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metadata
pretty_name: Mathematics Dataset
language:
  - en
paperswithcode_id: mathematics
dataset_info:
  - config_name: algebra__linear_1d
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      - name: question
        dtype: string
      - name: answer
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  - config_name: algebra__polynomial_roots
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  - config_name: algebra__sequence_next_term
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  - config_name: numbers__base_conversion
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      - name: answer
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  - config_name: probability__swr_p_level_set
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Dataset Card for "math_dataset"

Table of Contents

Dataset Description

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

Languages

More Information Needed

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

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

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, @lewtun, @thomwolf for adding this dataset.