Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
License:
File size: 2,452 Bytes
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---
language:
- en
license: mit
size_categories:
- 10K<n<100K
task_categories:
- text-generation
pretty_name: MATH
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- config_name: original
data_files:
- split: train
path: original/train-*
- split: test
path: original/test-*
dataset_info:
- config_name: default
features:
- name: problem
dtype: string
- name: level
dtype: string
- name: type
dtype: string
- name: solution
dtype: string
- name: extracted_solution
dtype: string
splits:
- name: train
num_bytes: 6062403
num_examples: 7500
- name: test
num_bytes: 3783919
num_examples: 5000
download_size: 4921628
dataset_size: 9846322
- config_name: original
features:
- name: problem
dtype: string
- name: level
dtype: string
- name: type
dtype: string
- name: solution
dtype: string
- name: extracted_solution
dtype: string
splits:
- name: train
num_bytes: 6062403
num_examples: 7500
- name: test
num_bytes: 3783919
num_examples: 5000
download_size: 4921628
dataset_size: 9846322
---
# Dataset Card for "competition_math"
Added column with final solution extracted from \boxed{} tags.
## 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}
} |