Train and test your model iteratively so that its intents and entities are validated
and compiled, and your model is assigned a version number.
In this example scenario, you've already created and
trained numerous intents, utterances, entities, and their associated annotations. In
this example procedure, you're testing the NLU model by providing the system with
utterances so it can deliver prediction results and confidence scores.
- Make sure that the NLU Model Builder - Core plugin, NLU Model Builder
plugin, and Predictive Intelligence plugin are all installed and
- Create an NLU model.
- Create one or more NLU intents and their associated entities.
- Role required: Admin or Delegated Developer role (with permission of All
In the Application Explorer, navigate to
NLU for Access Requests
is the name you saved in the system when
you created your NLU model. See Create an NLU model
Make sure the objects and data you've created are complete in the NLU Model,
NLU Intent, and NLU Entity screens. If key data is missing or incomplete, remedy
In the Intents section of the NLU Model screen, click
The system validates and compiles any changes you made to the NLU model. If
the training doesn't succeed, an error message appears with guidance for
In the Test Model panel, enter an utterance or partial utterance from your
The system predicts the top intents and entities and shows you their
matching confidence scores. In this scenario, you enter reset my
password to email@example.com
. That utterance result has a 94% match
(confidence score) to the utterances you provided in your Reset
- If you want to update the current Confidence Threshold(%) value, click the
Properties button in your NLU for Access
Requests NLU Model screen, set a new value, and click
- Publish your NLU model.