tasksource/deberta-small-long-nli
Zero-Shot Classification
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Datasets:
Tasks:
Text Classification
Sub-tasks:
natural-language-inference
Languages:
English
Size:
10K<n<100K
ArXiv:
License:
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Dataset Card for "hans"
Dataset Summary
The HANS dataset is an NLI evaluation set that tests specific hypotheses about invalid heuristics that NLI models are likely to learn.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
plain_text
- Size of downloaded dataset files: 30.94 MB
- Size of the generated dataset: 31.81 MB
- Total amount of disk used: 62.76 MB
An example of 'train' looks as follows.
Data Fields
The data fields are the same among all splits.
plain_text
premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),non-entailment(1).parse_premise: astringfeature.parse_hypothesis: astringfeature.binary_parse_premise: astringfeature.binary_parse_hypothesis: astringfeature.heuristic: astringfeature.subcase: astringfeature.template: astringfeature.
Data Splits
| name | train | validation |
|---|---|---|
| plain_text | 30000 | 30000 |
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
@article{DBLP:journals/corr/abs-1902-01007,
author = {R. Thomas McCoy and
Ellie Pavlick and
Tal Linzen},
title = {Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural
Language Inference},
journal = {CoRR},
volume = {abs/1902.01007},
year = {2019},
url = {http://arxiv.org/abs/1902.01007},
archivePrefix = {arXiv},
eprint = {1902.01007},
timestamp = {Tue, 21 May 2019 18:03:36 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1902-01007.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Contributions
Thanks to @TevenLeScao, @thomwolf for adding this dataset.
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Models trained or fine-tuned on jhu-cogsci/hans
