English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Background. The development of empirical studies in software engineeringmainly relies on the data available on code hosting platforms, being GitHub themost representative. Nevertheless, in the last years, the emergence of MachineLearning (ML) has led to the development of platforms specifically designed fordeveloping ML-based projects, being Hugging Face Hub (HFH) the most popularone. With over 250k repositories, and growing fast, HFH is becoming a promisingecosystem of ML artifacts and therefore a potential source of data forempirical studies. However, so far there have been no studies evaluating thepotential of HFH for such studies. Objective. In this proposal for a registeredreport, we aim at performing an exploratory study of the current state of HFHin order to investigate its suitability to be used as a source platform forempirical studies. Method. We conduct a qualitative and quantitative analysisof HFH for empirical studies. The former will be performed by comparing thefeatures of HFH with those of other code hosting platforms, such as GitHub andGitLab. The latter will be performed by analyzing the data available in HFH.

On the Suitability of Hugging Face Hub for Empirical Studies