math_qa

• Description:

A large-scale dataset of math word problems and an interpretable neural math problem solver that learns to map problems to operation programs.

• Additional Documentation: Explore on Papers With Code

• Homepage: https://math-qa.github.io/

• Source code: tfds.datasets.math_qa.Builder

• Versions:

  • 1.0.0 (default): Initial release.

• Download size: 6.96 MiB

• Dataset size: 27.15 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'test'	2,985
'train'	29,837
'validation'	4,475

• Feature structure:

FeaturesDict({
    'Problem': Text(shape=(), dtype=string),
    'Rationale': Text(shape=(), dtype=string),
    'annotated_formula': Text(shape=(), dtype=string),
    'category': Text(shape=(), dtype=string),
    'correct': Text(shape=(), dtype=string),
    'correct_option': Text(shape=(), dtype=string),
    'linear_formula': Text(shape=(), dtype=string),
    'options': Text(shape=(), dtype=string),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
Problem	Text		string
Rationale	Text		string
annotated_formula	Text		string
category	Text		string
correct	Text		string
correct_option	Text		string
linear_formula	Text		string
options	Text		string

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@misc{amini2019mathqa,
      title={MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms},
      author={Aida Amini and Saadia Gabriel and Peter Lin and Rik Koncel-Kedziorski and Yejin Choi and Hannaneh Hajishirzi},
      year={2019},
      eprint={1905.13319},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}