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  6. EvaluationJob

Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi

google-native.datalabeling/v1beta1.EvaluationJob

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Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi

Creates an evaluation job. Auto-naming is currently not supported for this resource.

Create EvaluationJob Resource

Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

Constructor syntax

new EvaluationJob(name: string, args: EvaluationJobArgs, opts?: CustomResourceOptions);
@overload
def EvaluationJob(resource_name: str,
                  args: EvaluationJobArgs,
                  opts: Optional[ResourceOptions] = None)

@overload
def EvaluationJob(resource_name: str,
                  opts: Optional[ResourceOptions] = None,
                  annotation_spec_set: Optional[str] = None,
                  description: Optional[str] = None,
                  evaluation_job_config: Optional[GoogleCloudDatalabelingV1beta1EvaluationJobConfigArgs] = None,
                  label_missing_ground_truth: Optional[bool] = None,
                  model_version: Optional[str] = None,
                  schedule: Optional[str] = None,
                  project: Optional[str] = None)
func NewEvaluationJob(ctx *Context, name string, args EvaluationJobArgs, opts ...ResourceOption) (*EvaluationJob, error)
public EvaluationJob(string name, EvaluationJobArgs args, CustomResourceOptions? opts = null)
public EvaluationJob(String name, EvaluationJobArgs args)
public EvaluationJob(String name, EvaluationJobArgs args, CustomResourceOptions options)
type: google-native:datalabeling/v1beta1:EvaluationJob
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

Parameters

name This property is required. string
The unique name of the resource.
args This property is required. EvaluationJobArgs
The arguments to resource properties.
opts CustomResourceOptions
Bag of options to control resource's behavior.
resource_name This property is required. str
The unique name of the resource.
args This property is required. EvaluationJobArgs
The arguments to resource properties.
opts ResourceOptions
Bag of options to control resource's behavior.
ctx Context
Context object for the current deployment.
name This property is required. string
The unique name of the resource.
args This property is required. EvaluationJobArgs
The arguments to resource properties.
opts ResourceOption
Bag of options to control resource's behavior.
name This property is required. string
The unique name of the resource.
args This property is required. EvaluationJobArgs
The arguments to resource properties.
opts CustomResourceOptions
Bag of options to control resource's behavior.
name This property is required. String
The unique name of the resource.
args This property is required. EvaluationJobArgs
The arguments to resource properties.
options CustomResourceOptions
Bag of options to control resource's behavior.

Constructor example

The following reference example uses placeholder values for all input properties.

var evaluationJobResource = new GoogleNative.DataLabeling.V1Beta1.EvaluationJob("evaluationJobResource", new()
{
    AnnotationSpecSet = "string",
    Description = "string",
    EvaluationJobConfig = new GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1EvaluationJobConfigArgs
    {
        BigqueryImportKeys = 
        {
            { "string", "string" },
        },
        EvaluationConfig = new GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1EvaluationConfigArgs
        {
            BoundingBoxEvaluationOptions = new GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptionsArgs
            {
                IouThreshold = 0,
            },
        },
        ExampleCount = 0,
        ExampleSamplePercentage = 0,
        BoundingPolyConfig = new GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1BoundingPolyConfigArgs
        {
            AnnotationSpecSet = "string",
            InstructionMessage = "string",
        },
        EvaluationJobAlertConfig = new GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfigArgs
        {
            Email = "string",
            MinAcceptableMeanAveragePrecision = 0,
        },
        HumanAnnotationConfig = new GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1HumanAnnotationConfigArgs
        {
            AnnotatedDatasetDisplayName = "string",
            Instruction = "string",
            AnnotatedDatasetDescription = "string",
            ContributorEmails = new[]
            {
                "string",
            },
            LabelGroup = "string",
            LanguageCode = "string",
            QuestionDuration = "string",
            ReplicaCount = 0,
            UserEmailAddress = "string",
        },
        ImageClassificationConfig = new GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1ImageClassificationConfigArgs
        {
            AnnotationSpecSet = "string",
            AllowMultiLabel = false,
            AnswerAggregationType = GoogleNative.DataLabeling.V1Beta1.GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationType.StringAggregationTypeUnspecified,
        },
        InputConfig = new GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1InputConfigArgs
        {
            DataType = GoogleNative.DataLabeling.V1Beta1.GoogleCloudDatalabelingV1beta1InputConfigDataType.DataTypeUnspecified,
            AnnotationType = GoogleNative.DataLabeling.V1Beta1.GoogleCloudDatalabelingV1beta1InputConfigAnnotationType.AnnotationTypeUnspecified,
            BigquerySource = new GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1BigQuerySourceArgs
            {
                InputUri = "string",
            },
            ClassificationMetadata = new GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1ClassificationMetadataArgs
            {
                IsMultiLabel = false,
            },
            GcsSource = new GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1GcsSourceArgs
            {
                InputUri = "string",
                MimeType = "string",
            },
            TextMetadata = new GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1TextMetadataArgs
            {
                LanguageCode = "string",
            },
        },
        TextClassificationConfig = new GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1TextClassificationConfigArgs
        {
            AnnotationSpecSet = "string",
            AllowMultiLabel = false,
            SentimentConfig = new GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1SentimentConfigArgs
            {
                EnableLabelSentimentSelection = false,
            },
        },
    },
    LabelMissingGroundTruth = false,
    ModelVersion = "string",
    Schedule = "string",
    Project = "string",
});
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example, err := datalabeling.NewEvaluationJob(ctx, "evaluationJobResource", &datalabeling.EvaluationJobArgs{
	AnnotationSpecSet: pulumi.String("string"),
	Description:       pulumi.String("string"),
	EvaluationJobConfig: &datalabeling.GoogleCloudDatalabelingV1beta1EvaluationJobConfigArgs{
		BigqueryImportKeys: pulumi.StringMap{
			"string": pulumi.String("string"),
		},
		EvaluationConfig: &datalabeling.GoogleCloudDatalabelingV1beta1EvaluationConfigArgs{
			BoundingBoxEvaluationOptions: &datalabeling.GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptionsArgs{
				IouThreshold: pulumi.Float64(0),
			},
		},
		ExampleCount:            pulumi.Int(0),
		ExampleSamplePercentage: pulumi.Float64(0),
		BoundingPolyConfig: &datalabeling.GoogleCloudDatalabelingV1beta1BoundingPolyConfigArgs{
			AnnotationSpecSet:  pulumi.String("string"),
			InstructionMessage: pulumi.String("string"),
		},
		EvaluationJobAlertConfig: &datalabeling.GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfigArgs{
			Email:                             pulumi.String("string"),
			MinAcceptableMeanAveragePrecision: pulumi.Float64(0),
		},
		HumanAnnotationConfig: &datalabeling.GoogleCloudDatalabelingV1beta1HumanAnnotationConfigArgs{
			AnnotatedDatasetDisplayName: pulumi.String("string"),
			Instruction:                 pulumi.String("string"),
			AnnotatedDatasetDescription: pulumi.String("string"),
			ContributorEmails: pulumi.StringArray{
				pulumi.String("string"),
			},
			LabelGroup:       pulumi.String("string"),
			LanguageCode:     pulumi.String("string"),
			QuestionDuration: pulumi.String("string"),
			ReplicaCount:     pulumi.Int(0),
			UserEmailAddress: pulumi.String("string"),
		},
		ImageClassificationConfig: &datalabeling.GoogleCloudDatalabelingV1beta1ImageClassificationConfigArgs{
			AnnotationSpecSet:     pulumi.String("string"),
			AllowMultiLabel:       pulumi.Bool(false),
			AnswerAggregationType: datalabeling.GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationTypeStringAggregationTypeUnspecified,
		},
		InputConfig: &datalabeling.GoogleCloudDatalabelingV1beta1InputConfigArgs{
			DataType:       datalabeling.GoogleCloudDatalabelingV1beta1InputConfigDataTypeDataTypeUnspecified,
			AnnotationType: datalabeling.GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeAnnotationTypeUnspecified,
			BigquerySource: &datalabeling.GoogleCloudDatalabelingV1beta1BigQuerySourceArgs{
				InputUri: pulumi.String("string"),
			},
			ClassificationMetadata: &datalabeling.GoogleCloudDatalabelingV1beta1ClassificationMetadataArgs{
				IsMultiLabel: pulumi.Bool(false),
			},
			GcsSource: &datalabeling.GoogleCloudDatalabelingV1beta1GcsSourceArgs{
				InputUri: pulumi.String("string"),
				MimeType: pulumi.String("string"),
			},
			TextMetadata: &datalabeling.GoogleCloudDatalabelingV1beta1TextMetadataArgs{
				LanguageCode: pulumi.String("string"),
			},
		},
		TextClassificationConfig: &datalabeling.GoogleCloudDatalabelingV1beta1TextClassificationConfigArgs{
			AnnotationSpecSet: pulumi.String("string"),
			AllowMultiLabel:   pulumi.Bool(false),
			SentimentConfig: &datalabeling.GoogleCloudDatalabelingV1beta1SentimentConfigArgs{
				EnableLabelSentimentSelection: pulumi.Bool(false),
			},
		},
	},
	LabelMissingGroundTruth: pulumi.Bool(false),
	ModelVersion:            pulumi.String("string"),
	Schedule:                pulumi.String("string"),
	Project:                 pulumi.String("string"),
})
Copy
var evaluationJobResource = new EvaluationJob("evaluationJobResource", EvaluationJobArgs.builder()
    .annotationSpecSet("string")
    .description("string")
    .evaluationJobConfig(GoogleCloudDatalabelingV1beta1EvaluationJobConfigArgs.builder()
        .bigqueryImportKeys(Map.of("string", "string"))
        .evaluationConfig(GoogleCloudDatalabelingV1beta1EvaluationConfigArgs.builder()
            .boundingBoxEvaluationOptions(GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptionsArgs.builder()
                .iouThreshold(0)
                .build())
            .build())
        .exampleCount(0)
        .exampleSamplePercentage(0)
        .boundingPolyConfig(GoogleCloudDatalabelingV1beta1BoundingPolyConfigArgs.builder()
            .annotationSpecSet("string")
            .instructionMessage("string")
            .build())
        .evaluationJobAlertConfig(GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfigArgs.builder()
            .email("string")
            .minAcceptableMeanAveragePrecision(0)
            .build())
        .humanAnnotationConfig(GoogleCloudDatalabelingV1beta1HumanAnnotationConfigArgs.builder()
            .annotatedDatasetDisplayName("string")
            .instruction("string")
            .annotatedDatasetDescription("string")
            .contributorEmails("string")
            .labelGroup("string")
            .languageCode("string")
            .questionDuration("string")
            .replicaCount(0)
            .userEmailAddress("string")
            .build())
        .imageClassificationConfig(GoogleCloudDatalabelingV1beta1ImageClassificationConfigArgs.builder()
            .annotationSpecSet("string")
            .allowMultiLabel(false)
            .answerAggregationType("STRING_AGGREGATION_TYPE_UNSPECIFIED")
            .build())
        .inputConfig(GoogleCloudDatalabelingV1beta1InputConfigArgs.builder()
            .dataType("DATA_TYPE_UNSPECIFIED")
            .annotationType("ANNOTATION_TYPE_UNSPECIFIED")
            .bigquerySource(GoogleCloudDatalabelingV1beta1BigQuerySourceArgs.builder()
                .inputUri("string")
                .build())
            .classificationMetadata(GoogleCloudDatalabelingV1beta1ClassificationMetadataArgs.builder()
                .isMultiLabel(false)
                .build())
            .gcsSource(GoogleCloudDatalabelingV1beta1GcsSourceArgs.builder()
                .inputUri("string")
                .mimeType("string")
                .build())
            .textMetadata(GoogleCloudDatalabelingV1beta1TextMetadataArgs.builder()
                .languageCode("string")
                .build())
            .build())
        .textClassificationConfig(GoogleCloudDatalabelingV1beta1TextClassificationConfigArgs.builder()
            .annotationSpecSet("string")
            .allowMultiLabel(false)
            .sentimentConfig(GoogleCloudDatalabelingV1beta1SentimentConfigArgs.builder()
                .enableLabelSentimentSelection(false)
                .build())
            .build())
        .build())
    .labelMissingGroundTruth(false)
    .modelVersion("string")
    .schedule("string")
    .project("string")
    .build());
Copy
evaluation_job_resource = google_native.datalabeling.v1beta1.EvaluationJob("evaluationJobResource",
    annotation_spec_set="string",
    description="string",
    evaluation_job_config={
        "bigquery_import_keys": {
            "string": "string",
        },
        "evaluation_config": {
            "bounding_box_evaluation_options": {
                "iou_threshold": 0,
            },
        },
        "example_count": 0,
        "example_sample_percentage": 0,
        "bounding_poly_config": {
            "annotation_spec_set": "string",
            "instruction_message": "string",
        },
        "evaluation_job_alert_config": {
            "email": "string",
            "min_acceptable_mean_average_precision": 0,
        },
        "human_annotation_config": {
            "annotated_dataset_display_name": "string",
            "instruction": "string",
            "annotated_dataset_description": "string",
            "contributor_emails": ["string"],
            "label_group": "string",
            "language_code": "string",
            "question_duration": "string",
            "replica_count": 0,
            "user_email_address": "string",
        },
        "image_classification_config": {
            "annotation_spec_set": "string",
            "allow_multi_label": False,
            "answer_aggregation_type": google_native.datalabeling.v1beta1.GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationType.STRING_AGGREGATION_TYPE_UNSPECIFIED,
        },
        "input_config": {
            "data_type": google_native.datalabeling.v1beta1.GoogleCloudDatalabelingV1beta1InputConfigDataType.DATA_TYPE_UNSPECIFIED,
            "annotation_type": google_native.datalabeling.v1beta1.GoogleCloudDatalabelingV1beta1InputConfigAnnotationType.ANNOTATION_TYPE_UNSPECIFIED,
            "bigquery_source": {
                "input_uri": "string",
            },
            "classification_metadata": {
                "is_multi_label": False,
            },
            "gcs_source": {
                "input_uri": "string",
                "mime_type": "string",
            },
            "text_metadata": {
                "language_code": "string",
            },
        },
        "text_classification_config": {
            "annotation_spec_set": "string",
            "allow_multi_label": False,
            "sentiment_config": {
                "enable_label_sentiment_selection": False,
            },
        },
    },
    label_missing_ground_truth=False,
    model_version="string",
    schedule="string",
    project="string")
Copy
const evaluationJobResource = new google_native.datalabeling.v1beta1.EvaluationJob("evaluationJobResource", {
    annotationSpecSet: "string",
    description: "string",
    evaluationJobConfig: {
        bigqueryImportKeys: {
            string: "string",
        },
        evaluationConfig: {
            boundingBoxEvaluationOptions: {
                iouThreshold: 0,
            },
        },
        exampleCount: 0,
        exampleSamplePercentage: 0,
        boundingPolyConfig: {
            annotationSpecSet: "string",
            instructionMessage: "string",
        },
        evaluationJobAlertConfig: {
            email: "string",
            minAcceptableMeanAveragePrecision: 0,
        },
        humanAnnotationConfig: {
            annotatedDatasetDisplayName: "string",
            instruction: "string",
            annotatedDatasetDescription: "string",
            contributorEmails: ["string"],
            labelGroup: "string",
            languageCode: "string",
            questionDuration: "string",
            replicaCount: 0,
            userEmailAddress: "string",
        },
        imageClassificationConfig: {
            annotationSpecSet: "string",
            allowMultiLabel: false,
            answerAggregationType: google_native.datalabeling.v1beta1.GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationType.StringAggregationTypeUnspecified,
        },
        inputConfig: {
            dataType: google_native.datalabeling.v1beta1.GoogleCloudDatalabelingV1beta1InputConfigDataType.DataTypeUnspecified,
            annotationType: google_native.datalabeling.v1beta1.GoogleCloudDatalabelingV1beta1InputConfigAnnotationType.AnnotationTypeUnspecified,
            bigquerySource: {
                inputUri: "string",
            },
            classificationMetadata: {
                isMultiLabel: false,
            },
            gcsSource: {
                inputUri: "string",
                mimeType: "string",
            },
            textMetadata: {
                languageCode: "string",
            },
        },
        textClassificationConfig: {
            annotationSpecSet: "string",
            allowMultiLabel: false,
            sentimentConfig: {
                enableLabelSentimentSelection: false,
            },
        },
    },
    labelMissingGroundTruth: false,
    modelVersion: "string",
    schedule: "string",
    project: "string",
});
Copy
type: google-native:datalabeling/v1beta1:EvaluationJob
properties:
    annotationSpecSet: string
    description: string
    evaluationJobConfig:
        bigqueryImportKeys:
            string: string
        boundingPolyConfig:
            annotationSpecSet: string
            instructionMessage: string
        evaluationConfig:
            boundingBoxEvaluationOptions:
                iouThreshold: 0
        evaluationJobAlertConfig:
            email: string
            minAcceptableMeanAveragePrecision: 0
        exampleCount: 0
        exampleSamplePercentage: 0
        humanAnnotationConfig:
            annotatedDatasetDescription: string
            annotatedDatasetDisplayName: string
            contributorEmails:
                - string
            instruction: string
            labelGroup: string
            languageCode: string
            questionDuration: string
            replicaCount: 0
            userEmailAddress: string
        imageClassificationConfig:
            allowMultiLabel: false
            annotationSpecSet: string
            answerAggregationType: STRING_AGGREGATION_TYPE_UNSPECIFIED
        inputConfig:
            annotationType: ANNOTATION_TYPE_UNSPECIFIED
            bigquerySource:
                inputUri: string
            classificationMetadata:
                isMultiLabel: false
            dataType: DATA_TYPE_UNSPECIFIED
            gcsSource:
                inputUri: string
                mimeType: string
            textMetadata:
                languageCode: string
        textClassificationConfig:
            allowMultiLabel: false
            annotationSpecSet: string
            sentimentConfig:
                enableLabelSentimentSelection: false
    labelMissingGroundTruth: false
    modelVersion: string
    project: string
    schedule: string
Copy

