How can one train the NLU in real-time / live without having to restart the NLU server?
So far I’m training the NLU using the following command: python -m rasa_nlu.train -c ./nlu_config.yml --data ./data/nlu_data -o models/nlu --fixed_model_name nlu --project current --verbose
In the nlu_data directory I have a few training files in .md and .json format.
As I’m testing the bot, I often run into bad intent identification / ner extraction issues, which I’m trying to solve by adding more samples to the training data. However, it seems like I always have to restart the NLU server to reflect the new model.
Since the new model is saved under the same name (due to --fixed_model_name), I thought it may be possible to autoreload the model while the NLU server is running.
I know there’s online / interactive training available for Rasa Core, but I’m interested in quickly testing and updating the NLU model by training it on a new batch of samples.
Any ideas or solutions? Feel like this should be fairly simple to achieve, but can’t figure it out.
Thanks a lot already!