Language Studio QS for CLU edits

Author: eric-urban

articles/cognitive-services/language-service/conversational-language-understanding/includes/language-studio/deploy-model.md

@@ -14,17 +14,19 @@ To deploy your model from within the [Language Studio](https://aka.ms/LanguageSt
 
 1. Select **Deploying a model** from the left side menu.
 
-2. Click on **Add deployment** to start a new deployment job.
+1. Select **Add deployment** to start the **Add deployment** wizard.
 
     :::image type="content" source="../../media/add-deployment-model.png" alt-text="A screenshot showing the model deployment button in Language Studio." lightbox="../../media/add-deployment-model.png":::
 
-3. Select **Create new deployment** to create a new deployment and assign a trained model from the dropdown below. You can also **Overwrite an existing deployment** by selecting this option and select the trained model you want to assign to it from the dropdown below.
+1. Select **Create a new deployment name** to create a new deployment and assign a trained model from the dropdown below. You can otherwise select **Overwrite an existing deployment name** to effectively replace the model that's used by an existing deployment.
 
     > [!NOTE]
     > Overwriting an existing deployment doesn't require changes to your [Prediction API](https://aka.ms/clu-runtime-api) call but the results you get will be based on the newly assigned model.
     
     :::image type="content" source="../../media/create-deployment-job.png" alt-text="A screenshot showing the screen for adding a new deployment in Language Studio." lightbox="../../media/create-deployment-job.png":::
 
-4. click on **Deploy** to start the deployment job.
+1. Select a trained model from the **Model** dropdown. 
 
-5. After deployment is successful, an expiration date will appear next to it. [Deployment expiration](../../../concepts/model-lifecycle.md#expiration-timeline) is when your deployed model will be unavailable to be used for prediction, which typically happens **twelve** months after a training configuration expires.
+1. Select **Deploy** to start the deployment job.
+
+1. After deployment is successful, an expiration date will appear next to it. [Deployment expiration](../../../concepts/model-lifecycle.md#expiration-timeline) is when your deployed model will be unavailable to be used for prediction, which typically happens **twelve** months after a training configuration expires.

articles/cognitive-services/language-service/conversational-language-understanding/includes/language-studio/import-project.md

@@ -15,7 +15,7 @@ ms.author: aahi
     :::image type="content" source="../../media/select-custom-clu.png" alt-text="A screenshot showing the location of Custom Language Understanding in the Language Studio landing page." lightbox="../../media/select-custom-clu.png"::: 
 
 
-2. This will bring you to the **Conversational language understanding projects** page. From the dropdown next to Create new project select **Import**.
+2. This will bring you to the **Conversational language understanding projects** page. Next to the **Create new project** button select **Import**.
 
     :::image type="content" source="../../media/projects-page.png" alt-text="A screenshot showing the conversation project page in Language Studio." lightbox="../../media/projects-page.png":::
 

articles/cognitive-services/language-service/conversational-language-understanding/includes/language-studio/test-model.md

@@ -9,23 +9,20 @@ ms.date: 10/26/2022
 ms.author: aahi
 ---
 
+## Test deployed model
 
-## Test the model
+To test your deployed models from within the [Language Studio](https://aka.ms/LanguageStudio):
+1. Select **Testing deployments** from the left side menu.
 
-To test your model from Language studio
+1. For multilingual projects, from the **Select text language** dropdown, select the language of the utterance you are testing.
 
-1. Select **Test model** from the left side menu.
+1. From the **Deployment name** dropdown, select the deployment name corresponding to the model that you want to test. You can only test models that are assigned to deployments.
 
-3. Select the model you want to test. You can only test models that are assigned to deployments.
+1. In the text box, enter an utterance to test. For example, if you created an application for email-related utterances you could enter *Delete this email*. 
 
-4. For multilingual projects, from the language dropdown, select the language of the utterance you are testing.
-
-5. From deployment name dropdown, select your deployment name.
-
-6. In the text box, enter an utterance to test. For example, if you created an application for email-related utterances you could type *Delete this email*. 
-
-7. From the top menu, click on **Run the test**.
+7. Towards the the top of the page, select **Run the test**.
 
 8. After you run the test, you should see the response of the model in the result. You can view the results in entities cards view, or view it in JSON format.
 
