_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
)[]
)[]
)
) |
entity_recognizer_properties
Describes information associated with an entity recognizer. | STRUCT(
"entity_recognizer_arn" VARCHAR,
"language_code" VARCHAR,
"status" VARCHAR,
"message" VARCHAR,
"submit_time" TIMESTAMP_S,
"end_time" TIMESTAMP_S,
"training_start_time" TIMESTAMP_S,
"training_end_time" TIMESTAMP_S,
"input_data_config" STRUCT(
"data_format" VARCHAR,
"entity_types" STRUCT(
"type" VARCHAR
)[],
"documents" STRUCT(
"s3_uri" VARCHAR,
"test_s3_uri" VARCHAR,
"input_format" VARCHAR
),
"annotations" STRUCT(
"s3_uri" VARCHAR,
"test_s3_uri" VARCHAR
),
"entity_list" STRUCT(
"s3_uri" 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
)[]
),
"recognizer_metadata" STRUCT(
"number_of_trained_documents" BIGINT,
"number_of_test_documents" BIGINT,
"evaluation_metrics" STRUCT(
"precision" DOUBLE,
"recall" DOUBLE,
"f1_score" DOUBLE
),
"entity_types" STRUCT(
"type" VARCHAR,
"evaluation_metrics" STRUCT(
"precision" DOUBLE,
"recall" DOUBLE,
"f1_score" DOUBLE
),
"number_of_train_mentions" BIGINT
)[]
),
"data_access_role_arn" VARCHAR,
"volume_kms_key_id" VARCHAR,
"vpc_config" STRUCT(
"security_group_ids" VARCHAR[],
"subnets" VARCHAR[]
),
"model_kms_key_id" VARCHAR,
"version_name" VARCHAR,
"source_model_arn" VARCHAR,
"flywheel_arn" VARCHAR,
"output_data_config" STRUCT(
"flywheel_stats_s3_prefix" VARCHAR
)
) |
Show child fields- entity_recognizer_properties.data_access_role_arn
The Amazon Resource Name (ARN) of the IAM role that grants Amazon Comprehend read access to your input data. - entity_recognizer_properties.end_time
The time that the recognizer creation completed. - entity_recognizer_properties.entity_recognizer_arn
The Amazon Resource Name (ARN) that identifies the entity recognizer. - entity_recognizer_properties.flywheel_arn
The Amazon Resource Number (ARN) of the flywheel - entity_recognizer_properties.input_data_config
The input data properties of an entity recognizer. Show child fields- entity_recognizer_properties.input_data_config.annotations
The S3 location of the CSV file that annotates your training documents. Show child fields- entity_recognizer_properties.input_data_config.annotations.s3_uri
Specifies the Amazon S3 location where the annotations for an entity recognizer are located. The URI must be in the same Region as the API endpoint that you are calling. - entity_recognizer_properties.input_data_config.annotations.test_s3_uri
Specifies the Amazon S3 location where the test annotations for an entity recognizer are located. The URI must be in the same Region as the API endpoint that you are calling.
- entity_recognizer_properties.input_data_config.augmented_manifests[]
Show child fields- entity_recognizer_properties.input_data_config.augmented_manifests[].annotation_data_s3_uri
The S3 prefix to the annotation files that are referred in the augmented manifest file. - entity_recognizer_properties.input_data_config.augmented_manifests[].attribute_names[]
- entity_recognizer_properties.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. - entity_recognizer_properties.input_data_config.augmented_manifests[].s3_uri
The Amazon S3 location of the augmented manifest file. - entity_recognizer_properties.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. - entity_recognizer_properties.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.
- entity_recognizer_properties.input_data_config.data_format
The format of your training data: -
COMPREHEND_CSV: A CSV file that supplements your training documents. The CSV file contains information about the custom entities that your trained model will detect. The required format of the file depends on whether you are providing annotations or an entity list. If you use this value, you must provide your CSV file by using either the Annotations or EntityList parameters. You must provide your training documents by using the Documents parameter. -
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 labels. Each label annotates a named entity in the training document. 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. - entity_recognizer_properties.input_data_config.documents
The S3 location of the folder that contains the training documents for your custom entity recognizer. This parameter is required if you set DataFormat to COMPREHEND_CSV. Show child fields- entity_recognizer_properties.input_data_config.documents.input_format
Specifies how the text in an input file should be processed. This is optional, and the default is ONE_DOC_PER_LINE. ONE_DOC_PER_FILE - Each file is considered a separate document. Use this option when you are processing large documents, such as newspaper articles or scientific papers. ONE_DOC_PER_LINE - Each line in a file is considered a separate document. Use this option when you are processing many short documents, such as text messages. - entity_recognizer_properties.input_data_config.documents.s3_uri
Specifies the Amazon S3 location where the training documents for an entity recognizer are located. The URI must be in the same Region as the API endpoint that you are calling. - entity_recognizer_properties.input_data_config.documents.test_s3_uri
Specifies the Amazon S3 location where the test documents for an entity recognizer are located. The URI must be in the same Amazon Web Services Region as the API endpoint that you are calling.
