README.md

metadata

pretty_name: tasksource-jev
language:
  - multilingual
license: other
task_categories:
  - text-classification
  - question-answering
tags:
  - tasksource
  - jev
  - system-one
  - runtime-defined-decisions
  - decision-models
  - multiple-choice
size_categories:
  - 100K<n<1M
dataset_info:
  features:
    - name: state
      dtype: string
    - name: id
      dtype: string
    - name: kind
      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
  splits:
    - name: train
      num_bytes: 411325775
      num_examples: 450000
    - name: validation
      num_bytes: 25098023
      num_examples: 25000
    - name: test
      num_bytes: 23026201
      num_examples: 25000
  download_size: 221068324
  dataset_size: 459449999
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

tasksource-jev

tasksource-jev recasts Tasksource classification and multiple-choice datasets as runtime-defined decisions. Each example supplies its state and candidate criteria at inference time. The dataset is intended for training and evaluating bounded decision models; it is not tied to one Jev implementation.

This is an independent data transformation. It is not an official TypeSafe Jev dataset and is not produced by or affiliated with TypeSafe or OpenJev.

Build status: a 105,000-example preview is currently available while the source-balanced 500,000-row release is built from 730 selected English and multilingual tasks. Checkpointed successes are retained and incompatible upstream sources are reported explicitly.

Schema

field	type	meaning
id	string	Stable identifier derived from task, split, and row index.
kind	string	System One primitive: choice, noul, or score.
options	list of strings	Candidate labels or answers supplied at runtime. Order is significant.
target	list of floats	One-hot target distribution aligned with options.
state	string	Text or question on which the decision is based. Paired classification inputs are marked text_A and text_B.
question	string	The decision requested from the model.
source	string	Tasksource task identifier used to load the source data.
variant	string	Direct decision or a named deterministic subrecast.
split	string	Source split normalized to train, dev, or test.

Classification criteria are the source task's label names. Multiple-choice criteria are the answer choices. Every source row is retained as a direct choice. A deterministic augmentation pass adds label-verification noul questions to about 5% of rows. Ordered-rubric score augmentation is available for genuinely ordinal sources but is disabled by default. Native regression and ordinal recasting will be used for score examples rather than imposing an order on nominal classification labels.

Deterministic subrecasts

The release adds two conservative, low-frequency variants while retaining every direct row:

variant	default rate	purpose
label_verification	5%	A noul judgement asking whether a deterministically proposed label is correct. Correct and incorrect proposals are balanced.
criteria_permutation	5%	The same choice decision with options and targets permuted together, reducing option-position shortcuts.
instruction_paraphrase	5%	The same decision with a manually vetted equivalent instruction. Common NLI and sentiment label groups receive specific wording; other tasks use conservative generic alternatives.
paired_text_format	5% of paired rows	Neutral alternatives to repeated text_A/text_B field labels, without assuming a task-specific relation between the texts.

All transformations are derived exactly from the source target and introduce no teacher-generated claims. Candidate-subset decisions are a possible later addition. Synthetic uncertainty, abstention, and nominal-to-ordinal conversions are intentionally excluded because one-hot classification labels do not justify them. For noul, target contains the scalar truth probability and options is empty; for choice and score, target is aligned with options. The direct canonical decision is unchanged; lower-frequency variants are identified explicitly by the variant field.

from datasets import load_dataset

dataset = load_dataset("tasksource/tasksource-jev")
row = dataset["train"][0]
answer = row["options"][max(range(len(row["target"])), key=row["target"].__getitem__)]

With Tasksource installed, the same representation can be produced directly:

from tasksource import load_task, render_systemone

dataset = load_task("glue/rte", recast="jev")
request = render_systemone(dataset["train"][0], model="openjev")

Construction

Tasksource standardizes heterogeneous datasets into common classification and multiple-choice templates. This release applies recast_jev to compatible English and multilingual tasks, retains the standard train/validation/test splits, and records the Tasksource identifier in every row. Tasks that fail to download or preprocess are recorded by the build report rather than silently represented as complete. To keep very large sources balanced, the build caps each task at 30,000 training rows and 3,000 validation or test rows using Tasksource's deterministic sampling (seed 0). The published release is capped at 500,000 rows using a source-balanced 90/5/5 train/dev/test allocation; selection preserves relative row order.

For a useful Dataset Viewer preview, only the first 1,000 training rows are ordered round-robin by source. This is a deterministic permutation, not a random shuffle. After that display prefix, all remaining examples retain their original relative order.

The selected catalog includes 100 BIG-bench task configurations and all 57 MMLU subjects currently registered in Tasksource. Their original split identity is preserved in the row-level split field, with validation normalized to dev. This makes source/split exclusion explicit when constructing a training mixture.

The repository includes failed-tasks.json and outdated-datasets.json. The latter specifically tracks upstream datasets that still depend on loading scripts no longer supported by current Hugging Face Datasets, so they can be migrated to data-only Parquet repositories and incorporated in a later build.

The build is reproducible from the Tasksource repository:

python scripts/build_jev_dataset.py --output build/tasksource-jev --finalize

Licensing and provenance

Tasksource is a preprocessing framework and catalog, not the original publisher of the constituent datasets. Copyright, license, and usage restrictions remain those of each upstream dataset. Users should consult the upstream dataset card identified by source before redistributing or using a subset. The aggregate is therefore marked license: other; no single license is asserted over all rows.

Citation

If this recast is useful, cite Tasksource, which provides the task collection and harmonization framework:

@inproceedings{sileo-2024-tasksource,
    title = "tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework",
    author = "Sileo, Damien",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    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/",
    pages = "15655--15684",
    abstract = "The HuggingFace Datasets Hub hosts thousands of datasets, offering exciting opportunities for language model training and evaluation. However, datasets for a specific task type often have different structures, making harmonization challenging which prevents the interchangeable use of comparable datasets. As a result, multi-task training or evaluation necessitates manual work to fit data into task templates. Several initiatives independently tackle this issue by releasing harmonized datasets or providing harmonization codes to preprocess datasets into a consistent format. We identify patterns in such preprocessings, such as column renaming, or more complex patterns. We then propose an annotation framework that enables concise, readable, and reusable preprocessing annotations. tasksource annotates more than 600 task preprocessings and provides a backend to automate dataset alignment. We fine-tune a multi-task text encoder on all tasksource tasks, outperforming every publicly available text encoder of comparable parameter count according to an external evaluation."
}