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Datasets:
Boredoom17
/
Nepali-Flow-Roman

Tasks:
Text Classification
Translation
Modalities:
Text
Languages:
Nepali
Size:
100K - 1M
Tags:
nepali
roman-nepali
romanized
transliteration
social-media
low-resource
License:
Dataset card Data Studio Files
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Nepali-Flow-Roman / DATA_PROCESSING.md
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Data Processing Pipeline

This document describes the technical workflow for constructing and maintaining the Nepali Text Corpus.

Overview

The corpus is built using a DuckDB-based pipeline (scripts/merge.py) that ingests raw CSVs, applies filtering and normalization, and produces stratified parquet outputs optimized for different research domains.

Raw Data (CSV) → Validation & Normalization → Domain Stratification → Parquet Output

Input Sources

Source File Format Records Notes
IRIISNEPAL iriisnepal_raw.csv CSV ~6.1M Manually curated formal Nepali
YouTube youtube_comments_clean.csv CSV ~431K Pre-cleaned by clean.py
Wikipedia wikipedia_nepali.csv CSV ~291K Extracted from wiki dump
News nepali_news.csv CSV ~87K Scraped from live news feeds

Preprocessing Steps

1. Text Validation

All records are filtered on:

  • Non-null check: text IS NOT NULL
  • Non-empty check: trim(text) <> ''
  • Minimum length (IRIIS only): length(split(trim(text), ' ')) >= 5 (5+ words)
  • Script validation (IRIIS only): Must contain at least one Devanagari character

2. Script Detection

Automatic classification for news and YouTube sources:

IF text contains [ऀ-ॿ] THEN 'devanagari'
ELSE IF text contains [A-Za-z] THEN 'latin'
ELSE 'other'

3. Metadata Assignment

Each row is enriched with:

  • source: Origin identifier (e.g., iriisnepal, youtube_comments, wikipedia_nepali)
  • domain: Content category (formal, colloquial, encyclopedia, news)
  • script: Writing system detected
  • lang: ISO 639-1 code (ne for Nepali)
  • date_collected: Processing date
  • license: Source-specific license

4. Deduplication

  • No exact-duplicate removal (preserves all unique utterances)
  • Partial duplicates retained (colloquial speech naturally repeats common phrases)

Output Datasets

nepali_corpus_full.parquet

Purpose: Complete merged corpus for general research
Rows: 7,167,456
Ordering:

  1. Formal domain (IRIISNEPAL)
  2. Encyclopedia domain (Wikipedia)
  3. News domain
  4. Colloquial domain (YouTube)

Within each domain, ordered by source, then length DESC for visibility.

nepali_corpus_formal.parquet

Purpose: Formal writing for LM pretraining
Rows: 6,378,206 (formal + encyclopedia + news)
Domains included: formal, encyclopedia, news
Ordering: Domain priority → source → length DESC

Professional dataset preview: Leading rows are formal Wikipedia and news articles (clean, representative examples).

nepali_corpus_colloquial.parquet

Purpose: Conversational Nepali for sociolinguistic analysis
Rows: 431,648 (YouTube comments only)
Script distribution:

  • Devanagari: 123,804 comments
  • Latin (Roman): 307,999 comments
  • Mixed: 19,845 comments

Ordering: Devanagari (longest first) → Latin → Mixed (ensures Hugging Face viewer surfaces Devanagari examples first)

nepali_corpus_roman.parquet

Purpose: Roman-script Nepali subset
Rows: 307,999 (latin script from YouTube)
Derived from: Colloquial corpus with script = 'latin'

nepali_corpus_wikipedia.parquet

Purpose: Encyclopedia-style Nepali (NOT published separately; kept for analysis)
Rows: 291,767
Note: Wikipedia data is merged into nepali_corpus_formal.parquet for public release.

Performance Optimizations

DuckDB Configuration 

PRAGMA threads = 2              -- CPU parallelism
PRAGMA preserve_insertion_order = false
PRAGMA memory_limit = '8GB'     -- RAM cap
PRAGMA temp_directory = '...'   -- Disk spillover

Parquet Compression

  • Codec: Zstandard (ZSTD)
  • Compression Level: Default (best balance)
  • Result: 5.95 GB file for 7.1M rows (≈850 bytes/row average)

Processing Speed

  • Typical merge run: ~2-5 minutes on 8GB RAM
  • DuckDB streaming keeps memory footprint constant

Quality Metrics

Content Representation

  • Formal (88%): Academic, journalistic, encyclopedic content
  • Colloquial (6%): Conversational, social media discourse
  • Roman script (4%): Transliterated Nepali
  • Mixed script (<1%): Code-switching examples

Text Statistics

  • Average length: ~120 UTF-8 characters
  • Median length: ~85 characters
  • Max length: ~50,000 characters (rare outliers)
  • Devanagari percentage: ~87% of rows

Coverage

  • Unique sources: 5 primary (IRIIS, Wikipedia, YouTube, Kantipur, Setopati, +3 others)
  • Time span: Circa 2016–2026 (mixed historical and current)
  • Geographic scope: Nepal-centric; diaspora content included in YouTube

Maintenance & Updates

Incremental Updates

To add new data:

  1. Append new rows to source CSV (e.g., nepali_news.csv)
  2. Re-run scripts/merge.py
  3. Output parquets are regenerated from scratch
  4. Run scripts/publish.py to sync to Hugging Face

Re-running the Pipeline 

cd /Users/ad/research/nepali-text
venv/bin/python scripts/merge.py

Schema Reference

All parquet files use this schema:

Column Type Description
text string UTF-8 Nepali text
source string Data source identifier
domain string Content type (formal, colloquial, encyclopedia, news)
script string Writing system (devanagari, latin, mixed)
lang string ISO 639-1 language code (always 'ne')
date_collected string ISO 8601 processing date
license string Source license (MIT, CC BY 4.0, CC BY-SA 4.0, source-dependent)

Last Updated: April 2, 2026
Pipeline Version: 1.0
Maintainer: Boredoom17