Update README.md

README.md

@@ -6,7 +6,7 @@ The BART model was pre-trained on the CNN-DailyMail dataset, but it was re-train
 
 According to huggingface, 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 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). This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs.
+BART is particularly effective when fine-tuned for summarization on the Amazon Review data, which hosts a large collection of reviews.
 
 ## Intended uses & limitations