Release v1.1: state_is_record column + card (source links, train-on-it, tables)
#3
by davanstrien HF Staff - opened
- README.md +56 -47
- data/arxiv/train.parquet +2 -2
- data/base/train.parquet +2 -2
- data/decisionbank/train.parquet +2 -2
- data/harvard/train.parquet +2 -2
- data/nls/train.parquet +2 -2
- data/procpile/train.parquet +2 -2
- data/prompt/train.parquet +2 -2
- data/tool/train.parquet +2 -2
- data/web/train.parquet +2 -2
- scripts/release_build.py +9 -4
README.md
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@@ -31,15 +31,15 @@ This card is also the data recipe: it says which sources were used, why, and whi
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[github.com/davanstrien/curation-decision-recipe](https://github.com/davanstrien/curation-decision-recipe). If you
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train decision models, please share how you made your data too so others can learn from your work and open models can keep improving!
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| Rows
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| Sources
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| Row shapes
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| Labels made by | dataset gold, library cataloguers, programs, and LLMs (named per source and per row)
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| Licence
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| Trains
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| Build
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```python
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import json
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@@ -57,36 +57,37 @@ q = json.loads(decisions[0]["question"]) # n
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One config per source, plus `all`. `web` rows have an empty `state` by design (see Web text). Splitting by source keeps licence terms together, so a user can take only the parts
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whose terms suit them.
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| Config
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|---|---:|---|
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| `base`
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| `decisionbank` |
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| `prompt`
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| `web`
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| `nls`
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| `tool`
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| `harvard`
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| `arxiv`
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| `procpile`
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`manifest/training_mix_manifest.parquet` lists all 535,730 rows of the training mix (including the 18,766 rows not
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released here): row id, source, licence class, labeller and a SHA-256 hash of each row's text.
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Columns (union of both shapes; unused fields are null):
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| Column
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|---|---|
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| `id`
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| `config`, `src`
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| `row_type`
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| `teacher`
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| `licence_class`, `licence`
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| `state`
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| `
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| `
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| `
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| `
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| `
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Example decision row (DecisionBank, shortened):
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@@ -124,21 +125,27 @@ web = load_dataset("davanstrien/curation-decision-mix", "web", split="train").ma
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Row indices assume the source datasets are unchanged since October 2026. The snippet loads each source dataset in full; it has not been tested end to end.
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---
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## Sources
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| Source
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|---|---:|---|---|---|
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| base (tasksource +
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| DecisionBank-260k
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| prompt slice
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| web slice (**ids and labels only**)
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| National Library of Scotland catalogue
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| tool lane (code decisions)
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| Harvard modern catalogue
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| arXiv
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| procedural pile
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---
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@@ -158,7 +165,9 @@ the pool).
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data neutral or harmful, gains staying inside the task families covered, and source audits worth more than extra rows.
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Our own runs agreed: adding bulk without audits did not help; diverse, audited sources did (+4 points over the base
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alone on a sample of a public decision benchmark suite). So every source was audited before training:
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- **Text-blind baselines** on every source (majority label, longest option, options-only ranker)
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source that looked like +4.5 turned out to be mostly a "longest option is right" shortcut (real gain ≈ +2.3), so
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sources with option-only leaks were fixed or dropped.
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- **Distractors weighted by gold frequency.** With uniformly sampled wrong options, a real-label source (arXiv)
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@@ -168,8 +177,8 @@ alone on a sample of a public decision benchmark suite). So every source was aud
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"LLM style means unsupported" and was dropped.
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**3. Train on the labels real curation pipelines use.** The curation slices reuse public labels from curation work:
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WebOrganizer topic and format (Wettig et al., 2025, *Organize the Web*), FineWeb-Edu educational value
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al., 2024), and prompt tags from NuminaMath-1.5, DeepMath-103K, WebInstruct-verified and LMArena. Fixed-head
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classifiers trained on the same labels were used as a ceiling check before any claim (they still win their own
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labels: 0.87 / 0.83 vs 0.84 / 0.74 for WebOrganizer topic/format).
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[github.com/davanstrien/curation-decision-recipe](https://github.com/davanstrien/curation-decision-recipe). If you
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train decision models, please share how you made your data too so others can learn from your work and open models can keep improving!
