langquality 1.0.1

Language Quality Toolkit for Low-Resource Languages

LangQuality - Language Quality Toolkit for Low-Resource Languages

A modular, extensible Python toolkit for analyzing the quality of text and audio datasets for low-resource languages. LangQuality helps researchers and developers ensure high-quality datasets for training NLP models (ASR, machine translation, language models) across diverse languages.

✨ Key Features

🌍 Multi-Language Support via Language Packs

• Language-agnostic architecture: Works with any language through configurable Language Packs
• Pre-built packs: Fongbe, French, English, and more
• Easy customization: Create your own Language Pack in minutes
• Community-driven: Share and discover Language Packs from the community

🔍 Comprehensive Quality Analysis

• Structural Analysis: Sentence length distribution, outlier detection, statistical metrics
• Linguistic Analysis: Readability scores, lexical complexity, morphological features
• Diversity Analysis: Vocabulary richness (TTR), n-gram distributions, duplicate detection
• Domain Analysis: Thematic balance, under/over-represented categories
• Gender Bias Detection: Gender representation, stereotype detection, balance metrics

🔌 Extensible Plugin System

• Custom analyzers: Add your own analysis modules without modifying core code
• Automatic discovery: Drop plugins into a directory and they're automatically loaded
• Language-specific analyzers: Create analyzers tailored to specific languages

📊 Rich Output Formats

• Interactive Dashboard: Beautiful HTML visualizations with Plotly
• Actionable Recommendations: Prioritized suggestions based on best practices
• Multiple Exports: JSON, CSV, PDF reports, execution logs
• Per-sentence annotations: Quality scores and flags for each sentence

🚀 Quick Start

Installation

# Install from PyPI
pip install langquality

# Install with all optional dependencies
pip install langquality[all]

# Download language models (if using spaCy-based packs)
python -m spacy download fr_core_news_md  # For French
python -m spacy download en_core_web_md   # For English

Basic Usage

Analyze a dataset with a specific language:

# Analyze Fongbe data
langquality analyze --input data/fongbe_sentences --output results --language fon

# Analyze French data
langquality analyze --input data/french_sentences --output results --language fra

# Analyze English data
langquality analyze --input data/english_sentences --output results --language eng

View Results

# Open the interactive dashboard
open results/dashboard.html

Python API

from langquality.pipeline import PipelineController
from langquality.language_packs import LanguagePackManager
from langquality.data import GenericDataLoader

# Load a language pack
pack_manager = LanguagePackManager()
language_pack = pack_manager.load_language_pack("fon")

# Load your data
loader = GenericDataLoader(language_pack)
sentences = loader.load_from_csv("data/sentences.csv")

# Run analysis
controller = PipelineController(language_pack)
results = controller.run(sentences)

# Access results
print(f"Total sentences: {results.structural.total_sentences}")
print(f"Average readability: {results.linguistic.avg_readability_score}")

📦 Language Packs

Language Packs are self-contained configurations that adapt LangQuality to specific languages. Each pack includes:

• Language-specific configuration (tokenization, thresholds, etc.)
• Linguistic resources (lexicons, stopwords, gender terms, etc.)
• Optional custom analyzers

Available Language Packs

Language	Code	Status	Resources
Fongbe	fon	✅ Stable	Full (lexicon, gender terms, ASR vocabulary)
French	fra	✅ Stable	Full (lexicon, stopwords, gender terms, professions)
English	eng	✅ Stable	Full (lexicon, stopwords, gender terms, professions)
Minangkabau	min	🚧 Minimal	Basic configuration only
Your Language	xxx	💡 Create one!	See Language Pack Guide

Managing Language Packs

# List installed packs
langquality pack list

# Show pack details
langquality pack info fon

# Create a new pack template
langquality pack create <language_code>

# Validate a pack
langquality pack validate path/to/pack

Creating Your Own Language Pack

Creating a Language Pack for your language is straightforward:

1. Generate a template:

   langquality pack create <your_language_code>
   

2. Configure the pack: Edit config.yaml with language-specific settings

3. Add resources (optional): Add lexicons, stopwords, or other linguistic resources

4. Test it:

   langquality pack validate path/to/your_pack
   langquality analyze --input test_data --output results --language <your_language_code>
   

See the Language Pack Guide for detailed instructions.

