From b51e6013453a74429b6801e1ad7fb127048ef434 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Fri, 11 Dec 2020 22:39:34 +0100 Subject: [PATCH] Migrate model card from transformers-repo Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755 Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/facebook/bart-large/README.md --- README.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..6531417 --- /dev/null +++ b/README.md @@ -0,0 +1,15 @@ +--- +license: mit +thumbnail: https://huggingface.co/front/thumbnails/facebook.png +--- + +The Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract, + +Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT). + +The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token. + +BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. + +The Authors’ code can be found here: +https://github.com/pytorch/fairseq/tree/master/examples/bart