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| Rows | 516,964 released, from a 535,730-row training mix; 18,766 base rows without a verified open licence are left out (see Licences) |
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| Sources | 9: public task collections, DecisionBank, curation labels from web/prompt datasets, library catalogues, program-computed tasks |
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| Row shapes | decision rows `{state, question, target}` and GLiClass rows `{text, all_labels, true_labels, prompt}` |
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| Labels made by | dataset gold, library cataloguers, programs, and LLMs (named per source and per row) |
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| Licence | mixed, per row; web page text is **not** included (labels plus a pointer to the source row; rebuild snippet below) |
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| Trains | [davanstrien/ultrabert-380m-curation](https://huggingface.co/davanstrien/ultrabert-380m-curation) (UltraBERT-380M + GLiClass head; 12,000 steps over this mix) |
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| Build | mix builder on Hugging Face Jobs (CPU), then [`scripts/release_build.py`](scripts/release_build.py) for the release (a record of how this repo was made: it reads the mix from a private bucket); test sets fixed before the mix was built, and every row checked against them |
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```python
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import json
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One config per source, plus `all`. `web` rows have an empty `state` by design (see Web text). Splitting by source keeps licence terms together, so a user can take only the parts
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whose terms suit them.
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| Config | Rows (training mix) | Shape |
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| -------------- | ------------------------------------------------: | -------- |
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| `base` | 173,859 (192,625 in training; licence filter) | GLiClass |
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| `decisionbank` | 165,277 | decision |
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| `prompt` | 52,491 | decision |
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| `web` | 41,890 (labels only; `state` empty until rebuilt) | decision |
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| `nls` | 23,846 | GLiClass |
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| `tool` | 21,089 | GLiClass |
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| `harvard` | 13,918 | decision |
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| `arxiv` | 13,597 | GLiClass |
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| `procpile` | 10,997 | GLiClass |
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`manifest/training_mix_manifest.parquet` lists all 535,730 rows of the training mix (including the 18,766 rows not
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released here): row id, source, licence class, labeller and a SHA-256 hash of each row's text.
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Columns (union of both shapes; unused fields are null):
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| Column | Meaning |
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| --------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `id` | stable row id; prefix names the source (`db/…`, `procpile-…`, `knowledgator/gliclass-v2.0:…`) |
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| `config`, `src` | source group and the finer origin (e.g. `db_bitext_support`, a tasksource task) |
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| `row_type` | `decision` or `gliclass` |
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| `teacher` | who made the label (e.g. `Llama-3.1-405B`, `cataloguer`, `program`, `50% source label + 50% Qwen3.8-27B`) |
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| `licence_class`, `licence` | `permissive` / `sa` (share-alike, e.g. CC BY-SA, CDLA-Sharing) / `other_open` (MPL-2.0, AFL-3.0) / `publisher` (text not redistributed), and the licence id |
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| `state` | the input (decision rows): a string, or a record as a JSON string |
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| `state_is_record` | decision rows: true when `state` was a record (dict or list) in training; false when it was text (some text states look like JSON) |
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| `question` | JSON string: `{type, instructions, criteria}`, `type` is `noul` or `choice`; `noul` = yes/no; `choice` options are `{key: description}`; graded scales use `level_<k>` keys, lowest first |
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| `target` | JSON string: probability per option (soft targets allowed; sums to 1 for single-answer rows) |
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| `text`, `all_labels`, `true_labels`, `prompt` | GLiClass rows: text, label list, `{label: score}` or a list (JSON string), optional prompt |
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| `multi_label` | true when several options can be correct (per-option yes/no loss in training) |
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| `source_dataset`, `source_row` | `web` rows only: source dataset and row index in its `train` split |
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Example decision row (DecisionBank, shortened):
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Row indices assume the source datasets are unchanged since October 2026. The snippet loads each source dataset in full; it has not been tested end to end.
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**Train on it.** [`data/release_to_jsonl.py`](https://github.com/davanstrien/curation-decision-recipe/blob/main/data/release_to_jsonl.py)
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in the recipe repo converts this dataset to the trainer's JSONL input (`uv run data/release_to_jsonl.py --out
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mix.jsonl`; filter with `--configs` and `--licence-class`). We checked it against the private training mix: all
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475,074 released rows with text are identical on every field the trainer reads. The recipe README has a tested
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command that converts and trains on one GPU Job.
