Lots-of-LoRAs/task047_miscellaneous_answering_science_questions

该数据集名为task047_miscellaneous_answering_science_questions，属于文本生成任务类别。数据集包含200个训练样本、25个验证样本和26个测试样本。每个样本包含输入、输出和ID三个特征，输入和输出均为字符串类型。数据集由众包方式创建，语言为英语，采用Apache 2.0许可证。数据集的相关信息可以在其主页和两篇论文中找到。

This dataset is designed for answering science questions, containing input and output fields along with an ID field. The dataset is divided into train, validation, and test sets, with 200, 25, and 26 examples respectively. The creation and language sources are crowdsourced, using English, and licensed under Apache 2.0.

原始信息汇总

数据集概述

基本信息

• 数据集名称: task047_miscellaneous_answering_science_questions
• 语言: 英语 (en)
• 许可证: Apache 2.0
• 任务类别: 文本生成 (text-generation)

数据集详情

• 配置名称: plain_text
• 特征:
  • 输入: 字符串 (string)
  • 输出: 字符串 (string)
  • ID: 字符串 (string)
• 数据分割:
  • 训练集: 200 个样本
  • 验证集: 25 个样本
  • 测试集: 26 个样本

引用信息

• 主要引用: 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}, }

• 额外引用: bibtex @misc{brüelgabrielsson2024compressserveservingthousands, title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead}, author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon}, year={2024}, eprint={2407.00066}, archivePrefix={arXiv}, primaryClass={cs.DC}, url={https://arxiv.org/abs/2407.00066}, }