Struct EntityRecognizerInputDataConfig

#[non_exhaustive]
pub struct EntityRecognizerInputDataConfig {
    pub data_format: Option<EntityRecognizerDataFormat>,
    pub entity_types: Vec<EntityTypesListItem>,
    pub documents: Option<EntityRecognizerDocuments>,
    pub annotations: Option<EntityRecognizerAnnotations>,
    pub entity_list: Option<EntityRecognizerEntityList>,
    pub augmented_manifests: Option<Vec<AugmentedManifestsListItem>>,
}

Specifies the format and location of the input 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<EntityRecognizerDataFormat>

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.

entity_types: Vec<EntityTypesListItem>

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).

documents: Option<EntityRecognizerDocuments>

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.

annotations: Option<EntityRecognizerAnnotations>

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

entity_list: Option<EntityRecognizerEntityList>

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

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 EntityRecognizerInputDataConfig

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

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) -> &[EntityTypesListItem]

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) -> Option<&EntityRecognizerDocuments>

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) -> Option<&EntityRecognizerAnnotations>

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

pub fn entity_list(&self) -> Option<&EntityRecognizerEntityList>

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

pub fn augmented_manifests(&self) -> &[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.

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().

impl EntityRecognizerInputDataConfig

pub fn builder() -> EntityRecognizerInputDataConfigBuilder

Creates a new builder-style object to manufacture EntityRecognizerInputDataConfig.

Trait Implementations

impl Clone for EntityRecognizerInputDataConfig

fn clone(&self) -> EntityRecognizerInputDataConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for EntityRecognizerInputDataConfig

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

Formats the value using the given formatter.

impl PartialEq for EntityRecognizerInputDataConfig

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for EntityRecognizerInputDataConfig