pretty_name: tasksource-jev-typed-decisions
language:
- en
- multilingual
license: other
task_categories:
- zero-shot-classification
- text-classification
- question-answering
- token-classification
tags:
- tasksource
- jev
- system-one
- runtime-defined-decisions
- decision-models
- multiple-choice
- decision-model
- extraction
- information-extraction
- extractive-qa
- span-extraction
- named-entity-recognition
- structured-extraction
- clairic
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-*
- config_name: extraction
data_files:
- split: train
path: extraction/train-*
- split: validation
path: extraction/validation-*
- split: test
path: extraction/test-*
- config_name: filtered-full
data_files:
- split: train
path: filtered-full/train-*.parquet
- config_name: full
data_files:
- split: train
path: full/train-*
- split: validation
path: full/validation-*
- split: test
path: full/test-*
- config_name: vision
data_files:
- split: train
path: vision/train-*
- split: test
path: vision/test-*
- split: validation
path: vision/validation-*
dataset_info:
- config_name: default
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: source_index
dtype: string
- name: group_id
dtype: string
- name: question_id
dtype: string
- name: example_id
dtype: string
- name: license
dtype: string
- name: license_use
dtype: string
splits:
- name: train
num_bytes: 1534153675
num_examples: 999832
- name: validation
num_bytes: 23419061
num_examples: 15000
- name: test
num_bytes: 21143086
num_examples: 15000
download_size: 694326150
dataset_size: 1578715822
- config_name: extraction
features:
- name: id
dtype: string
- name: source
dtype: string
- name: split
dtype: string
- name: state
dtype: string
- name: fields
list:
- name: name
dtype: string
- name: type
dtype: string
- name: instruction
dtype: string
- name: answers
list:
- name: 'null'
dtype: bool
- name: values
list:
- name: text
dtype: string
- name: start
dtype: int32
- name: end
dtype: int32
- name: metadata
dtype: string
- name: license
dtype: string
- name: license_use
dtype: string
splits:
- name: train
num_bytes: 1067215371
num_examples: 498522
- name: validation
num_bytes: 83457905
num_examples: 35743
- name: test
num_bytes: 81350588
num_examples: 38574
download_size: 834331789
dataset_size: 1232023864
- config_name: filtered-full
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: 1633306939
num_examples: 1305478
download_size: 335270018
dataset_size: 1633306939
- config_name: full
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: source_index
dtype: string
- name: group_id
dtype: string
- name: question_id
dtype: string
- name: example_id
dtype: string
- name: license
dtype: string
- name: license_use
dtype: string
splits:
- name: train
num_bytes: 3871839670
num_examples: 2550012
- name: validation
num_bytes: 23419061
num_examples: 15000
- name: test
num_bytes: 21143086
num_examples: 15000
download_size: 1950002463
dataset_size: 3916401817
- config_name: vision
features:
- name: images
list: image
- name: metadata
dtype: string
- name: id
dtype: string
- name: kind
dtype: string
- name: options
list: string
- name: target
list: float64
- name: state
dtype: string
- 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: example_id
dtype: string
- name: license
dtype: string
- name: license_use
dtype: string
splits:
- name: train
num_bytes: 236883226428
num_examples: 722314
- name: test
num_bytes: 146575133
num_examples: 475
- name: validation
num_bytes: 274444703
num_examples: 972
download_size: 140122509371
dataset_size: 237304246264
task_ids:
- extractive-qa
- named-entity-recognition
tasksource-jev-typed-decisions
Typed decisions from hundreds of curated Tasksource tasks: classification, multiple choice, ordered ratings, probabilities and visual reasoning. Targets come from source annotations or deterministic transformations of them. Native annotation noise and subjective judgments remain.
Load
from datasets import load_dataset
text = load_dataset("tasksource/tasksource-jev-typed-decisions")
vision = load_dataset("tasksource/tasksource-jev-typed-decisions", "vision")
extraction = load_dataset("tasksource/tasksource-jev-typed-decisions", "extraction")
row = text["train"][0]
print(row["state"], row["question"], row["options"], row["target"])
| Config | Contents |
|---|---|
default |
Steered text training mix, plus reviewed incremental additions; validation and test included. |
full |
Larger text pool with per-source caps, including incremental additions; validation and test included. |
filtered-full |
Filtered, training-only historical snapshot. Later additions are not included. See selection policy and counts. |
vision |
Multimodal decisions with ordered images and text choices. See visual sources, sampling and provenance. |
extraction |
A text and several typed fields (questions, entity types) answered with character spans or null, Clairic-style. See extraction format and sources. |
Exact split sizes are in the Dataset Viewer metadata. Task coverage and counts are in sources.yaml and vision/sources.yaml.
