Hi there!
Maybe some suggestions
- You could “re-process” the .json files generated by the
rasa test nluyourself in a custom script (by combining the DIETClassifier_report.json & DIETClassifier_errors.json you could extract some metrics). However in your case, you have multiple entities in 1 sentence and this info is lost in the json files unfortunately. - A second option would be to write a “test script” yourself, where you load your trained model and parse all your test utterances. You will get a very detailed results per sentence (ranking all predicted intents and the confidence, but also the predicted entities (if any)). If you do some post-processing on these results, I think you can get the insights you are referring to. If you want an example of this, rasa has a very good blog about how to write some custom python code to evaluate models → Evaluating Rasa NLU Models in Jupyter
Good luck! 