  The field of natural language processing (NLP) has seen remarkable
advancements, thanks to the power of deep learning and foundation models.
Language models, and specifically BERT, have been key players in this progress.
In this study, we trained and introduced two new BERT models using Persian
data. We put our models to the test, comparing them to seven existing models
across 14 diverse Persian natural language understanding (NLU) tasks. The
results speak for themselves: our larger model outperforms the competition,
showing an average improvement of at least +2.8 points. This highlights the
effectiveness and potential of our new BERT models for Persian NLU tasks.
