@@ -9,7 +9,7 @@ ms.custom:
   - ignite-2024
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 11/18/2025
+ms.date: 12/05/2025
 ms.author: lajanuar
 ---
 # Configure Language docker containers

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 11/18/2025
+ms.date: 12/05/2025
 ms.author: lajanuar
 ms.custom: language-service-clu, ignite-2024
 ---
@@ -20,7 +20,8 @@ ms.custom: language-service-clu, ignite-2024
 > * [Custom named entity recognition (NER)](../../custom-named-entity-recognition/overview.md)
 > * [Orchestration workflow](../../orchestration-workflow/overview.md)
 
-Custom Language features enable you to deploy your project to more than one region. This capability makes it much easier to access your project globally while you manage only one instance of your project in one place. As of November 2024, custom Language features also enable you to deploy your project to multiple resources within a single region via the API, so that you can use your custom model wherever you need.
+Custom Language features enable you to deploy your project to more than one region. This capability makes it much easier to access your project globally while you manage only one instance of your project in one place. Beginning in November 2024, custom Language features allow you to deploy your project to multiple resources within a single region using the API. Thus, you can access and utilize your custom model wherever needed.
+
 
 Before you deploy a project, you can assign *deployment resources* in other regions. Each deployment resource is a different Language resource from the one that you use to author your project. You deploy to those resources and then target your prediction requests to that resource in their respective regions and your queries are served directly from that region.
 
@@ -46,7 +47,7 @@ Assigning deployment resources requires Microsoft Entra authentication. Microsof
 
 Your project name and resource are used as its main identifiers. A Language resource can only have a specific project name in each resource. Any other projects with the same name can't be deployed to that resource.
 
-For example, if a project `ContosoSupport` was created by the resource `MyWestUS2` in West US 2 and deployed to the resource `MyEastUS` in East US, the resource `MyEastUS` can't create a different project called `ContosoSupport` and deploy a project to that region. Similarly, your collaborators can't then create a project `ContosoSupport` with the resource `MyCentralIndia` in Central India and deploy it to either `MyWestUS2` or `MyEastUS`.
+For example, if a project `ContosoSupport` was created via the resource `MyWestUS2` in West US 2 and deployed to the resource `MyEastUS` in East US, the resource `MyEastUS` can't create a different project called `ContosoSupport` and deploy a project to that region. Similarly, your collaborators can't then create a project `ContosoSupport` with the resource `MyCentralIndia` in Central India and deploy it to either `MyWestUS2` or `MyEastUS`.
 
 You can only swap deployments that are available in the exact same regions. Otherwise, swapping fails.
 

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 11/18/2025
+ms.date: 12/05/2025
 ms.author: lajanuar
 ms.custom: language-service-clu
 ---

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: overview
-ms.date: 11/18/2025
+ms.date: 12/05/2025
 ms.author: lajanuar
 ms.custom: language-service-clu
 ---

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 11/18/2025
+ms.date: 12/05/2025
 ms.author: lajanuar
 ms.custom: language-service-clu, mode-other
 zone_pivot_groups: foundry-rest-api

@@ -7,21 +7,21 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 11/18/2025
+ms.date: 12/05/2025
 ms.author: lajanuar
 ms.custom: language-service-custom-classification, mode-other
 zone_pivot_groups: usage-custom-language-features
 ---
 # Quickstart: Custom text classification
 
-Use this article to get started with creating a custom text classification project where you can train custom models for text classification. A model is artificial intelligence software that's trained to do a certain task. For this system, the models classify text, and are trained by learning from tagged data.
+Use this article to get started with creating a custom text classification project where you can train custom models for text classification. A model is AI software trained to do a certain task. For this system, the models classify text, and are trained by learning from tagged data.
 
 Custom text classification supports two types of projects: 
 
-* **Single label classification** - you can assign a single class for each document in your dataset. For example, a movie script could only be classified as "Romance" or "Comedy". 
-* **Multi label classification** - you can assign multiple classes for each document in your dataset. For example, a movie script could be classified as "Comedy" or "Romance" and "Comedy".
+* **Single label classification** - you can assign a single class for each document in your dataset. For example, a movie script could only be classified as "Romance" or "Comedy." 
+* **Multi label classification** - you can assign multiple classes for each document in your dataset. For example, a movie script could be classified as "Comedy" or "Romance" and "Comedy."
 
