conversational meta data

laujan · laujan · commit 28378ad66002 · 2025-05-28T12:13:06.000-07:00
diff --git a/articles/ai-services/language-service/conversational-language-understanding/how-to/use-containers.md b/articles/ai-services/language-service/conversational-language-understanding/how-to/use-containers.md
@@ -24,12 +24,12 @@ If you don't have an Azure subscription, create a [free account](https://azure.m
 
 ## Prerequisites
 
-You must meet the following prerequisites before using CLU containers. 
+You must meet the following prerequisites before using CLU containers.
 
 * If you don't have an Azure subscription, create a [free account](https://azure.microsoft.com/free/cognitive-services/).
-* [Docker](https://docs.docker.com/) installed on a host computer. Docker must be configured to allow the containers to connect with and send billing data to Azure. 
+* [Docker](https://docs.docker.com/) installed on a host computer. Docker must be configured to allow the containers to connect with and send billing data to Azure.
     * On Windows, Docker must also be configured to support Linux containers.
-    * You should have a basic understanding of [Docker concepts](https://docs.docker.com/get-started/overview/). 
+    * You should have a basic understanding of [Docker concepts](https://docs.docker.com/get-started/overview/).
 * A <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics"  title="Create a Language resource"  target="_blank">Language resource </a>
 
 [!INCLUDE [Gathering required parameters](../../../containers/includes/container-gathering-required-parameters.md)]
@@ -42,31 +42,31 @@ The following table describes the minimum and recommended specifications for the
 
 It's recommended to have a CPU with AVX-512 instruction set, for the best experience (performance and accuracy).
 
-|                     | Minimum host specs     | Recommended host specs |
+|     | Minimum host specs     | Recommended host specs |
 |---------------------|------------------------|------------------------|
-| **CLU**             | 1 core, 2 GB memory     | 4 cores, 8 GB memory    |
+| **CLU**     | 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.
 
-## Export your Conversational Language Understanding model 
+## Export your Conversational Language Understanding model
 
-Before you proceed with running the docker image, you need to export your own trained model to expose it to your container. Use the following command to extract your model and replace the placeholders below with your own values: 
+Before you proceed with running the docker image, you need to export your own trained model to expose it to your container. Use the following command to extract your model and replace the placeholders below with your own values:
 
 |Placeholder |Value|Format or example|
 |------------|-----|-----------------|
 |**{API_KEY}** |The key for your Language resource. You can find it on your resource's **Key and endpoint** page, on the Azure portal.|xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx|
 |**{ENDPOINT_URI}**|The endpoint for accessing the Conversational Language Understanding API. You can find it on your resource's **Key and endpoint** page, on the Azure portal.|`https://<your-custom-subdomain>.cognitiveservices.azure.com`|
 |**{PROJECT_NAME}**|The name of the project containing the model that you want to export. You can find it on your projects tab in the Language Studio portal.|myProject|
-|**{TRAINED_MODEL_NAME}** |The name of the trained model you want to export. You can find your trained models on your model evaluation tab under your project in the Language Studio portal|myTrainedModel 
+|**{TRAINED_MODEL_NAME}** |The name of the trained model you want to export. You can find your trained models on your model evaluation tab under your project in the Language Studio portal|myTrainedModel
 |**{EXPORTED_MODEL_NAME}** |The name to assign for the new exported model created.|myExportedModel |
 
 ```bash
-curl --location --request PUT '{ENDPOINT_URI}/language/authoring/analyze-conversations/projects/{PROJECT_NAME}/exported-models/{EXPORTED_MODEL_NAME}?api-version=2024-11-15-preview' \ 
---header 'Ocp-Apim-Subscription-Key: {API_KEY}' \ 
---header 'Content-Type: application/json' \ 
---data-raw '{ 
-    "TrainedModelLabel": "{TRAINED_MODEL_NAME}" 
-}' 
+curl --location --request PUT '{ENDPOINT_URI}/language/authoring/analyze-conversations/projects/{PROJECT_NAME}/exported-models/{EXPORTED_MODEL_NAME}?api-version=2024-11-15-preview' \
+--header 'Ocp-Apim-Subscription-Key: {API_KEY}' \
+--header 'Content-Type: application/json' \
+--data-raw '{
+    "TrainedModelLabel": "{TRAINED_MODEL_NAME}"
+}'
 ```
 
 ## Get the container image with `docker pull`
@@ -75,50 +75,50 @@ The CLU container image can be found on the `mcr.microsoft.com` container regist
 
  To use the latest version of the container, you can use the `latest` tag, which is for English. You can also find a full list of containers for supported languages using the [tags on the MCR](https://mcr.microsoft.com/product/azure-cognitive-services/language/clu/tags).
 
-The latest CLU container is available in several languages. To download the container for the English container, use the command below. 
+The latest CLU container is available in several languages. To download the container for the English container, use the command below.
 
 ```
 docker pull mcr.microsoft.com/azure-cognitive-services/language/clu:latest
 ```
 
 [!INCLUDE [Tip for using docker list](../../../includes/cognitive-services-containers-docker-list-tip.md)]
 
-## Run the container in download model mode 
+## Run the container in download model mode
 
-After creating the exported model in the section above, users have to run the container in order to download the deployment package that was created specifically for their exported models. 
+After creating the exported model in the section above, users have to run the container in order to download the deployment package that was created specifically for their exported models.
 
