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.

翻译：自然语言处理领域因深度学习与基础模型的强大能力而取得了显著进展。语言模型，特别是BERT，已成为推动这一进步的关键因素。本研究利用波斯语数据训练并推出了两种新型BERT模型。我们通过14项多样化的波斯语自然语言理解任务，将所提模型与七种现有模型进行了全面对比测试。结果不言而喻：我们的大规模模型在竞争中表现优异，平均性能提升至少达到+2.8个百分点。这充分证明了我们提出的新型BERT模型在波斯语自然语言理解任务中的有效性与应用潜力。