What I am doing for now as a temp solution is (at least works):
- Tune model with train and val sets.
- Store these hyperparameters (lr, dimension of embeddings)
- Combine train and test (add target column to test)
- Run model as you are running with val
- Make predictions with m.predict()
Note: I am very fascinated by expressing categorical variables in Eucledian spaces, especially after seeing some t-SNE visualizations on them. Very cool 