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

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


Co-authored-by: Daniel Levenson <[email protected]>

README.md

@@ -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).