Merge pull request #187581 from aahill/include-files

GitHubber17 · web-flow · commit e3ccb63249d2 · 2022-02-22T12:34:18.000-08:00
include files for custom features
diff --git a/articles/cognitive-services/language-service/custom-named-entity-recognition/how-to/call-api.md b/articles/cognitive-services/language-service/custom-named-entity-recognition/how-to/call-api.md
@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: how-to
-ms.date: 01/07/2022
+ms.date: 02/16/2022
 ms.author: aahi
 ms.custom: language-service-custom-ner, ignite-fall-2021
 ---
@@ -30,11 +30,11 @@ See the [application development lifecycle](../overview.md#application-developme
 
 ## Deploy your model
 
-1. Go to your project in [Language studio](https://aka.ms/custom-extraction)
+Go to your project in [Language studio](https://aka.ms/custom-extraction).
 
-2. Select **Deploy model** from the left side menu.
+[!INCLUDE [Deploy a model using Language Studio](../includes/deploy-model-language-studio.md)]
 
-3. Select the model you want to deploy, then select **Deploy model**. If you deploy your model through the Language Studio, your `deployment-name` is `prod`.
+If you deploy your model through the Language Studio, your `deployment-name` is `prod`.
 
 > [!TIP]
 > You can test your model in Language Studio by sending samples of text for it to classify. 
diff --git a/articles/cognitive-services/language-service/custom-named-entity-recognition/how-to/create-project.md b/articles/cognitive-services/language-service/custom-named-entity-recognition/how-to/create-project.md
@@ -156,8 +156,12 @@ For information on authorizing access to your Azure blob storage account and dat
 
 ## Create a custom named entity recognition project
 
+Once your resource and storage container are configured, create a new custom NER project. A project is a work area for building your custom AI models based on your data. Your project can only be accessed by you and others who have contributor access to the Azure resource being used.
+
 [!INCLUDE [Create custom NER project](../includes/create-project.md)]
 
+Review the data you entered and select **Create Project**.
+
 ## Next steps
 
 After your project is created, you can start [tagging your data](tag-data.md), which will inform your entity extraction model how to interpret text, and is used for training and evaluation.
diff --git a/articles/cognitive-services/language-service/custom-named-entity-recognition/includes/create-project.md b/articles/cognitive-services/language-service/custom-named-entity-recognition/includes/create-project.md
@@ -10,32 +10,27 @@ ms.author: aahi
 ms.custom: ignite-fall-2021
 ---
 
-Once your resource and storage container are configured, create a new custom NER project. A project is a work area for building your custom AI models based on your data. Your project can only be accessed by you and others who have contributor access to the Azure resource being used.
-
 1. Sign into the [Language Studio portal](https://aka.ms/languageStudio). A window will appear to let you select your subscription and Language resource. Select the resource you created in the above step. 
 
 2. Find the **Entity extraction** section, and select **Custom named entity recognition** from the available services.
 
+    :::image type="content" source="../media/select-custom-ner.png" alt-text="A screenshot showing the location of custom NER in the Language Studio landing page." lightbox="../media/select-custom-ner.png":::
+
 3. Select **Create new project** from the top menu in your projects page. Creating a project will let you tag data, train, evaluate, improve, and deploy your models. 
 
-    >[!NOTE]
-    > If your dataset contains files of different languages or if you expect different languages during runtime, you can enable the multi-lingual option.
-    
     :::image type="content" source="../media/create-project.png" alt-text="A screenshot of the project creation page." lightbox="../media/create-project.png":::
 
 
-4.  After you click, **Create new project**, a screen will appear to let you connect your storage account. If you cannot find your storage account, make sure you created a resource using the steps above. 
+4.  After you click, **Create new project**, a screen will appear to let you connect your storage account. If you can’t find your storage account, make sure you created a resource using the steps above.
 
     >[!NOTE]
     > * You only need to do this step once for each new resource you use. 
     > * This process is irreversible, if you connect a storage account to your resource you cannot disconnect it later.
     > * You can only connect your resource to one storage account.
-    > * If you've already connected a storage account, you will see a **Select project type** screen instead. See the next step.
+    > * If you've already connected a storage account, you will see a **Enter basic information** screen instead. See the next step.
     
     :::image type="content" source="../media/connect-storage.png" alt-text="A screenshot showing the storage connection screen." lightbox="../media/connect-storage.png":::
 
-<!--If you're using a preexisting resource, see [creating Azure resources](../concepts/use-azure-resources.md). When you are done, select **Next**.--> 
-
-5. Enter the project information, including a name, description, and the language of the files in your project. You will not be able to change the name of your project later. 
+5. Enter the project information, including a name, description, and the language of the files in your project. You won’t be able to change the name of your project later. 
 
-6. Review the data you entered and select **Create Project**.
+6. Select the container where you’ve uploaded your data.
diff --git a/articles/cognitive-services/language-service/custom-named-entity-recognition/includes/deploy-model-language-studio.md b/articles/cognitive-services/language-service/custom-named-entity-recognition/includes/deploy-model-language-studio.md
@@ -0,0 +1,14 @@
+---
+services: cognitive-services
+author: aahill
+manager: nitinme
+ms.service: cognitive-services
+ms.subservice: language-service
+ms.topic: include
+ms.date: 02/04/2022
+ms.author: aahi
+---
+
+1. Select **Deploy model** from the left side menu.
+
+2. Select the model you want to deploy, then select **Deploy model**.
diff --git a/articles/cognitive-services/language-service/custom-named-entity-recognition/includes/quickstarts/language-studio.md b/articles/cognitive-services/language-service/custom-named-entity-recognition/includes/quickstarts/language-studio.md
@@ -5,7 +5,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: include
-ms.date: 01/24/2022
+ms.date: 02/04/2022
 ms.author: aahi
 ms.custom: ignite-fall-2021
 ---
@@ -48,35 +48,9 @@ Before you can use custom NER, you’ll need to create an Azure Language resourc
 
 Once your resource and storage container are configured, create a new conversational NER project. A project is a work area for building your custom AI models based on your data. Your project can only be accessed by you and others who have contributor access to the Azure resource being used.
 
