DibiaCorp85/fine-tuning_nllb-200_600M

This project aims to fine-tune the NLLB-200 (No Language Left Behind model from Meta AI for language translations. Yoruba, Igbo, Hausa, Wolof, Swahili, and Zulu being a low-resource African languages, lacks robust machine translation systems, and this work contributes toward addressing that gap. # 🧠 Fine-Tuning NLLB for Language Translation This project fine-tunes Meta AI’s NLLB (No Language Left Behind) model to translate from **English** to **selected language (LaTn)**, especially low-resource languages. The training pipeline leverages Hugging Face’s ecosystem to tokenize data, fine-tune multilingual transformers, and evaluate translation quality using standard metrics. --- ## 📌 Project Highlights - 🔤 Language Pair: `English → ....` - 🧰 Model Base: facebook/nllb-200-distilled-1.3B - ⚙️ Tools: Hugging Face Transformers, Datasets, Evaluate, and Colab (or local GPU) - 📄 Format: Python code implemented in a Jupyter Notebook --- ## 🚀 Example Usage ### ✅ Install Required Packages ```bash pip install transformers datasets sacrebleu evaluate sentencepiece ... ``` ## 🧪 Evaluation After training, the model is evaluated using: BLEU Score – Measures n-gram overlap between candidate and reference translations. ## 🙏 Acknowledgements - Meta AI – for releasing the NLLB-200 model - Hugging Face – for transformers, datasets, and evaluation libraries - JW300 / MENYO-20k / ROGENDO – multilingual parallel corpora for low-resource African languages - Google Colab – free GPU access for prototyping and training ## Take-Away Efficient translation models capable of an appreciable degree of accurate English-to-African-language translation. These notebooks show the implementation steps for a Seq2Seq fine-tuning process.