| --- |
| 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](https://huggingface.co/meet5568/lma_models) --- unigram |
| 10,000 for both languages. Each line stores the token **pieces** rather than |
| numeric ids: |
|
|
| ```json |
| {"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. |
|
|
