IN3’s Systems, Software and Models Research Lab (SOM Research Lab) is pleased to invite you to the Seminar: «Making Machine learning community analysis possible: A Tool to Analyze the Hugging Face Hub Community», given by Adem Ait, PhD student at the SnT-University of Luxembourg.
The seminar will be held, on-site, on Tuesday, October 29 at 15:00 pm (CET) in Room C1.17 of the Interdisciplinary R&I Hub (Building C).
Venue
Interdisciplinary R&I Hub (Building C - Room C1.17)
Rambla del Poblenou, 154
08018 Barcelona
Espanya
When
29/10/2024 15.00h
Organized by
Universitat Oberta de Catalunya, IN3's Systems, Software and Models Research Lab (SOM Research Lab)
Program
Abstract
In recent years, empirical studies on software engineering practices have primarly relied on general-purpose social coding platform such as GitHub. However, with the emergence of Machine Learning (ML), platforms specifically designed for hosting and developing ML-based projects have appeared, being Hugging Face Hub (HFH) one of the most popular ones. HFH is experiencing rapid growth, skyrocketing from 100,000 hosted public repositories at the end of 2022 to over 1 million today. As such, it is a promising source for all types of empirical studies aimed at analyzing the collaborative development and evolution of ML artifacts. Nevertheless, apart from the API provided by the platform, there are no easy-to-use solutions to collect and explore the different facets of HFH data. In this seminar, I will present HFCommunity, a relational database populated with HFH data to facilitate empirical analysis on the growing number of ML-related development projects.
Adem Ait
PhD student at the SnT-University of Luxembourg. Before (2021-2024) he was a research assistant in the SOM Research Lab at IN3-UOC, in Barcelona, Spain. I graduated in Computer Engineering at the FIB-UPC (Technical University of Catalonia) on 2021 and he graduated in Data Science master at the UOC on 2023. His research interests are on LLM-agents, agentic workflows and the modeling of these. Currently working on his PhD thesis focused on providing modeling methodologies to the definition of LLM-agents systems. Furthermore, he collaborated in works of social analysis in Open Source systems conducting empirical and mining software repositories (MSR) studies and graph oriented machine learning techniques.