Docs Packages
  1. Packages
  2. Google Cloud Native
  3. API Docs
  4. datalabeling
  5. datalabeling/v1beta1
  6. getEvaluationJob

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
pulumi/pulumi-google-native

google-native.datalabeling/v1beta1.getEvaluationJob

Explore with Pulumi AI

google-native logo

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
pulumi/pulumi-google-native

    Gets an evaluation job by resource name.

    Using getEvaluationJob

    Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

    function getEvaluationJob(args: GetEvaluationJobArgs, opts?: InvokeOptions): Promise<GetEvaluationJobResult>
function getEvaluationJobOutput(args: GetEvaluationJobOutputArgs, opts?: InvokeOptions): Output<GetEvaluationJobResult>
    def get_evaluation_job(evaluation_job_id: Optional[str] = None,
                       project: Optional[str] = None,
                       opts: Optional[InvokeOptions] = None) -> GetEvaluationJobResult
def get_evaluation_job_output(evaluation_job_id: Optional[pulumi.Input[str]] = None,
                       project: Optional[pulumi.Input[str]] = None,
                       opts: Optional[InvokeOptions] = None) -> Output[GetEvaluationJobResult]
    func LookupEvaluationJob(ctx *Context, args *LookupEvaluationJobArgs, opts ...InvokeOption) (*LookupEvaluationJobResult, error)
func LookupEvaluationJobOutput(ctx *Context, args *LookupEvaluationJobOutputArgs, opts ...InvokeOption) LookupEvaluationJobResultOutput

    > Note: This function is named LookupEvaluationJob in the Go SDK.

    public static class GetEvaluationJob 
{
    public static Task<GetEvaluationJobResult> InvokeAsync(GetEvaluationJobArgs args, InvokeOptions? opts = null)
    public static Output<GetEvaluationJobResult> Invoke(GetEvaluationJobInvokeArgs args, InvokeOptions? opts = null)
}
    public static CompletableFuture<GetEvaluationJobResult> getEvaluationJob(GetEvaluationJobArgs args, InvokeOptions options)
// Output-based functions aren't available in Java yet

    fn::invoke:
  function: google-native:datalabeling/v1beta1:getEvaluationJob
  arguments:
    # arguments dictionary

    The following arguments are supported:

    getEvaluationJob Result

    The following output properties are available:

    AnnotationSpecSet 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}"
    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.
    Description string
    Description of the job. The description can be up to 25,000 characters long.
    EvaluationJobConfig Pulumi.GoogleNative.DataLabeling.V1Beta1.Outputs.GoogleCloudDatalabelingV1beta1EvaluationJobConfigResponse
    Configuration details for the evaluation job.
    LabelMissingGroundTruth 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 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.
    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}"
    Schedule 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.
    State string
    Describes the current state of the job.
    AnnotationSpecSet 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}"
    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.
    Description string
    Description of the job. The description can be up to 25,000 characters long.
    EvaluationJobConfig GoogleCloudDatalabelingV1beta1EvaluationJobConfigResponse
    Configuration details for the evaluation job.
    LabelMissingGroundTruth 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 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.
    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}"
    Schedule 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.
    State string
    Describes the current state of the job.
    annotationSpecSet 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}"
    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.
    description String
    Description of the job. The description can be up to 25,000 characters long.
    evaluationJobConfig GoogleCloudDatalabelingV1beta1EvaluationJobConfigResponse
    Configuration details for the evaluation job.
    labelMissingGroundTruth 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 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.
    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}"
    schedule 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.
    state String
    Describes the current state of the job.
    annotationSpecSet 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}"
    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.
    description string
    Description of the job. The description can be up to 25,000 characters long.
    evaluationJobConfig GoogleCloudDatalabelingV1beta1EvaluationJobConfigResponse
    Configuration details for the evaluation job.
    labelMissingGroundTruth 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 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.
    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}"
    schedule 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.
    state string
    Describes the current state of the job.
    annotation_spec_set 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}"
    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.
    description str
    Description of the job. The description can be up to 25,000 characters long.
    evaluation_job_config GoogleCloudDatalabelingV1beta1EvaluationJobConfigResponse
    Configuration details for the evaluation job.
    label_missing_ground_truth 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 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.
    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}"
    schedule 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.
    state str
    Describes the current state of the job.
    annotationSpecSet 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}"
    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.
    description String
    Description of the job. The description can be up to 25,000 characters long.
    evaluationJobConfig Property Map
    Configuration details for the evaluation job.
    labelMissingGroundTruth 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 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.
    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}"
    schedule 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.
    state String
    Describes the current state of the job.

    Supporting Types

    GoogleCloudDatalabelingV1beta1AttemptResponse

    AttemptTime string
    PartialFailures []GoogleRpcStatusResponse
    Details of errors that occurred.
    attemptTime String
    partialFailures List<GoogleRpcStatusResponse>
    Details of errors that occurred.
    attemptTime string
    partialFailures GoogleRpcStatusResponse[]
    Details of errors that occurred.
    attemptTime String
    partialFailures List<Property Map>
    Details of errors that occurred.

    GoogleCloudDatalabelingV1beta1BigQuerySourceResponse

    InputUri 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 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 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 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 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 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.

    GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptionsResponse

    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.

    GoogleCloudDatalabelingV1beta1BoundingPolyConfigResponse

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

    GoogleCloudDatalabelingV1beta1ClassificationMetadataResponse

    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.

    GoogleCloudDatalabelingV1beta1EvaluationConfigResponse

    BoundingBoxEvaluationOptions 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 GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptionsResponse
    Only specify this field if the related model performs image object detection (IMAGE_BOUNDING_BOX_ANNOTATION). Describes how to evaluate bounding boxes.
    boundingBoxEvaluationOptions GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptionsResponse
    Only specify this field if the related model performs image object detection (IMAGE_BOUNDING_BOX_ANNOTATION). Describes how to evaluate bounding boxes.
    boundingBoxEvaluationOptions 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 GoogleCloudDatalabelingV1beta1BoundingBoxEvaluationOptionsResponse
    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.

    GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfigResponse

    Email string
    An email address to send alerts to.
    MinAcceptableMeanAveragePrecision 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 string
    An email address to send alerts to.
    MinAcceptableMeanAveragePrecision 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 String
    An email address to send alerts to.
    minAcceptableMeanAveragePrecision 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 string
    An email address to send alerts to.
    minAcceptableMeanAveragePrecision 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 str
    An email address to send alerts to.
    min_acceptable_mean_average_precision 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 String
    An email address to send alerts to.
    minAcceptableMeanAveragePrecision 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.

    GoogleCloudDatalabelingV1beta1EvaluationJobConfigResponse

    BigqueryImportKeys 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 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 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 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 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 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 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 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 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 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 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 GoogleCloudDatalabelingV1beta1BoundingPolyConfigResponse
    Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.
    EvaluationConfig 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 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 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 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 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 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 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 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 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 GoogleCloudDatalabelingV1beta1BoundingPolyConfigResponse
    Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.
    evaluationConfig 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 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 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 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 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 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 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 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 {[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 GoogleCloudDatalabelingV1beta1BoundingPolyConfigResponse
    Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.
    evaluationConfig 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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.

    GoogleCloudDatalabelingV1beta1GcsSourceResponse

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

    GoogleCloudDatalabelingV1beta1HumanAnnotationConfigResponse

    AnnotatedDatasetDescription string
    Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
    AnnotatedDatasetDisplayName string
    A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
    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/
    Instruction string
    Instruction resource name.
    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.
    AnnotatedDatasetDescription string
    Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
    AnnotatedDatasetDisplayName string
    A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
    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/
    Instruction string
    Instruction resource name.
    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.
    annotatedDatasetDescription String
    Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
    annotatedDatasetDisplayName String
    A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
    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/
    instruction String
    Instruction resource name.
    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.
    annotatedDatasetDescription string
    Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
    annotatedDatasetDisplayName string
    A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
    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/
    instruction string
    Instruction resource name.
    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_description str
    Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
    annotated_dataset_display_name str
    A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
    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/
    instruction str
    Instruction resource name.
    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.
    annotatedDatasetDescription String
    Optional. A human-readable description for AnnotatedDataset. The description can be up to 10000 characters long.
    annotatedDatasetDisplayName String
    A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .
    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/
    instruction String
    Instruction resource name.
    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.

    GoogleCloudDatalabelingV1beta1ImageClassificationConfigResponse

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

    GoogleCloudDatalabelingV1beta1InputConfigResponse

    AnnotationType 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 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 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 string
    Data type must be specifed when user tries to import data.
    GcsSource Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1GcsSourceResponse
    Source located in Cloud Storage.
    TextMetadata Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1TextMetadataResponse
    Required for text import, as language code must be specified.
    AnnotationType 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 GoogleCloudDatalabelingV1beta1BigQuerySourceResponse
    Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
    ClassificationMetadata 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 string
    Data type must be specifed when user tries to import data.
    GcsSource GoogleCloudDatalabelingV1beta1GcsSourceResponse
    Source located in Cloud Storage.
    TextMetadata GoogleCloudDatalabelingV1beta1TextMetadataResponse
    Required for text import, as language code must be specified.
    annotationType 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 GoogleCloudDatalabelingV1beta1BigQuerySourceResponse
    Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
    classificationMetadata 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 String
    Data type must be specifed when user tries to import data.
    gcsSource GoogleCloudDatalabelingV1beta1GcsSourceResponse
    Source located in Cloud Storage.
    textMetadata GoogleCloudDatalabelingV1beta1TextMetadataResponse
    Required for text import, as language code must be specified.
    annotationType 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 GoogleCloudDatalabelingV1beta1BigQuerySourceResponse
    Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
    classificationMetadata 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 string
    Data type must be specifed when user tries to import data.
    gcsSource GoogleCloudDatalabelingV1beta1GcsSourceResponse
    Source located in Cloud Storage.
    textMetadata GoogleCloudDatalabelingV1beta1TextMetadataResponse
    Required for text import, as language code must be specified.
    annotation_type 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 GoogleCloudDatalabelingV1beta1BigQuerySourceResponse
    Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
    classification_metadata 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 str
    Data type must be specifed when user tries to import data.
    gcs_source GoogleCloudDatalabelingV1beta1GcsSourceResponse
    Source located in Cloud Storage.
    text_metadata GoogleCloudDatalabelingV1beta1TextMetadataResponse
    Required for text import, as language code must be specified.
    annotationType 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 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.
    dataType String
    Data type must be specifed when user tries to import data.
    gcsSource Property Map
    Source located in Cloud Storage.
    textMetadata Property Map
    Required for text import, as language code must be specified.

    GoogleCloudDatalabelingV1beta1SentimentConfigResponse

    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.

    GoogleCloudDatalabelingV1beta1TextClassificationConfigResponse

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

    GoogleCloudDatalabelingV1beta1TextMetadataResponse

    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.

    GoogleRpcStatusResponse

    Code int
    The status code, which should be an enum value of google.rpc.Code.
    Details 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 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 int
    The status code, which should be an enum value of google.rpc.Code.
    Details []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 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 Integer
    The status code, which should be an enum value of google.rpc.Code.
    details 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 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 number
    The status code, which should be an enum value of google.rpc.Code.
    details {[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 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 int
    The status code, which should be an enum value of google.rpc.Code.
    details 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 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 Number
    The status code, which should be an enum value of google.rpc.Code.
    details 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 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-native logo

    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
    pulumi/pulumi-google-native