Update README.md

README.md

@@ -1,23 +1,49 @@
 ---
-tags:
-- structured-data-classification
 library_name: generic
+
+language: 
+  - en
+
+thumbnail:
+
+tags:
+- classification
+- gradient boosted trees
+- keras
+- TensorFlow
+
+license: apache-2.0
+libraries: TensorBoard
+
+metrics:
+- accuracy
+
+model-index:
+- name: TF_Decision_Trees
+  results:
+  - task: 
+      type: structured-data-classification
+    dataset:
+      type: census
+      name: Census-Income Data Set
+    metrics:
+    - type: accuracy
+      value: 96.57    
+
+pipeline_tag: "structured-data-classification"
+
 ---
-# Structured Data Classification repository template
-
-This is a template repository for structured data classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
-1. Specify the requirements by defining a `requirements.txt` file.
-2. Implement the `pipeline.py` `__init__` and `__call__` methods. These methods are called by the Inference API. The `__init__` method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The `__call__` method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.
-
-Example repos
-* https://huggingface.co/osanseviero/wine-quality
-
-## How to start
-First create a repo in https://hf.co/new. 
-Then clone this template and push it to your repo.
-```
-git clone https://huggingface.co/templates/structured-data-classification
-cd structured-data-classification
-git remote set-url origin https://huggingface.co/$YOUR_USER/$YOUR_REPO_NAME
-git push --force
-```
+
+# Classification with TensorFlow Decision Forests 
+#### Using TensorFlow Decision Forests for structured data classification
+
+ <br />
+##### This example uses Gradient Boosted Trees model in binary classification of structured data, and covers the following scenarios:
+
+1. Build a decision forests model by specifying the input feature usage.
+2. Implement a custom Binary Target encoder as a Keras Preprocessing layer to encode the categorical features with respect to their target value co-occurrences, and then use the encoded features to build a decision forests model.
+
+The example uses Tensorflow 7.0 or higher. It uses the US Census Income Dataset containing approximately 300k instances with 41 numerical and categorical variables. This is a binary classification problem to determine whether a person makes over 50k a year. 
+
+Author: Khalid Salama  <br /> 
+Adapted implementation: Tannia Dubon