EvaluationJob Resource Properties

To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

Inputs

In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

The EvaluationJob resource accepts the following input properties:

AnnotationSpecSet This property is required. string
Name of the AnnotationSpecSet describing all the labels that your machine learning model outputs. You must create this resource before you create an evaluation job and provide its name in the following format: "projects/{project_id}/annotationSpecSets/{annotation_spec_set_id}"
Description This property is required. string
Description of the job. The description can be up to 25,000 characters long.
EvaluationJobConfig This property is required. Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1EvaluationJobConfig
Configuration details for the evaluation job.
LabelMissingGroundTruth This property is required. bool
Whether you want Data Labeling Service to provide ground truth labels for prediction input. If you want the service to assign human labelers to annotate your data, set this to true. If you want to provide your own ground truth labels in the evaluation job's BigQuery table, set this to false.
ModelVersion This property is required. string
The AI Platform Prediction model version to be evaluated. Prediction input and output is sampled from this model version. When creating an evaluation job, specify the model version in the following format: "projects/{project_id}/models/{model_name}/versions/{version_name}" There can only be one evaluation job per model version.
Schedule This property is required. string
Describes the interval at which the job runs. This interval must be at least 1 day, and it is rounded to the nearest day. For example, if you specify a 50-hour interval, the job runs every 2 days. You can provide the schedule in crontab format or in an English-like format. Regardless of what you specify, the job will run at 10:00 AM UTC. Only the interval from this schedule is used, not the specific time of day.
Project Changes to this property will trigger replacement. string
AnnotationSpecSet This property is required. string
Name of the AnnotationSpecSet describing all the labels that your machine learning model outputs. You must create this resource before you create an evaluation job and provide its name in the following format: "projects/{project_id}/annotationSpecSets/{annotation_spec_set_id}"
Description This property is required. string
Description of the job. The description can be up to 25,000 characters long.
EvaluationJobConfig This property is required. GoogleCloudDatalabelingV1beta1EvaluationJobConfigArgs
Configuration details for the evaluation job.
LabelMissingGroundTruth This property is required. bool
Whether you want Data Labeling Service to provide ground truth labels for prediction input. If you want the service to assign human labelers to annotate your data, set this to true. If you want to provide your own ground truth labels in the evaluation job's BigQuery table, set this to false.
ModelVersion This property is required. string
The AI Platform Prediction model version to be evaluated. Prediction input and output is sampled from this model version. When creating an evaluation job, specify the model version in the following format: "projects/{project_id}/models/{model_name}/versions/{version_name}" There can only be one evaluation job per model version.
Schedule This property is required. string
Describes the interval at which the job runs. This interval must be at least 1 day, and it is rounded to the nearest day. For example, if you specify a 50-hour interval, the job runs every 2 days. You can provide the schedule in crontab format or in an English-like format. Regardless of what you specify, the job will run at 10:00 AM UTC. Only the interval from this schedule is used, not the specific time of day.
Project Changes to this property will trigger replacement. string
annotationSpecSet This property is required. String
Name of the AnnotationSpecSet describing all the labels that your machine learning model outputs. You must create this resource before you create an evaluation job and provide its name in the following format: "projects/{project_id}/annotationSpecSets/{annotation_spec_set_id}"
description This property is required. String
Description of the job. The description can be up to 25,000 characters long.
evaluationJobConfig This property is required. GoogleCloudDatalabelingV1beta1EvaluationJobConfig
Configuration details for the evaluation job.
labelMissingGroundTruth This property is required. Boolean
Whether you want Data Labeling Service to provide ground truth labels for prediction input. If you want the service to assign human labelers to annotate your data, set this to true. If you want to provide your own ground truth labels in the evaluation job's BigQuery table, set this to false.
modelVersion This property is required. String
The AI Platform Prediction model version to be evaluated. Prediction input and output is sampled from this model version. When creating an evaluation job, specify the model version in the following format: "projects/{project_id}/models/{model_name}/versions/{version_name}" There can only be one evaluation job per model version.
schedule This property is required. String
Describes the interval at which the job runs. This interval must be at least 1 day, and it is rounded to the nearest day. For example, if you specify a 50-hour interval, the job runs every 2 days. You can provide the schedule in crontab format or in an English-like format. Regardless of what you specify, the job will run at 10:00 AM UTC. Only the interval from this schedule is used, not the specific time of day.
project Changes to this property will trigger replacement. String
annotationSpecSet This property is required. string
Name of the AnnotationSpecSet describing all the labels that your machine learning model outputs. You must create this resource before you create an evaluation job and provide its name in the following format: "projects/{project_id}/annotationSpecSets/{annotation_spec_set_id}"
description This property is required. string
Description of the job. The description can be up to 25,000 characters long.
evaluationJobConfig This property is required. GoogleCloudDatalabelingV1beta1EvaluationJobConfig
Configuration details for the evaluation job.
labelMissingGroundTruth This property is required. boolean
Whether you want Data Labeling Service to provide ground truth labels for prediction input. If you want the service to assign human labelers to annotate your data, set this to true. If you want to provide your own ground truth labels in the evaluation job's BigQuery table, set this to false.
modelVersion This property is required. string
The AI Platform Prediction model version to be evaluated. Prediction input and output is sampled from this model version. When creating an evaluation job, specify the model version in the following format: "projects/{project_id}/models/{model_name}/versions/{version_name}" There can only be one evaluation job per model version.
schedule This property is required. string
Describes the interval at which the job runs. This interval must be at least 1 day, and it is rounded to the nearest day. For example, if you specify a 50-hour interval, the job runs every 2 days. You can provide the schedule in crontab format or in an English-like format. Regardless of what you specify, the job will run at 10:00 AM UTC. Only the interval from this schedule is used, not the specific time of day.
project Changes to this property will trigger replacement. string
annotation_spec_set This property is required. str
Name of the AnnotationSpecSet describing all the labels that your machine learning model outputs. You must create this resource before you create an evaluation job and provide its name in the following format: "projects/{project_id}/annotationSpecSets/{annotation_spec_set_id}"
description This property is required. str
Description of the job. The description can be up to 25,000 characters long.
evaluation_job_config This property is required. GoogleCloudDatalabelingV1beta1EvaluationJobConfigArgs
Configuration details for the evaluation job.
label_missing_ground_truth This property is required. bool
Whether you want Data Labeling Service to provide ground truth labels for prediction input. If you want the service to assign human labelers to annotate your data, set this to true. If you want to provide your own ground truth labels in the evaluation job's BigQuery table, set this to false.
model_version This property is required. str
The AI Platform Prediction model version to be evaluated. Prediction input and output is sampled from this model version. When creating an evaluation job, specify the model version in the following format: "projects/{project_id}/models/{model_name}/versions/{version_name}" There can only be one evaluation job per model version.
schedule This property is required. str
Describes the interval at which the job runs. This interval must be at least 1 day, and it is rounded to the nearest day. For example, if you specify a 50-hour interval, the job runs every 2 days. You can provide the schedule in crontab format or in an English-like format. Regardless of what you specify, the job will run at 10:00 AM UTC. Only the interval from this schedule is used, not the specific time of day.
project Changes to this property will trigger replacement. str
annotationSpecSet This property is required. String
Name of the AnnotationSpecSet describing all the labels that your machine learning model outputs. You must create this resource before you create an evaluation job and provide its name in the following format: "projects/{project_id}/annotationSpecSets/{annotation_spec_set_id}"
description This property is required. String
Description of the job. The description can be up to 25,000 characters long.
evaluationJobConfig This property is required. Property Map
Configuration details for the evaluation job.
labelMissingGroundTruth This property is required. Boolean
Whether you want Data Labeling Service to provide ground truth labels for prediction input. If you want the service to assign human labelers to annotate your data, set this to true. If you want to provide your own ground truth labels in the evaluation job's BigQuery table, set this to false.
modelVersion This property is required. String
The AI Platform Prediction model version to be evaluated. Prediction input and output is sampled from this model version. When creating an evaluation job, specify the model version in the following format: "projects/{project_id}/models/{model_name}/versions/{version_name}" There can only be one evaluation job per model version.
schedule This property is required. String
Describes the interval at which the job runs. This interval must be at least 1 day, and it is rounded to the nearest day. For example, if you specify a 50-hour interval, the job runs every 2 days. You can provide the schedule in crontab format or in an English-like format. Regardless of what you specify, the job will run at 10:00 AM UTC. Only the interval from this schedule is used, not the specific time of day.
project Changes to this property will trigger replacement. String

Outputs

All input properties are implicitly available as output properties. Additionally, the EvaluationJob resource produces the following output properties:

Attempts List<Pulumi.GoogleNative.DataLabeling.V1Beta1.Outputs.GoogleCloudDatalabelingV1beta1AttemptResponse>
Every time the evaluation job runs and an error occurs, the failed attempt is appended to this array.
CreateTime string
Timestamp of when this evaluation job was created.
Id string
The provider-assigned unique ID for this managed resource.
Name string
After you create a job, Data Labeling Service assigns a name to the job with the following format: "projects/{project_id}/evaluationJobs/ {evaluation_job_id}"
State string
Describes the current state of the job.
Attempts []GoogleCloudDatalabelingV1beta1AttemptResponse
Every time the evaluation job runs and an error occurs, the failed attempt is appended to this array.
CreateTime string
Timestamp of when this evaluation job was created.
Id string
The provider-assigned unique ID for this managed resource.
Name string
After you create a job, Data Labeling Service assigns a name to the job with the following format: "projects/{project_id}/evaluationJobs/ {evaluation_job_id}"
State string
Describes the current state of the job.
attempts List<GoogleCloudDatalabelingV1beta1AttemptResponse>
Every time the evaluation job runs and an error occurs, the failed attempt is appended to this array.
createTime String
Timestamp of when this evaluation job was created.
id String
The provider-assigned unique ID for this managed resource.
name String
After you create a job, Data Labeling Service assigns a name to the job with the following format: "projects/{project_id}/evaluationJobs/ {evaluation_job_id}"
state String
Describes the current state of the job.
attempts GoogleCloudDatalabelingV1beta1AttemptResponse[]
Every time the evaluation job runs and an error occurs, the failed attempt is appended to this array.
createTime string
Timestamp of when this evaluation job was created.
id string
The provider-assigned unique ID for this managed resource.
name string
After you create a job, Data Labeling Service assigns a name to the job with the following format: "projects/{project_id}/evaluationJobs/ {evaluation_job_id}"
state string
Describes the current state of the job.
attempts Sequence[GoogleCloudDatalabelingV1beta1AttemptResponse]
Every time the evaluation job runs and an error occurs, the failed attempt is appended to this array.
create_time str
Timestamp of when this evaluation job was created.
id str
The provider-assigned unique ID for this managed resource.
name str
After you create a job, Data Labeling Service assigns a name to the job with the following format: "projects/{project_id}/evaluationJobs/ {evaluation_job_id}"
state str
Describes the current state of the job.
attempts List<Property Map>
Every time the evaluation job runs and an error occurs, the failed attempt is appended to this array.
createTime String
Timestamp of when this evaluation job was created.
id String
The provider-assigned unique ID for this managed resource.
name String
After you create a job, Data Labeling Service assigns a name to the job with the following format: "projects/{project_id}/evaluationJobs/ {evaluation_job_id}"
state String
Describes the current state of the job.

