
AraBART is the first Arabic model in which the encoder and the decoder are pretrained end-to-end, based on BART. AraBART follows the architecture of BART-Base
which has 6 encoder and 6 decoder layers and 768 hidden dimensions. In total AraBART has 139M parameters.

AraBART achieves the best performance on multiple abstractive summarization datasets, outperforming strong baselines including a pretrained Arabic BERT-based models and multilingual mBART and mT5 models.



{"language": ["ar"], "license": "apache-2.0", "tags": ["summarization", "bart"], "widget": [{"text": "\u0628\u064a\u0631\u0648\u062a \u0647\u064a \u0639\u0627\u0635\u0645\u0629 <mask>."}], "pipeline_tag": "fill-mask"}
transformers pytorch mbart feature-extraction summarization bart fill-mask ar license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #mbart #feature-extraction #summarization #bart #fill-mask #ar #license-apache-2.0 #endpoints_compatible #has_space #region-us 


AraBART is the first Arabic model in which the encoder and the decoder are pretrained end-to-end, based on BART. AraBART follows the architecture of BART-Base
which has 6 encoder and 6 decoder layers and 768 hidden dimensions. In total AraBART has 139M parameters.

AraBART achieves the best performance on multiple abstractive summarization datasets, outperforming strong baselines including a pretrained Arabic BERT-based models and multilingual mBART and mT5 models.



TAGS
#transformers #pytorch #mbart #feature-extraction #summarization #bart #fill-mask #ar #license-apache-2.0 #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #mbart #feature-extraction #summarization #bart #fill-mask #ar #license-apache-2.0 #endpoints_compatible #has_space #region-us 
