Merge pull request #1363 from cdpark/refresh-oct-patfarley-3

Feature 322546: Q&M: Freshness - AI Services & Search 180d target - Oct sprint - patfarley-3

Author: v-dirichards

articles/ai-services/content-moderator/text-moderation-api.md

@@ -1,27 +1,29 @@
 ---
 title: Text Moderation - Content Moderator
 titleSuffix: Azure AI services
-description: Use text moderation for possible unwanted text, personal data, and custom lists of terms.
+description: Use text moderation to detect potentially unwanted text, personal data, and custom lists of terms.
 #services: cognitive-services
 author: PatrickFarley
 manager: nitinme
 ms.service: azure-ai-content-moderator
 ms.topic: conceptual
-ms.date: 01/18/2024
+ms.date: 11/06/2024
 ms.author: pafarley
 
 ---
 
-# Learn text moderation concepts
+# Text moderation concepts
 
-Use Content Moderator's text moderation models to analyze text content, such as chat rooms, discussion boards, chatbots, e-commerce catalogs, and documents.
+[!INCLUDE [deprecation notice](includes/tool-deprecation.md)]
+
+You can use Azure Content Moderator's text moderation models to analyze text content, such as chat rooms, discussion boards, chatbots, e-commerce catalogs, and documents.
 
 The service response includes the following information:
 
 - Profanity: term-based matching with built-in list of profane terms in various languages
 - Classification: machine-assisted classification into three categories
 - Personal data
-- Auto-corrected text
+- Autocorrected text
 - Original text
 - Language
 
@@ -40,13 +42,13 @@ If the API detects any profane terms in any of the [supported languages](./langu
 ```
 
 > [!NOTE]
-> For the **language** parameter, assign `eng` or leave it empty to see the machine-assisted **classification** response (preview feature). **This feature supports English only**.
+> For the `language` parameter, assign `eng` or leave it empty to see the machine-assisted *classification* response (preview feature). **This feature supports English only**.
 >
-> For **profanity terms** detection, use the [ISO 639-3 code](http://www-01.sil.org/iso639-3/codes.asp) of the supported languages listed in this article, or leave it empty.
+> For *profanity terms* detection, use the [ISO 639-3 code](http://www-01.sil.org/iso639-3/codes.asp) of the supported languages listed in this article, or leave it empty.
 
 ## Classification
 
-Content Moderator's machine-assisted **text classification feature** supports **English only**, and helps detect potentially undesired content. The flagged content may be assessed as inappropriate depending on context. It conveys the likelihood of each category. The feature uses a trained model to identify possible abusive, derogatory or discriminatory language. This includes slang, abbreviated words, offensive, and intentionally misspelled words.
+Content Moderator's machine-assisted *text classification feature* supports *English only*, and helps detect potentially undesired content. The flagged content might be assessed as inappropriate depending on context. It conveys the likelihood of each category. The feature uses a trained model to identify possible abusive, derogatory, or discriminatory language. This includes slang, abbreviated words, offensive, and intentionally misspelled words.
 
 The following extract in the JSON extract shows an example output:
 
@@ -67,10 +69,10 @@ The following extract in the JSON extract shows an example output:
 
 ### Explanation
 
-- `Category1` refers to potential presence of language that may be considered sexually explicit or adult in certain situations.
-- `Category2` refers to potential presence of language that may be considered sexually suggestive or mature in certain situations.
-- `Category3` refers to potential presence of language that may be considered offensive in certain situations.
-- `Score` is between 0 and 1. The higher the score, the higher the model is predicting that the category may be applicable. This feature relies on a statistical model rather than manually coded outcomes. We recommend testing with your own content to determine how each category aligns to your requirements.
+- `Category1` refers to the potential presence of language that might be considered sexually explicit or adult in certain situations.
+- `Category2` refers to the potential presence of language that might be considered sexually suggestive or mature in certain situations.
+- `Category3` refers to the potential presence of language that might be considered offensive in certain situations.
+- `Score` is between 0 and 1. The higher the score, the higher the probability that the category might be applicable. This feature relies on a statistical model rather than manually coded outcomes. We recommend testing with your own content to determine how each category aligns to your requirements.
 - `ReviewRecommended` is either true or false depending on the internal score thresholds. Customers should assess whether to use this value or decide on custom thresholds based on their content policies.
 
 ## Personal data
@@ -120,24 +122,24 @@ The following example shows a sample response:
 }
 ```
 
-## Auto-correction
+## Autocorrection
 
-The text moderation response can optionally return the text with basic auto-correction applied. 
+The text moderation response can optionally return the text with basic autocorrection applied. 
 
