schnitsum 0.3.0

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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
• 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
• 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

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 schnitsum import SchnitSum
model = SchnitSum("sobamchan/bart-large-scitldr-distilled-3-3")

docs = [
    "Document you want to summarize."
]

summaries = model(docs)
print(summaries)