Docs Home Pulumi IaC Pulumi ESC Pulumi Cloud Packages Tutorials
  1. Packages
  2. Oracle Cloud Infrastructure
  3. API Docs
  4. AiDocument
  5. Model
Oracle Cloud Infrastructure v2.17.0 published on Friday, Nov 15, 2024 by Pulumi
pulumi/pulumi-oci

oci.AiDocument.Model

Explore with Pulumi AI

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

    This resource provides the Model resource in Oracle Cloud Infrastructure Ai Document service.

    Create a new model.

    Updates the model metadata only selected path parameter.

    Create Model Resource

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

    Constructor syntax

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

@overload
def Model(resource_name: str,
          opts: Optional[ResourceOptions] = None,
          compartment_id: Optional[str] = None,
          project_id: Optional[str] = None,
          model_type: Optional[str] = None,
          description: Optional[str] = None,
          display_name: Optional[str] = None,
          freeform_tags: Optional[Mapping[str, str]] = None,
          is_quick_mode: Optional[bool] = None,
          max_training_time_in_hours: Optional[float] = None,
          model_id: Optional[str] = None,
          defined_tags: Optional[Mapping[str, str]] = None,
          model_version: Optional[str] = None,
          component_models: Optional[Sequence[_aidocument.ModelComponentModelArgs]] = None,
          testing_dataset: Optional[_aidocument.ModelTestingDatasetArgs] = None,
          training_dataset: Optional[_aidocument.ModelTrainingDatasetArgs] = None,
          validation_dataset: Optional[_aidocument.ModelValidationDatasetArgs] = None)
    func NewModel(ctx *Context, name string, args ModelArgs, opts ...ResourceOption) (*Model, error)
    public Model(string name, ModelArgs args, CustomResourceOptions? opts = null)
    public Model(String name, ModelArgs args)
public Model(String name, ModelArgs args, CustomResourceOptions options)

    type: oci:AiDocument:Model
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

    Parameters

    name string
    The unique name of the resource.
    args ModelArgs
    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 ModelArgs
    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 ModelArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args ModelArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args ModelArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Constructor example

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

    var ociModelResource = new Oci.AiDocument.Model("ociModelResource", new()
{
    CompartmentId = "string",
    ProjectId = "string",
    ModelType = "string",
    Description = "string",
    DisplayName = "string",
    FreeformTags = 
    {
        { "string", "string" },
    },
    IsQuickMode = false,
    MaxTrainingTimeInHours = 0,
    ModelId = "string",
    DefinedTags = 
    {
        { "string", "string" },
    },
    ModelVersion = "string",
    ComponentModels = new[]
    {
        new Oci.AiDocument.Inputs.ModelComponentModelArgs
        {
            ModelId = "string",
        },
    },
    TestingDataset = new Oci.AiDocument.Inputs.ModelTestingDatasetArgs
    {
        DatasetType = "string",
        Bucket = "string",
        DatasetId = "string",
        Namespace = "string",
        Object = "string",
    },
    TrainingDataset = new Oci.AiDocument.Inputs.ModelTrainingDatasetArgs
    {
        DatasetType = "string",
        Bucket = "string",
        DatasetId = "string",
        Namespace = "string",
        Object = "string",
    },
    ValidationDataset = new Oci.AiDocument.Inputs.ModelValidationDatasetArgs
    {
        DatasetType = "string",
        Bucket = "string",
        DatasetId = "string",
        Namespace = "string",
        Object = "string",
    },
});

