@@ -7,7 +7,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 01/16/2024
+ms.date: 01/31/2025
 ms.author: jboback
 ---
 

@@ -7,7 +7,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 12/19/2023
+ms.date: 01/31/2025
 ms.author: jboback
 ms.custom: language-service-custom-ner, mode-other
 zone_pivot_groups: usage-custom-language-features

@@ -1,168 +0,0 @@
----
-title: Custom Text Analytics for health data formats
-titleSuffix: Azure AI services
-description: Learn about the data formats accepted by custom text analytics for health.
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: conceptual
-ms.date: 11/21/2024
-ms.author: jboback
-ms.custom: language-service-custom-ta4h
----
-
-# Accepted data formats in custom text analytics for health
-
-Use this article to learn about formatting your data to be imported into custom text analytics for health.
-
-If you are trying to [import your data](../how-to/create-project.md#import-project) into custom Text Analytics for health, it has to follow a specific format. If you don't have data to import, you can [create your project](../how-to/create-project.md) and use the Language Studio to [label your documents](../how-to/label-data.md).
-
-Your Labels file should be in the `json` format below to be used when importing your labels into a project.
-
-```json
-{
-	"projectFileVersion": "{API-VERSION}",
-	"stringIndexType": "Utf16CodeUnit",
-	"metadata": {
-		"projectName": "{PROJECT-NAME}",
-		"projectKind": "CustomHealthcare",
-		"description": "Trying out custom Text Analytics for health",
-		"language": "{LANGUAGE-CODE}",
-		"multilingual": true,
-		"storageInputContainerName": "{CONTAINER-NAME}",
-		"settings": {}
-	},
-	"assets": {
-		"projectKind": "CustomHealthcare",
-		"entities": [
-			{
-				"category": "Entity1",
-				"compositionSetting": "{COMPOSITION-SETTING}",
-				"list": {
-					"sublists": [
-						{
-							"listKey": "One",
-							"synonyms": [
-								{
-									"language": "en",
-									"values": [
-										"EntityNumberOne",
-										"FirstEntity"
-									]
-								}
-							]
-						}
-					]
-				}
-			},
-			{
-				"category": "Entity2"
-			},
-			{
-				"category": "MedicationName",
-				"list": {
-					"sublists": [
-						{
-							"listKey": "research drugs",
-							"synonyms": [
-								{
-									"language": "en",
-									"values": [
-										"rdrug a",
-										"rdrug b"
-									]
-								}
-							]
-
-						}
-					]
-				}
-				"prebuilts": "MedicationName"
-			}
-		],
-		"documents": [
-			{
-				"location": "{DOCUMENT-NAME}",
-				"language": "{LANGUAGE-CODE}",
-				"dataset": "{DATASET}",
-				"entities": [
-					{
-						"regionOffset": 0,
-						"regionLength": 500,
-						"labels": [
-							{
-								"category": "Entity1",
-								"offset": 25,
-								"length": 10
-							},
-							{
-								"category": "Entity2",
-								"offset": 120,
-								"length": 8
-							}
-						]
-					}
-				]
-			},
-			{
-				"location": "{DOCUMENT-NAME}",
-				"language": "{LANGUAGE-CODE}",
-				"dataset": "{DATASET}",
-				"entities": [
-					{
-						"regionOffset": 0,
-						"regionLength": 100,
-						"labels": [
-							{
-								"category": "Entity2",
-								"offset": 20,
-								"length": 5
-							}
-						]
-					}
-				]
-			}
-		]
-	}
-}
-
-```
-
-|Key  |Placeholder  |Value  | Example |
-|---------|---------|----------|--|
-| `multilingual` | `true`| A boolean value that enables you to have documents in multiple languages in your dataset and when your model is deployed you can query the model in any supported language (not necessarily included in your training documents). See [language support](../language-support.md#) to learn more about multilingual support. | `true`|
-|`projectName`|`{PROJECT-NAME}`|Project name|`myproject`|
-| `storageInputContainerName` |`{CONTAINER-NAME}`|Container name|`mycontainer`|
-| `entities` | | Array containing all the entity types you have in the project. These are the entity types that will be extracted from your documents into.|  |
-| `category` | | The name of the entity type, which can be user defined for new entity definitions, or predefined for prebuilt entities. For more information, see the entity naming rules below.|  |
-|`compositionSetting`|`{COMPOSITION-SETTING}`|Rule that defines how to manage multiple components in your entity. Options are `combineComponents` or `separateComponents`. |`combineComponents`|
-| `list` | | Array containing all the sublists you have in the project for a specific entity. Lists can be added to prebuilt entities or new entities with learned components.|  |
-|`sublists`|`[]`|Array containing sublists. Each sublist is a key and its associated values.|`[]`|
-| `listKey`| `One` | A normalized value for the list of synonyms to map back to in prediction. | `One` |
-|`synonyms`|`[]`|Array containing all the synonyms|synonym|
-| `language` | `{LANGUAGE-CODE}` |  A string specifying the language code for the synonym in your sublist. If your project is a multilingual project and you want to support your list of synonyms for all the languages in your project, you have to explicitly add your synonyms to each language. See [Language support](../language-support.md) for more information about supported language codes. |`en`|
-| `values`| `"EntityNumberone"`, `"FirstEntity"`  | A list of comma separated strings that will be matched exactly for extraction and map to the list key. | `"EntityNumberone"`, `"FirstEntity"` |
-| `prebuilts` | `MedicationName` | The name of the prebuilt component populating the prebuilt entity. [Prebuilt entities](../../text-analytics-for-health/concepts/health-entity-categories.md) are automatically loaded into your project by default but you can extend them with list components in your labels file.  | `MedicationName` |
-| `documents` | | Array containing all the documents in your project and list of the entities labeled within each document. | [] |
-| `location` | `{DOCUMENT-NAME}` |  The location of the documents in the storage container. Since all the documents are in the root of the container this should be the document name.|`doc1.txt`|
-| `dataset` | `{DATASET}` |  The test set to which this file goes to when split before training. Learn more about data splitting [here](../how-to/train-model.md#data-splitting). Possible values for this field are `Train` and `Test`.      |`Train`|
-| `regionOffset` |  |  The inclusive character position of the start of the text.      |`0`|
-| `regionLength` |  |  The length of the bounding box in terms of UTF16 characters. Training only considers the data in this region.      |`500`|
-| `category` |  |  The type of entity associated with the span of text specified. | `Entity1`|
-| `offset` |  |  The start position for the entity text. | `25`|
-| `length` |  |  The length of the entity in terms of UTF16 characters. | `20`|
-| `language` | `{LANGUAGE-CODE}` |  A string specifying the language code for the document used in your project. If your project is a multilingual project, choose the language code of the majority of the documents. See [Language support](../language-support.md) for more information about supported language codes. |`en`|
-
-## Entity naming rules
-
-1. [Prebuilt entity names](../../text-analytics-for-health/concepts/health-entity-categories.md) are predefined. They must be populated with a prebuilt component and it must match the entity name.
-2. New user defined entities (entities with learned components or labeled text) can't use prebuilt entity names.
-3. New user defined entities can't be populated with prebuilt components as prebuilt components must match their associated entities names and have no labeled data assigned to them in the documents array.
-
-
-
-## Next steps
-* You can import your labeled data into your project directly. Learn how to [import project](../how-to/create-project.md#import-project)
-* See the [how-to article](../how-to/label-data.md)  more information about labeling your data. 
-* When you're done labeling your data, you can [train your model](../how-to/train-model.md).  

@@ -1,106 +0,0 @@
----
-title: Entity components in custom Text Analytics for health
-titleSuffix: Azure AI services
-description: Learn how custom Text Analytics for health extracts entities from text
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: conceptual
-ms.date: 11/21/2024
-ms.author: jboback
-ms.custom: language-service-custom-ta4h
----
-
-# Entity components in custom text analytics for health
-
-In custom Text Analytics for health, entities are relevant pieces of information that are extracted from your unstructured input text. An entity can be extracted by different methods. They can be learned through context, matched from a list, or detected by a prebuilt recognized entity. Every entity in your project is composed of one or more of these methods, which are defined as your entity's components. When an entity is defined by more than one component, their predictions can overlap. You can determine the behavior of an entity prediction when its components overlap by using a fixed set of options in the **Entity options**.
-
-## Component types
-
-An entity component determines a way you can extract the entity. An entity can contain one component, which would determine the only method that would be used to extract the entity, or multiple components to expand the ways in which the entity is defined and extracted. 
-
-The [Text Analytics for health entities](../../text-analytics-for-health/concepts/health-entity-categories.md) are automatically loaded into your project as entities with prebuilt components. You can define list components for entities with prebuilt components but you can't add learned components. Similarly, you can create new entities with learned and list components, but you can't populate them with additional prebuilt components.
-
-### Learned component
-
-The learned component uses the entity tags you label your text with to train a machine learned model. The model learns to predict where the entity is, based on the context within the text. Your labels provide examples of where the entity is expected to be present in text, based on the meaning of the words around it and as the words that were labeled. This component is only defined if you add labels to your data for the entity. If you do not label any data, it will not have a learned component.
-
-The Text Analytics for health entities, which by default have prebuilt components can't be extended with learned components, meaning they do not require or accept further labeling to function.
-
-:::image type="content" source="../media/learned-component.png" alt-text="A screenshot showing an example of learned components for entities." lightbox="../media/learned-component.png":::
-
-### List component
-
-The list component represents a fixed, closed set of related words along with their synonyms. The component performs an exact text match against the list of values you provide as synonyms. Each synonym belongs to a "list key", which can be used as the normalized, standard value for the synonym that will return in the output if the list component is matched. List keys are **not** used for matching.
-
-In multilingual projects, you can specify a different set of synonyms for each language. While using the prediction API, you can specify the language in the input request, which will only match the synonyms associated to that language.
-
-
-:::image type="content" source="../media/list-component.png" alt-text="A screenshot showing an example of list components for entities." lightbox="../media/list-component.png":::
-
-### Prebuilt component
-
-The [Text Analytics for health entities](../../text-analytics-for-health/concepts/health-entity-categories.md) are automatically loaded into your project as entities with prebuilt components. You can define list components for entities with prebuilt components but you cannot add learned components. Similarly, you can create new entities with learned and list components, but you cannot populate them with additional prebuilt components. Entities with prebuilt components are pretrained and can extract information relating to their categories without any labels.
-
-:::image type="content" source="../media/prebuilt-component.png" alt-text="A screenshot showing an example of prebuilt components for entities." lightbox="../media/prebuilt-component.png":::
-
-
-## Entity options
-
-When multiple components are defined for an entity, their predictions may overlap. When an overlap occurs, each entity's final prediction is determined by one of the following options.
-
-### Combine components
-
-Combine components as one entity when they overlap by taking the union of all the components.
-
-Use this to combine all components when they overlap. When components are combined, you get all the extra information that’s tied to a list or prebuilt component when they are present.
-
-#### Example
-
-Suppose you have an entity called Software that has a list component, which contains “Proseware OS” as an entry. In your input data, you have “I want to buy Proseware OS 9” with “Proseware OS 9” tagged as Software:
-
-:::image type="content" source="../media/union-overlap-example-1.svg" alt-text="A screenshot showing a learned and list entity overlapped." lightbox="../media/union-overlap-example-1.svg":::
-
-By using combine components, the entity will return with the full context as “Proseware OS 9” along with the key from the list component:
-
-:::image type="content" source="../media/union-overlap-example-1-part-2.svg" alt-text="A screenshot showing the result of a combined component." lightbox="../media/union-overlap-example-1-part-2.svg":::
-
-Suppose you had the same utterance but only “OS 9” was predicted by the learned component:
-
-:::image type="content" source="../media/union-overlap-example-2.svg" alt-text="A screenshot showing an utterance with O S 9 predicted by the learned component." lightbox="../media/union-overlap-example-2.svg":::
-
-With combine components, the entity will still return as “Proseware OS 9” with the key from the list component:
-
-:::image type="content" source="../media/union-overlap-example-2-part-2.svg" alt-text="A screenshot showing the returned software entity." lightbox="../media/union-overlap-example-2-part-2.svg":::
-
-
-### Don't combine components
-
-Each overlapping component will return as a separate instance of the entity. Apply your own logic after prediction with this option.
-
-#### Example
-
-Suppose you have an entity called Software that has a list component, which contains “Proseware Desktop” as an entry. In your labeled data, you have “I want to buy Proseware Desktop Pro” with “Proseware Desktop Pro” labeled as Software:
-
-:::image type="content" source="../media/separated-overlap-example-1.svg" alt-text="A screenshot showing an example of a learned and list entity overlapped." lightbox="../media/separated-overlap-example-1.svg":::
-
-When you do not combine components, the entity will return twice:
-
-:::image type="content" source="../media/separated-overlap-example-1-part-2.svg" alt-text="A screenshot showing the entity returned twice." lightbox="../media/separated-overlap-example-1-part-2.svg":::
-
-
-## How to use components and options
-
-Components give you the flexibility to define your entity in more than one way. When you combine components, you make sure that each component is represented and you reduce the number of entities returned in your predictions. 
-
-A common practice is to extend a prebuilt component with a list of values that the prebuilt might not support. For example, if you have a **Medication Name** entity, which has a `Medication.Name` prebuilt component added to it, the entity may not predict all the medication names specific to your domain. You can use a list component to extend the values of the Medication Name entity and thereby extending the prebuilt with your own values of Medication Names.
-
-Other times you may be interested in extracting an entity through context such as a **medical device**. You would label for the learned component of the medical device to learn _where_ a medical device is based on its position within the sentence. You may also have a list of medical devices that you already know before hand that you'd like to always extract. Combining both components in one entity allows you to get both options for the entity.
-
-When you do not combine components, you allow every component to act as an independent entity extractor. One way of using this option is to separate the entities extracted from a list to the ones extracted through the learned or prebuilt components to handle and treat them differently.
-
-
-## Next steps
-
-* [Entities with prebuilt components](../../text-analytics-for-health/concepts/health-entity-categories.md)

