Correct the Nepali figures: post-downsample, 22% manual

README.md

@@ -12,18 +12,31 @@ tags:
 - pretraining-corpus
 ---
 
-# LMA Phase 1 — Hindi and Nepali pretraining corpora
+# 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
+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) |
+| 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
 
@@ -34,14 +47,20 @@ 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.
+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. The manual share
-is reported above and is the graded part of the assignment.
+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.
@@ -57,7 +76,7 @@ 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
+two corpora share nothing --- including a character n-gram classifier that
 separates them at 100% accuracy.
 
 ## Splits
@@ -74,24 +93,33 @@ after splitting: **zero** in both languages.
 ## Tokenized files
 
 Produced by the tokenizers at
-[meet5568/lma_models](https://huggingface.co/meet5568/lma_models). Each line
-stores the token **pieces** rather than numeric ids:
+[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 ��ेलः पाकिस्तान ने भारत को हराया …",
- "tokens": ["▁एशियाई", "▁खेल", "ः", "▁पाकिस्तान", "▁ने", …]}
+ "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 —
+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.
+120x, which is why the language-ID stage runs before the cheap filters.