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Project description
Schnitsum: Easy to use neural network based summarization models
This package enables to generate summaries of you documents of interests.
Currently, we support following models,
- BART (large) fine-tuned on computer science papers (ref. SciTLDR).
- Model name:
sobamchan/bart-large-scitldr
- Model name:
- BART (large) fine-tuned on computer science papers (ref. SciTLDR). Then distilled (by
shrink and fine-tune
) to have 65% parameters less.- Model name:
sobamchan/bart-large-scitldr-distilled-3-3
- Model name:
- BART (large) fine-tuned on computer science papers (ref. SciTLDR). Then distilled (by
shrink and fine-tune
) to have 37% parameters less.- Model name:
sobamchan/bart-large-scitldr-distilled-12-3
- Model name:
- mBART (large, en-to-de) fine-tuned on computer science papers, English papers and German summaries (ref. X-SciTLDR).
- Model name:
sobamchan/mbart-large-xscitldr-de
- Model name:
- BART (large) fine-tuned on XSum, BCC news article lead sentences in English. (ref. X-SciTLDR).
- Model name:
facebook/bart-large-xsum
- Model name:
we are planning to expand coverage soon to other sizes, domains, languages, models soon.
Installation
pip install schnitsum # or poetry add schnitsum
This will let you generate summaries with CPUs only, if you want to utilize your GPUs, please follow the instruction by PyTorch, here.
Usage
From Command Line
Pass document as an argument and print the summary
> schnitsum --model-name sobamchan/bart-large-scitldr-distilled-3-3 --text "Text to summarize"
Pass documents as a file and save summaries in a file. Input file needs to contain documents line by line. example
> schnitsum --model-name sobamchan/bart-large-scitldr-distilled-3-3 --file docs.txt --opath sums.txt
From Python
from schnitsum import SchnitSum
model = SchnitSum("sobamchan/bart-large-scitldr-distilled-3-3")
docs = [
"Document you want to summarize."
]
summaries = model(docs)
print(summaries)
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