Metadata-Version: 2.1
Name: schnitsum
Version: 0.2.0
Summary: 
Author: sobamchan
Author-email: oh.sore.sore.soutarou@gmail.com
Requires-Python: >=3.9,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Dist: torch (>=1.12.1,<2.0.0)
Requires-Dist: transformers (>=4.22.2,<5.0.0)
Description-Content-Type: text/markdown

# 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)](https://aclanthology.org/2020.acl-main.703) fine-tuned on computer science papers (ref. [SciTLDR](https://aclanthology.org/2020.findings-emnlp.428)).
  - Model name: `sobamchan/bart-large-scitldr`
- [BART (large)](https://aclanthology.org/2020.acl-main.703) fine-tuned on computer science papers (ref. [SciTLDR](https://aclanthology.org/2020.findings-emnlp.428)). Then distilled to have 65% parameters less.
  - Model name: `sobamchan/bart-large-scitldr-distilled-3-3`

we are planning to expand coverage soon to other sizes, domains, languages, models soon.


# Installation

```bash
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](https://pytorch.org/get-started/locally/).


# Usage

```py3
from schnitsum import SchnitSum
model = SchnitSum("sobamchan/bart-large-scitldr-distilled-3-3")

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

summaries = model(docs)
print(summaries)
```

