Skip to content
master
Switch branches/tags
Code

Latest commit

Refactor deprecated unittest aliases for Python 3.11 compatibility.
900a081

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 

Mathematics Dataset

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 questions

Question: Solve -42*r + 27*c = -1167 and 130*r + 4*c = 372 for r.
Answer: 4

Question: Calculate -841880142.544 + 411127.
Answer: -841469015.544

Question: Let x(g) = 9*g + 1. Let q(c) = 2*c + 1. Let f(i) = 3*i - 39. Let w(j) = q(x(j)). Calculate f(w(a)).
Answer: 54*a - 30

Question: Let e(l) = l - 6. Is 2 a factor of both e(9) and 2?
Answer: False

Question: Let u(n) = -n**3 - n**2. Let e(c) = -2*c**3 + c. Let l(j) = -118*e(j) + 54*u(j). What is the derivative of l(a)?
Answer: 546*a**2 - 108*a - 118

Question: Three letters picked without replacement from qqqkkklkqkkk. Give prob of sequence qql.
Answer: 1/110

Pre-generated data

Pre-generated files

Version 1.0

This is the version released with the original paper. It contains 2 million (question, answer) pairs per module, with questions limited to 160 characters in length, and answers to 30 characters in length. Note the training data for each question type is split into "train-easy", "train-medium", and "train-hard". This allows training models via a curriculum. The data can also be mixed together uniformly from these training datasets to obtain the results reported in the paper. Categories:

  • algebra (linear equations, polynomial roots, sequences)
  • arithmetic (pairwise operations and mixed expressions, surds)
  • calculus (differentiation)
  • comparison (closest numbers, pairwise comparisons, sorting)
  • measurement (conversion, working with time)
  • numbers (base conversion, remainders, common divisors and multiples, primality, place value, rounding numbers)
  • polynomials (addition, simplification, composition, evaluating, expansion)
  • probability (sampling without replacement)

Getting the source

PyPI

The easiest way to get the source is to use pip:

$ pip install mathematics_dataset

From GitHub

Alternately you can get the source by cloning the mathematics_dataset repository:

$ git clone https://github.com/deepmind/mathematics_dataset
$ pip install --upgrade mathematics_dataset/

Generating examples

Generated examples can be printed to stdout via the generate script. For example:

python -m mathematics_dataset.generate --filter=linear_1d

will generate example (question, answer) pairs for solving linear equations in one variable.

We've also included generate_to_file.py as an example of how to write the generated examples to text files. You can use this directly, or adapt it for your generation and training needs.

Dataset Metadata

The following table is necessary for this dataset to be indexed by search engines such as Google Dataset Search.

property value
name Mathematics Dataset
url
sameAs https://github.com/deepmind/mathematics_dataset
description This dataset consists of 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.\n \n ## Example questions\n \n ```\n Question: Solve -42*r + 27*c = -1167 and 130*r + 4*c = 372 for r.\n Answer: 4\n \n Question: Calculate -841880142.544 + 411127.\n Answer: -841469015.544\n \n Question: Let x(g) = 9*g + 1. Let q(c) = 2*c + 1. Let f(i) = 3*i - 39. Let w(j) = q(x(j)). Calculate f(w(a)).\n Answer: 54*a - 30\n ```\n \n It contains 2 million (question, answer) pairs per module, with questions limited to 160 characters in length, and answers to 30 characters in length. Note the training data for each question type is split into "train-easy", "train-medium", and "train-hard". This allows training models via a curriculum. The data can also be mixed together uniformly from these training datasets to obtain the results reported in the paper. Categories:\n \n * **algebra** (linear equations, polynomial roots, sequences)\n * **arithmetic** (pairwise operations and mixed expressions, surds)\n * **calculus** (differentiation)\n * **comparison** (closest numbers, pairwise comparisons, sorting)\n * **measurement** (conversion, working with time)\n * **numbers** (base conversion, remainders, common divisors and multiples,\n primality, place value, rounding numbers)\n * **polynomials** (addition, simplification, composition, evaluating, expansion)\n * **probability** (sampling without replacement)
provider
property value
name DeepMind
sameAs https://en.wikipedia.org/wiki/DeepMind
citation https://identifiers.org/arxiv:1904.01557

About

This dataset code generates mathematical question and answer pairs, from a range of question types at roughly school-level difficulty.

Resources

License

Releases

No releases published

Packages

No packages published

Languages