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typo: encoder-encoder -> encoder-decoder (#1) 

- typo: encoder-encoder -> encoder-decoder (58509d68aee6f8131fbafbf0d4881c71cbe457d3)


Co-authored-by: Daniel Levenson <dleve123@users.noreply.huggingface.co>
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patrickvonplaten 2022-06-03 10:00:20 +00:00 committed by system
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@ -11,7 +11,7 @@ Disclaimer: The team releasing BART did not write a model card for this model so
## Model description
BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).