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docs/composition.md

@@ -1,59 +1,59 @@
-# Composition, measured
-
-Numbers below are **text bytes after cleaning**, not file sizes (JSONL adds
-~10 % of metadata). The final mix is produced by `build`, which applies
-deduplication and the quotas; the "fetched" column is the pool it draws from.
-
-## Fetched pool
-
-| | EN | RU | total |
-|---|---:|---:|---:|
-| text | 4.9 GB | 2.4 GB | **7.3 GB** |
-| documents | 690k | 105k | **795k** |
-
-At the measured 3.34 bytes/token on the 8K bilingual BPE this is ≈ 2.15B
-tokens before dedup and quota selection.
-
-## By domain
-
-| domain | fetched share | notes |
-|---|---:|---|
-| mathematics | 26 % | OpenWebMath is the bulk; Wikipedia math adds definitions |
-| science | 22 % | arXiv full text dominates; cosmology curated separately |
-| encyclopedic | 16 % | Wikipedia EN/RU |
-| code | 14 % | CodeParrot Clean + CPython stdlib |
-| literature | 11 % | Gutenberg novels, Russian classics from Wikisource |
-| ML | 5 % | arXiv abstracts, textbooks, framework docs |
-| logic | 2 % | Lean mathlib proofs + Wikipedia logic |
-| CS / engineering | 3 % | curated Wikipedia categories (chips, VLSI, XP, agile) |
-| humour | <1 % | Gutenberg satire, transcripts |
-| identity | <0.1 % | synthetic |
-
-## Language ratio
-
-60/40 **by bytes** is the contract. Because Cyrillic costs ~1.8× more bytes per
-character, the same split is ≈ 55/45 by bytes but only ≈ 33 % Russian by
-tokens with the 8K BPE. Both numbers are reported in `tokenizer_meta.json`;
-the byte contract is the stable one, the token share depends on the tokenizer.
-
-## Shortfalls (kept visible)
-
-* **logic** — 10 % quota, ~2 % realised. Formal proofs exist (mathlib), but
-  general logic prose is thin.
-* **ML** — 12 % quota, ~5 % realised. arXiv abstracts are high quality but
-  short; textbooks are paywalled.
-* **humour** — 1 % quota, ~1.5 MB realised. Public-domain satire and dialogue
-  are small by nature.
-* **Russian code** — the code quota is filled with English source; Russian
-  code comments are not a coherent corpus of their own.
-
-The mixer emits these as `shortfalls` in `corpus_report.json` rather than
-padding the quota with mislabelled text.
-
-## Dedup and held-out
-
-* MinHash, 64 permutations, 5-shingles, Jaccard threshold 0.8.
-* Held-out: 2 % of each (language, domain) bucket, at most one document
-  boundary of overshoot.
-* Buckets under 5 MB (identity, rare slices) are **not** split, so they stay in
-  training in full.
+# Composition, measured
+
+Numbers below are **text bytes after cleaning**, not file sizes (JSONL adds
+~10 % of metadata). The final mix is produced by `build`, which applies
+deduplication and the quotas; the "fetched" column is the pool it draws from.
+
+## Fetched pool
+
+| | EN | RU | total |
+|---|---:|---:|---:|
+| text | 4.9 GB | 2.4 GB | **7.3 GB** |
+| documents | 690k | 105k | **795k** |
+
+At the measured 3.34 bytes/token on the 8K bilingual BPE this is ≈ 2.15B
+tokens before dedup and quota selection.
+
+## By domain
+
+| domain | fetched share | notes |
+|---|---:|---|
+| mathematics | 26 % | OpenWebMath is the bulk; Wikipedia math adds definitions |
+| science | 22 % | arXiv full text dominates; cosmology curated separately |
+| encyclopedic | 16 % | Wikipedia EN/RU |
+| code | 14 % | CodeParrot Clean + CPython stdlib |
+| literature | 11 % | Gutenberg novels, Russian classics from Wikisource |
+| ML | 5 % | arXiv abstracts, textbooks, framework docs |
+| logic | 2 % | Lean mathlib proofs + Wikipedia logic |
+| CS / engineering | 3 % | curated Wikipedia categories (chips, VLSI, XP, agile) |
+| humour | <1 % | Gutenberg satire, transcripts |
+| identity | <0.1 % | synthetic, 800 plain + 850 pastiche per language, no numeric facts |
+
+## Language ratio
+
+60/40 **by bytes** is the contract. Because Cyrillic costs ~1.8× more bytes per
+character, the same split is ≈ 55/45 by bytes but only ≈ 33 % Russian by
+tokens with the 8K BPE. Both numbers are reported in `tokenizer_meta.json`;
+the byte contract is the stable one, the token share depends on the tokenizer.
+
+## Shortfalls (kept visible)
+
+* **logic** — 10 % quota, ~2 % realised. Formal proofs exist (mathlib), but
+  general logic prose is thin.
+* **ML** — 12 % quota, ~5 % realised. arXiv abstracts are high quality but
+  short; textbooks are paywalled.
+* **humour** — 1 % quota, ~1.5 MB realised. Public-domain satire and dialogue
+  are small by nature.
+* **Russian code** — the code quota is filled with English source; Russian
+  code comments are not a coherent corpus of their own.
+
+The mixer emits these as `shortfalls` in `corpus_report.json` rather than
+padding the quota with mislabelled text.
+
+## Dedup and held-out
+
+* MinHash, 64 permutations, 5-shingles, Jaccard threshold 0.8.
+* Held-out: 2 % of each (language, domain) bucket, at most one document
+  boundary of overshoot.
+* Buckets under 5 MB (identity, rare slices) are **not** split, so they stay in
+  training in full.