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Connects directly to the Python Bridge, to send text or sections of the interpretation graph to be process by ML algorithms in Python |
Operates On: Lexical Items with TEXT_BLOCK, EOF, others.
The Python Classification Watcher Stage is an implementation of the abstract class called Watcher Stage, this Stage is used to classify large blocks of texts as for example: complete paragraphs or whole documents. The Watcher saves the classification paths for every text block and then verifies the vertices to look for triggers which mark the end of the saving cycle and start to process the saved data, then returns a weight vector that would be the classification of the text block.
Saga_is_recognizer
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The difference between this and the Python Model Recognizer Stage is that this stage requires a trigger flag to start processing the text. |
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"dependencyTags": [], "modelName": "bert-base-nli-stsb-mean-tokens", "modelVersion": "1", "modelMethod": "predict", "normalizeTags": false, "hostname": "localhost", "port": 5000, |
The output of the Watcher Stage is at the metadata of the vertex flagged as the trigger, for this example it is the EOF but it could be configured to work with TEXT_BLOCK_SPLIT or any other flag.
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V--------------[abraham lincoln likes macaroni and cheese]--------------------V <=== EOF, at this vertex's metadata is the embedded vector ^--[abraham]--V--[lincoln]--V--[likes]--V--[macaroni]--V--[and]--V--[cheese]--^ ^---{place}---^ ^----{food}----^ ^---{food}---^ ^----------{person}---------^ ^-----------------{food}--------------^ |