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---
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## Sources
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| Source | Rows | What the rows ask | Labels made by | Licence |
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| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------: | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
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| base ([tasksource](https://huggingface.co/datasets/tasksource/tasksource-jev-typed-decisions) + Knowledgator GLiClass data: [v2.0](https://huggingface.co/datasets/knowledgator/gliclass-v2.0), [v3-logic](https://huggingface.co/datasets/knowledgator/gliclass-v3-logic-dataset)) | 173,859 (192,625 trained on) | many public classification tasks | dataset gold; knowledgator gliclass-v2.0 / v3-logic (LLM-generated) | per source repo, in `licence`; only rows with a verified permissive, share-alike or other open licence are released (see Licences) |
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| [DecisionBank-260k](https://huggingface.co/datasets/jgeuter/DecisionBank) | 165,277 | public tasks, written and programmatic decision questions | 50 % source label + 50 % Qwen3.8-27B; Qwen3.8-27B with thinking; 50 % Jeff 1.0 teachers (Qwen3.6-27B, Qwen3.5-122B-A10B) + 50 % Qwen3.8-27B (4,188 rows); code | per row: CC BY / CC BY-SA, Apache, MIT, CC0 (public domain), CDLA-Sharing-1.0; non-commercial, research-only, unknown and other terms dropped |
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| prompt slice | 52,491 | [NuminaMath-1.5](https://huggingface.co/datasets/AI-MO/NuminaMath-1.5) question type / problem type / validity; [DeepMath-103K](https://huggingface.co/datasets/zwhe99/DeepMath-103K) difficulty / topic; [WebInstruct-verified](https://huggingface.co/datasets/TIGER-Lab/WebInstruct-verified) answer type / category / difficulty; [LMArena](https://huggingface.co/datasets/lmarena-ai/arena-human-preference-140k) criteria and tags | dataset columns (NuminaMath, WebInstruct: labeller not documented); GPT-4o (DeepMath); LMArena classifiers | Apache-2.0; MIT; Apache-2.0; CC-BY-4.0 |
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| web slice (**ids and labels only**) | 41,890 | WebOrganizer [topic](https://huggingface.co/datasets/WebOrganizer/TopicAnnotations-Llama-3.1-405B-FP8) and [format](https://huggingface.co/datasets/WebOrganizer/FormatAnnotations-Llama-3.1-405B-FP8); [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu-llama3-annotations) educational value | Llama-3.1-405B (WebOrganizer; the annotation datasets state no licence); Llama-3-70B (FineWeb-Edu) | labels: none stated; ODC-By. Page text: publisher copyright, not included (rebuild snippet above) |
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| [National Library of Scotland](https://data.nls.uk/) catalogue | 23,846 | subject areas of library records | National Library of Scotland cataloguers | CC0 |
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| tool lane (code decisions) | 21,089 | linter findings (classification and yes/no) and complexity comparisons on functions from Python packages | program-computed (linter and complexity tools) | permissive package licences (MIT / BSD / Apache-2.0 / PSF); the package is not recorded per row |
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| [Harvard Library](https://library.harvard.edu/open-metadata) modern catalogue | 13,918 | Library of Congress subject class, literary form, government publication | Harvard Library cataloguers (from their catalogue records) | CC0 |
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| arXiv | 13,597 | arXiv subject category | author-chosen categories | arXiv metadata (CC0) |
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| [procedural pile](https://huggingface.co/datasets/reasoning-core/procedural-pile) | 10,997 | program-checkable reasoning | program-computed | CC BY 4.0 (attribute) |
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---
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|
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data neutral or harmful, gains staying inside the task families covered, and source audits worth more than extra rows.
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Our own runs agreed: adding bulk without audits did not help; diverse, audited sources did (+4 points over the base
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alone on a sample of a public decision benchmark suite). So every source was audited before training:
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+
- **Text-blind baselines** on every source (majority label, longest option, options-only ranker), in the spirit of
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hypothesis-only baselines for NLI ([Poliak et al., 2018](https://arxiv.org/abs/1805.01042);
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[Gururangan et al., 2018](https://arxiv.org/abs/1803.02324)). A generated-schema
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source that looked like +4.5 turned out to be mostly a "longest option is right" shortcut (real gain ≈ +2.3), so
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sources with option-only leaks were fixed or dropped.
