# Datasets: allenai /math_qa

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# 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.

## Dataset Structure

### Data Instances

#### default

• 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: a string feature.
• Rationale: a string feature.
• options: a string feature.
• correct: a string feature.
• annotated_formula: a string feature.
• linear_formula: a string feature.
• category: a string feature.

### Data Splits

name train validation test
default 29837 4475 2985

## Considerations for Using the Data

### 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
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",
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.