Merge pull request #5414 from aahill/lang-freshness

acrolinx pass

Author: prmerger-automator[bot]

articles/ai-services/language-service/conversational-language-understanding/concepts/data-formats.md

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 11/21/2024
+ms.date: 06/05/2025
 ms.author: lajanuar
 ms.custom: language-service-custom-clu
 ---

articles/ai-services/language-service/custom-text-classification/concepts/evaluation-metrics.md

@@ -7,7 +7,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 11/21/2024
+ms.date: 6/6/2025
 ms.author: lajanuar
 ms.custom: language-service-custom-classification
 ---
@@ -26,12 +26,12 @@ Model evaluation is triggered automatically after training is completed successf
 
     `Recall = #True_Positive / (#True_Positive + #False_Negatives)`
 
-* **F1 score**: The F1 score is a function of Precision and Recall. It's needed when you seek a balance between Precision and Recall.
+* **F1 score**: The F1 score is a function of precision and recall. It's needed when you seek a balance between precision and recall.
 
     `F1 Score = 2 * Precision * Recall / (Precision + Recall)` <br> 
 
 >[!NOTE]
-> Precision, recall and F1 score are calculated for each class separately (*class-level* evaluation) and for the model collectively (*model-level* evaluation).
+> Precision, recall, and F1 score are calculated for each class separately (*class-level* evaluation) and for the model collectively (*model-level* evaluation).
 ## Model-level and Class-level evaluation metrics
 
 The definitions of precision, recall, and evaluation are the same for both class-level and model-level evaluations. However, the count of *True Positive*, *False Positive*, and *False Negative* differ as shown in the following example.
@@ -89,7 +89,7 @@ The below sections use the following example dataset:
 **F1 Score** = `2 * Precision * Recall / (Precision + Recall) =  (2 * 0.8 * 0.67) / (0.8 + 0.67) = 0.73`
 
 > [!NOTE] 
-> For single-label classification models, the count of false negatives and false positives are always equal. Custom single-label classification models always predict one class for each document. If the prediction is not correct, FP count of the predicted class increases by one and FN of the actual class increases by one, overall count of FP and FN for the model will always be equal. This is not the case for multi-label classification, because failing to predict one of the classes of a document is counted as a false negative. 
+> For single-label classification models, the number of false negatives and false positives are always equal. Custom single-label classification models always predict one class for each document. If the prediction is not correct, FP count of the predicted class increases by one and FN of the actual class increases by one, overall count of FP and FN for the model will always be equal. This is not the case for multi-label classification, because failing to predict one of the classes of a document is counted as a false negative. 
 ## Interpreting class-level evaluation metrics
 
 So what does it actually mean to have a high precision or a high recall for a certain class?

articles/ai-services/language-service/question-answering/how-to/network-isolation.md

@@ -5,13 +5,13 @@ ms.service: azure-ai-language
 ms.topic: how-to
 author: laujan
 ms.author: lajanuar
-ms.date: 11/21/2024
+ms.date: 06/06/2025
 ms.custom: language-service-question-answering
 ---
 
 #  Network isolation and private endpoints
 
-The steps below describe how to restrict public access to custom question answering resources as well as how to enable Azure Private Link. Protect an AI Foundry resource from public access by [configuring the virtual network](../../../cognitive-services-virtual-networks.md?tabs=portal).
+The following steps describe how to restrict public access to custom question answering resources as well as how to enable Azure Private Link. Protect an AI Foundry resource from public access by [configuring the virtual network](../../../cognitive-services-virtual-networks.md?tabs=portal).
 
 ## Private Endpoints
 
@@ -21,12 +21,12 @@ Private endpoints are provided by [Azure Private Link](/azure/private-link/priva
 
 ## Steps to enable private endpoint
 
-1. Assign *Contributor* role to language resource (Depending on the context this may appear as a Text Analytics resource) in the Azure Search Service instance. This operation requires *Owner* access to the subscription. Go to Identity tab in the service resource to get the identity.
+1. Assign the *contributor* role to your resource in the Azure Search Service instance. This operation requires *Owner* access to the subscription. Go to Identity tab in the service resource to get the identity.
 
 > [!div class="mx-imgBorder"]
 > ![Text Analytics Identity](../../../QnAMaker/media/qnamaker-reference-private-endpoints/private-endpoints-identity.png)
 
-2. Add the above identity as *Contributor* by going to Azure Search Service IAM tab.
+2. Add the above identity as *Contributor* by going to the Azure Search Service access control tab.
 
 ![Managed service IAM](../../../QnAMaker/media/qnamaker-reference-private-endpoints/private-endpoint-access-control.png)
 
@@ -54,9 +54,9 @@ This will establish a private endpoint connection between language resource and
 
 ## Restrict access to Azure AI Search resource
 
-Follow the steps below to restrict public access to custom question answering language resources. Protect an AI Foundry resource from public access by [configuring the virtual network](../../../cognitive-services-virtual-networks.md?tabs=portal).
+Follow these steps to restrict public access to custom question answering language resources. Protect an AI Foundry resource from public access by [configuring the virtual network](../../../cognitive-services-virtual-networks.md?tabs=portal).
 
-After restricting access to an AI Foundry resource based on VNet, To browse projects on Language Studio from your on-premises network or your local browser.
+After you restrict access to an AI Foundry resource based on virtual network, to browse projects on Language Studio from your on-premises network or your local browser:
 - Grant access to [on-premises network](../../../cognitive-services-virtual-networks.md?tabs=portal#configure-access-from-on-premises-networks).
 - Grant access to your [local browser/machine](../../../cognitive-services-virtual-networks.md?tabs=portal#managing-ip-network-rules).
 - Add the **public IP address of the machine  under the Firewall** section of the **Networking** tab. By default `portal.azure.com` shows the current browsing machine's public IP (select this entry) and then select **Save**.