README.md

metadata

title: SlangGPT
emoji: 🌍
colorFrom: green
colorTo: yellow
sdk: gradio
sdk_version: 6.14.0
python_version: '3.10'
app_file: app.py
pinned: false
license: mit
short_description: Egyptian Arabic slang → Modern Standard Arabic translation

SlangGPT – Egyptian Arabic → Modern Standard Arabic

⚡ Real-time Egyptian Arabic slang translation powered by AraGPT-2.

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🧠 About the Project

SlangGPT is a fine-tuned AraGPT-2 model designed to translate Egyptian Arabic slang/dialect into Modern Standard Arabic (MSA).

The project also includes:

• ✅ A translation verification (detection) model
• ⭐ Human feedback collection
• 📊 Public research datasets
• 🤖 RLHF-ready feedback pipeline

👉 Type an Egyptian Arabic sentence below and get the MSA translation instantly!

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✨ Features

• 🇪🇬 Egyptian Arabic slang understanding
• 📘 Translation into Modern Standard Arabic (MSA)
• 🤖 Fine-tuned AraGPT-2 language model
• 🧠 Translation verification / detection model
• ⭐ Human feedback collection pipeline
• 📊 Public feedback dataset for research
• 🌐 Interactive Gradio interface

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💡 Example Inputs

Try these examples:

• عامل ايه؟
• إيه الأخبار؟
• هو انت رايح فين؟
• عايز أروح البيت
• أنا زهقان جدًا
• الدنيا حر النهاردة

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📄 Full Project Report

For all technical details — architecture, training, hyperparameters, evaluation, error analysis, and comparison with Stanford CS224N baselines — read the full report:

📄 https://github.com/adhamashraf7788/SlangGPT/blob/main/SlangGPT_report.pdf

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🧠 How It Works

The model expects the following prompt format:

dialect: {your sentence} ↔ msa:

The model then autoregressively generates the corresponding MSA translation.

Decoding Strategy

• Temperature = 0.7
• Top-k = 50
• Top-p = 0.92
• Repetition penalty = 1.3

These settings improve fluency while reducing repetitive outputs.

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📝 Feedback System

After each translation, users can provide feedback:

1. ✅ Is the translation correct? (Yes / No)
2. ✍️ Provide a corrected MSA translation (optional)
3. ⭐ Rate translation quality (1–5)

All feedback is stored in the public dataset:

🔗 https://huggingface.co/datasets/AdhamAshraf/slanggpt-feedback-dataset

Collected feedback will help improve future SlangGPT versions and support Arabic RLHF research.

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📊 Model Performance

Generation Quality

Metric	Zero-shot (Base AraGPT-2)	SlangGPT
chrF	10.62	29.08
BLEU	0.02	6.63

Detection Model

Task	Accuracy
Translation Verification	0.956

Improvements

• 📈 chrF improvement: +18.46
• 📈 Detection accuracy improvement: +45.6 points

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🔬 Research Contributions

This project contributes:

• A fine-tuned Egyptian Arabic → MSA generation model
• A translation verification classifier
• A public human-feedback dataset
• An RLHF-ready Arabic NLP pipeline

The project aims to support future Arabic dialect NLP research and low-resource language modeling.

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⚠️ Limitations

The model may struggle with:

• Rare slang expressions
• Mixed Arabic-English text
• Heavy sarcasm or idioms
• Long conversational context

Translations are generated probabilistically and may occasionally contain inaccuracies.

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🔐 Feedback & Privacy

Submitted feedback may be stored publicly in the research feedback dataset.

Please avoid submitting:

• Personal information
• Phone numbers
• Addresses
• Sensitive/private content

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🚀 Future Work

Planned future improvements include:

• Larger instruction-tuned Arabic models
• RLHF fine-tuning using collected feedback
• Better dialect generalization
• Arabic-English code-switching support
• Faster inference optimization

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🏗️ Technical Stack

• Transformers 🤗
• PyTorch
• Gradio
• Hugging Face Spaces
• AraGPT-2
• pandas
• scikit-learn

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📚 Resources

Resource	Link
Live Space	https://huggingface.co/spaces/AdhamAshraf/SlangGPT
Model on HF Hub	https://huggingface.co/AdhamAshraf/SlangGPT
Dataset (Egyptian ↔ MSA)	https://huggingface.co/datasets/AdhamAshraf/egyptian-2-arabic
Feedback Dataset	https://huggingface.co/datasets/AdhamAshraf/slanggpt-feedback-dataset
GitHub Repository	https://github.com/adhamashraf7788/SlangGPT
Full Report	https://github.com/adhamashraf7788/SlangGPT/blob/main/SlangGPT_report.pdf

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🙏 Acknowledgements

• AraGPT-2 by Antoun et al. (2021)
• Stanford CS224N educational framework
• The Arabic NLP open-source community
• All users who provide feedback to improve SlangGPT

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⭐ Support the Project

If you find SlangGPT useful:

• ⭐ Star the GitHub repository
• 🤝 Contribute improvements
• 📝 Submit feedback
• 📢 Share the project

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📜 License

This Space, model, and datasets are released under the MIT License.

Free for academic and commercial use with attribution.

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🚀 Enjoy Translating!

Thank you for using SlangGPT and helping improve Arabic NLP research.