  EuroVoc is a multilingual thesaurus that was built for organizing the
legislative documentary of the European Union institutions. It contains
thousands of categories at different levels of specificity and its descriptors
are targeted by legal texts in almost thirty languages. In this work we propose
a unified framework for EuroVoc classification on 22 languages by fine-tuning
modern Transformer-based pretrained language models. We study extensively the
performance of our trained models and show that they significantly improve the
results obtained by a similar tool - JEX - on the same dataset. The code and
the fine-tuned models were open sourced, together with a programmatic interface
that eases the process of loading the weights of a trained model and of
classifying a new document.