@@ -1,152 +0,0 @@
----
-title: Custom text analytics for health evaluation metrics
-titleSuffix: Azure AI services
-description: Learn about evaluation metrics in custom Text Analytics for health
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: conceptual 
-ms.date: 11/21/2024
-ms.author: jboback
-ms.custom: language-service-custom-ta4h
----
-
-# Evaluation metrics for custom Text Analytics for health models
-
-Your [dataset is split](../how-to/train-model.md#data-splitting) into two parts: a set for training, and a set for testing. The training set is used to train the model, while the testing set is used as a test for model after training to calculate the model performance and evaluation. The testing set is not introduced to the model through the training process, to make sure that the model is tested on new data.
-
-Model evaluation is triggered automatically after training is completed successfully. The evaluation process starts by using the trained model to predict user defined entities for documents in the test set, and compares them with the provided data labels (which establishes a baseline of truth). The results are returned so you can review the model’s performance. User defined entities are **included** in the evaluation factoring in Learned and List components; Text Analytics for health prebuilt entities are **not** factored in the model evaluation. For evaluation, custom Text Analytics for health uses the following metrics:
-
-* **Precision**: Measures how precise/accurate your model is. It is the ratio between the correctly identified positives (true positives) and all identified positives. The precision metric reveals how many of the predicted entities are correctly labeled. 
-
-    `Precision = #True_Positive / (#True_Positive + #False_Positive)`
-
-* **Recall**: Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted entities are correct.
-
-    `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 = 2 * Precision * Recall / (Precision + Recall)` <br> 
-
->[!NOTE]
-> Precision, recall and F1 score are calculated for each entity separately (*entity-level* evaluation) and for the model collectively (*model-level* evaluation).
-
-## Model-level and entity-level evaluation metrics
-
-Precision, recall, and F1 score are calculated for each entity separately (entity-level evaluation) and for the model collectively (model-level evaluation).
-
-The definitions of precision, recall, and evaluation are the same for both entity-level and model-level evaluations. However, the counts for *True Positives*, *False Positives*, and *False Negatives* differ can differ. For example, consider the following text.
-
-### Example
-
-*The first party of this contract is John Smith, resident of 5678 Main Rd., City of Frederick, state of Nebraska. And the second party is Forrest Ray, resident of 123-345 Integer Rd., City of Corona, state of New Mexico. There is also Fannie Thomas resident of 7890 River Road, city of Colorado Springs, State of Colorado.*
-
-The model extracting entities from this text could have the following predictions:
-
-| Entity | Predicted as | Actual type |
-|--|--|--|
-| John Smith | Person | Person |
-| Frederick | Person | City |
-| Forrest | City | Person |
-| Fannie Thomas | Person | Person |
-| Colorado Springs | City | City |
-
-### Entity-level evaluation for the *person* entity 
-
-The model would have the following entity-level evaluation, for the *person* entity:
-
-| Key | Count | Explanation |
-|--|--|--|
-| True Positive | 2 | *John Smith* and *Fannie Thomas* were correctly predicted as *person*. |
-| False Positive | 1 | *Frederick* was incorrectly predicted as *person* while it should have been *city*. |
-| False Negative | 1 | *Forrest* was incorrectly predicted as *city* while it should have been *person*. |
-
-* **Precision**: `#True_Positive / (#True_Positive + #False_Positive)` = `2 / (2 + 1) = 0.67`
-* **Recall**: `#True_Positive / (#True_Positive + #False_Negatives)` = `2 / (2 + 1) = 0.67`
-* **F1 Score**: `2 * Precision * Recall / (Precision + Recall)` = `(2 * 0.67 * 0.67) / (0.67 + 0.67) = 0.67`
-
-### Entity-level evaluation for the *city* entity
-
-The model would have the following entity-level evaluation, for the *city* entity:
-
-| Key | Count | Explanation |
-|--|--|--|
-| True Positive | 1 | *Colorado Springs* was correctly predicted as *city*. |
-| False Positive | 1 | *Forrest* was incorrectly predicted as *city* while it should have been *person*. |
-| False Negative | 1 | *Frederick* was incorrectly predicted as *person* while it should have been *city*. |
-
-* **Precision** = `#True_Positive / (#True_Positive + #False_Positive)` = `1 / (1 + 1) = 0.5`
-* **Recall** = `#True_Positive / (#True_Positive + #False_Negatives)` = `1 / (1 + 1) = 0.5`
-* **F1 Score** = `2 * Precision * Recall / (Precision + Recall)` =  `(2 * 0.5 * 0.5) / (0.5 + 0.5) = 0.5`
-
-### Model-level evaluation for the collective model
-
-The model would have the following evaluation for the model in its entirety:
-
-| Key | Count | Explanation |
-|--|--|--|
-| True Positive | 3 | *John Smith* and *Fannie Thomas* were correctly predicted as *person*. *Colorado Springs* was correctly predicted as *city*. This is the sum of true positives for all entities. |
-| False Positive | 2 | *Forrest* was incorrectly predicted as *city* while it should have been *person*. *Frederick* was incorrectly predicted as *person* while it should have been *city*. This is the sum of false positives for all entities. |
-| False Negative | 2 | *Forrest* was incorrectly predicted as *city* while it should have been *person*. *Frederick* was incorrectly predicted as *person* while it should have been *city*. This is the sum of false negatives for all entities. |
-
-* **Precision** = `#True_Positive / (#True_Positive + #False_Positive)` = `3 / (3 + 2) = 0.6`
-* **Recall** = `#True_Positive / (#True_Positive + #False_Negatives)` = `3 / (3 + 2) = 0.6`
-* **F1 Score** = `2 * Precision * Recall / (Precision + Recall)` =  `(2 * 0.6 * 0.6) / (0.6 + 0.6) = 0.6`
-
-## Interpreting entity-level evaluation metrics
-
-So what does it actually mean to have high precision or high recall for a certain entity?
-
-| Recall | Precision | Interpretation |
-|--|--|--|
-| High | High | This entity is handled well by the model. |
-| Low | High | The model cannot always extract this entity, but when it does it is with high confidence. |
-| High | Low | The model extracts this entity well, however it is with low confidence as it is sometimes extracted as another type. |
-| Low | Low | This entity type is poorly handled by the model, because it is not usually extracted. When it is, it is not with high confidence. |
-
-## Guidance
-
-After you trained your model, you will see some guidance and recommendation on how to improve the model. It's recommended to have a model covering all points in the guidance section.
-
-* Training set has enough data: When an entity type has fewer than 15 labeled instances in the training data, it can lead to lower accuracy due to the model not being adequately trained on these cases. In this case, consider adding more labeled data in the training set. You can check the *data distribution* tab for more guidance.
-
-* All entity types are present in test set: When the testing data lacks labeled instances for an entity type, the model’s test performance may become less comprehensive due to untested scenarios. You can check the *test set data distribution* tab for more guidance.
-
-* Entity types are balanced within training and test sets: When sampling bias causes an inaccurate representation of an entity type’s frequency, it can lead to lower accuracy due to the model expecting that entity type to occur too often or too little. You can check the *data distribution* tab for more guidance.
-
-* Entity types are evenly distributed between training and test sets: When the mix of entity types doesn’t match between training and test sets, it can lead to lower testing accuracy due to the model being trained differently from how it’s being tested. You can check the *data distribution* tab for more guidance.
-
-* Unclear distinction between entity types in training set: When the training data is similar for multiple entity types, it can lead to lower accuracy because the entity types may be frequently misclassified as each other. Review the following entity types and consider merging them if they’re similar. Otherwise, add more examples to better distinguish them from each other. You can check the *confusion matrix* tab for more guidance.
-
-
-## Confusion matrix
-
-A Confusion matrix is an N x N matrix used for model performance evaluation, where N is the number of entities.
-The matrix compares the expected labels with the ones predicted by the model.
-This gives a holistic view of how well the model is performing and what kinds of errors it is making.
-
-You can use the Confusion matrix to identify entities that are too close to each other and often get mistaken (ambiguity). In this case consider merging these entity types together. If that isn't possible, consider adding more tagged examples of both entities to help the model differentiate between them.
-
-The highlighted diagonal in the image below is the correctly predicted entities, where the predicted tag is the same as the actual tag.
-
-:::image type="content" source="../../media/custom/confusion.png" alt-text="A screenshot that shows an example confusion matrix." lightbox="../../media/custom/confusion.png":::
-
-You can calculate the entity-level and model-level evaluation metrics from the confusion matrix:
-
-* The values in the diagonal are the *True Positive* values of each entity.
-* The sum of the values in the entity rows (excluding the diagonal) is the *false positive* of the model.
-* The sum of the values in the entity columns (excluding the diagonal) is the *false Negative* of the model.
-
-Similarly,
-
-* The *true positive* of the model is the sum of *true Positives* for all entities.
-* The *false positive* of the model is the sum of *false positives* for all entities.
-* The *false Negative* of the model is the sum of *false negatives* for all entities. 
-
-## Next steps
-
-* [Custom text analytics for health overview](../overview.md)
-* [View a model's performance in Language Studio](../how-to/view-model-evaluation.md)
-* [Train a model](../how-to/train-model.md)

