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Oracle Cloud Infrastructure v2.17.0 published on Friday, Nov 15, 2024 by Pulumi
pulumi/pulumi-oci

oci.AiDocument.getModel

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Oracle Cloud Infrastructure v2.17.0 published on Friday, Nov 15, 2024 by Pulumi
pulumi/pulumi-oci

    This data source provides details about a specific Model resource in Oracle Cloud Infrastructure Ai Document service.

    Get a model by identifier.

    Example Usage

    import * as pulumi from "@pulumi/pulumi";
import * as oci from "@pulumi/oci";

const testModel = oci.AiDocument.getModel({
    modelId: testModelOciAiDocumentModel.id,
});

    import pulumi
import pulumi_oci as oci

test_model = oci.AiDocument.get_model(model_id=test_model_oci_ai_document_model["id"])

    package main

import (
	"github.com/pulumi/pulumi-oci/sdk/v2/go/oci/AiDocument"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := AiDocument.GetModel(ctx, &aidocument.GetModelArgs{
			ModelId: testModelOciAiDocumentModel.Id,
		}, nil)
		if err != nil {
			return err
		}
		return nil
	})
}

    using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Oci = Pulumi.Oci;

return await Deployment.RunAsync(() => 
{
    var testModel = Oci.AiDocument.GetModel.Invoke(new()
    {
        ModelId = testModelOciAiDocumentModel.Id,
    });

});

    package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.oci.AiDocument.AiDocumentFunctions;
import com.pulumi.oci.AiDocument.inputs.GetModelArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var testModel = AiDocumentFunctions.getModel(GetModelArgs.builder()
            .modelId(testModelOciAiDocumentModel.id())
            .build());

    }
}

    variables:
  testModel:
    fn::invoke:
      Function: oci:AiDocument:getModel
      Arguments:
        modelId: ${testModelOciAiDocumentModel.id}

    Using getModel

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

    function getModel(args: GetModelArgs, opts?: InvokeOptions): Promise<GetModelResult>
function getModelOutput(args: GetModelOutputArgs, opts?: InvokeOptions): Output<GetModelResult>
    def get_model(model_id: Optional[str] = None,
              opts: Optional[InvokeOptions] = None) -> GetModelResult
def get_model_output(model_id: Optional[pulumi.Input[str]] = None,
              opts: Optional[InvokeOptions] = None) -> Output[GetModelResult]
    func GetModel(ctx *Context, args *GetModelArgs, opts ...InvokeOption) (*GetModelResult, error)
func GetModelOutput(ctx *Context, args *GetModelOutputArgs, opts ...InvokeOption) GetModelResultOutput

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

    public static class GetModel 
{
    public static Task<GetModelResult> InvokeAsync(GetModelArgs args, InvokeOptions? opts = null)
    public static Output<GetModelResult> Invoke(GetModelInvokeArgs args, InvokeOptions? opts = null)
}
    public static CompletableFuture<GetModelResult> getModel(GetModelArgs args, InvokeOptions options)
// Output-based functions aren't available in Java yet

    fn::invoke:
  function: oci:AiDocument/getModel:getModel
  arguments:
    # arguments dictionary

    The following arguments are supported:

    ModelId string
    A unique model identifier.
    ModelId string
    A unique model identifier.
    modelId String
    A unique model identifier.
    modelId string
    A unique model identifier.
    model_id str
    A unique model identifier.
    modelId String
    A unique model identifier.

    getModel Result

    The following output properties are available:

