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 URL hoping to encourage new research directions and applications for Arabic NLP.
Recently, pre-trained 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 are 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 a large Arabic corpus 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. The Mega model was evaluated and showed success on different tasks including synthetic news generation, and zero-shot question answering. For text generation, our best model achieves a perplexity of 29.8 on held-out Wikipedia articles. A study conducted with human evaluators showed the significant success of AraGPT2-mega in generating news articles that are difficult to distinguish from articles written by humans. We thus develop and release an automatic discriminator model with a 98\% percent accuracy in detecting model-generated text. The models are also publicly available, hoping to encourage new research directions and applications for Arabic NLP.
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We evaluate different size variants of AraGPT2 using the perplexity measure, where AraGPT2-mega
The Mega model was evaluated and showed success on different tasks including synthetic news generation, and zero-shot question answering. For text generation, our best model
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articles from Wikipedia . Pretrained variants of AraGPT2 (base, medium, large, mega) are publicly available on URL
Wikipedia articles. A study conducted with human evaluators showed the significant success of AraGPT2-mega in generating news articles that are difficult to distinguish from articles written by humans. We thus develop and release an automatic discriminator model with a 98\% percent accuracy in detecting model-generated text. The models are also publicly available,