
# Dataset Card for "math_dataset"

## 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://github.com/deepmind/mathematics_dataset](https://github.com/deepmind/mathematics_dataset)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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:** 130.65 GB
- **Size of the generated dataset:** 9.08 GB
- **Total amount of disk used:** 139.73 GB

### Dataset Summary

Mathematics database.

This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).

Example usage:
train_examples, val_examples = datasets.load_dataset(
    'math_dataset/arithmetic__mul',
    split=['train', 'test'],
    as_supervised=True)

### 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

#### algebra__linear_1d

- **Size of downloaded dataset files:** 2.33 GB
- **Size of the generated dataset:** 92.60 MB
- **Total amount of disk used:** 2.43 GB

An example of 'train' looks as follows.
```

```

#### algebra__linear_1d_composed

- **Size of downloaded dataset files:** 2.33 GB
- **Size of the generated dataset:** 200.58 MB
- **Total amount of disk used:** 2.53 GB

An example of 'train' looks as follows.
```

```

#### algebra__linear_2d

- **Size of downloaded dataset files:** 2.33 GB
- **Size of the generated dataset:** 127.41 MB
- **Total amount of disk used:** 2.46 GB

An example of 'train' looks as follows.
```

```

#### algebra__linear_2d_composed

- **Size of downloaded dataset files:** 2.33 GB
- **Size of the generated dataset:** 235.59 MB
- **Total amount of disk used:** 2.57 GB

An example of 'train' looks as follows.
```

```

#### algebra__polynomial_roots

- **Size of downloaded dataset files:** 2.33 GB
- **Size of the generated dataset:** 164.01 MB
- **Total amount of disk used:** 2.50 GB

An example of 'train' looks as follows.
```

```

### Data Fields

The data fields are the same among all splits.

#### algebra__linear_1d
- `question`: a `string` feature.
- `answer`: a `string` feature.

#### algebra__linear_1d_composed
- `question`: a `string` feature.
- `answer`: a `string` feature.

#### algebra__linear_2d
- `question`: a `string` feature.
- `answer`: a `string` feature.

#### algebra__linear_2d_composed
- `question`: a `string` feature.
- `answer`: a `string` feature.

#### algebra__polynomial_roots
- `question`: a `string` feature.
- `answer`: a `string` feature.

### Data Splits

|           name            | train |test |
|---------------------------|------:|----:|
|algebra__linear_1d         |1999998|10000|
|algebra__linear_1d_composed|1999998|10000|
|algebra__linear_2d         |1999998|10000|
|algebra__linear_2d_composed|1999998|10000|
|algebra__polynomial_roots  |1999998|10000|

## 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

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

### Citation Information

```

@article{2019arXiv,
  author = {Saxton, Grefenstette, Hill, Kohli},
  title = {Analysing Mathematical Reasoning Abilities of Neural Models},
  year = {2019},
  journal = {arXiv:1904.01557}
}

```


### Contributions

Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
language:en region:us
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TAGS
#language-English #region-us 

Dataset Card for "math\_dataset"
================================


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:
* Point of Contact:
* Size of downloaded dataset files: 130.65 GB
* Size of the generated dataset: 9.08 GB
* Total amount of disk used: 139.73 GB


### Dataset Summary


Mathematics database.


This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.


Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).


Example usage:
train\_examples, val\_examples = datasets.load\_dataset(
'math\_dataset/arithmetic\_\_mul',
split=['train', 'test'],
as\_supervised=True)


### Supported Tasks and Leaderboards


### Languages


Dataset Structure
-----------------


### Data Instances


#### algebra\_\_linear\_1d


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 92.60 MB
* Total amount of disk used: 2.43 GB


An example of 'train' looks as follows.


#### algebra\_\_linear\_1d\_composed


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 200.58 MB
* Total amount of disk used: 2.53 GB


An example of 'train' looks as follows.


#### algebra\_\_linear\_2d


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 127.41 MB
* Total amount of disk used: 2.46 GB


An example of 'train' looks as follows.


#### algebra\_\_linear\_2d\_composed


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 235.59 MB
* Total amount of disk used: 2.57 GB


An example of 'train' looks as follows.


#### algebra\_\_polynomial\_roots


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 164.01 MB
* Total amount of disk used: 2.50 GB


An example of 'train' looks as follows.


### Data Fields


The data fields are the same among all splits.


#### algebra\_\_linear\_1d


* 'question': a 'string' feature.
* 'answer': a 'string' feature.


#### algebra\_\_linear\_1d\_composed


* 'question': a 'string' feature.
* 'answer': a 'string' feature.


#### algebra\_\_linear\_2d


* 'question': a 'string' feature.
* 'answer': a 'string' feature.


#### algebra\_\_linear\_2d\_composed


* 'question': a 'string' feature.
* 'answer': a 'string' feature.


