--- sourceDocument: Yokohama Enable AI sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/yokohama/intelligent-experiences Release : - yokohama ft:locale : - en-US ft:publication_title : - Yokohama Enable AI ft:clusterId : - platai bundleId : - platai workflow : - Platform --- # Test and publish your model # Test and publish your model {#ariaid-title1} Release version: Yokohama Updated January 30, 2025 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete. ## 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. Show full answer Show less 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 {#testing-your-model__section_wxp_kz2_xrb} 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. Note: Testing your model requires the Multi-model Batch Testing feature, available with the NLU Workbench - Advanced Features application from ServiceNow® Store. For more information, see [Install NLU Workbench - Advanced Features](https://servicenow-prod.fluidtopics.net/e6w1em~rJVUJ6NXkr4vAbg "You can install the NLU Workbench - Advanced Features application (com.snc.nlu.workbench.advanced) if you have the admin role."). To test your model, navigate to NLU WorkbenchModels. 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 {#testing-your-model__section_b2f_lf2_dwb} 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 {#testing-your-model__section_y4w_kqy_dwb} The test results show how your model responded to the utterances in the test set. The bar chart shows the prediction percentages for correct, correct among multiple, missed, and incorrect:{#testing-your-model__table_cyz_yt3_3qb__entry__2} | 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-your-model__table_cyz_yt3_3qb} 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](https://servicenow-prod.fluidtopics.net/9DCue~pqIqM_bEihwOdQIQ "Change your NLU model's name, description, or confidence threshold on the Settings page of the model overview."). For information about utterances which should not have any intent predicted, see [Irrelevance detection in NLU](https://servicenow-prod.fluidtopics.net/NwbaT5ofR5JghXSGZ~0gYg "Keep Virtual Agent chats focused with Irrelevance detection. Use the Irrelevance detection feature to train your NLU model to avoid making predictions for utterances that are not relevant."). ## Publish model {#testing-your-model__section_arr_pn2_dwb} The Publish model button makes the current version of the model available to other applications such as Virtual Agent. Note: If the model has not been trained, the Publish model button is unavailable. Return to the Build and train your model phase to train the model before publishing. For more information on publishing your model, see [Publish your NLU model](https://servicenow-prod.fluidtopics.net/~qrDzWr0FQtpEuEXdn7B6g "Publish your Natural Language Understanding (NLU) model to activate it and make it available for use in other applications that consume NLU."). ## Multi-model Batch Testing {#testing-your-model__section_qh3_lpr_prb} 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 NLU WorkbenchNLU Advanced FeaturesMulti-model Batch Testing. For more information, see [Multi-model Batch Testing](https://servicenow-prod.fluidtopics.net/4P~K156R15CxwKnHcMcKpw "Test multiple Natural Language Understanding (NLU) models against a large set of utterances to evaluate the performance of the models. Add test sets, test multiple models, and see test results."). For more information about test sets, see: * [Create a test set](https://servicenow-prod.fluidtopics.net/ljfFSXm2YCrfIfIaBa4y7g "To create or add to an NLU test set, you can upload a file of test utterances matched with correct intents. Use the test set to assess the performance of your model.") * [Test set creation and management](https://servicenow-prod.fluidtopics.net/wP~RQuZhfFfx16bQPE~ccA "Use the default test set of your NLU model to test the model's performance and accuracy. Manage your test set over time by building or updating its content in the NLU Workbench.") For information about the process of testing, see [Test your model](https://servicenow-prod.fluidtopics.net/ybhJ_V~DIsEwX3YwMF7qHw "Test your Natural Language Understanding (NLU) model against its default test set. Testing helps determine how your model is performing with the current content."). * **[Test your model](https://servicenow-prod.fluidtopics.net/ybhJ_V~DIsEwX3YwMF7qHw)** Test your Natural Language Understanding (NLU) model against its default test set. Testing helps determine how your model is performing with the current content. * **[Publish your NLU model](https://servicenow-prod.fluidtopics.net/~qrDzWr0FQtpEuEXdn7B6g)** Publish your Natural Language Understanding (NLU) model to activate it and make it available for use in other applications that consume NLU. * **[Compare draft and published versions of your NLU model](https://servicenow-prod.fluidtopics.net/GxcwAmCqocSo4vTXzofTKA)** Compare a draft trained Natural Language Understanding (NLU) model to its most recent published version. Test and review the changes to make sure that your draft model will have increased performance. *[\>]: and then