    example, err := AiDocument.NewModel(ctx, "ociModelResource", &AiDocument.ModelArgs{
	CompartmentId: pulumi.String("string"),
	ProjectId:     pulumi.String("string"),
	ModelType:     pulumi.String("string"),
	Description:   pulumi.String("string"),
	DisplayName:   pulumi.String("string"),
	FreeformTags: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	IsQuickMode:            pulumi.Bool(false),
	MaxTrainingTimeInHours: pulumi.Float64(0),
	ModelId:                pulumi.String("string"),
	DefinedTags: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	ModelVersion: pulumi.String("string"),
	ComponentModels: aidocument.ModelComponentModelArray{
		&aidocument.ModelComponentModelArgs{
			ModelId: pulumi.String("string"),
		},
	},
	TestingDataset: &aidocument.ModelTestingDatasetArgs{
		DatasetType: pulumi.String("string"),
		Bucket:      pulumi.String("string"),
		DatasetId:   pulumi.String("string"),
		Namespace:   pulumi.String("string"),
		Object:      pulumi.String("string"),
	},
	TrainingDataset: &aidocument.ModelTrainingDatasetArgs{
		DatasetType: pulumi.String("string"),
		Bucket:      pulumi.String("string"),
		DatasetId:   pulumi.String("string"),
		Namespace:   pulumi.String("string"),
		Object:      pulumi.String("string"),
	},
	ValidationDataset: &aidocument.ModelValidationDatasetArgs{
		DatasetType: pulumi.String("string"),
		Bucket:      pulumi.String("string"),
		DatasetId:   pulumi.String("string"),
		Namespace:   pulumi.String("string"),
		Object:      pulumi.String("string"),
	},
})

    var ociModelResource = new Model("ociModelResource", ModelArgs.builder()
    .compartmentId("string")
    .projectId("string")
    .modelType("string")
    .description("string")
    .displayName("string")
    .freeformTags(Map.of("string", "string"))
    .isQuickMode(false)
    .maxTrainingTimeInHours(0)
    .modelId("string")
    .definedTags(Map.of("string", "string"))
    .modelVersion("string")
    .componentModels(ModelComponentModelArgs.builder()
        .modelId("string")
        .build())
    .testingDataset(ModelTestingDatasetArgs.builder()
        .datasetType("string")
        .bucket("string")
        .datasetId("string")
        .namespace("string")
        .object("string")
        .build())
    .trainingDataset(ModelTrainingDatasetArgs.builder()
        .datasetType("string")
        .bucket("string")
        .datasetId("string")
        .namespace("string")
        .object("string")
        .build())
    .validationDataset(ModelValidationDatasetArgs.builder()
        .datasetType("string")
        .bucket("string")
        .datasetId("string")
        .namespace("string")
        .object("string")
        .build())
    .build());

    oci_model_resource = oci.ai_document.Model("ociModelResource",
    compartment_id="string",
    project_id="string",
    model_type="string",
    description="string",
    display_name="string",
    freeform_tags={
        "string": "string",
    },
    is_quick_mode=False,
    max_training_time_in_hours=0,
    model_id="string",
    defined_tags={
        "string": "string",
    },
    model_version="string",
    component_models=[{
        "model_id": "string",
    }],
    testing_dataset={
        "dataset_type": "string",
        "bucket": "string",
        "dataset_id": "string",
        "namespace": "string",
        "object": "string",
    },
    training_dataset={
        "dataset_type": "string",
        "bucket": "string",
        "dataset_id": "string",
        "namespace": "string",
        "object": "string",
    },
    validation_dataset={
        "dataset_type": "string",
        "bucket": "string",
        "dataset_id": "string",
        "namespace": "string",
        "object": "string",
    })

    const ociModelResource = new oci.aidocument.Model("ociModelResource", {
    compartmentId: "string",
    projectId: "string",
    modelType: "string",
    description: "string",
    displayName: "string",
    freeformTags: {
        string: "string",
    },
    isQuickMode: false,
    maxTrainingTimeInHours: 0,
    modelId: "string",
    definedTags: {
        string: "string",
    },
    modelVersion: "string",
    componentModels: [{
        modelId: "string",
    }],
    testingDataset: {
        datasetType: "string",
        bucket: "string",
        datasetId: "string",
        namespace: "string",
        object: "string",
    },
    trainingDataset: {
        datasetType: "string",
        bucket: "string",
        datasetId: "string",
        namespace: "string",
        object: "string",
    },
    validationDataset: {
        datasetType: "string",
        bucket: "string",
        datasetId: "string",
        namespace: "string",
        object: "string",
    },
});

    type: oci:AiDocument:Model
properties:
    compartmentId: string
    componentModels:
        - modelId: string
    definedTags:
        string: string
    description: string
    displayName: string
    freeformTags:
        string: string
    isQuickMode: false
    maxTrainingTimeInHours: 0
    modelId: string
    modelType: string
    modelVersion: string
    projectId: string
    testingDataset:
        bucket: string
        datasetId: string
        datasetType: string
        namespace: string
        object: string
    trainingDataset:
        bucket: string
        datasetId: string
        datasetType: string
        namespace: string
        object: string
    validationDataset:
        bucket: string
        datasetId: string
        datasetType: string
        namespace: string
        object: string

