DatarrX/Myanmar-Style-Classification-Corpus

Myanmar Style Classification Corpus (MSCC)

Dataset Description

The Myanmar Style Classification Corpus (MSCC) is a specialized dataset for binary text classification, designed to help AI models distinguish between Written Style (Formal) and Spoken Style (Informal) Burmese text.

This dataset is a reconstructed version of the Myanmar Written-Spoken Parallel Corpus (MWSPC), reformatted to support supervised learning tasks such as style detection and linguistic analysis.

• Curated by: Khant Sint Heinn (Kalix Louis)
• Organization: DatarrX | ဒေတာ-အက်စ်
• Language: Burmese (Myanmar)
• License: Creative Commons Attribution 4.0 International (CC-BY-4.0)

Understanding the Styles

In Myanmar linguistics, the distinction between written and spoken forms is vital:

• Label 0 (Written Style): Formal, literary language used in official documents, news, and academic papers. (e.g., "...သည်", "...၏")
• Label 1 (Spoken Style): Informal, colloquial language used in daily conversations and social media. (e.g., "...တယ်", "...ရဲ့")

Dataset Structure

The dataset contains 11,110 rows of unique Burmese sentences.

Field	Type	Description
text	string	The Burmese sentence to be classified
label	int	0 for Written Style, 1 for Spoken Style

Data Quality & Uniqueness

Every entry in this corpus has been strictly filtered to ensure 100% uniqueness. The dataset contains 5,555 unique written sentences and 5,555 unique spoken sentences, totaling 11,110 distinct data points.

Uses

Direct Use

• Style Detection: Building classifiers to detect if a text is formal or informal.
• Grammar & Style Checking: Assisting in tools that highlight inconsistent style usage in Burmese writing.
• Data Filtering: Automatically categorizing large-scale scraped Burmese text for specialized model training.

Dataset Creation

Curation Rationale

One of the biggest challenges in Myanmar NLP is the "Style Mix" problem. By providing a clean, labeled dataset for style classification, MSCC enables developers to build smarter tools that respect the traditional and modern boundaries of the Myanmar language.

Bias, Risks, and Limitations

This dataset focuses on standard Burmese usage. While it is highly accurate for general formal and informal styles, it may not fully capture extremely localized dialects or very recent internet slang that deviates significantly from standard colloquial Burmese.

Citation

If you use this dataset in your research or project, please cite it as follows:

APA: Khant Sint Heinn, (2026). Myanmar Style Classification Corpus (MSCC). DatarrX Foundation. Retrieved from [Hugging Face Link]

BibTeX:

@dataset{mscc_2026,
  author       = {Khant Sint Heinn (Kalix Louis)},
  title        = {Myanmar Style Classification Corpus (MSCC)},
  year         = {2026},
  publisher    = {Hugging Face},
  organization = {DatarrX},
  url          = {[https://huggingface.co/datasets/DatarrX/MSCC]}
}

Dataset Card Contact

For inquiries, please contact through DatarrX or Kalix Louis.

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