Lots-of-LoRAs/task687_mmmlu_answer_generation_college_chemistry

该数据集名为task687_mmmlu_answer_generation_college_chemistry，主要用于生成大学化学问题的答案。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88、11和11个样本。数据集的语言为英语，创建方式为众包，任务类别为文本生成。数据集的主页和相关论文提供了更多详细信息。

The dataset, named task687_mmmlu_answer_generation_college_chemistry, is primarily used for generating answers to college chemistry questions. It includes three features: input, output, and ID. The data is divided into training, validation, and test sets, containing 88, 11, and 11 samples respectively. The dataset is in English, created through crowdsourcing, and falls under the text generation task category. More details can be found on the datasets homepage and related papers.

原始信息汇总

数据集概述

基本信息

• 数据集名称: task687_mmmlu_answer_generation_college_chemistry
• 别名: plain_text
• 任务类别: 文本生成
• 语言: 英语
• 许可证: Apache 2.0
• 数据创建者: 众包
• 注释创建者: 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

• train: 88个样本
• valid: 11个样本
• test: 11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}, }