# google-deepmind/mathematics_dataset

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

Repository: https://github.com/google-deepmind/mathematics_dataset
Canonical: https://ross.abutalabs.com/products/mathematics_dataset
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2024-12-23T14:21:10+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2716, "days_push": 618, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1965, forks 275 (observed 2026-08-28T04:05:59.900401+00:00)

## What it is
A Python library from DeepMind that generates synthetic mathematical question-and-answer pairs at roughly school-level difficulty across topics like algebra, arithmetic, calculus, and probability. It was created to benchmark the mathematical reasoning abilities of neural models, with pre-generated data available for download.

## Use cases
- generate math question-answer pairs for training models
- benchmark a model's algebraic reasoning skills
- create curriculum learning data with easy/medium/hard splits
- get synthetic arithmetic and calculus word problems
- evaluate neural networks on school-level mathematics
- generate probability and polynomial questions programmatically

## When to choose
- you need large volumes of synthetic math QA data for training or evaluating models
- you want curriculum-style difficulty splits (train-easy/medium/hard)
- you need reproducible school-level math questions across many topics

## When to avoid
- you need real-world or competition-level math problems
- you want a static benchmark dataset rather than a generator
- you need advanced topics beyond school-level math like topology or statistics

## Facets
- artifact type: dataset
- maturity: maintenance
- function: data-generation, math, machine-learning
- domain: mathematics, machine-learning, education
- platform: python, cross-platform
- tags: synthetic-data, mathematical-reasoning, question-answering, benchmark, school-level-math, datasets

## Member repositories
- google-deepmind/mathematics_dataset (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:59.900401+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:05:28.557797+00:00, confidence not recorded.
  - readme: https://github.com/google-deepmind/mathematics_dataset (fetched 2026-08-28T04:05:59.900401+00:00, sha 4030caa4bf83)
- Data as of 2026-08-30T08:39:29.467469+00:00.