> For the complete documentation index, see [llms.txt](https://docs.tonic.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tonic.ai/textual/dataset-configure-redaction/dataset-custom-entity-types.md). # Working with additional entity types The entity types list includes the following additional entity types that you activated for the dataset: * Custom regex-based and model-based types. * Types that you [imported from the type library](/textual/entity-types/importing-types-from-the-textual-library.md). From the **Entity settings** page, you can enable and disable additional types. You can also update the configuration of a regex-based type, and go to the details page for a model-based type. ## Enabling and disabling additional types {% hint style="info" %} **Required dataset permission:** Edit dataset settings {% endhint %} The entity types list includes the additional entity types that are active for the dataset. To manage the additional types that are active for the dataset: 1. Click **Custom entity types**. 2. On the **Enable custom entity types** panel, to search for specific entity types, begin to type text in the entity type name. As you type, Textual updates the list to only display matching entity types.

Enable custom entity types panel for a dataset

3. To enable an additional entity type for the dataset, set its toggle to the on position. 4. When you enable the entity type, you can also set how Textual prioritizes the entity type when a detected value matches both the additional type and a built-in type. The options are: * **Prioritized -** This is the default value. With this option, the additional type always takes precedence over the built-in type. * **Standard -** With this option, Textual uses the confidence scores to determine which type to apply.

Configuration to determine the custom type priority when a value matches both the custom type and a built-in type

5. To disable an additional entity type for the dataset, set its toggle to the off position. 6. To enable all of the additional entity types for the dataset, click **Bulk**, then: 1. To enable all of the additinal types, and always prioritize the additional types over built-in types, select **Enable (Prioritized)**. 2. To enable all of the additional types, and use the confidence scores to determine which type takes precedence, select **Enable (Standard)**. 7. To disable all of the additional entity types for the dataset, click **Bulk**, then click **Disable all**. ## Setting the confidence threshold for additional types The **Confidence Thresholds** tab displays the additional entity types that are enabled for the dataset. From the **Confidence Thresholds** tab, you can: * View the confidence score range for detected values. * Set a minimum confidence score threshold for detections.

Confidence Thresholds tab for a dataset showing the confidence values for detected types and the confidence thresholds for detection

### Viewing the confidence scores for detected values For types that were detected in the dataset, the entry displays a mark for each confidence score that occurred in the detection. The mark indicates the number of detected values with that score. To display the score, hover over the mark.

### Setting the confidence score threshold for a type The confidence threshold is the minimum confidence score for a detected value. If the confidence score for a detected value is below the threshold, then Textual ignores that value. For each entity type, Textual indicates whether any of the values are ignored based on the current threshold setting. To set the confidence threshold for an entity type, either: * Drag the threshold dot to the threshold. * Type the threshold value in the field to the left. The field value also updates as you drag the threshold dot. You can configure a confidence threshold for any type, even if the type was not detected in the dataset.

Threshold setting options for entity types - you can drag the dot or type the value in the field

### Replicating an entity type threshold across your datasets To replicate the threshold for an entity type across all of the datasets that you have access to: 1. Click **Apply to all datasets**. 2. On the confirmation panel, click **Apply**. ## Updating the configuration of a regex-based custom entity type {% hint style="info" %} **Required global permission - either:** * Create custom entity types * Edit any custom entity type {% endhint %} From the dataset details, you can edit the configuration of a regex-based custom entity type. To edit a regex-based custom entity type, click the settings icon for that type. Note that any changes to the custom entity type settings affect all of the datasets that use the custom entity type. For information on how to configure a regex-based custom entity type, go to [Adding regex-based types](/textual/entity-types/entity-types-custom.md). ## Viewing the details for a model-based custom entity type {% hint style="info" %} **Required global permission - either:** * Create custom entity types * Edit any custom entity type {% endhint %} For a model-based custom entity type, to display the details for the entity type, click the settings icon. Textual displays the entity type details in a new browser tab. ## Running a new scan to reflect changes to the active additional types When you enable, disable, or edit additional entity types, the changes do not take effect until you run a new scan on each file. Textual marks each file as requiring a rescan, and displays a prompt to rescan all of the files.

To run a new scan for a file, click its scan option.

Rescan option for an individual dataset file

--- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.tonic.ai/textual/dataset-configure-redaction/dataset-custom-entity-types.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.