| Column Name | Column Data Type |
|---|
filter Input Column
Filters the jobs that are returned. You can filter jobs on their name, status, or the date and time that they were submitted. You can only set one filter at a time. | STRUCT(
"status" VARCHAR,
"document_classifier_name" VARCHAR,
"submit_time_before" TIMESTAMP_S,
"submit_time_after" TIMESTAMP_S
) |
Show child fields- filter.document_classifier_name
The name that you assigned to the document classifier - filter.status
Filters the list of classifiers based on status. - filter.submit_time_after
Filters the list of classifiers based on the time that the classifier was submitted for processing. Returns only classifiers submitted after the specified time. Classifiers are returned in descending order, newest to oldest. - filter.submit_time_before
Filters the list of classifiers based on the time that the classifier was submitted for processing. Returns only classifiers submitted before the specified time. Classifiers are returned in ascending order, oldest to newest.
|
_aws_profile Input Column
The AWS profile defines the AWS identity used. It can be defined via credentials or by assuming a IAM role. | STRUCT(
"type" VARCHAR,
"name" VARCHAR,
"account_id" VARCHAR,
"via_profile_name" VARCHAR,
"assumed_role_arn" VARCHAR,
"organization" STRUCT(
"account_name" VARCHAR,
"id" VARCHAR,
"tags" STRUCT(
"key" VARCHAR,
"value" VARCHAR
)[],
"master_account" STRUCT(
"id" VARCHAR,
"email" VARCHAR
),
"parents" STRUCT(
"type" VARCHAR,
"id" VARCHAR,
"name" VARCHAR,
"tags" STRUCT(
"key" VARCHAR,
"value" VARCHAR
)[]
)[]
)
) |
Show child fields- _aws_profile.account_id
The AWS account id - _aws_profile.assumed_role_arn
The ARN of the assumed role - _aws_profile.name
The unique name of the profile. - _aws_profile.organization
Information about this profile's membership in the AWS organization. Show child fields- _aws_profile.organization.account_name
The name of account speciifed by the organization - _aws_profile.organization.id
The organization id - _aws_profile.organization.master_account
Show child fields- _aws_profile.organization.master_account.email
The organization master account email address - _aws_profile.organization.master_account.id
The organization master account id
- _aws_profile.organization.parents[]
Show child fields- _aws_profile.organization.parents[].id
The id of the parent - _aws_profile.organization.parents[].name
The name of the parent - _aws_profile.organization.parents[].tags[]
Show child fields- _aws_profile.organization.parents[].tags[].key
- _aws_profile.organization.parents[].tags[].value
- _aws_profile.organization.parents[].type
The type of parent can be an organization unit or a root
- _aws_profile.organization.tags[]
Show child fields- _aws_profile.organization.tags[].key
- _aws_profile.organization.tags[].value
- _aws_profile.type
The type of profile, either 'credentials' or 'assumed_role' - _aws_profile.via_profile_name
This IAM role for this profile is assumed by first utilizing another profile with this name to obtain credentials.
|
_aws_region Input Column
The AWS region to use. | VARCHAR |
classifier_metadata
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. | STRUCT(
"number_of_labels" BIGINT,
"number_of_trained_documents" BIGINT,
"number_of_test_documents" BIGINT,
"evaluation_metrics" STRUCT(
"accuracy" DOUBLE,
"precision" DOUBLE,
"recall" DOUBLE,
"f1_score" DOUBLE,
"micro_precision" DOUBLE,
"micro_recall" DOUBLE,
"micro_f1_score" DOUBLE,
"hamming_loss" DOUBLE
)
) |
Show child fields- classifier_metadata.evaluation_metrics
Describes the result metrics for the test data associated with an documentation classifier. Show child fields- classifier_metadata.evaluation_metrics.accuracy
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. - classifier_metadata.evaluation_metrics.f1_score
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. - classifier_metadata.evaluation_metrics.hamming_loss
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. - classifier_metadata.evaluation_metrics.micro_f1_score
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. - classifier_metadata.evaluation_metrics.micro_precision
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. - classifier_metadata.evaluation_metrics.micro_recall
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. - classifier_metadata.evaluation_metrics.precision
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. - classifier_metadata.evaluation_metrics.recall
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.
