3.8 or higher - CUDA-capable GPU (recommended for training, optional for inference) ### Install from Source 1. Clone the repository: ```bash git clone git@github-second:Galsenaicommunity/Wolof-NMT.git cd Wolof-NMT ``` 2. Install dependencies: ```bash pip install -r requirements.txt ``` 3. Configure environment variables: ```bash # Copy the example environment file cp .env.example .env # Edit .env with your settings # At minimum, you may want to set: # - MODEL_CHECKPOINT (if using a custom trained model) # - HF_TOKEN (if pushing models to HuggingFace Hub) # - WANDB_API_KEY (if using Weights & Biases) ``` 4. (Optional) Install as a package: ```bash pip install -e . ``` ## 🏃 Quick Start ### Configuration Before using the translator, configure your environment variables. Create a `.env` file from the example: ```bash cp .env …