Datasets:
Tasks:
Text Classification
Sub-tasks:
natural-language-inference
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
metadata
annotations_creators:
- expert-generated
language_creators:
- expert-generated
languages:
- en
licenses:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: hans
pretty_name: Heuristic Analysis for NLI Systems
Dataset Card for "hans"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://github.com/tommccoy1/hans
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 29.51 MB
- Size of the generated dataset: 30.34 MB
- Total amount of disk used: 59.85 MB
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
We show detailed information for up to 5 configurations of the dataset.
Data Instances
plain_text
- Size of downloaded dataset files: 29.51 MB
- Size of the generated dataset: 30.34 MB
- Total amount of disk used: 59.85 MB
An example of 'train' looks as follows.
Data Fields
The data fields are the same among all splits.
plain_text
premise
: astring
feature.hypothesis
: astring
feature.label
: a classification label, with possible values includingentailment
(0),non-entailment
(1).parse_premise
: astring
feature.parse_hypothesis
: astring
feature.binary_parse_premise
: astring
feature.binary_parse_hypothesis
: astring
feature.heuristic
: astring
feature.subcase
: astring
feature.template
: astring
feature.
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.