---
license: apache-2.0
language:
- my
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- myanmar
- burmese
- nlp
library_name: sentence-transformers
dataset_size: 500000
loss: MSELoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
- source_sentence: ▁ထို့ကြောင့် ကြော်ငြာ ရှင် သည် နှိပ် လိုက်ပါ ကသာ ပေးချေ လိမ့်မည်။
  sentences:
  - ▁ကိုယ်ပိုင် စိတ်ကူး ဉာဏ် ဖြင့် ▁တီထွင် ရေးသား နိုင်သည်။
  - ▁ထိုအရာ အားလုံးက ▁အလွန် စိတ်လေး စရာ၊ ▁ကြောက်စရာကောင်း လှ သည်ဟု ▁ခံစား မိသည်။
datasets:
- DatarrX/myX-Mega-Corpus
---

# 📝 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)](https://huggingface.co/kalixlouiis)
*   **Published by:** [DatarrX (Myanmar Open Source NGO)](https://huggingface.co/DatarrX)
*   **Training Dataset:** [DatarrX/myX-Mega-Corpus](https://huggingface.co/datasets/DatarrX/myX-Mega-Corpus) (500,000 Rows)
*   **Tokenization:** Processed using [DatarrX/myX-Tokenizer](https://huggingface.co/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
```text
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
```bash
pip install -U sentence-transformers
```

### Direct Usage (Inference)
```python
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](https://huggingface.co/DatarrX/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
```bibtex
@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](https://github.com/kalixlouiis) | [Hugging Face](https://huggingface.co/kalixlouiis) | [Kaggle](https://www.kaggle.com/organizations/kalixlouiis)