Struct aws_sdk_comprehend::model::entity_recognizer_input_data_config::Builder

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

A builder for EntityRecognizerInputDataConfig

Implementations

impl Builder

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

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.

pub fn set_data_format(self, input: Option<EntityRecognizerDataFormat>) -> Self

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.

pub fn entity_types(self, input: EntityTypesListItem) -> Self

Appends an item to entity_types.

To override the contents of this collection use set_entity_types.

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).

pub fn set_entity_types(self, input: Option<Vec<EntityTypesListItem>>) -> Self

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).

pub fn documents(self, input: EntityRecognizerDocuments) -> Self

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.

pub fn set_documents(self, input: Option<EntityRecognizerDocuments>) -> Self

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.

pub fn annotations(self, input: EntityRecognizerAnnotations) -> Self

The S3 location of the CSV file that annotates your training documents.

pub fn set_annotations(self, input: Option<EntityRecognizerAnnotations>) -> Self

The S3 location of the CSV file that annotates your training documents.

pub fn entity_list(self, input: EntityRecognizerEntityList) -> Self

The S3 location of the CSV file that has the entity list for your custom entity recognizer.

pub fn set_entity_list(self, input: Option<EntityRecognizerEntityList>) -> Self

The S3 location of the CSV file that has the entity list for your custom entity recognizer.

pub fn augmented_manifests(self, input: AugmentedManifestsListItem) -> 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.

This parameter is required if you set DataFormat to AUGMENTED_MANIFEST.

pub fn set_augmented_manifests(
    self,
    input: Option<Vec<AugmentedManifestsListItem>>
) -> 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.

This parameter is required if you set DataFormat to AUGMENTED_MANIFEST.

pub fn build(self) -> EntityRecognizerInputDataConfig

Consumes the builder and constructs a EntityRecognizerInputDataConfig

Trait Implementations

impl Clone for Builder

fn clone(&self) -> Builder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Builder

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

Formats the value using the given formatter.

impl Default for Builder

fn default() -> Builder

Returns the “default value” for a type.

impl PartialEq<Builder> for Builder

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

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

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

This method tests for !=.

impl StructuralPartialEq for Builder