oddadmix/English-Moroccan-Darija-v1

🕌 English → Moroccan Darija Translator

This repository provides a machine translation model for translating English into Moroccan Darija (الدارجة المغربية).
The model is fine-tuned to handle conversational, cultural, and everyday expressions, producing natural Moroccan Darija output.

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🚀 Model Details

• Model ID: oddadmix/English-Moroccan-Darija-v1
• Framework: Hugging Face transformers
• Task: English → Moroccan Darija translation
• Language Pair: English → Moroccan Darija
• Context Window: 32K tokens

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📖 Usage

Install the required libraries:

pip install transformers

Run the translation:

from transformers import pipeline

model_id = "oddadmix/English-Moroccan-Darija-v1"
translate = pipeline("text-generation", model=model_id)

messages = [
    {"role": "system", "content": "Translate to Moroccan Darija"},
    {"role": "user", "content": "How are you today?"}
]

translation = translate(
    messages,
    max_new_tokens=8000,
    do_sample=True,
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05
)

print(translation)

Example Output:

كيف داير اليوم؟

⚠️ Important Note: The system prompt ({"role": "system", "content": "Translate to Moroccan Darija"}) is crucial.
Without it, the model will not perform at its best capacity.

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📊 Benchmark Results

The model has been evaluated against other strong LLMs on the English → Moroccan Darija task as a proxy benchmark.
For Moroccan Darija evaluation, a dataset of 300 sentences manually translated by a Moroccan translator was used.

🧾 Evaluation Dataset Coverage

The dataset spans diverse domains, ensuring wide coverage:

• Daily Life & Family: greetings, weather, school, transportation, family meals.
• Food & Cooking: couscous, tagines, vegetables, desserts, cooking instructions.
• Travel & Geography: Marrakech, Tangier, Casablanca, Rabat, Agadir, public transport.
• Work & Business: meetings, HR, finance, reports, management.
• Politics & Government: parliament debates, policies, laws, elections.
• Arts & Culture: music, painting, poetry, theater, sculpture.
• Education & Health: doctors, hospitals, lessons, assignments, public health.

This diversity makes the benchmark a strong representation of real-world translation scenarios.

Model	BLEU	METEOR	chrF	Task
Claude-Sonnet-4	0.312	0.566	62.09	English → Moroccan Darija
GPT-5-mini	0.381	0.637	66.58	English → Moroccan Darija
GPT-5	0.284	0.551	61.73	English → Moroccan Darija
GPT-4.1	0.306	0.575	61.87	English → Moroccan Darija
oddadmix/English-Moroccan-Darija-v1	0.423	0.644	67.31	English → Moroccan Darija

➡️ Our model achieves state-of-the-art performance while delivering specialized Moroccan Darija output across a wide variety of contexts.

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🌍 Applications

• Translating English educational material into Moroccan Darija.
• Supporting Moroccan dialect localization for chatbots, apps, and websites.
• Preserving cultural nuances in translations (not just MSA literalism).

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⚠️ Notes

• Output is optimized for natural conversational Moroccan Darija, not Modern Standard Arabic (MSA).
• Since Darija is a primarily spoken dialect, spelling conventions may vary slightly.
• Thanks to its 32K context window, the model can handle long documents and complex conversations seamlessly.
• Always include the system prompt to unlock the model’s best performance.

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🔎 Limitations & Future Work

• Spelling Variations: Moroccan Darija lacks standardized spelling. The model may generate slight differences (e.g., "بزاف" vs "بزّاف").
• Code-Switching: Common Darija usage mixes in French and occasionally Spanish. The model currently prioritizes pure Darija but may benefit from code-switching support.
• Niche Domains: Performance may vary for highly technical or domain-specific text. Future fine-tuning on specialized datasets could improve this.
• Evaluation Scope: Current evaluation is based on 300 manually translated sentences across diverse fields. Expanding the dataset will strengthen benchmarks further.
• Future Improvements:
  • Add multilingual code-switching training.
  • Expand context-specific datasets (education, health, e-commerce).
  • Release an interactive demo/Colab notebook for easier testing.

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📬 Contact

For feedback, contributions, or collaborations, please open an issue or reach out.