@@ -1,57 +0,0 @@
----
-title: Send a custom Text Analytics for health request to your custom model
-description: Learn how to send a request for custom text analytics for health.
-titleSuffix: Azure AI services
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: how-to
-ms.date: 11/21/2024
-ms.author: jboback
-ms.devlang: http
-ms.custom: language-service-custom-ta4h
----
-
-# Send queries to your custom Text Analytics for health model
-
-After the deployment is added successfully, you can query the deployment to extract entities from your text based on the model you assigned to the deployment.
-You can query the deployment programmatically using the [Prediction API](/rest/api/language/text-analysis-runtime/analyze-text). 
-
-## Test deployed model
-
-You can use Language Studio to submit the custom Text Analytics for health task and visualize the results. 
-
-[!INCLUDE [Test model](../../includes/custom/language-studio/test-model.md)]
-
-:::image type="content" source="../media/test-model-results.png" alt-text="A screenshot showing the deployment testing screen in Language Studio for Custom text analytics of health." lightbox="../media/test-model-results.png":::
-
-
-## Send a custom text analytics for health request to your model
-
-# [Language Studio](#tab/language-studio)
-
-[!INCLUDE [Get prediction URL](../../includes/custom/language-studio/get-prediction-url.md)]
-
-
-# [REST API](#tab/rest-api)
-
-First you will need to get your resource key and endpoint:
-
-[!INCLUDE [Get keys and endpoint Azure Portal](../../includes/key-endpoint-page-azure-portal.md)]
-
-
-### Submit a custom Text Analytics for health task
-
-[!INCLUDE [submit a custom Text Analytics for health task using the REST API](../includes/rest-api/submit-task.md)]
-
-### Get task results
-
-[!INCLUDE [get custom Text Analytics for health task results](../includes/rest-api/get-results.md)]
-
-
----
-
-## Next steps
-
-* [Custom text analytics for health](../overview.md)

@@ -1,117 +0,0 @@
----
-title: Using Azure resources in custom Text Analytics for health
-titleSuffix: Azure AI services
-description: Learn about the steps for using Azure resources with custom text analytics for health.
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: how-to
-ms.date: 11/21/2024
-ms.author: jboback
-ms.custom: language-service-custom-ta4h, references_regions
----
-
-# How to create custom Text Analytics for health project
-
-Use this article to learn how to set up the requirements for starting with custom text analytics for health and create a project.
-
-## Prerequisites
-
-Before you start using custom text analytics for health, you need:
-
-* An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services).
-
-## Create a Language resource 
-
-Before you start using custom text analytics for health, you'll need an Azure AI Language resource. It's recommended to create your Language resource and connect a storage account to it in the Azure portal. Creating a resource in the Azure portal lets you create an Azure storage account at the same time, with all of the required permissions preconfigured. You can also read further in the article to learn how to use a pre-existing resource, and configure it to work with custom text analytics for health.
-
-You also will need an Azure storage account where you will upload your `.txt` documents that will be used to train a model to extract entities.
-
-> [!NOTE]
->  * You need to have an **owner** role assigned on the resource group to create a Language resource.
->  * If you will connect a pre-existing storage account, you should have an owner role assigned to it.
-
-## Create Language resource and connect storage account
-
-You can create a resource in the following ways:
-
-* The Azure portal
-* Language Studio
-* PowerShell
-
-> [!Note]
-> You shouldn't move the storage account to a different resource group or subscription once it's linked with the Language resource.
-
-[!INCLUDE [create a new resource from the Azure portal](../../includes/custom/resource-creation-azure-portal.md)]
-
-[!INCLUDE [create a new resource from Language Studio](../../includes/custom/resource-creation-language-studio.md)]
-
-[!INCLUDE [create a new resource with Azure PowerShell](../../includes/custom/resource-creation-powershell.md)]
-
-
-> [!NOTE]
-> * The process of connecting a storage account to your Language resource is irreversible, it cannot be disconnected later.
-> * You can only connect your language resource to one storage account.
-
-## Using a pre-existing Language resource
-
-[!INCLUDE [use an existing resource](../includes/use-pre-existing-resource.md)]
-
-## Create a custom Text Analytics for health project
-
-Once your resource and storage container are configured, create a new custom text analytics for health project. A project is a work area for building your custom AI models based on your data. Your project can only be accessed by you and others who have access to the Azure resource being used. If you have labeled data, you can use it to get started by [importing a project](#import-project).
-
-### [Language Studio](#tab/language-studio)
-
-[!INCLUDE [Language Studio project creation](../includes/language-studio/create-project.md)]
-
-### [REST APIs](#tab/rest-api)
-
-[!INCLUDE [REST APIs project creation](../includes/rest-api/create-project.md)]
-
----
-
-## Import project
-
-If you have already labeled data, you can use it to get started with the service. Make sure that your labeled data follows the [accepted data formats](../concepts/data-formats.md).
-
-### [Language Studio](#tab/language-studio)
-
-[!INCLUDE [Import project](../includes/language-studio/import-project.md)]
-
-### [REST APIs](#tab/rest-api)
-
-[!INCLUDE [Import project](../includes/rest-api/import-project.md)]
-
----
-
-## Get project details
-
-### [Language Studio](#tab/language-studio)
-
-[!INCLUDE [Language Studio project details](../../includes/custom/project-details.md)]
-
-### [REST APIs](#tab/rest-api)
-
-[!INCLUDE [REST APIs project details](../includes/rest-api/project-details.md)]
-
----
-
-## Delete project
-
-### [Language Studio](#tab/language-studio)
-
-[!INCLUDE [Delete project using Language studio](../includes/language-studio/delete-project.md)]
-
-### [REST APIs](#tab/rest-api)
-
-[!INCLUDE [Delete project using the REST API](../includes/rest-api/delete-project.md)]
-
----
-
-## Next steps
-
-* You should have an idea of the [project schema](design-schema.md) you will use to label your data.
-
-* After you define your schema, you can start [labeling your data](label-data.md), which will be used for model training, evaluation, and finally making predictions.

@@ -1,105 +0,0 @@
----
-title: Deploy a custom Text Analytics for health model
-titleSuffix: Azure AI services
-description: Learn about deploying a model for custom Text Analytics for health.
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: how-to
-ms.date: 11/21/2024
-ms.author: jboback
-ms.custom: language-service-custom-ta4h
----
-
-# Deploy a custom text analytics for health model
-
-Once you're satisfied with how your model performs, it's ready to be deployed and used to recognize entities in text. Deploying a model makes it available for use through the [prediction API](https://aka.ms/ct-runtime-swagger).
-
-## Prerequisites
-
-* A successfully [created project](create-project.md) with a configured Azure storage account.
-* Text data that has [been uploaded](design-schema.md#data-preparation) to your storage account.
-* [Labeled data](label-data.md) and a successfully [trained model](train-model.md).
-* Reviewed the [model evaluation details](view-model-evaluation.md) to determine how your model is performing.
-
-For more information, see [project development lifecycle](../overview.md#project-development-lifecycle).
-
-## Deploy model
-
-After you've reviewed your model's performance and decided it can be used in your environment, you need to assign it to a deployment. Assigning the model to a deployment makes it available for use through the [prediction API](https://aka.ms/ct-runtime-swagger). It is recommended to create a deployment named *production* to which you assign the best model you have built so far and use it in your system. You can create another deployment called *staging* to which you can assign the model you're currently working on to be able to test it. You can have a maximum of 10 deployments in your project. 
-
-# [Language Studio](#tab/language-studio)
-
-[!INCLUDE [Deploy a model using Language Studio](../includes/language-studio/deploy-model.md)]
-   
-# [REST APIs](#tab/rest-api)
-
-### Submit deployment job
-
-[!INCLUDE [deploy model](../includes/rest-api/deploy-model.md)]
-
-### Get deployment job status
-
-[!INCLUDE [get deployment status](../includes/rest-api/get-deployment-status.md)]
-
----
-
-## Swap deployments
-
-After you are done testing a model assigned to one deployment and you want to assign this model to another deployment you can swap these two deployments. Swapping deployments involves taking the model assigned to the first deployment, and assigning it to the second deployment. Then taking the model assigned to second deployment, and assigning it to the first deployment. You can use this process to swap your *production* and *staging* deployments when you want to take the model assigned to *staging* and assign it to *production*. 
-
-# [Language Studio](#tab/language-studio)
-
-[!INCLUDE [Swap deployments](../includes/language-studio/swap-deployment.md)]
-
-# [REST APIs](#tab/rest-api)
-
-[!INCLUDE [Swap deployments](../includes/rest-api/swap-deployment.md)]
-
----
-
-
-## Delete deployment
-
-# [Language Studio](#tab/language-studio)
-
-[!INCLUDE [Delete deployment](../includes/language-studio/delete-deployment.md)]
-
-# [REST APIs](#tab/rest-api)
-
-[!INCLUDE [Delete deployment](../includes/rest-api/delete-deployment.md)]
-
----
-
-## Assign deployment resources
-
-You can [deploy your project to multiple regions](../../concepts/custom-features/multi-region-deployment.md) by assigning different Language resources that exist in different regions.
-
-# [Language Studio](#tab/language-studio)
-
-[!INCLUDE [Assign resource](../../conversational-language-understanding/includes/language-studio/assign-resources.md)]
-
-# [REST APIs](#tab/rest-api)
-
-[!INCLUDE [Assign resource](../../custom-text-classification/includes/rest-api/assign-resources.md)]
-
----
-
-## Unassign deployment resources
-
-When unassigning or removing a deployment resource from a project, you will also delete all the deployments that have been deployed to that resource's region.
-
-# [Language Studio](#tab/language-studio)
-
-[!INCLUDE [Unassign resource](../../conversational-language-understanding/includes/language-studio/unassign-resources.md)]
-
-# [REST APIs](#tab/rest-api)
-
-[!INCLUDE [Unassign resource](../../custom-text-classification/includes/rest-api/unassign-resources.md)]
-
----
-
-## Next steps
-
-After you have a deployment, you can use it to [extract entities](call-api.md) from text.

