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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 TOKEN and 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, |
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V--------------[abraham lincoln likes macaroni and cheese]--------------------V ^--[abraham]--V--[lincoln]--V--[likes]--V--[macaroni]--V--[and]--V--[cheese]--^ ^---{place}---^ ^----{food}----^ ^---{food}---^ ^----------{person}---------^ ^-----------------{food}--------------^ |
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No vertices are created in this stage |
Description of resource.
Saga_json | ||
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"_id" : "KGAAJGsBemSwA0nZTLXA", "tag": "recipe", "pattern": "("how many"|"how much") {ingredient} ", "confAdjust": 0.95 . . . additional fields as needed go here . . . |
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These will all be added to the interpretation graph with the SEMANTIC_TAG flag.
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Tags are hierarchical representations of the same intent. For example, {city} → {administrative-area} → {geographical-area} |
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