    CompartmentId string
    The compartment identifier.
    ComponentModels List<GetModelComponentModel>
    The OCID collection of active custom Key Value models that need to be composed.
    DefinedTags Dictionary<string, string>
    Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    Description string
    An optional description of the model.
    DisplayName string
    A human-friendly name for the model, which can be changed.
    FreeformTags Dictionary<string, string>
    A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    Id string
    A unique identifier that is immutable after creation.
    IsComposedModel bool
    Set to true when the model is created by using multiple key value extraction models.
    IsQuickMode bool
    Set to true when experimenting with a new model type or dataset, so model training is quick, with a predefined low number of passes through the training data.
    Labels List<string>
    The collection of labels used to train the custom model.
    LifecycleDetails string
    A message describing the current state in more detail, that can provide actionable information if training failed.
    MaxTrainingTimeInHours double
    The maximum model training time in hours, expressed as a decimal fraction.
    Metrics List<GetModelMetric>
    Trained Model Metrics.
    ModelId string
    The OCID of active custom Key Value model that need to be composed.
    ModelType string
    The type of the Document model.
    ModelVersion string
    The version of the model.
    ProjectId string
    The OCID of the project that contains the model.
    State string
    The current state of the model.
    SystemTags Dictionary<string, string>
    Usage of system tag keys. These predefined keys are scoped to namespaces. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    TenancyId string
    The tenancy id of the model.
    TestingDatasets List<GetModelTestingDataset>
    The base entity which is the input for creating and training a model.
    TimeCreated string
    When the model was created, as an RFC3339 datetime string.
    TimeUpdated string
    When the model was updated, as an RFC3339 datetime string.
    TrainedTimeInHours double
    The total hours actually used for model training.
    TrainingDatasets List<GetModelTrainingDataset>
    The base entity which is the input for creating and training a model.
    ValidationDatasets List<GetModelValidationDataset>
    The base entity which is the input for creating and training a model.
    CompartmentId string
    The compartment identifier.
    ComponentModels []GetModelComponentModel
    The OCID collection of active custom Key Value models that need to be composed.
    DefinedTags map[string]string
    Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    Description string
    An optional description of the model.
    DisplayName string
    A human-friendly name for the model, which can be changed.
    FreeformTags map[string]string
    A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    Id string
    A unique identifier that is immutable after creation.
    IsComposedModel bool
    Set to true when the model is created by using multiple key value extraction models.
    IsQuickMode bool
    Set to true when experimenting with a new model type or dataset, so model training is quick, with a predefined low number of passes through the training data.
    Labels []string
    The collection of labels used to train the custom model.
    LifecycleDetails string
    A message describing the current state in more detail, that can provide actionable information if training failed.
    MaxTrainingTimeInHours float64
    The maximum model training time in hours, expressed as a decimal fraction.
    Metrics []GetModelMetric
    Trained Model Metrics.
    ModelId string
    The OCID of active custom Key Value model that need to be composed.
    ModelType string
    The type of the Document model.
    ModelVersion string
    The version of the model.
    ProjectId string
    The OCID of the project that contains the model.
    State string
    The current state of the model.
    SystemTags map[string]string
    Usage of system tag keys. These predefined keys are scoped to namespaces. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    TenancyId string
    The tenancy id of the model.
    TestingDatasets []GetModelTestingDataset
    The base entity which is the input for creating and training a model.
    TimeCreated string
    When the model was created, as an RFC3339 datetime string.
    TimeUpdated string
    When the model was updated, as an RFC3339 datetime string.
    TrainedTimeInHours float64
    The total hours actually used for model training.
    TrainingDatasets []GetModelTrainingDataset
    The base entity which is the input for creating and training a model.
    ValidationDatasets []GetModelValidationDataset
    The base entity which is the input for creating and training a model.
    compartmentId String
    The compartment identifier.
    componentModels List<GetModelComponentModel>
    The OCID collection of active custom Key Value models that need to be composed.
    definedTags Map<String,String>
    Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description String
    An optional description of the model.
    displayName String
    A human-friendly name for the model, which can be changed.
    freeformTags Map<String,String>
    A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    id String
    A unique identifier that is immutable after creation.
    isComposedModel Boolean
    Set to true when the model is created by using multiple key value extraction models.
    isQuickMode Boolean
    Set to true when experimenting with a new model type or dataset, so model training is quick, with a predefined low number of passes through the training data.
    labels List<String>
    The collection of labels used to train the custom model.
    lifecycleDetails String
    A message describing the current state in more detail, that can provide actionable information if training failed.
    maxTrainingTimeInHours Double
    The maximum model training time in hours, expressed as a decimal fraction.
    metrics List<GetModelMetric>
    Trained Model Metrics.
    modelId String
    The OCID of active custom Key Value model that need to be composed.
    modelType String
    The type of the Document model.
    modelVersion String
    The version of the model.
    projectId String
    The OCID of the project that contains the model.
    state String
    The current state of the model.
    systemTags Map<String,String>
    Usage of system tag keys. These predefined keys are scoped to namespaces. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    tenancyId String
    The tenancy id of the model.
    testingDatasets List<GetModelTestingDataset>
    The base entity which is the input for creating and training a model.
    timeCreated String
    When the model was created, as an RFC3339 datetime string.
    timeUpdated String
    When the model was updated, as an RFC3339 datetime string.
    trainedTimeInHours Double
    The total hours actually used for model training.
    trainingDatasets List<GetModelTrainingDataset>
    The base entity which is the input for creating and training a model.
    validationDatasets List<GetModelValidationDataset>
    The base entity which is the input for creating and training a model.