Supporting Types

GoogleCloudDatalabelingV1beta1AttemptResponse
, GoogleCloudDatalabelingV1beta1AttemptResponseArgs

AttemptTime This property is required. string
PartialFailures This property is required. List<Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleRpcStatusResponse>
Details of errors that occurred.
AttemptTime This property is required. string
PartialFailures This property is required. []GoogleRpcStatusResponse
Details of errors that occurred.
attemptTime This property is required. String
partialFailures This property is required. List<GoogleRpcStatusResponse>
Details of errors that occurred.
attemptTime This property is required. string
partialFailures This property is required. GoogleRpcStatusResponse[]
Details of errors that occurred.
attempt_time This property is required. str
partial_failures This property is required. Sequence[GoogleRpcStatusResponse]
Details of errors that occurred.
attemptTime This property is required. String
partialFailures This property is required. List<Property Map>
Details of errors that occurred.

GoogleCloudDatalabelingV1beta1BigQuerySource
, GoogleCloudDatalabelingV1beta1BigQuerySourceArgs

InputUri This property is required. string
BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the correct schema. Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" Learn more.
InputUri This property is required. string
BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the correct schema. Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" Learn more.
inputUri This property is required. String
BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the correct schema. Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" Learn more.
inputUri This property is required. string
BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the correct schema. Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" Learn more.
input_uri This property is required. str
BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the correct schema. Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" Learn more.
inputUri This property is required. String
BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the correct schema. Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" Learn more.

GoogleCloudDatalabelingV1beta1BigQuerySourceResponse
, GoogleCloudDatalabelingV1beta1BigQuerySourceResponseArgs

InputUri This property is required. string
BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the correct schema. Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" Learn more.
InputUri This property is required. string
BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the correct schema. Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" Learn more.
inputUri This property is required. String
BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the correct schema. Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" Learn more.
inputUri This property is required. string
BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the correct schema. Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" Learn more.
input_uri This property is required. str
BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the correct schema. Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" Learn more.
inputUri This property is required. String
BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the correct schema. Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" Learn more.

GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptions
, GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptionsArgs

IouThreshold double
Minimum intersection-over-union (IOU) required for 2 bounding boxes to be considered a match. This must be a number between 0 and 1.
IouThreshold float64
Minimum intersection-over-union (IOU) required for 2 bounding boxes to be considered a match. This must be a number between 0 and 1.
iouThreshold Double
Minimum intersection-over-union (IOU) required for 2 bounding boxes to be considered a match. This must be a number between 0 and 1.
iouThreshold number
Minimum intersection-over-union (IOU) required for 2 bounding boxes to be considered a match. This must be a number between 0 and 1.
iou_threshold float
Minimum intersection-over-union (IOU) required for 2 bounding boxes to be considered a match. This must be a number between 0 and 1.
iouThreshold Number
Minimum intersection-over-union (IOU) required for 2 bounding boxes to be considered a match. This must be a number between 0 and 1.

GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptionsResponse
, GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptionsResponseArgs

IouThreshold This property is required. double
Minimum intersection-over-union (IOU) required for 2 bounding boxes to be considered a match. This must be a number between 0 and 1.
IouThreshold This property is required. float64
Minimum intersection-over-union (IOU) required for 2 bounding boxes to be considered a match. This must be a number between 0 and 1.
iouThreshold This property is required. Double
Minimum intersection-over-union (IOU) required for 2 bounding boxes to be considered a match. This must be a number between 0 and 1.
iouThreshold This property is required. number
Minimum intersection-over-union (IOU) required for 2 bounding boxes to be considered a match. This must be a number between 0 and 1.
iou_threshold This property is required. float
Minimum intersection-over-union (IOU) required for 2 bounding boxes to be considered a match. This must be a number between 0 and 1.
iouThreshold This property is required. Number
Minimum intersection-over-union (IOU) required for 2 bounding boxes to be considered a match. This must be a number between 0 and 1.

GoogleCloudDatalabelingV1beta1BoundingPolyConfig
, GoogleCloudDatalabelingV1beta1BoundingPolyConfigArgs

AnnotationSpecSet This property is required. string
Annotation spec set resource name.
InstructionMessage string
Optional. Instruction message showed on contributors UI.
AnnotationSpecSet This property is required. string
Annotation spec set resource name.
InstructionMessage string
Optional. Instruction message showed on contributors UI.
annotationSpecSet This property is required. String
Annotation spec set resource name.
instructionMessage String
Optional. Instruction message showed on contributors UI.
annotationSpecSet This property is required. string
Annotation spec set resource name.
instructionMessage string
Optional. Instruction message showed on contributors UI.
annotation_spec_set This property is required. str
Annotation spec set resource name.
instruction_message str
Optional. Instruction message showed on contributors UI.
annotationSpecSet This property is required. String
Annotation spec set resource name.
instructionMessage String
Optional. Instruction message showed on contributors UI.

GoogleCloudDatalabelingV1beta1BoundingPolyConfigResponse
, GoogleCloudDatalabelingV1beta1BoundingPolyConfigResponseArgs

AnnotationSpecSet This property is required. string
Annotation spec set resource name.
InstructionMessage This property is required. string
Optional. Instruction message showed on contributors UI.
AnnotationSpecSet This property is required. string
Annotation spec set resource name.
InstructionMessage This property is required. string
Optional. Instruction message showed on contributors UI.
annotationSpecSet This property is required. String
Annotation spec set resource name.
instructionMessage This property is required. String
Optional. Instruction message showed on contributors UI.
annotationSpecSet This property is required. string
Annotation spec set resource name.
instructionMessage This property is required. string
Optional. Instruction message showed on contributors UI.
annotation_spec_set This property is required. str
Annotation spec set resource name.
instruction_message This property is required. str
Optional. Instruction message showed on contributors UI.
annotationSpecSet This property is required. String
Annotation spec set resource name.
instructionMessage This property is required. String
Optional. Instruction message showed on contributors UI.

GoogleCloudDatalabelingV1beta1ClassificationMetadata
, GoogleCloudDatalabelingV1beta1ClassificationMetadataArgs

IsMultiLabel bool
Whether the classification task is multi-label or not.
IsMultiLabel bool
Whether the classification task is multi-label or not.
isMultiLabel Boolean
Whether the classification task is multi-label or not.
isMultiLabel boolean
Whether the classification task is multi-label or not.
is_multi_label bool
Whether the classification task is multi-label or not.
isMultiLabel Boolean
Whether the classification task is multi-label or not.

GoogleCloudDatalabelingV1beta1ClassificationMetadataResponse
, GoogleCloudDatalabelingV1beta1ClassificationMetadataResponseArgs

IsMultiLabel This property is required. bool
Whether the classification task is multi-label or not.
IsMultiLabel This property is required. bool
Whether the classification task is multi-label or not.
isMultiLabel This property is required. Boolean
Whether the classification task is multi-label or not.
isMultiLabel This property is required. boolean
Whether the classification task is multi-label or not.
is_multi_label This property is required. bool
Whether the classification task is multi-label or not.
isMultiLabel This property is required. Boolean
Whether the classification task is multi-label or not.

GoogleCloudDatalabelingV1beta1EvaluationConfig
, GoogleCloudDatalabelingV1beta1EvaluationConfigArgs

BoundingBoxEvaluationOptions Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptions
Only specify this field if the related model performs image object detection (IMAGE_BOUNDING_BOX_ANNOTATION). Describes how to evaluate bounding boxes.
BoundingBoxEvaluationOptions GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptions
Only specify this field if the related model performs image object detection (IMAGE_BOUNDING_BOX_ANNOTATION). Describes how to evaluate bounding boxes.
boundingBoxEvaluationOptions GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptions
Only specify this field if the related model performs image object detection (IMAGE_BOUNDING_BOX_ANNOTATION). Describes how to evaluate bounding boxes.
boundingBoxEvaluationOptions GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptions
Only specify this field if the related model performs image object detection (IMAGE_BOUNDING_BOX_ANNOTATION). Describes how to evaluate bounding boxes.
bounding_box_evaluation_options GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptions
Only specify this field if the related model performs image object detection (IMAGE_BOUNDING_BOX_ANNOTATION). Describes how to evaluate bounding boxes.
boundingBoxEvaluationOptions Property Map
Only specify this field if the related model performs image object detection (IMAGE_BOUNDING_BOX_ANNOTATION). Describes how to evaluate bounding boxes.

GoogleCloudDatalabelingV1beta1EvaluationConfigResponse
, GoogleCloudDatalabelingV1beta1EvaluationConfigResponseArgs

BoundingBoxEvaluationOptions This property is required. Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptionsResponse
Only specify this field if the related model performs image object detection (IMAGE_BOUNDING_BOX_ANNOTATION). Describes how to evaluate bounding boxes.
BoundingBoxEvaluationOptions This property is required. GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptionsResponse
Only specify this field if the related model performs image object detection (IMAGE_BOUNDING_BOX_ANNOTATION). Describes how to evaluate bounding boxes.
boundingBoxEvaluationOptions This property is required. GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptionsResponse
Only specify this field if the related model performs image object detection (IMAGE_BOUNDING_BOX_ANNOTATION). Describes how to evaluate bounding boxes.
boundingBoxEvaluationOptions This property is required. GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptionsResponse
Only specify this field if the related model performs image object detection (IMAGE_BOUNDING_BOX_ANNOTATION). Describes how to evaluate bounding boxes.
bounding_box_evaluation_options This property is required. GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptionsResponse
Only specify this field if the related model performs image object detection (IMAGE_BOUNDING_BOX_ANNOTATION). Describes how to evaluate bounding boxes.
boundingBoxEvaluationOptions This property is required. Property Map
Only specify this field if the related model performs image object detection (IMAGE_BOUNDING_BOX_ANNOTATION). Describes how to evaluate bounding boxes.

GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfig
, GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfigArgs

Email This property is required. string
An email address to send alerts to.
MinAcceptableMeanAveragePrecision This property is required. double
A number between 0 and 1 that describes a minimum mean average precision threshold. When the evaluation job runs, if it calculates that your model version's predictions from the recent interval have meanAveragePrecision below this threshold, then it sends an alert to your specified email.
Email This property is required. string
An email address to send alerts to.
MinAcceptableMeanAveragePrecision This property is required. float64
A number between 0 and 1 that describes a minimum mean average precision threshold. When the evaluation job runs, if it calculates that your model version's predictions from the recent interval have meanAveragePrecision below this threshold, then it sends an alert to your specified email.
email This property is required. String
An email address to send alerts to.
minAcceptableMeanAveragePrecision This property is required. Double
A number between 0 and 1 that describes a minimum mean average precision threshold. When the evaluation job runs, if it calculates that your model version's predictions from the recent interval have meanAveragePrecision below this threshold, then it sends an alert to your specified email.
email This property is required. string
An email address to send alerts to.
minAcceptableMeanAveragePrecision This property is required. number
A number between 0 and 1 that describes a minimum mean average precision threshold. When the evaluation job runs, if it calculates that your model version's predictions from the recent interval have meanAveragePrecision below this threshold, then it sends an alert to your specified email.
email This property is required. str
An email address to send alerts to.
min_acceptable_mean_average_precision This property is required. float
A number between 0 and 1 that describes a minimum mean average precision threshold. When the evaluation job runs, if it calculates that your model version's predictions from the recent interval have meanAveragePrecision below this threshold, then it sends an alert to your specified email.
email This property is required. String
An email address to send alerts to.
minAcceptableMeanAveragePrecision This property is required. Number
A number between 0 and 1 that describes a minimum mean average precision threshold. When the evaluation job runs, if it calculates that your model version's predictions from the recent interval have meanAveragePrecision below this threshold, then it sends an alert to your specified email.

GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfigResponse
, GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfigResponseArgs

Email This property is required. string
An email address to send alerts to.
MinAcceptableMeanAveragePrecision This property is required. double
A number between 0 and 1 that describes a minimum mean average precision threshold. When the evaluation job runs, if it calculates that your model version's predictions from the recent interval have meanAveragePrecision below this threshold, then it sends an alert to your specified email.
Email This property is required. string
An email address to send alerts to.
MinAcceptableMeanAveragePrecision This property is required. float64
A number between 0 and 1 that describes a minimum mean average precision threshold. When the evaluation job runs, if it calculates that your model version's predictions from the recent interval have meanAveragePrecision below this threshold, then it sends an alert to your specified email.
email This property is required. String
An email address to send alerts to.
minAcceptableMeanAveragePrecision This property is required. Double
A number between 0 and 1 that describes a minimum mean average precision threshold. When the evaluation job runs, if it calculates that your model version's predictions from the recent interval have meanAveragePrecision below this threshold, then it sends an alert to your specified email.
email This property is required. string
An email address to send alerts to.
minAcceptableMeanAveragePrecision This property is required. number
A number between 0 and 1 that describes a minimum mean average precision threshold. When the evaluation job runs, if it calculates that your model version's predictions from the recent interval have meanAveragePrecision below this threshold, then it sends an alert to your specified email.
email This property is required. str
An email address to send alerts to.
min_acceptable_mean_average_precision This property is required. float
A number between 0 and 1 that describes a minimum mean average precision threshold. When the evaluation job runs, if it calculates that your model version's predictions from the recent interval have meanAveragePrecision below this threshold, then it sends an alert to your specified email.
email This property is required. String
An email address to send alerts to.
minAcceptableMeanAveragePrecision This property is required. Number
A number between 0 and 1 that describes a minimum mean average precision threshold. When the evaluation job runs, if it calculates that your model version's predictions from the recent interval have meanAveragePrecision below this threshold, then it sends an alert to your specified email.

GoogleCloudDatalabelingV1beta1EvaluationJobConfig
, GoogleCloudDatalabelingV1beta1EvaluationJobConfigArgs

BigqueryImportKeys This property is required. Dictionary<string, string>
Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field: * data_json_key: the data key for prediction input. You must provide either this key or reference_json_key. * reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key. * label_json_key: the label key for prediction output. Required. * label_score_json_key: the score key for prediction output. Required. * bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.
EvaluationConfig This property is required. Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1EvaluationConfig
Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.
ExampleCount This property is required. int
The maximum number of predictions to sample and save to BigQuery during each evaluation interval. This limit overrides example_sample_percentage: even if the service has not sampled enough predictions to fulfill example_sample_perecentage during an interval, it stops sampling predictions when it meets this limit.
ExampleSamplePercentage This property is required. double
Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.
BoundingPolyConfig Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1BoundingPolyConfig
Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.
EvaluationJobAlertConfig Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfig
Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.
HumanAnnotationConfig Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1HumanAnnotationConfig
Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.
ImageClassificationConfig Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1ImageClassificationConfig
Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
InputConfig Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1InputConfig
Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields: * dataType must be one of IMAGE, TEXT, or GENERAL_DATA. * annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection). * If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel. * You must specify bigquerySource (not gcsSource).
TextClassificationConfig Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1TextClassificationConfig
Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
BigqueryImportKeys This property is required. map[string]string
Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field: * data_json_key: the data key for prediction input. You must provide either this key or reference_json_key. * reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key. * label_json_key: the label key for prediction output. Required. * label_score_json_key: the score key for prediction output. Required. * bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.
EvaluationConfig This property is required. GoogleCloudDatalabelingV1beta1EvaluationConfig
Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.
ExampleCount This property is required. int
The maximum number of predictions to sample and save to BigQuery during each evaluation interval. This limit overrides example_sample_percentage: even if the service has not sampled enough predictions to fulfill example_sample_perecentage during an interval, it stops sampling predictions when it meets this limit.
ExampleSamplePercentage This property is required. float64
Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.
BoundingPolyConfig GoogleCloudDatalabelingV1beta1BoundingPolyConfig
Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.
EvaluationJobAlertConfig GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfig
Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.
HumanAnnotationConfig GoogleCloudDatalabelingV1beta1HumanAnnotationConfig
Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.
ImageClassificationConfig GoogleCloudDatalabelingV1beta1ImageClassificationConfig
Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
InputConfig GoogleCloudDatalabelingV1beta1InputConfig
Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields: * dataType must be one of IMAGE, TEXT, or GENERAL_DATA. * annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection). * If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel. * You must specify bigquerySource (not gcsSource).
TextClassificationConfig GoogleCloudDatalabelingV1beta1TextClassificationConfig
Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
bigqueryImportKeys This property is required. Map<String,String>
Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field: * data_json_key: the data key for prediction input. You must provide either this key or reference_json_key. * reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key. * label_json_key: the label key for prediction output. Required. * label_score_json_key: the score key for prediction output. Required. * bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.
evaluationConfig This property is required. GoogleCloudDatalabelingV1beta1EvaluationConfig
Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.
exampleCount This property is required. Integer
The maximum number of predictions to sample and save to BigQuery during each evaluation interval. This limit overrides example_sample_percentage: even if the service has not sampled enough predictions to fulfill example_sample_perecentage during an interval, it stops sampling predictions when it meets this limit.
exampleSamplePercentage This property is required. Double
Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.
boundingPolyConfig GoogleCloudDatalabelingV1beta1BoundingPolyConfig
Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.
evaluationJobAlertConfig GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfig
Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.
humanAnnotationConfig GoogleCloudDatalabelingV1beta1HumanAnnotationConfig
Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.
imageClassificationConfig GoogleCloudDatalabelingV1beta1ImageClassificationConfig
Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
inputConfig GoogleCloudDatalabelingV1beta1InputConfig
Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields: * dataType must be one of IMAGE, TEXT, or GENERAL_DATA. * annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection). * If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel. * You must specify bigquerySource (not gcsSource).
textClassificationConfig GoogleCloudDatalabelingV1beta1TextClassificationConfig
Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
bigqueryImportKeys This property is required. {[key: string]: string}
Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field: * data_json_key: the data key for prediction input. You must provide either this key or reference_json_key. * reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key. * label_json_key: the label key for prediction output. Required. * label_score_json_key: the score key for prediction output. Required. * bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.
evaluationConfig This property is required. GoogleCloudDatalabelingV1beta1EvaluationConfig
Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.
exampleCount This property is required. number
The maximum number of predictions to sample and save to BigQuery during each evaluation interval. This limit overrides example_sample_percentage: even if the service has not sampled enough predictions to fulfill example_sample_perecentage during an interval, it stops sampling predictions when it meets this limit.
exampleSamplePercentage This property is required. number
Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.
boundingPolyConfig GoogleCloudDatalabelingV1beta1BoundingPolyConfig
Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.
evaluationJobAlertConfig GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfig
Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.
humanAnnotationConfig GoogleCloudDatalabelingV1beta1HumanAnnotationConfig
Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.
imageClassificationConfig GoogleCloudDatalabelingV1beta1ImageClassificationConfig
Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
inputConfig GoogleCloudDatalabelingV1beta1InputConfig
Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields: * dataType must be one of IMAGE, TEXT, or GENERAL_DATA. * annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection). * If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel. * You must specify bigquerySource (not gcsSource).
textClassificationConfig GoogleCloudDatalabelingV1beta1TextClassificationConfig
Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
bigquery_import_keys This property is required. Mapping[str, str]
Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field: * data_json_key: the data key for prediction input. You must provide either this key or reference_json_key. * reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key. * label_json_key: the label key for prediction output. Required. * label_score_json_key: the score key for prediction output. Required. * bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.
evaluation_config This property is required. GoogleCloudDatalabelingV1beta1EvaluationConfig
Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.
example_count This property is required. int
The maximum number of predictions to sample and save to BigQuery during each evaluation interval. This limit overrides example_sample_percentage: even if the service has not sampled enough predictions to fulfill example_sample_perecentage during an interval, it stops sampling predictions when it meets this limit.
example_sample_percentage This property is required. float
Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.
bounding_poly_config GoogleCloudDatalabelingV1beta1BoundingPolyConfig
Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.
evaluation_job_alert_config GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfig
Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.
human_annotation_config GoogleCloudDatalabelingV1beta1HumanAnnotationConfig
Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.
image_classification_config GoogleCloudDatalabelingV1beta1ImageClassificationConfig
Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
input_config GoogleCloudDatalabelingV1beta1InputConfig
Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields: * dataType must be one of IMAGE, TEXT, or GENERAL_DATA. * annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection). * If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel. * You must specify bigquerySource (not gcsSource).
text_classification_config GoogleCloudDatalabelingV1beta1TextClassificationConfig
Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
bigqueryImportKeys This property is required. Map<String>
Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field: * data_json_key: the data key for prediction input. You must provide either this key or reference_json_key. * reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key. * label_json_key: the label key for prediction output. Required. * label_score_json_key: the score key for prediction output. Required. * bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.
evaluationConfig This property is required. Property Map
Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.
exampleCount This property is required. Number
The maximum number of predictions to sample and save to BigQuery during each evaluation interval. This limit overrides example_sample_percentage: even if the service has not sampled enough predictions to fulfill example_sample_perecentage during an interval, it stops sampling predictions when it meets this limit.
exampleSamplePercentage This property is required. Number
Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.
boundingPolyConfig Property Map
Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.
evaluationJobAlertConfig Property Map
Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.
humanAnnotationConfig Property Map
Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.
imageClassificationConfig Property Map
Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
inputConfig Property Map
Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields: * dataType must be one of IMAGE, TEXT, or GENERAL_DATA. * annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection). * If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel. * You must specify bigquerySource (not gcsSource).
textClassificationConfig Property Map
Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

GoogleCloudDatalabelingV1beta1EvaluationJobConfigResponse
, GoogleCloudDatalabelingV1beta1EvaluationJobConfigResponseArgs

BigqueryImportKeys This property is required. Dictionary<string, string>
Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field: * data_json_key: the data key for prediction input. You must provide either this key or reference_json_key. * reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key. * label_json_key: the label key for prediction output. Required. * label_score_json_key: the score key for prediction output. Required. * bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.
BoundingPolyConfig This property is required. Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1BoundingPolyConfigResponse
Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.
EvaluationConfig This property is required. Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1EvaluationConfigResponse
Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.
EvaluationJobAlertConfig This property is required. Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfigResponse
Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.
ExampleCount This property is required. int
The maximum number of predictions to sample and save to BigQuery during each evaluation interval. This limit overrides example_sample_percentage: even if the service has not sampled enough predictions to fulfill example_sample_perecentage during an interval, it stops sampling predictions when it meets this limit.
ExampleSamplePercentage This property is required. double
Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.
HumanAnnotationConfig This property is required. Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1HumanAnnotationConfigResponse
Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.
ImageClassificationConfig This property is required. Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1ImageClassificationConfigResponse
Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
InputConfig This property is required. Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1InputConfigResponse
Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields: * dataType must be one of IMAGE, TEXT, or GENERAL_DATA. * annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection). * If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel. * You must specify bigquerySource (not gcsSource).
TextClassificationConfig This property is required. Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1TextClassificationConfigResponse
Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
BigqueryImportKeys This property is required. map[string]string
Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field: * data_json_key: the data key for prediction input. You must provide either this key or reference_json_key. * reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key. * label_json_key: the label key for prediction output. Required. * label_score_json_key: the score key for prediction output. Required. * bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.
BoundingPolyConfig This property is required. GoogleCloudDatalabelingV1beta1BoundingPolyConfigResponse
Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.
EvaluationConfig This property is required. GoogleCloudDatalabelingV1beta1EvaluationConfigResponse
Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.
EvaluationJobAlertConfig This property is required. GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfigResponse
Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.
ExampleCount This property is required. int
The maximum number of predictions to sample and save to BigQuery during each evaluation interval. This limit overrides example_sample_percentage: even if the service has not sampled enough predictions to fulfill example_sample_perecentage during an interval, it stops sampling predictions when it meets this limit.
ExampleSamplePercentage This property is required. float64
Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.
HumanAnnotationConfig This property is required. GoogleCloudDatalabelingV1beta1HumanAnnotationConfigResponse
Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.
ImageClassificationConfig This property is required. GoogleCloudDatalabelingV1beta1ImageClassificationConfigResponse
Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
InputConfig This property is required. GoogleCloudDatalabelingV1beta1InputConfigResponse
Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields: * dataType must be one of IMAGE, TEXT, or GENERAL_DATA. * annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection). * If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel. * You must specify bigquerySource (not gcsSource).
TextClassificationConfig This property is required. GoogleCloudDatalabelingV1beta1TextClassificationConfigResponse
Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
bigqueryImportKeys This property is required. Map<String,String>
Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field: * data_json_key: the data key for prediction input. You must provide either this key or reference_json_key. * reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key. * label_json_key: the label key for prediction output. Required. * label_score_json_key: the score key for prediction output. Required. * bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.
boundingPolyConfig This property is required. GoogleCloudDatalabelingV1beta1BoundingPolyConfigResponse
Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.
evaluationConfig This property is required. GoogleCloudDatalabelingV1beta1EvaluationConfigResponse
Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.
evaluationJobAlertConfig This property is required. GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfigResponse
Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.
exampleCount This property is required. Integer
The maximum number of predictions to sample and save to BigQuery during each evaluation interval. This limit overrides example_sample_percentage: even if the service has not sampled enough predictions to fulfill example_sample_perecentage during an interval, it stops sampling predictions when it meets this limit.
exampleSamplePercentage This property is required. Double
Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.
humanAnnotationConfig This property is required. GoogleCloudDatalabelingV1beta1HumanAnnotationConfigResponse
Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.
imageClassificationConfig This property is required. GoogleCloudDatalabelingV1beta1ImageClassificationConfigResponse
Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
inputConfig This property is required. GoogleCloudDatalabelingV1beta1InputConfigResponse
Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields: * dataType must be one of IMAGE, TEXT, or GENERAL_DATA. * annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection). * If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel. * You must specify bigquerySource (not gcsSource).
textClassificationConfig This property is required. GoogleCloudDatalabelingV1beta1TextClassificationConfigResponse
Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
bigqueryImportKeys This property is required. {[key: string]: string}
Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field: * data_json_key: the data key for prediction input. You must provide either this key or reference_json_key. * reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key. * label_json_key: the label key for prediction output. Required. * label_score_json_key: the score key for prediction output. Required. * bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.
boundingPolyConfig This property is required. GoogleCloudDatalabelingV1beta1BoundingPolyConfigResponse
Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.
evaluationConfig This property is required. GoogleCloudDatalabelingV1beta1EvaluationConfigResponse
Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.
evaluationJobAlertConfig This property is required. GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfigResponse
Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.
exampleCount This property is required. number
The maximum number of predictions to sample and save to BigQuery during each evaluation interval. This limit overrides example_sample_percentage: even if the service has not sampled enough predictions to fulfill example_sample_perecentage during an interval, it stops sampling predictions when it meets this limit.
exampleSamplePercentage This property is required. number
Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.
humanAnnotationConfig This property is required. GoogleCloudDatalabelingV1beta1HumanAnnotationConfigResponse
Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.
imageClassificationConfig This property is required. GoogleCloudDatalabelingV1beta1ImageClassificationConfigResponse
Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
inputConfig This property is required. GoogleCloudDatalabelingV1beta1InputConfigResponse
Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields: * dataType must be one of IMAGE, TEXT, or GENERAL_DATA. * annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection). * If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel. * You must specify bigquerySource (not gcsSource).
textClassificationConfig This property is required. GoogleCloudDatalabelingV1beta1TextClassificationConfigResponse
Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
bigquery_import_keys This property is required. Mapping[str, str]
Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field: * data_json_key: the data key for prediction input. You must provide either this key or reference_json_key. * reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key. * label_json_key: the label key for prediction output. Required. * label_score_json_key: the score key for prediction output. Required. * bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.
bounding_poly_config This property is required. GoogleCloudDatalabelingV1beta1BoundingPolyConfigResponse
Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.
evaluation_config This property is required. GoogleCloudDatalabelingV1beta1EvaluationConfigResponse
Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.
evaluation_job_alert_config This property is required. GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfigResponse
Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.
example_count This property is required. int
The maximum number of predictions to sample and save to BigQuery during each evaluation interval. This limit overrides example_sample_percentage: even if the service has not sampled enough predictions to fulfill example_sample_perecentage during an interval, it stops sampling predictions when it meets this limit.
example_sample_percentage This property is required. float
Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.
human_annotation_config This property is required. GoogleCloudDatalabelingV1beta1HumanAnnotationConfigResponse
Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.
image_classification_config This property is required. GoogleCloudDatalabelingV1beta1ImageClassificationConfigResponse
Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
input_config This property is required. GoogleCloudDatalabelingV1beta1InputConfigResponse
Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields: * dataType must be one of IMAGE, TEXT, or GENERAL_DATA. * annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection). * If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel. * You must specify bigquerySource (not gcsSource).
text_classification_config This property is required. GoogleCloudDatalabelingV1beta1TextClassificationConfigResponse
Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
bigqueryImportKeys This property is required. Map<String>
Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field: * data_json_key: the data key for prediction input. You must provide either this key or reference_json_key. * reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key. * label_json_key: the label key for prediction output. Required. * label_score_json_key: the score key for prediction output. Required. * bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.
boundingPolyConfig This property is required. Property Map
Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.
evaluationConfig This property is required. Property Map
Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.
evaluationJobAlertConfig This property is required. Property Map
Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.
exampleCount This property is required. Number
The maximum number of predictions to sample and save to BigQuery during each evaluation interval. This limit overrides example_sample_percentage: even if the service has not sampled enough predictions to fulfill example_sample_perecentage during an interval, it stops sampling predictions when it meets this limit.
exampleSamplePercentage This property is required. Number
Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.
humanAnnotationConfig This property is required. Property Map
Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.
imageClassificationConfig This property is required. Property Map
Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.
inputConfig This property is required. Property Map
Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields: * dataType must be one of IMAGE, TEXT, or GENERAL_DATA. * annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection). * If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel. * You must specify bigquerySource (not gcsSource).
textClassificationConfig This property is required. Property Map
Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

