comprehend_create_document_classifier: Creates a new document classifier that you can use to...
In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

comprehend_create_document_classifier

R Documentation

Creates a new document classifier that you can use to categorize documents

Description

Creates a new document classifier that you can use to categorize documents. To create a classifier, you provide a set of training documents that labeled with the categories that you want to use. After the classifier is trained you can use it to categorize a set of labeled documents into the categories. For more information, see how-document-classification.

See https://paws-r.github.io/docs/comprehend/create_document_classifier.html for full documentation.

Usage

comprehend_create_document_classifier(
  DocumentClassifierName,
  VersionName = NULL,
  DataAccessRoleArn,
  Tags = NULL,
  InputDataConfig,
  OutputDataConfig = NULL,
  ClientRequestToken = NULL,
  LanguageCode,
  VolumeKmsKeyId = NULL,
  VpcConfig = NULL,
  Mode = NULL,
  ModelKmsKeyId = NULL,
  ModelPolicy = NULL
)

Arguments

`DocumentClassifierName`	[required] The name of the document classifier.
`VersionName`	The version name given to the newly created classifier. Version names can have a maximum of 256 characters. Alphanumeric characters, hyphens (-) and underscores (_) are allowed. The version name must be unique among all models with the same classifier name in the account/AWS Region.
`DataAccessRoleArn`	[required] The Amazon Resource Name (ARN) of the AWS Identity and Management (IAM) role that grants Amazon Comprehend read access to your input data.
`Tags`	Tags to be associated with the document classifier being created. 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 for the job.
`OutputDataConfig`	Enables the addition of output results configuration parameters for custom classifier jobs.
`ClientRequestToken`	A unique identifier for the request. If you don't set the client request token, Amazon Comprehend generates one.
`LanguageCode`	[required] The language of the input documents. You can specify any of the following languages supported by Amazon Comprehend: German ("de"), English ("en"), Spanish ("es"), French ("fr"), Italian ("it"), or Portuguese ("pt"). All documents must be in the same language.
`VolumeKmsKeyId`	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`	Configuration parameters for an optional private Virtual Private Cloud (VPC) containing the resources you are using for your custom classifier. For more information, see Amazon VPC.
`Mode`	Indicates the mode in which the classifier will be trained. The classifier can be trained in multi-class mode, which identifies one and only one class for each document, or multi-label mode, which identifies one or more labels for each document. In multi-label mode, multiple labels for an individual document are separated by a delimiter. The default delimiter between labels is a pipe (\|).
`ModelKmsKeyId`	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"`
`ModelPolicy`	The resource-based policy to attach to your custom document classifier model. You can use this policy to allow another AWS account to import your custom model. Provide your policy as a JSON body that you enter as a UTF-8 encoded string without line breaks. To provide valid JSON, 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"]}'`