Training classification models
To train a model for custom classification, you define the categories and provide example documents to train the custom model. You train the model in either multi-class or multi-label mode. Multi-class mode associates a single class with each document. Multi-label mode associates one or more classes with each document.
Custom classification supports two types of classifier models: plain-text models and native document models. A plain-text model classifies documents based on their text content. A native document model also classifies documents based on text content. A native document model can also use additional signals, such as from the layout of the document. You train a native document model with native documents for the model to learn the layout information.
Plain-text models have the following characteristics:
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You train the model using UTF-8 encoded text documents.
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You can train the model using documents in one of following languages: English, Spanish, German, Italian, French, or Portuguese.
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The training documents for a given classifier must all use the same language.
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Training documents are plain text, so there are no additional charges for text extraction.
Native document models have the following characteristics:
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You train the model using semi-structured documents, which includes the following document types:
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Digital and scanned PDF documents.
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Word documents (DOCX).
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Images: JPG files, PNG files, and single-page TIFF files.
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Textract API output JSON files.
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You train the model using English documents.
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If your training documents include scanned document files, you incur additional charges for text extraction. See the Amazon Comprehend Pricing
page for details.
You can classify any of the supported document types using either type of model. However, for the most accurate results, we recommend using a plain-text model to classify plain-text documents and a native document model to classify semi-structured documents.
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