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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.

Latihan ini adalah bagian dari kursus

Building Chatbots in Python

Lihat Kursus

Petunjuk latihan

  • 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.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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"))
Edit dan Jalankan Kode