-In this quickstart you can use the sample datasets provided to build a multi label classification where you can classify movie scripts into one or more categories or you can use single label classification dataset where you can classify abstracts of scientific papers into one of the defined domains.
+In this quickstart, you can use the sample datasets provided to build a multi label classification to classify movie scripts into one or more categories. Alternatively, you can use single label classification dataset to classify abstracts of scientific papers into one of the defined domains.
 
 
 ::: zone pivot="language-studio"
@@ -38,7 +38,7 @@ In this quickstart you can use the sample datasets provided to build a multi lab
 
 ## Next steps
 
-After you've created a custom text classification model, you can:
+After you create a custom text classification model, you can:
 * [Use the runtime API to classify text](how-to/call-api.md)
 
 When you start to create your own custom text classification projects, use the how-to articles to learn more about developing your model in greater detail:

@@ -6,39 +6,39 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 11/18/2025
+ms.date: 12/05/2025
 ms.author: lajanuar
 ms.custom: language-service-ner, ignite-2024
 ---
 # Entity types and tags
 
-Use this article to get an overview of the new API changes starting from version `2024-11-01`. This API change mainly introduces two new concepts (`entity types` and `entity tags`) replacing the `category` and `subcategory` fields in the current Generally Available API. A detailed overview of each API parameter and the supported API versions it corresponds to can be found on the [Skill Parameters][../how-to/skill-parameters.md] page.
+Use this article to get an overview of the new API changes starting from version `2024-11-01`. This API change mainly introduces two new concepts (`entity types` and `entity tags`) replacing the `category` and `subcategory` fields in the current Generally Available API. A detailed overview of each API parameter and the corresponding supported API versions are found on the [Skill Parameters][../how-to/skill-parameters.md] page.
 
-Since an entity like “Seattle” could be classified as a City, GPE (Geo Political Entity), and a Location, the `type` attribute is used to define the most granular classification, in this case City. The `tags` attribute in the service output is a list all possible classifications (City, GPE, and Location) and their respective confidence score. A full mapping of possible tags for each type can be found below. The `metadata` attributes in the service output contains additional information about the entity, such as the integer value associated with the entity. 
+Since an entity like "Seattle" could be classified as a City, GPE (Geo Political Entity), and a Location, the `type` attribute is used to define the most granular classification, in this case City. The `tags` attribute in the service output is a list all possible classifications (City, GPE, and Location) and their respective confidence score. A full mapping of possible tags for each are listed. The `metadata` attributes in the service output contain additional information about the entity, such as the integer value associated with the entity. 
 
 ## Entity types
-Entity types represent the lowest (or finest) granularity at which the entity has been detected and can be considered to be the base class that has been detected.
+Entity types represent the lowest (or finest) granularity at which the entity is detected. Types are considered to be the base class detected.
 
 ## Entity tags
-Entity tags are used to further identify an entity where a detected entity is tagged by the entity type and additional tags to differentiate the identified entity. The entity tags list could be considered to include categories, subcategories, sub-subcategories, and so on.
+Entity tags are used to further identify an entity where a detected entity is tagged to indicate the entity type. The entity tags list can include categories, subcategories, and sub-subcategories.
 
 ## Changes from versions `2022-05-01` and `2023-04-01` to version `2024-11-01` API
 The changes introduce better flexibility for the named entity recognition service, including:
 
 Updates to the structure of input formats:
-•	InclusionList
-•	ExclusionList
-•	Overlap policy
+*    InclusionList
+*    ExclusionList
+*    Overlap policy
 
 Updates to the handling of output formats:
 
-* More granular entity recognition outputs through introducing the tags list where an entity could be tagged by more than one entity tag.
+* More granular entity recognition outputs through introducing the tags list where an entity is tagged with more than one entity tag.
 * Overlapping entities where entities could be recognized as more than one entity type and if so, this entity would be returned twice. If an entity was recognized to belong to two entity tags under the same entity type, both entity tags are returned in the tags list.
-* Filtering entities using entity tags, you can learn more about this by navigating to [this article](../how-to-call.md#select-which-entities-to-be-returned).
-* Metadata Objects which contain additional information about the entity but currently only act as a wrapper for the existing entity resolution feature. You can learn more about this new feature [here](entity-metadata.md).
+* Filtering entities using entity tags: to learn more, *see*  [Select returned entities](../how-to-call.md#select-which-entities-to-be-returned).
+* Metadata Objects that contain additional information about the entity but currently only act as a wrapper for the existing entity resolution feature. You can learn more about this new feature [here](entity-metadata.md).
 
