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Copy file name to clipboardExpand all lines: articles/applied-ai-services/form-recognizer/concept-composed-models.md
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@@ -42,7 +42,7 @@ With composed models, you can assign multiple custom models to a composed model
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::: moniker range="form-recog-3.0.0"
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With the introduction of [****custom classifier models****](./concept-custom-classifier.md), you can choose to use [**composed models**](./concept-composed-models.md) or the classifier model as an explicit step before analysis. For a deeper understanding of when to use a classifier or composed model, _see_[**Custom classifier models**](concept-custom-classifier.md).
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With the introduction of [****custom classification models****](./concept-custom-classifier.md), you can choose to use [**composed models**](./concept-composed-models.md) or the classification model as an explicit step before analysis. For a deeper understanding of when to use a classification or composed model, _see_[**Custom classification models**](concept-custom-classifier.md).
Custom classifier models are deep-learning-model types that combine layout and language features to accurately detect and identify documents you process within your application. Custom classifier models can classify each page in an input file to identify the document(s) within and can also identify multiple documents or multiple instances of a single document within an input file.
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Custom classification models are deep-learning-model types that combine layout and language features to accurately detect and identify documents you process within your application. Custom classification models can classify each page in an input file to identify the document(s) within and can also identify multiple documents or multiple instances of a single document within an input file.
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## Model capabilities
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Custom classifier models can analyze a single- or multi-file documents to identify if any of the trained document types are contained within an input file. Here are the currently supported scenarios:
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Custom classification models can analyze a single- or multi-file documents to identify if any of the trained document types are contained within an input file. Here are the currently supported scenarios:
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* A single file containing one document. For instance, a loan application form.
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* A single file containing multiple documents. For instance, a loan application package containing a loan application form, payslip, and bank statement.
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* A single file containing multiple instances of the same document. For instance, a collection of scanned invoices.
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Training a custom classifier model requires at least two distinct classes and a minimum of five samples per class.
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Training a custom classification model requires at least two distinct classes and a minimum of five samples per class.
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### Compare custom classifier and composed models
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### Compare custom classification and composed models
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A custom classifier model can replace [a composed model](concept-composed-models.md) in some scenarios but there are a few differences to be aware of:
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A custom classification model can replace [a composed model](concept-composed-models.md) in some scenarios but there are a few differences to be aware of:
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| Capability | Custom classifier process | Composed model process |
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|--|--|--|
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|Analyze a single document of unknown type belonging to one of the types trained for extraction model processing.|● Requires multiple calls. </br> ● Call the classifier models based on the document class. This step allows for a confidence-based check before invoking the extraction model analysis.</br> ● Invoke the extraction model. |● Requires a single call to a composed model containing the model corresponding to the input document type. |
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|Analyze a single document of unknown type belonging to several types trained for extraction model processing.|●Requires multiple calls.</br> ● Make a call to the classifier that ignores documents not matching a designated type for extraction.</br> ● Invoke the extraction model. |● Requires a single call to a composed model. The service selects a custom model within the composed model with the highest match.</br> ● A composed model can't ignore documents.|
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|Analyze a single document of unknown type belonging to one of the types trained for extraction model processing.|● Requires multiple calls. </br> ● Call the classification models based on the document class. This step allows for a confidence-based check before invoking the extraction model analysis.</br> ● Invoke the extraction model. |● Requires a single call to a composed model containing the model corresponding to the input document type. |
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|Analyze a single document of unknown type belonging to several types trained for extraction model processing.|●Requires multiple calls.</br> ● Make a call to the classification that ignores documents not matching a designated type for extraction.</br> ● Invoke the extraction model. |● Requires a single call to a composed model. The service selects a custom model within the composed model with the highest match.</br> ● A composed model can't ignore documents.|
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|Analyze a file containing multiple documents of known or unknown type belonging to one of the types trained for extraction model processing.|● Requires multiple calls. </br> ● Call the extraction model for each identified document in the input file.</br> ● Invoke the extraction model. |● Requires a single call to a composed model.</br> ● The composed model invokes the component model once on the first instance of the document. </br> ●The remaining documents are ignored. |
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## Language support
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## Best practices
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Custom classifier models require a minimum of five samples per class to train. If the classes are similar, adding extra training samples improves model accuracy.
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Custom classification models require a minimum of five samples per class to train. If the classes are similar, adding extra training samples improves model accuracy.
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## Training a model
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Custom classifier models are only available in the [v3.0 API](v3-migration-guide.md) starting with API version ```2023-02-28-preview```. [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio) provides a no-code user interface to interactively train a custom classifier.
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Custom classification models are only available in the [v3.0 API](v3-migration-guide.md) starting with API version ```2023-02-28-preview```. [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio) provides a no-code user interface to interactively train a custom classifier.
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When using the REST API, if you've organized your documents by folders, you can use the ```azureBlobSource``` property of the request to train a classifier model.
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When using the REST API, if you've organized your documents by folders, you can use the ```azureBlobSource``` property of the request to train a classification model.
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Form Recognizer uses advanced machine learning technology to identify documents, detect and extract information from forms and documents, and return the extracted data in a structured JSON output. With Form Recognizer, you can use document analysis models, pre-built/pre-trained, or your trained standalone custom models.
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Custom models now include [custom classifier models](./concept-custom-classifier.md) for scenarios where you need to identify the document type prior to invoking the extraction model. Classifier models are available starting with the ```2023-02-28-preview``` API. A classifier model can be paired with a custom extraction model to analyze and extract fields from forms and documents specific to your business to create a document processing solution. Standalone custom extraction models can be combined to create [composed models](concept-composed-models.md).
