  The Latin language has received attention from the computational linguistics
research community, which has built, over the years, several valuable
resources, ranging from detailed annotated corpora to sophisticated tools for
linguistic analysis. With the recent advent of large language models,
researchers have also started developing models capable of generating vector
representations of Latin texts. The performances of such models remain behind
the ones for modern languages, given the disparity in available data. In this
paper, we present the LiMe dataset, a corpus of 325 documents extracted from a
series of medieval manuscripts called Libri sententiarum potestatis Mediolani,
and thoroughly annotated by experts, in order to be employed for masked
language model, as well as supervised natural language processing tasks.
