Update README.md

README.md

@@ -2,26 +2,45 @@
 library_name: keras
 tags:
 - structured-data-classification
+- transformer
 ---
 
 ## Model description
 
 More information needed
+### Keras Implementation of Structured data learning with TabTransformer
+This repo contains the trained model of [Structured data learning with TabTransformer](https://keras.io/examples/structured_data/tabtransformer/#define-dataset-metadata).
+The full credit goes to: [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)
 
-## Intended uses & limitations
+Spaces Link: 
 
-More information needed
+### Model summary:
+- The trained model uses self-attention based Transformers structure following by multiple feed forward layers in order to serve supervised and semi-supervised learning.
+- The model's inputs can contain both numerical and categorical features. 
+- All the categorical features will be encoded into embedding vector with the same number of embedding dimensions, before adding (point-wise) with each other and feeding into a stack of Transformer blocks.
+- The contextual embeddings of the categorical features after the final Transformer layer, are concatenated with the input numerical features, and fed into a final MLP block.
+- A SoftMax function is applied at the end of the model.
 
-## Training and evaluation data
+## Intended uses & limitations:
+- This model can be used for both supervised and semi-supervised tasks on tabular data.
 
-More information needed
+## Training and evaluation data:
+- This model was trained using the [United States Census Income Dataset](https://archive.ics.uci.edu/ml/datasets/census+income) provided by the UC Irvine Machine Learning Repository. The task of the dataset is to predict whether a person is likely to be making over USD 50,000 a year (binary classification).
+- The dataset consists of 14 input features: 5 numerical features and 9 categorical features.
 
 ## Training procedure
-
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- optimizer: {'name': 'AdamW', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay': 1e-04, 'exclude_from_weight_decay': None}
+- optimizer: 'AdamW'
+- learning_rate: 0.001
+- weight decay: 1e-04
+- loss: 'sparse_categorical_crossentropy'
+- beta_1: 0.9
+- beta_2: 0.999
+- epsilon: 1e-07
+- epochs: 50
+- batch_size: 16
 - training_precision: float32
 
  ## Training Metrics