task_categories:text-generation size_categories:10K<n<100K language:en license:mit arxiv:2103.03874 region:us
MATH is a dataset of 12,500 challenging competition mathematics problems. Each
problem in Math has a full step-by-step solution which can be used to teach
models to generate answer derivations and explanations.
@article{hendrycksmath2021,
  title={Measuring Mathematical Problem Solving With the Math Dataset},
  author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},
  journal={NeurIPS},
  year={2021}
}
---
license: mit
task_categories:
- text-generation
language:
- en
pretty_name: MATH
size_categories:
- 10K<n<100K
---

# Dataset Card for Dataset Name

## Dataset Description

- **Homepage:** https://github.com/hendrycks/math/blob/main/README.md
- **Repository:** https://github.com/hendrycks/math
- **Paper:** https://arxiv.org/abs/2103.03874

### Dataset Summary

MATH contains 12,500 challenging competition mathematics problems. Each problem in MATH has a full step-by-step solution which can be used to teach models to generate answer derivations and explanation
This dataset card aims to be a base template for new datasets.


### Languages

[English]

## Dataset Structure

### Data Instances

7 sub-datasets

### Data Splits

training: 7500
test: 5000


## Additional Information


### Licensing Information

MIT but check the [Legal Compliance](https://arxiv.org/pdf/2103.03874.pdf) section in appendix B of the paper as well as the [repo](https://github.com/hendrycks/math/blob/main/LICENSE).

### Citation Information

@article{hendrycksmath2021,
  title={Measuring Mathematical Problem Solving With the MATH Dataset},
  author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},
  journal={NeurIPS},
  year={2021}
}
