[ aws . comprehend ]

describe-entity-recognizer

Description

Provides details about an entity recognizer including status, S3 buckets containing training data, recognizer metadata, metrics, and so on.

See also: AWS API Documentation

See ‘aws help’ for descriptions of global parameters.

Synopsis

  describe-entity-recognizer
--entity-recognizer-arn <value>
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]

Options

--entity-recognizer-arn (string)

The Amazon Resource Name (ARN) that identifies the entity recognizer.

--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

EntityRecognizerProperties -> (structure)

Describes information associated with an entity recognizer.

EntityRecognizerArn -> (string)

The Amazon Resource Name (ARN) that identifies the entity recognizer.

LanguageCode -> (string)

The language of the input documents. All documents must be in the same language. Only English (“en”) is currently supported.

Status -> (string)

Provides the status of the entity recognizer.

Message -> (string)

A description of the status of the recognizer.

SubmitTime -> (timestamp)

The time that the recognizer was submitted for processing.

EndTime -> (timestamp)

The time that the recognizer creation completed.

TrainingStartTime -> (timestamp)

The time that training of the entity recognizer started.

TrainingEndTime -> (timestamp)

The time that training of the entity recognizer was completed.

InputDataConfig -> (structure)

The input data properties of an entity recognizer.

DataFormat -> (string)

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.

EntityTypes -> (list)

The entity types in the labeled training data that Amazon Comprehend uses to train the custom entity recognizer. Any entity types that you don’t specify are ignored.

A maximum of 25 entity types can be used at one time to train an 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), space, and , (comma).

(structure)

An entity type within a labeled training dataset that Amazon Comprehend uses to train a custom entity recognizer.

Type -> (string)

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), space, and , (comma).

Documents -> (structure)

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 .

S3Uri -> (string)

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.

Annotations -> (structure)

The S3 location of the CSV file that annotates your training documents.

S3Uri -> (string)

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.

EntityList -> (structure)

The S3 location of the CSV file that has the entity list for your custom entity recognizer.

S3Uri -> (string)

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.

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.

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)

RecognizerMetadata -> (structure)

Provides information about an entity recognizer.

NumberOfTrainedDocuments -> (integer)

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.

NumberOfTestDocuments -> (integer)

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.

EvaluationMetrics -> (structure)

Detailed information about the accuracy of an entity recognizer.

Precision -> (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.

Recall -> (double)

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.

F1Score -> (double)

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. The highest score is 1, and the worst score is 0.

EntityTypes -> (list)

Entity types from the metadata of an entity recognizer.

(structure)

Individual item from the list of entity types in the metadata of an entity recognizer.

Type -> (string)

Type of entity from the list of entity types in the metadata of an entity recognizer.

EvaluationMetrics -> (structure)

Detailed information about the accuracy of the entity recognizer for a specific item on the list of entity types.

Precision -> (double)

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.

Recall -> (double)

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.

F1Score -> (double)

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.

NumberOfTrainMentions -> (integer)

Indicates the number of times the given entity type was seen in the training data.

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 entity recognizer. 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)