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Badges #23

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merged 11 commits into from
Apr 17, 2024
Merged

Badges #23

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69c0961
Update README.md
tcoroller Apr 16, 2024
073650a
Added badges to Readme
melodiemonod Apr 17, 2024
9862f6e
change color of arxiv badge
melodiemonod Apr 17, 2024
12964ea
change name of qc workflow
melodiemonod Apr 17, 2024
ccba6b2
Add emoji in QC workflow name
melodiemonod Apr 17, 2024
06f6067
change name of docs workflow
melodiemonod Apr 17, 2024
e54a10c
Add emoji to doc workflow name
melodiemonod Apr 17, 2024
7ac9aeb
Update README.md
tcoroller Apr 17, 2024
b985b35
Add files via upload
tcoroller Apr 17, 2024
e5fb650
Update README.md
tcoroller Apr 17, 2024
68cbd83
Update README.md
tcoroller Apr 17, 2024
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4 changes: 2 additions & 2 deletions .github/workflows/codeqc.yml
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
name: torchsurv-codeqc
name: "QC 🛠️"

on: [push]

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with:
name: docs.tar.gz
path: docs.tar.gz
retention-days: 5
retention-days: 5
4 changes: 2 additions & 2 deletions .github/workflows/docs.yml
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@@ -1,4 +1,4 @@
name: torchsurv-docs-cicd
name: "Docs 📚"

on:
push:
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touch .nojekyll # Prevents GitHub Pages from ignoring files that start with an underscore
git add .
git commit -m "Deploy GitHub Pages"
git push -f origin gh-pages
git push -f origin gh-pages
5 changes: 5 additions & 0 deletions README.md
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Expand Up @@ -4,6 +4,11 @@

![CodeQC](https://github.com/Novartis/torchsurv/actions/workflows/codeqc.yml/badge.svg?branch=main)
![Docs](https://github.com/Novartis/torchsurv/actions/workflows/docs.yml/badge.svg?branch=main)
[![arXiv](https://img.shields.io/badge/arXiv-2404.10761-f9f107.svg)](https://arxiv.org/abs/2404.10761)
[![Documentation](https://img.shields.io/badge/GitBook-Docu-lightblue)](https://opensource.nibr.com/torchsurv/)
[![PyPI version](https://badge.fury.io/py/torchsurv.svg)](https://badge.fury.io/py/torchsurv)
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Can be done also with
https://img.shields.io/pypi/v/torchsurv?label=pypi%20package

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Do you recommend this over the fury.io?

[![Downloads](https://static.pepy.tech/badge/torchsurv)](https://pepy.tech/project/torchsurv)


`TorchSurv` is a Python package that serves as a companion tool to perform deep survival modeling within the `PyTorch` environment. Unlike existing libraries that impose specific parametric forms on users, `TorchSurv` enables the use of custom `PyTorch`-based deep survival models. With its lightweight design, minimal input requirements, full `PyTorch` backend, and freedom from restrictive survival model parameterizations, `TorchSurv` facilitates efficient survival model implementation, particularly beneficial for high-dimensional input data scenarios.

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