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summarization mask_token: <mask>
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https://api-inference.huggingface.co/models/sshleifer/distilbart-xsum-1-1
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sshleifer/distilbart-xsum-1-1 sshleifer/distilbart-xsum-1-1
180 downloads
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pytorch

tf

Contributed by

sshleifer Sam Shleifer
70 models

How to use this model directly from the πŸ€—/transformers library:

			
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from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-xsum-1-1") model = AutoModelWithLMHead.from_pretrained("sshleifer/distilbart-xsum-1-1")
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Usage

This checkpoint should be loaded into BartForConditionalGeneration.from_pretrained. See the BART docs for more information.

Metrics for DistilBART models

Model Name MM Params Inference Time (MS) Speedup Rouge 2 Rouge-L
distilbart-xsum-12-1 222 90 2.54 18.31 33.37
distilbart-xsum-6-6 230 132 1.73 20.92 35.73
distilbart-xsum-12-3 255 106 2.16 21.37 36.39
distilbart-xsum-9-6 268 136 1.68 21.72 36.61
bart-large-xsum (baseline) 406 229 1 21.85 36.50
distilbart-xsum-12-6 306 137 1.68 22.12 36.99
bart-large-cnn (baseline) 406 381 1 21.06 30.63
distilbart-12-3-cnn 255 214 1.78 20.57 30.00
distilbart-12-6-cnn 306 307 1.24 21.26 30.59
distilbart-6-6-cnn 230 182 2.09 20.17 29.70