Abstract
Context
Empirical studies in software engineering mainly rely on the data available on code-hosting platforms, being GitHub the most representative. Nevertheless, in the last years, the emergence of Machine Learning (ML) has led to the development of platforms specifically designed for hosting ML-based projects, with Hugging Face Hub (HFH) as the most popular one. So far, there have been no studies evaluating the potential of HFH for such studies.
Objective
We aim at performing an exploratory study of the current state of HFH and its suitability to be used as a source platform for empirical studies.
Method
We conduct a qualitative and quantitative analysis of HFH. The former will be performed by comparing the features of HFH with those of other code-hosting platforms, such as GitHub and GitLab. The latter will be performed by analyzing the data available in HFH.
Results
We propose a feature framework to characterize HFH and report on the current usage of the platform, both in terms of number and types of projects (and surrounding community) and the features they mostly rely on.
Conclusions
The results confirm that HFH offers enough features and diverse enough data to be the source of relevant empirical studies on the development, evolution and usage of AI-related projects. The results also triggered a discussion on aspects of HFH that should be considered when performing such empirical studies.










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Data Availability
The data used in RQ1 (i.e., survey results and interview transcription) is available in a Zenodo repository with the identifier https://doi.org/10.5281/zenodo.11072131. The data used in RQ2 is the October 2023 release of HFCommunity, available with the identifier https://doi.org/10.5281/zenodo.10020642.
Notes
In particular, BitBucket, Codeberg, Forgejo, GitHub, GitLab, HFH, Kallithea, Launchpad, Savannah GNU and SourceForge.
This study was performed on October, 2023.
Queries available in Appendix A.1.1
Queries available in Appendix A.1.2
Query available in Appendix A.1.3
An example of a complete social profile: https://huggingface.co/clefourrier
Libraries supported by HFH: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/model-libraries.ts
For more information: https://huggingface.co/spaces/DIBT/prompt-collective
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Acknowledgements
This work is part of the project TED2021-130331B-I00 funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR; and BESSER, funded by the Luxembourg National Research Fund (FNR) PEARL program, grant agreement 16544475.
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A. Appendix
A. Appendix
1.1 A.1 Queries of Digital Libraries
1.1.1 A.1.1 Review of platform studies
To check how many articles are published for each platform, we queried each digital library with each one of the ten platforms identified in Section 4.2.1.\(^a\)
\(^a\) Platforms with names with a possible space separator have been searched with both forms (e.g., GitHub or Git Hub, HuggingFace or Hugging Face, etc.)



1.1.2 A.1.2 Review of Literature
In order to show a generalization of the queries, we show the structure we follow, in which the platform keyword is one of four code-hosting platforms identified with results in Section 5.1 (Review of literature step), and the feature keyword can be one of the following values: Branches, CICD - Development Workflow, Collaboration/Cloud Coding,\(^b\) Code Review, CVS, External Integrations, Following, Fork, Groups, Issues, Licensing, Marketplace, Packages, Pull Request, Project Relations, Q&A, Release, Repo Type, Roles, Security, Snippets, Stream Analytics, Tagging, Webhooks, Wiki, Work Management,\(^c\) Web Publish.\(^d\)
\(^b\) In GitHub we used the keyword Codespaces.
\(^c\) In GitHub we used the keyword GitHub Projects.
\(^d\) In GitHub and GitLab we used the keyword Pages.



1.1.3 A.1.3 Review of Datasets
The platform keyword is one of the four code-hosting platforms identified with results in Section 5.1 (Review of literature step) and the data keyword is either: data source, dataset or tool.



1.2 B.2 Top-100 Repositories
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Ait, A., Cánovas Izquierdo, J.L. & Cabot, J. On the suitability of hugging face hub for empirical studies. Empir Software Eng 30, 57 (2025). https://doi.org/10.1007/s10664-024-10608-8
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DOI: https://doi.org/10.1007/s10664-024-10608-8

