AraBERT is a transformer-based model specifically trained for the Arabic language to achieve state-of-the-art performance in Arabic Natural Language Processing (NLP) tasks. Unlike multilingual BERT, which is trained on a wide range of languages, AraBERT is pre-trained on a large Arabic corpus to better capture the linguistic characteristics of Arabic, including its morphological complexity and syntactic structure. The model is based on the BERT architecture and is fine-tuned for three key NLP tasks: Sentiment Analysis (SA), Named Entity Recognition (NER), and Question Answering (QA). AraBERT outperforms previous state-of-the-art models, including multilingual BERT, on most tested Arabic NLP tasks. It is publicly available on GitHub, encouraging further research and applications in Arabic NLP. The model was pre-trained on a large dataset of 70 million sentences, covering Modern Standard Arabic (MSA) and Dialectal Arabic (DA), and includes a custom subword tokenization to handle the lexical sparsity of Arabic. The pre-training process involved two main tasks: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). The model was trained on a combination of Arabic news articles, Wikipedia data, and other large Arabic corpora. The fine-tuning process was applied to three downstream tasks, with AraBERT achieving high performance on all of them. In Sentiment Analysis, AraBERT outperformed other models, including the hULMonA model, on multiple Arabic sentiment datasets. In Named Entity Recognition, AraBERTv0.1 improved the F1 score by 2.53 points compared to the Bi-LSTM-CRF model. In Question Answering, AraBERT showed improved performance in F1-score but had lower exact match scores, indicating some challenges in accurately identifying the correct answer span. The results suggest that pre-training on a single language can lead to better performance than multilingual models, especially for languages with limited resources. AraBERT's success highlights the importance of language-specific models in NLP tasks and provides a new baseline for Arabic NLP research. The model is also smaller than multilingual BERT, making it more accessible for various applications. Future work includes developing a version of AraBERT that does not rely on external tokenizers and improving the model's ability to handle different Arabic dialects.AraBERT is a transformer-based model specifically trained for the Arabic language to achieve state-of-the-art performance in Arabic Natural Language Processing (NLP) tasks. Unlike multilingual BERT, which is trained on a wide range of languages, AraBERT is pre-trained on a large Arabic corpus to better capture the linguistic characteristics of Arabic, including its morphological complexity and syntactic structure. The model is based on the BERT architecture and is fine-tuned for three key NLP tasks: Sentiment Analysis (SA), Named Entity Recognition (NER), and Question Answering (QA). AraBERT outperforms previous state-of-the-art models, including multilingual BERT, on most tested Arabic NLP tasks. It is publicly available on GitHub, encouraging further research and applications in Arabic NLP. The model was pre-trained on a large dataset of 70 million sentences, covering Modern Standard Arabic (MSA) and Dialectal Arabic (DA), and includes a custom subword tokenization to handle the lexical sparsity of Arabic. The pre-training process involved two main tasks: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). The model was trained on a combination of Arabic news articles, Wikipedia data, and other large Arabic corpora. The fine-tuning process was applied to three downstream tasks, with AraBERT achieving high performance on all of them. In Sentiment Analysis, AraBERT outperformed other models, including the hULMonA model, on multiple Arabic sentiment datasets. In Named Entity Recognition, AraBERTv0.1 improved the F1 score by 2.53 points compared to the Bi-LSTM-CRF model. In Question Answering, AraBERT showed improved performance in F1-score but had lower exact match scores, indicating some challenges in accurately identifying the correct answer span. The results suggest that pre-training on a single language can lead to better performance than multilingual models, especially for languages with limited resources. AraBERT's success highlights the importance of language-specific models in NLP tasks and provides a new baseline for Arabic NLP research. The model is also smaller than multilingual BERT, making it more accessible for various applications. Future work includes developing a version of AraBERT that does not rely on external tokenizers and improving the model's ability to handle different Arabic dialects.