Lots-of-LoRAs/task1453_person_entity_extraction_btc_corpus

该数据集名为task1453_person_entity_extraction_btc_corpus，主要用于文本生成任务，特别是人物实体提取。数据集包含208个训练样本、26个验证样本和27个测试样本。每个样本包含输入、输出和ID三个特征。数据集的语言为英语，创建者和注释者均为众包。数据集的相关论文和详细信息可以在提供的链接中找到。

The dataset is named task1453_person_entity_extraction_btc_corpus and is primarily used for text generation tasks, specifically person entity extraction. The dataset contains 208 training samples, 26 validation samples, and 27 test samples. Each sample includes three features: input, output, and ID. The language of the dataset is English, and both the creators and annotators are crowdsourced. Related papers and detailed information about the dataset can be found in the provided links.

原始信息汇总

数据集概述

基本信息

• 数据集名称: task1453_person_entity_extraction_btc_corpus
• 数据集别名: plain_text
• 语言: 英语 (en)
• 许可证: Apache 2.0
• 任务类别: 文本生成 (text-generation)

数据集结构

特征

• input: 字符串类型 (string)
• output: 字符串类型 (string)
• id: 字符串类型 (string)

数据分割

• 训练集 (train): 208个样本
• 验证集 (valid): 26个样本
• 测试集 (test): 27个样本

引用信息

如果使用该数据集进行研究，请引用以下论文： bibtex @misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions, title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}, author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi}, year={2022}, eprint={2204.07705}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2204.07705}, }