added code factor score

README.md

@@ -16,11 +16,13 @@ https://anaconda.org/conda-forge/torchsurv)
 
 ![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)
+[![CodeFactor](https://www.codefactor.io/repository/github/novartis/torchsurv/badge/main)](https://www.codefactor.io/repository/github/novartis/torchsurv/overview/main)
 [![JOSS](https://joss.theoj.org/papers/02d7496da2b9cc34f9a6e04cabf2298d/status.svg)](https://joss.theoj.org/papers/02d7496da2b9cc34f9a6e04cabf2298d)
 [![License](https://img.shields.io/badge/License-MIT-black)](https://opensource.org/licenses/MIT)
 [![Documentation](https://img.shields.io/badge/GithubPage-Sphinx-blue)](https://opensource.nibr.com/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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