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Connects directly to the Saga Python Bridge, to send tokens without SEMANTIC_TAGs to be process by ML algorithms in Python |
Operates On: Lexical Items with TOKEN and not with SEMANTIC_TAG.
Saga_is_recognizer
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The difference between this and the Python Model Stage is that this stage does requires to be associated to a tag |
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Configuration Parameters
Parameter summary Model name registered in the python bridge name modelName required true Parameter summary Model version registered in the python wrapper to query default latest name modelVersion Parameter summary Model method to call for the model name modelMethod required true Parameter summary Python server communication hostname default localhost name hostname Parameter summary Python server communication port default 5000 name port
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"modelName": "lsi" "modelVersion": "1" "modelMethod": "predict" "hostname": "localhost", "port": 5000, |
Example Output
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V----[THOMAS B. SEEKER]-----V-------[1234 Northern Star Circle]-------V-------[Baltimore, MD 12345]--------V
^---[THOMAS B]---V-[SEEKER]-^-[1234]-V-[Northern]-V-[Star]-V-[Circle]-^---[Baltimore,]----V-[MD]-V-[12345]-^
^-[THOMAS]-V-[B]-^ ^-[northern]-^-[star]-^-[circle]-^-[Baltimore]-V-[,]-^-[md]-^ ^
^-[thomas]-^-[b]-^-[SEEKER]-^-----------------[{lsi}]-----------------^---[baltimore,]----^ ^
^----[{lsi}]-----^-[seeker]-^ ^-[baltimore]-^ ^
^-[{lsi}]--^ ^--------------[{lsi}]---------------^ |
Output Flags
Lex-Item Flags:
- WEIGHT_VECTOR - Identifies the tag as a weight vector representation of a sentence
- ML_PREDICT- Result from a machine learning algorithm for prediction
- ML_CLASSIFY- Result from a machine learning algorithm for classification
- ML_REGRESS- Result from a machine learning algorithm for regression
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No vertices are created in this stage |
Overview
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