Lots-of-LoRAs/task341_winomt_classification_gender_anti

该数据集名为task341_winomt_classification_gender_anti，主要用于文本生成任务，特别是性别分类的反偏见任务。数据集的特征包括输入、输出和ID，分割为训练集、验证集和测试集。训练集包含1261个样本，验证集和测试集各包含158个样本。数据集的创建者是众包，语言为英语，许可证为Apache-2.0。相关论文和联系信息也在README中提供。

The dataset is named task341_winomt_classification_gender_anti and is primarily used for text generation tasks, specifically for gender classification anti-bias tasks. The dataset features include input, output, and ID, and is split into training, validation, and test sets. The training set contains 1261 examples, while the validation and test sets each contain 158 examples. The dataset is created by crowdsourcing, is in English, and is licensed under Apache-2.0. Relevant papers and contact information are also provided in the README.

原始信息汇总

数据集概述

基本信息

• 数据集名称: task341_winomt_classification_gender_anti
• 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

数据集结构

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: 1261 条样本
• 验证集: 158 条样本
• 测试集: 158 条样本

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}, }