Hi [gez-mycroft] - thank you very much for replying to my request eventually!
I have good news here - as with O in Open Source I made it to adapt the code to something like that. It gave me headaches since little or almost none is documented here But thanks to being Python code it was possible to pull through.
I guess I should made an official PULL REQUEST here? But I would like to ask for assistance from the dev team here since I am rather not familiar with those things yet (would be my first PR ;-). Also I would like to document and probably unit-test it before I commit it to the official base.
Another thing is: It occassionally (by chance) happens that I get a malloc error during training. It simply helps to repeat it and in almost all cases it worked then with the respective model. But I guess it is not “production-grade” then…
Regarding the technical background: I almost made no changes to padatious - I leveraged the “cache to file” functionality and adjusted some of the internal interfaces to “fake” a cache access when actually accessing a saved model (as a snapshot of the cached model so to speak). I guess, it’s probably not the best way but since I want a quick and practical solution it NOW somehow serves my purpose. Btw. I have combined it with the RASA NLU stack and created a new NLU component there for both entity extraction and intent detection.
It works almost perfectly! Better than the RASA NLU components available by the framework, I have to admit.
So - if anyone from the dev team or else is interested in having a look into incorporating my “fork” I would be happy to deliver to the code base in one way or another!