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UgannA Siyabasa V2 — FastText Sinhala Embedding Model 🇱🇰

UgannA_SiyabasaV2 (උගන්නැ සියබස) is the first public FastText embedding model released by Remeinium Corp. The name comes from Kumaratunga Munidasa’s timeless quote: “උගන්නැ සියබස – මත් වන්නැ එහි රසයෙන්” Learn Sinhala – be intoxicated with its beauty.

Just as Munidasa envisioned nurturing the Sinhala language, this model represents teaching it to machines.

📌 Key Features

Type: FastText
Vector size: 300 dimensions
File size: ~3.94GB
Training data: 17GB processed Sinhala text

🔧 Usage

import fasttext
# Load model
model = fasttext.load_model("Remeinium/UgannA_Siyabasa/UgannA_Siyabasa.bin")

# Get vector for a word
vector = model.get_word_vector("අම්මා")

# Get nearest neighbors
neighbors = model.get_nearest_neighbors("අම්මා", k=10)
print(neighbors)

Use API

Test Live: Embedding Playground
API Docs: Go to API Console

📂 Training Data

Processed and cleaned training corpus: ~17GB
Preprocessing: tokenization, normalization, deduplication

🗜️ License

This model is released under the Remeinium Open Model License (ROML).
It permits research and commercial use with attribution.
See the LICENSE file for full terms.

⚠️ Limitations

May reflect cultural/linguistic biases from sources.
Optimized for Sinhala; not multilingual.

🤝 Collaboration

You are welcome to:

Use this model for research & your projects
Share improvements, benchmarks, or downstream applications
Contact : 📧 support@remeinium.com

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