[ aws . comprehend ]

classify-document

Description

Creates a new document classification request to analyze a single document in real-time, using a previously created and trained custom model and an endpoint.

See also: AWS API Documentation

See ‘aws help’ for descriptions of global parameters.

Synopsis

  classify-document
--text <value>
--endpoint-arn <value>
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]

Options

--text (string)

The document text to be analyzed.

--endpoint-arn (string)

The Amazon Resource Number (ARN) of the endpoint.

--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

Classes -> (list)

The classes used by the document being analyzed. These are used for multi-class trained models. Individual classes are mutually exclusive and each document is expected to have only a single class assigned to it. For example, an animal can be a dog or a cat, but not both at the same time.

(structure)

Specifies the class that categorizes the document being analyzed

Name -> (string)

The name of the class.

Score -> (float)

The confidence score that Amazon Comprehend has this class correctly attributed.

Labels -> (list)

The labels used the document being analyzed. These are used for multi-label trained models. Individual labels represent different categories that are related in some manner and are not mutually exclusive. For example, a movie can be just an action movie, or it can be an action movie, a science fiction movie, and a comedy, all at the same time.

(structure)

Specifies one of the label or labels that categorize the document being analyzed.

Name -> (string)

The name of the label.

Score -> (float)

The confidence score that Amazon Comprehend has this label correctly attributed.