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Create Entity Recognizer

comprehend_create_entity_recognizer R Documentation

Creates an entity recognizer using submitted files

Description

Creates an entity recognizer using submitted files. After your create_entity_recognizer request is submitted, you can check job status using the describe_entity_recognizer API.

Usage

comprehend_create_entity_recognizer(RecognizerName, VersionName,
  DataAccessRoleArn, Tags, InputDataConfig, ClientRequestToken,
  LanguageCode, VolumeKmsKeyId, VpcConfig, ModelKmsKeyId, ModelPolicy)

Arguments

RecognizerName

[required] The name given to the newly created recognizer. Recognizer names can be a maximum of 256 characters. Alphanumeric characters, hyphens (-) and underscores (_) are allowed. The name must be unique in the account/Region.

VersionName

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

DataAccessRoleArn

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

Tags

Tags to associate with the entity recognizer. 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.

InputDataConfig

[required] Specifies the format and location of the input data. The S3 bucket containing the input data must be located in the same Region as the entity recognizer being created.

ClientRequestToken

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

LanguageCode

[required] You can specify any of the following languages: English ("en"), Spanish ("es"), French ("fr"), Italian ("it"), German ("de"), or Portuguese ("pt"). If you plan to use this entity recognizer with PDF, Word, or image input files, you must specify English as the language. All training documents must be in the same language.

VolumeKmsKeyId

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

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

ModelKmsKeyId

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

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

Provide your JSON as a UTF-8 encoded string without line breaks. To provide valid JSON for your policy, 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"]}'

Value

A list with the following syntax: 

list(
  EntityRecognizerArn = "string"
)

Request syntax

svc$create_entity_recognizer(
  RecognizerName = "string",
  VersionName = "string",
  DataAccessRoleArn = "string",
  Tags = list(
    list(
      Key = "string",
      Value = "string"
    )
  ),
  InputDataConfig = list(
    DataFormat = "COMPREHEND_CSV"|"AUGMENTED_MANIFEST",
    EntityTypes = list(
      list(
        Type = "string"
      )
    ),
    Documents = list(
      S3Uri = "string",
      TestS3Uri = "string",
      InputFormat = "ONE_DOC_PER_FILE"|"ONE_DOC_PER_LINE"
    ),
    Annotations = list(
      S3Uri = "string",
      TestS3Uri = "string"
    ),
    EntityList = list(
      S3Uri = "string"
    ),
    AugmentedManifests = list(
      list(
        S3Uri = "string",
        Split = "TRAIN"|"TEST",
        AttributeNames = list(
          "string"
        ),
        AnnotationDataS3Uri = "string",
        SourceDocumentsS3Uri = "string",
        DocumentType = "PLAIN_TEXT_DOCUMENT"|"SEMI_STRUCTURED_DOCUMENT"
      )
    )
  ),
  ClientRequestToken = "string",
  LanguageCode = "en"|"es"|"fr"|"de"|"it"|"pt"|"ar"|"hi"|"ja"|"ko"|"zh"|"zh-TW",
  VolumeKmsKeyId = "string",
  VpcConfig = list(
    SecurityGroupIds = list(
      "string"
    ),
    Subnets = list(
      "string"
    )
  ),
  ModelKmsKeyId = "string",
  ModelPolicy = "string"
)