- entity_recognizer_properties.input_data_config.entity_list
The S3 location of the CSV file that has the entity list for your custom entity recognizer. Show child fields- entity_recognizer_properties.input_data_config.entity_list.s3_uri
Specifies the Amazon S3 location where the entity list is located. The URI must be in the same Region as the API endpoint that you are calling.
- entity_recognizer_properties.input_data_config.entity_types[]
Show child fields- entity_recognizer_properties.input_data_config.entity_types[].type
An entity type within a labeled training dataset that Amazon Comprehend uses to train a custom entity recognizer. Entity types must not contain the following invalid characters: \n (line break), \\n (escaped line break, \r (carriage return), \\r (escaped carriage return), \t (tab), \\t (escaped tab), and , (comma).
- entity_recognizer_properties.language_code
The language of the input documents. All documents must be in the same language. Only English ("en") is currently supported. - entity_recognizer_properties.message
A description of the status of the recognizer. - entity_recognizer_properties.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: - entity_recognizer_properties.output_data_config
Output data configuration. Show child fields- entity_recognizer_properties.output_data_config.flywheel_stats_s3_prefix
The Amazon S3 prefix for the data lake location of the flywheel statistics.
- entity_recognizer_properties.recognizer_metadata
Provides information about an entity recognizer. Show child fields- entity_recognizer_properties.recognizer_metadata.entity_types[]
Show child fields- entity_recognizer_properties.recognizer_metadata.entity_types[].evaluation_metrics
Detailed information about the accuracy of the entity recognizer for a specific item on the list of entity types. Show child fields- entity_recognizer_properties.recognizer_metadata.entity_types[].evaluation_metrics.f1_score
A measure of how accurate the recognizer results are for a specific entity type in 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. - entity_recognizer_properties.recognizer_metadata.entity_types[].evaluation_metrics.precision
A measure of the usefulness of the recognizer results for a specific entity type in the test data. High precision means that the recognizer returned substantially more relevant results than irrelevant ones. - entity_recognizer_properties.recognizer_metadata.entity_types[].evaluation_metrics.recall
A measure of how complete the recognizer results are for a specific entity type in the test data. High recall means that the recognizer returned most of the relevant results.
- entity_recognizer_properties.recognizer_metadata.entity_types[].number_of_train_mentions
Indicates the number of times the given entity type was seen in the training data. - entity_recognizer_properties.recognizer_metadata.entity_types[].type
Type of entity from the list of entity types in the metadata of an entity recognizer.
- entity_recognizer_properties.recognizer_metadata.evaluation_metrics
Detailed information about the accuracy of an entity recognizer. Show child fields- entity_recognizer_properties.recognizer_metadata.evaluation_metrics.f1_score
A measure of how accurate the recognizer 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. For plain text entity recognizer models, the range is 0 to 100, where 100 is the best score. For PDF/Word entity recognizer models, the range is 0 to 1, where 1 is the best score. - entity_recognizer_properties.recognizer_metadata.evaluation_metrics.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. - entity_recognizer_properties.recognizer_metadata.evaluation_metrics.recall
A measure of how complete the recognizer results are for the test data. High recall means that the recognizer returned most of the relevant results.
- entity_recognizer_properties.recognizer_metadata.number_of_test_documents
The number of documents in the input data that were used to test the entity recognizer. Typically this is 10 to 20 percent of the input documents. - entity_recognizer_properties.recognizer_metadata.number_of_trained_documents
The number of documents in the input data that were used to train the entity recognizer. Typically this is 80 to 90 percent of the input documents.
- entity_recognizer_properties.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 entity recognizer model in your Amazon Web Services account. - entity_recognizer_properties.status
Provides the status of the entity recognizer. - entity_recognizer_properties.submit_time
The time that the recognizer was submitted for processing. - entity_recognizer_properties.training_end_time
The time that training of the entity recognizer was completed. - entity_recognizer_properties.training_start_time
The time that training of the entity recognizer started. - entity_recognizer_properties.version_name
The version name you assigned to the entity recognizer. - entity_recognizer_properties.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: - entity_recognizer_properties.vpc_config
Configuration parameters for a private Virtual Private Cloud (VPC) containing the resources you are using for your custom entity recognizer. For more information, see Amazon VPC. Show child fields- entity_recognizer_properties.vpc_config.security_group_ids[]
- entity_recognizer_properties.vpc_config.subnets[]
|