有没有相关的论文或文献参考？
这个数据集是基于什么背景创建的？
数据集的作者是谁？
能帮我联系到这个数据集的作者吗？
这个数据集如何下载？
MATH-500|数学问题数据集|文本生成数据集
收藏huggingface2024-11-15 更新2024-12-12 收录
下载链接：
https://huggingface.co/datasets/HuggingFaceH4/MATH-500
下载链接
链接失效反馈资源简介：
MATH-500数据集包含500个问题，这些问题是OpenAI在其论文《Let's Verify Step by Step》中创建的MATH基准测试的一部分。数据集的类别是文本生成，语言为英语。
提供机构：
Hugging Face H4
创建时间：
2024-11-15
原始信息汇总
数据集概述
基本信息
- 数据集名称: MATH-500
- 任务类别: 文本生成
- 语言: 英语
数据集描述
该数据集包含从MATH基准测试中选择的500个问题，这些问题是OpenAI在其论文《Lets Verify Step by Step》中创建的。
AI搜集汇总
数据集介绍
构建方式
MATH-500数据集源自OpenAI在其《Let's Verify Step by Step》论文中创建的MATH基准测试，从中精选了500道数学问题。这些问题的选取基于其复杂性和多样性，旨在为文本生成任务提供高质量的数学问题样本。数据集的构建过程严格遵循学术标准，确保了数据的代表性和可靠性。
特点
MATH-500数据集以其高质量和多样性著称，涵盖了广泛的数学领域和难度级别。每个问题都经过精心挑选，以确保其在数学逻辑和解题步骤上的完整性。数据集的语言为英语，适合用于训练和评估文本生成模型，尤其是在数学问题求解方面的表现。
使用方法
MATH-500数据集主要用于文本生成任务，特别是数学问题的自动求解和步骤验证。研究人员和开发者可以通过该数据集训练模型，评估其在数学问题理解和解答上的能力。数据集的使用方法包括加载数据、预处理、模型训练和性能评估，具体操作可参考OpenAI提供的GitHub仓库中的详细指南。
背景与挑战
背景概述
MATH-500数据集源自OpenAI在其论文《Let's Verify Step by Step》中提出的MATH基准测试，旨在推动数学问题求解领域的研究。该数据集由OpenAI团队于2023年创建，包含500道精选数学问题，涵盖了代数、几何、概率等多个数学分支。其核心研究问题在于通过逐步验证的方法，提升模型在复杂数学问题上的推理能力。MATH-500的发布为自然语言处理与数学推理的交叉领域提供了重要的研究资源，推动了相关技术的发展与应用。
当前挑战
MATH-500数据集在解决数学问题生成与推理领域面临多重挑战。首先，数学问题的多样性与复杂性要求模型具备高度的逻辑推理能力，这对现有模型的泛化能力提出了严峻考验。其次，数据集的构建过程中，如何确保问题的代表性、难度分布的合理性以及标注的准确性，是研究人员需要克服的关键问题。此外，逐步验证方法的实现需要精确的步骤分解与逻辑一致性，这对数据集的标注与模型训练提出了更高的技术要求。
常用场景
经典使用场景
MATH-500数据集在自然语言处理领域中被广泛用于文本生成任务，特别是在数学问题求解的自动化系统中。该数据集通过提供500个精心挑选的数学问题，为研究人员提供了一个标准化的测试平台，用于评估和优化文本生成模型在复杂数学推理任务中的表现。
实际应用
在实际应用中，MATH-500数据集被用于开发智能教育工具，如自动解题系统和个性化学习平台。这些工具能够根据学生的解题步骤提供即时反馈，帮助他们更好地理解数学概念，提升学习效率。
衍生相关工作
MATH-500数据集衍生了一系列经典工作，特别是在基于步骤验证的数学问题求解领域。例如，OpenAI的研究团队利用该数据集开发了PRM800K模型，该模型通过逐步验证解题步骤，显著提高了数学问题求解的准确性和可靠性。
以上内容由AI搜集并总结生成
数据主题
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集
Canadian Census
**Overview** The data package provides demographics for Canadian population groups according to multiple location categories: Forward Sortation Areas (FSAs), Census Metropolitan Areas (CMAs) and Census Agglomerations (CAs), Federal Electoral Districts (FEDs), Health Regions (HRs) and provinces. **Description** The data are available through the Canadian Census and the National Household Survey (NHS), separated or combined. The main demographic indicators provided for the population groups, stratified not only by location but also for the majority by demographical and socioeconomic characteristics, are population number, females and males, usual residents and private dwellings. The primary use of the data at the Health Region level is for health surveillance and population health research. Federal and provincial departments of health and human resources, social service agencies, and other types of government agencies use the information to monitor, plan, implement and evaluate programs to improve the health of Canadians and the efficiency of health services. Researchers from various fields use the information to conduct research to improve health. Non-profit health organizations and the media use the health region data to raise awareness about health, an issue of concern to all Canadians. The Census population counts for a particular geographic area representing the number of Canadians whose usual place of residence is in that area, regardless of where they happened to be on Census Day. Also included are any Canadians who were staying in that area on Census Day and who had no usual place of residence elsewhere in Canada, as well as those considered to be 'non-permanent residents'. National Household Survey (NHS) provides demographic data for various levels of geography, including provinces and territories, census metropolitan areas/census agglomerations, census divisions, census subdivisions, census tracts, federal electoral districts and health regions. In order to provide a comprehensive overview of an area, this product presents data from both the NHS and the Census. NHS data topics include immigration and ethnocultural diversity; aboriginal peoples; education and labor; mobility and migration; language of work; income and housing. 