sileod/deberta-v3-small-tasksource-nli
Zero-Shot Classification
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Dataset Card for "winogrande"
Dataset Summary
WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a fill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires commonsense reasoning.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
winogrande_debiased
- Size of downloaded dataset files: 3.40 MB
- Size of the generated dataset: 1.59 MB
- Total amount of disk used: 4.99 MB
An example of 'train' looks as follows.
winogrande_l
- Size of downloaded dataset files: 3.40 MB
- Size of the generated dataset: 1.71 MB
- Total amount of disk used: 5.11 MB
An example of 'validation' looks as follows.
winogrande_m
- Size of downloaded dataset files: 3.40 MB
- Size of the generated dataset: 0.72 MB
- Total amount of disk used: 4.12 MB
An example of 'validation' looks as follows.
winogrande_s
- Size of downloaded dataset files: 3.40 MB
- Size of the generated dataset: 0.47 MB
- Total amount of disk used: 3.87 MB
An example of 'validation' looks as follows.
winogrande_xl
- Size of downloaded dataset files: 3.40 MB
- Size of the generated dataset: 5.58 MB
- Total amount of disk used: 8.98 MB
An example of 'train' looks as follows.
Data Fields
The data fields are the same among all splits.
winogrande_debiased
sentence: astringfeature.option1: astringfeature.option2: astringfeature.answer: astringfeature.
winogrande_l
sentence: astringfeature.option1: astringfeature.option2: astringfeature.answer: astringfeature.
winogrande_m
sentence: astringfeature.option1: astringfeature.option2: astringfeature.answer: astringfeature.
winogrande_s
sentence: astringfeature.option1: astringfeature.option2: astringfeature.answer: astringfeature.
winogrande_xl
sentence: astringfeature.option1: astringfeature.option2: astringfeature.answer: astringfeature.
Data Splits
| name | train | validation | test |
|---|---|---|---|
| winogrande_debiased | 9248 | 1267 | 1767 |
| winogrande_l | 10234 | 1267 | 1767 |
| winogrande_m | 2558 | 1267 | 1767 |
| winogrande_s | 640 | 1267 | 1767 |
| winogrande_xl | 40398 | 1267 | 1767 |
| winogrande_xs | 160 | 1267 | 1767 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@InProceedings{ai2:winogrande,
title = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},
authors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi
},
year={2019}
}
Contributions
Thanks to @thomwolf, @TevenLeScao, @patrickvonplaten, @lewtun for adding this dataset.
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