-| Placeholder                 | Value                                                                                                                                 |Format or example                                              |
-|-----------------------------|---------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------|
-| **{API_KEY}**               | The key for your Language resource. You can find it on your resource's **Key and endpoint** page, on the Azure portal.                | xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx                              |  
-| **{ENDPOINT_URI}**          | The endpoint for accessing the API. You can find it on your resource's **Key and endpoint** page, on the Azure portal.                | `https://<your-custom-subdomain>.cognitiveservices.azure.com` |
-| **{IMAGE_TAG}**             | The image tag representing the language of the container you want to run. Make sure this matches the `docker pull` command you used.  | latest                                                        |
-| **{LOCAL_CLU_PORT}**        | Port number assigned for the container in local machine.                                                                              | 5000                                                          |
-| **{LOCAL_MODEL_DIRECTORY}** | Absolute directory in host machine where exported models are saved in.                                                            | `C:\usr\local\myDeploymentPackage`                            |
-| **{PROJECT_NAME}**          | Name of the project that the exported model belongs to                                                                                | myProject                                                     |
-| **{EXPORTED_MODEL_NAME}**   | Exported model to be downloaded                                                                                                       | myExportedModel                                               |
+| Placeholder| Value|Format or example  |
+|---|---|---|
+| **{API_KEY}**| The key for your Language resource. You can find it on your resource's **Key and endpoint** page, on the Azure portal. | xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx   | 
+| **{ENDPOINT_URI}**     | The endpoint for accessing the API. You can find it on your resource's **Key and endpoint** page, on the Azure portal. | `https://<your-custom-subdomain>.cognitiveservices.azure.com` |
+| **{IMAGE_TAG}**   | The image tag representing the language of the container you want to run. Make sure this matches the `docker pull` command you used.  | latest|
+| **{LOCAL_CLU_PORT}**| Port number assigned for the container in local machine.| 5000 |
+| **{LOCAL_MODEL_DIRECTORY}** | Absolute directory in host machine where exported models are saved in. | `C:\usr\local\myDeploymentPackage` |
+| **{PROJECT_NAME}**| Name of the project that the exported model belongs to  | myProject  |
+| **{EXPORTED_MODEL_NAME}**   | Exported model to be downloaded | myExportedModel   |
 
 ```bash
-docker run --rm -it -p {LOCAL_CLU_PORT}:80 \ 
-mcr.microsoft.com/azure-cognitive-services/language/clu:{IMAGE_TAG} \   
--v {LOCAL_MODEL_DIRECTORY}:/DeploymentPackage \ 
-Billing={ENDPOINT_URI} \   
-ApiKey={API_KEY} \ 
-downloadmodel \ 
-projectName={PROJECT_NAME} \ 
-exportedModelName={EXPORTED_MODEL_NAME} 
+docker run --rm -it -p {LOCAL_CLU_PORT}:80 \
+mcr.microsoft.com/azure-cognitive-services/language/clu:{IMAGE_TAG} \ 
+-v {LOCAL_MODEL_DIRECTORY}:/DeploymentPackage \
+Billing={ENDPOINT_URI} \ 
+ApiKey={API_KEY} \
+downloadmodel \
+projectName={PROJECT_NAME} \
+exportedModelName={EXPORTED_MODEL_NAME}
 ```
 
-DO NOT alter the downloaded files. Even altering the name or folder structure can affect the integrity of the container and might break it. 
+DO NOT alter the downloaded files. Even altering the name or folder structure can affect the integrity of the container and might break it.
 
-Repeat those steps to download as many models as you'd like to test. They can belong to different projects and have different exported model names. 
+Repeat those steps to download as many models as you'd like to test. They can belong to different projects and have different exported model names.
 
 ## Run the container with `docker run`
 
 Once the container is on the host computer, use the [docker run](https://docs.docker.com/engine/reference/commandline/run/) command to run the containers. The container continues to run until you stop it. Replace the placeholders below with your own values:
 
 
 > [!IMPORTANT]
-> * The docker commands in the following sections use the back slash, `\`, as a line continuation character. Replace or remove this based on your host operating system's requirements. 
+> * The docker commands in the following sections use the back slash, `\`, as a line continuation character. Replace or remove this based on your host operating system's requirements.
 > * The `Eula`, `Billing`, and `ApiKey` options must be specified to run the container; otherwise, the container won't start.  For more information, see [Billing](#billing).
 
 To run the CLU container, execute the following `docker run` command. Replace the placeholders below with your own values:
@@ -150,12 +150,12 @@ This command:
 
 [!INCLUDE [Running multiple containers on the same host](../../../includes/cognitive-services-containers-run-multiple-same-host.md)]
 
-## Running NER Container 
-CLU relies on NER to handle prebuilt entities. The CLU container works properly without NER if users decide not to integrate it. NER billing is disabled when it’s used through CLU, no extra charges are generated unless a call is made directly to NER’s container. 
- 
-To set up NER in CLU container 
-- Follow the [NER container documentation](../../named-entity-recognition/how-to/use-containers.md). 
-- When running CLU container, make sure to set the parameter `Ner_Url `so that `Ner_Url=http://host.docker.internal:{LOCAL_NER_PORT}` 
+## Running NER Container
+CLU relies on NER to handle prebuilt entities. The CLU container works properly without NER if users decide not to integrate it. NER billing is disabled when it's used through CLU, no extra charges are generated unless a call is made directly to NER's container.
+
+To set up NER in CLU container
+- Follow the [NER container documentation](../../named-entity-recognition/how-to/use-containers.md).
+- When running CLU container, make sure to set the parameter `Ner_Url `so that `Ner_Url=http://host.docker.internal:{LOCAL_NER_PORT}`
 
 ## Query the container's prediction endpoint
 
