Comprehend / Client / create_document_classifier

create_document_classifier#

Comprehend.Client.create_document_classifier(**kwargs)#

Creates a new document classifier that you can use to categorize documents. To create a classifier, you provide a set of training documents that are labeled with the categories that you want to use. For more information, see Training classifier models in the Comprehend Developer Guide.

See also: AWS API Documentation

Request Syntax

response = client.create_document_classifier(
    DocumentClassifierName='string',
    VersionName='string',
    DataAccessRoleArn='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    InputDataConfig={
        'DataFormat': 'COMPREHEND_CSV'|'AUGMENTED_MANIFEST',
        'S3Uri': 'string',
        'TestS3Uri': 'string',
        'LabelDelimiter': 'string',
        'AugmentedManifests': [
            {
                'S3Uri': 'string',
                'Split': 'TRAIN'|'TEST',
                'AttributeNames': [
                    'string',
                ],
                'AnnotationDataS3Uri': 'string',
                'SourceDocumentsS3Uri': 'string',
                'DocumentType': 'PLAIN_TEXT_DOCUMENT'|'SEMI_STRUCTURED_DOCUMENT'
            },
        ],
        'DocumentType': 'PLAIN_TEXT_DOCUMENT'|'SEMI_STRUCTURED_DOCUMENT',
        'Documents': {
            'S3Uri': 'string',
            'TestS3Uri': 'string'
        },
        'DocumentReaderConfig': {
            'DocumentReadAction': 'TEXTRACT_DETECT_DOCUMENT_TEXT'|'TEXTRACT_ANALYZE_DOCUMENT',
            'DocumentReadMode': 'SERVICE_DEFAULT'|'FORCE_DOCUMENT_READ_ACTION',
            'FeatureTypes': [
                'TABLES'|'FORMS',
            ]
        }
    },
    OutputDataConfig={
        'S3Uri': 'string',
        'KmsKeyId': 'string',
        'FlywheelStatsS3Prefix': 'string'
    },
    ClientRequestToken='string',
    LanguageCode='en'|'es'|'fr'|'de'|'it'|'pt'|'ar'|'hi'|'ja'|'ko'|'zh'|'zh-TW',
    VolumeKmsKeyId='string',
    VpcConfig={
        'SecurityGroupIds': [
            'string',
        ],
        'Subnets': [
            'string',
        ]
    },
    Mode='MULTI_CLASS'|'MULTI_LABEL',
    ModelKmsKeyId='string',
    ModelPolicy='string'
)
Parameters:

  • DocumentClassifierName (string) –

    [REQUIRED]

    The name of the document classifier.

  • VersionName (string) – The version name given to the newly created classifier. Version names can have a maximum of 256 characters. Alphanumeric characters, hyphens (-) and underscores (_) are allowed. The version name must be unique among all models with the same classifier name in the Amazon Web Services account/Amazon Web Services Region.

  • DataAccessRoleArn (string) –

    [REQUIRED]

    The Amazon Resource Name (ARN) of the IAM role that grants Amazon Comprehend read access to your input data.

  • Tags (list) –

    Tags to associate with the document classifier. A tag is a key-value pair that adds as a metadata to a resource used by Amazon Comprehend. For example, a tag with “Sales” as the key might be added to a resource to indicate its use by the sales department.

    • (dict) –

      A key-value pair that adds as a metadata to a resource used by Amazon Comprehend. For example, a tag with the key-value pair ‘Department’:’Sales’ might be added to a resource to indicate its use by a particular department.

      • Key (string) – [REQUIRED]

        The initial part of a key-value pair that forms a tag associated with a given resource. For instance, if you want to show which resources are used by which departments, you might use “Department” as the key portion of the pair, with multiple possible values such as “sales,” “legal,” and “administration.”

      • Value (string) –

        The second part of a key-value pair that forms a tag associated with a given resource. For instance, if you want to show which resources are used by which departments, you might use “Department” as the initial (key) portion of the pair, with a value of “sales” to indicate the sales department.

  • InputDataConfig (dict) –

    [REQUIRED]

    Specifies the format and location of the input data for the job.

    • DataFormat (string) –

      The format of your training data:

      • 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 the S3Uri parameter 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.

    • S3Uri (string) –

      The Amazon S3 URI for the input data. 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.

    • TestS3Uri (string) –

      This specifies the Amazon S3 location that contains the test annotations for the document classifier. The URI must be in the same Amazon Web Services Region as the API endpoint that you are calling.

    • LabelDelimiter (string) –

      Indicates the delimiter used to separate each label for training a multi-label classifier. 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.

    • AugmentedManifests (list) –

      A list of augmented manifest files that provide training data for your custom model. 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.