Row format
| Field | Meaning |
|---|---|
state |
Text to reason about. |
question |
The decision to make. |
kind |
choice, score or noul. |
options |
Criteria in their presented order; empty for noul. |
target |
Distribution aligned with options, or [p_true] for noul. |
id, group_id, question_id |
Decision identity and grouping of related questions. |
example_id |
Content-derived example identity used by the build. |
source_index |
Original row of the example in its source dataset, as split:row (the split before any re-splitting); empty for packed and generated rows. |
source, split, variant |
Task identity, split annotation and recast variant. |
license, license_use |
Recorded source terms and their usage classification. |
images |
Vision only: ordered Hugging Face image features, decoded on access. |
metadata |
Vision only: JSON-string source identity, geometry, augmentation and license evidence. |
{"state": "My body cast a shadow over the grass. What was the cause of this?",
"question": "Choose the criterion that best answers the question.",
"kind": "choice", "options": ["The sun was rising.", "The grass was cut."],
"target": [1.0, 0.0], "source": "super_glue/copa"}
choice selects a criterion; score uses an ordered scale. Soft targets preserve annotator disagreement or interpolate numerical ratings between neighboring scale anchors. noul represents the probability that a proposition is true. Scale meaning is determined by the question and criteria.
Rows sharing group_id can be assembled into a multi-question request. Vision images stay separate from text and must be passed through the model's image adapter; answer options remain text. See AGENTS.md for request assembly and row semantics.
Construction and evaluation
Tasksource standardizes native labels and choices, adds task-specific questions where needed, and applies deterministic option permutations and low-frequency text variants. Some text rows pack related decisions over a shared state. A decision's majority label keeps at most three rows per row of its other labels (graded answers count as yes above 0.5; a question with a single label is dropped), so skewed binary sources such as answer selection or toxicity no longer reward always answering the common label; procedural questions keep each label under 60%. The visual config contains direct decisions and shares sampling budgets across related task views.
Native source partitions are retained where available; derived group-based holdouts and overlap exclusions are documented per source. These checks do not establish absence of all cross-dataset or benchmark contamination. For example, Mind2Web text tasks use derived holdouts from public training trajectories, not official benchmark tests; alignment with private Decision Index evaluation rows is unverified. Mind2Web is excluded from the visual config.
Source and recast reviews are coarse checks, not corpus-wide correctness guarantees. English examples illustrate the exported and canonical representations.
Licenses
This collection has mixed source terms, hence license: other. Original annotation and image terms still apply. license_use is a best-effort classification: commercial, non-commercial or unspecified; it is not legal clearance. Missing or scoped evidence is retained explicitly.
text = text.filter(lambda use: use == "commercial", input_columns="license_use")
Visual annotation licenses may not cover third-party images. The visual license review records unresolved image rights and explicit restrictions, including FigureQA's prohibition on redistribution. See component-level evidence and the vision documentation before reuse.
Documentation and provenance
- Text sources and revisions, release audit, build manifest and failed sources.
- Vision documentation and filtered-full selection evidence.
- Build runbook, base build recipe and task catalogs.
- Release history. Addition manifests and sampled reviews retain their original snapshot context.
Citation
If you use tasksource, its standardized task annotations, or datasets derived from them in published research, please cite the tasksource paper (Sileo, LREC-COLING 2024). The procedural data is generated with Reasoning Core; please also cite the Reasoning Core paper (Sileo et al., 2026).
@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)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1361",
doi = "10.63317/2h4pkr685mkq",
pages = "15655--15684",
}
@article{sileo2026reasoning,
title={Reasoning Core: Designing Broad Procedural Data for Completion-Supervised Reasoning Training},
author={Sileo, Damien and Lacombe, Valentin and Kachler, Dimitri},
journal={arXiv preprint arXiv:2608.05148},
year={2026},
url={https://arxiv.org/abs/2608.05148}
}