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