Blame for validation_report.md

# Validation report — data `v1`

**ALL CHECKS PASS**

- generated by `python -m data.validate --config configs/data/v1.yaml`
- config fingerprints: pile `3f4303d9b926b730`, bios `7a8c48a96792d725`, pack `15643c90477c31a7`
- Pile revision `3be90335b66f24456a5d6659d9c8d208c0357119`
- bundle: 59 files, 1,151.6 MB (753.6 MB without `raw/`)
- git `ff1bc917cb40`

| check | what | result | measured |
|---|---|:---:|---|
| **A1** | Rebuild is byte-identical | **PASS** | 59 files hashed; 0 changed, 0 added, 0 removed |
| **A2** | `data.fetch --verify-only` passes | **PASS** | 59/59 files verified against v1.json (0 mismatched, 0 missing) |
| **A3** | Token counts per subject and half | **PASS** | every Pile subject exactly 27,000,000 in train.bin; every held-out half within 1% of 2,500,000 per subject |
| **A4** | Shares in every 1,000,000-token window | **PASS** | worst deviation 0.416 points across 598 overlapping windows (stride 250,000), tolerance 2.0 |
| **A5** | Format: dtype, vocab bound, EOT, index tiling | **PASS** | train.bin: 150,475,264 tokens, 332,886 segments, 333,027 EOT separator/pad tokens, max id 50256 \| label.bin: 15,021,806 tokens, 13,306 segments, 13,305 EOT separator/pad tokens, max id 50256 \| test.bin: 15,023,952 tokens, 13,381 segments, 13,380 EOT separator/pad tokens, max id 50256 |
| **A6** | Decoding index segments reproduces the source | **PASS** | 1,000 random segments checked token for token (820 of them whole documents, also compared as text); 0 mismatches |
| **A7** | Leaks: ids, exact hashes, 13-gram overlap | **PASS** | ids disjoint across train/label/test; 0 exact-hash matches; 0 documents over 50%; worst single document 50.0%; aggregates Pile-CC=0.64%, Github=14.35%, PubMed=2.93%, FreeLaw=9.21%, Wikipedia=2.14%, USPTO=0.00% |
| **A8** | Halves: disjoint, >=500 docs, <=4096 tokens | **PASS** | halves share 0 documents; smallest half/subject is 708 documents (minimum 500); longest document 4096 tokens (cap 4096) |
| **A9** | No held-out-only subject in train.bin | **PASS** | ['USPTO Backgrounds'] appear in 0 train.bin segments by document id and 0 by subject label; train.bin carries ['FreeLaw', 'Github', 'Pile-CC', 'PubMed Central', 'Wikipedia (en)', 'bio'] |
| **A10** | Subject labels agree with the source | **PASS** | 142,886 Pile segments: index subject == raw-archive meta.pile_set_name == the document id's slug; 190,000 biography segments all labelled `bio` with a known person id |
| **A11** | Bag-of-words subject classifier (sanity) | **PASS** | accuracy 94.87% on 13,381 test-half documents (threshold 90%); per subject Pile-CC=89.7%, Github=97.8%, PubMed=97.6%, FreeLaw=99.1%, Wikipedia=95.0%, USPTO=96.2% |
| **A12** | Biography facts | **PASS** | 2,500 people, names unique and absent from the Pile, genders {'female': 1250, 'male': 1250}, 5 facts each; first-token uniqueness holds per list (first two for birth_date, which has only 12 distinct first tokens); chi-square p birth_date=0.124, birth_city=0.877, university=0.183, major=0.940, employer=0.347 |
| **A13** | Fact prompts: no answer leaks | **PASS** | 100,000 rows: **0** leak the value verbatim (0), **0** via the person's name (0), **0** via another of the person's values (0); 50,000 training prompts are exact prefixes of their biography; 50,000 paraphrase prompts, 200 of them searched in full against train.bin and absent; held-out markers inside biography text: 'Question:'=0, 'When it comes to'=0, 'is on record as'=0, 'reads as follows'=0 (the same markers occur 571 times in ordinary Pile text, which is not a leak); REPORTED: 48 rows whose ranking key is a TEMPLATE word (birth_city=Thessaloniki, Greece, employer=Heineken, major=Theology, university=Aalborg University) |
| **A14** | Exposure counts and control isolation | **PASS** | 190,000 copies over 2,000 trained people, every count equal to its tier target and spread over the 4 rewordings; 0 cross a 512-token boundary; 0 of 500 control names appear in train text |
| **A15** | Probe floor: random and untrained sit mid-list | **PASS** | random scorer: mean rank fraction 0.502, top-1 0.558% over 36,000 prompts; untrained model: 0.501, top-1 0.525% over 4,000; mean candidate list 210 |
| **A16** | Loader: order, resume, shapes | **PASS** | 293,896 sequences of 512; same seed reproduces the order, a different seed does not, the order is a permutation, and a resume from (seed, position) matches an unbroken run |
| **A17** | Model beats the per-subject unigram baseline | **PASS** | Pile-CC: model 6.7886 vs unigram 7.559; Github: model 5.7371 vs unigram 6.3888; PubMed: model 6.5792 vs unigram 7.4554; FreeLaw: model 5.8735 vs unigram 6.8493; Wikipedia: model 7.0163 vs unigram 7.72; USPTO: model 7.0021 vs unigram 7.9605 |
| **A18** | Biography facts are learnable | **PASS** | 5,830 steps / 96M tokens in 2.03 h of training. test top-1: frequent 40.5%, normal 3.1%, rare 0.8%, control 0.6% (frequent/control = 64.003x, needs 2x; monotone yes). paraphrase top-1: frequent 2.2%, control 0.4% |

## Timings

| check | seconds |
|---|---:|
| A1 | 253.0 |
| A2 | 0.4 |
| A3 | 0.0 |
| A4 | 2.9 |
| A5 | 0.3 |
| A6 | 2.8 |
| A7 | 88.3 |
| A8 | 0.0 |
| A9 | 0.0 |
| A10 | 4.9 |
| A11 | 0.5 |
| A12 | 0.0 |
| A13 | 8.0 |
| A14 | 3.4 |
| A15 | 10.0 |
| A16 | 0.1 |
| A17 | 7.0 |
| A18 | 0.0 |

Total 382 s.