DocumentClassifierInputDataConfigProperty Struct
The input properties for training a document classifier.
For more information on how the input file is formatted, see Preparing training data in the Comprehend Developer Guide.
Example
// The code below shows an example of how to instantiate this type.
// The values are placeholders you should change.
import { aws_comprehend as comprehend } from 'aws-cdk-lib';
const documentClassifierInputDataConfigProperty: comprehend.CfnDocumentClassifier.DocumentClassifierInputDataConfigProperty = {
augmentedManifests: [{
attributeNames: ['attributeNames'],
s3Uri: 's3Uri',
// the properties below are optional
split: 'split',
}],
dataFormat: 'dataFormat',
documentReaderConfig: {
documentReadAction: 'documentReadAction',
// the properties below are optional
documentReadMode: 'documentReadMode',
featureTypes: ['featureTypes'],
},
documents: {
s3Uri: 's3Uri',
// the properties below are optional
testS3Uri: 'testS3Uri',
},
documentType: 'documentType',
labelDelimiter: 'labelDelimiter',
s3Uri: 's3Uri',
testS3Uri: 'testS3Uri',
};
Properties
augmentedManifestsIResolvable | IResolvable | CfnDocumentClassifier.AugmentedManifestsListItemProperty[]augmented_manifeststyping.Union[IResolvable, typing.List[typing.Union[IResolvable, AugmentedManifestsListItemProperty]]]augmentedManifestsIResolvableAugmentedManifestsIResolvableAugmentedManifestsinterface{}#An augmented manifest file is a labeled dataset that is produced by Amazon SageMaker Ground Truth.
This parameter is required if you set DataFormat to AUGMENTED_MANIFEST .
notes
COMPREHEND_CSV: A two-column CSV file, where labels are provided in the first column, and documents are provided in the second. If you use this value, you must provide theS3Uriparameter in your request.AUGMENTED_MANIFEST: A labeled dataset that is produced by Amazon SageMaker Ground Truth. This file is in JSON lines format. Each line is a complete JSON object that contains a training document and its associated labels.
If you use this value, you must provide the AugmentedManifests parameter in your request.
If you don't specify a value, Amazon Comprehend uses COMPREHEND_CSV as the default.
documentReaderConfigIResolvable | CfnDocumentClassifier.DocumentReaderConfigPropertydocument_reader_configtyping.Union[IResolvable, DocumentReaderConfigProperty]documentReaderConfigIResolvableDocumentReaderConfigIResolvableDocumentReaderConfiginterface{}#documentsIResolvable | CfnDocumentClassifier.DocumentClassifierDocumentsPropertydocumentstyping.Union[IResolvable, DocumentClassifierDocumentsProperty]documentsIResolvableDocumentsIResolvableDocumentsinterface{}#This parameter is required in a request to create a native document model.
Provide plain-text documents to create a plain-text model, and provide semi-structured documents to create a native document model.
labelDelimiterstringlabel_delimiterstrlabelDelimiterStringLabelDelimiterstringLabelDelimiter*string#notes
The default delimiter between labels is a pipe (|). You can use a different character as a delimiter (if it's an allowed character) by specifying it under Delimiter for labels. If the training documents use a delimiter other than the default or the delimiter you specify, the labels on that line will be combined to make a single unique label, such as LABELLABELLABEL.
notes
The S3 bucket must be in the same Region as the API endpoint that you are calling. The URI can point to a single input file or it can provide the prefix for a collection of input files.
For example, if you use the URI S3://bucketName/prefix , if the prefix is a single file, Amazon Comprehend uses that file as input. If more than one file begins with the prefix, Amazon Comprehend uses all of them as input.
This parameter is required if you set DataFormat to COMPREHEND_CSV .
The URI must be in the same AWS Region as the API endpoint that you are calling.