Hi Akshit, Thanks , I moved a bit forward to understand the setup flow thanks to you!
Long story short : After a couple of test I upgraded the core model and the NLU model
Now let me share my flow from training to run the model:
Now I am using
rasa_nlu: 0.13.8
rasa_core: 0.13.0a5
On Osx
Trianing NLU
For training NLU I used:
from rasa_nlu.training_data import load_data
from rasa_nlu.config import RasaNLUModelConfig
from rasa_nlu.model import Trainer
from rasa_nlu import config
# loading the nlu training samples
training_data = load_data("NLU/nlu.json")
# trainer to educate our pipeline
trainer = Trainer(config.load("NLU/config.yml"))
# train the model!
interpreter = trainer.train(training_data)
# store it for future use
model_directory = trainer.persist("./models/nlu",
fixed_model_name="current")
Confusion matrix
Question 1:
The confusion matrix is not great ( how I can improve it ?)
Now is time to train the core model, I use:
from rasa_core.policies import FallbackPolicy, KerasPolicy, MemoizationPolicy
from rasa_core.agent import Agent
# this will catch predictions the model isn't very certain about
# there is a threshold for the NLU predictions as well as the action predictions
agent = Agent('domain.yml', policies=[MemoizationPolicy(), KerasPolicy()])
# loading our neatly defined training dialogues
training_data = agent.load_data('stories/stories.md')
agent.train(
training_data,
validation_split=1.0
)
agent.persist('models/dialogue')
2. Run core and NLU model
From console something weird happens ( I cannot see any reply from my bot ) and I have a couples of warning about some pythons libraries :
$ python -m rasa_core.run -d models/dialogue -u ./models/nlu/default/current/
python -m rasa_core.run --enable_api -d models/dialogue -u ./models/nlu/default/current/ -o out.log
API mode is running with :
python -m rasa_core.run --enable_api -d models/dialogue -u ./models/nlu/default/current/ -o out.log