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Dataset: quarel 🏷
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How to load this dataset directly with the πŸ€—/datasets library:

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from datasets import load_dataset dataset = load_dataset("quarel")


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Models trained or fine-tuned on quarel

Dataset Card for "quarel"

Table of Contents

Dataset Description

Dataset Summary

QuaRel is a crowdsourced dataset of 2771 multiple-choice story questions, including their logical forms.

Supported Tasks

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Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances


  • Size of downloaded dataset files: 0.60 MB
  • Size of the generated dataset: 1.46 MB
  • Total amount of disk used: 2.07 MB

An example of 'train' looks as follows.

    "answer_index": 0,
    "id": "QuaRel_V1_B5_1403",
    "logical_form_pretty": "qrel(time, lower, world1) -> qrel(distance, higher, world2) ; qrel(distance, higher, world1)",
    "logical_forms": ["(infer (time lower world1) (distance higher world2) (distance higher world1))", "(infer (time lower world2) (distance higher world1) (distance higher world2))"],
    "question": "John and Rita are going for a run.  Rita gets tired and takes a break on the park bench.  After twenty minutes in the park, who has run farther? (A) John (B) Rita",
    "world_literals": {
        "world1": ["Rita"],
        "world2": ["John"]

Data Fields

The data fields are the same among all splits.


  • id: a string feature.
  • answer_index: a int32 feature.
  • logical_forms: a list of string features.
  • logical_form_pretty: a string feature.
  • world_literals: a dictionary feature containing:
    • world1: a string feature.
    • world2: a string feature.
  • question: a string feature.

Data Splits Sample Size

name train validation test
default 1941 278 552

Dataset Creation

Curation Rationale

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Source Data

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

    title={QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships},
    author={Oyvind Tafjord, Peter Clark, Matt Gardner, Wen-tau Yih, Ashish Sabharwal},