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This recognizer uses a frozen Universal Sentence Encoder TensorFlow model to encode, using sentence embedding vectors, a list of Frequently Asked Questions and tag sentences that match a question/answer pair given a specified threshold of accuracy with the question/answer from the FAQ. The recognizer also gives the option to use a python model instead of TensorFlow. You'll need the Python Bridge running for this.
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Before adding the FAQ Recognizer, download the tensorflow model from here and unzip it under %SAGA_SERVER%\tf-models
Configuration using TensorFlow:
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Configuration using Python:
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Click on the button which will popup the "Add new FAQ" dialog
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Clicking on the button will encode all question/answer pairs for evaluations.
When icon shows in orange, it means that the vectors have been loaded and its ready to use for matches.
Click onicon to copy the sentence embedding vector of the question (top) or answer (bottom) for debugging purposes.
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