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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.

Bu egzersiz

Building Chatbots in Python

kursunun bir parçasıdır
Kursu Görüntüle

Egzersiz talimatları

  • 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.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import necessary modules
from rasa_nlu.converters import load_data
from rasa_nlu.config import RasaNLUConfig
from rasa_nlu.model import Trainer

# Create args dictionary
args = ____

# Create a configuration and trainer
config = ____
trainer = ____

# Load the training data
training_data = load_data("./training_data.json")

# Create an interpreter by training the model
interpreter = ____

# Test the interpreter
print(interpreter.parse("I'm looking for a Mexican restaurant in the North of town"))
Kodu Düzenle ve Çalıştır