     <!--:::image type="content" source="../../media/test-model.png" alt-text="A screenshot showing testing the model." lightbox="../../media/test-model.png":::-->
+

articles/cognitive-services/language-service/conversational-language-understanding/includes/language-studio/train-model.md

@@ -15,21 +15,21 @@ To start training your model from within the [Language Studio](https://aka.ms/la
 
 2. Select **Start a training job** from the top menu.
 
-3. Select **Train a new model** and type in the model name in the text box. You can also **overwrite an existing model** by selecting this option and choosing the model you want to overwrite from the dropdown menu. Overwriting a trained model is irreversible, but it won't affect your deployed models until you deploy the new model.
+3. Select **Train a new model** and enter a new model name in the text box. Otherwise to replace an existing model with a model trained on the new data, select **Overwrite an existing model** and then select an existing model. Overwriting a trained model is irreversible, but it won't affect your deployed models until you deploy the new model.
 
 4. Select training mode. You can choose **Standard training** for faster training, but it is only available for English. Or you can choose **Advanced training** which is supported for other languages and multilingual projects, but it involves longer training times. Learn more about [training modes](../../how-to/train-model.md#training-modes).
 
 
 5. Select a [data splitting](../../how-to/train-model.md#data-splitting) method. You can choose **Automatically splitting the testing set from training data** where the system will split your utterances between the training and testing sets, according to the specified percentages. Or you can **Use a manual split of training and testing data**, this option is only enabled if you have added utterances to your testing set when you [labeled your utterances](../../how-to/tag-utterances.md). 
 
-6. Click on the **Train** button.
+6. Select the **Train** button.
 
     :::image type="content" source="../../media/train-model.png" alt-text="A screenshot showing the training page in Language Studio." lightbox="../../media/train-model.png":::
 
-7. Click on the training job ID from the list. a panel will appear where you can check the **Training progress**, **Job status** and other details for this job.
+7. Select the training job ID from the list. A panel will appear where you can check the training progress, job status, and other details for this job.
 
     > [!NOTE]
     > * Only successfully completed training jobs will generate models.
     > * Training can take some time between a couple of minutes and couple of hours based on the count of utterances.
-    > * You can only have one training job running at a time. You can't start other training job within the same project until the running job is completed.
-    > * The machine learning used to train models is regularly updated. If you would like to train on a previous configuration, select **Click here to change** and choose a previous [training configuration version](../../../concepts/model-lifecycle.md).
+    > * You can only have one training job running at a time. You can't start other training jobs within the same project until the running job is completed.
+    > * The machine learning used to train models is regularly updated. To train on a previous [configuration version](../../../concepts/model-lifecycle.md), select **Click here to change** from the **Start a training job** page and choose a previous version.

articles/cognitive-services/language-service/conversational-language-understanding/includes/quickstarts/language-studio.md

@@ -26,9 +26,9 @@ ms.custom: ignite-fall-2021
 
 ## Create a conversational language understanding project
 
-Once you have a Language resource created, create a conversational language understanding project. A project is a work area for building your custom ML models based on your data. Your project can only be accessed by you and others who have access to the Language resource being used.
+Once you have a Language resource selected, create a conversational language understanding project. A project is a work area for building your custom ML models based on your data. Your project can only be accessed by you and others who have access to the Language resource being used.
 
-For this quickstart, you can download [this sample project](https://go.microsoft.com/fwlink/?linkid=2196152) and import it. This project can predict the intended commands from user input, such as: reading emails, deleting emails, and attaching a document to an email. 
+For this quickstart, you can download [this sample project file](https://go.microsoft.com/fwlink/?linkid=2196152) and import it. This project can predict the intended commands from user input, such as: reading emails, deleting emails, and attaching a document to an email. 
 
 [!INCLUDE [Import project](../language-studio/import-project.md)]
 

articles/cognitive-services/language-service/custom-named-entity-recognition/includes/language-studio/delete-project.md

@@ -10,4 +10,4 @@ ms.author: aahi
 ms.custom: language-service-custom-classification, event-tier1-build-2022
 ---
 
-When you don't need your project anymore, you can delete your project using [Language Studio](https://aka.ms/custom-extraction). Select **Custom named entity recognition (NER)** from the top, select project you want to delete and click on **Delete** from the top menu.
+When you don't need your project anymore, you can delete your project using [Language Studio](https://aka.ms/custom-extraction). Select **Custom named entity recognition (NER)** from the top, select the project you want to delete, and then select **Delete** from the top menu.