Card: straight to the value proposition

README.md

@@ -17,62 +17,12 @@ tags:
 - multiple-choice
 size_categories:
 - 1M<n<10M
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
-dataset_info:
-  features:
-  - name: state
-    dtype: string
-  - name: kind
-    dtype: string
-  - name: id
-    dtype: string
-  - name: options
-    list: string
-  - name: target
-    list: float64
-  - name: question
-    dtype: string
-  - name: source
-    dtype: string
-  - name: variant
-    dtype: string
-  - name: split
-    dtype: string
-  - name: group_id
-    dtype: string
-  - name: question_id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 2852935264
-    num_examples: 2500000
-  - name: validation
-    num_bytes: 16541972
-    num_examples: 15000
-  - name: test
-    num_bytes: 16383876
-    num_examples: 15000
-  download_size: 1666874588
-  dataset_size: 2885861112
 ---
 
 # tasksource-jev-typed-decisions
 
-**Two million human-labeled decisions from 600+ tasks, in one format for models
-that read their answer criteria at runtime.**
-
-Most instruction data teaches a model to *write*. This dataset teaches it to
-*decide*: given a state and a question, pick among the options it is handed,
-rate on a scale it is handed, or give a calibrated probability. The options
-change from row to row, so a model has to read them rather than memorize a label set.
+**2.5 million decisions from 670 sources, in one format for models that read
+their answer criteria at runtime.**
 
 ## Why use it
 
@@ -128,6 +78,7 @@ print(row["state"], row["question"], row["options"], row["target"])
 | `target` | Distribution over `options`, or `[p]` for `noul` |
 | `id`, `group_id`, `question_id` | Link decisions over the same source example |
 | `source`, `split`, `variant` | Originating task, original split, and recast variant |
+| `license`, `license_use` | The source's license(s), and `commercial`, `non-commercial` or `unspecified` (see below) |
 
 Splits: 2,500,000 train, 15,000 validation (`dev` in `split`), and 15,000 test,
 following each source's own train/dev/test splits where it has them.
@@ -145,16 +96,35 @@ following each source's own train/dev/test splits where it has them.
 - **Variants.** Low-frequency, deterministic variants cover label verification as `noul`, criterion order, and instruction wording.
 - **Packing.** Up to 10% of each classification task's examples are packed, two to four at a time, into `packed_derived` states. Their questions (an item's label, agreement, existence, counts) follow exactly from the gold labels.
 - **Mixing.** Formats get fixed shares of the train rows (47% classification, 30% multiple choice, 3% token labeling, 10% graded (soft-label sources), 10% procedural). Within a format, dataset families get equal shares, scaled by [hand-set weights](https://github.com/sileod/tasksource/blob/main/src/tasksource/metadata/weights.py) (more for adversarial NLI, long documents and preference pairs; less for templated probes). Related questions are kept together.
-  - The first 1,000 train rows are interleaved to show variety in the Dataset Viewer; the rest is shuffled.
+  - The first 1,000 train rows are interleaved to show variety in the Dataset Viewer; the rest is shuffled. Questions of a group stay adjacent throughout.
   - Evaluation benchmarks (BIG-bench, MMLU, BLiMP, MATH test, ...) are left out so they stay clean for evaluation.
-- **Sources.** [sources.yaml](sources.yaml) lists every source with its rows, the Hub dataset and revision it was loaded from, and the original dataset behind each tasksource copy.
+- **Sources.** [sources.yaml](sources.yaml) lists every source with its rows, the Hub dataset and revision it was loaded from, the original dataset behind each tasksource copy, and its licenses.
 - **Audit trail.** The [source mix](release-audit.json), [failed source list](failed-tasks.json), and [build manifest](build-manifest.json) ship with the data.
 - **Reproducible.** The [build runbook](https://github.com/sileod/tasksource/blob/main/docs/jev/README.md) rebuilds the release from [Tasksource](https://github.com/sileod/tasksource)'s [task catalog](https://github.com/sileod/tasksource/blob/main/tasks.md).
 
 ## License and scope
 
 Tasksource harmonizes datasets from many publishers; their original licenses
-and terms still apply, hence `license: other`. This recast is independent of
+and terms still apply, hence `license: other`.
+
+Each row carries its source's license, to help filter:
+
+```python
+ds = ds.filter(lambda use: use == "commercial", input_columns="license_use")
+```
+
+- `license` lists the `license` of the Hub dataset card the source was loaded from,
+  and of the original dataset behind a tasksource copy. It also lists licenses recorded
+  by the [Data Provenance Initiative](https://www.dataprovenance.org/), marked `(DPI)`.
+- `license_use` takes the most restrictive of those: `non-commercial` if any is
+  non-commercial or academic-only, `commercial` if one allows commercial use (share-alike
+  and copyleft included), and `unspecified` otherwise. That covers missing licenses and
+  `other`, bare `cc`, and no-derivatives licenses.
+- [sources.yaml](sources.yaml) records each card and DPI license per source.
+
+This is a best-effort aid, not legal advice. Licenses on cards can be wrong or
+incomplete, and a source's terms may differ from its card's. Check the
+original terms before relying on them. This recast is independent of
 TypeSafe and OpenJev.
 
 ## Citation