Mbashas/gandabert

Fine-tuned mBERT for Luganda news classification across 5 categories (Politics, Business, Sports, Health, Religion) — advancing NLP for low-resource African languages. # GandaBERT: Luganda News Classification using Fine-tuned mBERT A final-year project that fine-tunes **multilingual BERT (mBERT)** for **Luganda news topic classification** across five categories: business, health, politics, religion, and sports. ## Overview This project builds a Luganda-language news classifier by: 1. **Combining multiple data sources** — MasakhaNEWS Luganda corpus, BBC News articles translated to Luganda via Google Cloud Translation API, and GPT-4o-generated synthetic articles translated to Luganda. 2. **Fine-tuning mBERT** (`bert-base-multilingual-cased`) on the combined dataset using Hugging Face Transformers. 3. **Evaluating** the model on a stratified held-out test set with per-category precision, recall, and F1-score. 4. **Running inference** on unlabeled Luganda news articles to classify them. ## Project Structure ``` FYP_2026/ ├── gandabert_complete.ipynb # Complete pipeline notebook (Colab) ├── news_classifier_model/ # Saved model artifacts │ ├── config.json # Model architecture config │ ├── label_mapping.json # Label-to-ID mapping │ ├── tokenizer.json # Tokenizer vocabulary │ └── tokenizer_config.json # Tokenizer settings ├── combined_final.tsv # Combined training dataset ├── split_train.tsv # Training split ├── split_val.tsv # Validation split ├── split_test.tsv # Test split ├── new_train_split.tsv # MasakhaNEWS train split ├── new_test_split.tsv # MasakhaNEWS test split ├── new_train_luganda.tsv # MasakhaNEWS Luganda training data ├── train_Luganda.tsv # Original Luganda training set ├── test_Luganda.tsv # Original Luganda test set ├── generated_news_articles.csv # GPT-4o generated articles (English) ├── translated_generated.csv # Translated synthetic articles (Luganda) ├── training_curves.png # Training/validation …