Poetry holds immense significance within the cultural and traditional fabric
of any nation. It serves as a vehicle for poets to articulate their emotions,
preserve customs, and convey the essence of their culture. Arabic poetry is no
exception, having played a cherished role in the heritage of the Arabic
community throughout history and maintaining its relevance in the present era.
Typically, comprehending Arabic poetry necessitates the expertise of a linguist
who can analyze its content and assess its quality. This paper presents the
introduction of a framework called \textit{Ashaar}
this https URL, which encompasses a collection of datasets and
pre-trained models designed specifically for the analysis and generation of
Arabic poetry. The pipeline established within our proposed approach
encompasses various aspects of poetry, such as meter, theme, and era
classification. It also incorporates automatic poetry diacritization, enabling
more intricate analyses like automated extraction of the \textit{Arudi} style.
Additionally, we explore the feasibility of generating conditional poetry
through the pre-training of a character-based GPT model. Furthermore, as part
of this endeavor, we provide four datasets: one for poetry generation, another
for diacritization, and two for Arudi-style prediction. These datasets aim to
facilitate research and development in the field of Arabic poetry by enabling
researchers and enthusiasts to delve into the nuances of this rich literary
tradition.

本文介绍了一种名为 Ashaar 的框架，它包括一系列数据集和预训练模型，旨在分析和生成阿拉伯诗歌，并探索生成条件诗歌的可行性，为阿拉伯诗歌领域的研究和发展提供了数据集。