@erohmensing correct me if I am wrong here, but the Interpreter tries to load the model with a deprecated style. Before Rasa 1.0, a model was separated by Core and NLU hence the metadata.json was placed inside the coresponding folder.
I already expressed the suspicion that the pogrammatical code is not up to date in that area but I am not quite sure.
Hm, I think what’s wrong here is that you’re not pointing to the model correctly: You’re training to the output folder/tar models/generic/model but then trying to load from models/generic. The metadata should be in there inside the tar file, it’s essentially a zipped dir. But I’m not 100% sure about how Interpreter.load() is getting the files. Can you try
Hi Julian, there used to be a programmatical example to load the model on previous versions of Rasa. These instructions worked ok by then, training the model using command line tools and loading the model with a snippet of Python.
I’m now using the latest for both, command line tools and Python snippet, but the latest is a guess since there is no longer a programmatical example in the docs.
I’m also not sure as I’m not a Python programmer, but taking a look at rasa/nlu/model.py suggests I’m doing the right thing.
Hi @erohmensing, I’ve tried many ways but they all result in the same error. Interpreter.load() always considers the input parameter to be a folder, even if I specify a file.
For example, all of these behave in the same way:
Interpreter.load("./models/generic")
Interpreter.load("./models/generic/model")
Interpreter.load("./models/generic/model.tar.gz")
When a folder is specified the error is about not finding metadata.json. When a file is specified the error is about the path not being a folder.