Datasets:

tasksource
/

tasksource-instruct

pretty_name: tasksource-instruct
language:
  - en
license: other
size_categories:
  - 1M<n<10M
task_categories:
  - text-generation
  - text-classification
  - token-classification
  - zero-shot-classification
tags:
  - instructions
  - instruction-tuning
  - instruction-finetuning
  - flan
  - promptsource
  - tasksource
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: validation
        path: data/validation-*
dataset_info:
  features:
    - name: inputs
      dtype: string
    - name: targets
      dtype: string
    - name: task
      dtype: string
    - name: license
      dtype: string
    - name: license_use
      dtype: string
  splits:
    - name: train
      num_bytes: 6509818491
      num_examples: 7282255
    - name: validation
      num_bytes: 183888515
      num_examples: 184914
    - name: test
      num_bytes: 184116352
      num_examples: 192830
  download_size: 3122792007
  dataset_size: 6877823358

tasksource-instruct

Instruction-tuning data recast from the ~480 English classification, multiple-choice and token-classification tasks of tasksource.

Every example comes from a human-built dataset (NLI, logical reasoning, sentiment, hate speech, discourse, argumentation, ...), not from a teacher model. Each task is capped at 30k training examples, so no task dominates. Many tasks aren't in FLAN v2, for example DynaSent, DynaHate, discriminative bAbI, epistemic logic, RuleTaker, veridicality and dozens of NLI datasets.

from datasets import load_dataset

ds = load_dataset("tasksource/tasksource-instruct", split="train")
ds = ds.filter(lambda use: use == "commercial", input_columns="license_use")  # optional

Format

column	content
`inputs`	the instruction, the example, and the answer options
`targets`	the answer: an option (`entailment.`), a letter (`B.`), or `word: TAG` lines for token tasks
`task`	the tasksource task id
`license`, `license_use`	the source's licenses, see below

Prompts ask for the answer with no explanation, so the short targets don't teach a model to stop explaining in general. Tasks are interleaved round-robin, so any slice of the split mixes them. Validation and test keep up to 500 examples per task.

tasksource-instruct works well mixed with FLAN v2 or other instruction data. It covers discriminative reasoning tasks that those sets cover less.

For preference pairs built from the same rows, see tasksource_dpo_pairs. For soft labels, ratings and multi-question requests, see tasksource-jev-typed-decisions.

Reproducibility

The dataset is built by scripts/build_instruct_dataset.py:

PYTHONPATH=.:src python scripts/build_instruct_dataset.py --finalize

Sources are loaded at pinned Hub revisions. sources.yaml records, per task, the Hub dataset, revision, original dataset, licenses and row counts, and build-report.jsonl records the code commit of each task's build. MMLU, BIG-bench and BLiMP are left out, so they stay clean for evaluation. Other public benchmarks (GLUE, SuperGLUE, HellaSwag, PIQA, ...) are in the data through their training splits.

License and scope

Tasksource harmonizes datasets from many publishers; their original licenses and terms still apply, hence license: other.

license lists the license of the Hub dataset card the task was loaded from, and of the original dataset behind a tasksource copy. It also lists licenses recorded by the Data Provenance Initiative, marked (DPI).
license_use takes the most restrictive of those: non-commercial if any is non-commercial or academic-only, commercial if one allows commercial use (share-alike and copyleft included), and unspecified otherwise.

This is a best-effort aid, not legal advice. Check the original terms before relying on them.

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)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1361/",
    pages = "15655--15684",
}