TY - JOUR
T1 - PTMTorrent: A Dataset for Mining Open-source Pre-trained Model Packages
AU - Jiang, Wenxin
AU - Synovic, Nicholas
AU - Jajal, Purvish
AU - Schorlemmer, Taylor R.
AU - Tewari, Arav
AU - Pareek, Bhavesh
AU - Thiruvathukal, George K.
AU - Davis, James C
N1 - Jiang, Wenxin; Synovic, Nicholas; Jajal, Purvish; Schorlemmer, Taylor R.; Tewari, Arav; Pareek, Bhavesh; et al. (2023): PTMTorrent: A Dataset for Mining Open-source Pre-trained Model Packages. figshare. Dataset. https://doi.org/10.6084/m9.figshare.22009880.v3
PY - 2023/5/15
Y1 - 2023/5/15
N2 - 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.
AB - 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.
KW - open source software
KW - data mining
KW - machine learning
KW - empirical software engineering
UR - https://ecommons.luc.edu/cs_facpubs/320
U2 - 10.1109/MSR59073.2023.00021
DO - 10.1109/MSR59073.2023.00021
M3 - Article
JO - Computer Science: Faculty Publications and Other Works
JF - Computer Science: Faculty Publications and Other Works
ER -