Query your custom model

After the deployment is added successfully, you can query the deployment to extract entities from your text based on the model you assigned to the deployment. You can query the deployment programmatically using the Prediction API or through the client libraries (Azure SDK).

Test deployed model

You can use Language Studio to submit the custom entity recognition task and visualize the results.

To test your deployed models from within the Language Studio:

1. Select Testing deployments from the left side menu.

2. Select the deployment you want to test. You can only test models that are assigned to deployments.

3. For multilingual projects, from the language dropdown, select the language of the text you are testing.

4. Select the deployment you want to query/test from the dropdown.

5. You can enter the text you want to submit to the request or upload a .txt file to use.

6. Select Run the test from the top menu.

7. In the Result tab, you can see the extracted entities from your text and their types. You can also view the JSON response under the JSON tab.

Send an entity recognition request to your model

Language Studio

1. After the deployment job is completed successfully, select the deployment you want to use and from the top menu select Get prediction URL.

   

2. In the window that appears, under the Submit pivot, copy the sample request URL and body. Replace the placeholder values such as YOUR_DOCUMENT_HERE and YOUR_DOCUMENT_LANGUAGE_HERE with the actual text and language you want to process.

3. Submit the POST cURL request in your terminal or command prompt. You'll receive a 202 response with the API results if the request was successful.

4. In the response header you receive extract {JOB-ID} from operation-location, which has the format: {ENDPOINT}/language/analyze-text/jobs/<JOB-ID}>

5. Back to Language Studio; select Retrieve pivot from the same window you got the example request you got earlier and copy the sample request into a text editor.

6. Add your job ID after /jobs/ to the URL, using the ID you extracted from the previous step.

7. Submit the GET cURL request in your terminal or command prompt.

REST API

First you need to get your resource key and endpoint:

1. Go to your resource overview page in the Azure portal

2. From the menu on the left side, select Keys and Endpoint. You'll use the endpoint and key for the API requests

   

Submit a custom NER task

Use this POST request to start a text classification task.

{ENDPOINT}/language/analyze-text/jobs?api-version={API-VERSION}

Placeholder	Value	Example
{ENDPOINT}	The endpoint for authenticating your API request.	https://<your-custom-subdomain>.cognitiveservices.azure.com
{API-VERSION}	The version of the API you are calling. The value referenced here is for the latest version released. See Model lifecycle to learn more about other available API versions.	2022-05-01

Headers

Key	Value
Ocp-Apim-Subscription-Key	Your key that provides access to this API.

Body

{
  "displayName": "Extracting entities",
  "analysisInput": {
    "documents": [
      {
        "id": "1",
        "language": "{LANGUAGE-CODE}",
        "text": "Text1"
      },
      {
        "id": "2",
        "language": "{LANGUAGE-CODE}",
        "text": "Text2"
      }
    ]
  },
  "tasks": [
     {
      "kind": "CustomEntityRecognition",
      "taskName": "Entity Recognition",
      "parameters": {
        "projectName": "{PROJECT-NAME}",
        "deploymentName": "{DEPLOYMENT-NAME}"
      }
    }
  ]
}

Key	Placeholder	Value	Example
displayName	{JOB-NAME}	Your job name.	MyJobName
documents	[{},{}]	List of documents to run tasks on.	[{},{}]
id	{DOC-ID}	Document name or ID.	doc1
language	{LANGUAGE-CODE}	A string specifying the language code for the document. If this key isn't specified, the service will assume the default language of the project that was selected during project creation. See language support for a list of supported language codes.	en-us
text	{DOC-TEXT}	Document task to run the tasks on.	Lorem ipsum dolor sit amet
tasks		List of tasks we want to perform.	[]
taskName	CustomEntityRecognition	The task name	CustomEntityRecognition
parameters		List of parameters to pass to the task.
project-name	{PROJECT-NAME}	The name for your project. This value is case-sensitive.	myProject
deployment-name	{DEPLOYMENT-NAME}	The name of your deployment. This value is case-sensitive.	prod

Response

You will receive a 202 response indicating that your task has been submitted successfully. In the response headers, extract operation-location. operation-location is formatted like this:

{ENDPOINT}/language/analyze-text/jobs/{JOB-ID}?api-version={API-VERSION}

You can use this URL to query the task completion status and get the results when task is completed.

Get task results

Use the following GET request to query the status/results of the custom entity recognition task.

{ENDPOINT}/language/analyze-text/jobs/{JOB-ID}?api-version={API-VERSION}

Placeholder	Value	Example
{ENDPOINT}	The endpoint for authenticating your API request.	https://<your-custom-subdomain>.cognitiveservices.azure.com
{API-VERSION}	The version of the API you are calling. The value referenced here is for the latest version released. See Model lifecycle to learn more about other available API versions.	2022-05-01

Headers

Key	Value
Ocp-Apim-Subscription-Key	Your key that provides access to this API.

Response Body

The response will be a JSON document with the following parameters

{
  "createdDateTime": "2021-05-19T14:32:25.578Z",
  "displayName": "MyJobName",
  "expirationDateTime": "2021-05-19T14:32:25.578Z",
  "jobId": "xxxx-xxxx-xxxxx-xxxxx",
  "lastUpdateDateTime": "2021-05-19T14:32:25.578Z",
  "status": "succeeded",
  "tasks": {
    "completed": 1,
    "failed": 0,
    "inProgress": 0,
    "total": 1,
    "items": [
      {
        "kind": "EntityRecognitionLROResults",
        "taskName": "Recognize Entities",
        "lastUpdateDateTime": "2020-10-01T15:01:03Z",
        "status": "succeeded",
        "results": {
          "documents": [
            {
              "entities": [
                {
                  "category": "Event",
                  "confidenceScore": 0.61,
                  "length": 4,
                  "offset": 18,
                  "text": "trip"
                },
                {
                  "category": "Location",
                  "confidenceScore": 0.82,
                  "length": 7,
                  "offset": 26,
                  "subcategory": "GPE",
                  "text": "Seattle"
                },
                {
                  "category": "DateTime",
                  "confidenceScore": 0.8,
                  "length": 9,
                  "offset": 34,
                  "subcategory": "DateRange",
                  "text": "last week"
                }
              ],
              "id": "1",
              "warnings": []
            }
          ],
          "errors": [],
          "modelVersion": "2020-04-01"
        }
      }
    ]
  }
}

Client libraries (Azure SDK)

First you need to get your resource key and endpoint:

Get your key and endpoint

Next you will need the key and endpoint from the resource to connect your application to the API. You'll paste your key and endpoint into the code later in the quickstart.

1. After the Language resource deploys successfully, click the Go to Resource button under Next Steps.

   

2. On the screen for your resource, select Keys and endpoint on the left navigation menu. You will use one of your keys and your endpoint in the steps below.

   

3. Download and install the client library package for your language of choice:

   Language	Package version
   .NET	5.2.0-beta.3
   Java	5.2.0-beta.3
   JavaScript	6.0.0-beta.1
   Python	5.2.0b4

4. After you've installed the client library, use the following samples on GitHub to start calling the API.

   • C#
   • Java
   • JavaScript
   • Python

5. See the following reference documentation for more information on the client, and return object:

   • C#
   • Java
   • JavaScript
   • Python

Next steps

• Frequently asked questions