@@ -1,109 +0,0 @@
----
-title: Preparing data and designing a schema for custom Text Analytics for health
-titleSuffix: Azure AI services
-description: Learn about how to select and prepare data, to be successful in creating custom TA4H projects.
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: how-to
-ms.date: 11/21/2024
-ms.author: jboback
-ms.custom: language-service-custom-ta4h
----
-
-# How to prepare data and define a schema for custom Text Analytics for health
-
-In order to create a custom TA4H model, you will need quality data to train it. This article covers how you should select and prepare your data, along with defining a schema. Defining the schema is the first step in [project development lifecycle](../overview.md#project-development-lifecycle), and it entailing defining the entity types or categories that you need your model to extract from the text at runtime.
-
-## Schema design
-
-Custom Text Analytics for health allows you to extend and customize the Text Analytics for health entity map. The first step of the process is building your schema, which allows you to define the new entity types or categories that you need your model to extract from text in addition to the Text Analytics for health existing entities at runtime.  
-
-* Review documents in your dataset to be familiar with their format and structure.
-
-* Identify the entities you want to extract from the data.
-
-    For example, if you are extracting entities from support emails, you might need to extract "Customer name", "Product name", "Request date", and "Contact information".
-
-* Avoid entity types ambiguity.
-
-    **Ambiguity** happens when entity types you select are similar to each other. The more ambiguous your schema the more labeled data you will need to differentiate between different entity types.
-
-    For example, if you are extracting data from a legal contract, to extract "Name of first party" and "Name of second party" you will need to add more examples to overcome ambiguity since the names of both parties look similar. Avoid ambiguity as it saves time, effort, and yields better results.
-
-* Avoid complex entities. Complex entities can be difficult to pick out precisely from text, consider breaking it down into multiple entities.
-
-    For example, extracting "Address" would be challenging if it's not broken down to smaller entities. There are so many variations of how addresses appear, it would take large number of labeled entities to teach the model to extract an address, as a whole, without breaking it down. However, if you replace "Address" with "Street Name", "PO Box", "City", "State" and "Zip", the model will require fewer labels per entity.
-
-
-## Add entities
-
-To add entities to your project:
-
-1. Move to **Entities** pivot from the top of the page.
-
-2. [Text Analytics for health entities](../../text-analytics-for-health/concepts/health-entity-categories.md) are automatically loaded into your project. To add additional entity categories, select **Add** from the top menu. You will be prompted to type in a name before completing creating the entity.
-
-3. After creating an entity, you'll be routed to the entity details page where you can define the composition settings for this entity.
-
-4. Entities are defined by [entity components](../concepts/entity-components.md): learned, list or prebuilt. Text Analytics for health entities are by default populated with the prebuilt component and cannot have learned components. Your newly defined entities can be populated with the learned component once you add labels for them in your data but cannot be populated with the prebuilt component. 
-
-5. You can add a [list](../concepts/entity-components.md#list-component)  component to any of your entities. 
-
-   
-### Add list component
-
-To add a **list** component, select **Add new list**. You can add multiple lists to each entity.
-
-1. To create a new list, in the *Enter value* text box enter this is the normalized value that will be returned when any of the synonyms values is extracted.
-
-2. For multilingual projects, from the *language* drop-down menu, select the language of the synonyms list and start typing in your synonyms and hit enter after each one. It is recommended to have synonyms lists in multiple languages.
-
-   <!--:::image type="content" source="../media/add-list-component.png" alt-text="A screenshot showing a list component in Language Studio." lightbox="../media/add-list-component.png":::-->
-   
-### Define entity options
-
-Change to the **Entity options** pivot in the entity details page. When multiple components are defined for an entity, their predictions may overlap. When an overlap occurs, each entity's final prediction is determined based on the [entity option](../concepts/entity-components.md#entity-options) you select in this step. Select the one that you want to apply to this entity and select the **Save** button at the top.
-
-   <!--:::image type="content" source="../media/entity-options.png" alt-text="A screenshot showing an entity option in Language Studio." lightbox="../media/entity-options.png":::-->
-
-
-After you create your entities, you can come back and edit them. You can **Edit entity components** or **delete** them by selecting this option from the top menu.
-
-
-## Data selection
-
-The quality of data you train your model with affects model performance greatly.
-
-* Use real-life data that reflects your domain's problem space to effectively train your model. You can use synthetic data to accelerate the initial model training process, but it will likely differ from your real-life data and make your model less effective when used.
-
-* Balance your data distribution as much as possible without deviating far from the distribution in real-life. For example, if you are training your model to extract entities from legal documents that may come in many different formats and languages, you should provide examples that exemplify the diversity as you would expect to see in real life.
-
-* Use diverse data whenever possible to avoid overfitting your model. Less diversity in training data may lead to your model learning spurious correlations that may not exist in real-life data. 
- 
-* Avoid duplicate documents in your data. Duplicate data has a negative effect on the training process, model metrics, and model performance. 
-
-* Consider where your data comes from. If you are collecting data from one person, department, or part of your scenario, you are likely missing diversity that may be important for your model to learn about. 
-
-> [!NOTE]
-> If your documents are in multiple languages, select the **enable multi-lingual** option during [project creation](../quickstart.md) and set the **language** option to the language of the majority of your documents.
-
-## Data preparation
-
-As a prerequisite for creating a project, your training data needs to be uploaded to a blob container in your storage account. You can create and upload training documents from Azure directly, or through using the Azure Storage Explorer tool. Using the Azure Storage Explorer tool allows you to upload more data quickly.  
-
-* [Create and upload documents from Azure](/azure/storage/blobs/storage-quickstart-blobs-portal#create-a-container)
-* [Create and upload documents using Azure Storage Explorer](/azure/vs-azure-tools-storage-explorer-blobs)
-
-You can only use `.txt` documents. If your data is in other format, you can use [CLUtils parse command](https://github.com/microsoft/CognitiveServicesLanguageUtilities/blob/main/CustomTextAnalytics.CLUtils/Solution/CogSLanguageUtilities.ViewLayer.CliCommands/Commands/ParseCommand/README.md) to change your document format.
-
-You can upload an annotated dataset, or you can upload an unannotated one and [label your data](../how-to/label-data.md) in Language studio. 
- 
-## Test set
-
-When defining the testing set, make sure to include example documents that are not present in the training set. Defining the testing set is an important step to calculate the [model performance](view-model-evaluation.md#model-details). Also, make sure that the testing set includes documents that represent all entities used in your project.
-
-## Next steps
-
-If you haven't already, create a custom Text Analytics for health project. If it's your first time using custom Text Analytics for health, consider following the [quickstart](../quickstart.md) to create an example project. You can also see the [how-to article](../how-to/create-project.md) for more details on what you need to create a project.

@@ -1,140 +0,0 @@
----
-title: Back up and recover your custom Text Analytics for health models
-titleSuffix: Azure AI services
-description: Learn how to save and recover your custom Text Analytics for health models.
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: conceptual
-ms.date: 11/21/2024
-ms.author: jboback
-ms.custom: language-service-custom-ta4h
----
-
-# Back up and recover your custom Text Analytics for health models
-
-When you create a Language resource, you specify a region for it to be created in. From then on, your resource and all of the operations related to it take place in the specified Azure server region. It's rare, but not impossible, to encounter a network issue that affects an entire region. If your solution needs to always be available, then you should design it to fail over into another region. This requires two Azure AI Language resources in different regions and synchronizing custom models across them. 
-
-If your app or business depends on the use of a custom Text Analytics for health model, we recommend that you create a replica of your project in an additional supported region. If a regional outage occurs, you can then access your model in the other fail-over region where you replicated your project.
-
-Replicating a project means that you export your project metadata and assets, and import them into a new project. This only makes a copy of your project settings and tagged data. You still need to [train](./train-model.md) and [deploy](./deploy-model.md) the models to be available for use with [prediction APIs](https://aka.ms/ct-runtime-swagger).
-
-In this article, you will learn to how to use the export and import APIs to replicate your project from one resource to another existing in different supported geographical regions, guidance on keeping your projects in sync and changes needed to your runtime consumption.
-
-##  Prerequisites
-
-* Two Azure AI Language resources in different Azure regions. [Create your resources](./create-project.md#create-a-language-resource) and connect them to an Azure storage account. It's recommended that you connect each of your Language resources to different storage accounts. Each storage account should be located in the same respective regions that your separate Language resources are in. You can follow the [quickstart](../quickstart.md?pivots=rest-api#create-a-new-azure-ai-language-resource-and-azure-storage-account) to create an additional Language resource and storage account.
-
-
-## Get your resource keys endpoint
-
-Use the following steps to get the keys and endpoint of your primary and secondary resources. These will be used in the following steps.
-
-[!INCLUDE [Get keys and endpoint Azure Portal](../includes/get-keys-endpoint-azure.md)]
-
-> [!TIP]
-> Keep a note of keys and endpoints for both primary and secondary resources as well as the primary and secondary container names. Use these values to replace the following placeholders:
-`{PRIMARY-ENDPOINT}`, `{PRIMARY-RESOURCE-KEY}`, `{PRIMARY-CONTAINER-NAME}`, `{SECONDARY-ENDPOINT}`, `{SECONDARY-RESOURCE-KEY}`, and `{SECONDARY-CONTAINER-NAME}`.
-> Also take note of your project name, your model name and your deployment name. Use these values to replace the following placeholders:  `{PROJECT-NAME}`, `{MODEL-NAME}` and `{DEPLOYMENT-NAME}`.
-
-## Export your primary project assets
-
-Start by exporting the project assets from the project in your primary resource.
-
-### Submit export job
-
-Replace the placeholders in the following request with your `{PRIMARY-ENDPOINT}` and `{PRIMARY-RESOURCE-KEY}` that you obtained in the first step.
-
-[!INCLUDE [Export project assets using the REST API](../includes/rest-api/export-project.md)]
-
-### Get export job status
-
-Replace the placeholders in the following request with your `{PRIMARY-ENDPOINT}` and `{PRIMARY-RESOURCE-KEY}` that you obtained in the first step.
-
-[!INCLUDE [Export project assets using the REST API](../includes/rest-api/get-export-status.md)]
-
-
-Copy the response body as you will use it as the body for the next import job.
-
-## Import to a new project 
-
-Now go ahead and import the exported project assets in your new project in the secondary region so you can replicate it.
-
-### Submit import job
-
-Replace the placeholders in the following request with your `{SECONDARY-ENDPOINT}`, `{SECONDARY-RESOURCE-KEY}`, and `{SECONDARY-CONTAINER-NAME}` that you obtained in the first step.
-
-[!INCLUDE [Import project using the REST API](../includes/rest-api/import-project.md)]
-
-### Get import job status
-
-Replace the placeholders in the following request with your `{SECONDARY-ENDPOINT}` and `{SECONDARY-RESOURCE-KEY}` that you obtained in the first step.
-
-[!INCLUDE [Import project using the REST API](../includes/rest-api/get-import-status.md)]
-
-
-## Train your model
-
-After importing your project, you only have copied the project's assets and metadata and assets. You still need to train your model, which will incur usage on your account. 
-
-### Submit training job
-
-Replace the placeholders in the following request with your `{SECONDARY-ENDPOINT}` and `{SECONDARY-RESOURCE-KEY}` that you obtained in the first step.
-
-[!INCLUDE [train model](../includes/rest-api/train-model.md)]
-
-
-### Get training status
-
-Replace the placeholders in the following request with your `{SECONDARY-ENDPOINT}` and `{SECONDARY-RESOURCE-KEY}` that you obtained in the first step.
-
-[!INCLUDE [get training model status](../includes/rest-api/get-training-status.md)]
-
-## Deploy your model
-
-This is the step where you make your trained model available form consumption via the [runtime prediction API](https://aka.ms/ct-runtime-swagger). 
-
-> [!TIP]
-> Use the same deployment name as your primary project for easier maintenance and minimal changes to your system to handle redirecting your traffic.
-
-### Submit deployment job 
-
-Replace the placeholders in the following request with your `{SECONDARY-ENDPOINT}` and `{SECONDARY-RESOURCE-KEY}` that you obtained in the first step.
-
-[!INCLUDE [deploy model](../includes/rest-api/deploy-model.md)]
-
-### Get the deployment status
-
-Replace the placeholders in the following request with your `{SECONDARY-ENDPOINT}` and `{SECONDARY-RESOURCE-KEY}` that you obtained in the first step.
-
-[!INCLUDE [get deploy status](../includes/rest-api/get-deployment-status.md)]
-
-## Changes in calling the runtime
-
-Within your system, at the step where you call [runtime prediction API](https://aka.ms/ct-runtime-swagger) check for the response code returned from the submit task API. If you observe a **consistent** failure in submitting the request, this could indicate an outage in your primary region. Failure once doesn't mean an outage, it may be transient issue. Retry submitting the job through the secondary resource you have created. For the second request use your `{SECONDARY-ENDPOINT}` and `{SECONDARY-RESOURCE-KEY}`, if you have followed the steps above, `{PROJECT-NAME}` and `{DEPLOYMENT-NAME}` would be the same so no changes are required to the request body. 
-
-In case you revert to using your secondary resource you will observe slight increase in latency because of the difference in regions where your model is deployed. 
-
-## Check if your projects are out of sync
-
-Maintaining the freshness of both projects is an important part of the process. You need to frequently check if any updates were made to your primary project so that you move them over to your secondary project. This way if your primary region fails and you move into the secondary region you should expect similar model performance since it already contains the latest updates. Setting the frequency of checking if your projects are in sync is an important choice. We recommend that you do this check daily in order to guarantee the freshness of data in your secondary model.
-
-### Get project details
-
-Use the following url to get your project details, one of the keys returned in the body indicates the last modified date of the project. 
-Repeat the following step twice, one for your primary project and another for your secondary project and compare the timestamp returned for both of them to check if they are out of sync.
-
-  [!INCLUDE [get project details](../includes/rest-api/get-project-details.md)]
-
-
-Repeat the same steps for your replicated project using `{SECONDARY-ENDPOINT}` and `{SECONDARY-RESOURCE-KEY}`. Compare the returned `lastModifiedDateTime` from both projects. If your primary project was modified sooner than your secondary one, you need to repeat the steps of [exporting](#export-your-primary-project-assets), [importing](#import-to-a-new-project), [training](#train-your-model) and [deploying](#deploy-your-model).
-
-
-## Next steps
-
-In this article, you have learned how to use the export and import APIs to replicate your project to a secondary Language resource in other region. Next, explore the API reference docs to see what else you can do with authoring APIs.
-
-* [Authoring REST API reference](https://aka.ms/ct-authoring-swagger)
-
-* [Runtime prediction REST API reference](https://aka.ms/ct-runtime-swagger)

