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)
1 maintainer
Metadata
1 maintainer
Release files for schnitsum 0.4.10
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| schnitsum-0.4.10.tar.gz | 7.5 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| schnitsum-0.4.10-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 15.7 kB
Release files / schnitsum-0.4.10.tar.gz
| Download URL | schnitsum-0.4.10.tar.gz |
|---|---|
| Size | 7.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
165b82bb3ebae06042ec750ff6ca6ec1f0918eb633150673718f97656f83219a
|
|
BLAKE2b-256 checksum How to use checksums |
d6c6f7cc5f322eb763f19ca6a5ce461e00847a05f17c08c8bc06d20111d4f352
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/1.2.2 CPython/3.9.5 Darwin/22.4.0
|
Release files / schnitsum-0.4.10-py3-none-any.whl
| Download URL | schnitsum-0.4.10-py3-none-any.whl |
|---|---|
| Size | 8.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
22d9b27c967fc94254d88efad8b71d5dd6dcfb4bdf3862f301d0a1bcda44247f
|
|
BLAKE2b-256 checksum How to use checksums |
60804585e217753e02dc0b45af4db88c401a77f004036f49bca36c9a53a9a685
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/1.2.2 CPython/3.9.5 Darwin/22.4.0
|
Security for schnitsum 0.4.10
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
