Struct aws_sdk_comprehend::types::DatasetInputDataConfig

#[non_exhaustive]
pub struct DatasetInputDataConfig {
    pub augmented_manifests: Option<Vec<DatasetAugmentedManifestsListItem>>,
    pub data_format: Option<DatasetDataFormat>,
    pub document_classifier_input_data_config: Option<DatasetDocumentClassifierInputDataConfig>,
    pub entity_recognizer_input_data_config: Option<DatasetEntityRecognizerInputDataConfig>,
}

Specifies the format and location of the input data for the dataset.

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.

augmented_manifests: Option<Vec<DatasetAugmentedManifestsListItem>>

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.

data_format: Option<DatasetDataFormat>

COMPREHEND_CSV: The data format is a two-column CSV file, where the first column contains labels and the second column contains documents.

AUGMENTED_MANIFEST: The data format

document_classifier_input_data_config: Option<DatasetDocumentClassifierInputDataConfig>

The input properties for training a document classifier model.

For more information on how the input file is formatted, see Preparing training data in the Comprehend Developer Guide.

entity_recognizer_input_data_config: Option<DatasetEntityRecognizerInputDataConfig>

The input properties for training an entity recognizer model.

Implementations

impl DatasetInputDataConfig

pub fn augmented_manifests(&self) -> &[DatasetAugmentedManifestsListItem]

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.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .augmented_manifests.is_none().

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

COMPREHEND_CSV: The data format is a two-column CSV file, where the first column contains labels and the second column contains documents.

AUGMENTED_MANIFEST: The data format

pub fn document_classifier_input_data_config( &self ) -> Option<&DatasetDocumentClassifierInputDataConfig>

The input properties for training a document classifier model.

For more information on how the input file is formatted, see Preparing training data in the Comprehend Developer Guide.

pub fn entity_recognizer_input_data_config( &self ) -> Option<&DatasetEntityRecognizerInputDataConfig>

The input properties for training an entity recognizer model.

impl DatasetInputDataConfig

pub fn builder() -> DatasetInputDataConfigBuilder

Creates a new builder-style object to manufacture DatasetInputDataConfig.

Trait Implementations

impl Clone for DatasetInputDataConfig

fn clone(&self) -> DatasetInputDataConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for DatasetInputDataConfig

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

Formats the value using the given formatter.

impl PartialEq for DatasetInputDataConfig

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

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

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

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for DatasetInputDataConfig