padie-extended 0.0.2

An AI powered Nigerian Language Text Classification Model

👩🏿 padie-extended 👩🏿

padie-extended is an open-source Python package designed to predict Nigerian languages, including Pidgin, Yoruba, Hausa, and Igbo. It provides AI-powered tools for language detection and fosters community collaboration to enhance its capabilities.

🔧 Note:

Padie-extended is a work in progress. It is an extension developed by Ayooluwaposi Olomo, building upon the original Padie repository by @sir-temi and @pythonisoft. Their open-source work laid the foundation for this project. Contributions are welcome. Be sure to check out their repository!

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Features

• 🚀 Fast and accurate language detection for Nigerian languages
• 🤖 Pre-trained transformer model for high-quality predictions
• 🌍 Supports 5 languages: English, Nigerian Pidgin, Yoruba, Hausa, and Igbo
• 📦 Simple API - just a few lines of code
• 🔧 Easy integration into existing Python projects
• 💻 Lightweight and efficient for production use

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🚫 Dataset Contributions

Please do NOT submit datasets to this repository. All dataset contributions should be made to the original Padie repository. This ensures all Padie-based projects benefit from your contributions.

🤝 How You Can Contribute:

We welcome contributions from developers, linguists, and data scientists interested in improving Nigerian language technology.

Here are some impactful ways you can help:

• Expand Language Coverage:
  Add support for more Nigerian and African languages beyond those currently included.

• Improve Short-Form Text Handling:
  The model performs better on long-form text. Training and fine-tuning it on short-form (social media, chat, etc.) data can boost performance.

• Optimize Inference Efficiency:
  Reduce model size or latency for deployment on resource-limited environments (mobile, low-bandwidth servers).

• Enhance Evaluation Metrics:
  Add multilingual or domain-specific benchmarks (e.g., dialectal variations, code-switching).

• Augment the Dataset:
  Contribute curated, diverse, and balanced text data to the main Padie repository, not this one.

• Improve Documentation & Examples:
  Add usage examples, Jupyter notebooks, or tutorials showing real-world use cases.

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🧠 Quick Contribution Steps

1. Fork the Repository:
   Click the "Fork" button at the top of the repository page to create your copy.

2. Clone Your Fork:

   git clone https://github.com/sir-temi/Padie.git
   

3. Create a Branch:

   git checkout -b feature-name
   

4. Make Your Changes:

   • Model improvements and training techniques
   • Bug fixes and code optimizations
   • Documentation and examples
   • Evaluation tools and metrics

5. Commit and Push:

   git commit -m "Describe your changes"
   git push origin feature-name
   

6. Submit a Pull Request:
   Open a pull request against the dev branch with a clear description of your changes.

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📦 Installation

pip install padie-extended

⌛ Quick Start

from padie_extended import LanguageDetector

# Initialize the detector
detector = LanguageDetector()

# Detect language from text
text = "Bawo ni, se daadaa ni?"
result = detector.predict(text)

print(f"Language: {result['language']}")
print(f"Confidence: {result['confidence']:.2%}")

Output:

Language: Yoruba
Confidence: 98.50%

🌍 Supported Languages

Language	Code	Example
English	en	"Hello, how are you?"
Nigerian Pidgin	pidgin	"How you dey?"
Yoruba	yo	"Bawo ni?"
Hausa	ha	"Sannu"
Igbo	ig	"Kedu?"

💡Usage Examples

Basic Detection

from padie_extended import LanguageDetector

detector = LanguageDetector()

# Single text
text = "I dey kampe, na God"
result = detector.predict(text)
print(result)
# {'language': 'pidgin', 'all_scores': {...}, 'confidence': 0.96}

Batch Processing

texts = [
    "Good morning everyone",
    "Ẹ káàárọ̀",
    "Sannu da safe",
    "Wetin dey happen?"
]

results = detector.predict_batch(texts)
for text, result in zip(texts, results):
    print(f"{text} -> {result['language']}")

Get All Confidence Scores

result = detector.predict("This is a mixed text")
print(result['all_scores'])
# {
#     'english': 0.85,
#     'pidgin': 0.10,
#     'yoruba': 0.03,
#     'hausa': 0.01,
#     'igbo': 0.01
# }

🧠Advanced Usage

Custom Model Path

detector = LanguageDetector(model_path="path/to/your/model")

Custom Confidence Threshold

# Set threshold at initialization (default is 0.5)
detector = LanguageDetector(confidence_threshold=0.7)

# Or override for a specific prediction
result = detector.predict("Maybe pidgin", threshold=0.8)

# Change threshold after initialization
detector.set_threshold(0.6)

Model Information

• Base Model: afro-xlmr-base Transformer-based model
• Training Data: Diverse corpus of Nigerian language texts
• Model Size: 1GB

Performance

Tested on a diverse dataset of Nigerian texts:

Metric	Score
Overall Accuracy	95.3%
F1 Score (weighted)	95.3%
Inference Speed	~4.5 ms per text (measured on GPU)

Requirements [End User]

If you’re using this package to detect languages in your own projects (not for model training or development), you only need the following dependencies:

• Python 3.8+
• PyTorch 2.0+
• Transformers 4.30+
• SentencePiece 0.1.99+

Use Cases

• 🌐 Content moderation - Detect language in user-generated content
• 📱 Social media analysis - Analyze multilingual Nigerian social media posts
• 🤖 Chatbots - Route conversations based on detected language
• 📊 Research - Analyze language distribution in datasets
• 🎯 Language-specific processing - Trigger different pipelines per language

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Citation

If you use this package in your research, please cite:

@software{padie_extended,
  author = {Olomo, Ayooluwaposi},
  title = {padie-extended: AI-powered Nigerian Language Detection},
  year = {2025},
  url = {https://github.com/posi-olomo/padie-extended}
}

Acknowledgments

• Built upon the Padie project
• Built with AWS cloud credits generously provided by Dr. Wálé Akínfadérìn
• Built with Hugging Face Transformers
• Inspired by the need for better Nigerian language NLP tools
• Thanks to all future contributors and the Nigerian NLP community

Links

• GitHub: posi-olomo/padie-extended
• PyPI: padie-extended
• Issues: Report a bug
• Documentation: Full Documentation

Support

If you encounter any issues or have questions:

1. Check the documentation
2. Search existing issues
3. Create a new issue

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🌍 Open Source Contribution

padie-extended is licensed under the MIT License, ensuring it remains free and open for everyone to use, contribute to, and enhance.

Made with ❤️ for the Nigerian tech community