AWS CLI Command Reference

[ aws . comprehend ]

describe-document-classifier

Description

Gets the properties associated with a document classifier.

See also: AWS API Documentation

See ‘aws help’ for descriptions of global parameters.

Synopsis

  describe-document-classifier
--document-classifier-arn <value>
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]

Options

--document-classifier-arn (string)

The Amazon Resource Name (ARN) that identifies the document classifier. The operation returns this identifier in its response.

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

See ‘aws help’ for descriptions of global parameters.

Output

DocumentClassifierProperties -> (structure)

An object that contains the properties associated with a document classifier.

DocumentClassifierArn -> (string)

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

LanguageCode -> (string)

The language code for the language of the documents that the classifier was trained on.

Status -> (string)

The status of the document classifier. If the status is TRAINED the classifier is ready to use. If the status is FAILED you can see additional information about why the classifier wasn’t trained in the Message field.

Message -> (string)

Additional information about the status of the classifier.

SubmitTime -> (timestamp)

The time that the document classifier was submitted for training.

EndTime -> (timestamp)

The time that training the document classifier completed.

TrainingStartTime -> (timestamp)

Indicates the time when the training starts on documentation classifiers. You are billed for the time interval between this time and the value of TrainingEndTime.

TrainingEndTime -> (timestamp)

The time that training of the document classifier was completed. Indicates the time when the training completes on documentation classifiers. You are billed for the time interval between this time and the value of TrainingStartTime.

InputDataConfig -> (structure)

The input data configuration that you supplied when you created the document classifier for training.

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)

The Amazon S3 URI for the input data. The Amazon S3 bucket must be in the same AWS 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.

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 .

(structure)

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)

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)

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.

OutputDataConfig -> (structure)

Provides output results configuration parameters for custom classifier jobs.

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. 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 AWS 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"

ClassifierMetadata -> (structure)

Information about the document classifier, including the number of documents used for training the classifier, the number of documents used for test the classifier, and an accuracy rating.

NumberOfLabels -> (integer)

The number of labels in the input data.

NumberOfTrainedDocuments -> (integer)

The number of documents in the input data that were used to train the classifier. Typically this is 80 to 90 percent of the input documents.

NumberOfTestDocuments -> (integer)

The number of documents in the input data that were used to test the classifier. Typically this is 10 to 20 percent of the input documents, up to 10,000 documents.

EvaluationMetrics -> (structure)

Describes the result metrics for the test data associated with an documentation classifier.

Accuracy -> (double)

The fraction of the labels that were correct recognized. It is computed by dividing the number of labels in the test documents that were correctly recognized by the total number of labels in the test documents.

Precision -> (double)

A measure of the usefulness of the classifier results in the test data. High precision means that the classifier returned substantially more relevant results than irrelevant ones.

Recall -> (double)

A measure of how complete the classifier results are for the test data. High recall means that the classifier returned most of the relevant results.

F1Score -> (double)

A measure of how accurate the classifier results are for the test data. It is derived from the Precision and Recall values. The F1Score is the harmonic average of the two scores. The highest score is 1, and the worst score is 0.

MicroPrecision -> (double)

A measure of the usefulness of the recognizer results in the test data. High precision means that the recognizer returned substantially more relevant results than irrelevant ones. Unlike the Precision metric which comes from averaging the precision of all available labels, this is based on the overall score of all precision scores added together.

MicroRecall -> (double)

A measure of how complete the classifier results are for the test data. High recall means that the classifier returned most of the relevant results. Specifically, this indicates how many of the correct categories in the text that the model can predict. It is a percentage of correct categories in the text that can found. Instead of averaging the recall scores of all labels (as with Recall), micro Recall is based on the overall score of all recall scores added together.

MicroF1Score -> (double)

A measure of how accurate the classifier results are for the test data. It is a combination of the Micro Precision and Micro Recall values. The Micro F1Score is the harmonic mean of the two scores. The highest score is 1, and the worst score is 0.

HammingLoss -> (double)

Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong labels compared to the total number of labels. Scores closer to zero are better.

DataAccessRoleArn -> (string)

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

VolumeKmsKeyId -> (string)

ID for the AWS 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 -> (structure)

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

SecurityGroupIds -> (list)

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)

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 specific classifier was trained. This also indicates the format of input documents and the format of the confusion matrix. Each classifier can only be trained in one mode and this cannot be changed once the classifier is trained.

ModelKmsKeyId -> (string)

ID for the AWS Key Management Service (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"

VersionName -> (string)

The version name that you assigned to the document classifier.

SourceModelArn -> (string)

The Amazon Resource Name (ARN) of the source model. This model was imported from a different AWS account to create the document classifier model in your AWS account.

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