 For example, the following input text has a misspelling.
 
 > The quick brown fox jumps over the lazzy dog.
 
-If you specify auto-correction, the response contains the corrected version of the text:
+If you specify autocorrection, the response contains the corrected version of the text:
 
 > The quick brown fox jumps over the lazy dog.
 
-## Creating and managing your custom lists of terms
+## Create and manage your custom lists of terms
 
-While the default, global list of terms works great for most cases, you may want to screen against terms that are specific to your business needs. For example, you may want to filter out any competitive brand names from posts by users.
+While the default, global list of terms works great for most cases, you might want to screen against terms that are specific to your business needs. For example, you might want to filter out any competitive brand names from posts by users.
 
 > [!NOTE]
-> There is a maximum limit of **5 term lists** with each list to **not exceed 10,000 terms**.
+> There is a maximum limit of *five term lists* with each list to *not exceed 10,000 terms*.
 >
 
 The following example shows the matching List ID:
@@ -152,8 +154,8 @@ The following example shows the matching List ID:
     }
 ```
 
-The Content Moderator provides a [Term List API](/rest/api/cognitiveservices/contentmoderator/list-management-term-lists) with operations for managing custom term lists. Check out the [Term Lists .NET quickstart](term-lists-quickstart-dotnet.md) if you are familiar with Visual Studio and C#.
+The Content Moderator provides a [Term List API](/rest/api/cognitiveservices/contentmoderator/list-management-term-lists) with operations for managing custom term lists. Check out the [Term Lists .NET quickstart](term-lists-quickstart-dotnet.md) if you're familiar with Visual Studio and C#.
 
-## Next steps
+## Related content
 
-Test out the APIs with the [Quickstart](client-libraries.md).
+- [Quickstart: Use the Content Moderator client library](client-libraries.md)

articles/ai-services/custom-vision-service/export-your-model.md

@@ -1,80 +1,80 @@
 ---
 title: Export your model to mobile - Custom Vision Service
 titleSuffix: Azure AI services
-description: This article will show you how to export your model for use in creating mobile applications or run locally for real-time classification.
+description: Learn how to export your model for use in creating mobile applications or to run locally for real-time classification.
 #services: cognitive-services
 author: PatrickFarley
 manager: nitinme
 
 ms.service: azure-ai-custom-vision
 ms.topic: how-to
-ms.date: 01/21/2024
+ms.date: 11/05/2024
 ms.author: pafarley
 ---
 
 # Export your model for use with mobile devices
 
-Custom Vision Service lets you export your classifiers to run offline. You can embed your exported classifier into an application and run it locally on a device for real-time classification.
+Azure AI Custom Vision Service lets you export your classifiers to run offline. You can embed your exported classifier into an application and run it locally on a device for real-time classification.
 
 ## Export options
 
 Custom Vision Service supports the following exports:
 
-* **TensorFlow** for **Android**.
-* **TensorFlow.js** for JavaScript frameworks like React, Angular, and Vue. This will run on both **Android** and **iOS** devices.
-* **CoreML** for **iOS11**.
-* **ONNX** for **Windows ML**, **Android**, and **iOS**.
-* **[Vision AI Developer Kit](https://azure.github.io/Vision-AI-DevKit-Pages/)**.
-* A **Docker container** for Windows, Linux, or ARM architecture. The container includes a TensorFlow model and service code to use the Custom Vision API.
+* **TensorFlow** for **Android**
+* **TensorFlow.js** for JavaScript frameworks like React, Angular, and Vue. This runs on both **Android** and **iOS** devices
+* **CoreML** for **iOS 11 and later**
+* **ONNX** for **Windows ML**, **Android**, and **iOS**
+* **[Vision AI Developer Kit](https://azure.github.io/Vision-AI-DevKit-Pages/)**
+* A **Docker container** for Windows, Linux, or ARM architecture. The container includes a TensorFlow model and service code to use the Custom Vision API
 
 > [!IMPORTANT]
-> Custom Vision Service only exports projects with **compact** domains. The models generated by compact domains are optimized for the constraints of real-time classification on mobile devices. Classifiers built with a compact domain may be slightly less accurate than a standard domain with the same amount of training data.
