Trains a text classification model on top of the provided BERT model. The model is extended with 3 layers:
- GlobalAveragePooling1D layer
- Dropout layer
- Dense layer
Besides the typical classification task where every row is assigned a single class it is also possible to train a model for multi-label classification where a row can be assigned multiple labels.
If a validation table is provided, then the model performance is evaluated on that data after every epoch.