    compartmentId string
    The compartment identifier.
    componentModels GetModelComponentModel[]
    The OCID collection of active custom Key Value models that need to be composed.
    definedTags {[key: string]: string}
    Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description string
    An optional description of the model.
    displayName string
    A human-friendly name for the model, which can be changed.
    freeformTags {[key: string]: string}
    A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    id string
    A unique identifier that is immutable after creation.
    isComposedModel boolean
    Set to true when the model is created by using multiple key value extraction models.
    isQuickMode boolean
    Set to true when experimenting with a new model type or dataset, so model training is quick, with a predefined low number of passes through the training data.
    labels string[]
    The collection of labels used to train the custom model.
    lifecycleDetails string
    A message describing the current state in more detail, that can provide actionable information if training failed.
    maxTrainingTimeInHours number
    The maximum model training time in hours, expressed as a decimal fraction.
    metrics GetModelMetric[]
    Trained Model Metrics.
    modelId string
    The OCID of active custom Key Value model that need to be composed.
    modelType string
    The type of the Document model.
    modelVersion string
    The version of the model.
    projectId string
    The OCID of the project that contains the model.
    state string
    The current state of the model.
    systemTags {[key: string]: string}
    Usage of system tag keys. These predefined keys are scoped to namespaces. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    tenancyId string
    The tenancy id of the model.
    testingDatasets GetModelTestingDataset[]
    The base entity which is the input for creating and training a model.
    timeCreated string
    When the model was created, as an RFC3339 datetime string.
    timeUpdated string
    When the model was updated, as an RFC3339 datetime string.
    trainedTimeInHours number
    The total hours actually used for model training.
    trainingDatasets GetModelTrainingDataset[]
    The base entity which is the input for creating and training a model.
    validationDatasets GetModelValidationDataset[]
    The base entity which is the input for creating and training a model.
    compartment_id str
    The compartment identifier.
    component_models Sequence[aidocument.GetModelComponentModel]
    The OCID collection of active custom Key Value models that need to be composed.
    defined_tags Mapping[str, str]
    Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description str
    An optional description of the model.
    display_name str
    A human-friendly name for the model, which can be changed.
    freeform_tags Mapping[str, str]
    A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    id str
    A unique identifier that is immutable after creation.
    is_composed_model bool
    Set to true when the model is created by using multiple key value extraction models.
    is_quick_mode bool
    Set to true when experimenting with a new model type or dataset, so model training is quick, with a predefined low number of passes through the training data.
    labels Sequence[str]
    The collection of labels used to train the custom model.
    lifecycle_details str
    A message describing the current state in more detail, that can provide actionable information if training failed.
    max_training_time_in_hours float
    The maximum model training time in hours, expressed as a decimal fraction.
    metrics Sequence[aidocument.GetModelMetric]
    Trained Model Metrics.
    model_id str
    The OCID of active custom Key Value model that need to be composed.
    model_type str
    The type of the Document model.
    model_version str
    The version of the model.
    project_id str
    The OCID of the project that contains the model.
    state str
    The current state of the model.
    system_tags Mapping[str, str]
    Usage of system tag keys. These predefined keys are scoped to namespaces. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    tenancy_id str
    The tenancy id of the model.
    testing_datasets Sequence[aidocument.GetModelTestingDataset]
    The base entity which is the input for creating and training a model.
    time_created str
    When the model was created, as an RFC3339 datetime string.
    time_updated str
    When the model was updated, as an RFC3339 datetime string.
    trained_time_in_hours float
    The total hours actually used for model training.
    training_datasets Sequence[aidocument.GetModelTrainingDataset]
    The base entity which is the input for creating and training a model.
    validation_datasets Sequence[aidocument.GetModelValidationDataset]
    The base entity which is the input for creating and training a model.
    compartmentId String
    The compartment identifier.
    componentModels List<Property Map>
    The OCID collection of active custom Key Value models that need to be composed.
    definedTags Map<String>
    Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description String
    An optional description of the model.
    displayName String
    A human-friendly name for the model, which can be changed.
    freeformTags Map<String>
    A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    id String
    A unique identifier that is immutable after creation.
    isComposedModel Boolean
    Set to true when the model is created by using multiple key value extraction models.
    isQuickMode Boolean
    Set to true when experimenting with a new model type or dataset, so model training is quick, with a predefined low number of passes through the training data.
    labels List<String>
    The collection of labels used to train the custom model.
    lifecycleDetails String
    A message describing the current state in more detail, that can provide actionable information if training failed.
    maxTrainingTimeInHours Number
    The maximum model training time in hours, expressed as a decimal fraction.
    metrics List<Property Map>
    Trained Model Metrics.
    modelId String
    The OCID of active custom Key Value model that need to be composed.
    modelType String
    The type of the Document model.
    modelVersion String
    The version of the model.
    projectId String
    The OCID of the project that contains the model.
    state String
    The current state of the model.
    systemTags Map<String>
    Usage of system tag keys. These predefined keys are scoped to namespaces. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    tenancyId String
    The tenancy id of the model.
    testingDatasets List<Property Map>
    The base entity which is the input for creating and training a model.
    timeCreated String
    When the model was created, as an RFC3339 datetime string.
    timeUpdated String
    When the model was updated, as an RFC3339 datetime string.
    trainedTimeInHours Number
    The total hours actually used for model training.
    trainingDatasets List<Property Map>
    The base entity which is the input for creating and training a model.
    validationDatasets List<Property Map>
    The base entity which is the input for creating and training a model.

