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？
FewRel|少样本学习数据集|关系分类数据集
收藏arXiv2018-10-27 更新2024-06-21 收录
下载链接：
http://zhuhao.me/fewrel
下载链接
链接失效反馈资源简介：
FewRel是由清华大学智能技术与系统国家重点实验室开发的一个大规模监督式少样本关系分类数据集。该数据集包含70,000个从维基百科中提取并由众包工作者标注的句子，涉及100种关系。数据集的创建过程包括使用远监督方法识别句子关系，并通过众包工作者进行筛选。FewRel主要用于解决少样本学习中的关系分类问题，旨在通过有限的数据实例训练模型，以适应新的关系分类任务。
提供机构：
清华大学
创建时间：
2018-10-24
AI搜集汇总
数据集介绍
构建方式
FewRel数据集的构建基于大规模的文本语料库，通过自动化的关系抽取技术，从海量文本中识别并标注出多种实体之间的关系。具体而言，该数据集采用了远程监督的方法，结合预定义的关系类型，对文本中的句子进行标注，从而生成包含丰富关系实例的训练和测试数据。
特点
FewRel数据集以其小样本学习的特性著称，特别适用于关系抽取任务中的少样本场景。该数据集包含了多种实体关系类型，且每种关系类型仅提供少量标注实例，这使得模型在处理新关系时能够展现出较强的泛化能力。此外，FewRel还提供了多样化的数据分布，以确保模型的鲁棒性和适应性。
使用方法
FewRel数据集主要用于训练和评估关系抽取模型，特别是在少样本学习场景下。研究者可以通过该数据集进行模型的预训练和微调，以提升模型在处理新关系时的表现。使用时，建议结合小样本学习算法，如元学习或迁移学习，以充分利用数据集的少样本特性，从而在实际应用中实现高效的关系抽取。
背景与挑战
背景概述
FewRel数据集由清华大学和微软亚洲研究院于2018年联合发布，专注于少样本关系分类任务。该数据集的核心研究问题是如何在仅有少量标注样本的情况下，实现高效的关系分类。FewRel的创建标志着少样本学习在自然语言处理领域的重要进展，为研究者提供了一个标准化的评估平台，推动了少样本学习技术的发展，特别是在关系抽取和信息检索等应用场景中。
当前挑战
FewRel数据集面临的挑战主要集中在少样本学习的固有难题上。首先，如何在有限的标注数据中提取有效的特征，以实现高精度的关系分类，是一个关键问题。其次，数据集的构建过程中，如何确保样本的多样性和代表性，以避免模型过拟合，也是一个重要挑战。此外，少样本学习模型的泛化能力，即在未见过的关系类型上的表现，也是研究者需要克服的难题。
发展历史
创建时间与更新
FewRel数据集由Pengcheng Yang等人于2018年首次提出，旨在解决关系抽取任务中的小样本学习问题。该数据集自创建以来，未有官方更新记录。
重要里程碑
FewRel的提出标志着小样本学习在自然语言处理领域的重要突破。其核心贡献在于提供了一个包含70,000个实例和100个关系的标注数据集，有效推动了小样本学习方法的研究与应用。此外，FewRel还引入了跨域关系抽取任务，进一步扩展了数据集的应用场景，为后续研究提供了丰富的实验平台。
当前发展情况
FewRel数据集的当前发展主要体现在其对小样本学习方法的持续影响和推动。随着深度学习技术的进步，FewRel被广泛应用于各种小样本学习模型的训练与评估，促进了相关算法的创新与优化。同时，FewRel的成功应用也激发了更多研究者关注小样本学习问题，推动了该领域的快速发展。未来，FewRel有望继续引领小样本学习研究的前沿，为自然语言处理领域带来更多突破。
发展历程
- FewRel数据集首次发表，由Pengcheng Yang等人提出，旨在解决少样本关系分类问题。
- FewRel数据集在自然语言处理领域得到广泛应用，成为少样本学习研究的重要基准。
- FewRel数据集的扩展版本FewRel 2.0发布，增加了更多的关系类别和实例，进一步提升了数据集的多样性和挑战性。
- FewRel数据集在多个国际会议和期刊上被引用和讨论，推动了少样本学习技术的发展。
常用场景
经典使用场景
FewRel数据集在自然语言处理领域中，主要用于关系抽取任务，特别是在少样本学习（Few-Shot Learning）的背景下。该数据集包含了大量实体对及其关系标签，为研究者提供了一个丰富的资源来探索如何在有限的标注数据下进行高效的关系分类。通过FewRel，研究者可以开发和评估少样本学习算法，以应对实际应用中标注数据稀缺的问题。
解决学术问题
FewRel数据集解决了自然语言处理领域中一个重要的学术问题，即如何在少样本情况下进行有效的关系抽取。传统的监督学习方法通常依赖于大量的标注数据，而FewRel通过提供少量的标注样本，促使研究者开发出能够在有限数据下表现良好的模型。这不仅推动了少样本学习技术的发展，还为实际应用中的数据标注成本问题提供了新的解决方案。
衍生相关工作
FewRel数据集的发布激发了许多相关研究工作，特别是在少样本学习和关系抽取领域。例如，研究者们基于FewRel开发了多种少样本学习算法，如基于元学习的模型和基于迁移学习的方法，这些方法在FewRel上的表现显著优于传统方法。此外，FewRel还促进了跨领域的关系抽取研究，如将自然语言处理技术应用于图像和视频的关系抽取，进一步拓宽了其应用范围。
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
Google Scholar
Google Scholar是一个学术搜索引擎，旨在检索学术文献、论文、书籍、摘要和文章等。它涵盖了广泛的学科领域，包括自然科学、社会科学、艺术和人文学科。用户可以通过关键词搜索、作者姓名、出版物名称等方式查找相关学术资源。
scholar.google.com 收录
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
jpft/danbooru2023
Danbooru2023是一个大规模的动漫图像数据集，包含超过500万张由爱好者社区贡献并详细标注的图像。图像标签涵盖角色、场景、版权、艺术家等方面，平均每张图像有30个标签。该数据集可用于训练图像分类、多标签标注、角色检测、生成模型等多种计算机视觉任务。数据集基于danbooru2021构建，扩展至包含ID #6,857,737的图像，增加了超过180万张新图像，总大小约为8TB。图像以原始格式提供，分为1000个子目录，使用图像ID的模1000进行分桶，以避免文件系统性能问题。
hugging_face 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
DAT
DAT是一个统一的跨场景跨领域基准，用于开放世界无人机主动跟踪。它提供了24个视觉复杂的场景，以评估算法的跨场景和跨领域泛化能力，并具有高保真度的现实机器人动力学建模。
github 收录