Model Integration

Tensoract Studio supports integration with custom models that can be leveraged for pre-labeling and auto-labeling of tasks in a project. Following are the steps to add a custom model to the workbench.

1. Navigate to Models on the left navigation bar.

   

2. Click Add Model to bring up the model setup screen.

3. Select a suitable Model Type. Currently supported model types are as below

   • NER Labeling - used to pre/auto label tasks in a NER project.

   • OCR Labeling - used to pre/auto label tasks in OCR project.

   • Bulk Image Classification-Labeling - used to pre/auto label tasks in Bulk Image Classification project.

   • OCR - used for extraction for documents in Scanned(OCR) datasets.

4. Provide a name for the custom model.

5. Specify the Rest API Endpoint URL for the custom model. Please note that Tensoract Studio should have network connectivity with this endpoint URL for the custom model integration to work properly.

6. Specify any optional model parameters. For example, you can include model authentication parameters as key-value pairs in the Header.

7. You can test the model integration with the default payload, which can be customized. Click Test and validate the model output to ensure the integration works as expected.

   For Model request payloads and response , please refer to this section Model Integration: Request Payloads and Responses.

8. Configure one or more Labels and Relationship for the model.

9. Click Save Model and the model is now ready for use in projects for pre/auto labeling of tasks.

Refer to the following video for an overview of custom model integration steps: