updating README.md

README.md

@@ -1,2 +1,24 @@
 # ML4EFT
-Studies of ML for EFT applications
+ML4EFT is a general open-source framework for the integration of unbinned multivariate observables into global fits of particle physics data.
+It makes use of machine learning regression and classification techniques to parameterise high-dimensional likelihood ratios,
+and can be seamlessly integrated into
+global analyses of, for example, the Standard Model Effective Field Theory and Parton Distribution Functions.
+
+The ML4EFT framework is made available via the Python Package Index (pip) and can be installed directly 
+by running 
+
+``pip install ml4eft``
+
+or alternatively the code can be downloaded from this public GitHub repository, and then installed by running
+
+```shell
+cd code
+pip install -e .
+```  
+
+The framework is documented on a dedicated website
+https://lhcfitnikhef.github.io/ML4EFT,
+where, in addition, one can find a self-standing tutorial (which can also
+be run in Google Colab) where the user is guided step by step in how
+unbinned multivariate observables can be constructed given a choice of
+EFT coefficients and of final-state kinematic features.