Lots-of-LoRAs/task625_xlwic_true_or_false_answer_generation

该数据集名为task625_xlwic_true_or_false_answer_generation，属于文本生成任务类别。数据集包含输入、输出和ID三个特征，数据分为训练集、验证集和测试集，分别包含115、14和15个样本。数据集的语言为英语，创建方式为众包，许可证为Apache-2.0。

The dataset is named task625_xlwic_true_or_false_answer_generation and belongs to the text generation task category. It includes three features: input, output, and ID. The data is divided into training, validation, and test sets, containing 115, 14, and 15 samples respectively. The dataset is in English, created through crowdsourcing, 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: task625_xlwic_true_or_false_answer_generation
• 语言: 英语 (en)
• 许可证: Apache 2.0
• 任务类别: 文本生成 (text-generation)
• 数据集配置: plain_text

数据集结构

特征

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

数据分割

• 训练集: 115个样本
• 验证集: 14个样本
• 测试集: 15个样本

引用信息

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}, }

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}, }