
# Dataset Card for MathQA

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [https://math-qa.github.io/math-QA/](https://math-qa.github.io/math-QA/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms](https://aclanthology.org/N19-1245/)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 7.30 MB
- **Size of the generated dataset:** 22.96 MB
- **Total amount of disk used:** 30.27 MB

### 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

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Languages

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## 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`: 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|

## Dataset Creation

### Curation Rationale

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the source language producers?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Annotations

#### Annotation process

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the annotators?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Personal and Sensitive Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Discussion of Biases

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Other Known Limitations

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Additional Information

### Dataset Curators

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Licensing Information

The dataset is licensed under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-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](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
task_categories:question-answering task_ids:multiple-choice-qa annotations_creators:crowdsourced language_creators:crowdsourced language_creators:expert-generated multilinguality:monolingual size_categories:10K<n<100K source_datasets:extended|aqua_rat language:en license:apache-2.0 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced", "expert-generated"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|aqua_rat"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "mathqa", "pretty_name": "MathQA", "dataset_info": {"features": [{"name": "Problem", "dtype": "string"}, {"name": "Rationale", "dtype": "string"}, {"name": "options", "dtype": "string"}, {"name": "correct", "dtype": "string"}, {"name": "annotated_formula", "dtype": "string"}, {"name": "linear_formula", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1844184, "num_examples": 2985}, {"name": "train", "num_bytes": 18368826, "num_examples": 29837}, {"name": "validation", "num_bytes": 2752969, "num_examples": 4475}], "download_size": 7302821, "dataset_size": 22965979}}
TAGS
#task_categories-question-answering #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|aqua_rat #language-English #license-apache-2.0 #region-us 

Dataset Card for MathQA
=======================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ 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
	+ Contributions


Dataset Description
-------------------


* Homepage: URL
* Repository:
* Paper: MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
* Point of Contact:
* Size of downloaded dataset files: 7.30 MB
* Size of the generated dataset: 22.96 MB
* Total amount of disk used: 30.27 MB


### 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.


### 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



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.


### Contributions


Thanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset.



### 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. ### 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



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. ### Contributions


Thanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset.
TAGS
#task_categories-question-answering #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|aqua_rat #language-English #license-apache-2.0 #region-us 
 ### 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. ### 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



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. ### Contributions


Thanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset.
passage: TAGS
#task_categories-question-answering #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|aqua_rat #language-English #license-apache-2.0 #region-us 
### 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.### 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



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