    Supporting Types

    GetModelComponentModel

    ModelId string
    A unique model identifier.
    ModelId string
    A unique model identifier.
    modelId String
    A unique model identifier.
    modelId string
    A unique model identifier.
    model_id str
    A unique model identifier.
    modelId String
    A unique model identifier.

    GetModelMetric

    DatasetSummaries List<GetModelMetricDatasetSummary>
    Summary of count of samples used during model training.
    LabelMetricsReports List<GetModelMetricLabelMetricsReport>
    List of metrics entries per label.
    ModelType string
    The type of the Document model.
    OverallMetricsReports List<GetModelMetricOverallMetricsReport>
    Overall Metrics report for Document Classification Model.
    DatasetSummaries []GetModelMetricDatasetSummary
    Summary of count of samples used during model training.
    LabelMetricsReports []GetModelMetricLabelMetricsReport
    List of metrics entries per label.
    ModelType string
    The type of the Document model.
    OverallMetricsReports []GetModelMetricOverallMetricsReport
    Overall Metrics report for Document Classification Model.
    datasetSummaries List<GetModelMetricDatasetSummary>
    Summary of count of samples used during model training.
    labelMetricsReports List<GetModelMetricLabelMetricsReport>
    List of metrics entries per label.
    modelType String
    The type of the Document model.
    overallMetricsReports List<GetModelMetricOverallMetricsReport>
    Overall Metrics report for Document Classification Model.
    datasetSummaries GetModelMetricDatasetSummary[]
    Summary of count of samples used during model training.
    labelMetricsReports GetModelMetricLabelMetricsReport[]
    List of metrics entries per label.
    modelType string
    The type of the Document model.
    overallMetricsReports GetModelMetricOverallMetricsReport[]
    Overall Metrics report for Document Classification Model.
    dataset_summaries Sequence[aidocument.GetModelMetricDatasetSummary]
    Summary of count of samples used during model training.
    label_metrics_reports Sequence[aidocument.GetModelMetricLabelMetricsReport]
    List of metrics entries per label.
    model_type str
    The type of the Document model.
    overall_metrics_reports Sequence[aidocument.GetModelMetricOverallMetricsReport]
    Overall Metrics report for Document Classification Model.
    datasetSummaries List<Property Map>
    Summary of count of samples used during model training.
    labelMetricsReports List<Property Map>
    List of metrics entries per label.
    modelType String
    The type of the Document model.
    overallMetricsReports List<Property Map>
    Overall Metrics report for Document Classification Model.

