As we are usually not building models on the fly, it often makes sense to train a model once and use it repeatedly to classify new data.

Note that, if you'd like to classify new documents, they need go through the same pipeline as other documents—the pipe needs to be the same for both training and classification. During training, the pipe's data alphabet is updated with each training instance. If you create a new pipe with the same steps, you don't produce the same pipeline as its data alphabet is empty. Therefore, to use the model on new data, we have to save or load the pipe along with the model and use this pipe to add new instances.