Datasets:
language:
- hi
- ne
license: cc-by-4.0
task_categories:
- text-generation
tags:
- hindi
- nepali
- devanagari
- pretraining-corpus
LMA Phase 1 --- Hindi and Nepali pretraining corpora
Two monolingual corpora built for a pair of ~25M-parameter decoder-only Transformers. Hindi is the higher-resource language, Nepali the lower-resource one. Both are written in Devanagari (U+0900-U+097F), so script cannot be used to tell them apart --- separating them is the central technical problem this dataset solves rather than assumes.
| language | documents | characters | manual (chars) | tokens | manual (tokens) | train | val | test | unseen domain |
|---|---|---|---|---|---|---|---|---|---|
| Hindi | 5,094,185 | 2.588B | 23.1% | 655.2M | 24.5% | 3,564,832 | 762,609 | 763,607 | 3,137 (bbc_hindi) |
| Nepali | 6,755,888 | 2.513B | 22.0% | 558.9M | 22.0% | 4,726,832 | 1,014,710 | 1,011,740 | 2,606 (nagarik_news) |
Both corpora clear the project's requirement that at least 20% of final tokens come from manually collected text.
Nepali was downsampled, Hindi was not
Nepali's cleaned corpus held 9,031,132 download documents against 207,956 manual ones, a manual share of 17.0% by characters -- below the 20% the project requires. Manual text is the scarce side and cannot be conjured, so the download bucket was thinned instead: 6,547,932 documents kept out of 9,031,132 (72.5%), lifting the manual share to 22.0%.
The selection is a seeded shuffle accumulating to a character budget, so it is uniform over documents and leaves the length distribution unchanged; keeping the longest documents instead would have biased the tokenizer's training data. Only the download bucket was touched, because it is a single source (IndicCorp v2) and thinning it costs no register diversity, unlike the manual bucket which carries all the literature, encyclopedic and long-form text.
Nothing was deleted. stage2.jsonl is intact and the selection lives in a committed list of document ids, so a choice that turns out wrong is fixed by regenerating one text file rather than by re-downloading and re-cleaning. The files published here are the downsampled corpus.
Hindi needed none of this: at 23.1% manual it already cleared the floor, and trimming it would have shrunk the corpus for no benefit.
Layout
hindi/raw/ what the collectors fetched, unmodified
hindi/clean/ after the nine cleaning stages
hindi/tokenized/ one line per document, with the actual token pieces
nepali/... the same three
All files are gzipped JSONL, one JSON object per line. For a browsable copy of the tokenized data, see the Kaggle mirrors linked at the end.
A note on the clean/ files for Nepali. stage2.jsonl there holds all
9,239,088 cleaned documents, which is the corpus before downsampling. The
6,755,888-document selection published as the Nepali corpus is defined by the
committed id list, not by a separate file. Use the split id lists if you want
exactly the corpus described in the table above.
Provenance
Two buckets. download is IndicCorp v2, a public corpus. manual is text whose fetching and cleaning code was written for this project --- news sitemaps and link crawls, MediaWiki API harvests, and literature sites.
Every document carries a doc_id of the form <lang>:<bucket>:<source>:<hash>,
so any line can be traced to the source and the collection run that produced it.
Cleaning
Nine stages in two passes. Pass 1 streams: Unicode NFC and Indic normalization; a Hindi/Nepali language-ID ensemble; foreign-script repair and removal; quality filters (length, script ratio, symbols, digits, repetition, PII anonymisation); exact deduplication by content hash; and CCNet-style learned filters. Pass 2 needs the whole corpus at once: paragraph deduplication, then MinHash+LSH near-duplicate removal at Jaccard 0.8.
Verification is part of the pipeline, not an afterthought: 0 foreign letters across all 14,333,273 documents, and five independent layers checking that the two corpora share nothing --- including a character n-gram classifier that separates them at 100% accuracy.
Splits
Document level, never line level, stratified by source, seed 1337, 70/15/15
by characters. One entire source is additionally held out per language as an
unseen-domain test set (bbc_hindi, nagarik_news), which measures how the
model handles text whose house style it has never seen.
Splits were made before the tokenizer was trained, so the tokenizer never saw validation or test text. Cross-split near-duplicate leakage was measured after splitting: zero in both languages.
Tokenized files
Produced by the tokenizers at meet5568/lma_models --- unigram 10,000 for both languages. Each line stores the token pieces rather than numeric ids:
{"doc_id": "hi:manual:bbc_hindi:f79a...", "bucket": "manual",
"source": "bbc_hindi", "n_tokens": 256,
"text": "एशियाई खेलः पाकिस्तान ने भारत को हराया ...",
"tokens": ["▁एशियाई", "▁खेल", "ः", "▁पाकिस्तान", "▁ने"]}
Ids are one piece_to_id lookup away and storing both would add ~2 GB per
language for no new information.
Browsable mirrors
The gzipped files here cannot be previewed in a browser. The tokenized data is also on Kaggle, where it can be read directly:
- https://kaggle.com/datasets/meet6868/lma-hindi-corpus
- https://kaggle.com/datasets/meet6868/lma-nepali-corpus
Limitations
Web-scraped text carries the biases of what is published online: news dominates, and formal registers are over-represented relative to conversational Nepali and Hindi. PII is anonymised with placeholders rather than removed. Contamination between the two languages was one-directional before cleaning --- Nepali documents misidentified as Hindi outnumbered the reverse by roughly 120x, which is why the language-ID stage runs before the cheap filters.