@@ -1,109 +0,0 @@
----
-title: How to label your data for custom Text Analytics for health
-titleSuffix: Azure AI services
-description: Learn how to label your data for use with custom Text Analytics for health.
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: how-to
-ms.date: 11/21/2024
-ms.author: jboback
-ms.custom: language-service-custom-ner
----
-
-# Label your data using the Language Studio
-
-Data labeling is a crucial step in development lifecycle. In this step, you  label your documents with the new entities you defined in your schema to populate their learned components. This data will be used in the next step when training your model so that your model can learn from the labeled data to know which entities to extract. If you already have labeled data, you can directly [import](create-project.md#import-project) it into your project, but you need to make sure that your data follows the [accepted data format](../concepts/data-formats.md). See [create project](create-project.md#import-project) to learn more about importing labeled data into your project. If your data isn't labeled already, you can label it in the [Language Studio](https://aka.ms/languageStudio).
-
-## Prerequisites
-
-Before you can label your data, you need:
-
-* A successfully [created project](create-project.md) with a configured Azure blob storage account
-* Text data that [has been uploaded](design-schema.md#data-preparation) to your storage account.
-
-See the [project development lifecycle](../overview.md#project-development-lifecycle) for more information.
-
-## Data labeling guidelines
-
-After preparing your data, designing your schema and creating your project, you will need to label your data. Labeling your data is important so your model knows which words will be associated with the entity types you need to extract. When you label your data in [Language Studio](https://aka.ms/languageStudio) (or import labeled data), these labels are stored in the JSON document in your storage container that you have connected to this project. 
-
-As you label your data, keep in mind:
-
-* You can't add labels for Text Analytics for health entities as they're pretrained prebuilt entities. You can only add labels to new entity categories that you defined during schema definition. 
-
-If you want to improve the recall for a prebuilt entity, you can extend it by adding a list component while you are [defining your schema](design-schema.md).
-
-* In general, more labeled data leads to better results, provided the data is labeled accurately.
-
-* The precision, consistency and completeness of your labeled data are key factors to determining model performance. 
-
-    * **Label precisely**: Label each entity to its right type always. Only include what you want extracted, avoid unnecessary data in your labels.
-    * **Label consistently**:  The same entity should have the same label across all the documents.
-    * **Label completely**: Label all the instances of the entity in all your documents. 
-
-   > [!NOTE]
-   > There is no fixed number of labels that can guarantee your model will perform the best. Model performance is dependent on possible ambiguity in your schema, and the quality of your labeled data. Nevertheless, we recommend having around 50 labeled instances per entity type.
-
-## Label your data
-
-Use the following steps to label your data:
-
-1. Go to your project page in [Language Studio](https://aka.ms/languageStudio).
-
-2. From the left side menu, select **Data labeling**. You can find a list of all documents in your storage container.
-
-    <!--:::image type="content" source="../media/tagging-files-view.png" alt-text="A screenshot showing the Language Studio screen for labeling data." lightbox="../media/tagging-files-view.png":::-->
-
-    >[!TIP]
-    > You can use the filters in top menu to view the unlabeled documents so that you can start labeling them.
-    > You can also use the filters to view the documents that are labeled with a specific entity type.
-
-3. Change to a single document view from the left side in the top menu or select a specific document to start labeling. You can find a list of all `.txt` documents available in your project to the left. You can use the **Back** and **Next** button from the bottom of the page to navigate through your documents.
-
-    > [!NOTE]
-    > If you enabled multiple languages for your project, you will find a **Language** dropdown in the top menu, which lets you select the language of each document. Hebrew is not supported with multi-lingual projects.
-
-4. In the right side pane, you can use the **Add entity type** button to add additional entities to your project that you missed during schema definition.
-
-    <!--:::image type="content" source="../media/tag-1.png" alt-text="A screenshot showing complete data labeling." lightbox="../media/tag-1.png":::-->
-
-5. You have two options to label your document:
-    
-    |Option |Description  |
-    |---------|---------|
-    |Label using a brush     | Select the brush icon next to an entity type in the right pane, then highlight the text in the document you want to annotate with this entity type.           |
-    |Label using a menu    | Highlight the word you want to label as an entity, and a menu will appear. Select the entity type you want to assign for this entity.        |
-    
-    The below screenshot shows labeling using a brush.
-    
-    :::image type="content" source="../media/tag-options.png" alt-text="A screenshot showing the labeling options offered in Custom NER." lightbox="../media/tag-options.png":::
-    
-6. In the right side pane under the **Labels** pivot you can find all the entity types in your project and the count of labeled instances per each. The prebuilt entities will be shown for reference but you will not be able to label for these prebuilt entities as they are pretrained.
-
-7. In the bottom section of the right side pane you can add the current document you are viewing to the training set or the testing set. By default all the documents are added to your training set. See [training and testing sets](train-model.md#data-splitting) for information on how they are used for model training and evaluation.
-
-    > [!TIP]
-    > If you are planning on using **Automatic** data splitting, use the default option of assigning all the documents into your training set.
-
-7. Under the **Distribution** pivot you can view the distribution across training and testing sets. You have two options for viewing:
-   * *Total instances* where you can view count of all labeled instances of a specific entity type.
-   * *Documents with at least one label* where each document is counted if it contains at least one labeled instance of this entity.
-  
-7. When you're labeling, your changes are synced periodically, if they have not been saved yet you will find a warning at the top of your page. If you want to save manually, select **Save labels** button at the bottom of the page.
-
-## Remove labels
-
-To remove a label
-
-1. Select the entity you want to remove a label from.
-2. Scroll through the menu that appears, and select **Remove label**.
-
-## Delete entities
-
-You cannot delete any of the Text Analytics for health pretrained entities because they have a prebuilt component. You are only permitted to delete newly defined entity categories. To delete an entity, select the delete icon next to the entity you want to remove. Deleting an entity removes all its labeled instances from your dataset. 
-
-## Next steps
-
-After you've labeled your data, you can begin [training a model](train-model.md) that will learn based on your data.

@@ -1,82 +0,0 @@
----
-title: How to train your custom Text Analytics for health model
-titleSuffix: Azure AI services
-description: Learn about how to train your model for custom Text Analytics for health.
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: how-to
-ms.date: 11/21/2024
-ms.author: jboback
-ms.custom: language-service-custom-ta4h
----
-
-# Train your custom Text Analytics for health model
-
-Training is the process where the model learns from your [labeled data](label-data.md). After training is completed, you'll be able to view the [model's performance](view-model-evaluation.md) to determine if you need to improve your model.
-
-To train a model, you start a training job and only successfully completed jobs create a model. Training jobs expire after seven days, which means you won't be able to retrieve the job details after this time. If your training job completed successfully and a model was created, the model won't be affected. You can only have one training job running at a time, and you can't start other jobs in the same project. 
-
-The training times can be anywhere from a few minutes when dealing with few documents, up to several hours depending on the dataset size and the complexity of your schema.
-
-
-## Prerequisites
-
-* A successfully [created project](create-project.md) with a configured Azure blob storage account
-* Text data that [has been uploaded](design-schema.md#data-preparation) to your storage account.
-* [Labeled data](label-data.md)
-
-See the [project development lifecycle](../overview.md#project-development-lifecycle) for more information.
-
-## Data splitting
-
-Before you start the training process, labeled documents in your project are divided into a training set and a testing set. Each one of them serves a different function.
-The **training set** is used in training the model, this is the set from which the model learns the labeled entities and what spans of text are to be extracted as entities. 
-The **testing set** is a blind set that is not introduced to the model during training but only during evaluation. 
-After model training is completed successfully, the model is used to make predictions from the documents in the testing and based on these predictions [evaluation metrics](../concepts/evaluation-metrics.md) are calculated. Model training and evaluation are only for newly defined entities with learned components; therefore, Text Analytics for health entities are excluded from model training and evaluation due to them being entities with prebuilt components. It's recommended to make sure that all your labeled entities are adequately represented in both the training and testing set.
-
-Custom Text Analytics for health supports two methods for data splitting:
-
-* **Automatically splitting the testing set from training data**:The system splits your labeled data between the training and testing sets, according to the percentages you choose. The recommended percentage split is 80% for training and 20% for testing. 
-
- > [!NOTE]
- > If you choose the **Automatically splitting the testing set from training data** option, only the data assigned to training set will be split according to the percentages provided.
-
-* **Use a manual split of training and testing data**: This method enables users to define which labeled documents should belong to which set. This step is only enabled if you have added documents to your testing set during [data labeling](label-data.md).
-
-## Train model
-
-# [Language studio](#tab/Language-studio)
-
-[!INCLUDE [Train model](../includes/language-studio/train-model.md)]
-
-# [REST APIs](#tab/REST-APIs)
-
-### Start training job
-
-[!INCLUDE [train model](../includes/rest-api/train-model.md)]
-
-### Get training job status
-
-Training could take sometime depending on the size of your training data and complexity of your schema. You can use the following request to keep polling the status of the training job until it's successfully completed.
-
- [!INCLUDE [get training model status](../includes/rest-api/get-training-status.md)]
-
----
-
-### Cancel training job
-
-# [Language Studio](#tab/language-studio)
-
-[!INCLUDE [Cancel training](../includes/language-studio/cancel-training.md)]
-
-# [REST APIs](#tab/rest-api)
-
-[!INCLUDE [Cancel training](../includes/rest-api/cancel-training.md)]
-
----
-
-## Next steps
-
-After training is completed, you'll be able to view the [model's performance](view-model-evaluation.md) to optionally improve your model if needed. Once you're satisfied with your model, you can deploy it, making it available to use for [extracting entities](call-api.md) from text.

