Lots-of-LoRAs/task1204_atomic_classification_hinderedby

该数据集名为Natural Instructions，主要用于文本生成任务。数据集包含输入、输出和ID三个特征，数据分割为训练集、验证集和测试集，分别包含5192、649和650个样本。数据集的相关信息可以在其主页和两篇论文中找到，其中一篇论文详细介绍了数据集的构建和泛化能力，另一篇论文则讨论了如何高效地服务数千个LoRA适配器。

The dataset is named Natural Instructions and is primarily used for text generation tasks. It includes three features: input, output, and ID. The data is split into training, validation, and test sets, containing 5192, 649, and 650 examples respectively. More details about the dataset can be found on its homepage and in two associated papers. One paper introduces the datasets construction and generalization capabilities, while the other discusses efficient serving of thousands of LoRA adapters.

原始信息汇总

数据集概述

基本信息

• 数据集名称: task1204_atomic_classification_hinderedby
• 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
• 标注创建者: 众包
• 语言创建者: 众包

数据集结构

特征

• input: 字符串类型
• output: 字符串类型
• id: 字符串类型

数据分割

• 训练集: 5192个样本
• 验证集: 649个样本
• 测试集: 650个样本

引用信息

• 主要论文: 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}, }

• 补充论文: 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}, }