Sadeed: Advancing Arabic Diacritization Through Small Language Model
Authors: name: Zeina Aldallal, name: Sara Chrouf, name: Khalil Hennara, name: Mohamed Motaism Hamed, name: Muhammad Hreden, name: Safwan AlModhayan
Organization: Misraj Ai

Arabic text diacritization remains a persistent challenge in natural language
processing due to the language's morphological richness. In this paper, we
introduce Sadeed, a novel approach based on a fine-tuned decoder-only language
model adapted from Kuwain 1.5B Hennara et al. [2025], a compact model
originally trained on diverse Arabic corpora. Sadeed is fine-tuned on carefully
curated, high-quality diacritized datasets, constructed through a rigorous
data-cleaning and normalization pipeline. Despite utilizing modest
computational resources, Sadeed achieves competitive results compared to
proprietary large language models and outperforms traditional models trained on
similar domains. Additionally, we highlight key limitations in current
benchmarking practices for Arabic diacritization. To address these issues, we
introduce SadeedDiac-25, a new benchmark designed to enable fairer and more
comprehensive evaluation across diverse text genres and complexity levels.
Together, Sadeed and SadeedDiac-25 provide a robust foundation for advancing
Arabic NLP applications, including machine translation, text-to-speech, and
language learning tools.