fixes

Author: aahill

articles/cognitive-services/language-service/custom-text-analytics-for-health/includes/get-keys-endpoint-azure.md

@@ -12,6 +12,6 @@ ms.author: aahi
 
 1. Go to your resource overview page in the [Azure portal](https://portal.azure.com/#home)
 
-2. From the menu on the left side, select **Keys and Endpoint**. You will use the endpoint and key for the API requests 
+2. From the menu on the left side, select **Keys and Endpoint**. You'll use the endpoint and key for the API requests 
 
     :::image type="content" source="../../custom-text-classification/media/get-endpoint-azure.png" alt-text="A screenshot showing the key and endpoint page in the Azure portal" lightbox="../../custom-text-classification/media/get-endpoint-azure.png":::

articles/cognitive-services/language-service/custom-text-analytics-for-health/includes/quickstarts/rest-api.md

@@ -89,7 +89,7 @@ Training could take sometime between 10 and 30 minutes for this sample dataset.
 
 ## Deploy your model
 
-Generally after training a model you would review it's evaluation details and make improvements if necessary. In this quickstart, you will just deploy your model, and make it available for you to try in Language Studio, or you can call the [prediction API](https://aka.ms/ct-runtime-swagger).
+Generally after training a model you would review its evaluation details and make improvements if necessary. In this quickstart, you will just deploy your model, and make it available for you to try in Language Studio, or you can call the [prediction API](https://aka.ms/ct-runtime-swagger).
 
 ### Start deployment job
 
@@ -105,7 +105,7 @@ Generally after training a model you would review it's evaluation details and ma
 > [!div class="nextstepaction"]
 > <a href="https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=REST API&Pillar=Language&Product=Custom-text-analytics-for-health&Page=quickstart&Section=Get-deployment-job-status" target="_target">I ran into an issue</a>
 
-## Make predictions using your trained model
+## Make predictions with your trained model
 
 After your model is deployed, you can start using it to extract entities from your text using the [prediction API](https://aka.ms/ct-runtime-swagger). In the sample dataset you downloaded earlier you can find some test documents that you can use in this step.
 

articles/cognitive-services/language-service/custom-text-analytics-for-health/includes/rest-api/deploy-model.md

@@ -21,7 +21,7 @@ Submit a **PUT** request using the following URL, headers, and JSON body to subm
 | `{ENDPOINT}` | The endpoint for authenticating your API request.   | `https://<your-custom-subdomain>.cognitiveservices.azure.com` |
 | `{PROJECT-NAME}` | The name of your project. This value is case-sensitive.   | `myProject` |
 | `{DEPLOYMENT-NAME}`     | The name of your deployment. This value is case-sensitive.  | `staging` |
-|`{API-VERSION}`     | The version of the API you are calling. The value referenced here is for the latest version released. See [Model lifecycle](../../../concepts/model-lifecycle.md#choose-the-model-version-used-on-your-data) to learn more about other available API versions.  | `2022-05-01` |
+|`{API-VERSION}`     | The version of the API you're calling. The value referenced here is for the latest version released. See [Model lifecycle](../../../concepts/model-lifecycle.md#choose-the-model-version-used-on-your-data) to learn more about other available API versions.  | `2022-05-01` |
 
 #### Headers
 

articles/cognitive-services/language-service/custom-text-analytics-for-health/includes/rest-api/get-deployment-status.md

@@ -22,7 +22,7 @@ Use the following **GET** request to query the status of the deployment job. You
 | `{PROJECT-NAME}` | The name of your project. This value is case-sensitive.   | `myProject` |
 | `{DEPLOYMENT-NAME}`     | The name of your deployment. This value is case-sensitive.  | `staging` |
 |`{JOB-ID}`     | The ID for locating your model's training status. This is in the `location` header value you received in the previous step.  | `xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxxx` |
-|`{API-VERSION}`     | The version of the API you are calling. The value referenced here is for the latest version released. See [Model lifecycle](../../../concepts/model-lifecycle.md#choose-the-model-version-used-on-your-data) to learn more about other available API versions.  | `2022-05-01` |
+|`{API-VERSION}`     | The version of the API you're calling. The value referenced here is for the latest version released. See [Model lifecycle](../../../concepts/model-lifecycle.md#choose-the-model-version-used-on-your-data) to learn more about other available API versions.  | `2022-05-01` |
 
 #### Headers
 
@@ -35,7 +35,7 @@ Use the following header to authenticate your request.
 
