albertvillanova
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Convert dataset sizes from base 2 to base 10 in the dataset card (#2)
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metadata
language:
- en
paperswithcode_id: wiqa
pretty_name: What-If Question Answering
dataset_info:
features:
- name: question_stem
dtype: string
- name: question_para_step
sequence: string
- name: answer_label
dtype: string
- name: answer_label_as_choice
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: metadata_question_id
dtype: string
- name: metadata_graph_id
dtype: string
- name: metadata_para_id
dtype: string
- name: metadata_question_type
dtype: string
- name: metadata_path_len
dtype: int32
splits:
- name: train
num_bytes: 17089298
num_examples: 29808
- name: test
num_bytes: 1532223
num_examples: 3003
- name: validation
num_bytes: 3779584
num_examples: 6894
download_size: 5247733
dataset_size: 22401105
Dataset Card for "wiqa"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://allenai.org/data/wiqa
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 5.24 MB
- Size of the generated dataset: 22.40 MB
- Total amount of disk used: 27.65 MB
Dataset Summary
The WIQA dataset V1 has 39705 questions containing a perturbation and a possible effect in the context of a paragraph. The dataset is split into 29808 train questions, 6894 dev questions and 3003 test questions.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
default
- Size of downloaded dataset files: 5.24 MB
- Size of the generated dataset: 22.40 MB
- Total amount of disk used: 27.65 MB
An example of 'validation' looks as follows.
{
"answer_label": "more",
"answer_label_as_choice": "A",
"choices": {
"label": ["A", "B", "C"],
"text": ["more", "less", "no effect"]
},
"metadata_graph_id": "481",
"metadata_para_id": "528",
"metadata_path_len": 3,
"metadata_question_id": "influence_graph:528:481:77#0",
"metadata_question_type": "INPARA_EFFECT",
"question_para_step": ["A male and female rabbit mate", "The female rabbit becomes pregnant", "Baby rabbits form inside of the mother rabbit", "The female rabbit gives birth to a litter", "The newborn rabbits grow up to become adults", "The adult rabbits find mates."],
"question_stem": "suppose the female is sterile happens, how will it affect LESS rabbits."
}
Data Fields
The data fields are the same among all splits.
default
question_stem
: astring
feature.question_para_step
: alist
ofstring
features.answer_label
: astring
feature.answer_label_as_choice
: astring
feature.choices
: a dictionary feature containing:text
: astring
feature.label
: astring
feature.
metadata_question_id
: astring
feature.metadata_graph_id
: astring
feature.metadata_para_id
: astring
feature.metadata_question_type
: astring
feature.metadata_path_len
: aint32
feature.
Data Splits
name | train | validation | test |
---|---|---|---|
default | 29808 | 6894 | 3003 |
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{wiqa,
author = {Niket Tandon and Bhavana Dalvi Mishra and Keisuke Sakaguchi and Antoine Bosselut and Peter Clark}
title = {WIQA: A dataset for "What if..." reasoning over procedural text},
journal = {arXiv:1909.04739v1},
year = {2019},
}
Contributions
Thanks to @patrickvonplaten, @lewtun, @thomwolf, @mariamabarham, @lhoestq for adding this dataset.