metadata

annotations_creators:
  - crowdsourced
  - expert-generated
language:
  - en
license:
  - apache-2.0
multilinguality:
  - monolingual
size_categories:
  - 100M<n<1B
task_categories:
  - other
pretty_name: P3
dataset_info:
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configs:
  - config_name: adversarial_qa_dbert_answer_the_following_q
    data_files:
      - split: train
        path: adversarial_qa_dbert_answer_the_following_q/train-*
      - split: validation
        path: adversarial_qa_dbert_answer_the_following_q/validation-*
  - config_name: adversarial_qa_dbert_based_on
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        path: adversarial_qa_dbert_based_on/train-*
      - split: validation
        path: adversarial_qa_dbert_based_on/validation-*
  - config_name: adversarial_qa_dbert_generate_question
    data_files:
      - split: train
        path: adversarial_qa_dbert_generate_question/train-*
      - split: validation
        path: adversarial_qa_dbert_generate_question/validation-*
      - split: test
        path: adversarial_qa_dbert_generate_question/test-*
  - config_name: adversarial_qa_dbert_question_context_answer
    data_files:
      - split: train
        path: adversarial_qa_dbert_question_context_answer/train-*
      - split: validation
        path: adversarial_qa_dbert_question_context_answer/validation-*
  - config_name: adversarial_qa_dbert_tell_what_it_is
    data_files:
      - split: train
        path: adversarial_qa_dbert_tell_what_it_is/train-*
      - split: validation
        path: adversarial_qa_dbert_tell_what_it_is/validation-*
  - config_name: adversarial_qa_dbidaf_answer_the_following_q
    data_files:
      - split: train
        path: adversarial_qa_dbidaf_answer_the_following_q/train-*
      - split: validation
        path: adversarial_qa_dbidaf_answer_the_following_q/validation-*
  - config_name: adversarial_qa_dbidaf_based_on
    data_files:
      - split: train
        path: adversarial_qa_dbidaf_based_on/train-*
      - split: validation
        path: adversarial_qa_dbidaf_based_on/validation-*
  - config_name: adversarial_qa_dbidaf_generate_question
    data_files:
      - split: train
        path: adversarial_qa_dbidaf_generate_question/train-*
      - split: validation
        path: adversarial_qa_dbidaf_generate_question/validation-*
      - split: test
        path: adversarial_qa_dbidaf_generate_question/test-*
  - config_name: adversarial_qa_dbidaf_question_context_answer
    data_files:
      - split: train
        path: adversarial_qa_dbidaf_question_context_answer/train-*
      - split: validation
        path: adversarial_qa_dbidaf_question_context_answer/validation-*
  - config_name: adversarial_qa_dbidaf_tell_what_it_is
    data_files:
      - split: train
        path: adversarial_qa_dbidaf_tell_what_it_is/train-*
      - split: validation
        path: adversarial_qa_dbidaf_tell_what_it_is/validation-*
  - config_name: adversarial_qa_droberta_answer_the_following_q
    data_files:
      - split: train
        path: adversarial_qa_droberta_answer_the_following_q/train-*
      - split: validation
        path: adversarial_qa_droberta_answer_the_following_q/validation-*
  - config_name: adversarial_qa_droberta_based_on
    data_files:
      - split: train
        path: adversarial_qa_droberta_based_on/train-*
      - split: validation
        path: adversarial_qa_droberta_based_on/validation-*
  - config_name: adversarial_qa_droberta_generate_question
    data_files:
      - split: train
        path: adversarial_qa_droberta_generate_question/train-*
      - split: validation
        path: adversarial_qa_droberta_generate_question/validation-*
      - split: test
        path: adversarial_qa_droberta_generate_question/test-*

Dataset Card for P3

Table of Contents
Dataset Description
Dataset Structure
Dataset Creation
Additional Information

Dataset Description

Homepage: https://bigscience.huggingface.co/promptsource
Repository: https://github.com/bigscience-workshop/promptsource/
Paper: Multitask Prompted Training Enables Zero-Shot Task Generalization
Point of Contact: Victor Sanh

Dataset Summary

P3 (Public Pool of Prompts) is a collection of prompted English datasets covering a diverse set of NLP tasks. A prompt is the combination of an input template and a target template. The templates are functions mapping a data example into natural language for the input and target sequences. For example, in the case of an NLI dataset, the data example would include fields for Premise, Hypothesis, Label. An input template would be If {Premise} is true, is it also true that {Hypothesis}?, whereas a target template can be defined with the label choices Choices[label]. Here Choices is prompt-specific metadata that consists of the options yes, maybe, no corresponding to label being entailment (0), neutral (1) or contradiction (2).

Prompts are collected using Promptsource, an interface to interactively write prompts on datasets, and collect prompt-specific metadata such as evaluation metrics. As of October 13th, there are 2'000 prompts collected for 270+ data(sub)sets. The collection of prompts of P3 is publicly available on Promptsource.

To train T0*, we used a subset of the prompts available in Promptsource (see details here). However, some of the prompts use random.choice, a method that selects uniformly at random an option in a list of valid possibilities. For reproducibility purposes, we release the collection of prompted examples used to train T0*. The data available here are the materialized version of the prompted datasets used in Multitask Prompted Training Enables Zero-Shot Task Generalization which represent only a subset of the datasets for which there is at least one prompt in Promptsource.

Supported Tasks and Leaderboards

The tasks represented in P3 cover a diverse set of NLP tasks including multiple-choice QA, sentiment analysis or natural language inference. We detail the full list of datasets in Source Data.

