Struct aws_sdk_comprehend::model::DocumentClassifierInputDataConfig

#[non_exhaustive]
pub struct DocumentClassifierInputDataConfig {
    pub data_format: Option<DocumentClassifierDataFormat>,
    pub s3_uri: Option<String>,
    pub test_s3_uri: Option<String>,
    pub label_delimiter: Option<String>,
    pub augmented_manifests: Option<Vec<AugmentedManifestsListItem>>,
}

The input properties for training a document classifier.

For more information on how the input file is formatted, see how-document-classification-training-data.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

data_format: Option<DocumentClassifierDataFormat>

The format of your training data:

• COMPREHEND_CSV: A two-column CSV file, where labels are provided in the first column, and documents are provided in the second. If you use this value, you must provide the S3Uri parameter in your request.

• 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 associated labels.

  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.

s3_uri: Option<String>

The Amazon S3 URI for the input data. The S3 bucket must be in the same region as the API endpoint that you are calling. The URI can point to a single input file or it can provide the prefix for a collection of input files.

For example, if you use the URI S3://bucketName/prefix, if the prefix is a single file, Amazon Comprehend uses that file as input. If more than one file begins with the prefix, Amazon Comprehend uses all of them as input.

This parameter is required if you set DataFormat to COMPREHEND_CSV.

test_s3_uri: Option<String>

The Amazon S3 URI for the input data. The Amazon S3 bucket must be in the same AWS Region as the API endpoint that you are calling. The URI can point to a single input file or it can provide the prefix for a collection of input files.

label_delimiter: Option<String>

Indicates the delimiter used to separate each label for training a multi-label classifier. The default delimiter between labels is a pipe (|). You can use a different character as a delimiter (if it's an allowed character) by specifying it under Delimiter for labels. If the training documents use a delimiter other than the default or the delimiter you specify, the labels on that line will be combined to make a single unique label, such as LABELLABELLABEL.

augmented_manifests: Option<Vec<AugmentedManifestsListItem>>

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.

Implementations

impl DocumentClassifierInputDataConfig

pub fn data_format(&self) -> Option<&DocumentClassifierDataFormat>

The format of your training data:

• COMPREHEND_CSV: A two-column CSV file, where labels are provided in the first column, and documents are provided in the second. If you use this value, you must provide the S3Uri parameter in your request.

• 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 associated labels.

  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.

pub fn s3_uri(&self) -> Option<&str>

The Amazon S3 URI for the input data. The S3 bucket must be in the same region as the API endpoint that you are calling. The URI can point to a single input file or it can provide the prefix for a collection of input files.

For example, if you use the URI S3://bucketName/prefix, if the prefix is a single file, Amazon Comprehend uses that file as input. If more than one file begins with the prefix, Amazon Comprehend uses all of them as input.

This parameter is required if you set DataFormat to COMPREHEND_CSV.

pub fn test_s3_uri(&self) -> Option<&str>

The Amazon S3 URI for the input data. The Amazon S3 bucket must be in the same AWS Region as the API endpoint that you are calling. The URI can point to a single input file or it can provide the prefix for a collection of input files.

pub fn label_delimiter(&self) -> Option<&str>

Indicates the delimiter used to separate each label for training a multi-label classifier. The default delimiter between labels is a pipe (|). You can use a different character as a delimiter (if it's an allowed character) by specifying it under Delimiter for labels. If the training documents use a delimiter other than the default or the delimiter you specify, the labels on that line will be combined to make a single unique label, such as LABELLABELLABEL.

pub fn augmented_manifests(&self) -> Option<&[AugmentedManifestsListItem]>

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.

impl DocumentClassifierInputDataConfig

pub fn builder() -> Builder

Creates a new builder-style object to manufacture DocumentClassifierInputDataConfig

Trait Implementations

impl Clone for DocumentClassifierInputDataConfig

fn clone(&self) -> DocumentClassifierInputDataConfig

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for DocumentClassifierInputDataConfig

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq<DocumentClassifierInputDataConfig> for DocumentClassifierInputDataConfig

fn eq(&self, other: &DocumentClassifierInputDataConfig) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &DocumentClassifierInputDataConfig) -> bool

This method tests for !=.

impl StructuralPartialEq for DocumentClassifierInputDataConfig