Struct aws_sdk_comprehend::types::builders::DatasetInputDataConfigBuilder

#[non_exhaustive]
pub struct DatasetInputDataConfigBuilder { /* private fields */ }

A builder for DatasetInputDataConfig.

Implementations

impl DatasetInputDataConfigBuilder

pub fn augmented_manifests( self, input: DatasetAugmentedManifestsListItem ) -> Self

Appends an item to augmented_manifests.

To override the contents of this collection use set_augmented_manifests.

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.

pub fn set_augmented_manifests( self, input: Option<Vec<DatasetAugmentedManifestsListItem>> ) -> Self

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.

pub fn get_augmented_manifests( &self ) -> &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.

pub fn data_format(self, input: DatasetDataFormat) -> Self

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 set_data_format(self, input: Option<DatasetDataFormat>) -> Self

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 get_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, input: DatasetDocumentClassifierInputDataConfig ) -> Self

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 set_document_classifier_input_data_config( self, input: Option<DatasetDocumentClassifierInputDataConfig> ) -> Self

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 get_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, input: DatasetEntityRecognizerInputDataConfig ) -> Self

The input properties for training an entity recognizer model.

pub fn set_entity_recognizer_input_data_config( self, input: Option<DatasetEntityRecognizerInputDataConfig> ) -> Self

The input properties for training an entity recognizer model.

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

The input properties for training an entity recognizer model.

pub fn build(self) -> DatasetInputDataConfig

Consumes the builder and constructs a DatasetInputDataConfig.

Trait Implementations

impl Clone for DatasetInputDataConfigBuilder

fn clone(&self) -> DatasetInputDataConfigBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for DatasetInputDataConfigBuilder

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

Formats the value using the given formatter.

impl Default for DatasetInputDataConfigBuilder

fn default() -> DatasetInputDataConfigBuilder

Returns the “default value” for a type.

impl PartialEq for DatasetInputDataConfigBuilder

fn eq(&self, other: &DatasetInputDataConfigBuilder) -> 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 DatasetInputDataConfigBuilder