Test and publish your model
Summarize
Summary of Test and publish your model
This process enables ServiceNow customers to evaluate the performance of their Natural Language Understanding (NLU) models by testing them against a default test set. Testing helps identify strengths and areas for improvement before publishing the model for use in applications such as Virtual Agent.
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The Test and publish your model phase offers an Overview page where you can run new tests, view previous test results summarized in bar charts, and analyze detailed results for each test utterance and its predicted intent.
Understanding Test Results
Test results categorize predictions into four types:
- Correct: Model accurately predicted the intent, including correctly identifying 'Not relevant' utterances.
- Correct among multiple: Model predicted the correct intent(s) but also included incorrect intents.
- Missed: Model failed to predict an expected intent.
- Incorrect: Model predicted an incorrect intent.
These results inform adjustments to the model’s confidence threshold, which determines the minimum confidence level required for intent prediction.
Publishing Your Model
Once testing confirms satisfactory performance, you can publish the trained model to activate it for use by other ServiceNow applications such as Virtual Agent. Note that publishing is only available after the model has been trained.
Multi-model Batch Testing
For advanced testing scenarios, the Multi-model Batch Testing feature allows you to test multiple models and various test sets simultaneously. This requires the NLU Workbench - Advanced Features application and provides comprehensive test management and comparison capabilities.
Practical Benefits
- Confidently assess and improve your NLU model’s accuracy and intent recognition before deployment.
- Understand detailed test feedback to fine-tune confidence thresholds and improve predictions.
- Publish tested and trained models to extend NLU capabilities across ServiceNow applications.
- Leverage batch testing to efficiently manage and evaluate multiple models and test sets at scale.
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 of testing and publishing your model
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.