tasksource/deberta-small-long-nli
Zero-Shot Classification
•
Updated
•
59.9k
•
38
Datasets:
Tasks:
Question Answering
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
10K<n<100K
License:
Dataset Viewer
Full Screen Viewer
Full Screen
The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider
removing the
loading script
and relying on
automated data support
(you can use
convert_to_parquet
from the datasets library). If this is not possible, please
open a discussion
for direct help.
Dataset Card for MathQA
Dataset Summary
We introduce a large-scale dataset of math word problems.
Our dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset with fully-specified operational programs.
AQuA-RAT has provided the questions, options, rationale, and the correct options.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
default
- Size of downloaded dataset files: 7.30 MB
- Size of the generated dataset: 22.96 MB
- Total amount of disk used: 30.27 MB
An example of 'train' looks as follows.
{
"Problem": "a multiple choice test consists of 4 questions , and each question has 5 answer choices . in how many r ways can the test be completed if every question is unanswered ?",
"Rationale": "\"5 choices for each of the 4 questions , thus total r of 5 * 5 * 5 * 5 = 5 ^ 4 = 625 ways to answer all of them . answer : c .\"",
"annotated_formula": "power(5, 4)",
"category": "general",
"correct": "c",
"linear_formula": "power(n1,n0)|",
"options": "a ) 24 , b ) 120 , c ) 625 , d ) 720 , e ) 1024"
}
Data Fields
The data fields are the same among all splits.
default
Problem: astringfeature.Rationale: astringfeature.options: astringfeature.correct: astringfeature.annotated_formula: astringfeature.linear_formula: astringfeature.category: astringfeature.
Data Splits
| name | train | validation | test |
|---|---|---|---|
| default | 29837 | 4475 | 2985 |
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
The dataset is licensed under the Apache License, Version 2.0.
Citation Information
@inproceedings{amini-etal-2019-mathqa,
title = "{M}ath{QA}: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms",
author = "Amini, Aida and
Gabriel, Saadia and
Lin, Shanchuan and
Koncel-Kedziorski, Rik and
Choi, Yejin and
Hajishirzi, Hannaneh",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1245",
doi = "10.18653/v1/N19-1245",
pages = "2357--2367",
}
Contributions
Thanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset.
- Downloads last month
- 25,694
Homepage:
math-qa.github.io
Paper:
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
Size of downloaded dataset files:
7.30 MB
