## 3 Literature Review  
Arabic, as one of the most widely spoken languages worldwide, plays a crucial role in areas such as NLP, computational linguistics, and language technology development. For the advancement of research in these fields, the availability of high-quality Arabic corpora is paramount. However, despite the growing interest in Arabic language resources and Arabic NLP, there is a noticeable lack of studies on publishing Arabic corpora [4].  
In 2022, [PERSON] et al. [11] introduced \"Masader\", a public catalogue of Arabic NLP datasets that includes 200 datasets annotated with 25 attributes. The authors also proposed a metadata annotation strategy that can be extended to other languages. However, Masader only contains Arabic datasets for testing and fine-tuning machine learning and deep learning models; it does not include corpora for training transformer models.  
[PERSON] et al. [3] conducted a scoping review to identify freely available Arabic corpora and subsequently presented a collection of 48 freely accessible sources. In their review, they provided key information about each corpus, including its name, type or purpose, corresponding website URL, size, and the primary sources from which it was compiled. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, only published, peer-reviewed studies were included [12]. However, not all available corpora were included, as the focus was on freely accessible resources; corpora that required a subscription or contacting the owners were excluded [3].  
[PERSON] [8] discussed, in their study, the design and creation of an extensive Arabic corpus comprising over 500 GB of data. The primary objective of their study was to enhance the cross-domain knowledge of large-scale language models. The above corpus was developedby collecting text from 22 diverse sources. However, despite being the largest, most diverse, clean Arabic corpus ever compiled, it is not publicly accessible to other researchers aiming to improve models and systems for NLP tasks.  
There is a shortage of Arabic corpora and collections of related published papers, and access is limited. Compared to other languages, there are few freely available and comprehensive Arabic corpora. This limitation hinders the development and evaluation of NLP models and algorithms specifically tailored to Arabic. Additionally, diverse data sources are often required to ensure the robustness and generalizability of models used for Arabic NLP tasks. While efforts have been made to create Arabic corpora, there is still a need for diverse, representative resources that cover a wide range of genres, registers, and writing styles. Such diversity is crucial for developing training models that can effectively handle the intricacies of various types of Arabic text.  
In Table 1, a comparison of our study and related works is presented, taking into account criteria such as the year of the study, the type of data, and the type of Arabic text analyzed. The data presented in the table also indicate whether the study authors proposed a text preprocessing algorithm and, if so, whether they specified the text size before and after the preprocessing stage; initial preprocessing generally involves crucial steps for cleaning the text and preparing it for training transformer models. Furthermore, in the Table below, the availability of public data is also highlighted.