Update README.md

README.md

@@ -1,3 +1,135 @@
----
-license: apache-2.0
----
+---
+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)