Deploy a model and extract entities from text using the runtime API.

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Once you are satisfied with how your model performs, it is ready to be deployed, and used to recognize entities in text. You can only send entity recognition tasks through the API, not from Language Studio.

Prerequisites

• A successfully created project with a configured Azure blob storage account
  • Text data that has been uploaded to your storage account.
• Tagged data
• A successfully trained model
• Reviewed the model evaluation details to determine how your model is performing.
  • (optional) Made improvements to your model if its performance isn't satisfactory.

See the application development lifecycle for more information.

Deploy your model

Deploying a model hosts it, and makes it available for predictions through an endpoint.

When a model is deployed, you will be able to test the model directly in the portal or by calling the API associated with it.

Note

You can only have ten deployment names

1. Go to your project in Language studio.

2. From the left panel, select Deploy model.

3. Click on Add deployment to submit a new deployment job.

   

4. In the window that appears, you can create a new deployment name or override an existing one. Then, you can add a trained model to this deployment name.

   

Delete deployment

To delete a deployment, select the deployment you want to delete and select Delete deployment

Tip

You can test your model in Language Studio by sending samples of text for it to classify.

1. Select Test model from the menu on the left side of your project in Language Studio.
2. Select the model you want to test.
3. Add your text to the textbox, you can also upload a .txt file.
4. Click on Run the test.
5. In the Result tab, you can see the extracted entities from your text. You can also view the JSON response under the JSON tab.

Send an entity recognition request to your model

Using Language Studio

Using Language studio

1. After the deployment is completed, select the model you want to use and from the top menu click on Get prediction URL and copy the URL and body.

   

2. In the window that appears, under the Submit pivot, copy the sample request into your command line

3. Replace <YOUR_DOCUMENT_HERE> with the actual text you want to classify.

   

4. Submit the request

5. In the response header you receive extract jobId from operation-location, which has the format: {YOUR-ENDPOINT}/text/analytics/v3.2-preview.2/analyze/jobs/<jobId}>

6. Copy the retrieve request and replace jobId and submit the request.

   

Retrieve the results of your job

1. Select Retrieve from the same window you got the example request you got earlier and copy the sample request into a text editor.

   

2. Replace <OPERATION_ID> with the jobId from the previous step.

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

Using the REST API

Use the REST API

First you will 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. Use endpoint for the API requests and you will need the key for Ocp-Apim-Subscription-Key header.

   

Submit custom NER task

Note

Project names is case sensitive.

Use this POST request to start an entity extraction task. Replace {projectName} with the project name where you have the model you want to use.

{YOUR-ENDPOINT}/text/analytics/v3.2-preview.2/analyze

Headers

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

Body

    {
    "displayName": "MyJobName",
    "analysisInput": {
        "documents": [
            {
                "id": "doc1", 
                "text": "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nunc tempus, felis sed vehicula lobortis, lectus ligula facilisis quam, quis aliquet lectus diam id erat. Vivamus eu semper tellus. Integer placerat sem vel eros iaculis dictum. Sed vel congue urna."
            },
            {
                "id": "doc2",
                "text": "Mauris dui dui, ultricies vel ligula ultricies, elementum viverra odio. Donec tempor odio nunc, quis fermentum lorem egestas commodo. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos."
            }
        ]
    },
    "tasks": {
        "customEntityRecognitionTasks": [      
            {
                "parameters": {
                      "project-name": "MyProject",
                      "deployment-name": "MyDeploymentName"
                      "stringIndexType": "TextElements_v8"
                }
            }
        ]
    }
}

Key	Sample Value	Description
displayName	"MyJobName"	Your job Name
documents	[{},{}]	List of documents to run tasks on
ID	"doc1"	a string document identifier
text	"Lorem ipsum dolor sit amet"	You document in string format
"tasks"	[]	List of tasks we want to perform.
--	customEntityRecognitionTasks	Task identifer for task we want to perform.
parameters	[]	List of parameters to pass to task
project-name	"MyProject"	Your project name. The project name is case-sensitive.
deployment-name	"MyDeploymentName"	Your deployment name

Response

You will receive a 202 response indicating success. In the response headers, extract operation-location. operation-location is formatted like this:

{YOUR-ENDPOINT}/text/analytics/v3.2-preview.2/analyze/jobs/<jobId>

You will use this endpoint in the next step to get the custom recognition task results.

Get the task results

Note

Project names is case sensitive.

Use this POST request to start an entity extraction task. Replace {projectName} with the project name where you have the model you want to use.

{YOUR-ENDPOINT}/text/analytics/v3.2-preview.2/analyze

Headers

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

Body

    {
    "displayName": "MyJobName",
    "analysisInput": {
        "documents": [
            {
                "id": "doc1", 
                "text": "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nunc tempus, felis sed vehicula lobortis, lectus ligula facilisis quam, quis aliquet lectus diam id erat. Vivamus eu semper tellus. Integer placerat sem vel eros iaculis dictum. Sed vel congue urna."
            },
            {
                "id": "doc2",
                "text": "Mauris dui dui, ultricies vel ligula ultricies, elementum viverra odio. Donec tempor odio nunc, quis fermentum lorem egestas commodo. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos."
            }
        ]
    },
    "tasks": {
        "customEntityRecognitionTasks": [      
            {
                "parameters": {
                      "project-name": "MyProject",
                      "deployment-name": "MyDeploymentName"
                      "stringIndexType": "TextElements_v8"
                }
            }
        ]
    }
}

Key	Sample Value	Description
displayName	"MyJobName"	Your job Name
documents	[{},{}]	List of documents to run tasks on
ID	"doc1"	a string document identifier
text	"Lorem ipsum dolor sit amet"	You document in string format
"tasks"	[]	List of tasks we want to perform.
--	customEntityRecognitionTasks	Task identifer for task we want to perform.
parameters	[]	List of parameters to pass to task
project-name	"MyProject"	Your project name. The project name is case-sensitive.
deployment-name	"MyDeploymentName"	Your deployment name

Response

You will receive a 202 response indicating success. In the response headers, extract operation-location. operation-location is formatted like this:

{YOUR-ENDPOINT}/text/analytics/v3.2-preview.2/analyze/jobs/<jobId>

You will use this endpoint in the next step to get the custom recognition task results.

Using the client libraries (Azure SDK)

Use the client libraries

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

2. From the menu on the left side, select Keys and Endpoint. Use endpoint for the API requests and you will need the key for Ocp-Apim-Subscription-Key header.

   

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

   Language	Package version
   .NET	5.2.0-beta.2
   Java	5.2.0-beta.2
   JavaScript	5.2.0-beta.2
   Python	5.2.0b2

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