Update README.md

model_cards/facebook/rag-sequence-nq/README.md

 ## RAG
 
 This is the RAG-Sequence Model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf) 
-by Aleksandra Piktus et al.
+by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.
 
 ## Usage:
 
@@ -18,7 +18,7 @@ outputs = model(input_ids=input_dict["input_ids"], labels=input_dict["labels"])
 
 # outputs.loss should give 76.2978
 
-generated = model.generate(input_ids=input_dict["input_ids"], num_beams=4)
+generated = model.generate(input_ids=input_dict["input_ids"])
 generated_string = tokenizer.batch_decode(generated, skip_special_tokens=True)
 
 # generated_string should give 270,000,000 -> not quite correct the answer, but it also only uses a dummy index