    GetModelMetricDatasetSummary

    TestSampleCount int
    Number of samples used for testing the model.
    TrainingSampleCount int
    Number of samples used for training the model.
    ValidationSampleCount int
    Number of samples used for validating the model.
    TestSampleCount int
    Number of samples used for testing the model.
    TrainingSampleCount int
    Number of samples used for training the model.
    ValidationSampleCount int
    Number of samples used for validating the model.
    testSampleCount Integer
    Number of samples used for testing the model.
    trainingSampleCount Integer
    Number of samples used for training the model.
    validationSampleCount Integer
    Number of samples used for validating the model.
    testSampleCount number
    Number of samples used for testing the model.
    trainingSampleCount number
    Number of samples used for training the model.
    validationSampleCount number
    Number of samples used for validating the model.
    test_sample_count int
    Number of samples used for testing the model.
    training_sample_count int
    Number of samples used for training the model.
    validation_sample_count int
    Number of samples used for validating the model.
    testSampleCount Number
    Number of samples used for testing the model.
    trainingSampleCount Number
    Number of samples used for training the model.
    validationSampleCount Number
    Number of samples used for validating the model.

    GetModelMetricLabelMetricsReport

    ConfidenceEntries List<GetModelMetricLabelMetricsReportConfidenceEntry>
    List of document classification confidence report.
    DocumentCount int
    Total test documents in the label.
    Label string
    Label name
    MeanAveragePrecision double
    Mean average precision under different thresholds
    ConfidenceEntries []GetModelMetricLabelMetricsReportConfidenceEntry
    List of document classification confidence report.
    DocumentCount int
    Total test documents in the label.
    Label string
    Label name
    MeanAveragePrecision float64
    Mean average precision under different thresholds
    confidenceEntries List<GetModelMetricLabelMetricsReportConfidenceEntry>
    List of document classification confidence report.
    documentCount Integer
    Total test documents in the label.
    label String
    Label name
    meanAveragePrecision Double
    Mean average precision under different thresholds
    confidenceEntries GetModelMetricLabelMetricsReportConfidenceEntry[]
    List of document classification confidence report.
    documentCount number
    Total test documents in the label.
    label string
    Label name
    meanAveragePrecision number
    Mean average precision under different thresholds
    confidence_entries Sequence[aidocument.GetModelMetricLabelMetricsReportConfidenceEntry]
    List of document classification confidence report.
    document_count int
    Total test documents in the label.
    label str
    Label name
    mean_average_precision float
    Mean average precision under different thresholds
    confidenceEntries List<Property Map>
    List of document classification confidence report.
    documentCount Number
    Total test documents in the label.
    label String
    Label name
    meanAveragePrecision Number
    Mean average precision under different thresholds

    GetModelMetricLabelMetricsReportConfidenceEntry

    Accuracy double
    accuracy under the threshold
    F1score double
    f1Score under the threshold
    Precision double
    Precision under the threshold
    Recall double
    Recall under the threshold
    Threshold double
    Threshold used to calculate precision and recall.
    Accuracy float64
    accuracy under the threshold
    F1score float64
    f1Score under the threshold
    Precision float64
    Precision under the threshold
    Recall float64
    Recall under the threshold
    Threshold float64
    Threshold used to calculate precision and recall.
    accuracy Double
    accuracy under the threshold
    f1score Double
    f1Score under the threshold
    precision Double
    Precision under the threshold
    recall Double
    Recall under the threshold
    threshold Double
    Threshold used to calculate precision and recall.
    accuracy number
    accuracy under the threshold
    f1score number
    f1Score under the threshold
    precision number
    Precision under the threshold
    recall number
    Recall under the threshold
    threshold number
    Threshold used to calculate precision and recall.
    accuracy float
    accuracy under the threshold
    f1score float
    f1Score under the threshold
    precision float
    Precision under the threshold
    recall float
    Recall under the threshold
    threshold float
    Threshold used to calculate precision and recall.
    accuracy Number
    accuracy under the threshold
    f1score Number
    f1Score under the threshold
    precision Number
    Precision under the threshold
    recall Number
    Recall under the threshold
    threshold Number
    Threshold used to calculate precision and recall.

