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  3. Building Chatbots in Python

Exercise

Rasa NLU

In this exercise, you'll use Rasa NLU to create an interpreter, which parses incoming user messages and returns a set of entities. Your job is to train an interpreter using the MITIE entity recognition model in Rasa NLU.

Instructions

100 XP
  • Create a dictionary called args with a single key "pipeline" with value "spacy_sklearn".
  • Create a config by calling RasaNLUConfig() with the single argument cmdline_args with value args.
  • Create a trainer by calling Trainer() using the configuration as the argument.
  • Create a interpreter by calling trainer.train() with the training_data.