#### algebra\_\_polynomial\_roots


* 'question': a 'string' feature.
* 'answer': 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


### Contributions


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



### Dataset Summary


Mathematics database.


This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.


Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).


Example usage:
train\_examples, val\_examples = datasets.load\_dataset(
'math\_dataset/arithmetic\_\_mul',
split=['train', 'test'],
as\_supervised=True) ### Supported Tasks and Leaderboards ### Languages


Dataset Structure
----------------- ### Data Instances #### algebra\_\_linear\_1d


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 92.60 MB
* Total amount of disk used: 2.43 GB


An example of 'train' looks as follows. #### algebra\_\_linear\_1d\_composed


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 200.58 MB
* Total amount of disk used: 2.53 GB


An example of 'train' looks as follows. #### algebra\_\_linear\_2d


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 127.41 MB
* Total amount of disk used: 2.46 GB


An example of 'train' looks as follows. #### algebra\_\_linear\_2d\_composed


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 235.59 MB
* Total amount of disk used: 2.57 GB


An example of 'train' looks as follows. #### algebra\_\_polynomial\_roots


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 164.01 MB
* Total amount of disk used: 2.50 GB


An example of 'train' looks as follows. ### Data Fields


The data fields are the same among all splits. #### algebra\_\_linear\_1d


* 'question': a 'string' feature.
* 'answer': a 'string' feature. #### algebra\_\_linear\_1d\_composed


* 'question': a 'string' feature.
* 'answer': a 'string' feature. #### algebra\_\_linear\_2d


* 'question': a 'string' feature.
* 'answer': a 'string' feature. #### algebra\_\_linear\_2d\_composed


* 'question': a 'string' feature.
* 'answer': a 'string' feature. #### algebra\_\_polynomial\_roots


* 'question': a 'string' feature.
* 'answer': 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 ### Contributions


Thanks to @patrickvonplaten, @lewtun, @thomwolf for adding this dataset.
TAGS
#language-English #region-us 
 ### Dataset Summary


Mathematics database.


This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.


Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).


Example usage:
train\_examples, val\_examples = datasets.load\_dataset(
'math\_dataset/arithmetic\_\_mul',
split=['train', 'test'],
as\_supervised=True) ### Supported Tasks and Leaderboards ### Languages


Dataset Structure
----------------- ### Data Instances #### algebra\_\_linear\_1d


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 92.60 MB
* Total amount of disk used: 2.43 GB


An example of 'train' looks as follows. #### algebra\_\_linear\_1d\_composed


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 200.58 MB
* Total amount of disk used: 2.53 GB


An example of 'train' looks as follows. #### algebra\_\_linear\_2d


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 127.41 MB
* Total amount of disk used: 2.46 GB


An example of 'train' looks as follows. #### algebra\_\_linear\_2d\_composed


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 235.59 MB
* Total amount of disk used: 2.57 GB


An example of 'train' looks as follows. #### algebra\_\_polynomial\_roots


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 164.01 MB
* Total amount of disk used: 2.50 GB


An example of 'train' looks as follows. ### Data Fields


The data fields are the same among all splits. #### algebra\_\_linear\_1d


* 'question': a 'string' feature.
* 'answer': a 'string' feature. #### algebra\_\_linear\_1d\_composed


* 'question': a 'string' feature.
* 'answer': a 'string' feature. #### algebra\_\_linear\_2d


* 'question': a 'string' feature.
* 'answer': a 'string' feature. #### algebra\_\_linear\_2d\_composed


* 'question': a 'string' feature.
* 'answer': a 'string' feature. #### algebra\_\_polynomial\_roots


* 'question': a 'string' feature.
* 'answer': 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 ### Contributions


Thanks to @patrickvonplaten, @lewtun, @thomwolf for adding this dataset.
passage: TAGS
#language-English #region-us 
### Dataset Summary


Mathematics database.


This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.


Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).


Example usage:
train\_examples, val\_examples = datasets.load\_dataset(
'math\_dataset/arithmetic\_\_mul',
split=['train', 'test'],
as\_supervised=True)### Supported Tasks and Leaderboards### Languages


Dataset Structure
-----------------### Data Instances#### algebra\_\_linear\_1d


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 92.60 MB
* Total amount of disk used: 2.43 GB


An example of 'train' looks as follows.#### algebra\_\_linear\_1d\_composed


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 200.58 MB
* Total amount of disk used: 2.53 GB


An example of 'train' looks as follows.#### algebra\_\_linear\_2d


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 127.41 MB
* Total amount of disk used: 2.46 GB


An example of 'train' looks as follows.#### algebra\_\_linear\_2d\_composed


* Size of downloaded dataset files: 2.33 GB
* Size of the generated dataset: 235.59 MB
* Total amount of disk used: 2.57 GB


An example of 'train' looks as follows.