@@ -1,75 +0,0 @@
----
-title: Evaluate a Custom Text Analytics for health model
-titleSuffix: Azure AI services
-description: Learn how to evaluate and score your Custom Text Analytics for health model
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: how-to
-ms.date: 11/21/2024
-ms.author: jboback
-ms.custom: language-service-custom-ta4h
----
-
-
-# View a custom text analytics for health model's evaluation and details
-
-After your model has finished training, you can view the model performance and see the extracted entities for the documents in the test set. 
-
-> [!NOTE]
-> Using the **Automatically split the testing set from training data** option may result in different model evaluation result every time you train a new model, as the test set is selected randomly from the data. To make sure that the evaluation is calculated on the same test set every time you train a model, make sure to use the **Use a manual split of training and testing data** option when starting a training job and define your **Test** documents when [labeling data](label-data.md).
-
-## Prerequisites
-
-Before viewing model evaluation, you need:
-
-* A successfully [created project](create-project.md) with a configured Azure blob storage account.
-* Text data that [has been uploaded](design-schema.md#data-preparation) to your storage account.
-* [Labeled data](label-data.md)
-* A [successfully trained model](train-model.md)
-
-
-## Model details
-
-There are several metrics you can use to evaluate your mode. See the [performance metrics](../concepts/evaluation-metrics.md) article for more information on the model details described in this article.
-
-### [Language studio](#tab/language-studio)
-
-[!INCLUDE [View model evaluation using Language Studio](../../includes/custom/model-evaluation-language-studio.md)]
-
-### [REST APIs](#tab/rest-api)
-
-[!INCLUDE [Model evaluation](../includes/rest-api/model-evaluation.md)]
-
----
-
-## Load or export model data
-
-### [Language studio](#tab/Language-studio)
-
-[!INCLUDE [Load export model](../../includes/custom/load-export-model-language-studio.md)]
-
-
-### [REST APIs](#tab/REST-APIs)
-
-[!INCLUDE [Load export model](../../includes/custom/load-export-model-rest-api.md)]
-
----
-
-## Delete model
-
-### [Language studio](#tab/language-studio)
-
-[!INCLUDE [Delete model](../../includes/custom/delete-model-language-studio.md)]
-
-### [REST APIs](#tab/rest-api)
-
-[!INCLUDE [Delete model](../../includes/custom/delete-model-rest-api.md)]
-
----
-
-## Next steps
-
-* [Deploy your model](deploy-model.md)
-* Learn about the [metrics used in evaluation](../concepts/evaluation-metrics.md). 

@@ -1,15 +0,0 @@
----
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-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'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":::

@@ -1,11 +0,0 @@
----
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-To cancel a training job from within [Language Studio](https://aka.ms/languageStudio), go to the **Training jobs** page. Select the training job you want to cancel and select **Cancel** from the top menu. 

@@ -1,39 +0,0 @@
----
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-1. Sign into the [Language Studio](https://aka.ms/languageStudio). A window will appear to let you select your subscription and Language resource. Select the Language resource you created in the above step. 
-
-2. Under the **Extract information** section of Language Studio, select **Custom Text Analytics for health**.
-
-    <!--:::image type="content" source="../../media/select-custom-TA4H.png" alt-text="A screenshot showing the location of custom TA4H in the Language Studio landing page." lightbox="../../media/select-custom-TA4H.png":::-->
-
-3. Select **Create new project** from the top menu in your projects page. Creating a project lets you label data, train, evaluate, improve, and deploy your models. 
-
-    :::image type="content" source="../../media/create-project.png" alt-text="A screenshot of the project creation page." lightbox="../../media/create-project.png":::
-
-4. Enter the project information, including a name, description, and the language of the files in your project. If you're using the [example dataset](https://aka.ms/custom-ta4h-quickstart-samples), select **English**. You can't change the name of your project later. Select **Next**
-
-    > [!TIP]
-    > Your dataset doesn't have to be entirely in the same language. You can have multiple documents, each with different supported languages. If your dataset contains documents of different languages or if you expect text from different languages during runtime, select **enable multi-lingual dataset** option when you enter the basic information for your project. This option can be enabled later from the **Project settings** page.
-
-5.  After you select **Create new project**, a window will appear to let you connect your storage account. If you've already connected a storage account, you will see the storage accounted connected. If not, choose your storage account from the dropdown that appears and select **Connect storage account**; this will set the required roles for your storage account. This step will possibly return an error if you are not assigned as **owner** on the storage account.
-
-    >[!NOTE]
-    > * You only need to do this step once for each new resource you use. 
-    > * This process is irreversible, if you connect a storage account to your Language resource you cannot disconnect it later.
-    > * You can only connect your Language resource to one storage account.
-    
-    :::image type="content" source="../../media/connect-storage.png" alt-text="A screenshot showing the storage connection screen." lightbox="../../media/connect-storage.png":::
-
-6. Select the container where you have uploaded your dataset.
-
-7. If you have already labeled data make sure it follows the supported format and select **Yes, my files are already labeled and I have formatted JSON labels file** and select the labels file from the drop-down menu. Select **Next**. If you are using the dataset from the QuickStart, there is no need to review the formatting of the JSON labels file. 
-
-8. Review the data you entered and select **Create Project**.

@@ -1,12 +0,0 @@
----
-titleSuffix: Azure AI services
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-To delete a deployment from within [Language Studio](https://aka.ms/laguageStudio), go to the **Deploying a model** page. Select the deployment you want to delete and select **Delete deployment** from the top menu. 

@@ -1,15 +0,0 @@
----
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
-ms.custom: language-service-custom-classification
----
-
-When you don't need your project anymore, you can delete your project using [Language Studio](https://aka.ms/custom-extraction). 
-1. Select the Language service feature you're using at the top of the page, s
-1. Select the project you want to delete
-1. Select **Delete** from the top menu.

@@ -1,28 +0,0 @@
----
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-To deploy your model from within the [Language Studio](https://aka.ms/LanguageStudio):
-
-1. Select **Deploying a model** from the left side menu.
-
-2. Select **Add deployment** to start a new deployment job.
-
-    :::image type="content" source="../../media/deploy-model.png" alt-text="A screenshot showing the deployment button in Language Studio." lightbox="../../media/deploy-model.png":::
-
-3. Select **Create new deployment** to create a new deployment and assign a trained model from the dropdown below. You can also **Overwrite an existing deployment** by selecting this option and select the trained model you want to assign to it from the dropdown below.
-
-    > [!NOTE]
-    > Overwriting an existing deployment doesn't require changes to your [prediction API](https://aka.ms/ct-runtime-swagger) call but the results you get will be based on the newly assigned model.
-    
-   :::image type="content" source="../../media/add-deployment.png" alt-text="A screenshot showing the model deployment options in Language Studio." lightbox="../../media/add-deployment.png":::
-     
-4. Select **Deploy** to start the deployment job.
-
-5. After deployment is successful, an expiration date will appear next to it. [Deployment expiration](../../../concepts/model-lifecycle.md#expiration-timeline) is when your deployed model will be unavailable to be used for prediction, which typically happens **twelve** months after a training configuration expires.

@@ -1,44 +0,0 @@
----
-titleSuffix: Azure AI services
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-1. Sign into the [Language Studio](https://aka.ms/languageStudio). A window will appear to let you select your subscription and Language resource. Select your Language resource. 
-
-2. Under the **Extract information** section of Language Studio, select **Custom text analytics for health**.
-
-    <!--:::image type="content" source="../../media/select-custom-ner.png" alt-text="A screenshot showing the location of the custom NER feature in the Language Studio landing page." lightbox="../../media/select-custom-ner.png":::-->
-        
-
-3. Select **Create new project** from the top menu in your projects page. Creating a project will let you tag data, train, evaluate, improve, and deploy your models. 
-
-    <!--:::image type="content" source="../../media/create-project.png" alt-text="A screenshot of the project creation page." lightbox="../../media/create-project.png":::-->
-
-
-4.  After you select **Create new project**, a screen will appear to let you connect your storage account. If you can’t find your storage account, make sure you created a resource using the recommended steps. If you've already connected a storage account to your Language resource, you will see your storage account connected.
-
-    >[!NOTE]
-    > * You only need to do this step once for each new language resource you use. 
-    > * This process is irreversible, if you connect a storage account to your Language resource you cannot disconnect it later.
-    > * You can only connect your Language resource to one storage account.
-
-    :::image type="content" source="../../media/connect-storage.png" alt-text="A screenshot of the storage connection screen for new projects." lightbox="../../media/connect-storage.png":::
-
-4. Enter the project information, including a name, description, and the language of the files in your project. You won’t be able to change the name of your project later. Select **Next**.
-       
-    >[!TIP]
-    > Your dataset doesn't have to be entirely in the same language. You can have multiple documents, each with different supported languages. If your dataset contains documents of different languages or if you expect text from different languages during runtime, select **enable multi-lingual dataset** option when you enter the basic information for your project. This option can be enabled later from the **Project settings** page.
-
-5. Select the container where you have uploaded your dataset. 
-
-7. Select **Yes, my files are already labeled and I have formatted JSON labels file** and select the labels file from the drop-down menu below to import your JSON labels file. Make sure it follows the [supported format](../../concepts/data-formats.md).
-
-8.   Select **Next**.
-
-9. Review the data you entered and select **Create Project**.

@@ -1,16 +0,0 @@
----
-titleSuffix: Azure AI services
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-To swap deployments from within [Language Studio](https://aka.ms/laguageStudio):
-
-1. In the **Deploying a model** page, select the two deployments you want to swap and select **Swap deployments** from the top menu. 
-
-2. From the window that appears, select the names of the deployments you want to swap. 

