Update README.md

README.md

@@ -36,7 +36,7 @@ Used the example/seq2seq/run_summarization.py script from the transformers sourc
   max_target_length: 128
 
 ## Eval results
-
+  
   eval_gen_len: 35.89,\
   eval_loss: 1.3807392120361328,\
   eval_rouge1: 47.3372,\
@@ -54,7 +54,13 @@ Used the example/seq2seq/run_summarization.py script from the transformers sourc
   tokenizer = PegasusTokenizer.from_pretrained(model_name)
   model = PegasusForConditionalGeneration.from_pretrained(model_name)
   
-  src_text = """Carter: Hey Alexis, I just wanted to let you know that I had a really nice time with you tonight.\r\n Alexis: Thanks Carter. Yeah, I really enjoyed myself as well.\r\n Carter: If you are up for it, I would really like to see you again soon.\r\n Alexis: Thanks Carter, I'm flattered. But I have a really busy week coming up.\r\n Carter: Yeah, no worries. I totally understand. But if you ever want to go grab dinner again, just let me know.\r\n Alexis: Yeah of course. Thanks again for tonight. Carter: Sure. Have a great night.\r\n"""
+  src_text = """Carter: Hey Alexis, I just wanted to let you know that I had a really nice time with you tonight.\\r\
+  Alexis: Thanks Carter. Yeah, I really enjoyed myself as well.\\r\
+  Carter: If you are up for it, I would really like to see you again soon.\\r\
+  Alexis: Thanks Carter, I'm flattered. But I have a really busy week coming up.\\r\
+  Carter: Yeah, no worries. I totally understand. But if you ever want to go grab dinner again, just let me know.\\r\
+  Alexis: Yeah of course. Thanks again for tonight. Carter: Sure. Have a great night.\\r\
+  """
   
   token_params = dict(max_length=512, truncation=True, padding='longest', return_tensors="pt")
   batch = tokenizer(src_text, **token_params)