create-entity-recognizer¶

Description¶

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

Synopsis¶

  create-entity-recognizer
--recognizer-name <value>
--data-access-role-arn <value>
[--tags <value>]
--input-data-config <value>
[--client-request-token <value>]
--language-code <value>
[--volume-kms-key-id <value>]
[--vpc-config <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]

Options¶

--recognizer-name (string)

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.

--data-access-role-arn (string)

The Amazon Resource Name (ARN) of the AWS Identity and Management (IAM) role that grants Amazon Comprehend read access to your input data.

--tags (list)

Tags to be associated with the entity recognizer 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.

(structure)

A key-value pair that adds as a metadata to a resource used by Amazon Comprehend. For example, a tag with the key-value pair ‘Department’:’Sales’ might be added to a resource to indicate its use by a particular department.

Key -> (string)

The initial part of a key-value pair that forms a tag associated with a given resource. For instance, if you want to show which resources are used by which departments, you might use “Department” as the key portion of the pair, with multiple possible values such as “sales,” “legal,” and “administration.”

Value -> (string)

The second part of a key-value pair that forms a tag associated with a given resource. For instance, if you want to show which resources are used by which departments, you might use “Department” as the initial (key) portion of the pair, with a value of “sales” to indicate the sales department.

Shorthand Syntax:

Key=string,Value=string ...

JSON Syntax:

[
  {
    "Key": "string",
    "Value": "string"
  }
  ...
]

--input-data-config (structure)

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.

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)

JSON Syntax:

{
  "DataFormat": "COMPREHEND_CSV"|"AUGMENTED_MANIFEST",
  "EntityTypes": [
    {
      "Type": "string"
    }
    ...
  ],
  "Documents": {
    "S3Uri": "string"
  },
  "Annotations": {
    "S3Uri": "string"
  },
  "EntityList": {
    "S3Uri": "string"
  },
  "AugmentedManifests": [
    {
      "S3Uri": "string",
      "AttributeNames": ["string", ...]
    }
    ...
  ]
}

--client-request-token (string)

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

--language-code (string)

You can specify any of the following languages supported by Amazon Comprehend: English (“en”), Spanish (“es”), French (“fr”), Italian (“it”), German (“de”), or Portuguese (“pt”). All documents must be in the same language.

Possible values:

en

es

fr

de

it

pt

ar

hi

ja

ko

zh

zh-TW

--volume-kms-key-id (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"

--vpc-config (structure)

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 .

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)

Shorthand Syntax:

SecurityGroupIds=string,string,Subnets=string,string

JSON Syntax:

{
  "SecurityGroupIds": ["string", ...],
  "Subnets": ["string", ...]
}

--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¶

EntityRecognizerArn -> (string)

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

Table of Contents

Feedback

User Guide

create-entity-recognizer¶

Description¶

Synopsis¶

Options¶

Output¶