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

翻译：Hugging Face（HF）已成为机器学习（ML）模型开发与共享的关键平台。本研究通过HF Hub API收集的超过38万个模型数据，采用仓库挖掘方法，旨在探究HF上模型相关的社区参与、演化与维护情况——这些方面在文献中尚未得到全面探讨。我们首先考察HF的整体增长与流行趋势，揭示ML领域、框架使用、作者分组以及标签与数据集的演化趋势。通过对模型卡片描述的文本分析，我们还试图识别开发者社区中的常见主题与见解。研究进一步扩展到模型的维护方面：评估ML模型的维护状态，将提交信息分类为多种类型（修正型、完善型与适应型），分析提交指标在开发阶段中的演化，并引入一种基于多属性评估模型维护状态的新分类系统。本研究旨在为ML模型维护与演化提供有价值的见解，为未来在HF等社区驱动平台上的模型开发、维护及社区参与策略提供参考。