## 1 Introduction  
Arabic has linguistic complexities such as morphological richness, complex syntax, and multiple dialects spread over many geolinguistic environments, which affect Arabic natural language processing (NLP) task performance compared to several other languages. Arabic word embedding plays a crucial role in Arabic NLP research. Word embedding is mainly a type of distributed word representation that captures both the semantic and syntactic information of words. It is derived from a large unlabeled corpus of text data [1, 2]. Indeed, word embedding is a valuable technique that provides numerical representations of words, enabling meaningful comparisons. Therefore, word embedding models are trained on large text corpus using algorithms such as Word2 Vec, GloVe, or FastText. These pretrained models provide a robust method for representing Arabic words in a high-dimensionalvector space. By employing statistical techniques to analyze frequently occurring n-grams from diverse Arabic content domains, such as Tweets and Arabic Wikipedia articles, these models are generated. This vector representation enables researchers to enhance the effectiveness and accuracy of various NLP tasks. Moreover, these models can be easily downloaded and integrated into numerous NLP applications. [PERSON] et al. [3] have made significant contributions to this field with their widely used Word2 Vec model. This model efficiently learns vector representations of words from large text datasets, capturing semantic relationships and enhancing various NLP tasks such as sentiment analysis, machine translation, and information retrieval. However, their work is developed using English corpora and is primarily useful for English NLP tasks [4]. Furthermore, Facebook has published an enhanced version of Word2 Vec called FastText [5]. FastText incorporates the use of skip-gram models, which involve utilizing n-grams for each word. This approach enables faster training on large corpora and also allows the model to handle new words that were not present in the training data. The FastText model has gained popularity due to its efficiency and ability to capture the nuances of language [6]. Pretrained models, which are widely available to the public and trained on extensive datasets, have become a standard resource for various NLP applications. However, it is important to note that these models are primarily focused on the English language [7]. Indeed, these models greatly facilitate the efficient calculation of word similarities by utilizing low-dimensional matrix operations. This capability proves to be valuable in addressing the complexities of Arabic language features and mitigating issues related to semantic ambiguity. By representing Arabic words in a high-dimensional vector space, these models can capture the subtle nuances and context-specific meanings of Arabic words, enabling more accurate and effective analysis in Arabic natural language processing (NLP) tasks [8]. Despite the growing popularity of word embedding techniques, the development of Arabic word embedding is still in its early stages. The main hurdle lies in the scarcity of Arabic language resources. The creation of a robust Arabic corpus is challenging due to the limited availability of reliable sources. Evaluating Arabic word embedding models requires diverse datasets, incorporating analogy tests, sentiment-annotated data, and word pairs with synonymous meanings. These datasets are crucial for assessing a model's ability to accurately capture semantic relationships in the Arabic language. Only a few works have been published on Arabic word embedding models. For instance, in [9], the authors developed Arabic models with various hyperparameter values. However, these models are not readily available for use. Additionally, the researchers collected their corpora from online resources and utilized a translated version of the Google Analogy Test dataset. Despite its popularity, the structure of the Google Analogy Test dataset in terms of relations has been questioned.