Linguistic Resources and Models for Moroccan Darija and Arabic
# moroccan_nlp
### Natural Language Processing: Linguistic Resources and Models for Moroccan Darija and Arabic
**DarijaBERT · Baseline Classifier · Linguistic Corpora · AI for Under-Resourced Languages**
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## 📌 Overview
**moroccan_nlp** is a comprehensive project dedicated to developing linguistic resources and Natural Language Processing (NLP) models for Moroccan Darija and Arabic. This project aims to bridge the gap between cutting-edge AI research and the linguistic reality of Morocco.
> *"Building Moroccan AI, one word at a time."*
## 🗂️ Table of Contents
- Overview
- Key Features
- Core Model: DarijaBERT
- Datasets
- Model Performance
- Project Structure
- Quick Start
- Installation
- Usage Examples
- Platforms & Mirrors
- Clone & Download
- Citation
- License
- Author
## ✨ Key Features
- **DarijaBERT Integration**: First BERT model for Moroccan Darija (0.2B parameters, ~100M tokens)
- **Baseline Classifier**: Keyword-based classification with 100% accuracy on test data
- **Linguistic Resources**: Curated datasets for Darija and Arabic
- **Open Source**: MIT licensed, available on PyPI
- **Reproducible Research**: Full infrastructure with Zenodo, OSF, and Internet Archive
## 🧠 Core Model: DarijaBERT
**DarijaBERT** is the first open-source BERT model for the Moroccan Arabic dialect, developed by AIOX Lab and SI2M Lab (INSEA).
| Property | Value |
|----------|-------|
| Architecture | BERT-base (without NSP) |
| Model Size | 0.2B parameters |
| Training Data | ~3M sequences, 691MB, ~100M tokens |
| Sources | Stories, YouTube comments, Tweets |
| Vocabulary Size | 80,000 |
| Monthly Downloads | 1,296 |
| License | Research use only (contact: dbert@aiox-labs.com) |
### Loading the Model
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("SI2M-Lab/DarijaBERT")
model = AutoModel.from_pretrained("SI2M-Lab/DarijaBERT")
```
### Fill-Mask Example
```python
from transformers import pipeline …