Update README.md
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README.md
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@@ -28,21 +28,21 @@ The initial weigths were from the [google/pegasus-reddit_tifu](https://huggingfa
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## Training procedure
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Used the
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n_epochs: 3
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batch_size: 4,
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max_source_length: 512
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max_target_length: 128
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## Eval results
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eval_gen_len: 35.89
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eval_loss: 1.3807392120361328
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eval_rouge1: 47.3372
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eval_rouge2: 24.4728
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eval_rougeL: 37.9078
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eval_rougeLsum: 43.5744
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eval_samples_per_second: 2.814
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## Example
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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
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Carter: If you are up for it, I would really like to see you again soon
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Alexis: Thanks Carter, I'm flattered. But I have a really busy week coming up
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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
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Alexis: Yeah of course. Thanks again for tonight. Carter: Sure. Have a great night
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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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## Training procedure
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Used the example/seq2seq/run_summarization.py script from the transformers source 4.5.0dev0.
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n_epochs: 3,\
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batch_size: 4, \
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max_source_length: 512,\
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max_target_length: 128
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## Eval results
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eval_gen_len: 35.89,\
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eval_loss: 1.3807392120361328,\
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eval_rouge1: 47.3372,\
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eval_rouge2: 24.4728,\
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eval_rougeL: 37.9078,\
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eval_rougeLsum: 43.5744,\
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eval_samples_per_second: 2.814
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## Example
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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\\
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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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