Google Native

v0.26.0 published on Friday, Sep 16, 2022 by Pulumi

EvaluationJob

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

Create a 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.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 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 GoogleCloudDatalabelingV1beta1EvaluationJobConfigArgs

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 GoogleCloudDatalabelingV1beta1EvaluationJobConfigArgs

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

LanguageCode string

The language of this text, as a BCP-47. Default value is en-US.

LanguageCode string

The language of this text, as a BCP-47. Default value is en-US.

languageCode String

The language of this text, as a BCP-47. Default value is en-US.

languageCode string

The language of this text, as a BCP-47. Default value is en-US.

language_code str

The language of this text, as a BCP-47. Default value is en-US.

languageCode String

The language of this text, as a BCP-47. Default value is en-US.

GoogleRpcStatusResponse

Code int

The status code, which should be an enum value of google.rpc.Code.

Details List<ImmutableDictionary<string, string>>

A list of messages that carry the error details. There is a common set of message types for APIs to use.

Message string

A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

Code int

The status code, which should be an enum value of google.rpc.Code.

Details []map[string]string

A list of messages that carry the error details. There is a common set of message types for APIs to use.

Message string

A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code Integer

The status code, which should be an enum value of google.rpc.Code.

details List<Map<String,String>>

A list of messages that carry the error details. There is a common set of message types for APIs to use.

message String

A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code number

The status code, which should be an enum value of google.rpc.Code.

details {[key: string]: string}[]

A list of messages that carry the error details. There is a common set of message types for APIs to use.

message string

A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code int

The status code, which should be an enum value of google.rpc.Code.

details Sequence[Mapping[str, str]]

A list of messages that carry the error details. There is a common set of message types for APIs to use.

message str

A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code Number

The status code, which should be an enum value of google.rpc.Code.

details List<Map<String>>

A list of messages that carry the error details. There is a common set of message types for APIs to use.

message String

A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

Package Details

Repository
https://github.com/pulumi/pulumi-google-native
License
Apache-2.0