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Hugging Face (HF) has established itself as a crucial platform for thedevelopment and sharing of machine learning (ML) models. This repository miningstudy, which delves into more than 380,000 models using data gathered via theHF Hub API, aims to explore the community engagement, evolution, andmaintenance around models hosted on HF, aspects that have yet to becomprehensively explored in the literature. We first examine the overall growthand popularity of HF, uncovering trends in ML domains, framework usage, authorsgrouping and the evolution of tags and datasets used. Through text analysis ofmodel card descriptions, we also seek to identify prevalent themes and insightswithin the developer community. Our investigation further extends to themaintenance aspects of models, where we evaluate the maintenance status of MLmodels, classify commit messages into various categories (corrective,perfective, and adaptive), analyze the evolution across development stages ofcommits metrics and introduce a new classification system that estimates themaintenance status of models based on multiple attributes. This study aims toprovide valuable insights about ML model maintenance and evolution that couldinform future model development, maintenance, and community engagementstrategies on community-driven platforms like HF.

Analyzing the Evolution and Maintenance of ML Models on Hugging Face