diff --git a/.github/workflows/sphinx-build.yml b/.github/workflows/sphinx-build.yml
index 40a3804..1bcdecc 100644
--- a/.github/workflows/sphinx-build.yml
+++ b/.github/workflows/sphinx-build.yml
@@ -1,4 +1,4 @@
-name: Sphinx document with GitHub Pages dependencies preinstalled
+name: Documentation
 
 on:
   # Runs on pushes targeting the default branch
diff --git a/README.md b/README.md
index 73b1573..e9f787d 100644
--- a/README.md
+++ b/README.md
@@ -3,6 +3,11 @@
 
 # High Granularity Quantization
 
+[![License Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-green.svg)](LICENSE)
+[![Documentation](https://github.com/calad0i/HGQ/actions/workflows/sphinx-build.yml/badge.svg)](https://calad0i.github.io/HGQ/)
+[![PyPI version](https://badge.fury.io/py/hgq.svg)](https://badge.fury.io/py/hgq)
+
+
 HGQ is a framework for quantization aware training of neural networks to be deployed on FPGAs, which allows for per-weight and per-activation bitwidth optimization.
 
 Depending on the specific [application](https://arxiv.org/abs/2006.10159), HGQ could achieve up to 10x resource reduction compared to the traditional `AutoQkeras` approach, while maintaining the same accuracy. For some more challenging [tasks](https://arxiv.org/abs/2202.04976), where the model is already under-fitted, HGQ could still improve the performance under the same on-board resource consumption. For more details, please refer to our paper (link coming not too soon).
diff --git a/pyproject.toml b/pyproject.toml
index cb3828b..e49a9ea 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "HGQ"
-version = "0.2.0-rc1"
+version = "0.2.0b1"
 authors = [{ name = "Chang Sun", email = "chsun@cern.ch" }]
 description = "High Granularity Quantizarion"
 readme = "README.md"