Recent research has focused extensively on state-of-the-art pretrained language models, particularly those based on Transformer architectures, and how well they account for negation and other linguistic phenomena in various tasks. This study aims to evaluate the understanding of negation in Italian bert- and roberta-based models, contrasting the predominant English-focused prior research. We develop the SCIN Set, an Italian dataset designed to model the influence of polarity constraints on models in a masked predictions task. Applying the SCIN Set reveals that these models do not adjust their behaviour based on sentences polarity, even when the resulting sentence is contradictory. We conclude that the tested models lack a clear understanding of how negation alters sentence meaning.