Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downstream tasks. PTM registries known as "model hubs" support engineers in distributing and reusing deep learning models. PTM packages include pre-trained weights, documentation, model architectures, datasets, and metadata. Mining the information in PTM packages will enable the discovery of engineering phenomena and tools to support software engineers. However, accessing this information is difficult - there are many PTM registries, and both the registries and the individual packages may have rate limiting for accessing the data. We present an open-source dataset, PTMTorrent, to facilitate the evaluation and understanding of PTM packages. This paper describes the creation, structure, usage, and limitations of the dataset. The dataset includes a snapshot of 5 model hubs and a total of 15,913 PTM packages. These packages are represented in a uniform data schema for cross-hub mining. We describe prior uses of this data and suggest research opportunities for mining using our dataset. The PTMTorrent dataset (v1) is available at: https://app.globus.org/file-manager?origin_id=55e17a6e-9d8f-11ed-a2a2-8383522b48d9&origin_path=%2F~%2F. Our dataset generation tools are available on GitHub: https://doi.org/10.5281/zenodo.7570357.

翻译：由于从零开始开发和训练深度学习模型的成本高昂，机器学习工程师开始复用预训练模型（PTM）并针对下游任务进行微调。被称为“模型中心”的PTM注册库支持工程师分发和复用深度学习模型。PTM包包含预训练权重、文档、模型架构、数据集和元数据。挖掘PTM包中的信息有助于发现工程现象和工具，从而支持软件工程师。然而，访问这些信息存在困难——PTM注册库众多，且注册库和单个包均可能对数据访问设置速率限制。我们提出开源数据集PTMTorrent，以促进对PTM包的评估和理解。本文描述了该数据集的创建、结构、使用和局限性。该数据集包含5个模型中心的快照，共计15,913个PTM包。这些包以统一数据模式呈现，便于跨中心挖掘。我们描述了该数据的既往用途，并提出了利用本数据集进行挖掘的研究机会。PTMTorrent数据集（v1）可通过以下链接获取：https://app.globus.org/file-manager?origin_id=55e17a6e-9d8f-11ed-a2a2-8383522b48d9&origin_path=%2F~%2F。数据集生成工具可在GitHub上获取：https://doi.org/10.5281/zenodo.7570357。