📝 myX-Semantic-Light: An Efficient Burmese Sentence Embedding Model
Model Description
myX-Semantic-Light is a lightweight sentence-transformer model optimized for the Burmese (Myanmar 🇲🇲) language. It is designed for high-speed inference and low-resource environments while maintaining robust semantic understanding.
This model was trained using Knowledge Distillation from a multilingual teacher model. It maps Burmese sentences into a 384-dimensional dense vector space, making it twice as memory-efficient as the standard 768-dimensional versions.
Key Applications
- Real-time Semantic Search: Ideal for mobile or edge applications requiring fast retrieval.
- Efficient Clustering: Grouping large-scale Burmese datasets with reduced memory overhead.
- Similarity Scoring: Determining the relationship between short phrases and sentences.
Development & Distribution
- Developed by: Khant Sint Heinn (Kalix Louis)
- Published by: DatarrX (Myanmar Open Source NGO)
- Training Dataset: DatarrX/myX-Mega-Corpus (500,000 Rows)
- Tokenization: Processed using DatarrX/myX-Tokenizer.
Technical Specifications
- Base Model:
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 - Max Sequence Length: 128 tokens (Optimized for short-to-medium text)
- Output Dimension: 384 dimensions
- Similarity Function: Cosine Similarity
- Loss Function: MSELoss
Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False, 'architecture': 'BertModel'})
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_mean_tokens': True})
)
Usage
Installation
pip install -U sentence-transformers
Direct Usage (Inference)
from sentence_transformers import SentenceTransformer, util
# Load the lightweight model
model = SentenceTransformer("DatarrX/myX-Semantic-Light")
sentences = [
"ဝက်ခြံ ပျောက်ကင်းအောင် ဘယ်လိုလုပ်ရမလဲ။",
"မျက်နှာ အသားအရေ ထိန်းသိမ်းနည်းများ",
"နည်းပညာ သတင်းများ ဖတ်ရှုရန်"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities)
Implementation Guidelines (Thresholds)
Because this model is a lightweight variant trained on a smaller subset (500K rows), its score distribution differs slightly from the 1M SOTA version.
- Recommended Threshold: A Cosine Similarity score of 0.40 or higher is generally sufficient to indicate a semantic relationship.
- Note: For tasks requiring higher precision and deeper contextual reasoning, we recommend using the larger myX-Semantic (1M) version with a threshold of 0.60.
Training Details
- Samples: 500,000 training pairs.
- Batch Size: 64
- Epochs: 1
- Optimizer: AdamW (
adamw_torch_fused) - Training Time: ~37 minutes on multi-GPU setup.
Training Logs
| Epoch | Step | Training Loss |
|---|---|---|
| 0.13 | 500 | 0.0035 |
| 0.51 | 2000 | 0.0029 |
| 0.90 | 3500 | 0.0027 |
Limitations & Bias
- Encoding: Optimized for Unicode Burmese. Zawgyi encoding is not supported.
- Sequence Length: Performance may degrade for documents longer than 128 tokens due to the sequence length constraint during training.
License
This model is licensed under the Apache License 2.0.
Citation
@software{khantsintheinn2026myxsemantic_light,
author = {Khant Sint Heinn},
title = {myX-Semantic-Light: An Efficient Burmese Sentence Embedding Model},
year = {2026},
publisher = {DatarrX},
url = {[https://huggingface.co/DatarrX/myX-Semantic-Light}
}
About the Author
Khant Sint Heinn, working under the name Kalix Louis, is a Machine Learning Engineer focused on Natural Language Processing (NLP), data foundations, and open-source AI development. His work is centered on improving support for the Burmese (Myanmar) language in modern AI systems by building high-quality datasets, practical tools, and scalable infrastructure for language technology.
He is currently the Lead Developer at DatarrX, where he develops data pipelines, manages large-scale data collection workflows, and helps create open-source resources for researchers, developers, and organizations. His experience includes data engineering, web scripting, dataset curation, and building systems that support real-world machine learning applications.
Khant Sint Heinn is especially interested in advancing low-resource languages and making AI more accessible to underrepresented communities. Through his open-source contributions, he works to strengthen the Burmese (Myanmar) tech ecosystem and provide reliable building blocks for future language models, search systems, and intelligent applications.
His goal is simple: to turn limited language resources into practical opportunities through clean data, useful tools, and community-driven innovation.
Connect with the Author:
GitHub | Hugging Face | Kaggle