## 1 Introduction
Many works have used a translated version of the Google Analogy Test for non-English word embedding models; however, its efficiency in evaluating non-English word embedding models has been criticized [10]. For morphologically rich languages such as Arabic and German, using the Google Analogy Test may not be effective since it does not provide a deep representation of these languages from the outset. One of the major issues with using the Google Analogy Test is that it contains a (city-in-state) test section, which is irrelevant to some languages, such as Arabic, due to cultural differences. Additionally, some vocabulary used in the test does not have exact translations, leading the model to fail in producing the desired results. For example, in the (family) test section, the word (stepbrother) cannot be translated as a single word in Arabic. Another published Arabic model is Aravec [11], which is available for use. However, it has limitations in capturing certain Arabic words. Additionally, the authors did not publish the details of the corpora used or the testing datasets employed. From previous efforts in Arabic word embedding, we can conclude that these models face several limitations, including limited availability, reliance on unsuitable evaluation tests, such as the Google Analogy Test, vocabulary loss due to translation challenges, and a lack of transparency in corpus and test dataset details. These limitations high light the need for more comprehensive and openly available Arabic word embedding models and resources. Therefore, to address this gap, this paper proposes the development of a comprehensive set of high-quality Arabic word embedding models that can be effectively utilized for various natural language processing (NLP) tasks, including sentiment analysis and similarity testing. The contributions of this paper are as follows:  
* The creation of comprehensive Arabic embedding models: We developed 108 Arabic word embedding models using advanced techniques such as Word2 Vec and FastText, optimizing the hyperparameters to produce high-quality embeddings specifically suited for the unique linguistic structures of Arabic. * Rich language representation: By training the models on three diverse Arabic corpora--news articles, books, and Wikipedia--we capture the linguistic variety and morphological richness of Arabic, supporting robust, context-aware embeddings. * The development of Arabic-specific evaluation metrics: We adapted an Arabic benchmark from Google's Analogy Test to accurately assess semantic relationships within Arabic, ensuring the embeddings reflect true language understanding. In addition to the aforementioned contributions, this paper provides open access to the developed models, corpora, and testing datasets (Our work is available at [[https://github.com/[PERSON]/ArabicWordEmbedding](https://github.com/[PERSON]/ArabicWordEmbedding)]([https://github.com/[PERSON]/ArabicWordEmbedding](https://github.com/[PERSON]/ArabicWordEmbedding)) (accessed on 28 September 2024)). By doing so, we strive to contribute valuable tools that can enhance the Arabic NLP domain. The remainder of this paper is organized as follows. Section 2 provides background information about Word embeddings. Related works are summarized in Section 3. Section 4 elaborates on the proposed Arabic word embedding model-building process. This is followed by their evaluation in Section 5 and the discussion of the results in Section 6. Finally, we conclude in Section 7.