+> Custom Vision Service only exports projects with *compact* domains. The models generated by compact domains are optimized for the constraints of real-time classification on mobile devices. Classifiers built with a compact domain might be slightly less accurate than a standard domain with the same amount of training data.
 >
-> For information on improving your classifiers, see the [Improving your classifier](getting-started-improving-your-classifier.md) document.
+> For information on improving your classifiers, see [Improving your classifier](getting-started-improving-your-classifier.md).
 
 ## Convert to a compact domain
 
 > [!NOTE]
-> The steps in this section only apply if you have an existing model that is not set to compact domain.
+> The steps in this section only apply if you have an existing model that isn't set to compact domain.
 
-To convert the domain of an existing model, take the following steps:
+To convert the domain of an existing model, follow these steps:
 
-1. On the [Custom vision website](https://customvision.ai), select the **Home** icon to view a list of your projects.
+1. Go to the [Custom Vision website](https://customvision.ai), then sign in to view a list of your projects.
 
-    ![Image of the home icon and projects list](./media/export-your-model/projects-list.png)
+    :::image type="content" source="media/export-your-model/projects-list.png" alt-text="Screenshot that shows a list of projects.":::
 
 1. Select a project, and then select the **Gear** icon in the upper right of the page.
 
-    ![Image of the gear icon](./media/export-your-model/gear-icon.png)
+    :::image type="content" source="media/export-your-model/gear-icon.png" alt-text="Screenshot that shows the gear icon.":::
 
 1. In the **Domains** section, select one of the **compact** domains. Select **Save Changes** to save the changes. 
 
     > [!NOTE]
     > For Vision AI Dev Kit, the project must be created with the **General (Compact)** domain, and you must specify the **Vision AI Dev Kit** option under the **Export Capabilities** section.
 
-    ![Image of domains selection](./media/export-your-model/domains.png)
+    :::image type="content" source="media/export-your-model/domains.png" alt-text="Screenshot that shows the Domains selection.":::
 
 1. From the top of the page, select **Train** to retrain using the new domain.
 
 ## Export your model
 
 To export the model after retraining, use the following steps:
 
-1. Go to the **Performance** tab and select **Export**. 
+1. Go to the **Performance** tab and select **Export**.
 
-    ![Image of the export icon](./media/export-your-model/export.png)
+    :::image type="content" source="media/export-your-model/export.png" alt-text="Screenshot that shows the export icon.":::
 
     > [!TIP]
-    > If the **Export** entry is not available, then the selected iteration does not use a compact domain. Use the **Iterations** section of this page to select an iteration that uses a compact domain, and then select **Export**.
+    > If the **Export** entry isn't available, then the selected iteration doesn't use a compact domain. Use the **Iterations** section of this page to select an iteration that uses a compact domain, and then select **Export**.
 
 1. Select your desired export format, and then select **Export** to download the model.
 
-## Next steps
+## Related content
 
-Integrate your exported model into an application by exploring one of the following articles or samples:
+To integrate your exported model into an application, explore one of the following articles or samples:
 
-* [Use your TensorFlow model with Python](export-model-python.md)
+* [Run a TensorFlow model in Python](export-model-python.md)
 * [Use your ONNX model with Windows Machine Learning](custom-vision-onnx-windows-ml.md)
-* See the sample for [CoreML model in an iOS application](https://go.microsoft.com/fwlink/?linkid=857726) for real-time image classification with Swift.
-* See the sample for [TensorFlow model in an Android application](https://github.com/Azure-Samples/cognitive-services-android-customvision-sample) for real-time image classification on Android.
-* See the sample for [CoreML model with Xamarin](https://github.com/xamarin/ios-samples/tree/master/ios11/CoreMLAzureModel) for real-time image classification in a Xamarin iOS app.
+* See the Swift sample for [CoreML model in an iOS application](https://go.microsoft.com/fwlink/?linkid=857726)
+* See the Android sample for [TensorFlow model in an Android app](https://github.com/Azure-Samples/cognitive-services-android-customvision-sample)
+* See the Xamarin iOS sample for [CoreML model with Xamarin](https://github.com/xamarin/ios-samples/tree/master/ios11/CoreMLAzureModel)