GoogleCloudDatalabelingV1beta1GcsSource
, GoogleCloudDatalabelingV1beta1GcsSourceArgs

InputUri This property is required. string
The input URI of source file. This must be a Cloud Storage path (gs://...).
MimeType This property is required. string
The format of the source file. Only "text/csv" is supported.
InputUri This property is required. string
The input URI of source file. This must be a Cloud Storage path (gs://...).
MimeType This property is required. string
The format of the source file. Only "text/csv" is supported.
inputUri This property is required. String
The input URI of source file. This must be a Cloud Storage path (gs://...).
mimeType This property is required. String
The format of the source file. Only "text/csv" is supported.
inputUri This property is required. string
The input URI of source file. This must be a Cloud Storage path (gs://...).
mimeType This property is required. string
The format of the source file. Only "text/csv" is supported.
input_uri This property is required. str
The input URI of source file. This must be a Cloud Storage path (gs://...).
mime_type This property is required. str
The format of the source file. Only "text/csv" is supported.
inputUri This property is required. String
The input URI of source file. This must be a Cloud Storage path (gs://...).
mimeType This property is required. String
The format of the source file. Only "text/csv" is supported.

GoogleCloudDatalabelingV1beta1GcsSourceResponse
, GoogleCloudDatalabelingV1beta1GcsSourceResponseArgs

InputUri This property is required. string
The input URI of source file. This must be a Cloud Storage path (gs://...).
MimeType This property is required. string
The format of the source file. Only "text/csv" is supported.
InputUri This property is required. string
The input URI of source file. This must be a Cloud Storage path (gs://...).
MimeType This property is required. string
The format of the source file. Only "text/csv" is supported.
inputUri This property is required. String
The input URI of source file. This must be a Cloud Storage path (gs://...).
mimeType This property is required. String
The format of the source file. Only "text/csv" is supported.
inputUri This property is required. string
The input URI of source file. This must be a Cloud Storage path (gs://...).
mimeType This property is required. string
The format of the source file. Only "text/csv" is supported.
input_uri This property is required. str
The input URI of source file. This must be a Cloud Storage path (gs://...).
mime_type This property is required. str
The format of the source file. Only "text/csv" is supported.
inputUri This property is required. String
The input URI of source file. This must be a Cloud Storage path (gs://...).
mimeType This property is required. String
The format of the source file. Only "text/csv" is supported.

GoogleCloudDatalabelingV1beta1HumanAnnotationConfig
, GoogleCloudDatalabelingV1beta1HumanAnnotationConfigArgs

AnnotatedDatasetDisplayName This property is required. string
A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
Instruction This property is required. string
Instruction resource name.
AnnotatedDatasetDescription string
Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
ContributorEmails List<string>
Optional. If you want your own labeling contributors to manage and work on this labeling request, you can set these contributors here. We will give them access to the question types in crowdcompute. Note that these emails must be registered in crowdcompute worker UI: https://crowd-compute.appspot.com/
LabelGroup string
Optional. A human-readable label used to logically group labeling tasks. This string must match the regular expression [a-zA-Z\\d_-]{0,128}.
LanguageCode string
Optional. The Language of this question, as a BCP-47. Default value is en-US. Only need to set this when task is language related. For example, French text classification.
QuestionDuration string
Optional. Maximum duration for contributors to answer a question. Maximum is 3600 seconds. Default is 3600 seconds.
ReplicaCount int
Optional. Replication of questions. Each question will be sent to up to this number of contributors to label. Aggregated answers will be returned. Default is set to 1. For image related labeling, valid values are 1, 3, 5.
UserEmailAddress string
Email of the user who started the labeling task and should be notified by email. If empty no notification will be sent.
AnnotatedDatasetDisplayName This property is required. string
A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
Instruction This property is required. string
Instruction resource name.
AnnotatedDatasetDescription string
Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
ContributorEmails []string
Optional. If you want your own labeling contributors to manage and work on this labeling request, you can set these contributors here. We will give them access to the question types in crowdcompute. Note that these emails must be registered in crowdcompute worker UI: https://crowd-compute.appspot.com/
LabelGroup string
Optional. A human-readable label used to logically group labeling tasks. This string must match the regular expression [a-zA-Z\\d_-]{0,128}.
LanguageCode string
Optional. The Language of this question, as a BCP-47. Default value is en-US. Only need to set this when task is language related. For example, French text classification.
QuestionDuration string
Optional. Maximum duration for contributors to answer a question. Maximum is 3600 seconds. Default is 3600 seconds.
ReplicaCount int
Optional. Replication of questions. Each question will be sent to up to this number of contributors to label. Aggregated answers will be returned. Default is set to 1. For image related labeling, valid values are 1, 3, 5.
UserEmailAddress string
Email of the user who started the labeling task and should be notified by email. If empty no notification will be sent.
annotatedDatasetDisplayName This property is required. String
A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
instruction This property is required. String
Instruction resource name.
annotatedDatasetDescription String
Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
contributorEmails List<String>
Optional. If you want your own labeling contributors to manage and work on this labeling request, you can set these contributors here. We will give them access to the question types in crowdcompute. Note that these emails must be registered in crowdcompute worker UI: https://crowd-compute.appspot.com/
labelGroup String
Optional. A human-readable label used to logically group labeling tasks. This string must match the regular expression [a-zA-Z\\d_-]{0,128}.
languageCode String
Optional. The Language of this question, as a BCP-47. Default value is en-US. Only need to set this when task is language related. For example, French text classification.
questionDuration String
Optional. Maximum duration for contributors to answer a question. Maximum is 3600 seconds. Default is 3600 seconds.
replicaCount Integer
Optional. Replication of questions. Each question will be sent to up to this number of contributors to label. Aggregated answers will be returned. Default is set to 1. For image related labeling, valid values are 1, 3, 5.
userEmailAddress String
Email of the user who started the labeling task and should be notified by email. If empty no notification will be sent.
annotatedDatasetDisplayName This property is required. string
A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
instruction This property is required. string
Instruction resource name.
annotatedDatasetDescription string
Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
contributorEmails string[]
Optional. If you want your own labeling contributors to manage and work on this labeling request, you can set these contributors here. We will give them access to the question types in crowdcompute. Note that these emails must be registered in crowdcompute worker UI: https://crowd-compute.appspot.com/
labelGroup string
Optional. A human-readable label used to logically group labeling tasks. This string must match the regular expression [a-zA-Z\\d_-]{0,128}.
languageCode string
Optional. The Language of this question, as a BCP-47. Default value is en-US. Only need to set this when task is language related. For example, French text classification.
questionDuration string
Optional. Maximum duration for contributors to answer a question. Maximum is 3600 seconds. Default is 3600 seconds.
replicaCount number
Optional. Replication of questions. Each question will be sent to up to this number of contributors to label. Aggregated answers will be returned. Default is set to 1. For image related labeling, valid values are 1, 3, 5.
userEmailAddress string
Email of the user who started the labeling task and should be notified by email. If empty no notification will be sent.
annotated_dataset_display_name This property is required. str
A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
instruction This property is required. str
Instruction resource name.
annotated_dataset_description str
Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
contributor_emails Sequence[str]
Optional. If you want your own labeling contributors to manage and work on this labeling request, you can set these contributors here. We will give them access to the question types in crowdcompute. Note that these emails must be registered in crowdcompute worker UI: https://crowd-compute.appspot.com/
label_group str
Optional. A human-readable label used to logically group labeling tasks. This string must match the regular expression [a-zA-Z\\d_-]{0,128}.
language_code str
Optional. The Language of this question, as a BCP-47. Default value is en-US. Only need to set this when task is language related. For example, French text classification.
question_duration str
Optional. Maximum duration for contributors to answer a question. Maximum is 3600 seconds. Default is 3600 seconds.
replica_count int
Optional. Replication of questions. Each question will be sent to up to this number of contributors to label. Aggregated answers will be returned. Default is set to 1. For image related labeling, valid values are 1, 3, 5.
user_email_address str
Email of the user who started the labeling task and should be notified by email. If empty no notification will be sent.
annotatedDatasetDisplayName This property is required. String
A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
instruction This property is required. String
Instruction resource name.
annotatedDatasetDescription String
Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
contributorEmails List<String>
Optional. If you want your own labeling contributors to manage and work on this labeling request, you can set these contributors here. We will give them access to the question types in crowdcompute. Note that these emails must be registered in crowdcompute worker UI: https://crowd-compute.appspot.com/
labelGroup String
Optional. A human-readable label used to logically group labeling tasks. This string must match the regular expression [a-zA-Z\\d_-]{0,128}.
languageCode String
Optional. The Language of this question, as a BCP-47. Default value is en-US. Only need to set this when task is language related. For example, French text classification.
questionDuration String
Optional. Maximum duration for contributors to answer a question. Maximum is 3600 seconds. Default is 3600 seconds.
replicaCount Number
Optional. Replication of questions. Each question will be sent to up to this number of contributors to label. Aggregated answers will be returned. Default is set to 1. For image related labeling, valid values are 1, 3, 5.
userEmailAddress String
Email of the user who started the labeling task and should be notified by email. If empty no notification will be sent.

