Compose custom models

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This content applies to: v4.0 (GA) | Previous versions: v3.1 (GA) v3.0 (GA) v2.1 (GA)

Important

Model compose behavior is changed for api-version=2024-11-30 (GA). For more information refer to composed custom models. The following behavior only applies to v3.1 and previous versions.

A composed model is created by taking a collection of custom models and assigning them to a single model ID. You can assign up to 200 trained custom models to a single composed model ID. When a document is submitted to a composed model, the service performs a classification step to decide which custom model accurately represents the form presented for analysis. Composed models are useful when you train several models and want to group them to analyze similar form types. For example, your composed model might include custom models trained to analyze your supply, equipment, and furniture purchase orders. Instead of manually trying to select the appropriate model, you can use a composed model to determine the appropriate custom model for each analysis and extraction.

To learn more, see Composed custom models.

In this article, you learn how to create and use composed custom models to analyze your forms and documents.

Prerequisites

To get started, you need the following resources:

• An Azure subscription. You can create a free Azure subscription.

• A Document Intelligence instance. Once you have your Azure subscription, create a Document Intelligence resource in the Azure portal to get your key and endpoint. If you have an existing Document Intelligence resource, navigate directly to your resource page. You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.

  1. After the resource deploys, select Go to resource.

  2. Copy the Keys and Endpoint values from the Azure portal and paste them in a convenient location, such as Microsoft Notepad. You need the key and endpoint values to connect your application to the Document Intelligence API.

Tip

For more information, see create a Document Intelligence resource.

• An Azure storage account. If you don't know how to create an Azure storage account, follow the Azure Storage quickstart for Azure portal. You can use the free pricing tier (F0) to try the service, and upgrade later to a paid tier for production.

Create your custom models

First, you need a set of custom models to compose. You can use the Document Intelligence Studio, REST API, or client libraries. The steps are as follows:

• Assemble your training dataset
• Upload your training set to Azure blob storage
• Train your custom models

Assemble your training dataset

Building a custom model begins with establishing your training dataset. You need a minimum of five completed forms of the same type for your sample dataset. They can be of different file types (jpg, png, pdf, tiff) and contain both text and handwriting. Your forms must follow the input requirements for Document Intelligence.

Tip

Follow these tips to optimize your data set for training:

• If possible, use text-based PDF documents instead of image-based documents. Scanned PDFs are handled as images.
• For filled-in forms, use examples that have all of their fields filled in.
• Use forms with different values in each field.
• If your form images are of lower quality, use a larger data set (10-15 images, for example).

See Build a training data set for tips on how to collect your training documents.

Upload your training dataset

Once you gather a set of training documents, you need to upload your training data to an Azure blob storage container.

If you want to use manually labeled data, you have to upload the .labels.json and .ocr.json files that correspond to your training documents.

Train your custom model

When you train your model with labeled data, the model uses supervised learning to extract values of interest, using the labeled forms you provide. Labeled data results in better-performing models and can produce models that work with complex forms or forms containing values without keys.

Document Intelligence uses the prebuilt-layout model API to learn the expected sizes and positions of typeface and handwritten text elements and extract tables. Then it uses user-specified labels to learn the key/value associations and tables in the documents. We recommend that you use five manually labeled forms of the same type (same structure) to get started with training a new model. Then, add more labeled data, as needed, to improve the model accuracy. Document Intelligence enables training a model to extract key-value pairs and tables using supervised learning capabilities.

To create custom models, start with configuring your project:

1. From the Studio homepage, select Create new from the Custom model card.

2. Use the ➕ Create a project command to start the new project configuration wizard.

3. Enter project details, select the Azure subscription and resource, and the Azure Blob storage container that contains your data.

4. Review, submit your settings, and create the project.

While creating your custom models, you might need to extract data collections from your documents. The collections may appear one of two formats. Using tables as the visual pattern:

• Dynamic or variable count of values (rows) for a given set of fields (columns)

• Specific collection of values for a given set of fields (columns and/or rows)

See Document Intelligence Studio: labeling as tables

Training with labels leads to better performance in some scenarios. To train with labels, you need to have special label information files (<filename>.pdf.labels.json) in your blob storage container alongside the training documents.