@@ -1,26 +0,0 @@
----
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
-ms.custom: language-service-custom-text-analytics-for-health-model-testing
----
-
-To test your deployed models from within the [Language Studio](https://aka.ms/LanguageStudio):
-1. Select **Testing deployments** from the left side menu.
-
-2. Select the deployment you want to test. You can only test models that are assigned to deployments. 
-
-3. Select the deployment you want to query/test from the dropdown.
-
-4. You can enter the text you want to submit to the request or upload a `.txt` file to use.
-
-5. Select **Run the test** from the top menu.
-
-6. In the **Result** tab, you can see the extracted entities from your text and their types. You can also view the [JSON response](../../how-to/call-api.md#get-task-results) under the **JSON** tab.
-
-    :::image type="content" source="../../media/test-model-results.png" alt-text="A screenshot showing the deployment testing screen in Language Studio." lightbox="../../media/test-model-results.png":::
-

@@ -1,30 +0,0 @@
----
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-To start training your model from within the [Language Studio](https://aka.ms/LanguageStudio):
-
-1. Select **Training jobs** from the left side menu.
-
-2. Select **Start a training job** from the top menu.
-
-3. Select **Train a new model** and type in the model name in the text box. You can also **overwrite an existing model** by selecting this option and choosing the model you want to overwrite from the dropdown menu. Overwriting a trained model is irreversible, but it won't affect your deployed models until you deploy the new model.
-
-    :::image type="content" source="../../media/train-model.png" alt-text="A screenshot showing the training job creation screen in Language Studio." lightbox="../../media/train-model.png":::
-    
-4. Select data splitting method. You can choose **Automatically splitting the testing set from training data** where the system will split your labeled data between the training and testing sets, according to the specified percentages. Or you can **Use a manual split of training and testing data**, this option is only enabled if you have added documents to your testing set. See [data labeling](../../how-to/label-data.md) and [how to train a model](../../how-to/train-model.md#data-splitting) for information about data splitting.
-
-5. Select the **Train** button.
-
-6. If you select the Training Job ID from the list, a side pane will appear where you can check the **Training progress**, **Job status**, and other details for this job.
-
-    > [!NOTE]
-    > * Only successfully completed training jobs will generate models.
-    > * Training can take some time between a couple of minutes and several hours based on the size of your labeled data.
-    > * You can only have one training job running at a time. You can't start other training job within the same project until the running job is completed. 

@@ -1,29 +0,0 @@
----
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-After you have created an Azure storage account and connected it to your Language resource, you will need to upload the documents from the sample dataset to the root directory of your container. These documents will later be used to train your model.
-
-
-1. [Download the sample dataset](https://aka.ms/custom-ta4h-quickstart-samples) from GitHub. 
-
-2. Open the .zip file, and extract the folder containing the documents. 
-
-2. In the [Azure portal](https://portal.azure.com), navigate to the storage account you created, and select it.
-
-3. In your storage account, select **Containers** from the left menu, located below **Data storage**. On the screen that appears, select **+ Container**. Give the container the name **example-data** and leave the default **Public access level**.
-
-    :::image type="content" source="../../media/storage-screen.png" alt-text="A screenshot showing the main page for a storage account." lightbox="../../media/storage-screen.png":::
-
-4. After your container has been created, select it. Then select **Upload** button to select the `.txt` and `.json` files you downloaded earlier. 
-
-    :::image type="content" source="../../media/file-upload-screen.png" alt-text="A screenshot showing the button for uploading files to the storage account." lightbox="../../media/file-upload-screen.png":::
-
-
-The provided sample dataset contains 12 clinical notes. Each clinical note includes several medical entities and the treatment location. We will use the prebuilt entities to extract the medical entities and train the custom model to extract the treatment location using the entity's learned and list components.

@@ -1,81 +0,0 @@
----
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-## Prerequisites
-
-* Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
-
-> [!div class="nextstepaction"]
-> <a href="https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=Language-studio&Pillar=Language&Product=Custom-text-analytics-for-health&Page=quickstart&Section=Prerequisites" target="_target">I ran into an issue</a>
-
-## Create a new Azure AI Language resource and Azure storage account
-
-Before you can use custom Text Analytics for health, you need to create an Azure AI Language resource, which will give you the credentials that you need to create a project and start training a model. You'll also need an Azure storage account, where you can upload your dataset that is used to build your model.
-
-> [!IMPORTANT]
-> To quickly get started, we recommend creating a new Azure AI Language resource using the steps provided in this article. Using the steps in this article will let you create the Language resource and storage account at the same time, which is easier than doing it later.
->
-> If you have a pre-existing resource that you'd like to use, you will need to connect it to storage account. For more information, see [guidance to using a pre-existing resource](../../how-to/create-project.md#using-a-pre-existing-language-resource).
-
-[!INCLUDE [create a new resource from the Azure portal](../resource-creation-azure-portal.md)]
-
-> [!div class="nextstepaction"]
-> <a href="https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=Language-studio&Pillar=Language&Product=Custom-text-analytics-for-health&Page=quickstart&Section=Create-a-new-azure-language-resource-and-storage-account" target="_target">I ran into an issue</a>
-
-## Upload sample data to blob container
-
-[!INCLUDE [Uploading sample data for custom TA4H](blob-storage-upload.md)]
-
-> [!div class="nextstepaction"]
-> <a href="https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=Language-studio&Pillar=Language&Product=Custom-text-analytics-for-health&Page=quickstart&Section=Upload-sample-data-to-blob-container" target="_target">I ran into an issue</a>
-
-## Create a custom Text Analytics for health project
-
-Once your resource and storage account are configured, create a new custom Text Analytics for health project. A project is a work area for building your custom ML models based on your data. Your project can only be accessed by you and others who have access to the Language resource being used.
-
-[!INCLUDE [Create a custom Text Analytics for health project](../language-studio/create-project.md)]
-
-> [!div class="nextstepaction"]
-> <a href="https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=Language-studio&Pillar=Language&Product=Custom-text-analytics-for-health&Page=quickstart&Section=Create-custom-named-entity-recognition-project" target="_target">I ran into an issue</a>
-
-## Train your model
-
-Typically after you create a project, you go ahead and start labeling the documents you have in the container connected to your project. For this quickstart, you have imported a sample tagged dataset and initialized your project with the sample JSON labels file so there is no need to add additional labels.
-
-[!INCLUDE [Train a model using Language Studio](../language-studio/train-model.md)]
-
-> [!div class="nextstepaction"]
-> <a href="https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=Language-studio&Pillar=Language&Product=Custom-text-analytics-for-health&Page=quickstart&Section=Train-model" target="_target">I ran into an issue</a>
-
-## Deploy your model
-
-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).
-
-[!INCLUDE [Deploy a model using Language Studio](../language-studio/deploy-model.md)]
-
-> [!div class="nextstepaction"]
-> <a href="https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=Language-studio&Pillar=Language&Product=Custom-text-analytics-for-health&Page=quickstart&Section=Deploy-model" target="_target">I ran into an issue</a>
-
-
-## Test your model
-
-After your model is deployed, you can start using it to extract entities from your text via [Prediction API](https://aka.ms/ct-runtime-swagger). For this quickstart, you will use the [Language Studio](https://aka.ms/LanguageStudio) to submit the custom Text Analytics for health prediction task and visualize the results. In the sample dataset you downloaded earlier, you can find some test documents that you can use in this step.
-
-[!INCLUDE [Test a model using Language Studio](../language-studio/test-model.md)]
-
-> [!div class="nextstepaction"]
-> <a href="https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=Language-studio&Pillar=Language&Product=Custom-text-analytics-for-health&Page=quickstart&Section=Test-model" target="_target">I ran into an issue</a>
-
-## Clean up resources
-
-[!INCLUDE [Delete project using Language Studio](../language-studio/delete-project.md)]
-
-> [!div class="nextstepaction"]
-> <a href="https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=Language-studio&Pillar=Language&Product=Custom-text-analytics-for-health&Page=quickstart&Section=Clean-up-projects" target="_target">I ran into an issue</a>

@@ -1,128 +0,0 @@
----
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-## Prerequisites
-
-* Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services)
-
-> [!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=Trigger-import-project-job" target="_target">I ran into an issue</a>
-
-## Create a new Azure AI Language resource and Azure storage account
-
-Before you can use custom Text Analytics for health, you'll need to create an Azure AI Language resource, which will give you the credentials that you need to create a project and start training a model. You'll also need an Azure storage account, where you can upload your dataset that will be used in building your model.
-
-> [!IMPORTANT]
-> To get started quickly, we recommend creating a new Azure AI Language resource using the steps provided in this article, which will let you create the Language resource, and create and/or connect a storage account at the same time, which is easier than doing it later.
->
-> If you have a pre-existing resource that you'd like to use, you will need to connect it to storage account. See [create project](../../how-to/create-project.md#using-a-pre-existing-language-resource) for more information.
-
-[!INCLUDE [create a new resource from the Azure portal](../resource-creation-azure-portal.md)]
-
-> [!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=Create-new-resource" target="_target">I ran into an issue</a>
-
-## Upload sample data to blob container
-
-[!INCLUDE [Uploading sample data for custom Text Analytics for health](blob-storage-upload.md)]
-
-> [!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=Upload-sample-data-to-blob-container" target="_target">I ran into an issue</a>
-
-### Get your resource keys and endpoint
-
-[!INCLUDE [Get keys and endpoint Azure Portal](../get-keys-endpoint-azure.md)]
-
-> [!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-resource-keys-and-endpoint" target="_target">I ran into an issue</a>
-
-## Create a custom Text Analytics for health project
-
-Once your resource and storage account are configured, create a new custom Text Analytics for health project. A project is a work area for building your custom ML models based on your data. Your project can only be accessed by you and others who have access to the Language resource being used.
-
-Use the labels file you downloaded from the sample data in the previous step and add it to the body of the following request. 
-
-### Trigger import project job 
-
-[!INCLUDE [Import a project using the REST API](../rest-api/import-project.md)]
-
-> [!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=Trigger-import-project-job" target="_target">I ran into an issue</a>
-
-### Get import job status
-
- [!INCLUDE [get import project status](../rest-api/get-import-status.md)]
-
-> [!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-import-job-status" target="_target">I ran into an issue</a>
-
-## Train your model
-
-Typically after you create a project, you go ahead and start labeling the documents you have in the container connected to your project. For this quickstart, you have imported a sample tagged dataset and initialized your project with the sample JSON tags file.
-
-### Start training job
-
-After your project has been imported, you can start training your model. 
-
-[!INCLUDE [train model](../rest-api/train-model.md)]
-
-> [!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=Start-training-your-job" target="_target">I ran into an issue</a>
-
-### Get training job status
-
-Training could take sometime between 10 and 30 minutes for this sample dataset. You can use the following request to keep polling the status of the training job until it is successfully completed.
-
-[!INCLUDE [get training model status](../rest-api/get-training-status.md)]
-
-> [!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-training-job-status" target="_target">I ran into an issue</a>
-
-## Deploy your model
-
-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
-
-[!INCLUDE [deploy model](../rest-api/deploy-model.md)]
-
-> [!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=Submit-deployment-job" target="_target">I ran into an issue</a>
-
-### Get deployment job status
-
-[!INCLUDE [get deployment status](../rest-api/get-deployment-status.md)]
-
-> [!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 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.
-
-### Submit a custom Text Analytics for health task
-
-[!INCLUDE [submit a custom Text Analytics for health task using the REST API](../rest-api/submit-task.md)]
-
-> [!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=Submit-custom-text-analytics-for-health-task" target="_target">I ran into an issue</a>
-
-### Get task results
-
-[!INCLUDE [get custom Text Analytics for health task results](../rest-api/get-results.md)]
-
-> [!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-task-results" target="_target">I ran into an issue</a>
-
-## Clean up resources
-
-[!INCLUDE [Delete project using the REST API](../rest-api/delete-project.md)]
-
-> [!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=Clean-up-resources" target="_target">I ran into an issue</a>

@@ -1,39 +0,0 @@
----
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-### Create a new resource from the Azure portal
-
-1. Sign in to the [Azure portal](https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics) to create a new Azure AI Language resource.
-
-1. In the window that appears, select **Custom text classification & custom named entity recognition** from the custom features. Select **Continue to create your resource** at the bottom of the screen. 
-
-    :::image type="content" source="../media/select-custom-feature-azure-portal.png" alt-text="A screenshot showing custom text classification & custom named entity recognition in the Azure portal." lightbox="../media/select-custom-feature-azure-portal.png":::
-
-1. Create a Language resource with following details.
-
-    |Name  | Description  |
-    |---------|---------|
-    | Subscription | Your Azure subscription. |
-    | Resource group | A resource group that will contain your resource. You can use an existing one, or create a new one. |
-    |Region | The region for your Language resource. For example, "West US 2". |
-    | Name | A name for your resource. |
-    |Pricing tier     | The pricing tier for your Language resource. You can use the Free (F0) tier to try the service.       |
-
-    > [!NOTE]
-    > If you get a message saying "*your login account is not an owner of the selected storage account's resource group*", your account needs to have an owner role assigned on the resource group before you can create a Language resource. Contact your Azure subscription owner for assistance.
-
-1. In the **Custom text classification & custom named entity recognition** section, select an existing storage account or select **New storage account**. These values are to help you get started, and not necessarily the [storage account values](/azure/storage/common/storage-account-overview) you’ll want to use in production environments. To avoid latency during building your project connect to storage accounts in the same region as your Language resource.
-
-    |Storage account value  |Recommended value  |
-    |---------|---------|
-    | Storage account name | Any name |
-    | Storage account type | Standard LRS |
-
-1. Make sure the **Responsible AI Notice** is checked. Select **Review + create** at the bottom of the page, then select **Create**.