GoogleCloudDatalabelingV1beta1HumanAnnotationConfigResponse
, GoogleCloudDatalabelingV1beta1HumanAnnotationConfigResponseArgs

AnnotatedDatasetDescription This property is required. string
Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
AnnotatedDatasetDisplayName This property is required. string
A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
ContributorEmails This property is required. List<string>
Optional. If you want your own labeling contributors to manage and work on this labeling request, you can set these contributors here. We will give them access to the question types in crowdcompute. Note that these emails must be registered in crowdcompute worker UI: https://crowd-compute.appspot.com/
Instruction This property is required. string
Instruction resource name.
LabelGroup This property is required. string
Optional. A human-readable label used to logically group labeling tasks. This string must match the regular expression [a-zA-Z\\d_-]{0,128}.
LanguageCode This property is required. string
Optional. The Language of this question, as a BCP-47. Default value is en-US. Only need to set this when task is language related. For example, French text classification.
QuestionDuration This property is required. string
Optional. Maximum duration for contributors to answer a question. Maximum is 3600 seconds. Default is 3600 seconds.
ReplicaCount This property is required. int
Optional. Replication of questions. Each question will be sent to up to this number of contributors to label. Aggregated answers will be returned. Default is set to 1. For image related labeling, valid values are 1, 3, 5.
UserEmailAddress This property is required. string
Email of the user who started the labeling task and should be notified by email. If empty no notification will be sent.
AnnotatedDatasetDescription This property is required. string
Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
AnnotatedDatasetDisplayName This property is required. string
A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
ContributorEmails This property is required. []string
Optional. If you want your own labeling contributors to manage and work on this labeling request, you can set these contributors here. We will give them access to the question types in crowdcompute. Note that these emails must be registered in crowdcompute worker UI: https://crowd-compute.appspot.com/
Instruction This property is required. string
Instruction resource name.
LabelGroup This property is required. string
Optional. A human-readable label used to logically group labeling tasks. This string must match the regular expression [a-zA-Z\\d_-]{0,128}.
LanguageCode This property is required. string
Optional. The Language of this question, as a BCP-47. Default value is en-US. Only need to set this when task is language related. For example, French text classification.
QuestionDuration This property is required. string
Optional. Maximum duration for contributors to answer a question. Maximum is 3600 seconds. Default is 3600 seconds.
ReplicaCount This property is required. int
Optional. Replication of questions. Each question will be sent to up to this number of contributors to label. Aggregated answers will be returned. Default is set to 1. For image related labeling, valid values are 1, 3, 5.
UserEmailAddress This property is required. string
Email of the user who started the labeling task and should be notified by email. If empty no notification will be sent.
annotatedDatasetDescription This property is required. String
Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
annotatedDatasetDisplayName This property is required. String
A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
contributorEmails This property is required. List<String>
Optional. If you want your own labeling contributors to manage and work on this labeling request, you can set these contributors here. We will give them access to the question types in crowdcompute. Note that these emails must be registered in crowdcompute worker UI: https://crowd-compute.appspot.com/
instruction This property is required. String
Instruction resource name.
labelGroup This property is required. String
Optional. A human-readable label used to logically group labeling tasks. This string must match the regular expression [a-zA-Z\\d_-]{0,128}.
languageCode This property is required. String
Optional. The Language of this question, as a BCP-47. Default value is en-US. Only need to set this when task is language related. For example, French text classification.
questionDuration This property is required. String
Optional. Maximum duration for contributors to answer a question. Maximum is 3600 seconds. Default is 3600 seconds.
replicaCount This property is required. Integer
Optional. Replication of questions. Each question will be sent to up to this number of contributors to label. Aggregated answers will be returned. Default is set to 1. For image related labeling, valid values are 1, 3, 5.
userEmailAddress This property is required. String
Email of the user who started the labeling task and should be notified by email. If empty no notification will be sent.
annotatedDatasetDescription This property is required. string
Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
annotatedDatasetDisplayName This property is required. string
A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
contributorEmails This property is required. string[]
Optional. If you want your own labeling contributors to manage and work on this labeling request, you can set these contributors here. We will give them access to the question types in crowdcompute. Note that these emails must be registered in crowdcompute worker UI: https://crowd-compute.appspot.com/
instruction This property is required. string
Instruction resource name.
labelGroup This property is required. string
Optional. A human-readable label used to logically group labeling tasks. This string must match the regular expression [a-zA-Z\\d_-]{0,128}.
languageCode This property is required. string
Optional. The Language of this question, as a BCP-47. Default value is en-US. Only need to set this when task is language related. For example, French text classification.
questionDuration This property is required. string
Optional. Maximum duration for contributors to answer a question. Maximum is 3600 seconds. Default is 3600 seconds.
replicaCount This property is required. number
Optional. Replication of questions. Each question will be sent to up to this number of contributors to label. Aggregated answers will be returned. Default is set to 1. For image related labeling, valid values are 1, 3, 5.
userEmailAddress This property is required. string
Email of the user who started the labeling task and should be notified by email. If empty no notification will be sent.
annotated_dataset_description This property is required. str
Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
annotated_dataset_display_name This property is required. str
A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
contributor_emails This property is required. Sequence[str]
Optional. If you want your own labeling contributors to manage and work on this labeling request, you can set these contributors here. We will give them access to the question types in crowdcompute. Note that these emails must be registered in crowdcompute worker UI: https://crowd-compute.appspot.com/
instruction This property is required. str
Instruction resource name.
label_group This property is required. str
Optional. A human-readable label used to logically group labeling tasks. This string must match the regular expression [a-zA-Z\\d_-]{0,128}.
language_code This property is required. str
Optional. The Language of this question, as a BCP-47. Default value is en-US. Only need to set this when task is language related. For example, French text classification.
question_duration This property is required. str
Optional. Maximum duration for contributors to answer a question. Maximum is 3600 seconds. Default is 3600 seconds.
replica_count This property is required. int
Optional. Replication of questions. Each question will be sent to up to this number of contributors to label. Aggregated answers will be returned. Default is set to 1. For image related labeling, valid values are 1, 3, 5.
user_email_address This property is required. str
Email of the user who started the labeling task and should be notified by email. If empty no notification will be sent.
annotatedDatasetDescription This property is required. String
Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
annotatedDatasetDisplayName This property is required. String
A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
contributorEmails This property is required. List<String>
Optional. If you want your own labeling contributors to manage and work on this labeling request, you can set these contributors here. We will give them access to the question types in crowdcompute. Note that these emails must be registered in crowdcompute worker UI: https://crowd-compute.appspot.com/
instruction This property is required. String
Instruction resource name.
labelGroup This property is required. String
Optional. A human-readable label used to logically group labeling tasks. This string must match the regular expression [a-zA-Z\\d_-]{0,128}.
languageCode This property is required. String
Optional. The Language of this question, as a BCP-47. Default value is en-US. Only need to set this when task is language related. For example, French text classification.
questionDuration This property is required. String
Optional. Maximum duration for contributors to answer a question. Maximum is 3600 seconds. Default is 3600 seconds.
replicaCount This property is required. Number
Optional. Replication of questions. Each question will be sent to up to this number of contributors to label. Aggregated answers will be returned. Default is set to 1. For image related labeling, valid values are 1, 3, 5.
userEmailAddress This property is required. String
Email of the user who started the labeling task and should be notified by email. If empty no notification will be sent.

GoogleCloudDatalabelingV1beta1ImageClassificationConfig
, GoogleCloudDatalabelingV1beta1ImageClassificationConfigArgs

AnnotationSpecSet This property is required. string
Annotation spec set resource name.
AllowMultiLabel bool
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one image.
AnswerAggregationType Pulumi.GoogleNative.DataLabeling.V1Beta1.GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationType
Optional. The type of how to aggregate answers.
AnnotationSpecSet This property is required. string
Annotation spec set resource name.
AllowMultiLabel bool
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one image.
AnswerAggregationType GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationType
Optional. The type of how to aggregate answers.
annotationSpecSet This property is required. String
Annotation spec set resource name.
allowMultiLabel Boolean
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one image.
answerAggregationType GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationType
Optional. The type of how to aggregate answers.
annotationSpecSet This property is required. string
Annotation spec set resource name.
allowMultiLabel boolean
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one image.
answerAggregationType GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationType
Optional. The type of how to aggregate answers.
annotation_spec_set This property is required. str
Annotation spec set resource name.
allow_multi_label bool
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one image.
answer_aggregation_type GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationType
Optional. The type of how to aggregate answers.
annotationSpecSet This property is required. String
Annotation spec set resource name.
allowMultiLabel Boolean
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one image.
answerAggregationType "STRING_AGGREGATION_TYPE_UNSPECIFIED" | "MAJORITY_VOTE" | "UNANIMOUS_VOTE" | "NO_AGGREGATION"
Optional. The type of how to aggregate answers.

GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationType
, GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationTypeArgs

StringAggregationTypeUnspecified
STRING_AGGREGATION_TYPE_UNSPECIFIED
MajorityVote
MAJORITY_VOTEMajority vote to aggregate answers.
UnanimousVote
UNANIMOUS_VOTEUnanimous answers will be adopted.
NoAggregation
NO_AGGREGATIONPreserve all answers by crowd compute.
GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationTypeStringAggregationTypeUnspecified
STRING_AGGREGATION_TYPE_UNSPECIFIED
GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationTypeMajorityVote
MAJORITY_VOTEMajority vote to aggregate answers.
GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationTypeUnanimousVote
UNANIMOUS_VOTEUnanimous answers will be adopted.
GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationTypeNoAggregation
NO_AGGREGATIONPreserve all answers by crowd compute.
StringAggregationTypeUnspecified
STRING_AGGREGATION_TYPE_UNSPECIFIED
MajorityVote
MAJORITY_VOTEMajority vote to aggregate answers.
UnanimousVote
UNANIMOUS_VOTEUnanimous answers will be adopted.
NoAggregation
NO_AGGREGATIONPreserve all answers by crowd compute.
StringAggregationTypeUnspecified
STRING_AGGREGATION_TYPE_UNSPECIFIED
MajorityVote
MAJORITY_VOTEMajority vote to aggregate answers.
UnanimousVote
UNANIMOUS_VOTEUnanimous answers will be adopted.
NoAggregation
NO_AGGREGATIONPreserve all answers by crowd compute.
STRING_AGGREGATION_TYPE_UNSPECIFIED
STRING_AGGREGATION_TYPE_UNSPECIFIED
MAJORITY_VOTE
MAJORITY_VOTEMajority vote to aggregate answers.
UNANIMOUS_VOTE
UNANIMOUS_VOTEUnanimous answers will be adopted.
NO_AGGREGATION
NO_AGGREGATIONPreserve all answers by crowd compute.
"STRING_AGGREGATION_TYPE_UNSPECIFIED"
STRING_AGGREGATION_TYPE_UNSPECIFIED
"MAJORITY_VOTE"
MAJORITY_VOTEMajority vote to aggregate answers.
"UNANIMOUS_VOTE"
UNANIMOUS_VOTEUnanimous answers will be adopted.
"NO_AGGREGATION"
NO_AGGREGATIONPreserve all answers by crowd compute.

GoogleCloudDatalabelingV1beta1ImageClassificationConfigResponse
, GoogleCloudDatalabelingV1beta1ImageClassificationConfigResponseArgs

AllowMultiLabel This property is required. bool
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one image.
AnnotationSpecSet This property is required. string
Annotation spec set resource name.
AnswerAggregationType This property is required. string
Optional. The type of how to aggregate answers.
AllowMultiLabel This property is required. bool
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one image.
AnnotationSpecSet This property is required. string
Annotation spec set resource name.
AnswerAggregationType This property is required. string
Optional. The type of how to aggregate answers.
allowMultiLabel This property is required. Boolean
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one image.
annotationSpecSet This property is required. String
Annotation spec set resource name.
answerAggregationType This property is required. String
Optional. The type of how to aggregate answers.
allowMultiLabel This property is required. boolean
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one image.
annotationSpecSet This property is required. string
Annotation spec set resource name.
answerAggregationType This property is required. string
Optional. The type of how to aggregate answers.
allow_multi_label This property is required. bool
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one image.
annotation_spec_set This property is required. str
Annotation spec set resource name.
answer_aggregation_type This property is required. str
Optional. The type of how to aggregate answers.
allowMultiLabel This property is required. Boolean
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one image.
annotationSpecSet This property is required. String
Annotation spec set resource name.
answerAggregationType This property is required. String
Optional. The type of how to aggregate answers.

GoogleCloudDatalabelingV1beta1InputConfig
, GoogleCloudDatalabelingV1beta1InputConfigArgs

DataType This property is required. Pulumi.GoogleNative.DataLabeling.V1Beta1.GoogleCloudDatalabelingV1beta1InputConfigDataType
Data type must be specifed when user tries to import data.
AnnotationType Pulumi.GoogleNative.DataLabeling.V1Beta1.GoogleCloudDatalabelingV1beta1InputConfigAnnotationType
Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
BigquerySource Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1BigQuerySource
Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
ClassificationMetadata Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1ClassificationMetadata
Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
GcsSource Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1GcsSource
Source located in Cloud Storage.
TextMetadata Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1TextMetadata
Required for text import, as language code must be specified.
DataType This property is required. GoogleCloudDatalabelingV1beta1InputConfigDataType
Data type must be specifed when user tries to import data.
AnnotationType GoogleCloudDatalabelingV1beta1InputConfigAnnotationType
Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
BigquerySource GoogleCloudDatalabelingV1beta1BigQuerySource
Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
ClassificationMetadata GoogleCloudDatalabelingV1beta1ClassificationMetadata
Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
GcsSource GoogleCloudDatalabelingV1beta1GcsSource
Source located in Cloud Storage.
TextMetadata GoogleCloudDatalabelingV1beta1TextMetadata
Required for text import, as language code must be specified.
dataType This property is required. GoogleCloudDatalabelingV1beta1InputConfigDataType
Data type must be specifed when user tries to import data.
annotationType GoogleCloudDatalabelingV1beta1InputConfigAnnotationType
Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
bigquerySource GoogleCloudDatalabelingV1beta1BigQuerySource
Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
classificationMetadata GoogleCloudDatalabelingV1beta1ClassificationMetadata
Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
gcsSource GoogleCloudDatalabelingV1beta1GcsSource
Source located in Cloud Storage.
textMetadata GoogleCloudDatalabelingV1beta1TextMetadata
Required for text import, as language code must be specified.
dataType This property is required. GoogleCloudDatalabelingV1beta1InputConfigDataType
Data type must be specifed when user tries to import data.
annotationType GoogleCloudDatalabelingV1beta1InputConfigAnnotationType
Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
bigquerySource GoogleCloudDatalabelingV1beta1BigQuerySource
Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
classificationMetadata GoogleCloudDatalabelingV1beta1ClassificationMetadata
Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
gcsSource GoogleCloudDatalabelingV1beta1GcsSource
Source located in Cloud Storage.
textMetadata GoogleCloudDatalabelingV1beta1TextMetadata
Required for text import, as language code must be specified.
data_type This property is required. GoogleCloudDatalabelingV1beta1InputConfigDataType
Data type must be specifed when user tries to import data.
annotation_type GoogleCloudDatalabelingV1beta1InputConfigAnnotationType
Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
bigquery_source GoogleCloudDatalabelingV1beta1BigQuerySource
Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
classification_metadata GoogleCloudDatalabelingV1beta1ClassificationMetadata
Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
gcs_source GoogleCloudDatalabelingV1beta1GcsSource
Source located in Cloud Storage.
text_metadata GoogleCloudDatalabelingV1beta1TextMetadata
Required for text import, as language code must be specified.
dataType This property is required. "DATA_TYPE_UNSPECIFIED" | "IMAGE" | "VIDEO" | "TEXT" | "GENERAL_DATA"
Data type must be specifed when user tries to import data.
annotationType "ANNOTATION_TYPE_UNSPECIFIED" | "IMAGE_CLASSIFICATION_ANNOTATION" | "IMAGE_BOUNDING_BOX_ANNOTATION" | "IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATION" | "IMAGE_BOUNDING_POLY_ANNOTATION" | "IMAGE_POLYLINE_ANNOTATION" | "IMAGE_SEGMENTATION_ANNOTATION" | "VIDEO_SHOTS_CLASSIFICATION_ANNOTATION" | "VIDEO_OBJECT_TRACKING_ANNOTATION" | "VIDEO_OBJECT_DETECTION_ANNOTATION" | "VIDEO_EVENT_ANNOTATION" | "TEXT_CLASSIFICATION_ANNOTATION" | "TEXT_ENTITY_EXTRACTION_ANNOTATION" | "GENERAL_CLASSIFICATION_ANNOTATION"
Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
bigquerySource Property Map
Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
classificationMetadata Property Map
Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
gcsSource Property Map
Source located in Cloud Storage.
textMetadata Property Map
Required for text import, as language code must be specified.

