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bart-large-xsum-samsum - bnb 8bits

Original model description:

language: en tags: - bart - seq2seq - summarization license: apache-2.0 datasets: - samsum widget: - text: | Hannah: Hey, do you have Betty's number? Amanda: Lemme check Amanda: Sorry, can't find it. Amanda: Ask Larry Amanda: He called her last time we were at the park together Hannah: I don't know him well Amanda: Don't be shy, he's very nice Hannah: If you say so.. Hannah: I'd rather you texted him Amanda: Just text him 馃檪 Hannah: Urgh.. Alright Hannah: Bye Amanda: Bye bye model-index: - name: bart-large-xsum-samsum results: - task: name: Abstractive Text Summarization type: abstractive-text-summarization dataset: name: "SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization" type: samsum metrics: - name: Validation ROUGE-1 type: rouge-1 value: 54.3921 - name: Validation ROUGE-2 type: rouge-2 value: 29.8078 - name: Validation ROUGE-L type: rouge-l value: 45.1543 - name: Test ROUGE-1 type: rouge-1 value: 53.3059 - name: Test ROUGE-2 type: rouge-2 value: 28.355 - name: Test ROUGE-L type: rouge-l value: 44.0953

bart-large-xsum-samsum

This model was obtained by fine-tuning facebook/bart-large-xsum on Samsum dataset.

Usage

from transformers import pipeline

summarizer = pipeline("summarization", model="lidiya/bart-large-xsum-samsum")
conversation = '''Hannah: Hey, do you have Betty's number?
Amanda: Lemme check
Amanda: Sorry, can't find it.
Amanda: Ask Larry
Amanda: He called her last time we were at the park together
Hannah: I don't know him well
Amanda: Don't be shy, he's very nice
Hannah: If you say so..
Hannah: I'd rather you texted him
Amanda: Just text him 馃檪
Hannah: Urgh.. Alright
Hannah: Bye
Amanda: Bye bye                                       
'''
summarizer(conversation)

Training procedure

Results

key value
eval_rouge1 54.3921
eval_rouge2 29.8078
eval_rougeL 45.1543
eval_rougeLsum 49.942
test_rouge1 53.3059
test_rouge2 28.355
test_rougeL 44.0953
test_rougeLsum 48.9246
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