Nepali-Corpus

What Is This?

Everything combined—7.1 million rows of Nepali. News, Wikipedia, YouTube comments, all together. It's meant to be a solid foundation if you want to build NLP tools for Nepali.

Dataset Composition

Total rows: 7,167,456

Subset	Rows	Domain profile	Script profile
Full corpus	7,167,456	Formal + colloquial + encyclopedia + news	Devanagari, Latin, mixed
Formal subset	6,735,808	Formal/news/encyclopedia writing	Mostly Devanagari
Colloquial subset	431,648	Social media comments	Devanagari, Latin, mixed
Roman subset	307,999	Colloquial social text	Latin
Code-mixed subset	19,845	Colloquial mixed-script text	Mixed

Where It Came From

• IRIISNEPAL — a dataset of formal Nepali writing (6M rows)
• YouTube comments — real conversations (431k rows)
• Wikipedia — encyclopedia articles (291k rows)
• News articles — from Nepali news websites (87k rows)

Each row tells you where it came from and what license it's under.

Schema

Each record includes:

• text: textual content
• source: source identifier (for example iriisnepal, youtube_comments, wikipedia_nepali, ratopati)
• domain: formal, colloquial, encyclopedia, or news
• script: devanagari, latin, or mixed
• lang: language tag (for example ne, ne-roman, unknown)
• date_collected: collection or extraction date
• license: row-level license indicator

Construction Notes

• Pipeline-level deduplication is applied.
• Social text undergoes basic noise reduction (for example repetitive symbols, spam-like artifacts, and links).
• Wikipedia data is parsed from dump format and normalized to sentence-like rows.
• Script tags are assigned using character-range heuristics.
• The Hugging Face dataset viewer shows the first rows of the parquet file, so the corpus is ordered to surface more representative formal and encyclopedic examples first in the full preview.

Research Use Cases

• Nepali language model pretraining and domain adaptation
• Formal-vs-colloquial register analysis
• Script and code-mixing identification
• Retrieval and classification in low-resource settings

Limitations

• This is an aggregate corpus with mixed licenses; row-level license filtering is necessary for strict compliance workflows.
• Colloquial text contains non-standard spelling and platform-specific slang.
• Language and script labels are heuristic and may contain limited noise.
• The corpus is not released with fixed train/dev/test benchmarks.

Ethical and Responsible Use

This dataset should not be used to profile individuals or infer sensitive personal attributes. For production systems, users should implement additional filtering, auditing, and task-specific evaluation.

License Statement

The corpus is mixed-license. On Hugging Face, other means the dataset does not fit a single standard built-in license tag. The license column should be treated as the primary indicator for row-level usage conditions.

• IRIISNEPAL rows: MIT
• Wikipedia rows: CC BY-SA 4.0
• YouTube-derived rows: CC BY 4.0 metadata context
• Scraped news rows: source-dependent

Citation

If people ask how to cite this:

Aadarsha Chhetri. (2026). Nepali-Corpus. Hugging Face Datasets.
https://huggingface.co/datasets/Boredoom17/Nepali-Corpus

BibTeX:

@dataset{aadarsha2026nepali_text_corpus,
  author = {Aadarsha Chhetri},
  title = {Nepali-Corpus},
  year = {2026},
  url = {https://huggingface.co/datasets/Boredoom17/Nepali-Corpus}
}

Or just: "We used Aadarsha Chhetri's Nepali-Corpus (2026)."