Context Arabic is a rich and major world language. Recent advances in computational linguistics and AI can be applied to Arabic but not in the generic way most languages are treated. This dataset (Arabic articles from Wikipedia) will be used to train Word2Vec and compare performance with publicly available pre-trained model from FastText (Facebook) in a generic way. A related model is now available: https://www.kaggle.com/abedkhooli/arabic-ulmfit-model Content All Wikipedia Arabic articles from the January 20, 2018 data dump (compressed) in wikimedia format. Cntent is expected to be (mostly) in modern standard Arabic Acknowledgements Thanks to Wikipedia for making public data dumps available and for Facebook for releasing pre-trained models. Inspiration The challenges (opportunities) here are mostly in the pre-processing of the token and text normalization, plus hyper parameter tuning for different purposes. It is easy to isolate Arabic tokens (many articles have non-Arabic words), but tokenization is a challenge: how to treat accented (7arakaat or tashkeel) and non-accented word forms, same word form with different meanings, suffixes and prefixes (especially w).