From 9e80c130e8fcc2a7dd6ff8f4576c5c8059e3fc2e Mon Sep 17 00:00:00 2001 From: Manish Kumar Date: Wed, 29 May 2024 12:29:50 +0200 Subject: [PATCH 1/2] badges added --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 99fc164..b2d13a7 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,11 @@ -[![Documentation Status](https://readthedocs.org/projects/mambular/badge/?version=latest)](https://mambular.readthedocs.io/en/latest/?badge=latest) +[![PyPI](https://img.shields.io/pypi/v/mambular)](https://pypi.org/project/mambular) +![PyPI - Downloads](https://img.shields.io/pypi/dw/mambular) +[![docs build](https://readthedocs.org/projects/mambular/badge/?version=latest)](https://mambular.readthedocs.io/en/latest/?badge=latest) +[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mambular.readthedocs.io/en/latest/) +[![open issues](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/basf/mamba-tabular/issues) [📘Documentation](https://mambular.readthedocs.io/en/latest/index.html) | From d93ecf4e8dec67d0ff7d8cccb6bad17464be8218 Mon Sep 17 00:00:00 2001 From: Manish Kumar Date: Wed, 29 May 2024 12:32:12 +0200 Subject: [PATCH 2/2] cleanup --- README.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/README.md b/README.md index b2d13a7..47f5647 100644 --- a/README.md +++ b/README.md @@ -19,9 +19,6 @@ Mambular is a Python package that brings the power of Mamba architectures to tabular data, offering a suite of deep learning models for regression, classification, and distributional regression tasks. Designed with ease of use in mind, Mambular models adhere to scikit-learn's `BaseEstimator` interface, making them highly compatible with the familiar scikit-learn ecosystem. This means you can fit, predict, and transform using Mambular models just as you would with any traditional scikit-learn model, but with the added performance and flexibility of deep learning. - - - ## Features - **Comprehensive Model Suite**: Includes modules for regression (`MambularRegressor`), classification (`MambularClassifier`), and distributional regression (`MambularLSS`), catering to a wide range of tabular data tasks.