Datasets:

tasksource
/

tasksource-jev-typed-decisions

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
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: full
    data_files:
      - split: train
        path: full/train-*
      - split: validation
        path: full/validation-*
      - split: test
        path: full/test-*
  - config_name: filtered-full
    data_files:
      - split: train
        path: filtered-full/train-*.parquet
  - 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: 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: 1357605662
        num_examples: 999879
      - name: validation
        num_bytes: 19470492
        num_examples: 14023
      - name: test
        num_bytes: 18690297
        num_examples: 14202
    download_size: 673696926
    dataset_size: 1395766451
  - 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: 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: 3394424881
        num_examples: 2500000
      - name: validation
        num_bytes: 20827026
        num_examples: 15000
      - name: test
        num_bytes: 19740492
        num_examples: 15000
    download_size: 1728254328
    dataset_size: 3434992399
  - 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: 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: 12372469916
        num_examples: 106085
      - name: test
        num_bytes: 146575133
        num_examples: 475
      - name: validation
        num_bytes: 274444703
        num_examples: 972
    download_size: 12369389176
    dataset_size: 12793489752

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")
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.

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`, `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

@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/}
}