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？
joelniklaus/lextreme|自然语言处理数据集|法律文本分析数据集
收藏hugging_face2023-04-29 更新2024-03-04 收录
下载链接：
https://hf-mirror.com/datasets/joelniklaus/lextreme
下载链接
链接失效反馈资源简介：
LEXTREME数据集是一个多语言法律自然语言理解基准，包含11个多样化的多语言法律NLU数据集。其中6个数据集有单一配置，5个数据集有两个或三个配置，总共包含18个任务（8个单标签文本分类任务、5个多标签文本分类任务和5个标记分类任务）。数据集支持多种语言，包括保加利亚语、捷克语、丹麦语、德语、希腊语、英语、西班牙语、爱沙尼亚语、芬兰语、法语、爱尔兰语、克罗地亚语、匈牙利语、意大利语、立陶宛语、拉脱维亚语、马耳他语、荷兰语、波兰语、葡萄牙语、罗马尼亚语、斯洛伐克语、斯洛文尼亚语和瑞典语。
提供机构：
joelniklaus
原始信息汇总
数据集概述
数据集名称
LEXTREME: A Multilingual Legal Benchmark for Natural Language Understanding
数据集概要
该数据集包含11个多语言法律自然语言理解（NLU）数据集，总计18个任务，包括8个单标签文本分类任务、5个多标签文本分类任务和5个令牌分类任务。
支持的任务和配置
- Brazilian Court Decisions: 判决预测（判决、一致性）
- Swiss Judgment Prediction: 判决预测（默认）
- German Argument Mining: 论点挖掘（默认）
- Greek Legal Code: 主题分类（卷、章、主题）
- Online Terms of Service: 不公平分类（不公平级别、条款主题）
- Covid 19 Emergency Event: 事件分类（默认）
- MultiEURLEX: 主题分类（级别1、级别2、级别3）
- LeNER BR: 命名实体识别（默认）
- LegalNERo: 命名实体识别（默认）
- Greek Legal NER: 命名实体识别（默认）
- MAPA: 命名实体识别（粗粒度、细粒度）
语言支持
支持以下语言：bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv
数据集结构
- 文件格式: jsonl
- 数据分割: 每个配置包含三个数据分割（训练、验证和测试）
许可证
cc-by-4.0
数据集大小
10K<n<100K
数据集来源
- 源数据集: 扩展
任务类别
- 文本分类
- 令牌分类
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
MNLI
MNLI（Multi-Genre Natural Language Inference）是一个大规模的自然语言推理数据集，包含433,000多对句子对。该数据集用于评估模型在不同文本类型中的推理能力，包括新闻文章、小说、论坛帖子等。每个句子对都标注了三种可能的关系：蕴含（entailment）、矛盾（contradiction）和中性（neutral）。
cims.nyu.edu 收录
中国区域交通网络数据集
该数据集包含中国各区域的交通网络信息，包括道路、铁路、航空和水路等多种交通方式的网络结构和连接关系。数据集详细记录了各交通节点的位置、交通线路的类型、长度、容量以及相关的交通流量信息。
data.stats.gov.cn 收录
GHCN
GHCN（Global Historical Climatology Network）是一个全球历史气候网络数据集，包含了全球各地气象站记录的每日气象数据，如温度、降水、风速等。该数据集用于研究气候变化和天气模式。
www.ncei.noaa.gov 收录
Traditional-Chinese-Medicine-Dataset-SFT
该数据集是一个高质量的中医数据集，主要由非网络来源的内部数据构成，包含约1GB的中医各个领域临床案例、名家典籍、医学百科、名词解释等优质内容。数据集99%为简体中文内容，质量优异，信息密度可观。数据集适用于预训练或继续预训练用途，未来将继续发布针对SFT/IFT的多轮对话和问答数据集。数据集可以独立使用，但建议先使用配套的预训练数据集对模型进行继续预训练后，再使用该数据集进行进一步的指令微调。数据集还包含一定比例的中文常识、中文多轮对话数据以及古文/文言文<->现代文翻译数据，以避免灾难性遗忘并加强模型表现。
huggingface 收录