awesome-repositories.com
Blog
MCP
awesome-repositories.com

Discover the best open-source repositories with AI-powered search.

ExploreCurated searchesOpen-source alternativesSelf-hosted softwareBlogSitemap
ProjectMCP serverAboutHow we rankPress
LegalPrivacyTerms
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
google-deepmind avatar

google-deepmind/mathematics_dataset

0
View on GitHub↗

Mathematics Dataset

This project provides a structured repository of school-level mathematical problems designed to train and evaluate the reasoning capabilities of neural network models. It functions as a standardized benchmark for measuring the proficiency of artificial intelligence systems in arithmetic, algebra, and logical reasoning.

The dataset is generated through procedural synthesis, utilizing formal grammars and template-driven logic to create unique question and answer pairs. To support incremental learning, the content is organized into hierarchical difficulty levels, allowing for the structured sequencing of problems to help models master foundational concepts before progressing to more complex tasks.

The generation process ensures reproducibility through deterministic random seeding and maintains logical consistency via symbolic expression parsing. The repository is maintained through a peer-review process to ensure the quality and integrity of the generated data.

AI search

Explore more awesome repositories

Describe what you need in plain English — the AI ranks thousands of curated open-source projects by relevance.

Start searching with AI

Features

  • Mathematical Reasoning Datasets - Offers a collection of school-level mathematical question and answer pairs designed to evaluate and train neural reasoning.
  • Machine Learning Datasets - Creates large-scale collections of mathematical problems to train and evaluate neural network reasoning.
  • Model Performance Benchmarking - Tests the mathematical proficiency of artificial intelligence systems using standardized problem sets.
  • Model Evaluation Benchmarks - Provides a standardized set of mathematical tasks to measure language model proficiency in arithmetic, algebra, and logic.
  • Difficulty-Based Datasets - Organizes mathematical problem sets into hierarchical difficulty levels to support structured training and performance benchmarking.
  • Incremental Model Training - Sequences mathematical problems by difficulty to facilitate incremental learning and mastery of foundational concepts.
  • Multi-Hop Question Generators - Automates the construction of complex mathematical question-answer pairs requiring multi-step reasoning through procedural logic.
  • Reasoning Problem Synthesis - Synthesizes diverse mathematical queries by filling structural templates with randomized variables and operations.
  • Training Data Generation - Groups training examples into difficulty levels to support progressive learning and performance measurement.
  • Symbolic Expression Manipulators - Uses formal grammars to construct and evaluate mathematical expressions, ensuring logical consistency across generated problem sets.
1,954 stars·272 forks·Python·Apache-2.0·29 views

Star history

Star history chart for google-deepmind/mathematics_datasetStar history chart for google-deepmind/mathematics_dataset

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does google-deepmind/mathematics_dataset do?

This project provides a structured repository of school-level mathematical problems designed to train and evaluate the reasoning capabilities of neural network models. It functions as a standardized benchmark for measuring the proficiency of artificial intelligence systems in arithmetic, algebra, and logical reasoning.

What are the main features of google-deepmind/mathematics_dataset?

The main features of google-deepmind/mathematics_dataset are: Mathematical Reasoning Datasets, Machine Learning Datasets, Model Performance Benchmarking, Model Evaluation Benchmarks, Difficulty-Based Datasets, Incremental Model Training, Multi-Hop Question Generators, Reasoning Problem Synthesis.

Which projects share features with google-deepmind/mathematics_dataset?

Projects with overlapping indexed features include: opendcai/dataflow — DataFlow is an agent-based workflow orchestrator and data pipeline designed to synthesize, clean, and augment… facebookresearch/pythia — Pythia is a multimodal research framework and distributed training system designed for building, training, and… jaredpalmer/kev — Kev is a self-hosted decision model toolkit. It reads a document and answers typed questions about it (yes/no,… camel-ai/camel — This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified… google-research-datasets/natural-questions — Natural Questions is a large-scale machine learning research dataset designed for training and evaluating open-domain… unsplash/datasets — This project is an open-source visual dataset and machine learning image library. It provides large-scale collections…

Projects sharing features with Mathematics Dataset

These projects share indexed features with Mathematics Dataset. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • opendcai/dataflowOpenDCAI avatar

    OpenDCAI/DataFlow

    8,488View on GitHub↗

    DataFlow is an agent-based workflow orchestrator and data pipeline designed to synthesize, clean, and augment large-scale datasets for training large language models. It functions as a synthetic data generator and text curation tool, utilizing an intelligent assistant to assemble modular processing operators into functional pipelines based on user requirements. The project distinguishes itself through a low-code approach, providing a web-based visual interface for designing and monitoring multi-stage execution flows. It features an operator-based registry system that allows for the integratio

    Pythondatadata-agentdata-cleaning
    View on GitHub↗8,488
  • facebookresearch/pythiafacebookresearch avatar

    facebookresearch/pythia

    5,635View on GitHub↗

    Pythia is a multimodal research framework and distributed training system designed for building, training, and evaluating large models that combine visual and linguistic data. It provides a modular environment for developing vision-language models, focusing on the integration of image and text inputs into shared feature representations. The framework utilizes a modular architecture that decouples model building blocks into interchangeable components, allowing for flexible configuration of vision and language modules. It includes a benchmark suite for executing reference models against standar

    Python
    View on GitHub↗5,635
  • jaredpalmer/kevjaredpalmer avatar

    jaredpalmer/kev

    9,878View on GitHub↗

    Kev is a self-hosted decision model toolkit. It reads a document and answers typed questions about it (yes/no, multiple-choice, or ordinal rating) in a single forward pass, returning calibrated probabilities and a confidence value instead of free-form text. The underlying model is a compact open language model customized with low-rank adapters and a pointer head, so it picks among supplied options rather than generating output. What sets it apart is how the answers are served and trusted. Many questions can be scored over one shared document, with each question isolated from the others and ba

    Pythondecision-modeljevqwen3
    View on GitHub↗9,878
  • camel-ai/camelcamel-ai avatar

    camel-ai/camel

    18,018View on GitHub↗

    This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

    Pythonagentai-societiesartificial-intelligence
    View on GitHub↗18,018
Compare all 30 related projects→

Curated searches featuring Mathematics Dataset

Hand-picked collections where Mathematics Dataset appears.
  • NLP datasets