    GetModelMetricOverallMetricsReport

    ConfidenceEntries List<GetModelMetricOverallMetricsReportConfidenceEntry>
    List of document classification confidence report.
    DocumentCount int
    Total test documents in the label.
    MeanAveragePrecision double
    Mean average precision under different thresholds
    ConfidenceEntries []GetModelMetricOverallMetricsReportConfidenceEntry
    List of document classification confidence report.
    DocumentCount int
    Total test documents in the label.
    MeanAveragePrecision float64
    Mean average precision under different thresholds
    confidenceEntries List<GetModelMetricOverallMetricsReportConfidenceEntry>
    List of document classification confidence report.
    documentCount Integer
    Total test documents in the label.
    meanAveragePrecision Double
    Mean average precision under different thresholds
    confidenceEntries GetModelMetricOverallMetricsReportConfidenceEntry[]
    List of document classification confidence report.
    documentCount number
    Total test documents in the label.
    meanAveragePrecision number
    Mean average precision under different thresholds
    confidence_entries Sequence[aidocument.GetModelMetricOverallMetricsReportConfidenceEntry]
    List of document classification confidence report.
    document_count int
    Total test documents in the label.
    mean_average_precision float
    Mean average precision under different thresholds
    confidenceEntries List<Property Map>
    List of document classification confidence report.
    documentCount Number
    Total test documents in the label.
    meanAveragePrecision Number
    Mean average precision under different thresholds

    GetModelMetricOverallMetricsReportConfidenceEntry

    Accuracy double
    accuracy under the threshold
    F1score double
    f1Score under the threshold
    Precision double
    Precision under the threshold
    Recall double
    Recall under the threshold
    Threshold double
    Threshold used to calculate precision and recall.
    Accuracy float64
    accuracy under the threshold
    F1score float64
    f1Score under the threshold
    Precision float64
    Precision under the threshold
    Recall float64
    Recall under the threshold
    Threshold float64
    Threshold used to calculate precision and recall.
    accuracy Double
    accuracy under the threshold
    f1score Double
    f1Score under the threshold
    precision Double
    Precision under the threshold
    recall Double
    Recall under the threshold
    threshold Double
    Threshold used to calculate precision and recall.
    accuracy number
    accuracy under the threshold
    f1score number
    f1Score under the threshold
    precision number
    Precision under the threshold
    recall number
    Recall under the threshold
    threshold number
    Threshold used to calculate precision and recall.
    accuracy float
    accuracy under the threshold
    f1score float
    f1Score under the threshold
    precision float
    Precision under the threshold
    recall float
    Recall under the threshold
    threshold float
    Threshold used to calculate precision and recall.
    accuracy Number
    accuracy under the threshold
    f1score Number
    f1Score under the threshold
    precision Number
    Precision under the threshold
    recall Number
    Recall under the threshold
    threshold Number
    Threshold used to calculate precision and recall.

    GetModelTestingDataset

    Bucket string
    The name of the Object Storage bucket that contains the input data file.
    DatasetId string
    OCID of the Data Labeling dataset.
    DatasetType string
    The dataset type, based on where it is stored.
    Namespace string
    The namespace name of the Object Storage bucket that contains the input data file.
    Object string
    The object name of the input data file.
    Bucket string
    The name of the Object Storage bucket that contains the input data file.
    DatasetId string
    OCID of the Data Labeling dataset.
    DatasetType string
    The dataset type, based on where it is stored.
    Namespace string
    The namespace name of the Object Storage bucket that contains the input data file.
    Object string
    The object name of the input data file.
    bucket String
    The name of the Object Storage bucket that contains the input data file.
    datasetId String
    OCID of the Data Labeling dataset.
    datasetType String
    The dataset type, based on where it is stored.
    namespace String
    The namespace name of the Object Storage bucket that contains the input data file.
    object String
    The object name of the input data file.
    bucket string
    The name of the Object Storage bucket that contains the input data file.
    datasetId string
    OCID of the Data Labeling dataset.
    datasetType string
    The dataset type, based on where it is stored.
    namespace string
    The namespace name of the Object Storage bucket that contains the input data file.
    object string
    The object name of the input data file.
    bucket str
    The name of the Object Storage bucket that contains the input data file.
    dataset_id str
    OCID of the Data Labeling dataset.
    dataset_type str
    The dataset type, based on where it is stored.
    namespace str
    The namespace name of the Object Storage bucket that contains the input data file.
    object str
    The object name of the input data file.
    bucket String
    The name of the Object Storage bucket that contains the input data file.
    datasetId String
    OCID of the Data Labeling dataset.
    datasetType String
    The dataset type, based on where it is stored.
    namespace String
    The namespace name of the Object Storage bucket that contains the input data file.
    object String
    The object name of the input data file.

