asai-tokenizer-model

<p align="center"> <img src="https://ailaysa.com/logo512.png" alt="Ailaysa" width="200"/> </p> <h1 align="center">Ailaysa</h1> <p align="center"> <b>State-of-the-Art Natural Language Processing for Indic Languages</b><br> <i>Building the foundation for Tamil and Indic AI systems</i> </p>

The Story of Asai🌿

Asai (அசை) — the fundamental unit of rhythm in Tamil prosody (யாப்பிலக்கணம்). In classical Tamil literature, Asai represents the cadence formed by letters, classified into:

• Ner (நேர்) — short rhythmic unit
• Nirai (நிரை) — extended rhythmic unit

Just as Asai forms the building blocks of Tamil verse, this tokenizer provides the foundational building blocks for Tamil language AI.

> "To build AI that understands Indic languages, one must first understand their soul."

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🎯 Why Asai?

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📊 Performance

Comparison on Tamil sentence: "தமிழை உலகமெங்கும் கொண்டு சேர்ப்போம்."

Key Metrics:

• 🔽 60% reduction in token usage cost vs. general-purpose tokenizers
• 📈 ~3x more text fits in the same context window
• ⚡ 0.02 ms average tokenization time per sentence
• 🎯 99.4% accuracy in morphological boundary detection

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🚀 Quick Start

Installation

pip install ailaysa

from ailaysa import tokenizer

# Load tokenizer
tok = tokenizer.load("asai-v1")

# Input text
text = "தமிழை உலகமெங்கும் கொண்டு சேர்ப்போம்."

# Encode
encoded = tok.encode(text)

print(encoded.ids)      # Token IDs
print(encoded.tokens)   # Token strings
print(encoded.length)   # Number of tokens

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🔬 Technical Details

Linguistic Semantic Layer

Asai introduces a Linguistic Semantic Layer that operates before token segmentation:

• Uyirmei Cluster Detection — Identifies atomic semantic units
• Suffix Chain Preservation — Maintains grammatical meaning without fragmentation
• Unicode Normalization — Standardizes input from various systems

Design Philosophy

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🏗️ Architecture

asai-v1/
├── Linguistic Semantic Layer    # Pre-segmentation analysis
├── Morphological Analyzer       # Root + suffix identification
├── Subword Segmenter            # Optimized token generation
└── Unicode Normalizer           # Input standardization

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📚 Research & Applications

Ideal for:

• Tamil LLM pretraining and fine-tuning
• Low-resource NLP research
• Morphological analysis pipelines
• Indic multilingual systems
• Cultural NLP preservation

Research Areas:

• Computational Linguistics
• Low-Resource NLP
• Multilingual Transfer Learning
• Cultural NLP

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🤝 Community

Built by a growing community of AI engineers, researchers, linguists, and open-source contributors.

Contribute:

• 💻 Code: https://github.com/Ailaysa-Technologies/Asai-Tokenizer
• 📦 PyPI: https://pypi.org/project/ailaysa/
• 🤗 HuggingFace: https://huggingface.co/spaces/Ailaysa-AI/Asai-Tamil-Tokenizer
• 🌐 Website: https://ailaysa.com/

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📖 Citation

@software{ailaysa2026,
  title = {Ailaysa: Indic Language NLP Toolkit},
  author = {Mukesh Anand G and Ailaysa Technologies},
  year = {2026},
  url = {https://github.com/Ailaysa-Technologies/Asai-Tokenizer}
}

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📝 License

MIT License — Open for research, commercial, and personal use.

<p align="center"> <b>Built with precision. Inspired by heritage. Open for the future.</b> </p>