Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+language:
+- my
+license: cc-by-4.0
+size_categories:
+- 1K<n<10K
+task_categories:
+- text-generation
+- translation
+- summarization
+tags:
+- myanmar
+- burmese
+- formal-to-informal
+- written-to-spoken
+- nlp
+- mwspc
+dataset_info:
+  features:
+  - name: written_style
+    dtype: string
+  - name: spoken_style
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 5555
+pretty_name: Myanmar Written-Spoken Parallel Corpus (MWSPC)
+---
+
+# Myanmar Written-Spoken Parallel Corpus (MWSPC)
+
+## Dataset Description
+
+Myanmar Written-Spoken Parallel Corpus (MWSPC) is a high-quality open-source dataset designed to bridge the gap between formal written Burmese and daily spoken Burmese. This dataset is crucial for building natural-sounding AI models that understand the linguistic nuances of the Myanmar language.
+
+- **Curated by:** [Khant Sint Heinn (Kalix Louis)](https://huggingface.co/kalixlouiis)
+- **Organization:** [DatarrX | ဒေတာ-အက်စ်](https://huggingface.co/DatarrX)
+- **Language:** Burmese (Myanmar)
+- **License:** Creative Commons Attribution 4.0 International (CC-BY-4.0)
+
+### What is Written vs. Spoken Burmese?
+
+Burmese is a **diglossic** language, meaning there is a significant difference between the formal literary style and the informal colloquial style.
+
+- **Written Style (Literary/Formal):** Used in news, textbooks, legal documents, and official speeches. It uses specific grammatical markers like "သည်" (thi), "၏" (ei), and "၍" (yway).
+- **Spoken Style (Colloquial/Informal):** Used in daily conversations, social media, and fiction dialogue. It uses markers like "တယ်" (tal), "ရဲ့" (yae), and "ပြီး" (pyee).
+
+AI models trained only on formal texts often sound unnatural to native speakers. **MWSPC** provides direct mapping between these two styles to enable "Style Transfer" and "Natural Language Understanding."
+
+## Dataset Structure
+
+The dataset consists of **5,555 rows** of parallel text pairs.
+
+| Field | Description |
+|---|---|
+| `written_style` | Formal/Literary version of the sentence (Formal Burmese) |
+| `spoken_style` | Informal/Colloquial version of the sentence (Spoken Burmese) |
+
+### Development History
+This dataset was built by aggregating and refining:
+1. **[kalixlouiis/myanmar-written-spoken-text-pairs](https://huggingface.co/datasets/kalixlouiis/myanmar-written-spoken-text-pairs)** (1,643 rows).
+2. **Additional Curated Data:** 3,912 new rows added to ensure high diversity and accuracy.
+
+## Uses
+
+### Direct Use
+- **Style Transfer:** Converting formal news or documents into casual spoken language for chatbots.
+- **Machine Translation:** Improving the fluency of translations from English to Burmese.
+- **Preprocessing:** Normalizing social media text into formal Burmese for downstream NLP tasks.
+
+## Dataset Creation
+
+### Curation Rationale
+In the current AI era, most Myanmar datasets are scraped from news sites, which are heavily biased towards the "Written Style." This creates a lack of data for "Spoken Style" applications. MWSPC was created to empower the Burmese AI ecosystem with a data-rich foundation for next-generation innovation.
+
+## Bias, Risks, and Limitations
+
+While the dataset covers various patterns, Burmese is a dialect-rich language. This dataset primarily focuses on the standard dialect (Yangon/Mandalay). Users should be aware that regional slang or extremely casual internet lingo may not be fully represented.
+
+## Citation
+
+If you use this dataset in your research or project, please cite it as follows:
+
+**APA:**
+Khant Sint Heinn, (2026). Myanmar Written-Spoken Parallel Corpus (MWSPC). DatarrX Foundation. Retrieved from [https://huggingface.co/datasets/DatarrX/Myanmar-Written-Spoken-Parallel-Corpus]
+
+**BibTeX:**
+```bibtex
+@dataset{mwspc,
+  author       = {Khant Sint Heinn (Kalix Louis)},
+  title        = {Myanmar Written-Spoken Parallel Corpus (MWSPC)},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  organization = {DatarrX},
+  url          = {https://huggingface.co/datasets/DatarrX/MWSPC}
+}
+```
+
+---
+
+## 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)