# Generated by default/object.tt package Paws::SageMaker::HumanTaskConfig; use Moose; has AnnotationConsolidationConfig => (is => 'ro', isa => 'Paws::SageMaker::AnnotationConsolidationConfig', required => 1); has MaxConcurrentTaskCount => (is => 'ro', isa => 'Int'); has NumberOfHumanWorkersPerDataObject => (is => 'ro', isa => 'Int', required => 1); has PreHumanTaskLambdaArn => (is => 'ro', isa => 'Str', required => 1); has PublicWorkforceTaskPrice => (is => 'ro', isa => 'Paws::SageMaker::PublicWorkforceTaskPrice'); has TaskAvailabilityLifetimeInSeconds => (is => 'ro', isa => 'Int'); has TaskDescription => (is => 'ro', isa => 'Str', required => 1); has TaskKeywords => (is => 'ro', isa => 'ArrayRef[Str|Undef]'); has TaskTimeLimitInSeconds => (is => 'ro', isa => 'Int', required => 1); has TaskTitle => (is => 'ro', isa => 'Str', required => 1); has UiConfig => (is => 'ro', isa => 'Paws::SageMaker::UiConfig', required => 1); has WorkteamArn => (is => 'ro', isa => 'Str', required => 1); 1; ### main pod documentation begin ### =head1 NAME Paws::SageMaker::HumanTaskConfig =head1 USAGE This class represents one of two things: =head3 Arguments in a call to a service Use the attributes of this class as arguments to methods. You shouldn't make instances of this class. Each attribute should be used as a named argument in the calls that expect this type of object. As an example, if Att1 is expected to be a Paws::SageMaker::HumanTaskConfig object: $service_obj->Method(Att1 => { AnnotationConsolidationConfig => $value, ..., WorkteamArn => $value }); =head3 Results returned from an API call Use accessors for each attribute. If Att1 is expected to be an Paws::SageMaker::HumanTaskConfig object: $result = $service_obj->Method(...); $result->Att1->AnnotationConsolidationConfig =head1 DESCRIPTION Information required for human workers to complete a labeling task. =head1 ATTRIBUTES =head2 B AnnotationConsolidationConfig => L Configures how labels are consolidated across human workers. =head2 MaxConcurrentTaskCount => Int Defines the maximum number of data objects that can be labeled by human workers at the same time. Also referred to as batch size. Each object may have more than one worker at one time. The default value is 1000 objects. =head2 B NumberOfHumanWorkersPerDataObject => Int The number of human workers that will label an object. =head2 B PreHumanTaskLambdaArn => Str The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job. For built-in task types (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html), use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for C. For custom labeling workflows, see Pre-annotation Lambda (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates-step3.html#sms-custom-templates-step3-prelambda). B - Finds the most similar boxes from different workers based on the Jaccard index of the boxes. =over =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =back B - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers. =over =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =back B - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers. =over =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =back B - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label. =over =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =back B - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers. =over =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =back B - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers. =over =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =back B - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label. =over =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =item * C =back B