Code4ML: a Large-scale Dataset of annotated Machine Learning Code

TL;DR

Code4ML is a large, annotated dataset of approximately 2.5 million machine learning code snippets from Kaggle, designed to facilitate tasks like code classification, auto-completion, and generation in data science applications.

Contribution

The paper introduces Code4ML, a comprehensive annotated dataset of ML code snippets from Kaggle, enabling advanced machine learning tasks on code.

Findings

Contains ~2.5 million ML code snippets from Kaggle

Includes human-annotated subset for supervised tasks

Supports applications like code classification and auto-generation

Abstract

Program code as a data source is gaining popularity in the data science community. Possible applications for models trained on such assets range from classification for data dimensionality reduction to automatic code generation. However, without annotation number of methods that could be applied is somewhat limited. To address the lack of annotated datasets, we present the Code4ML corpus. It contains code snippets, task summaries, competitions and dataset descriptions publicly available from Kaggle - the leading platform for hosting data science competitions. The corpus consists of ~2.5 million snippets of ML code collected from ~100 thousand Jupyter notebooks. A representative fraction of the snippets is annotated by human assessors through a user-friendly interface specially designed for that purpose. Code4ML dataset can potentially help address a number of software engineering or data science challenges through a data-driven approach. For example, it can be helpful for semantic code classification, code auto-completion, and code generation for an ML task specified in natural language.