Align documentation with the verified Swedish-form evidence

Correct measurement scope and provenance before the accompanying article is published. README only; model weights, Core ML packages, evaluation files and dataset splits are unchanged. The model-card changes link to the merged public CPU/Neural Engine investigation.

README.md

@@ -25,10 +25,10 @@ configs:
 # One-Pass SV-Forms synthetic corpus (Swedish)
 
 **This dataset is entirely synthetic.** It was generated by a script from a concept catalogue, not
-collected from anyone. There is no real person in it: names are fictional, e-mail addresses use
-`.invalid`, organisations are invented, and the Swedish identifier-shaped values
-(`personnummer`, `organisationsnummer`, `bankgiro`, `plusgiro`, `IBAN`) are generated locally with
-valid checksums and belong to nobody. It is published so the recipe behind
+collected from people or customer submissions. Names and organisations are constructed,
+email addresses use `.invalid`, and identifier-shaped values are generated locally.
+They are not checked against registries; coincidental matches with real names or
+identifiers cannot be ruled out. The dataset is published so the recipe behind
 [`precisit/one-pass-sv-forms`](https://huggingface.co/precisit/one-pass-sv-forms) can be
 reproduced and refuted, not because the data has value as data.
 
@@ -39,40 +39,43 @@ of candidate decisions and returns one score per option, generating no text. Eac
 decision:
 
 ```json
-{"context": "UPPGIFT fyll i formuläret från dokumentet och skicka sedan in\nFORM ...\nELEMENT Edit \"Personnummer\" value=\"\"",
- "options": ["fyll Personnummer: 740721-3466", "fyll E-post: ...", "kryssa", "klicka", "hoppa över"],
+{"context": "UPPGIFT fyll i formuläret från dokumentet och skicka sedan in\nFORM ...\nELEMENT Edit \"E-postadress\" value=\"\"",
+ "options": ["fyll E-post: anna@example.invalid", "fyll Förnamn: Anna", "kryssa", "klicka", "hoppa över"],
  "label": 0,
- "meta": {"action": "fill", "form_signature": "adress|efternamn|fornamn|...", "role": "Edit", "seed": 2026}}
+ "meta": {"action": "fill", "form_signature": "adress|efternamn|epost|fornamn|...", "role": "Edit", "seed": 2026}}
 ```
 
-- `context` — the task line, the form title and the element in question, plus the entities
+- `context`: the task line, the form title and the element in question, plus the entities
   extracted from an attached document. Bytes are what the model sees; there is no tokenizer.
-- `options` — the label is an index into this list, so the option order *is* part of the sample.
-- `meta.action` — `fill` / `check` / `click` / `skip`, the higher-level decision.
-- `meta.form_signature` — the sorted field set of the form the row came from. **The splits are made
+- `options`: the label is an index into this list, so the option order *is* part of the sample.
+- `meta.action`: `fill` / `check` / `click` / `skip`, the higher-level decision.
+- `meta.form_signature`: the sorted field set of the form the row came from. **The splits are made
   on this value**, so a form's rows never straddle train/validation/test.
 
 ## How it was generated
 
 ```bash
 git clone https://github.com/precisit/one-pass-specialists
-cd one-pass-specialists && python -m onepass.synth --output data/sv --episodes 10000 --seed 2026
+cd one-pass-specialists
+git checkout 6a6def7887ea13a323049a972409b19aed953559
+# After installing the toolkit dependencies described in its README:
+python -m onepass.synth --output data/sv --episodes 10000 --seed 2026
 ```
 
-The generator is deterministic given `seed`; `manifest.json` records the episode count, the
+With the same generator revision and environment, generation is deterministic given `seed`; `manifest.json` records the episode count, the
 ratios, the generator configuration and a SHA-256 per split, so a regeneration can be checked
-rather than trusted. This upload was produced by that exact command, and the files here are
-gzipped copies of its splits (gzip SHA-256 values are listed below and in `manifest.json`).
+rather than trusted. This upload used that recipe, and the files here are gzipped copies
+of its splits. See `manifest.json` for the recorded checksums and split metadata.
 
 | Split | Episodes | Decisions | SHA-256 |
 | --- | ---: | ---: | --- |
 | train | 7 984 | 234 921 | in `manifest.json` |
 | validation | 1 007 | 29 649 | in `manifest.json` |
 | test | 1 009 | 29 839 | in `manifest.json` |
-| `demo-handwritten.jsonl` | — | 50 | hand-written, not generated: three forms with gold decisions, kept outside the splits as an out-of-distribution check |
+| `demo-handwritten.jsonl` | n/a | 50 | hand-written, not generated: three forms with gold decisions, kept outside the splits as an out-of-distribution check |
 
-Action mix in the training split: `skip` 112 006, `fill` 108 147, `click` 7 984, `check` 6 784 (the
-task is deliberately full of "leave this alone" decisions, because that is what a real form is).
+Action mix in the training split: `skip` 112 006, `fill` 108 147, `click` 7 984, `check` 6 784 (this
+mixture is a property of the generator, not a measured distribution of real form tasks).
 
 ## Intended use and limits
 
@@ -83,12 +86,14 @@ task is deliberately full of "leave this alone" decisions, because that is what
   catalogue with a fixed set of form templates; real Swedish forms have vocabulary, layouts and
   edge cases it does not contain. The released model's own card states the same limit, and the
   numbers on synthetic held-out rows are not a promise about any real form.
-- **Size matters more than it looks.** At 900 episodes the same pipeline and the same schedule
+- **Data-size comparison.** At 900 episodes the same pipeline and the same schedule
   score 77 % top-1; at 4 000, 98.94 %; at 10 000, 99.29 %. The corpus is published at 10 000
   episodes because that is what trained the released checkpoint, and the smaller configurations are
-  one command away if you want the curve for yourself.
-- **No personal data, and none may be added.** Keep any real form data out of this repository; the
-  value of it is that it is unambiguously synthetic.
+  one command away if you want the curve for yourself. Each run has its own generated
+  test set, and more episodes at fixed epochs mean more optimization steps. These
+  results do not isolate the effect of data size or establish a learning plateau.
+- **Keep real submissions out of this repository.** The corpus is generated for research.
+  Do not add personal data, customer data or real document contents.
 
 ## Licence