tasksource-jev-typed-decisions

One million decisions from 500+ Tasksource tasks across 300+ dataset families, in a single format for models that receive their answer criteria at runtime. The value is breadth with traceable supervision: most rows inherit labels, ratings, or annotator votes from existing datasets, not labels invented by a teacher model. The source field identifies the originating task; existing train/dev/test boundaries are retained where the source provides them. The Tasksource repository and task catalog document the source preprocessings.

The coverage is deliberately wide: GLUE and SuperGLUE inference and language understanding; SNLI and XNLI; HellaSwag, PIQA, and ScienceQA; AG News, Banking77, and real support-ticket classification; CoNLL-2003 and WNUT-17 entity tagging; MasakhaNEWS and other multilingual tasks; and graded sources such as HelpSteer and ChaosNLI. These are different decision problems with different criteria, made usable through one schema. A small, tagged procedural component adds controlled multi-question states.

Format

Each Parquet row has a state, a question, runtime options, and a target. kind is choice, noul (one truth probability), or score (ordered levels). For choice and score, target is a distribution aligned with options; for noul, options is empty. id, group_id, and question_id let related decisions share a source example without requiring nested rows. variant marks deterministic subrecasts; source and split preserve provenance.

from datasets import load_dataset

ds = load_dataset("tasksource/tasksource-jev-typed-decisions")
row = ds["train"][0]
print(row["state"], row["question"], row["options"], row["target"])

Most classification targets are one-hot because the source annotations are hard labels. Sources with vote distributions or ratings retain softer or ordinal targets where justified. Low-frequency, deterministic variants cover label verification, criterion order, instruction wording, and paired-text field wording. The first 1,000 training rows are interleaved to show task variety in the Dataset Viewer; no rows are added by that display order.

The release has 900,000 train, 50,000 validation (dev in the split field), and 50,000 test decisions. Publication balances dataset families while sampling their configurations and keeping related questions together. The full source mix, failed source list, and build manifest are published alongside the data. BIG-bench, MMLU, and BLiMP are not included.

Tasksource harmonizes datasets from many publishers; their original licenses and usage terms still apply. The aggregate is marked license: other because there is no single license for every source. The build runbook describes the resumable pipeline. This recast is independent of TypeSafe and OpenJev.

Citation

Please cite the Tasksource collection and preprocessing framework:

@inproceedings{sileo-2024-tasksource,
  title = {tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework},
  author = {Sileo, Damien},
  booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
  year = {2024},
  pages = {15655--15684},
  url = {https://aclanthology.org/2024.lrec-main.1361/}
}