| # 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 |
| ```sql |
| 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 |
| ```bash |
| 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) | |
| |
| ## Known Limitations |
| |
| 1. **YouTube content:** No content filtering; raw comments may contain offensive language |
| 2. **News licensing:** Publisher permissions uncertain; use cautiously in commercial settings |
| 3. **Script detection:** Simple regex-based; mixed-language text occasionally misclassified |
| 4. **Deduplication:** No semantic deduplication; similar paraphrases retained |
| 5. **Temporal bias:** Majority of data from 2020–2026; pre-2020 IRIIS content underrepresented |
| |
| ## Future Improvements |
| |
| - [ ] Semantic deduplication using embeddings |
| - [ ] Fine-grained toxicity filtering for colloquial subset |
| - [ ] Add date ranges per source for temporal filtering |
| - [ ] Multilingual metadata (code-mixed Hindi, English) |
| - [ ] Validation splits for supervised task benchmarking |
| |
| --- |
| |
| **Last Updated:** April 2, 2026 |
| **Pipeline Version:** 1.0 |
| **Maintainer:** Boredoom17 |
| |