Label files contain key-value associations that a user enters manually and are needed for labeled data training. However, not every source file needs to have a corresponding label file. Source files without labels are treated as ordinary training documents. We recommend five or more labeled files for reliable training. You can use a UI tool like Document Intelligence Studio to generate these files.

Label files contain key-value associations that a user entered manually. The files are needed for labeled data training, but not every source file needs to have a corresponding label file. Source files without labels are treated as ordinary training documents. We recommend five or more labeled files for reliable training. You can use a UI tool like Document Intelligence Studio to generate these files.

Once you have your label files, you can include them with by calling the training method with the useLabelFile parameter set to true.

Training with labels leads to better performance in some scenarios. To train with labels, you need to have special label information files (<filename>.pdf.labels.json) in your blob storage container alongside the training documents. Once you have them, you can call the training method with the useTrainingLabels parameter set to true.

Language	Method
C#	StartBuildModel
Java	beginBuildModel
JavaScript	beginBuildModel
Python	begin_build_document_model

Create a composed model

Note

the create compose model operation is only available for custom models trained with labels. Attempting to compose unlabeled models will produce an error.

With the create compose model operation, you can assign up to 100 trained custom models to a single model ID. When analyze documents with a composed model, Document Intelligence first classifies the form you submitted, then chooses the best matching assigned model, and returns results for that model. This operation is useful when incoming forms may belong to one of several templates.

Once the training process is successfully completed, you can begin to build your composed model. Here are the steps for creating and using composed models:

• Gather your custom model IDs
• Compose your custom models
• Analyze documents
• Manage your composed models

Gather your model IDs

When you train models using the Document Intelligence Studio, the model ID is located in the models menu under a project:

Compose your custom models

1. Select a custom models project.

2. In the project, select the Models menu item.

3. From the resulting list of models, select the models you wish to compose.

4. Choose the Compose button from the upper-left corner.

5. In the pop-up window, name your newly composed model and select Compose.

6. When the operation completes, your newly composed model appears in the list.

7. Once the model is ready, use the Test command to validate it with your test documents and observe the results.

Analyze documents

The custom model Analyze operation requires you to provide the modelID in the call to Document Intelligence. You should provide the composed model ID for the modelID parameter in your applications.

Manage your composed models

You can manage your custom models throughout life cycles:

• Test and validate new documents.
• Download your model to use in your applications.
• Delete your model when its lifecycle is complete.

Once the training process is successfully completed, you can begin to build your composed model. Here are the steps for creating and using composed models:

• Compose your custom models
• Analyze documents
• Manage your composed models

Compose your custom models

The composed models API accepts a list of model IDs to be composed.

Analyze documents

To make an Analyze document request, use a unique model name in the request parameters.

Manage your composed models

You can manage custom models throughout your development needs including copying, listing, and deleting your models.

Once the training process is successfully completed, you can begin to build your composed model. Here are the steps for creating and using composed models:

• Create a composed model
• Analyze documents
• Manage your composed models

Create a composed model

You can use the programming language of your choice to create a composed model:

Programming language	Code sample
C#	Model compose
Java	Model compose
JavaScript	Compose model
Python	Create composed model

Analyze documents

Once you build your composed model, you can use it to analyze forms and documents. Use your composed model ID and let the service decide which of your aggregated custom models fits best according to the document provided.

Programming language	Code sample
C#	Analyze a document with a custom/composed model using model ID
Java	Analyze a document with a custom/composed model using model ID
JavaScript	Analyze a document with a custom/composed model using model ID
Python	Analyze a document with a custom/composed model using model ID

Manage your composed models

You can manage a custom model at each stage in its life cycles. You can copy a custom model between resources, view a list of all custom models under your subscription, retrieve information about a specific custom model, and delete custom models from your account.

Programming language	Code sample
C#	Copy a custom model between Document Intelligence resources
Java	Copy a custom model between Document Intelligence resources
JavaScript	Copy a custom model between Document Intelligence resources
Python	Copy a custom model between Document Intelligence resources

Great! You learned the steps to create custom and composed models and use them in your Document Intelligence projects and applications.

Next steps

Try one of our Document Intelligence quickstarts:

Document Intelligence Studio

REST API

C#

Java

JavaScript

Python