Add tasksource-jev-typed-decisions dataset card

README.md

@@ -18,55 +18,6 @@ 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
-  - name: license
-    dtype: string
-  - name: license_use
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 2938193381
-    num_examples: 2500000
-  - name: validation
-    num_bytes: 17074873
-    num_examples: 15000
-  - name: test
-    num_bytes: 16910555
-    num_examples: 15000
-  download_size: 1453640142
-  dataset_size: 2972178809
 ---
 
 # tasksource-jev-typed-decisions
@@ -151,7 +102,7 @@ following each source's own train/dev/test splits where it has them.
     Label-verification and packed questions carry it too.
 - **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.
+- **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), times [audit weights](https://github.com/sileod/tasksource/blob/main/src/tasksource/metadata/audit_weights.py) from a per-task check of Jev on 200 examples: ×1.5 for hard tasks whose gold is right by construction (synthetic logic, theory of mind, spatial reasoning), ×0.5 for near-solved tasks and for hard tasks whose gold is a judgment call (ratings, preferences, crowd sentiment). The same check found and fixed inverted labels, hidden test labels and unclear questions in about 80 sources. Related questions are kept together.
   - 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, the original dataset behind each tasksource copy, and its licenses.