Tashkeel, or Arabic Text Diacritization (ATD), greatly enhances the comprehension of Arabic text by removing ambiguity and minimizing the risk of misinterpretations caused by its absence.It plays a crucial role in improving Arabic text processing, particularly in applications such as text-to-speech and machine translation.This paper introduces a new approach to training ATD models.First, we finetuned two transformers, encoder-only and encoder-decoder, that were initialized from a pretrained character-based BERT.Then, we applied the Noisy-Student approach to boost the performance of the best model.We evaluated our models alongside 11 commercial and open-source models using two manually labeled benchmark datasets: WikiNews and our CATT dataset.Our findings show that our top model surpasses all evaluated models by relative Diacritic Error Rates (DERs) of 30.83% and 35.21% on WikiNews and CATT, respectively, achieving state-of-the-art in ATD.In addition, we show that our model outperforms GPT-4-turbo on CATT dataset by a relative DER of 9.36%.We open-source our CATT models and benchmark dataset for the research community .