Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

chaima-Khenafif03/darija-latin-to-arabic-translator

Domain:

natural language processing

Record type:

model
Creator:
cha
Host:
Fine-tuned mBART-50 model that translates Algerian Darija from Latin script to Arabic script, handling telecom vocabulary and spelling variation # Darija Latin → Darija Arabic Translator A fine-tuned mBART-50 model that translates Algerian Darija written in Latin script into Darija written in Arabic script — handling French/English loanwords, telecom-specific terms (4G, GB, DA...), and the many different Latin spellings people use for the same word. ## Example | Input (Darija Latin) | Output (Darija Arabic) | |---|---| | `3andi offre tae 300DA w cnx lente` | `عندي عرض تاع 300دج وكونكسيون بطيئة` | | `salam 3alaykom` | `السلام عليكم` | | `wash kayen men les offres jdida?` | `واش كاين من العروض الجديدة؟` | **Handles spelling variation**: the dataset includes multiple real-world spellings of the same word, so the model generalizes across them rather than requiring one "correct" transliteration: ``` le téléphone te'i ysserkouli les données personnelles le téléphune ta3y yssar9oly les dunnées persunnelles le téléphone ta3i yssar9ouli les données personnelles ``` → all map to: `التيليفون تاعي يسرقولي البيانات الشخصية` ## Results Fine-tuned `facebook/mbart-large-50-many-to-many-mmt` on a custom 10,000-sample Darija Latin/Arabic parallel dataset, with custom telecom tokens added to the vocabulary (3G, 4G, 5G, GB, DA, Mbps, lte, adls). | Metric | Score | |---|---| | BLEU | 0.83 | ## Project structure ``` ├── data/ # sample of the parallel dataset (full 10K-row dataset not published) ├── notebooks/ # original development notebook ├── src/ │ ├── preprocess.py # dataset loading, tokenization, special tokens │ ├── 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_updated.csv # Translate (automatically downloads the model from Hugging Face Hub on first run) python src/predict.py "3andi offre tae 300DA w cnx lente" ``` ## Dataset Custom parallel dataset of 10,000 Darija Latin → Darija Arabic se …

Visit

github.com

Tasks

machine translationtext normalization

Languages

Arabic, Algerian Spoken

Licenses

MIT