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AraGPT2: Pre-Trained Transformer for Arabic Language Generation

by   Wissam Antoun, et al.

Recently, pretrained transformer-based architectures have proven to be very efficient at language modeling and understanding, given that they are trained on a large enough corpus. Applications in language generation for Arabic is still lagging in comparison to other NLP advances primarily due to the lack of advanced Arabic language generation models. In this paper, we develop the first advanced Arabic language generation model, AraGPT2, trained from scratch on large Arabic corpora of internet text and news articles. Our largest model, AraGPT2-mega, has 1.46 billion parameters, which makes it the largest Arabic language model available. We evaluate different size variants of AraGPT2 using the perplexity measure, where AraGPT2-mega achieves a perplexity of 29.8 on held-out articles from Wikipedia. Pretrained variants of AraGPT2 (base, medium, large, mega) are publicly available on hoping to encourage new research directions and applications for Arabic NLP.


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