Merge pull request #225692 from laujan/60934-add-videos-to-FR

60934 add videos to fr

Author: v-dirichards

articles/applied-ai-services/form-recognizer/concept-custom-label-tips.md

@@ -7,7 +7,7 @@ manager: nitinme
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: conceptual
-ms.date: 12/15/2022
+ms.date: 01/30/2023
 ms.author: vikurpad
 ms.custom: references_regions
 recommendations: false
@@ -17,6 +17,14 @@ recommendations: false
 
 This article highlights the best methods for labeling custom model datasets in the Form Recognizer Studio. Labeling documents can be time consuming when you have a large number of labels, long documents, or documents with varying structure. These tips should help you label documents more efficiently.
 
+## Video: Custom labels best practices
+
+* The following video is the second of two presentations intended to help you build custom models with higher accuracy (the first presentation explores [How to create a balanced data set](concept-custom-label.md#video-custom-label-tips-and-pointers)).
+
+* Here, we'll examine best practices for labeling your selected documents. With semantically relevant and consistent labeling, you should see an improvement in model performance.</br></br>
+
+  > [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE5fZKB ]
+
 ## Search
 
 The Studio now includes a search box for instances when you know you need to find specific words to label, but just don't know where they're located in the document. Simply search for the word or phrase and navigate to the specific section in the document to label the occurrence.

articles/applied-ai-services/form-recognizer/concept-custom-label.md

@@ -7,7 +7,7 @@ manager: nitinme
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: conceptual
-ms.date: 12/15/2022
+ms.date: 01/30/2023
 ms.author: vikurpad
 ms.custom: references_regions
 monikerRange: 'form-recog-3.0.0'
@@ -32,13 +32,21 @@ A labeled dataset consists of several files:
 
   * A `{file}.labels.json` file is created or updated when a field is labeled in a document. The label file contains the spans of text and associated polygons from the layout output for each span of text the user adds as a value for a specific field.
 
+## Video: Custom label tips and pointers
+
+* The following video is the first of two presentations intended to help you build custom models with higher accuracy (The second presentation examines [Best practices for labeling documents](concept-custom-label-tips.md#video-custom-labels-best-practices)).
+
+* Here, we'll explore how to create a balanced data set and select the right documents to label. This process will set you on the path to higher quality models.</br></br>
+
+  > [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RWWHru]
+
 ## Create a balanced dataset
 
 Before you start labeling, it's a good idea to look at a few different samples of the document to identify which samples you want to use in your labeled dataset. A balanced dataset represents all the typical variations you would expect to see for the document. Creating a balanced dataset will result in a model with the highest possible accuracy. A few examples to consider are:
 
 * **Document formats**: If you expect to analyze both digital and scanned documents, add a few examples of each type to the training dataset
 
-* **Variations (template model)**:  Consider splitting the dataset into folders and train a model for each of variation. Variations that include either structure or layout should be split into different models. You can then compose the individual models into a single [composed model](concept-composed-models.md).
+* **Variations (template model)**:  Consider splitting the dataset into folders and train a model for each of variation. Any variations that include either structure or layout should be split into different models. You can then compose the individual models into a single [composed model](concept-composed-models.md).
 
 * **Variations (Neural models)**: When your dataset has a manageable set of variations, about 15 or fewer, create a single dataset with a few samples of each of the different variations to train a single model. If the number of template variations is larger than 15, you'll train multiple models and [compose](concept-composed-models.md) them together.
 

articles/applied-ai-services/form-recognizer/how-to-guides/build-a-custom-model.md

@@ -7,7 +7,7 @@ manager: nitinme
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: how-to
-ms.date: 10/10/2022
+ms.date: 01/31/2023
 ms.author: lajanuar
 recommendations: false
 ---
@@ -46,6 +46,12 @@ Follow these tips to further optimize your data set for training:
 
 Once you've put together the set of forms or documents for training, you'll need to upload it to an Azure blob storage container. If you don't know how to create an Azure storage account with a container, following the [Azure Storage quickstart for Azure portal](../../../storage/blobs/storage-quickstart-blobs-portal.md). You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.
 
+## Video: Train your custom model
+
+* Once you've gathered and uploaded your training dataset, you're ready to train your custom model. In the following video, we'll create a project and explore some of the fundamentals for successfully labeling and training a model.</br></br>
+
+  > [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE5fX1c]
+
 ## Create a project in the Form Recognizer Studio
 
 The Form Recognizer Studio provides and orchestrates all the API calls required to complete your dataset and train your model.

articles/applied-ai-services/form-recognizer/overview.md

@@ -7,7 +7,7 @@ manager: nitinme
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: overview
-ms.date: 11/28/2022
+ms.date: 01/30/2023
 ms.author: lajanuar
 recommendations: false
 ---
@@ -32,6 +32,12 @@ Azure Form Recognizer is a cloud-based [Azure Applied AI Service](../../applied-
 | **Prebuilt models** | &#9679; [**W-2 form model**](concept-w2.md) </br>&#9679; [**Invoice model**](concept-invoice.md)</br>&#9679; [**Receipt model**](concept-receipt.md) </br>&#9679; [**Identity (ID) document model**](concept-id-document.md) </br>&#9679; [**Business card model**](concept-business-card.md) </br>
 | **Custom models** | &#9679; [**Custom model**](concept-custom.md) </br>&#9679; [**Composed model**](concept-model-overview.md)|
 
+## Video: Form Recognizer models
+
+The following video introduces Form Recognizer models and their associated output to help you choose the best model to address your document scenario needs.</br></br>
+
+  > [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE5fX1b]
+
 ## Which Form Recognizer model should I use?
 
 This section will help you decide which **Form Recognizer v3.0** supported model you should use for your application:

articles/applied-ai-services/form-recognizer/toc.yml

@@ -171,10 +171,10 @@ items:
     - name: Custom neural model
       displayName: selection, structure, tables, tabular, train, template, neural, build mode, signatures, custom
       href: concept-custom-neural.md
-    - name: Custom labels
+    - name: Create labeled datasets
       displayName: structure, selection, labels, tables, tabular, train
       href: concept-custom-label.md
-    - name: Custom labeling tips
+    - name: Labeling tips and pointers
       displayName: structure, selection, labels, tables, tabular, train, tips
       href: concept-custom-label-tips.md
     - name: Composed models