Connects directly to the Python Bridge, to send text or sections of the interpretation graph to be processed by ML algorithms in Python. This recognizer is used when there is a need to classify an entire document for example. That is the difference when compared to the Python Model Recognizer which will be processing or running for each token or text block. Processing an entire document has its benefits, it may be the best way to classify a document as a whole. By running only once per document we get a boost in performance when compared to run the recognizer for each individual token or text block. The other benefit is that we could normalize the text before sending it to the python model and also specify dependent tags so it runs in the order we need in the processing pipeline.
More settings could be displayed in the same dialog, it varies per recognizer.