-1. Sign into the [Language Studio portal](https://aka.ms/languageStudio). A window will appear to let you select your subscription and Language resource. Select the resource you created in the above step.
+[!INCLUDE [Create custom NER project](../create-project.md)]
 
-2. Find the **Entity extraction** section, and select **Custom named entity recognition** from the available services.
-    
-    :::image type="content" source="../../media/select-custom-ner.png" alt-text="A screenshot showing the location of custom NER in the Language Studio landing page." lightbox="../../media/select-custom-ner.png":::
-
-3. Select **Create new project** from the top menu in your projects page. Creating a project will let you tag data, train, evaluate, improve, and deploy your models. 
-
-    
-    :::image type="content" source="../../media/create-project.png" alt-text="A screenshot of the project creation page." lightbox="../../media/create-project.png":::
-
-
-4.  After you click, **Create new project**, a screen will appear to let you connect your storage account. If you can’t find your storage account, make sure you created a resource using the steps above. 
-
-    >[!NOTE]
-    > * You only need to do this step once for each new resource you use. 
-    > * This process is irreversible, if you connect a storage account to your resource you cannot disconnect it later.
-    > * You can only connect your resource to one storage account.
-    > * If you've already connected a storage account, you will see a **Enter basic information** screen instead. See the next step.
-    
-    :::image type="content" source="../../media/connect-storage.png" alt-text="A screenshot showing the storage connection screen." lightbox="../../media/connect-storage.png":::
-
-<!--If you're using a preexisting resource, see [creating Azure resources](../concepts/use-azure-resources.md). When you are done, select **Next**.--> 
-
-5. Enter the project information, including a name, description, and the language of the files in your project. You won’t be able to change the name of your project later. 
-
-6. Select the container where you’ve uploaded your data. When asked if your files are already tagged with classes, select **Yes** and choose the available file. Then click **Next**.
-
-7. Review the data you entered and select **Create Project**.
+Under **Are your files already tagged with entities**, select **Yes** and choose the available file. Then click **Next**. Review the data you entered and select **Create Project**.
 
 ## Train your model
 
@@ -88,9 +62,8 @@ Generally after training a model you would review it's [evaluation details](../.
 
 After your model is trained, you can deploy it. Deploying your model lets you start using it to extract named entities, using [Analyze API](https://aka.ms/ct-runtime-swagger).
 
-1. Select **Deploy model** from the left side menu.
+[!INCLUDE [Deploy a model using Language Studio](../deploy-model-language-studio.md)]
 
-2. Select the model you want to deploy, then select **Deploy model**.
 
 ## Test your model
 
diff --git a/articles/cognitive-services/language-service/custom-named-entity-recognition/tutorials/cognitive-search.md b/articles/cognitive-services/language-service/custom-named-entity-recognition/tutorials/cognitive-search.md
@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: tutorial
-ms.date: 02/02/2022
+ms.date: 02/04/2022
 ms.author: aahi
 ms.custom: language-service-custom-ner, ignite-fall-2021
 ---
@@ -34,31 +34,19 @@ In this tutorial, you learn how to:
 
 ## Create a custom NER project through Language studio
 
-1. Sign in to [Language Studio](https://aka.ms/languageStudio). A window will appear to let you select your subscription and Language resource. Select the resource you created in the above step.
+[!INCLUDE [Create custom NER project](../includes/create-project.md)]
 
-2. Under the **Extract information** section of Language Studio, select **custom named entity recognition** from the available services, and select it.
-    
-3. Select **Create new project** from the top menu in your projects page. Creating a project will let you tag data, train, evaluate, improve, and deploy your models. 
-
-4. If you’ve created your resource using the steps above in this [guide](../how-to/create-project.md#azure-resources), the **Connect storage** step will be completed already. If not, you need to assign [roles for your storage account](../how-to/create-project.md#required-roles-for-your-storage-account) before connecting it to your resource
-
-5. Enter project information, including a name, description, and the language of the files in your project. You won’t be able to change the name of your project later.
-    >[!TIP]
-    > Your dataset doesn't have to be entirely in the same language. You can have multiple files, each with different supported languages. If your dataset contains files of different languages or if you expect different languages during runtime, select **enable multi-lingual dataset** when you enter the basic information for your project.
-
-6. Select the container where you’ve uploaded your data. For this tutorial we’ll use the tags file you downloaded from the sample data.
-
-7. Review the data you entered and select **Create Project**.
+Select the container where you’ve uploaded your data. For this tutorial we’ll use the tags file you downloaded from the sample data. Review the data you entered and select **Create Project**.
 
 ## Train your model
 
 [!INCLUDE [Train a model using Language Studio](../includes/train-model-language-studio.md)]
 
 ## Deploy your model
 
-1. Select **Deploy model** from the left side menu.
+[!INCLUDE [Deploy a model using Language Studio](../includes/deploy-model-language-studio.md)]
 
-2. Select the model you want to deploy and from the top menu click on **Deploy model**. If you deploy your model through Language Studio, your `deployment-name` will be `prod`.
+If you deploy your model through Language Studio, your `deployment-name` will be `prod`.
 
 ## Use CogSvc language utilities tool for Cognitive search integration
  