 ### Response Body
 
-Once you send the request, you will get the following response. Keep polling this endpoint until the **status** parameter changes to "succeeded". You should get a `200` code to indicate the success of the request. 
+You'll receive the following request when you send the request. Keep polling this endpoint until the **status** parameter changes to "succeeded". You should get a `200` code to indicate the success of the request. 
 
 ```json
 {

articles/cognitive-services/language-service/custom-text-analytics-for-health/includes/rest-api/get-results.md

@@ -19,7 +19,7 @@ Use the following **GET** request to query the status/results of the custom enti
 |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](../../../concepts/model-lifecycle.md#choose-the-model-version-used-on-your-data) to learn more about other available API versions.  | `2022-05-01` |
+|`{API-VERSION}`     | The version of the API you're calling. The value referenced here is for the latest version released. See [Model lifecycle](../../../concepts/model-lifecycle.md#choose-the-model-version-used-on-your-data) to learn more about other available API versions.  | `2022-05-01` |
 
 #### Headers
 
@@ -29,7 +29,7 @@ Use the following **GET** request to query the status/results of the custom enti
 
 ### Response Body
 
-The response will be a JSON document with the following parameters
+The response is a JSON document with the following parameters
 
 ```json
 {

articles/cognitive-services/language-service/custom-text-analytics-for-health/includes/rest-api/train-model.md

@@ -10,7 +10,7 @@ ms.date: 06/29/2022
 ms.author: aahi
 ---
 
-Submit a **POST** request using the following URL, headers, and JSON body to submit a training job. Replace the placeholder values below with your own values. 
+Submit a **POST** request using the following URL, headers, and JSON body to submit a training job. Replace the placeholder values with your own values. 
 
 ```rest
 {ENDPOINT}/language/authoring/analyze-text/projects/{PROJECT-NAME}/:train?api-version={API-VERSION}
@@ -32,7 +32,7 @@ Use the following header to authenticate your request.
 
 #### Request body
 
-Use the following JSON in your request body. The model will be given the `{MODEL-NAME}` once training is complete. Only successful training jobs will produce models. 
+Use the following JSON in your request body. The model is given the `{MODEL-NAME}` once training is complete. Only successful training jobs produce models. 
 
 
 ```json
@@ -52,7 +52,7 @@ Use the following JSON in your request body. The model will be given the `{MODEL
 | modelLabel | `{MODEL-NAME}` | The model name that will be assigned to your model once trained successfully.  | `myModel` |
 | trainingConfigVersion | `{CONFIG-VERSION}` | This is the [model version](../../../concepts/model-lifecycle.md) that will be used to train the model. | `2022-05-01`| 
 | evaluationOptions |  | Option to split your data across training and testing sets. | `{}` |
-| kind | `percentage` |  Split methods. Possible values are `percentage` or `manual`. See [How to train a model](../../how-to/train-model.md#data-splitting) for more information. |`percentage`|
+| kind | `percentage` |  Split methods. Possible values are `percentage` or `manual`. <!--See [How to train a model](../../how-to/train-model.md#data-splitting) for more information.--> |`percentage`|
 | trainingSplitPercentage | `80`| Percentage of your tagged data to be included in the training set. Recommended value is `80`. | `80`|
 | testingSplitPercentage | `20` | Percentage of your tagged data to be included in the testing set. Recommended value is `20`.   | `20` |