GoogleCloudDatalabelingV1beta1InputConfigAnnotationType
, GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeArgs

AnnotationTypeUnspecified
ANNOTATION_TYPE_UNSPECIFIED
ImageClassificationAnnotation
IMAGE_CLASSIFICATION_ANNOTATIONClassification annotations in an image. Allowed for continuous evaluation.
ImageBoundingBoxAnnotation
IMAGE_BOUNDING_BOX_ANNOTATIONBounding box annotations in an image. A form of image object detection. Allowed for continuous evaluation.
ImageOrientedBoundingBoxAnnotation
IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATIONOriented bounding box. The box does not have to be parallel to horizontal line.
ImageBoundingPolyAnnotation
IMAGE_BOUNDING_POLY_ANNOTATIONBounding poly annotations in an image.
ImagePolylineAnnotation
IMAGE_POLYLINE_ANNOTATIONPolyline annotations in an image.
ImageSegmentationAnnotation
IMAGE_SEGMENTATION_ANNOTATIONSegmentation annotations in an image.
VideoShotsClassificationAnnotation
VIDEO_SHOTS_CLASSIFICATION_ANNOTATIONClassification annotations in video shots.
VideoObjectTrackingAnnotation
VIDEO_OBJECT_TRACKING_ANNOTATIONVideo object tracking annotation.
VideoObjectDetectionAnnotation
VIDEO_OBJECT_DETECTION_ANNOTATIONVideo object detection annotation.
VideoEventAnnotation
VIDEO_EVENT_ANNOTATIONVideo event annotation.
TextClassificationAnnotation
TEXT_CLASSIFICATION_ANNOTATIONClassification for text. Allowed for continuous evaluation.
TextEntityExtractionAnnotation
TEXT_ENTITY_EXTRACTION_ANNOTATIONEntity extraction for text.
GeneralClassificationAnnotation
GENERAL_CLASSIFICATION_ANNOTATIONGeneral classification. Allowed for continuous evaluation.
GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeAnnotationTypeUnspecified
ANNOTATION_TYPE_UNSPECIFIED
GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeImageClassificationAnnotation
IMAGE_CLASSIFICATION_ANNOTATIONClassification annotations in an image. Allowed for continuous evaluation.
GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeImageBoundingBoxAnnotation
IMAGE_BOUNDING_BOX_ANNOTATIONBounding box annotations in an image. A form of image object detection. Allowed for continuous evaluation.
GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeImageOrientedBoundingBoxAnnotation
IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATIONOriented bounding box. The box does not have to be parallel to horizontal line.
GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeImageBoundingPolyAnnotation
IMAGE_BOUNDING_POLY_ANNOTATIONBounding poly annotations in an image.
GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeImagePolylineAnnotation
IMAGE_POLYLINE_ANNOTATIONPolyline annotations in an image.
GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeImageSegmentationAnnotation
IMAGE_SEGMENTATION_ANNOTATIONSegmentation annotations in an image.
GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeVideoShotsClassificationAnnotation
VIDEO_SHOTS_CLASSIFICATION_ANNOTATIONClassification annotations in video shots.
GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeVideoObjectTrackingAnnotation
VIDEO_OBJECT_TRACKING_ANNOTATIONVideo object tracking annotation.
GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeVideoObjectDetectionAnnotation
VIDEO_OBJECT_DETECTION_ANNOTATIONVideo object detection annotation.
GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeVideoEventAnnotation
VIDEO_EVENT_ANNOTATIONVideo event annotation.
GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeTextClassificationAnnotation
TEXT_CLASSIFICATION_ANNOTATIONClassification for text. Allowed for continuous evaluation.
GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeTextEntityExtractionAnnotation
TEXT_ENTITY_EXTRACTION_ANNOTATIONEntity extraction for text.
GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeGeneralClassificationAnnotation
GENERAL_CLASSIFICATION_ANNOTATIONGeneral classification. Allowed for continuous evaluation.
AnnotationTypeUnspecified
ANNOTATION_TYPE_UNSPECIFIED
ImageClassificationAnnotation
IMAGE_CLASSIFICATION_ANNOTATIONClassification annotations in an image. Allowed for continuous evaluation.
ImageBoundingBoxAnnotation
IMAGE_BOUNDING_BOX_ANNOTATIONBounding box annotations in an image. A form of image object detection. Allowed for continuous evaluation.
ImageOrientedBoundingBoxAnnotation
IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATIONOriented bounding box. The box does not have to be parallel to horizontal line.
ImageBoundingPolyAnnotation
IMAGE_BOUNDING_POLY_ANNOTATIONBounding poly annotations in an image.
ImagePolylineAnnotation
IMAGE_POLYLINE_ANNOTATIONPolyline annotations in an image.
ImageSegmentationAnnotation
IMAGE_SEGMENTATION_ANNOTATIONSegmentation annotations in an image.
VideoShotsClassificationAnnotation
VIDEO_SHOTS_CLASSIFICATION_ANNOTATIONClassification annotations in video shots.
VideoObjectTrackingAnnotation
VIDEO_OBJECT_TRACKING_ANNOTATIONVideo object tracking annotation.
VideoObjectDetectionAnnotation
VIDEO_OBJECT_DETECTION_ANNOTATIONVideo object detection annotation.
VideoEventAnnotation
VIDEO_EVENT_ANNOTATIONVideo event annotation.
TextClassificationAnnotation
TEXT_CLASSIFICATION_ANNOTATIONClassification for text. Allowed for continuous evaluation.
TextEntityExtractionAnnotation
TEXT_ENTITY_EXTRACTION_ANNOTATIONEntity extraction for text.
GeneralClassificationAnnotation
GENERAL_CLASSIFICATION_ANNOTATIONGeneral classification. Allowed for continuous evaluation.
AnnotationTypeUnspecified
ANNOTATION_TYPE_UNSPECIFIED
ImageClassificationAnnotation
IMAGE_CLASSIFICATION_ANNOTATIONClassification annotations in an image. Allowed for continuous evaluation.
ImageBoundingBoxAnnotation
IMAGE_BOUNDING_BOX_ANNOTATIONBounding box annotations in an image. A form of image object detection. Allowed for continuous evaluation.
ImageOrientedBoundingBoxAnnotation
IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATIONOriented bounding box. The box does not have to be parallel to horizontal line.
ImageBoundingPolyAnnotation
IMAGE_BOUNDING_POLY_ANNOTATIONBounding poly annotations in an image.
ImagePolylineAnnotation
IMAGE_POLYLINE_ANNOTATIONPolyline annotations in an image.
ImageSegmentationAnnotation
IMAGE_SEGMENTATION_ANNOTATIONSegmentation annotations in an image.
VideoShotsClassificationAnnotation
VIDEO_SHOTS_CLASSIFICATION_ANNOTATIONClassification annotations in video shots.
VideoObjectTrackingAnnotation
VIDEO_OBJECT_TRACKING_ANNOTATIONVideo object tracking annotation.
VideoObjectDetectionAnnotation
VIDEO_OBJECT_DETECTION_ANNOTATIONVideo object detection annotation.
VideoEventAnnotation
VIDEO_EVENT_ANNOTATIONVideo event annotation.
TextClassificationAnnotation
TEXT_CLASSIFICATION_ANNOTATIONClassification for text. Allowed for continuous evaluation.
TextEntityExtractionAnnotation
TEXT_ENTITY_EXTRACTION_ANNOTATIONEntity extraction for text.
GeneralClassificationAnnotation
GENERAL_CLASSIFICATION_ANNOTATIONGeneral classification. Allowed for continuous evaluation.
ANNOTATION_TYPE_UNSPECIFIED
ANNOTATION_TYPE_UNSPECIFIED
IMAGE_CLASSIFICATION_ANNOTATION
IMAGE_CLASSIFICATION_ANNOTATIONClassification annotations in an image. Allowed for continuous evaluation.
IMAGE_BOUNDING_BOX_ANNOTATION
IMAGE_BOUNDING_BOX_ANNOTATIONBounding box annotations in an image. A form of image object detection. Allowed for continuous evaluation.
IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATION
IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATIONOriented bounding box. The box does not have to be parallel to horizontal line.
IMAGE_BOUNDING_POLY_ANNOTATION
IMAGE_BOUNDING_POLY_ANNOTATIONBounding poly annotations in an image.
IMAGE_POLYLINE_ANNOTATION
IMAGE_POLYLINE_ANNOTATIONPolyline annotations in an image.
IMAGE_SEGMENTATION_ANNOTATION
IMAGE_SEGMENTATION_ANNOTATIONSegmentation annotations in an image.
VIDEO_SHOTS_CLASSIFICATION_ANNOTATION
VIDEO_SHOTS_CLASSIFICATION_ANNOTATIONClassification annotations in video shots.
VIDEO_OBJECT_TRACKING_ANNOTATION
VIDEO_OBJECT_TRACKING_ANNOTATIONVideo object tracking annotation.
VIDEO_OBJECT_DETECTION_ANNOTATION
VIDEO_OBJECT_DETECTION_ANNOTATIONVideo object detection annotation.
VIDEO_EVENT_ANNOTATION
VIDEO_EVENT_ANNOTATIONVideo event annotation.
TEXT_CLASSIFICATION_ANNOTATION
TEXT_CLASSIFICATION_ANNOTATIONClassification for text. Allowed for continuous evaluation.
TEXT_ENTITY_EXTRACTION_ANNOTATION
TEXT_ENTITY_EXTRACTION_ANNOTATIONEntity extraction for text.
GENERAL_CLASSIFICATION_ANNOTATION
GENERAL_CLASSIFICATION_ANNOTATIONGeneral classification. Allowed for continuous evaluation.
"ANNOTATION_TYPE_UNSPECIFIED"
ANNOTATION_TYPE_UNSPECIFIED
"IMAGE_CLASSIFICATION_ANNOTATION"
IMAGE_CLASSIFICATION_ANNOTATIONClassification annotations in an image. Allowed for continuous evaluation.
"IMAGE_BOUNDING_BOX_ANNOTATION"
IMAGE_BOUNDING_BOX_ANNOTATIONBounding box annotations in an image. A form of image object detection. Allowed for continuous evaluation.
"IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATION"
IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATIONOriented bounding box. The box does not have to be parallel to horizontal line.
"IMAGE_BOUNDING_POLY_ANNOTATION"
IMAGE_BOUNDING_POLY_ANNOTATIONBounding poly annotations in an image.
"IMAGE_POLYLINE_ANNOTATION"
IMAGE_POLYLINE_ANNOTATIONPolyline annotations in an image.
"IMAGE_SEGMENTATION_ANNOTATION"
IMAGE_SEGMENTATION_ANNOTATIONSegmentation annotations in an image.
"VIDEO_SHOTS_CLASSIFICATION_ANNOTATION"
VIDEO_SHOTS_CLASSIFICATION_ANNOTATIONClassification annotations in video shots.
"VIDEO_OBJECT_TRACKING_ANNOTATION"
VIDEO_OBJECT_TRACKING_ANNOTATIONVideo object tracking annotation.
"VIDEO_OBJECT_DETECTION_ANNOTATION"
VIDEO_OBJECT_DETECTION_ANNOTATIONVideo object detection annotation.
"VIDEO_EVENT_ANNOTATION"
VIDEO_EVENT_ANNOTATIONVideo event annotation.
"TEXT_CLASSIFICATION_ANNOTATION"
TEXT_CLASSIFICATION_ANNOTATIONClassification for text. Allowed for continuous evaluation.
"TEXT_ENTITY_EXTRACTION_ANNOTATION"
TEXT_ENTITY_EXTRACTION_ANNOTATIONEntity extraction for text.
"GENERAL_CLASSIFICATION_ANNOTATION"
GENERAL_CLASSIFICATION_ANNOTATIONGeneral classification. Allowed for continuous evaluation.