📖 Documentation

• Quickstart Guide: Get up and running in 5 minutes
• User Guide: Comprehensive usage documentation
  • Installation
  • Analyzing Data
• Language Pack Guide: Create and customize Language Packs
• Developer Guide: Extend LangQuality
  • Architecture
  • Creating Analyzers
  • Plugin System
• API Reference: Complete API documentation
• FAQ: Frequently asked questions
• Migration Guide: Migrating from fongbe-data-quality

🎯 Use Cases

LangQuality is designed for researchers and developers working with low-resource languages:

• ASR Dataset Preparation: Ensure text quality before audio recording
• Machine Translation: Validate parallel corpora quality
• Language Model Training: Assess dataset diversity and balance
• Corpus Linguistics: Analyze linguistic properties of text collections
• Data Curation: Filter and improve existing datasets

🔧 Advanced Features

Custom Configuration

Override default thresholds and settings:

langquality analyze --input data --output results --language fon --config my_config.yaml

Example configuration:

thresholds:
  structural:
    min_words: 5
    max_words: 15
  diversity:
    target_ttr: 0.65
  gender:
    target_ratio: [0.45, 0.55]

Custom Analyzers

Create custom analyzers for specialized analysis:

from langquality.analyzers import Analyzer

class ToneAnalyzer(Analyzer):
    """Analyze tone and sentiment of sentences."""
    
    def analyze(self, sentences):
        # Your analysis logic
        return metrics
    
    def get_requirements(self):
        return ["tone_lexicon"]  # Required resources

Place your analyzer in the plugins directory and it will be automatically discovered.

See Creating Analyzers for details.

🤝 Contributing

We welcome contributions from the community! Whether you're:

• 🌍 Creating a Language Pack for your language
• 🔧 Adding new analyzers or features
• 📝 Improving documentation
• 🐛 Reporting bugs or issues
• 💡 Suggesting enhancements

Please see our Contributing Guide for:

• Code of Conduct
• Development setup
• Contribution workflow
• Coding standards
• Testing requirements

Quick Contribution Steps

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes and add tests
4. Ensure tests pass: pytest
5. Commit: git commit -m 'Add amazing feature'
6. Push: git push origin feature/amazing-feature
7. Open a Pull Request

👥 Community

Join our community to get help, share ideas, and collaborate:

• GitHub Discussions: Ask questions, share ideas, showcase your Language Packs
• Issue Tracker: Report bugs, request features
• Documentation: Comprehensive guides and API reference
• Contributing Guide: Learn how to contribute
• Code of Conduct: Our community standards

Support Channels

• 💬 Questions: Use GitHub Discussions Q&A
• 🐛 Bug Reports: Open an issue
• 💡 Feature Requests: Open an issue
• 🌍 Language Pack Submissions: Use our Language Pack template

📊 Project Status

LangQuality is actively maintained and under continuous development. See our CHANGELOG for recent updates and our Roadmap for planned features.

Current version: 1.0.0 (Stable)

📜 License

LangQuality is released under the MIT License. You are free to use, modify, and distribute this software for any purpose, including commercial applications.

🙏 Acknowledgments

LangQuality evolved from the Fongbe Data Quality Pipeline, originally developed to support dataset creation for Fongbe, a low-resource language from Benin. We're grateful to:

• The linguistic community working on African language preservation and NLP development
• Contributors who have created Language Packs and shared their expertise
• The open-source NLP community for tools and libraries that make this work possible

📚 Citation

If you use LangQuality in your research, please cite:

@software{langquality_toolkit,
  title={LangQuality: Language Quality Toolkit for Low-Resource Languages},
  author={LangQuality Community},
  year={2024},
  url={https://github.com/langquality/langquality},
  version={1.0.0}
}

🔗 Related Projects

• Common Voice: Crowdsourced voice dataset
• FLORES: Multilingual translation benchmark
• Masakhane: African NLP community

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Made with ❤️ for low-resource language communities worldwide

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