2011 Census data topics include population and dwelling counts; age and sex; families, households and marital status; structural type of dwelling and collectives; and language. The data are collected for private dwellings occupied by usual residents. A private dwelling is a dwelling in which a person or a group of persons permanently reside. Information for the National Household Survey does not include information for collective dwellings. Collective dwellings are dwellings used for commercial, institutional or communal purposes, such as a hotel, a hospital or a work camp. **Benefits** - Useful for canada public health stakeholders, for public health specialist or specialized public and other interested parties. for health surveillance and population health research. for monitoring, planning, implementation and evaluation of health-related programs. media agencies may use the health regions data to raise awareness about health, an issue of concern to all canadians. giving the addition of longitude and latitude in some of the datasets the data can be useful to transpose the values into geographical representations. the fields descriptions along with the dataset description are useful for the user to quickly understand the data and the dataset. **License Information** The use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. **Included Datasets** - [Canadian Population and Dwelling by FSA 2011](https://www.johnsnowlabs.com/marketplace/canadian-population-and-dwelling-by-fsa-2011) - This Canadian Census dataset covers data on population, total private dwellings and private dwellings occupied by usual residents by forward sortation area (FSA). It is enriched with the percentage of the population or dwellings versus the total amount as well as the geographical area, province, and latitude and longitude. The whole Canada's population is marked as 100, referring to 100% for the percentages. - [Detailed Canadian Population Statistics by CMAs and CAs 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-cmas-and-cas-2011) - This dataset covers the population statistics of Canada by Census Metropolitan Areas (CMAs) and Census Agglomerations (CAs). It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers. - [Detailed Canadian Population Statistics by FED 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-fed-2011) - This dataset covers the population statistics of Canada from 2011 by Federal Electoral District of 2013 Representation Order. It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers. - [Detailed Canadian Population Statistics by Health Region 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-health-region-2011) - This dataset covers the population statistics of Canada by health region. It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers. - [Detailed Canadian Population Statistics by Province 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-province-2011) - This dataset covers the population statistics of Canada by provinces and territories. It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers. **Data Engineering Overview** **We deliver high-quality data** - Each dataset goes through 3 levels of quality review - 2 Manual reviews are done by domain experts - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints - Data is normalized into one unified type system - All dates, unites, codes, currencies look the same - All null values are normalized to the same value - All dataset and field names are SQL and Hive compliant - Data and Metadata - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated - Data Updates - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted **Our data is curated and enriched by domain experts** Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts: - Field names, descriptions, and normalized values are chosen by people who actually understand their meaning - Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset - Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations - The data is always kept up to date – even when the source requires manual effort to get updates - Support for data subscribers is provided directly by the domain experts who curated the data sets - Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution. **Need Help?** If you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com).
Databricks 收录
poi
本项目收集国内POI兴趣点，当前版本数据来自于openstreetmap。
github 收录
UniMed
UniMed是一个大规模、开源的多模态医学数据集，包含超过530万张图像-文本对，涵盖六种不同的医学成像模态：X射线、CT、MRI、超声、病理学和眼底。该数据集通过利用大型语言模型（LLMs）将特定模态的分类数据集转换为图像-文本格式，并结合现有的医学领域的图像-文本数据，以促进可扩展的视觉语言模型（VLM）预训练。
github 收录
ChemBL
ChemBL是一个化学信息学数据库，包含大量生物活性数据，涵盖了药物发现和开发过程中的各种化学实体。数据集包括化合物的结构信息、生物活性数据、靶点信息等。
www.ebi.ac.uk 收录
AQA-7
AQA-7 是一个用于动作质量评估（AQA）的统一基准数据集，旨在通过整合多个领域的数据集来标准化评估方法。该数据集包含视频、骨骼数据和多模态输入，涵盖了体育分析、技能评估和医疗护理等多个应用领域。数据集的创建过程通过系统分析现有文献和实验协议，确保了评估的准确性和计算效率。AQA-7 的应用领域广泛，旨在解决动作质量评估中的偏差问题，提供客观的自动化评估，特别是在体育评分、技能评估和康复训练中具有重要意义。
arXiv 收录