DatarrX/myX-Burmese-Numbers

📝 Burmese Numbers - မြန်မာဂဏန်းများ

An exhaustive, high-fidelity mapping dataset containing 10,000,001 rows that covers every single integer from 0 to 10,000,000 (One Kote / တစ်ကုဋေ).

This dataset provides a structural translation and text-normalization bridge between Western Arabic numerals and the Burmese numeral system, explicitly broken down into individual digit-by-digit readouts and contextual full-text linguistic expansions. It is designed primarily for Machine Learning engineers, Automatic Speech Recognition (ASR) pipelines, Text-to-Speech (TTS) normalization, and linguistic researchers working on the Burmese (Myanmar) language.

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🎯 Project Purpose & Core Intent

In Burmese Natural Language Processing (NLP), handling numbers correctly (Text Normalization) presents unique challenges. This dataset serves as a deterministic reference for how numbers are structured, read digit-by-digit, and spoken fluently up to the traditional linguistic boundary of Kote (ကုဋေ).

🧮 Understanding the Schema & Feature Fields

To make this dataset fully accessible to non-Burmese developers, each row is structured into four distinct, parallel features:

1. en_num (Standard Arabic Numerals): The numerical representation in standard Western formats (e.g., 17, 10,000,000).
2. my_num (Burmese Digits): The corresponding representation using native Myanmar numeral glyphs (၁၇, ၁၀၀၀၀၀၀၀).
3. my_text_short (Literal Digit-by-Digit Spoken Style): Expresses how a number is read out loud one digit at a time. This is culturally analogous to reading "15" as "one five" instead of "fifteen". (e.g., 17 becomes တစ်ခုနစ် → "Ta Khwun-Na").
4. my_text_full (Formal/Full Linguistic Context Style): The full, grammatically complete linguistic phrase for the value. This is culturally analogous to reading "15" as "fifteen". (e.g., 17 becomes တစ်ဆယ့်ခုနစ် → "Ta Sair Khwun-Na").

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⚠️ Important Linguistic Variations & Edge Cases (Read Before Training)

For non-native engineers building AI models with this dataset, it is crucial to understand the distinct counting structure used here compared to casual spoken Burmese.

1. The Classical Scale: Up to "Kote"

Burmese numeric nomenclature scales sequentially through an explicit base-10 positional hierarchy:

• ခု (Khwut) - Ones
• ဆယ် (Sair) - Tens
• ရာ (Yar) - Hundreds
• ထောင် (Htaung) - Thousands
• သောင်း (Thaung) - Ten Thousands
• သိန်း (Thein) - Hundred Thousands
• သန်း (Than) - Millions
• ကုဋေ (Kote) - Ten Millions (10⁷)

This dataset precisely respects this structured chain up to 10,000,000 (တစ်ကုဋေ - Ta Kote).

2. Spoken Reality vs. Dataset Structure (Crucial for Contextual Models)

In daily colloquial Burmese, native speakers rarely use the word "Than" (သန်း / Million). Instead, they naturally shift the base to "Thein" (သိန်း / Hundred Thousands) to count higher amounts.

• Colloquial Example: A Burmese local will say "10 သိန်း" (10 Lakhs / 10 Hundred Thousands) instead of "၁ သန်း" (1 Million).

Dataset Implementation: While the colloquial compound approach is widely spoken, this dataset adheres to the strict, structurally ordered sequence (သောင်း → သိန်း → သန်း → ကုဋေ) to provide an absolute and mathematically linear foundation for machine learning algorithms.

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💡 Practical Applications

• Text-to-Speech (TTS) Engines: Serves as an ideal lookup or training matrix for text normalization graphs, transforming raw digits into predictable phonetic sequences.
• Automatic Speech Recognition (ASR): Aids models in mapping spoken number expressions back to numerical representations (my_text_full → en_num).
• Data Augmentation: Easily injects realistic, structured numeral phrases into existing low-resource Burmese text training sets.

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⚖️ License & Open-Source Terms

This dataset is released under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

You are free to share, copy, adapt, and commercially exploit this data, provided that you give appropriate credit to the original author/publisher and distribute any derivative works under the same license terms.

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📜 Citation & Academic Reference

If you incorporate the myX-Burmese-Numbers dataset into your academic research, speech synthesis pipelines, text normalization engines, or language modeling benchmarks, please acknowledge the author and publisher via the following official BibTeX citation:

@misc{datarrx_burmese_numbers_2026,
  author       = {Khant Sint Heinn},
  title        = {myX-Burmese-Numbers: A Comprehensive Parallel Text-Normalization Dataset Mapping 10 Million Burmese Numeral Progressions},
  year         = {2026},
  publisher    = {Hugging Face},
  organization = {DatarrX},
  howpublished = {https://huggingface.co/datasets/DatarrX/myX-Burmese-Numbers},
  note         = {Published under DatarrX. Open-source community asset released under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)}
}

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🧑‍💻 About the Author & Curator

Khant Sint Heinn (Burmese: ခန့်ဆင့်ဟိဏ်း), working under the professional moniker Kalix Louis, is a Machine Learning Engineer specialized in Natural Language Processing (NLP), data foundations, and open-source AI infrastructure. His primary engineering focus centers on elevating low-resource languages—specifically Burmese (Myanmar)—into data-rich assets capable of powering next-generation language models and scalable linguistic tools.

Currently serving as the Lead Developer at DatarrX, Khant Sint Heinn architects robust data pipelines, manages large-scale dataset curations, and builds the open-source building blocks necessary for accessible machine learning applications.

Connect with the Author:

• GitHub
• Hugging Face
• Kaggle

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🏛 Hosted by DatarrX

This dataset is proud to be maintained and distributed by DatarrX (Burmese: ဒေတာ-အက်စ်), a non-profit open-source foundation dedicated to building a robust digital and data foundation for the Burmese language in the AI era.

Rooting Burmese into AI.