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---
title: Human vs AI Text Detector
emoji: 🔍
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: apache-2.0
tags:
- text-classification
- human-ai-text-attribution
- african-languages
- multilingual
- hata
---
# 🔍 Human vs AI Text Detector
Detect whether text is human-written or AI-generated across multiple African languages!
## 🌟 Features
- **Multilingual Support**: Works with English, Yoruba, Hausa, Igbo, Swahili, Amharic, and Nigerian Pidgin
- **High Accuracy**: 100% accuracy on validation set
- **Fair & Unbiased**: Explicitly trained with fairness constraints across all languages
- **Easy to Use**: Simple interface for single text or batch processing
## 🎯 How to Use
1. **Single Text**: Paste your text in the input box and click "Classify Text"
2. **Batch Processing**: Upload a `.txt` file with one text per line for batch classification
3. **Examples**: Try the pre-loaded examples in different languages
## 🔬 Model Details
- **Base Model**: AfroXLMR-base
- **Parameters**: ~270M
- **Training**: Fine-tuned on PhD HATA African Dataset
- **Fairness**: EOD = 0.0, AAOD = 0.0 (perfect fairness)
## 📊 Performance
| Metric | Score |
|--------|-------|
| Accuracy | 100% |
| F1 Score | 100% |
| Precision | 100% |
| Recall | 100% |
## ⚠️ Limitations
- Optimized for African languages in training set
- Performance may vary on newer AI generation systems
- Should be used as part of a broader content verification system
## 🔗 Links
- [Model Repository](https://huggingface.co/msmaje/phdhatamodel)
- [Training Visualizations](https://huggingface.co/msmaje/phdhatamodel/tree/main/visualizations)
- [Dataset](https://huggingface.co/datasets/msmaje/phd-hata-african-dataset)
## 📚 Citation
```bibtex
@misc{msmaje2025hata,
author = {Maje, M.S.},
title = {AfroXLMR for Human-AI Text Attribution},
year = {2025},
publisher = {HuggingFace},
url = {https://huggingface.co/msmaje/phdhatamodel}
}
```
## 📧 Contact
For questions or feedback, please open an issue in the model repository. |