PubMedBERT for biomedical extractive summarization
Description
Work done for my Bachelor's thesis.
PubMedBERT fine-tuned
on MS^2 for extractive summarization.
The model architecture is similar to BERTSum.
Training code is available at biomed-ext-summ.
Usage
summarizer = pipeline("summarization",
model = "NotXia/pubmedbert-bio-ext-summ",
tokenizer = AutoTokenizer.from_pretrained("NotXia/pubmedbert-bio-ext-summ"),
trust_remote_code = True,
device = 0
)
sentences = ["sent1.", "sent2.", "sent3?"]
summarizer({"sentences": sentences}, strategy="count", strategy_args=2)
>>> (['sent1.', 'sent2.'], [0, 1])
Strategies
Strategies to summarize the document:
length
: summary with a maximum length (strategy_args
is the maximum length).count
: summary with the given number of sentences (strategy_args
is the number of sentences).ratio
: summary proportional to the length of the document (strategy_args
is the ratio [0, 1]).threshold
: summary only with sentences with a score higher than a given value (strategy_args
is the minimum score).
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