Languages

The data in P3 are in English (BCP-47 en).

Dataset Structure

Data Instances

An example of "train" looks as follows:

{
  'answer_choices': ['safe', 'trolley'],
  'inputs': [86, 8, 7142, 666, 6, 405, 8, 3, 834, 1518, 21, 1346, 42, 31682, 58, 37, 3, 929, 9, 3042, 63, 2765, 808, 8, 2045, 6448, 326, 13, 8, 31682, 11, 3, 24052, 135, 16, 8, 1346, 552, 8, 3, 834, 47, 6364, 5], 'inputs_pretokenized': 'In the sentence below, does the _ stand for safe or trolley?\nThe treasury workers took the gold bars off of the trolley and stacked them in the safe until the _ was empty.',
  'targets': [31682, 1],
  'targets_pretokenized': '\ntrolley'
}

In the case of rank classification (letting the model select its the prediction the option with the highest log-likelihood), an example looks as follows:

{
  'idx': [5, 0],
  'inputs': [86, 8, 7142, 666, 6, 405, 8, 3, 834, 1518, 21, 19454, 42, 22227, 58, 19454, 744, 31, 17, 2112, 4553, 17742, 7, 12, 1953, 6, 298, 22227, 966, 373, 405, 5, 3, 834, 19, 72, 952, 12, 619, 16, 3, 9, 17742, 3298, 5],
  'inputs_pretokenized': "In the sentence below, does the _ stand for Kyle or Logan?\nKyle doesn't wear leg warmers to bed, while Logan almost always does. _ is more likely to live in a warmer climate.",
  'is_correct': True,
  'targets': [19454, 1],
  'targets_pretokenized': 'Kyle',
  'weight': 1.0
}

To check all the prompted examples, you can use the Promptsource hosted tool and choose the Prompted dataset viewer mode in the left panel.

Data Fields

The data fields are the same among all splits:

answer_choices: the choices (in natural language) available to the model
inputs_pretokenized: the natural language input fed to the model
targets_pretokenized: the natural language target that the model has to generate
inputs: the tokenized input with T5's tokenizer
targets: the tokenized target with T5's tokenizer
idx: identifier of the (example, answer_option_id) in the case of rank classification
weight: a weight for the example produced by seqio (always set to 1.0 in practise)
is_correct: whether the (example, answer_option_id) is the correct one

Data Splits

The list of data splits and their respective sizes is very long. You'll find the whole list in this file.

Dataset Creation

Curation Rationale

The Public Pool of Prompts relies on the Hugging Face Dataset library. Any public dataset in the Datasets library can be prompted. We select the datasets that have at least one subset in English and excluded datasets containing (predominantly) non-natural language examples.

We conservatively decided not to prompt datasets that contain potentially harmful content (for instance, datasets built on social media content). However, we sometimes prompt datasets that are purposefully built to measure bias and fairness of trained models, and reserve these prompted datasets (the validation or test sets) for evaluation purposes.

Source Data

Here's the full list of the datasets present in the materialized version of P3:

Multiple-Choice QA
- CommonsenseQA
- DREAM
- QUAIL
- QuaRTz
- Social IQA
- WiQA
- Cosmos
- QASC
- Quarel
- SciQ
- Wiki Hop
- ARC
- OpenBookQA
- MultiRC
- PIQA
- RACE
- HellaSwag
- BoolQ
Extractive QA
- Adversarial QA
- Quoref
- DuoRC
- ROPES
- SQuAD v2
- ReCoRD
Close-book QA
- Hotpot QA
- Wiki QA
- Trivia QA
- Web Questions
Structure-to-text
- Common Gen
- Wiki Bio
Sentiment
- Amazon
- App Reviews
- IMDB
- Rotten Tomatoes
- Yelp
Summarization
- CNN Daily Mail
- Gigaword
- MultiNews
- SamSum
- XSum
Topic Classification
- AG News
- DBPedia
- TREC
Paraphrase Identification
- MRPC
- PAWS
- QQP
Natural Language Inference
- ANLI
- CB
- RTE
Coreference Resolution
- WSC
- Winogrande
Word Sense disambiguation
- WiC
Sentence Completion
- COPA
- HellaSwag
- Story Cloze

Annotations

The prompts available in Promptsource are collected as part of BigScience, one-year long research workshop on large multilingual models and datasets. 36 contributors affiliated with 24 institutions in 8 countries participated to the prompt collection. Contributors are in majority machine learning researchers or machine learning engineers.

The main annotation guideline was that prompts needed to be grammatical and understandable by a native English speaker with no prior experience of the tasks. Additionally, prompts that required explicit counting or numerical indexing were removed in favor of natural language variants, e.g., instead of predicting indices of a span to extract (e.g. in extractive question answering), the model was expected to copy the span's text instead. With these minimal constraints, prompt writers were encouraged to use both formal and creative prompts and various orderings of the data. Most of the prompts correspond directly to a version of the original proposed task, although we also allowed prompts that permuted the original task (for instance, generating a document from its summary) or allowed for ambiguous output (for instance, not indicating a list of available choices).

The full annotation given to the contributors can be found here. *Note to self: the link is currently being updated with the)

Additional Information

Licensing Information

The dataset is released under Apache 2.0.

Citation Information

@misc{sanh2021multitask,
      title={Multitask Prompted Training Enables Zero-Shot Task Generalization},
      author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Stella Biderman and Leo Gao and Tali Bers and Thomas Wolf and Alexander M. Rush},
      year={2021},
      eprint={2110.08207},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Contributions

Thanks to the contributors of promptsource for adding this dataset.