chaima-Khenafif03/darija-script-detector

Classifies Algerian Darija text as Arabic-script, Latin-script, or non-Darija # Darija Script Detector A fine-tuned XLM-RoBERTa model that classifies Algerian Arabic (Darija) text into three categories: **Darija written in Latin script**, **Darija written in Arabic script**, and **Not Darija**. Built to handle the code-switching and script-mixing common in Algerian social media, chat, and speech transcripts, where the same language is written inconsistently across Arabic and Latin alphabets. ## Example | Input | Prediction | |---|---| | `كي راك خويا؟` | Darija (Arabic script) | | `wach rak mlih wla la?` | Darija (Latin script) | | `How are you doing today?` | Not Darija | | `Je suis très fatigué aujourd'hui.` | Not Darija | ## Results Fine-tuned `xlm-roberta-base` for 3 epochs on a custom-labeled dataset. | Metric | Score | |---|---| | Accuracy | 99.7% | | F1 (macro) | 99.7% | | Precision (macro) | 99.8% | | Recall (macro) | 99.6% | ## Project structure ``` ├── data/ # sample of the labeled dataset (full dataset not published) ├── notebooks/ # original development notebook ├── src/ │ ├── preprocess.py # data loading, cleaning, tokenization │ ├── train.py # fine-tuning script │ └── predict.py # inference ├── examples/ # sample inputs/outputs └── requirements.txt ``` ## Usage ```bash pip install -r requirements.txt # Train python src/train.py path/to/darija_dataset.csv # Predict (automatically downloads the model from Hugging Face Hub on first run) python src/predict.py "wach rak mlih?" ``` ## Dataset Custom dataset built and labeled across three classes: `darija_latin`, `darija_arabic`, `not_darija`. A sample is included under `data/`; the full dataset is not published. ## Model Base model: `xlm-roberta-base`, fine-tuned for sequence classification. Fine-tuned weights hosted on Hugging Face Hub: Chaima-KHENAFIF/algerian-darija-script-detector