@@ -1,35 +0,0 @@
----
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-Create a **POST** request by using the following URL, headers, and JSON body to cancel a training job. 
-
-### Request URL
-
-Use the following URL when creating your API request. Replace the placeholder values below with your own values. 
-
-```rest
-{Endpoint}/language/authoring/analyze-text/projects/{PROJECT-NAME}/train/jobs/{JOB-ID}/:cancel?api-version={API-VERSION}
-```
-
-|Placeholder  |Value  | Example |
-|---------|---------|---------|
-|`{ENDPOINT}`     | The endpoint for authenticating your API request.   | `https://<your-custom-subdomain>.cognitiveservices.azure.com` |
-|`{PROJECT-NAME}`     | The name for your project. This value is case-sensitive.   | `EmailApp` |
-|`{JOB-ID}`       | This value is the training job ID.|  `XXXXX-XXXXX-XXXX-XX`|
-|`{API-VERSION}`     | The version of the API you're calling. The value referenced is for the latest released [model version](../../../concepts/model-lifecycle.md#choose-the-model-version-used-on-your-data).  | `2022-05-01` |
-
-### Headers
-
-Use the following header to authenticate your request. 
-
-|Key|Value|
-|--|--|
-|`Ocp-Apim-Subscription-Key`| The key to your resource. Used for authenticating your API requests.|
- 
-After you send your API request, you'll receive a 202 response with an `Operation-Location` header used to check the status of the job.

@@ -1,72 +0,0 @@
----
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-To start creating a custom Text Analytics for health model, you need to create a project. Creating a project will let you label data, train, evaluate, improve, and deploy your models.
-
-> [!NOTE]
-> The project name is case-sensitive for all operations.
-
-Create a **PATCH** request using the following URL, headers, and JSON body to create your project.
-
-### Request URL
-
-Use the following URL to create a project. Replace the placeholder values below with your own values. 
-
-```rest
-{Endpoint}/language/authoring/analyze-text/projects/{projectName}?api-version={API-VERSION}
-```
-
-|Placeholder  |Value  | Example |
-|---------|---------|---------|
-|`{ENDPOINT}`     | The endpoint for authenticating your API request.   | `https://<your-custom-subdomain>.cognitiveservices.azure.com` |
-|`{PROJECT-NAME}`     | The name for your project. This value is case-sensitive.   | `myProject` |
-|`{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` |
-
-
-### Headers
-
-Use the following header to authenticate your request. 
-
-|Key|Value|
-|--|--|
-|`Ocp-Apim-Subscription-Key`| The key to your resource. Used for authenticating your API requests.|
-
-### Body
-
-Use the following JSON in your request. Replace the placeholder values below with your own values.
-
-```json
-{
-  "projectName": "{PROJECT-NAME}",
-  "language": "{LANGUAGE-CODE}",
-  "projectKind": "CustomHealthcare",
-  "description": "Project description",
-  "multilingual": "True",
-  "storageInputContainerName": "{CONTAINER-NAME}"
-}
-
-```
-
-|Key  |Placeholder|Value  | Example |
-|---------|---------|---------|--|
-| projectName | `{PROJECT-NAME}` | The name of your project. This value is case-sensitive. | `myProject` |
-| language | `{LANGUAGE-CODE}` |  A string specifying the language code for the documents used in your project. If your project is a multilingual project, choose the language code of the majority of the documents. See [language support](../../language-support.md) to learn more about supported language codes. |`en-us`|
-| projectKind | `CustomHealthcare` | Your project kind. | `CustomHealthcare` |
-| multilingual | `true`| A boolean value that enables you to have documents in multiple languages in your dataset and when your model is deployed you can query the model in any supported language (not necessarily ones included in your training documents). See [language support](../../language-support.md) to learn more about multilingual support.  | `true`|
-| storageInputContainerName | `{CONTAINER-NAME` | The name of your Azure storage container where you have uploaded your documents.   | `myContainer` |
-
-
-
-
-This request will return a 201 response, which means that the project is created.
-
-
-This request will return an error if:
-* The selected resource doesn't have proper permission for the storage account. 
-

@@ -1,36 +0,0 @@
----
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-Create a **DELETE** request using the following URL, headers, and JSON body to delete a deployment.
-
-
-### Request URL
-
-```rest
-{Endpoint}/language/authoring/analyze-text/projects/{PROJECT-NAME}/deployments/{deploymentName}?api-version={API-VERSION}
-```
-
-|Placeholder  |Value  | Example |
-|---------|---------|---------|
-|`{ENDPOINT}`     | The endpoint for authenticating your API request.   | `https://<your-custom-subdomain>.cognitiveservices.azure.com` |
-|`{PROJECT-NAME}`     | The name for your project. This value is case-sensitive.   | `myProject` |
-|`{DEPLOYMENT-NAME}`     | The name for your deployment name. This value is case-sensitive.   | `prod` |
-|`{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` |
-
-### Headers
-
-Use the following header to authenticate your request. 
-
-|Key|Value|
-|--|--|
-|`Ocp-Apim-Subscription-Key`| The key to your resource. Used for authenticating your API requests.|
-
-
-Once you send your API request, you will receive a `202` response indicating success, which means your deployment has been deleted. A successful call results with an `Operation-Location` header used to check the status of the job.

@@ -1,31 +0,0 @@
----
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-When you no longer need your project, you can delete it with the following **DELETE** request. Replace the placeholder values with your own values.   
-
-```rest
-{Endpoint}/language/authoring/analyze-text/projects/{projectName}?api-version={API-VERSION}
-```
-
-|Placeholder  |Value  | Example |
-|---------|---------|---------|
-|`{ENDPOINT}`     | The endpoint for authenticating your API request.   | `https://<your-custom-subdomain>.cognitiveservices.azure.com` |
-|`{PROJECT-NAME}`     | The name for your project. This value is case-sensitive.  | `myProject` |
-|`{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` |
-
-### Headers
-
-Use the following header to authenticate your request. 
-
-|Key|Value|
-|--|--|
-|Ocp-Apim-Subscription-Key| The key to your resource. Used for authenticating your API requests.|
-
-
-Once you send your API request, you will receive a `202` response indicating success, which means your project has been deleted. A successful call results with an Operation-Location header used to check the status of the job.

@@ -1,52 +0,0 @@
----
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-Submit a **PUT** request using the following URL, headers, and JSON body to submit a deployment job. Replace the placeholder values below with your own values. 
-
-```rest
-{Endpoint}/language/authoring/analyze-text/projects/{projectName}/deployments/{deploymentName}?api-version={API-VERSION}
-```
-
-| Placeholder |Value | Example |
-|---------|---------|---------|
-| `{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'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
-
-Use the following header to authenticate your request. 
-
-|Key|Value|
-|--|--|
-|`Ocp-Apim-Subscription-Key`| The key to your resource. Used for authenticating your API requests.|
-
-#### Request body
-
-Use the following JSON in the body of your request. Use the name of the model you to assign to the deployment.  
-
-```json
-{
-  "trainedModelLabel": "{MODEL-NAME}"
-}
-```
-
-|Key  |Placeholder  |Value  | Example |
-|---------|---------|-----|----|
-| trainedModelLabel | `{MODEL-NAME}` | The model name that will be assigned to your deployment. You can only assign successfully trained models. This value is case-sensitive.   | `myModel` |
-
-Once you send your API request, you’ll receive a `202` response indicating that the job was submitted correctly. In the response headers, extract the `operation-location` value. It will be formatted like this: 
-
-```rest
-{ENDPOINT}/language/authoring/analyze-text/projects/{PROJECT-NAME}/deployments/{DEPLOYMENT-NAME}/jobs/{JOB-ID}?api-version={API-VERSION}
-``` 
-
-`{JOB-ID}` is used to identify your request, since this operation is asynchronous. You can use this URL to get the deployment status.

@@ -1,51 +0,0 @@
----
-#services: cognitive-services
-author: jboback
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 12/19/2023
-ms.author: jboback
----
-
-Create a **POST** request using the following URL, headers, and JSON body to export your project.
-
-### Request URL
-
-Use the following URL when creating your API request. Replace the placeholder values with your own values. 
-
-```rest
-{ENDPOINT}/language/authoring/analyze-text/projects/{PROJECT-NAME}/:export?stringIndexType=Utf16CodeUnit&api-version={API-VERSION}
-```
-
-|Placeholder  |Value  | Example |
-|---------|---------|---------|
-|`{ENDPOINT}`     | The endpoint for authenticating your API request.   | `https://<your-custom-subdomain>.cognitiveservices.azure.com` |
-|`{PROJECT-NAME}`     | The name for your project. This value is case-sensitive.   | `MyProject` |
-|`{API-VERSION}`     | The version of the API you are calling. The value referenced here is the latest [model version](../../../concepts/model-lifecycle.md#choose-the-model-version-used-on-your-data) released.  | `2022-05-01` |
-
-### Headers
-
-Use the following header to authenticate your request. 
-
-|Key|Value|
-|--|--|
-|`Ocp-Apim-Subscription-Key`| The key to your resource. Used for authenticating your API requests.|
-
-#### Body
-
-Use the following JSON in your request body specifying that you want to export all the assets.
-
-```json
-{
-  "assetsToExport": ["*"]
-}
-```
-
-Once you send your API request, you’ll receive a `202` response indicating that the job was submitted correctly. In the response headers, extract the `operation-location` value. It's formatted like this: 
-
-```rest
-{ENDPOINT}/language/authoring/analyze-text/projects/{PROJECT-NAME}/export/jobs/{JOB-ID}?api-version={API-VERSION}
-``` 
-
-`{JOB-ID}` is used to identify your request, since this operation is asynchronous. You’ll use this URL to get the export job status.