Preparing text data for model training.
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PIPELINE
Text after cleaning.
Text split into tokens.
Features used for analysis.
Recognized intent and method.
Values extracted from the request.
Context of this request.
Cleaning and tokens are computed from your text. Rules detect amounts, dates and currencies; NER detects people, places and organizations. A separate model predicts sentiment.