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- **Distractors weighted by gold frequency.** With uniformly sampled wrong options, a real-label source (arXiv)
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"LLM style means unsupported" and was dropped.
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**3. Train on the labels real curation pipelines use.** The curation slices reuse public labels from curation work:
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WebOrganizer topic and format ([Wettig et al., 2025](https://arxiv.org/abs/2502.10341), *Organize the Web*), FineWeb-Edu educational value
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([Penedo et al., 2024](https://arxiv.org/abs/2406.17557)), and prompt tags from NuminaMath-1.5, DeepMath-103K, WebInstruct-verified and LMArena. Fixed-head
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classifiers trained on the same labels were used as a ceiling check before any claim (they still win their own
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labels: 0.87 / 0.83 vs 0.84 / 0.74 for WebOrganizer topic/format).
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data/arxiv/train.parquet
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data/base/train.parquet
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data/decisionbank/train.parquet
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data/harvard/train.parquet
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data/nls/train.parquet
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data/web/train.parquet
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scripts/release_build.py
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(analysis/release/base_licence_map.json); drop GPL, unresolved and no-licence rows. Fill `licence` per row.
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- web: page text is not redistributed; `state` is set to "" and `source_dataset` / `source_row` point at the
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source row (train split) so the text can be rebuilt.
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- nested fields (state when it is a record, question, target, true_labels) are stored as JSON strings
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usage (CPU Job, dm-curation mounted at /c): python release_build.py --repo davanstrien/<name> [--dry-run]
|
| 14 |
"""
|
| 15 |
|
|
@@ -40,7 +41,7 @@ BASE_CLASS = {"permissive": "permissive", "sa": "sa", "other_open": "other_open"
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|
| 40 |
WEB_SOURCE = {"cur-wo-topic-": "WebOrganizer/TopicAnnotations-Llama-3.1-405B-FP8",
|
| 41 |
"cur-wo-format-": "WebOrganizer/FormatAnnotations-Llama-3.1-405B-FP8",
|
| 42 |
"cur-fwe-": "HuggingFaceFW/fineweb-edu-llama3-annotations"}
|
| 43 |
-
COLUMNS = ["id", "config", "src", "row_type", "teacher", "licence_class", "licence", "state", "question", "target",
|
| 44 |
"text", "all_labels", "true_labels", "prompt", "multi_label", "source_dataset", "source_row"]
|
| 45 |
|
| 46 |
|
|
@@ -52,6 +53,7 @@ def main():
|
|
| 52 |
ap = argparse.ArgumentParser()
|
| 53 |
ap.add_argument("--repo", required=True)
|
| 54 |
ap.add_argument("--dry-run", action="store_true")
|
|
|
|
| 55 |
args = ap.parse_args()
|
| 56 |
|
| 57 |
licmap = json.loads(LICMAP.read_text())
|
|
@@ -96,6 +98,7 @@ def main():
|
|
| 96 |
"id": r["id"], "config": config, "src": r.get("src"), "row_type": row_type,
|
| 97 |
"teacher": r.get("teacher"), "licence_class": licence_class, "licence": licence,
|
| 98 |
"state": state if state is None or isinstance(state, str) else as_json(state),
|
|
|
|
| 99 |
"question": as_json(r.get("question")), "target": as_json(r.get("target")),
|
| 100 |
"text": r.get("text"), "all_labels": r.get("all_labels"),
|
| 101 |
"true_labels": as_json(r.get("true_labels")), "prompt": r.get("prompt"),
|
|
@@ -114,6 +117,7 @@ def main():
|
|
| 114 |
schema = {c: pl.Utf8 for c in COLUMNS}
|
| 115 |
schema["all_labels"] = pl.List(pl.Utf8)
|
| 116 |
schema["multi_label"] = pl.Boolean
|
|
|
|
| 117 |
schema["source_row"] = pl.Int64
|
| 118 |
print(json.dumps(dict(sorted(stats.items())), indent=1))
|
| 119 |
print("dropped keys (not released):", dict(extra_keys))
|
|
@@ -134,8 +138,9 @@ def main():
|
|
| 134 |
pl.DataFrame(rs, schema=schema).write_parquet(p / "train.parquet")
|
| 135 |
(Path(d) / "release_stats.json").write_text(json.dumps(dict(sorted(stats.items())), indent=1))
|
| 136 |
api.upload_folder(repo_id=args.repo, repo_type="dataset", folder_path=d,
|
| 137 |
-
commit_message="Add release build of the cur-cand v1 training mix"
|
| 138 |
-
|
|
|
|
| 139 |
|
| 140 |
|
| 141 |
if __name__ == "__main__":
|
|
|
|
| 9 |
(analysis/release/base_licence_map.json); drop GPL, unresolved and no-licence rows. Fill `licence` per row.