 ## Versions `2022-05-01` and `2023-04-01` to current version API entity mappings
-You can see a comparison between the structure of the entity categories/types in the [Supported Named Entity Recognition (NER) entity categories and entity types article](./named-entity-categories.md). Below is a table describing the mappings between the results you would expect to see from versions `2022-05-01` and `2023-04-01` and the current version API.
+You can see a comparison between the structure of the entity categories/types in the [Supported Named Entity Recognition (NER) entity categories and entity types article](./named-entity-categories.md). The following table presents the mappings between the results you would expect to see from versions `2022-05-01` and `2023-04-01` and the current version API.
 
 | Type           | Tags                                   |
 |----------------|----------------------------------------|

@@ -8,12 +8,12 @@ ms.service: azure-ai-language
 ms.custom:
   - ignite-2024
 ms.topic: how-to
-ms.date: 11/18/2025
+ms.date: 12/05/2025
 ms.author: lajanuar
 ---
 # Using named entity recognition skill parameters
 
-Use this article to get an overview of the different API parameters used to adjust the input to a Named Entity Recognition (NER) API call. The Generally Available NER service now supports the ability to specify a list of entity tags to be included into the response or excluded from the response. If a piece of text is classified as more than one entity type, the `overlapPolicy` parameter allows customers to specify how the service will handle the overlap. The `inferenceOptions` parameter allows for users to adjust the inference, such as excluding the detected entity values from being normalized and included in the metadata.
+Use this article to get an overview of the different API parameters used to adjust the input to a Named Entity Recognition (NER) API call. The Generally Available NER service now supports the ability to specify a list of entity tags to be included into the response or excluded from the response. If a piece of text is classified as more than one entity type, the `overlapPolicy` parameter allows customers to specify how the service handles the overlap. The `inferenceOptions` parameter allows for users to adjust the inference, such as excluding the detected entity values from being normalized and included in the metadata.
 
 ## InclusionList parameter
 
@@ -34,7 +34,7 @@ Parameters by supported API version
 
 ## inferenceOptions parameter
 
-Defines a selection of options available for adjusting the inference. Currently we have only one property called `excludeNormalizedValues` which excludes the detected entity values to be normalized and included in the metadata. The numeric and temporal entity types support value normalization. 
+Defines a selection of options available for adjusting the inference. Currently we have only one property called `excludeNormalizedValues` that excludes the detected entity values to be normalized and included in the metadata. The numeric and temporal entity types support value normalization. 
 
 ## Sample
 

@@ -7,7 +7,7 @@ manager: nitinme
 ms.service: azure-ai-language
 ms.custom:
 ms.topic: how-to
-ms.date: 11/18/2025
+ms.date: 12/05/2025
 ms.author: lajanuar
 keywords: on-premises, Docker, container
 ---
@@ -42,7 +42,7 @@ We recommend that you have a CPU with AVX-512 instruction set, for the best expe
 
 |                     | Minimum host specs     | Recommended host specs |
 |---------------------|------------------------|------------------------|
-| **PII detection**   | 1 core, 2 GB memory     | 4 cores, 8 GB memory    |
+| **PII detection**   | 1-core, 2-GB memory     | 4-cores, 8-GB memory    |
 
 CPU core and memory correspond to the `--cpus` and `--memory` settings, which are used as part of the `docker run` command.
 

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 11/18/2025
+ms.date: 12/05/2025
 ms.author: lajanuar
 ms.custom: language-service-health, ignite-2024
 ---
@@ -41,7 +41,7 @@ When you use the REST API as part of building the request payload, you include a
 }
 ```
 
-Once the request has completed processing by Text Analytics for health and you pull the response from the REST API, you'll find the FHIR resource bundle in the output. You can locate the FHIR resource bundle inside each document processed using the property name `fhirBundle`. The following partial sample is output highlighting the `fhirBundle`.
+Once the request completes processing by Text Analytics for health and you pull the response from the REST API, you can find the FHIR resource bundle in the output. You can locate the FHIR resource bundle inside each document processed using the property name `fhirBundle`. The following partial sample is output highlighting the `fhirBundle`.
 
 ```json
 {