      • (dict) –

        An augmented manifest file that provides training data for your custom model. An augmented manifest file is a labeled dataset that is produced by Amazon SageMaker Ground Truth.

        • S3Uri (string) – [REQUIRED]

          The Amazon S3 location of the augmented manifest file.

        • Split (string) –

          The purpose of the data you’ve provided in the augmented manifest. You can either train or test this data. If you don’t specify, the default is train.

          TRAIN - all of the documents in the manifest will be used for training. If no test documents are provided, Amazon Comprehend will automatically reserve a portion of the training documents for testing.

          TEST - all of the documents in the manifest will be used for testing.

        • AttributeNames (list) – [REQUIRED]

          The JSON attribute that contains the annotations for your training documents. The number of attribute names that you specify depends on whether your augmented manifest file is the output of a single labeling job or a chained labeling job.

          If your file is the output of a single labeling job, specify the LabelAttributeName key that was used when the job was created in Ground Truth.

          If your file is the output of a chained labeling job, specify the LabelAttributeName key for one or more jobs in the chain. Each LabelAttributeName key provides the annotations from an individual job.

          • (string) –

        • AnnotationDataS3Uri (string) –

          The S3 prefix to the annotation files that are referred in the augmented manifest file.

        • SourceDocumentsS3Uri (string) –

          The S3 prefix to the source files (PDFs) that are referred to in the augmented manifest file.

        • DocumentType (string) –

          The type of augmented manifest. PlainTextDocument or SemiStructuredDocument. If you don’t specify, the default is PlainTextDocument.

          • PLAIN_TEXT_DOCUMENT A document type that represents any unicode text that is encoded in UTF-8.

          • SEMI_STRUCTURED_DOCUMENT A document type with positional and structural context, like a PDF. For training with Amazon Comprehend, only PDFs are supported. For inference, Amazon Comprehend support PDFs, DOCX and TXT.

    • DocumentType (string) –

      The type of input documents for training the model. Provide plain-text documents to create a plain-text model, and provide semi-structured documents to create a native document model.

    • Documents (dict) –

      The S3 location of the training documents. This parameter is required in a request to create a native document model.

      • S3Uri (string) – [REQUIRED]

        The S3 URI location of the training documents specified in the S3Uri CSV file.

      • TestS3Uri (string) –

        The S3 URI location of the test documents included in the TestS3Uri CSV file. This field is not required if you do not specify a test CSV file.

    • DocumentReaderConfig (dict) –

      Provides configuration parameters to override the default actions for extracting text from PDF documents and image files.

      By default, Amazon Comprehend performs the following actions to extract text from files, based on the input file type:

      • Word files - Amazon Comprehend parser extracts the text.

      • Digital PDF files - Amazon Comprehend parser extracts the text.

      • Image files and scanned PDF files - Amazon Comprehend uses the Amazon Textract DetectDocumentText API to extract the text.

      DocumentReaderConfig does not apply to plain text files or Word files.

      For image files and PDF documents, you can override these default actions using the fields listed below. For more information, see Setting text extraction options in the Comprehend Developer Guide.

      • DocumentReadAction (string) – [REQUIRED]

        This field defines the Amazon Textract API operation that Amazon Comprehend uses to extract text from PDF files and image files. Enter one of the following values:

        • TEXTRACT_DETECT_DOCUMENT_TEXT - The Amazon Comprehend service uses the DetectDocumentText API operation.

        • TEXTRACT_ANALYZE_DOCUMENT - The Amazon Comprehend service uses the AnalyzeDocument API operation.

      • DocumentReadMode (string) –

        Determines the text extraction actions for PDF files. Enter one of the following values:

        • SERVICE_DEFAULT - use the Amazon Comprehend service defaults for PDF files.

        • FORCE_DOCUMENT_READ_ACTION - Amazon Comprehend uses the Textract API specified by DocumentReadAction for all PDF files, including digital PDF files.

      • FeatureTypes (list) –

        Specifies the type of Amazon Textract features to apply. If you chose TEXTRACT_ANALYZE_DOCUMENT as the read action, you must specify one or both of the following values:

        • TABLES - Returns information about any tables that are detected in the input document.

        • FORMS - Returns information and the data from any forms that are detected in the input document.

        • (string) –

          Specifies the type of Amazon Textract features to apply. If you chose TEXTRACT_ANALYZE_DOCUMENT as the read action, you must specify one or both of the following values:

          • TABLES - Returns additional information about any tables that are detected in the input document.

          • FORMS - Returns additional information about any forms that are detected in the input document.

  • OutputDataConfig (dict) –

    Specifies the location for the output files from a custom classifier job. This parameter is required for a request that creates a native document model.

