DatarrX/Burmese-English-Code-Mixed-Corpus

🇲🇲 Burmese-English Code-Mixed Corpus ꒰ 1,111 Rows ꒱

A high-quality, human-curated dataset of code-mixed Burmese and English sentences, specifically designed for Natural Language Processing (NLP) and Machine Learning (ML) research.

Dataset Details

• Organization: DatarrX
• Creator: Khant Sint Heinn (Kalix Louis)
• Number of Rows: 1,111
• Language: Burmese (Unicode) & English Mix
• Dataset Format: .txt
• License: Apache 2.0

Description

The Burmese-English Code-Mixed Corpus is a general-purpose dataset containing 1,111 unique sentences reflecting the contemporary linguistic patterns of Myanmar's digital society. Unlike formal datasets, this collection captures the "natural flow" of conversation where English terms are seamlessly integrated into Burmese sentence structures.

Each sentence has been manually curated and verified to ensure:

• 100% Correct Spelling: Strict adherence to Myanmar Unicode standards.
• Grammatical Integrity: While the style is casual/conversational, the underlying Burmese grammar is preserved correctly.
• Authentic Social Context: Reflects real-world usage in social media, tech discussions, and daily life.

Use Cases (Purpose)

This dataset is highly versatile and can be utilized in various AI/NLP domains:

1. Large Language Model (LLM) Fine-tuning: Training models (like Llama, GPT, or Mistral) to understand and generate natural "Burmese-English" slang and professional mix-speech.
2. Sentiment Analysis: Improving the accuracy of sentiment detection in social media posts where users mix languages to express emotions.
3. Machine Translation (MT): Developing systems that can translate code-mixed sentences without losing the original context or "vibe."
4. Speech Recognition (ASR): Training acoustic models to recognize English loanwords used within Burmese speech.
5. Part-of-Speech (POS) Tagging: Researching how English nouns and verbs function within Burmese syntax.

Usage Rights

This dataset is released under the Apache 2.0 License. Users are encouraged to:

• Modify, expand, or clean the data.
• Use it for commercial or academic purposes.
• Redistribute or integrate it into larger datasets.

Citation & Attribution

If you use this dataset in your research or project, please provide credit using the following formats:

BibTeX

@dataset{khant_sint_heinn_2026_Burmese-English-Code-Mixed-Corpus,
  author       = {Khant Sint Heinn (Kalix Louis)},
  title        = {Burmese-English Code-Mixed Corpus (1,111 Rows)},
  month        = feb,
  year         = 2026,
  publisher    = {DatarrX},
  version      = {1.0.0},
  url          = {https://huggingface.co/datasets/DatarrX/Burmese-English-Code-Mixed-Corpus}
}

APA Format

Khant Sint Heinn (Kalix Louis). (2026). Burmese-English Code-Mixed Corpus (1,111 Rows) [Data set]. DatarrX. https://huggingface.co/datasets/DatarrX/Burmese-English-Code-Mixed-Corpus

Contact / Support:

For any inquiries or contributions, please contact through the DatarrX Organization or Kalix Louis's Profile.

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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