- classifier_metadata.number_of_labels
The number of labels in the input data. - classifier_metadata.number_of_test_documents
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. - classifier_metadata.number_of_trained_documents
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.
|
data_access_role_arn
The Amazon Resource Name (ARN) of the IAM role that grants Amazon Comprehend read access to your input data. | VARCHAR |
document_classifier_arn
The Amazon Resource Name (ARN) that identifies the document classifier. | VARCHAR |
end_time
The time that training the document classifier completed. | TIMESTAMP_S |
flywheel_arn
The Amazon Resource Number (ARN) of the flywheel | VARCHAR |
input_data_config
The input data configuration that you supplied when you created the document classifier for training. | STRUCT(
"data_format" VARCHAR,
"s3_uri" VARCHAR,
"test_s3_uri" VARCHAR,
"label_delimiter" VARCHAR,
"augmented_manifests" STRUCT(
"s3_uri" VARCHAR,
"split" VARCHAR,
"attribute_names" VARCHAR[],
"annotation_data_s3_uri" VARCHAR,
"source_documents_s3_uri" VARCHAR,
"document_type" VARCHAR
)[],
"document_type" VARCHAR,
"documents" STRUCT(
"s3_uri" VARCHAR,
"test_s3_uri" VARCHAR
),
"document_reader_config" STRUCT(
"document_read_action" VARCHAR,
"document_read_mode" VARCHAR,
"feature_types" VARCHAR[]
)
) |
Show child fields- input_data_config.augmented_manifests[]
Show child fields- input_data_config.augmented_manifests[].annotation_data_s3_uri
The S3 prefix to the annotation files that are referred in the augmented manifest file. - input_data_config.augmented_manifests[].attribute_names[]
- input_data_config.augmented_manifests[].document_type
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. - input_data_config.augmented_manifests[].s3_uri
The Amazon S3 location of the augmented manifest file. - input_data_config.augmented_manifests[].source_documents_s3_uri
The S3 prefix to the source files (PDFs) that are referred to in the augmented manifest file. - input_data_config.augmented_manifests[].split
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.
- input_data_config.data_format
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. - input_data_config.document_reader_config
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. Show child fields- input_data_config.document_reader_config.document_read_action
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. - input_data_config.document_reader_config.document_read_mode
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. - input_data_config.document_reader_config.feature_types[]
- input_data_config.document_type
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. - input_data_config.documents
The S3 location of the training documents. This parameter is required in a request to create a native document model. Show child fields- input_data_config.documents.s3_uri
The S3 URI location of the training documents specified in the S3Uri CSV file. - input_data_config.documents.test_s3_uri
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.
- input_data_config.label_delimiter
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. - input_data_config.s3_uri
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. - input_data_config.test_s3_uri
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.
|
language_code
The language code for the language of the documents that the classifier was trained on. | VARCHAR |
message
Additional information about the status of the classifier. | VARCHAR |
mode
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. | VARCHAR |
model_kms_key_id
ID for the KMS key that Amazon Comprehend uses to encrypt trained custom models. The ModelKmsKeyId can be either of the following formats: | VARCHAR |
output_data_config
Provides output results configuration parameters for custom classifier jobs. | STRUCT(
"s3_uri" VARCHAR,
"kms_key_id" VARCHAR,
"flywheel_stats_s3_prefix" VARCHAR
) |
Show child fields- output_data_config.flywheel_stats_s3_prefix
The Amazon S3 prefix for the data lake location of the flywheel statistics. - output_data_config.kms_key_id
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" - output_data_config.s3_uri
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.
|
source_model_arn
The Amazon Resource Name (ARN) of the source model. This model was imported from a different Amazon Web Services account to create the document classifier model in your Amazon Web Services account. | VARCHAR |
status
The status of the document classifier. If the status is TRAINED the classifier is ready to use. If the status is TRAINED_WITH_WARNINGS the classifier training succeeded, but you should review the warnings returned in the CreateDocumentClassifier response. If the status is FAILED you can see additional information about why the classifier wasn't trained in the Message field. | VARCHAR |
submit_time
The time that the document classifier was submitted for training. | TIMESTAMP_S |
training_end_time
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. | TIMESTAMP_S |
training_start_time
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. | TIMESTAMP_S |
version_name
The version name that you assigned to the document classifier. | VARCHAR |
volume_kms_key_id
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: | VARCHAR |
vpc_config
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. | STRUCT(
"security_group_ids" VARCHAR[],
"subnets" VARCHAR[]
) |
Show child fields- vpc_config.security_group_ids[]
- vpc_config.subnets[]
|