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.

Preview release: the first 105,000 examples are published now so the schema and loading path can be tested while the full 575-task build runs. The preview is versioned from completed tasks and will be replaced by the complete build.

Schema

field	type	meaning
id	string	Stable identifier derived from task, split, and row index.
kind	string	System One primitive. This release contains choice decisions.
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.

Classification criteria are the source task's label names. Multiple-choice criteria are the answer choices. The canonical conversion is deterministic: it does not shuffle, paraphrase, rename, or subsample criteria. Those operations can be applied later without changing the base dataset.

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 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 release caps each task at 30,000 training rows and 3,000 validation or test rows using Tasksource's deterministic sampling (seed 0); smaller tasks are retained in full.

The selected catalog includes 100 BIG-bench task configurations and all 57 MMLU subjects currently registered in Tasksource. Their original split identity is preserved. Because these are established benchmarks, users training on the aggregate should filter by source and split to avoid evaluation contamination.

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 task 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."
}