meet5568/lma_datasets

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 share	train	val	test	unseen domain
Hindi	5,094,185	2.588B	23.1%	3,564,832	762,609	763,607	3,137 (bbc_hindi)
Nepali	6,755,888	2.513B	17.0%	4,726,832	1,014,710	1,011,740	2,606 (nagarik_news)

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.

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. The manual share is reported above and is the graded part of the assignment.

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. 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.

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 120×, which is why the language-ID stage runs before the cheap filters.