Datasets:

precisit
/

one-pass-sv-forms-synthetic

license: mit
language:
  - sv
task_categories:
  - text-classification
tags:
  - synthetic
  - swedish
  - one-pass-scorer
  - jev
  - system-one
  - form-filling
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.jsonl.gz
      - split: validation
        path: data/validation.jsonl.gz
      - split: test
        path: data/test.jsonl.gz

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 precisit/one-pass-sv-forms can be reproduced and refuted, not because the data has value as data.

What it is

Training data for a one-pass option scorer: a model that receives a context and a supplied list of candidate decisions and returns one score per option, generating no text. Each line is one decision:

{"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"],
 "label": 0,
 "meta": {"action": "fill", "form_signature": "adress|efternamn|fornamn|...", "role": "Edit", "seed": 2026}}

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 on this value, so a form's rows never straddle train/validation/test.

How it was generated

git clone https://github.com/precisit/one-pass-specialists
cd one-pass-specialists && python -m onepass.synth --output data/sv --episodes 10000 --seed 2026

The generator 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).

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

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).

Intended use and limits

Use it for: reproducing the pipeline, learning the input contract, and testing whether a small Swedish scorer is the right component for a bounded interface task. It is the "data leg" of the recipe in precisit/one-pass-specialists.
Do not use it as a benchmark of production behaviour. It is generated from a 50-concept 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 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.

Licence

MIT, like the generator and the model. Please cite the toolkit repository rather than this dataset alone if you build on it.