Test and publish your model
Summarize
Summary of Test and Publish Your Model
The Test and publish your model phase allows you to assess the performance of your Natural Language Understanding (NLU) model and publish it for use in applications like Virtual Agent. Testing helps identify areas for improvement and ensures the model is effective before making it available to other applications.
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Key Features
- Testing Capabilities: Use the Multi-model Batch Testing feature to evaluate your model against its default test set and gain insights for enhancements.
- Test Results Insights: Review performance metrics through bar charts displaying the percentages of correct, correct among multiple, missed, and incorrect predictions.
- Publishing Functionality: The Publish model button enables you to make the current version of your model accessible to other applications, provided the model has been trained.
- Confidence Threshold: Understand how testing can influence the model's confidence threshold, impacting its intent predictions.
Key Outcomes
By testing and publishing your NLU model, you can:
- Enhance model accuracy based on detailed test results.
- Make informed decisions on publishing by ensuring the model meets performance standards.
- Utilize advanced testing features to verify multiple models and test sets simultaneously.
This process is essential for ensuring that your NLU model performs optimally in real-world applications. For further guidance, consult the related documentation on model settings and test set management.
Assess the performance of your NLU model to identify areas for improvement. Then publish your model to make it available to other applications such as Virtual Agent.
Summary usage
Test your Virtual Agent or AI Search model against its default test set to see how the model responds. Test results provide information you can use to improve your model.
To test your model, navigate to . Select the tab for your model's application, then select the name of the model.
In the Test and publish your model card, select View
phase.
Overview
The Test and publish your model phase opens in the Overview page by default. Buttons for Run new test and Publish model are located here.
Overview provides information about a previous test run, with bar charts summarizing the test results.
If you have earlier test runs, you can view those by selecting from the Test run date list.
To drill down into the test results table, select the Detailed results tab. Each test utterance is listed in Detailed results, with its prediction.
Understanding test results
The test results show how your model responded to the utterances in the test set.
| Percentage | Description |
|---|---|
| Correct | The percentage of utterances for which your model correctly predicted the intent. When the model predicts no intent for utterances marked as Not relevant, that result is counted as Correct. |
| Correct among multiple | For utterances that had more than one intent predicted. The percentage of utterances for which the model correctly predicted the intent or intents, but also predicted intents that did not belong to the utterance. |
| Missed | The percentage of utterances for which your model did not predict an intent, even though there was an expected intent. |
| Incorrect | The percentage of utterances for which your model predicted an intent that was not correct. |
Testing can affect the model's confidence threshold. The confidence threshold determines how confident a model must be to predict an intent for an utterance. For more information on confidence thresholds, see NLU model settings.
For information about utterances which should not have any intent predicted, see Irrelevance detection in NLU.
Publish model
For more information on publishing your model, see Publish your NLU model.
Multi-model Batch Testing
In the Test and publish your model phase, you test your model against its default test set. With Multi-model Batch Testing, you can test against other test sets, test multiple models at once, and see your test results. To use Multi-model Batch Testing, navigate to .
For more information, see Multi-model Batch Testing.
For information about the process of testing, see Test your model.