  This paper describes the second-placed approach developed by the Fraunhofer
SIT team in the CLEF-2023 CheckThat! lab Task 1B for English. Given a text
snippet from a political debate, the aim of this task is to determine whether
it should be assessed for check-worthiness. Detecting check-worthy statements
aims to facilitate manual fact-checking efforts by prioritizing the claims that
fact-checkers should consider first. It can also be considered as primary step
of a fact-checking system. Our best-performing method took advantage of an
ensemble classification scheme centered on Model Souping. When applied to the
English data set, our submitted model achieved an overall F1 score of 0.878 and
was ranked as the second-best model in the competition.
