  This study introduces the eFontes models for automatic linguistic annotation
of Medieval Latin texts, focusing on lemmatization, part-of-speech tagging, and
morphological feature determination. Using the Transformers library, these
models were trained on Universal Dependencies (UD) corpora and the newly
developed eFontes corpus of Polish Medieval Latin. The research evaluates the
models' performance, addressing challenges such as orthographic variations and
the integration of Latinized vernacular terms. The models achieved high
accuracy rates: lemmatization at 92.60%, part-of-speech tagging at 83.29%, and
morphological feature determination at 88.57%. The findings underscore the
importance of high-quality annotated corpora and propose future enhancements,
including extending the models to Named Entity Recognition.
