  In this work, we carried out a study about the use of attention-based
algorithms to automate the categorization of Brazilian case law documents. We
used data from the Kollemata Project to produce two distinct datasets with
adequate class systems. Then, we implemented a multi-class and multi-label
version of BERT and fine-tuned different BERT models with the produced
datasets. We evaluated several metrics, adopting the micro-averaged F1-Score as
our main metric for which we obtained a performance value of F1-micro=0.72
corresponding to gains of 30 percent points over the tested statistical
baseline. In this work, we carried out a study about the use of attention-based
algorithms to automate the categorization of Brazilian case law documents. We
used data from the \textit{Kollemata} Project to produce two distinct datasets
with adequate class systems. Then, we implemented a multi-class and multi-label
version of BERT and fine-tuned different BERT models with the produced
datasets. We evaluated several metrics, adopting the micro-averaged F1-Score as
our main metric for which we obtained a performance value of $\langle
\mathcal{F}_1 \rangle_{micro}=0.72$ corresponding to gains of 30 percent points
over the tested statistical baseline.