|
| 10 |
- web: page text is not redistributed; `state` is set to "" and `source_dataset` / `source_row` point at the
|
| 11 |
source row (train split) so the text can be rebuilt.
|
| 12 |
+
- nested fields (state when it is a record, question, target, true_labels) are stored as JSON strings;
|
| 13 |
+
`state_is_record` says whether a decision row's state was a record (dict or list) or text.
|
| 14 |
usage (CPU Job, dm-curation mounted at /c): python release_build.py --repo davanstrien/<name> [--dry-run]
|
| 15 |
"""
|
| 16 |
|
|
|
|
| 41 |
WEB_SOURCE = {"cur-wo-topic-": "WebOrganizer/TopicAnnotations-Llama-3.1-405B-FP8",
|
| 42 |
"cur-wo-format-": "WebOrganizer/FormatAnnotations-Llama-3.1-405B-FP8",
|
| 43 |
"cur-fwe-": "HuggingFaceFW/fineweb-edu-llama3-annotations"}
|
| 44 |
+
COLUMNS = ["id", "config", "src", "row_type", "teacher", "licence_class", "licence", "state", "state_is_record", "question", "target",
|
| 45 |
"text", "all_labels", "true_labels", "prompt", "multi_label", "source_dataset", "source_row"]
|
| 46 |
|
| 47 |
|
|
|
|
| 53 |
ap = argparse.ArgumentParser()
|
| 54 |
ap.add_argument("--repo", required=True)
|
| 55 |
ap.add_argument("--dry-run", action="store_true")
|
| 56 |
+
ap.add_argument("--create-pr", action="store_true", help="upload as a pull request to the dataset repo")
|
| 57 |
args = ap.parse_args()
|
| 58 |
|
| 59 |
licmap = json.loads(LICMAP.read_text())
|
|
|
|
| 98 |
"id": r["id"], "config": config, "src": r.get("src"), "row_type": row_type,
|
| 99 |
"teacher": r.get("teacher"), "licence_class": licence_class, "licence": licence,
|
| 100 |
"state": state if state is None or isinstance(state, str) else as_json(state),
|
| 101 |
+
"state_is_record": isinstance(state, (dict, list)) if row_type == "decision" else None,
|
| 102 |
"question": as_json(r.get("question")), "target": as_json(r.get("target")),
|
| 103 |
"text": r.get("text"), "all_labels": r.get("all_labels"),
|
| 104 |
"true_labels": as_json(r.get("true_labels")), "prompt": r.get("prompt"),
|
|
|
|
| 117 |
schema = {c: pl.Utf8 for c in COLUMNS}
|
| 118 |
schema["all_labels"] = pl.List(pl.Utf8)
|
| 119 |
schema["multi_label"] = pl.Boolean
|
| 120 |
+
schema["state_is_record"] = pl.Boolean
|
| 121 |
schema["source_row"] = pl.Int64
|
| 122 |
print(json.dumps(dict(sorted(stats.items())), indent=1))
|
| 123 |
print("dropped keys (not released):", dict(extra_keys))
|
|
|
|
| 138 |
pl.DataFrame(rs, schema=schema).write_parquet(p / "train.parquet")
|
| 139 |
(Path(d) / "release_stats.json").write_text(json.dumps(dict(sorted(stats.items())), indent=1))
|
| 140 |
api.upload_folder(repo_id=args.repo, repo_type="dataset", folder_path=d,
|
| 141 |
+
commit_message="Add release build of the cur-cand v1 training mix",
|
| 142 |
+
create_pr=args.create_pr)
|
| 143 |
+
print("uploaded", args.repo)
|
| 144 |
|
| 145 |
|
| 146 |
if __name__ == "__main__":
|