The Mathematics Dataset, developed by Google DeepMind, is a synthetic dataset designed to evaluate and train machine learning models on mathematical reasoning and symbolic manipulation. It generates question-and-answer pairs across a wide range of mathematical topics typically found in school-level curricula, testing a model’s ability to reason about algebra, arithmetic, calculus, probability, and more. Each question is programmatically generated with structured templates to ensure clear logic and reproducibility. The dataset enables models to learn mathematical problem-solving through examples that involve both numeric and symbolic reasoning. Version 1.0 includes over 2 million examples per category, with training splits labeled as “easy,” “medium,” and “hard,” supporting curriculum-based learning strategies. The data can be accessed via PyPI or generated locally using provided Python scripts, with outputs formatted for direct use in training or evaluation pipelines.
Features
- Generates large-scale question-answer pairs for mathematical reasoning tasks
- Covers multiple domains: algebra, arithmetic, calculus, probability, and more
- Provides difficulty-based splits (easy, medium, hard) for curriculum learning
- Fully customizable data generation through Python scripts
- Supports symbolic and numerical reasoning across diverse math concepts
- Available via PyPI or GitHub for easy access and integration
Project Samples
