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72 lines
1.8 KiB
72 lines
1.8 KiB
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import argparse
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import warnings
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from rasa_nlu.training_data import load_data
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from rasa_nlu import config
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from rasa_nlu.model import Trainer
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import rasa_core
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from rasa_core.agent import Agent
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from rasa_core.policies.keras_policy import KerasPolicy
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from rasa_core.policies.memoization import MemoizationPolicy
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def train_nlu():
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training_data = load_data('data/nlu-data.md')
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trainer = Trainer(config.load("nlu-config.yml"))
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trainer.train(training_data)
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model_directory = trainer.persist('models/nlu/', fixed_model_name="current")
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return model_directory
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def train_dialogue(
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domain_file="domain.yml",
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model_path="models/dialogue",
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training_data_file="data/stories.md"
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):
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agent = Agent(
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domain_file,
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policies=[MemoizationPolicy(max_history=3), KerasPolicy()]
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)
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training_data = agent.load_data(training_data_file)
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agent.train(
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training_data,
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epochs=400,
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batch_size=100,
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validation_split=0.2
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)
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agent.persist(model_path)
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return agent
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def train_all():
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model_directory = train_nlu()
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agent = train_dialogue()
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return [model_directory, agent]
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if __name__ == '__main__':
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warnings.filterwarnings(action='ignore', category=DeprecationWarning)
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rasa_core.utils.configure_colored_logging(loglevel="INFO")
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parser = argparse.ArgumentParser(
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description='starts the bot training')
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parser.add_argument(
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'task',
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choices=["train-nlu", "train-dialogue", "train-all"],
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help="what the bot should do?")
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task = parser.parse_args().task
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# decide what to do based on first parameter of the script
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if task == "train-nlu":
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train_nlu()
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elif task == "train-dialogue":
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train_dialogue()
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elif task == "train-all":
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train_all()
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