This model was trained using Microsoft's
Azure Machine Learning Service. It was fine-tuned on the
samsum corpus from
facebook/bart-large checkpoint.
1from transformers import pipeline
2summarizer = pipeline("summarization", model="linydub/bart-large-samsum")
3
4input_text = '''
5 Henry: Hey, is Nate coming over to watch the movie tonight?
6 Kevin: Yea, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you taken out the garbage yet?
7 Henry: Oh I forgot. I'll do that once I'm finished with my assignment for my math class.
8 Kevin: Yea, you should take it out as soon as possible. And also, Nate is bringing his girlfriend.
9 Henry: Nice, I'm really looking forward to seeing them again.
10'''
11summarizer(input_text)
More information about the fine-tuning process (including samples and benchmarks):
[Preview] https://github.com/linydub/azureml-greenai-txtsumThese results were retrieved from
Azure Monitor Metrics. All experiments were ran on AzureML low priority compute clusters.
*Compute cost ($) is estimated from the run duration, number of compute nodes utilized, and SKU's price per hour. Updated SKU pricing could be found
here.
These results were obtained using
CodeCarbon. The carbon emissions are estimated from training runtime only (excl. setup and evaluation runtimes).
