chore: Update the readme.

README.md

@@ -2,30 +2,17 @@
 license: apache-2.0
 ---
 
-# Template for Concrete ML
+# Synthetic dataset classification with a LogisticRegression with Concrete ML
 
-Concrete ML is Zama's open-source privacy-preserving ML package, based on fully homomorphic encryption (FHE). We refer the reader to fhe.org or Zama's websites for more information on FHE.
+In this repository, we classify a synthetic dataset. Inputs are sent encrypted to the HF endpoints, and are classified without the server seeing them in the clear, thanks to fully homomorphic encryption (FHE). This is done thanks to Zama's Concrete ML.
 
-This directory is used:
-- by ML practicioners, to create Concrete ML FHE-friendly models, and make them available to HF users
-- by companies, institutions or people to deploy those models over HF inference endpoints
-- by developers, to use these entry points to make applications on privacy-preserving ML
+Concrete ML is Zama's open-source privacy-preserving ML package, FHE. We refer the reader to fhe.org or Zama's websites for more information on FHE.
 
-## Creating models and making them available on HF
-
-This is quite easy. Fork this template (maybe use this experimental tool https://huggingface.co/spaces/huggingface-projects/repo_duplicator for that), and then:
-- install everything with: `pip install -r requirements.txt`
-- edit `creating_models.py`, and fill the part between "# BEGIN: insert your ML task here" and
-"# END: insert your ML task here"
-- run the python file: `python creating_models.py`
-
-At the end, if the script is successful, you'll have your compiled model ready in `compiled_model`. Now you can commit and push your repository (with in particular `compiled_model`, `handler.py`, `play_with_endpoint.py` and `requirements.txt`, but you can include the other files as well).
-
-We recommend you to tag your Concrete ML compiled repository with `Concrete ML FHE friendly` tag, such that people can find them easily.
+This directory was creating from the template https://huggingface.co/zama-fhe/concrete-ml-template-alpha.
 
 ## Deploying a compiled model on HF inference endpoint
 
-If you find an `Concrete ML FHE friendly` repository that you would like to deploy, it is very easy.
+If you would like to deploy, it is very easy.
 - click on 'Deploy' button in HF interface
 - chose "Inference endpoints"
 - chose the right model repository