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

Descoperă cele mai bune repository-uri open source cu căutare AI.

ExploreazăCăutări recomandateAlternative open-sourceSoftware self-hostedBlogHartă site
ProiectServer MCPDespreCum realizăm clasamentulPresă
LegalConfidențialitateTermeni
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·
google-deepmind avatar

google-deepmind/mathematics_dataset

0
View on GitHub↗
1,954 stele·272 fork-uri·Python·Apache-2.0·4 vizualizări

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.

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.

Istoric stele

Graficul istoricului de stele pentru google-deepmind/mathematics_datasetGraficul istoricului de stele pentru google-deepmind/mathematics_dataset

Căutare AI

Explorează mai multe repository-uri excelente

Descrie ce ai nevoie în limbaj simplu — AI-ul sortează mii de proiecte open source selectate în funcție de relevanță.

Start searching with AI

Colecții curatoriate care includ Mathematics Dataset

Colecții selectate manual în care apare Mathematics Dataset.
  • NLP datasets

Întrebări frecvente

Ce face google-deepmind/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.

Care sunt principalele funcționalități ale google-deepmind/mathematics_dataset?

Principalele funcționalități ale google-deepmind/mathematics_dataset sunt: 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.

Care sunt câteva alternative open-source pentru google-deepmind/mathematics_dataset?

Alternativele open-source pentru google-deepmind/mathematics_dataset includ: 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… 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… huggingface/datasets — Datasets is a library designed for the management, processing, and sharing of large-scale data collections for machine…

Alternative open-source pentru Mathematics Dataset

Proiecte open-source similare, clasificate după numărul de funcționalități comune cu Mathematics Dataset.
  • opendcai/dataflowAvatar OpenDCAI

    OpenDCAI/DataFlow

    2,926Vezi pe 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
    Vezi pe GitHub↗2,926
  • facebookresearch/pythiaAvatar facebookresearch

    facebookresearch/pythia

    5,635Vezi pe 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
    Vezi pe GitHub↗5,635
  • camel-ai/camelAvatar camel-ai

    camel-ai/camel

    17,253Vezi pe 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
    Vezi pe GitHub↗17,253
  • google-research-datasets/natural-questionsAvatar google-research-datasets

    google-research-datasets/natural-questions

    1,124Vezi pe GitHub↗

    Natural Questions is a large-scale machine learning research dataset designed for training and evaluating open-domain question answering systems. It consists of a corpus of real search queries paired with human-annotated Wikipedia document spans, providing a standardized foundation for advancing automated information retrieval and comprehension technologies. The project distinguishes itself by providing high-quality ground truth data that supports multiple answer formats, including binary, short-form, and long-form responses. By incorporating extractive span annotations and structured documen

    Python
    Vezi pe GitHub↗1,124
    • Vezi toate cele 30 alternative pentru Mathematics Dataset→