Update README.md

README.md

@@ -43,4 +43,25 @@ Used the _example/seq2seq/run_summarization.py_ script from the transformers sou
     eval_rouge2: 23.6137,\
     eval_rougeL: 37.2397,\
     eval_rougeLsum: 42.7126,\
-    eval_samples_per_second: 4.302
+    eval_samples_per_second: 4.302
+    
+## Example
+
+    from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+    
+    model_name = "jpcorb20/pegasus-large-reddit_tifu-samsum-256"
+    
+    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\nAlexis: Thanks Carter. Yeah, I really enjoyed myself as well.\r\nCarter: If you are up for it, I would really like to see you again soon.\r\nAlexis: Thanks Carter, I'm flattered. But I have a really busy week coming up.\r\nCarter: Yeah, no worries. I totally understand. But if you ever want to go grab dinner again, just let me know.\r\nAlexis: Yeah of course. Thanks again for tonight. Carter: Sure. Have a great night.\r\n"""
+    
+    token_params = dict(max_length=256, truncation=True, padding='longest', return_tensors="pt")
+    batch = tokenizer(src_text, **token_params)
+    
+    translated = model.generate(**batch)
+    
+    decode_params = dict(num_beams=5, min_length=16, max_length=128, length_penalty=2)
+    tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True, **decode_params)
+    
+    print(tgt_text)