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Custom models now include [custom classification models](./concept-custom-classifier.md) for scenarios where you need to identify the document type prior to invoking the extraction model. Classifier models are available starting with the ```2023-02-28-preview``` API. A classification model can be paired with a custom extraction model to analyze and extract fields from forms and documents specific to your business to create a document processing solution. Standalone custom extraction models can be combined to create [composed models](concept-composed-models.md).
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::: moniker range="form-recog-3.0.0"
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|Document variations | Requires a model per each variation | Uses a single model for all variations |
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|Language support | Multiple [language support](language-support.md#read-layout-and-custom-form-template-model)| English, with preview support for Spanish, French, German, Italian and Dutch [language support](language-support.md#custom-neural-model)|
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### Custom classifier model
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### Custom classification model
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Document classification is a new scenario supported by Form Recognizer with the ```2023-02-28-preview``` API. Document classifier supports classification and splitting scenarios. Train a classifier model to identify the different types of documents your application supports. The input file for the classifier model can contain multiple documents and classifies each document within an associated page range. See [custom classification](concept-custom-classifier.md) models to learn more.
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Document classification is a new scenario supported by Form Recognizer with the ```2023-02-28-preview``` API. Document classification supports classification and splitting scenarios. Train a classification model to identify the different types of documents your application supports. The input file for the classification model can contain multiple documents and classifies each document within an associated page range. See [custom classification](concept-custom-classifier.md) models to learn more.
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## Custom model tools
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1. Review and create your project.
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1. Label your documents to build and test your custom classifier model.
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1. Label your documents to build and test your custom classification model.
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> [!div class="nextstepaction"]
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> [Try Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio/document-classifier/projects)
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|**Custom models**||
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|[Custom model (overview)](#custom-models)| Extract data from forms and documents specific to your business. Custom models are trained for your distinct data and use cases. |
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|[Custom extraction models](#custom-extraction)|●**Custom template models** use layout cues to extract values from documents and are suitable to extract fields from highly structured documents with defined visual templates.</br>●**Custom neural models** are trained on various document types to extract fields from structured, semi-structured and unstructured documents.|
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| [Custom classifier model](#custom-classifier)| The **Custom classifier model** can classify each page in an input file to identify the document(s) within and can also identify multiple documents or multiple instances of a single document within an input file.
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| [Custom classification model](#custom-classifier)| The **Custom classification model** can classify each page in an input file to identify the document(s) within and can also identify multiple documents or multiple instances of a single document within an input file.
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| [Composed models](#composed-models) | Combine several custom models into a single model to automate processing of diverse document types with a single composed model.
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### Read OCR
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The custom classifier model enables you to identify the document type prior to invoking the extraction model. The classifier model is available starting with the 2023-02-28-preview. Training a custom classifier model requires at least two distinct classes and a minimum of five samples per class.
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The custom classification model enables you to identify the document type prior to invoking the extraction model. The classification model is available starting with the 2023-02-28-preview. Training a custom classification model requires at least two distinct classes and a minimum of five samples per class.
[!INCLUDE [applies to v3.0](../includes/applies-to-v3-0.md)]
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Custom classifier models can classify each page in an input file to identify the document(s) within. Classifier models can also identify multiple documents or multiple instances of a single document in the input file. Form Recognizer custom models require as few as five training documents per document class to get started. To get started training a custom classifier model, you need at least **five documents** for each class and **two classes** of documents.
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Custom classification models can classify each page in an input file to identify the document(s) within. Classifier models can also identify multiple documents or multiple instances of a single document in the input file. Form Recognizer custom models require as few as five training documents per document class to get started. To get started training a custom classification model, you need at least **five documents** for each class and **two classes** of documents.
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## Custom classifier model input requirements
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## Custom classification model input requirements
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Make sure your training data set follows the input requirements for Form Recognizer.
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1. Start by navigating to the [Form Recognizer Studio](https://formrecognizer.appliedai.azure.com/studio). The first time you use the Studio, you need to [initialize your subscription, resource group, and resource](../quickstarts/try-v3-form-recognizer-studio.md). Then, follow the [prerequisites for custom projects](../quickstarts/try-v3-form-recognizer-studio.md#additional-prerequisites-for-custom-projects) to configure the Studio to access your training dataset.
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1. In the Studio, select the **Custom classifier models** tile, on the custom models section of the page and select the **Create a project** button.
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1. In the Studio, select the **Custom classification models** tile, on the custom models section of the page and select the **Create a project** button.
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:::image type="content" source="../media/how-to/studio-create-classifier-project.png" alt-text="Screenshot of how to create a classifier project in the Form Recognizer Studio.":::
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1. Validate your model by evaluating the results for each document identified.
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Congratulations you've trained a custom classifier model in the Form Recognizer Studio! Your model is ready for use with the REST API or the SDK to analyze documents.
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Congratulations you've trained a custom classification model in the Form Recognizer Studio! Your model is ready for use with the REST API or the SDK to analyze documents.
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#### Custom
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*[**Custom extraction models**](https://formrecognizer-dogfood.appliedai.azure.com/studio/custommodel/projects): extract information from forms and documents with custom extraction models. Quickly train a model by labeling as few as five sample documents.
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*[**Custom classifier model**](https://formrecognizer-dogfood.appliedai.azure.com/studio/document-classifier/projects): train a custom classifier to distinguish between the different document types within your applications. Quickly train a model with as few as two classes and five samples per class.
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*[**Custom classification model**](https://formrecognizer-dogfood.appliedai.azure.com/studio/document-classifier/projects): train a custom classifier to distinguish between the different document types within your applications. Quickly train a model with as few as two classes and five samples per class.
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