    GetModelTrainingDataset

    Bucket string
    The name of the Object Storage bucket that contains the input data file.
    DatasetId string
    OCID of the Data Labeling dataset.
    DatasetType string
    The dataset type, based on where it is stored.
    Namespace string
    The namespace name of the Object Storage bucket that contains the input data file.
    Object string
    The object name of the input data file.
    Bucket string
    The name of the Object Storage bucket that contains the input data file.
    DatasetId string
    OCID of the Data Labeling dataset.
    DatasetType string
    The dataset type, based on where it is stored.
    Namespace string
    The namespace name of the Object Storage bucket that contains the input data file.
    Object string
    The object name of the input data file.
    bucket String
    The name of the Object Storage bucket that contains the input data file.
    datasetId String
    OCID of the Data Labeling dataset.
    datasetType String
    The dataset type, based on where it is stored.
    namespace String
    The namespace name of the Object Storage bucket that contains the input data file.
    object String
    The object name of the input data file.
    bucket string
    The name of the Object Storage bucket that contains the input data file.
    datasetId string
    OCID of the Data Labeling dataset.
    datasetType string
    The dataset type, based on where it is stored.
    namespace string
    The namespace name of the Object Storage bucket that contains the input data file.
    object string
    The object name of the input data file.
    bucket str
    The name of the Object Storage bucket that contains the input data file.
    dataset_id str
    OCID of the Data Labeling dataset.
    dataset_type str
    The dataset type, based on where it is stored.
    namespace str
    The namespace name of the Object Storage bucket that contains the input data file.
    object str
    The object name of the input data file.
    bucket String
    The name of the Object Storage bucket that contains the input data file.
    datasetId String
    OCID of the Data Labeling dataset.
    datasetType String
    The dataset type, based on where it is stored.
    namespace String
    The namespace name of the Object Storage bucket that contains the input data file.
    object String
    The object name of the input data file.

    GetModelValidationDataset

    Bucket string
    The name of the Object Storage bucket that contains the input data file.
    DatasetId string
    OCID of the Data Labeling dataset.
    DatasetType string
    The dataset type, based on where it is stored.
    Namespace string
    The namespace name of the Object Storage bucket that contains the input data file.
    Object string
    The object name of the input data file.
    Bucket string
    The name of the Object Storage bucket that contains the input data file.
    DatasetId string
    OCID of the Data Labeling dataset.
    DatasetType string
    The dataset type, based on where it is stored.
    Namespace string
    The namespace name of the Object Storage bucket that contains the input data file.
    Object string
    The object name of the input data file.
    bucket String
    The name of the Object Storage bucket that contains the input data file.
    datasetId String
    OCID of the Data Labeling dataset.
    datasetType String
    The dataset type, based on where it is stored.
    namespace String
    The namespace name of the Object Storage bucket that contains the input data file.
    object String
    The object name of the input data file.
    bucket string
    The name of the Object Storage bucket that contains the input data file.
    datasetId string
    OCID of the Data Labeling dataset.
    datasetType string
    The dataset type, based on where it is stored.
    namespace string
    The namespace name of the Object Storage bucket that contains the input data file.
    object string
    The object name of the input data file.
    bucket str
    The name of the Object Storage bucket that contains the input data file.
    dataset_id str
    OCID of the Data Labeling dataset.
    dataset_type str
    The dataset type, based on where it is stored.
    namespace str
    The namespace name of the Object Storage bucket that contains the input data file.
    object str
    The object name of the input data file.
    bucket String
    The name of the Object Storage bucket that contains the input data file.
    datasetId String
    OCID of the Data Labeling dataset.
    datasetType String
    The dataset type, based on where it is stored.
    namespace String
    The namespace name of the Object Storage bucket that contains the input data file.
    object String
    The object name of the input data file.

    Package Details

    Repository
    oci pulumi/pulumi-oci
    License
    Apache-2.0
    Notes
    This Pulumi package is based on the oci Terraform Provider.
    oci logo
    Oracle Cloud Infrastructure v2.17.0 published on Friday, Nov 15, 2024 by Pulumi
    pulumi/pulumi-oci