TookaBERT: A Step Forward for Persian NLU

• URL: http://arxiv.org/abs/2407.16382v1
• Date: Tue, 23 Jul 2024 11:12:47 GMT
• Title: TookaBERT: A Step Forward for Persian NLU
• Authors: MohammadAli SadraeiJavaheri, Ali Moghaddaszadeh, Milad Molazadeh, Fariba Naeiji, Farnaz Aghababaloo, Hamideh Rafiee, Zahra Amirmahani, Tohid Abedini, Fatemeh Zahra Sheikhi, Amirmohammad Salehoof,
• Abstract summary: We train and introduce two new BERT models using Persian data. We compare them to seven existing models across 14 diverse Persian natural language understanding (NLU) tasks. Our larger model outperforms the competition, showing an average improvement of at least +2.8 points.
• Score: 3.2769173057785212
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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