  One of the challenges in the NLP field is training large classification
models, a task that is both difficult and tedious. It is even harder when GPU
hardware is unavailable. The increased availability of pre-trained and
off-the-shelf word embeddings, models, and modules aim at easing the process of
training large models and achieving a competitive performance. We explore the
use of off-the-shelf BERT models and share the results of our experiments and
compare their results to those of LSTM networks and more simple baselines. We
show that the complexity and computational cost of BERT is not a guarantee for
enhanced predictive performance in the classification tasks at hand.