    • S3Uri (string) –

      When you use the OutputDataConfig object while creating a custom classifier, you specify the Amazon S3 location where you want to write the confusion matrix and other output files. The URI must be in the same Region as the API endpoint that you are calling. The location is used as the prefix for the actual location of this output file.

      When the custom classifier job is finished, the service creates the output file in a directory specific to the job. The S3Uri field contains the location of the output file, called output.tar.gz. It is a compressed archive that contains the confusion matrix.

    • KmsKeyId (string) –

      ID for the Amazon Web Services Key Management Service (KMS) key that Amazon Comprehend uses to encrypt the output results from an analysis job. The KmsKeyId can be one of the following formats:

      • KMS Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab"

      • Amazon Resource Name (ARN) of a KMS Key: "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

      • KMS Key Alias: "alias/ExampleAlias"

      • ARN of a KMS Key Alias: "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

    • FlywheelStatsS3Prefix (string) –

      The Amazon S3 prefix for the data lake location of the flywheel statistics.

  • ClientRequestToken (string) –

    A unique identifier for the request. If you don’t set the client request token, Amazon Comprehend generates one.

    This field is autopopulated if not provided.

  • LanguageCode (string) –

    [REQUIRED]

    The language of the input documents. You can specify any of the languages supported by Amazon Comprehend. All documents must be in the same language.

  • VolumeKmsKeyId (string) –

    ID for the Amazon Web Services Key Management Service (KMS) key that Amazon Comprehend uses to encrypt data on the storage volume attached to the ML compute instance(s) that process the analysis job. The VolumeKmsKeyId can be either of the following formats:

    • KMS Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab"

    • Amazon Resource Name (ARN) of a KMS Key: "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

  • VpcConfig (dict) –

    Configuration parameters for an optional private Virtual Private Cloud (VPC) containing the resources you are using for your custom classifier. For more information, see Amazon VPC.

    • SecurityGroupIds (list) – [REQUIRED]

      The ID number for a security group on an instance of your private VPC. Security groups on your VPC function serve as a virtual firewall to control inbound and outbound traffic and provides security for the resources that you’ll be accessing on the VPC. This ID number is preceded by “sg-”, for instance: “sg-03b388029b0a285ea”. For more information, see Security Groups for your VPC.

      • (string) –

    • Subnets (list) – [REQUIRED]

      The ID for each subnet being used in your private VPC. This subnet is a subset of the a range of IPv4 addresses used by the VPC and is specific to a given availability zone in the VPC’s Region. This ID number is preceded by “subnet-”, for instance: “subnet-04ccf456919e69055”. For more information, see VPCs and Subnets.

      • (string) –

  • Mode (string) – Indicates the mode in which the classifier will be trained. The classifier can be trained in multi-class mode, which identifies one and only one class for each document, or multi-label mode, which identifies one or more labels for each document. In multi-label mode, multiple labels for an individual document are separated by a delimiter. The default delimiter between labels is a pipe (|).

  • ModelKmsKeyId (string) –

    ID for the KMS key that Amazon Comprehend uses to encrypt trained custom models. The ModelKmsKeyId can be either of the following formats:

    • KMS Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab"

    • Amazon Resource Name (ARN) of a KMS Key: "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

  • ModelPolicy (string) –

    The resource-based policy to attach to your custom document classifier model. You can use this policy to allow another Amazon Web Services account to import your custom model.

    Provide your policy as a JSON body that you enter as a UTF-8 encoded string without line breaks. To provide valid JSON, enclose the attribute names and values in double quotes. If the JSON body is also enclosed in double quotes, then you must escape the double quotes that are inside the policy:

    "{\"attribute\": \"value\", \"attribute\": [\"value\"]}"

    To avoid escaping quotes, you can use single quotes to enclose the policy and double quotes to enclose the JSON names and values:

    '{"attribute": "value", "attribute": ["value"]}'

Return type:

dict

Returns:

Response Syntax

{
    'DocumentClassifierArn': 'string'
}

Response Structure

  • (dict) –

    • DocumentClassifierArn (string) –

      The Amazon Resource Name (ARN) that identifies the document classifier.

Exceptions

  • Comprehend.Client.exceptions.InvalidRequestException

  • Comprehend.Client.exceptions.ResourceInUseException

  • Comprehend.Client.exceptions.TooManyTagsException

  • Comprehend.Client.exceptions.TooManyRequestsException

  • Comprehend.Client.exceptions.ResourceLimitExceededException

  • Comprehend.Client.exceptions.UnsupportedLanguageException

  • Comprehend.Client.exceptions.KmsKeyValidationException

  • Comprehend.Client.exceptions.InternalServerException