GoogleCloudDatalabelingV1beta1InputConfigDataType
, GoogleCloudDatalabelingV1beta1InputConfigDataTypeArgs

DataTypeUnspecified
DATA_TYPE_UNSPECIFIEDData type is unspecified.
Image
IMAGEAllowed for continuous evaluation.
Video
VIDEOVideo data type.
Text
TEXTAllowed for continuous evaluation.
GeneralData
GENERAL_DATAAllowed for continuous evaluation.
GoogleCloudDatalabelingV1beta1InputConfigDataTypeDataTypeUnspecified
DATA_TYPE_UNSPECIFIEDData type is unspecified.
GoogleCloudDatalabelingV1beta1InputConfigDataTypeImage
IMAGEAllowed for continuous evaluation.
GoogleCloudDatalabelingV1beta1InputConfigDataTypeVideo
VIDEOVideo data type.
GoogleCloudDatalabelingV1beta1InputConfigDataTypeText
TEXTAllowed for continuous evaluation.
GoogleCloudDatalabelingV1beta1InputConfigDataTypeGeneralData
GENERAL_DATAAllowed for continuous evaluation.
DataTypeUnspecified
DATA_TYPE_UNSPECIFIEDData type is unspecified.
Image
IMAGEAllowed for continuous evaluation.
Video
VIDEOVideo data type.
Text
TEXTAllowed for continuous evaluation.
GeneralData
GENERAL_DATAAllowed for continuous evaluation.
DataTypeUnspecified
DATA_TYPE_UNSPECIFIEDData type is unspecified.
Image
IMAGEAllowed for continuous evaluation.
Video
VIDEOVideo data type.
Text
TEXTAllowed for continuous evaluation.
GeneralData
GENERAL_DATAAllowed for continuous evaluation.
DATA_TYPE_UNSPECIFIED
DATA_TYPE_UNSPECIFIEDData type is unspecified.
IMAGE
IMAGEAllowed for continuous evaluation.
VIDEO
VIDEOVideo data type.
TEXT
TEXTAllowed for continuous evaluation.
GENERAL_DATA
GENERAL_DATAAllowed for continuous evaluation.
"DATA_TYPE_UNSPECIFIED"
DATA_TYPE_UNSPECIFIEDData type is unspecified.
"IMAGE"
IMAGEAllowed for continuous evaluation.
"VIDEO"
VIDEOVideo data type.
"TEXT"
TEXTAllowed for continuous evaluation.
"GENERAL_DATA"
GENERAL_DATAAllowed for continuous evaluation.

GoogleCloudDatalabelingV1beta1InputConfigResponse
, GoogleCloudDatalabelingV1beta1InputConfigResponseArgs

AnnotationType This property is required. string
Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
BigquerySource This property is required. Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1BigQuerySourceResponse
Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
ClassificationMetadata This property is required. Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1ClassificationMetadataResponse
Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
DataType This property is required. string
Data type must be specifed when user tries to import data.
GcsSource This property is required. Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1GcsSourceResponse
Source located in Cloud Storage.
TextMetadata This property is required. Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1TextMetadataResponse
Required for text import, as language code must be specified.
AnnotationType This property is required. string
Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
BigquerySource This property is required. GoogleCloudDatalabelingV1beta1BigQuerySourceResponse
Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
ClassificationMetadata This property is required. GoogleCloudDatalabelingV1beta1ClassificationMetadataResponse
Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
DataType This property is required. string
Data type must be specifed when user tries to import data.
GcsSource This property is required. GoogleCloudDatalabelingV1beta1GcsSourceResponse
Source located in Cloud Storage.
TextMetadata This property is required. GoogleCloudDatalabelingV1beta1TextMetadataResponse
Required for text import, as language code must be specified.
annotationType This property is required. String
Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
bigquerySource This property is required. GoogleCloudDatalabelingV1beta1BigQuerySourceResponse
Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
classificationMetadata This property is required. GoogleCloudDatalabelingV1beta1ClassificationMetadataResponse
Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
dataType This property is required. String
Data type must be specifed when user tries to import data.
gcsSource This property is required. GoogleCloudDatalabelingV1beta1GcsSourceResponse
Source located in Cloud Storage.
textMetadata This property is required. GoogleCloudDatalabelingV1beta1TextMetadataResponse
Required for text import, as language code must be specified.
annotationType This property is required. string
Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
bigquerySource This property is required. GoogleCloudDatalabelingV1beta1BigQuerySourceResponse
Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
classificationMetadata This property is required. GoogleCloudDatalabelingV1beta1ClassificationMetadataResponse
Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
dataType This property is required. string
Data type must be specifed when user tries to import data.
gcsSource This property is required. GoogleCloudDatalabelingV1beta1GcsSourceResponse
Source located in Cloud Storage.
textMetadata This property is required. GoogleCloudDatalabelingV1beta1TextMetadataResponse
Required for text import, as language code must be specified.
annotation_type This property is required. str
Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
bigquery_source This property is required. GoogleCloudDatalabelingV1beta1BigQuerySourceResponse
Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
classification_metadata This property is required. GoogleCloudDatalabelingV1beta1ClassificationMetadataResponse
Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
data_type This property is required. str
Data type must be specifed when user tries to import data.
gcs_source This property is required. GoogleCloudDatalabelingV1beta1GcsSourceResponse
Source located in Cloud Storage.
text_metadata This property is required. GoogleCloudDatalabelingV1beta1TextMetadataResponse
Required for text import, as language code must be specified.
annotationType This property is required. String
Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
bigquerySource This property is required. Property Map
Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
classificationMetadata This property is required. Property Map
Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
dataType This property is required. String
Data type must be specifed when user tries to import data.
gcsSource This property is required. Property Map
Source located in Cloud Storage.
textMetadata This property is required. Property Map
Required for text import, as language code must be specified.

GoogleCloudDatalabelingV1beta1SentimentConfig
, GoogleCloudDatalabelingV1beta1SentimentConfigArgs

EnableLabelSentimentSelection bool
If set to true, contributors will have the option to select sentiment of the label they selected, to mark it as negative or positive label. Default is false.
EnableLabelSentimentSelection bool
If set to true, contributors will have the option to select sentiment of the label they selected, to mark it as negative or positive label. Default is false.
enableLabelSentimentSelection Boolean
If set to true, contributors will have the option to select sentiment of the label they selected, to mark it as negative or positive label. Default is false.
enableLabelSentimentSelection boolean
If set to true, contributors will have the option to select sentiment of the label they selected, to mark it as negative or positive label. Default is false.
enable_label_sentiment_selection bool
If set to true, contributors will have the option to select sentiment of the label they selected, to mark it as negative or positive label. Default is false.
enableLabelSentimentSelection Boolean
If set to true, contributors will have the option to select sentiment of the label they selected, to mark it as negative or positive label. Default is false.

GoogleCloudDatalabelingV1beta1SentimentConfigResponse
, GoogleCloudDatalabelingV1beta1SentimentConfigResponseArgs

EnableLabelSentimentSelection This property is required. bool
If set to true, contributors will have the option to select sentiment of the label they selected, to mark it as negative or positive label. Default is false.
EnableLabelSentimentSelection This property is required. bool
If set to true, contributors will have the option to select sentiment of the label they selected, to mark it as negative or positive label. Default is false.
enableLabelSentimentSelection This property is required. Boolean
If set to true, contributors will have the option to select sentiment of the label they selected, to mark it as negative or positive label. Default is false.
enableLabelSentimentSelection This property is required. boolean
If set to true, contributors will have the option to select sentiment of the label they selected, to mark it as negative or positive label. Default is false.
enable_label_sentiment_selection This property is required. bool
If set to true, contributors will have the option to select sentiment of the label they selected, to mark it as negative or positive label. Default is false.
enableLabelSentimentSelection This property is required. Boolean
If set to true, contributors will have the option to select sentiment of the label they selected, to mark it as negative or positive label. Default is false.

GoogleCloudDatalabelingV1beta1TextClassificationConfig
, GoogleCloudDatalabelingV1beta1TextClassificationConfigArgs

AnnotationSpecSet This property is required. string
Annotation spec set resource name.
AllowMultiLabel bool
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one text segment.
SentimentConfig Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1SentimentConfig
Optional. Configs for sentiment selection. We deprecate sentiment analysis in data labeling side as it is incompatible with uCAIP.
AnnotationSpecSet This property is required. string
Annotation spec set resource name.
AllowMultiLabel bool
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one text segment.
SentimentConfig GoogleCloudDatalabelingV1beta1SentimentConfig
Optional. Configs for sentiment selection. We deprecate sentiment analysis in data labeling side as it is incompatible with uCAIP.
annotationSpecSet This property is required. String
Annotation spec set resource name.
allowMultiLabel Boolean
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one text segment.
sentimentConfig GoogleCloudDatalabelingV1beta1SentimentConfig
Optional. Configs for sentiment selection. We deprecate sentiment analysis in data labeling side as it is incompatible with uCAIP.
annotationSpecSet This property is required. string
Annotation spec set resource name.
allowMultiLabel boolean
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one text segment.
sentimentConfig GoogleCloudDatalabelingV1beta1SentimentConfig
Optional. Configs for sentiment selection. We deprecate sentiment analysis in data labeling side as it is incompatible with uCAIP.
annotation_spec_set This property is required. str
Annotation spec set resource name.
allow_multi_label bool
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one text segment.
sentiment_config GoogleCloudDatalabelingV1beta1SentimentConfig
Optional. Configs for sentiment selection. We deprecate sentiment analysis in data labeling side as it is incompatible with uCAIP.
annotationSpecSet This property is required. String
Annotation spec set resource name.
allowMultiLabel Boolean
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one text segment.
sentimentConfig Property Map
Optional. Configs for sentiment selection. We deprecate sentiment analysis in data labeling side as it is incompatible with uCAIP.

GoogleCloudDatalabelingV1beta1TextClassificationConfigResponse
, GoogleCloudDatalabelingV1beta1TextClassificationConfigResponseArgs

AllowMultiLabel This property is required. bool
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one text segment.
AnnotationSpecSet This property is required. string
Annotation spec set resource name.
SentimentConfig This property is required. Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1SentimentConfigResponse
Optional. Configs for sentiment selection. We deprecate sentiment analysis in data labeling side as it is incompatible with uCAIP.
AllowMultiLabel This property is required. bool
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one text segment.
AnnotationSpecSet This property is required. string
Annotation spec set resource name.
SentimentConfig This property is required. GoogleCloudDatalabelingV1beta1SentimentConfigResponse
Optional. Configs for sentiment selection. We deprecate sentiment analysis in data labeling side as it is incompatible with uCAIP.
allowMultiLabel This property is required. Boolean
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one text segment.
annotationSpecSet This property is required. String
Annotation spec set resource name.
sentimentConfig This property is required. GoogleCloudDatalabelingV1beta1SentimentConfigResponse
Optional. Configs for sentiment selection. We deprecate sentiment analysis in data labeling side as it is incompatible with uCAIP.
allowMultiLabel This property is required. boolean
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one text segment.
annotationSpecSet This property is required. string
Annotation spec set resource name.
sentimentConfig This property is required. GoogleCloudDatalabelingV1beta1SentimentConfigResponse
Optional. Configs for sentiment selection. We deprecate sentiment analysis in data labeling side as it is incompatible with uCAIP.
allow_multi_label This property is required. bool
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one text segment.
annotation_spec_set This property is required. str
Annotation spec set resource name.
sentiment_config This property is required. GoogleCloudDatalabelingV1beta1SentimentConfigResponse
Optional. Configs for sentiment selection. We deprecate sentiment analysis in data labeling side as it is incompatible with uCAIP.
allowMultiLabel This property is required. Boolean
Optional. If allow_multi_label is true, contributors are able to choose multiple labels for one text segment.
annotationSpecSet This property is required. String
Annotation spec set resource name.
sentimentConfig This property is required. Property Map
Optional. Configs for sentiment selection. We deprecate sentiment analysis in data labeling side as it is incompatible with uCAIP.

GoogleCloudDatalabelingV1beta1TextMetadata
, GoogleCloudDatalabelingV1beta1TextMetadataArgs

LanguageCode string
The language of this text, as a BCP-47. Default value is en-US.
LanguageCode string
The language of this text, as a BCP-47. Default value is en-US.
languageCode String
The language of this text, as a BCP-47. Default value is en-US.
languageCode string
The language of this text, as a BCP-47. Default value is en-US.
language_code str
The language of this text, as a BCP-47. Default value is en-US.
languageCode String
The language of this text, as a BCP-47. Default value is en-US.

GoogleCloudDatalabelingV1beta1TextMetadataResponse
, GoogleCloudDatalabelingV1beta1TextMetadataResponseArgs

LanguageCode This property is required. string
The language of this text, as a BCP-47. Default value is en-US.
LanguageCode This property is required. string
The language of this text, as a BCP-47. Default value is en-US.
languageCode This property is required. String
The language of this text, as a BCP-47. Default value is en-US.
languageCode This property is required. string
The language of this text, as a BCP-47. Default value is en-US.
language_code This property is required. str
The language of this text, as a BCP-47. Default value is en-US.
languageCode This property is required. String
The language of this text, as a BCP-47. Default value is en-US.

GoogleRpcStatusResponse
, GoogleRpcStatusResponseArgs

Code This property is required. int
The status code, which should be an enum value of google.rpc.Code.
Details This property is required. List<ImmutableDictionary<string, string>>
A list of messages that carry the error details. There is a common set of message types for APIs to use.
Message This property is required. string
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
Code This property is required. int
The status code, which should be an enum value of google.rpc.Code.
Details This property is required. []map[string]string
A list of messages that carry the error details. There is a common set of message types for APIs to use.
Message This property is required. string
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
code This property is required. Integer
The status code, which should be an enum value of google.rpc.Code.
details This property is required. List<Map<String,String>>
A list of messages that carry the error details. There is a common set of message types for APIs to use.
message This property is required. String
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
code This property is required. number
The status code, which should be an enum value of google.rpc.Code.
details This property is required. {[key: string]: string}[]
A list of messages that carry the error details. There is a common set of message types for APIs to use.
message This property is required. string
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
code This property is required. int
The status code, which should be an enum value of google.rpc.Code.
details This property is required. Sequence[Mapping[str, str]]
A list of messages that carry the error details. There is a common set of message types for APIs to use.
message This property is required. str
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
code This property is required. Number
The status code, which should be an enum value of google.rpc.Code.
details This property is required. List<Map<String>>
A list of messages that carry the error details. There is a common set of message types for APIs to use.
message This property is required. String
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

Package Details

Repository
Google Cloud Native pulumi/pulumi-google-native
License
Apache-2.0

Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi