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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

    new EvaluationJob(name: string, args: EvaluationJobArgs, opts?: CustomResourceOptions);
    @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,
                      project: Optional[str] = None,
                      schedule: Optional[str] = None)
    @overload
    def EvaluationJob(resource_name: str,
                      args: EvaluationJobArgs,
                      opts: Optional[ResourceOptions] = 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.
    
    
    name string
    The unique name of the resource.
    args EvaluationJobArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    resource_name str
    The unique name of the resource.
    args 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 string
    The unique name of the resource.
    args EvaluationJobArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args EvaluationJobArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args EvaluationJobArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    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

    The EvaluationJob resource accepts the following input properties:

    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}"

    Description string

    Description of the job. The description can be up to 25,000 characters long.

    EvaluationJobConfig Pulumi.GoogleNative.DataLabeling.V1Beta1.Inputs.GoogleCloudDatalabelingV1beta1EvaluationJobConfig

    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.

    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.

    Project string
    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}"

    Description string

    Description of the job. The description can be up to 25,000 characters long.

    EvaluationJobConfig GoogleCloudDatalabelingV1beta1EvaluationJobConfigArgs

    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.

    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.

    Project string
    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}"

    description String

    Description of the job. The description can be up to 25,000 characters long.

    evaluationJobConfig GoogleCloudDatalabelingV1beta1EvaluationJobConfig

    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.

    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.

    project String
    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}"

    description string

    Description of the job. The description can be up to 25,000 characters long.

    evaluationJobConfig GoogleCloudDatalabelingV1beta1EvaluationJobConfig

    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.

    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.

    project string
    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}"

    description str

    Description of the job. The description can be up to 25,000 characters long.

    evaluation_job_config GoogleCloudDatalabelingV1beta1EvaluationJobConfigArgs

    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.

    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.

    project str
    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}"

    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.

    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.

    project 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 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.

    GoogleCloudDatalabelingV1beta1BigQuerySource, GoogleCloudDatalabelingV1beta1BigQuerySourceArgs

    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.

    GoogleCloudDatalabelingV1beta1BigQuerySourceResponse, GoogleCloudDatalabelingV1beta1BigQuerySourceResponseArgs

    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.

    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 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.

    GoogleCloudDatalabelingV1beta1BoundingPolyConfig, GoogleCloudDatalabelingV1beta1BoundingPolyConfigArgs

    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.

    GoogleCloudDatalabelingV1beta1BoundingPolyConfigResponse, GoogleCloudDatalabelingV1beta1BoundingPolyConfigResponseArgs

    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.

    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 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.

    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 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.

    GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfig, GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfigArgs

    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.

    GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfigResponse, GoogleCloudDatalabelingV1beta1EvaluationJobAlertConfigResponseArgs

    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.

    GoogleCloudDatalabelingV1beta1EvaluationJobConfig, GoogleCloudDatalabelingV1beta1EvaluationJobConfigArgs

    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.

    EvaluationConfig 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 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.

    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 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 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 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.

    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 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 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 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.

    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 {[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 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 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.

    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 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 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 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.

    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 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 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 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.

    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 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.

    GoogleCloudDatalabelingV1beta1GcsSource, GoogleCloudDatalabelingV1beta1GcsSourceArgs

    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.

    GoogleCloudDatalabelingV1beta1GcsSourceResponse, GoogleCloudDatalabelingV1beta1GcsSourceResponseArgs

    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.

    GoogleCloudDatalabelingV1beta1HumanAnnotationConfig, GoogleCloudDatalabelingV1beta1HumanAnnotationConfigArgs

    AnnotatedDatasetDisplayName string

    A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .

    Instruction 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 string

    A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .

    Instruction 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 String

    A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .

    instruction 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 string

    A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .

    instruction 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 str

    A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .

    instruction 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 String

    A human-readable name for AnnotatedDataset defined by users. Maximum of 64 characters .

    instruction 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 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.

    GoogleCloudDatalabelingV1beta1ImageClassificationConfig, GoogleCloudDatalabelingV1beta1ImageClassificationConfigArgs

    AnnotationSpecSet 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 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 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 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 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 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_VOTE

    Majority vote to aggregate answers.

    UnanimousVote
    UNANIMOUS_VOTE

    Unanimous answers will be adopted.

    NoAggregation
    NO_AGGREGATION

    Preserve all answers by crowd compute.

    GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationTypeStringAggregationTypeUnspecified
    STRING_AGGREGATION_TYPE_UNSPECIFIED
    GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationTypeMajorityVote
    MAJORITY_VOTE

    Majority vote to aggregate answers.

    GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationTypeUnanimousVote
    UNANIMOUS_VOTE

    Unanimous answers will be adopted.

    GoogleCloudDatalabelingV1beta1ImageClassificationConfigAnswerAggregationTypeNoAggregation
    NO_AGGREGATION

    Preserve all answers by crowd compute.

    StringAggregationTypeUnspecified
    STRING_AGGREGATION_TYPE_UNSPECIFIED
    MajorityVote
    MAJORITY_VOTE

    Majority vote to aggregate answers.

    UnanimousVote
    UNANIMOUS_VOTE

    Unanimous answers will be adopted.

    NoAggregation
    NO_AGGREGATION

    Preserve all answers by crowd compute.

    StringAggregationTypeUnspecified
    STRING_AGGREGATION_TYPE_UNSPECIFIED
    MajorityVote
    MAJORITY_VOTE

    Majority vote to aggregate answers.

    UnanimousVote
    UNANIMOUS_VOTE

    Unanimous answers will be adopted.

    NoAggregation
    NO_AGGREGATION

    Preserve all answers by crowd compute.

    STRING_AGGREGATION_TYPE_UNSPECIFIED
    STRING_AGGREGATION_TYPE_UNSPECIFIED
    MAJORITY_VOTE
    MAJORITY_VOTE

    Majority vote to aggregate answers.

    UNANIMOUS_VOTE
    UNANIMOUS_VOTE

    Unanimous answers will be adopted.

    NO_AGGREGATION
    NO_AGGREGATION

    Preserve all answers by crowd compute.

    "STRING_AGGREGATION_TYPE_UNSPECIFIED"
    STRING_AGGREGATION_TYPE_UNSPECIFIED
    "MAJORITY_VOTE"
    MAJORITY_VOTE

    Majority vote to aggregate answers.

    "UNANIMOUS_VOTE"
    UNANIMOUS_VOTE

    Unanimous answers will be adopted.

    "NO_AGGREGATION"
    NO_AGGREGATION

    Preserve all answers by crowd compute.

    GoogleCloudDatalabelingV1beta1ImageClassificationConfigResponse, GoogleCloudDatalabelingV1beta1ImageClassificationConfigResponseArgs

    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.

    GoogleCloudDatalabelingV1beta1InputConfig, GoogleCloudDatalabelingV1beta1InputConfigArgs

    DataType 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 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 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 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 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 "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_ANNOTATION

    Classification annotations in an image. Allowed for continuous evaluation.

    ImageBoundingBoxAnnotation
    IMAGE_BOUNDING_BOX_ANNOTATION

    Bounding box annotations in an image. A form of image object detection. Allowed for continuous evaluation.

    ImageOrientedBoundingBoxAnnotation
    IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATION

    Oriented bounding box. The box does not have to be parallel to horizontal line.

    ImageBoundingPolyAnnotation
    IMAGE_BOUNDING_POLY_ANNOTATION

    Bounding poly annotations in an image.

    ImagePolylineAnnotation
    IMAGE_POLYLINE_ANNOTATION

    Polyline annotations in an image.

    ImageSegmentationAnnotation
    IMAGE_SEGMENTATION_ANNOTATION

    Segmentation annotations in an image.

    VideoShotsClassificationAnnotation
    VIDEO_SHOTS_CLASSIFICATION_ANNOTATION

    Classification annotations in video shots.

    VideoObjectTrackingAnnotation
    VIDEO_OBJECT_TRACKING_ANNOTATION

    Video object tracking annotation.

    VideoObjectDetectionAnnotation
    VIDEO_OBJECT_DETECTION_ANNOTATION

    Video object detection annotation.

    VideoEventAnnotation
    VIDEO_EVENT_ANNOTATION

    Video event annotation.

    TextClassificationAnnotation
    TEXT_CLASSIFICATION_ANNOTATION

    Classification for text. Allowed for continuous evaluation.

    TextEntityExtractionAnnotation
    TEXT_ENTITY_EXTRACTION_ANNOTATION

    Entity extraction for text.

    GeneralClassificationAnnotation
    GENERAL_CLASSIFICATION_ANNOTATION

    General classification. Allowed for continuous evaluation.

    GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeAnnotationTypeUnspecified
    ANNOTATION_TYPE_UNSPECIFIED
    GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeImageClassificationAnnotation
    IMAGE_CLASSIFICATION_ANNOTATION

    Classification annotations in an image. Allowed for continuous evaluation.

    GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeImageBoundingBoxAnnotation
    IMAGE_BOUNDING_BOX_ANNOTATION

    Bounding box annotations in an image. A form of image object detection. Allowed for continuous evaluation.

    GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeImageOrientedBoundingBoxAnnotation
    IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATION

    Oriented bounding box. The box does not have to be parallel to horizontal line.

    GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeImageBoundingPolyAnnotation
    IMAGE_BOUNDING_POLY_ANNOTATION

    Bounding poly annotations in an image.

    GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeImagePolylineAnnotation
    IMAGE_POLYLINE_ANNOTATION

    Polyline annotations in an image.

    GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeImageSegmentationAnnotation
    IMAGE_SEGMENTATION_ANNOTATION

    Segmentation annotations in an image.

    GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeVideoShotsClassificationAnnotation
    VIDEO_SHOTS_CLASSIFICATION_ANNOTATION

    Classification annotations in video shots.

    GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeVideoObjectTrackingAnnotation
    VIDEO_OBJECT_TRACKING_ANNOTATION

    Video object tracking annotation.

    GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeVideoObjectDetectionAnnotation
    VIDEO_OBJECT_DETECTION_ANNOTATION

    Video object detection annotation.

    GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeVideoEventAnnotation
    VIDEO_EVENT_ANNOTATION

    Video event annotation.

    GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeTextClassificationAnnotation
    TEXT_CLASSIFICATION_ANNOTATION

    Classification for text. Allowed for continuous evaluation.

    GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeTextEntityExtractionAnnotation
    TEXT_ENTITY_EXTRACTION_ANNOTATION

    Entity extraction for text.

    GoogleCloudDatalabelingV1beta1InputConfigAnnotationTypeGeneralClassificationAnnotation
    GENERAL_CLASSIFICATION_ANNOTATION

    General classification. Allowed for continuous evaluation.

    AnnotationTypeUnspecified
    ANNOTATION_TYPE_UNSPECIFIED
    ImageClassificationAnnotation
    IMAGE_CLASSIFICATION_ANNOTATION

    Classification annotations in an image. Allowed for continuous evaluation.

    ImageBoundingBoxAnnotation
    IMAGE_BOUNDING_BOX_ANNOTATION

    Bounding box annotations in an image. A form of image object detection. Allowed for continuous evaluation.

    ImageOrientedBoundingBoxAnnotation
    IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATION

    Oriented bounding box. The box does not have to be parallel to horizontal line.

    ImageBoundingPolyAnnotation
    IMAGE_BOUNDING_POLY_ANNOTATION

    Bounding poly annotations in an image.

    ImagePolylineAnnotation
    IMAGE_POLYLINE_ANNOTATION

    Polyline annotations in an image.

    ImageSegmentationAnnotation
    IMAGE_SEGMENTATION_ANNOTATION

    Segmentation annotations in an image.

    VideoShotsClassificationAnnotation
    VIDEO_SHOTS_CLASSIFICATION_ANNOTATION

    Classification annotations in video shots.

    VideoObjectTrackingAnnotation
    VIDEO_OBJECT_TRACKING_ANNOTATION

    Video object tracking annotation.

    VideoObjectDetectionAnnotation
    VIDEO_OBJECT_DETECTION_ANNOTATION

    Video object detection annotation.

    VideoEventAnnotation
    VIDEO_EVENT_ANNOTATION

    Video event annotation.

    TextClassificationAnnotation
    TEXT_CLASSIFICATION_ANNOTATION

    Classification for text. Allowed for continuous evaluation.

    TextEntityExtractionAnnotation
    TEXT_ENTITY_EXTRACTION_ANNOTATION

    Entity extraction for text.

    GeneralClassificationAnnotation
    GENERAL_CLASSIFICATION_ANNOTATION

    General classification. Allowed for continuous evaluation.

    AnnotationTypeUnspecified
    ANNOTATION_TYPE_UNSPECIFIED
    ImageClassificationAnnotation
    IMAGE_CLASSIFICATION_ANNOTATION

    Classification annotations in an image. Allowed for continuous evaluation.

    ImageBoundingBoxAnnotation
    IMAGE_BOUNDING_BOX_ANNOTATION

    Bounding box annotations in an image. A form of image object detection. Allowed for continuous evaluation.

    ImageOrientedBoundingBoxAnnotation
    IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATION

    Oriented bounding box. The box does not have to be parallel to horizontal line.

    ImageBoundingPolyAnnotation
    IMAGE_BOUNDING_POLY_ANNOTATION

    Bounding poly annotations in an image.

    ImagePolylineAnnotation
    IMAGE_POLYLINE_ANNOTATION

    Polyline annotations in an image.

    ImageSegmentationAnnotation
    IMAGE_SEGMENTATION_ANNOTATION

    Segmentation annotations in an image.

    VideoShotsClassificationAnnotation
    VIDEO_SHOTS_CLASSIFICATION_ANNOTATION

    Classification annotations in video shots.

    VideoObjectTrackingAnnotation
    VIDEO_OBJECT_TRACKING_ANNOTATION

    Video object tracking annotation.

    VideoObjectDetectionAnnotation
    VIDEO_OBJECT_DETECTION_ANNOTATION

    Video object detection annotation.

    VideoEventAnnotation
    VIDEO_EVENT_ANNOTATION

    Video event annotation.

    TextClassificationAnnotation
    TEXT_CLASSIFICATION_ANNOTATION

    Classification for text. Allowed for continuous evaluation.

    TextEntityExtractionAnnotation
    TEXT_ENTITY_EXTRACTION_ANNOTATION

    Entity extraction for text.

    GeneralClassificationAnnotation
    GENERAL_CLASSIFICATION_ANNOTATION

    General classification. Allowed for continuous evaluation.

    ANNOTATION_TYPE_UNSPECIFIED
    ANNOTATION_TYPE_UNSPECIFIED
    IMAGE_CLASSIFICATION_ANNOTATION
    IMAGE_CLASSIFICATION_ANNOTATION

    Classification annotations in an image. Allowed for continuous evaluation.

    IMAGE_BOUNDING_BOX_ANNOTATION
    IMAGE_BOUNDING_BOX_ANNOTATION

    Bounding box annotations in an image. A form of image object detection. Allowed for continuous evaluation.

    IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATION
    IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATION

    Oriented bounding box. The box does not have to be parallel to horizontal line.

    IMAGE_BOUNDING_POLY_ANNOTATION
    IMAGE_BOUNDING_POLY_ANNOTATION

    Bounding poly annotations in an image.

    IMAGE_POLYLINE_ANNOTATION
    IMAGE_POLYLINE_ANNOTATION

    Polyline annotations in an image.

    IMAGE_SEGMENTATION_ANNOTATION
    IMAGE_SEGMENTATION_ANNOTATION

    Segmentation annotations in an image.

    VIDEO_SHOTS_CLASSIFICATION_ANNOTATION
    VIDEO_SHOTS_CLASSIFICATION_ANNOTATION

    Classification annotations in video shots.

    VIDEO_OBJECT_TRACKING_ANNOTATION
    VIDEO_OBJECT_TRACKING_ANNOTATION

    Video object tracking annotation.

    VIDEO_OBJECT_DETECTION_ANNOTATION
    VIDEO_OBJECT_DETECTION_ANNOTATION

    Video object detection annotation.

    VIDEO_EVENT_ANNOTATION
    VIDEO_EVENT_ANNOTATION

    Video event annotation.

    TEXT_CLASSIFICATION_ANNOTATION
    TEXT_CLASSIFICATION_ANNOTATION

    Classification for text. Allowed for continuous evaluation.

    TEXT_ENTITY_EXTRACTION_ANNOTATION
    TEXT_ENTITY_EXTRACTION_ANNOTATION

    Entity extraction for text.

    GENERAL_CLASSIFICATION_ANNOTATION
    GENERAL_CLASSIFICATION_ANNOTATION

    General classification. Allowed for continuous evaluation.

    "ANNOTATION_TYPE_UNSPECIFIED"
    ANNOTATION_TYPE_UNSPECIFIED
    "IMAGE_CLASSIFICATION_ANNOTATION"
    IMAGE_CLASSIFICATION_ANNOTATION

    Classification annotations in an image. Allowed for continuous evaluation.

    "IMAGE_BOUNDING_BOX_ANNOTATION"
    IMAGE_BOUNDING_BOX_ANNOTATION

    Bounding box annotations in an image. A form of image object detection. Allowed for continuous evaluation.

    "IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATION"
    IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATION

    Oriented bounding box. The box does not have to be parallel to horizontal line.

    "IMAGE_BOUNDING_POLY_ANNOTATION"
    IMAGE_BOUNDING_POLY_ANNOTATION

    Bounding poly annotations in an image.

    "IMAGE_POLYLINE_ANNOTATION"
    IMAGE_POLYLINE_ANNOTATION

    Polyline annotations in an image.

    "IMAGE_SEGMENTATION_ANNOTATION"
    IMAGE_SEGMENTATION_ANNOTATION

    Segmentation annotations in an image.

    "VIDEO_SHOTS_CLASSIFICATION_ANNOTATION"
    VIDEO_SHOTS_CLASSIFICATION_ANNOTATION

    Classification annotations in video shots.

    "VIDEO_OBJECT_TRACKING_ANNOTATION"
    VIDEO_OBJECT_TRACKING_ANNOTATION

    Video object tracking annotation.

    "VIDEO_OBJECT_DETECTION_ANNOTATION"
    VIDEO_OBJECT_DETECTION_ANNOTATION

    Video object detection annotation.

    "VIDEO_EVENT_ANNOTATION"
    VIDEO_EVENT_ANNOTATION

    Video event annotation.

    "TEXT_CLASSIFICATION_ANNOTATION"
    TEXT_CLASSIFICATION_ANNOTATION

    Classification for text. Allowed for continuous evaluation.

    "TEXT_ENTITY_EXTRACTION_ANNOTATION"
    TEXT_ENTITY_EXTRACTION_ANNOTATION

    Entity extraction for text.

    "GENERAL_CLASSIFICATION_ANNOTATION"
    GENERAL_CLASSIFICATION_ANNOTATION

    General classification. Allowed for continuous evaluation.

    GoogleCloudDatalabelingV1beta1InputConfigDataType, GoogleCloudDatalabelingV1beta1InputConfigDataTypeArgs

    DataTypeUnspecified
    DATA_TYPE_UNSPECIFIED

    Data type is unspecified.

    Image
    IMAGE

    Allowed for continuous evaluation.

    Video
    VIDEO

    Video data type.

    Text
    TEXT

    Allowed for continuous evaluation.

    GeneralData
    GENERAL_DATA

    Allowed for continuous evaluation.

    GoogleCloudDatalabelingV1beta1InputConfigDataTypeDataTypeUnspecified
    DATA_TYPE_UNSPECIFIED

    Data type is unspecified.

    GoogleCloudDatalabelingV1beta1InputConfigDataTypeImage
    IMAGE

    Allowed for continuous evaluation.

    GoogleCloudDatalabelingV1beta1InputConfigDataTypeVideo
    VIDEO

    Video data type.

    GoogleCloudDatalabelingV1beta1InputConfigDataTypeText
    TEXT

    Allowed for continuous evaluation.

    GoogleCloudDatalabelingV1beta1InputConfigDataTypeGeneralData
    GENERAL_DATA

    Allowed for continuous evaluation.

    DataTypeUnspecified
    DATA_TYPE_UNSPECIFIED

    Data type is unspecified.

    Image
    IMAGE

    Allowed for continuous evaluation.

    Video
    VIDEO

    Video data type.

    Text
    TEXT

    Allowed for continuous evaluation.

    GeneralData
    GENERAL_DATA

    Allowed for continuous evaluation.

    DataTypeUnspecified
    DATA_TYPE_UNSPECIFIED

    Data type is unspecified.

    Image
    IMAGE

    Allowed for continuous evaluation.

    Video
    VIDEO

    Video data type.

    Text
    TEXT

    Allowed for continuous evaluation.

    GeneralData
    GENERAL_DATA

    Allowed for continuous evaluation.

    DATA_TYPE_UNSPECIFIED
    DATA_TYPE_UNSPECIFIED

    Data type is unspecified.

    IMAGE
    IMAGE

    Allowed for continuous evaluation.

    VIDEO
    VIDEO

    Video data type.

    TEXT
    TEXT

    Allowed for continuous evaluation.

    GENERAL_DATA
    GENERAL_DATA

    Allowed for continuous evaluation.

    "DATA_TYPE_UNSPECIFIED"
    DATA_TYPE_UNSPECIFIED

    Data type is unspecified.

    "IMAGE"
    IMAGE

    Allowed for continuous evaluation.

    "VIDEO"
    VIDEO

    Video data type.

    "TEXT"
    TEXT

    Allowed for continuous evaluation.

    "GENERAL_DATA"
    GENERAL_DATA

    Allowed for continuous evaluation.

    GoogleCloudDatalabelingV1beta1InputConfigResponse, GoogleCloudDatalabelingV1beta1InputConfigResponseArgs

    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.

    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 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.

    GoogleCloudDatalabelingV1beta1TextClassificationConfig, GoogleCloudDatalabelingV1beta1TextClassificationConfigArgs

    AnnotationSpecSet 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 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 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 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 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 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 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.

    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 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, GoogleRpcStatusResponseArgs

    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