Machine learning code snippets semantic classification.

ABSTRACT: Program code has recently become a valuable active data source for training various data science models, from code classification to controlled code synthesis. Annotating code snippets play an essential role in such tasks. This article presents a novel approach that leverages CodeBERT, a powerful transformer-based model, to classify code snippets extracted from Code4ML automatically. Code4ML is a comprehensive machine learning code corpus compiled from Kaggle, a renowned data science competition platform. The corpus includes code snippets and information about the respective kernels and competitions, but it is limited in the quality of the tagged data, which is ~0.2%. Our method addresses the lack of labeled snippets for supervised model training by exploiting the internal am

SUBMITTER: Berezovskiy V 

PROVIDER: S-EPMC10703005 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature