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Code4ML:一个大规模的数据集,包含注释的机器学习代码

Anastasia Drozdova¹, Ekaterina Trofimova¹, Polina Guseva¹

¹Department of Computer Science, NRU Higher School of Economics, Moscow, Russia.

PeerJ. Computer science

|June 22, 2023

概括

研究人员开发了Code4ML集体,这是一个来自Kaggle的注释机器学习 (ML) 代码片段的大数据集. 该资源通过为分类和生成等任务提供标记代码来帮助ML开发.

科学领域:

计算机科学计算机科学
机器学习机器学习
软件工程软件工程软件工程
数据科学数据科学数据科学

背景情况:

程序代码越来越多地被用作数据科学中的数据源,用于语义分类和程序生成等任务.
机器学习模型的应用受到注释代码片段数据集的缺乏所阻碍.

研究的目的:

为了解决机器学习的注释代码数据集的稀缺性.
介绍Code4ML集体,这是一个全面的注释ML代码片段集.

主要方法:

从Kaggle上托管的10万个Jupyter笔记本中收集了大约250万个机器学习代码片段.
使用定制设计的,用户友好的界面,注释了这些代码片段的代表部分.
包括相关的元数据,如任务总结,竞赛细节和数据集描述.

主要成果:

Code4ML集体提供了一个大规模的,注释的ML代码片段数据集.
数据集来自Kaggle的公开数据,Kaggle是一个领先的数据科学竞争平台.
在收集的代码片段的很大一部分上进行了人类注释.

结论:

Code4ML数据集为数据科学和软件工程研究提供了宝贵的资源.
它可以促进数据驱动的方法来应对诸如语义代码分类,代码自动完成和基于自然语言的ML任务代码生成等挑战.

关键词:

Jupyter 的代码片段 ML 代码数据集的数据集

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