Keras Implementation of Structured data learning with TabTransformer
This repo contains the trained model of Structured data learning with TabTransformer.
The full credit goes to: Khalid Salama
Spaces Link:
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.
Intended uses & limitations:
- This model can be used for both supervised and semi-supervised tasks on tabular data.
Training and evaluation data:
- This model was trained using the United States Census Income Dataset 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: ‘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
Model history needed
Model Plot
View Model Plot
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