    Model Resource Properties

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

    Inputs

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

    The Model resource accepts the following input properties:

    CompartmentId string
    (Updatable) The compartment identifier.
    ModelType string
    The type of the Document model.
    ProjectId string
    The OCID of the project that contains the model.
    ComponentModels List<ModelComponentModel>
    The OCID list of active custom Key Value models that need to be composed.
    DefinedTags Dictionary<string, string>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    Description string
    (Updatable) An optional description of the model.
    DisplayName string
    (Updatable) A human-friendly name for the model, which can be changed.
    FreeformTags Dictionary<string, string>
    (Updatable) 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"}
    IsQuickMode bool
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    MaxTrainingTimeInHours double
    The maximum model training time in hours, expressed as a decimal fraction.
    ModelId string
    ModelVersion string
    The model version
    TestingDataset ModelTestingDataset
    The base entity which is the input for creating and training a model.
    TrainingDataset ModelTrainingDataset
    The base entity which is the input for creating and training a model.
    ValidationDataset ModelValidationDataset
    The base entity which is the input for creating and training a model.
    CompartmentId string
    (Updatable) The compartment identifier.
    ModelType string
    The type of the Document model.
    ProjectId string
    The OCID of the project that contains the model.
    ComponentModels []ModelComponentModelArgs
    The OCID list of active custom Key Value models that need to be composed.
    DefinedTags map[string]string
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    Description string
    (Updatable) An optional description of the model.
    DisplayName string
    (Updatable) A human-friendly name for the model, which can be changed.
    FreeformTags map[string]string
    (Updatable) 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"}
    IsQuickMode bool
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    MaxTrainingTimeInHours float64
    The maximum model training time in hours, expressed as a decimal fraction.
    ModelId string
    ModelVersion string
    The model version
    TestingDataset ModelTestingDatasetArgs
    The base entity which is the input for creating and training a model.
    TrainingDataset ModelTrainingDatasetArgs
    The base entity which is the input for creating and training a model.
    ValidationDataset ModelValidationDatasetArgs
    The base entity which is the input for creating and training a model.
    compartmentId String
    (Updatable) The compartment identifier.
    modelType String
    The type of the Document model.
    projectId String
    The OCID of the project that contains the model.
    componentModels List<ModelComponentModel>
    The OCID list of active custom Key Value models that need to be composed.
    definedTags Map<String,String>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description String
    (Updatable) An optional description of the model.
    displayName String
    (Updatable) A human-friendly name for the model, which can be changed.
    freeformTags Map<String,String>
    (Updatable) 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"}
    isQuickMode Boolean
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    maxTrainingTimeInHours Double
    The maximum model training time in hours, expressed as a decimal fraction.
    modelId String
    modelVersion String
    The model version
    testingDataset ModelTestingDataset
    The base entity which is the input for creating and training a model.
    trainingDataset ModelTrainingDataset
    The base entity which is the input for creating and training a model.
    validationDataset ModelValidationDataset
    The base entity which is the input for creating and training a model.
    compartmentId string
    (Updatable) The compartment identifier.
    modelType string
    The type of the Document model.
    projectId string
    The OCID of the project that contains the model.
    componentModels ModelComponentModel[]
    The OCID list of active custom Key Value models that need to be composed.
    definedTags {[key: string]: string}
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description string
    (Updatable) An optional description of the model.
    displayName string
    (Updatable) A human-friendly name for the model, which can be changed.
    freeformTags {[key: string]: string}
    (Updatable) 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"}
    isQuickMode boolean
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    maxTrainingTimeInHours number
    The maximum model training time in hours, expressed as a decimal fraction.
    modelId string
    modelVersion string
    The model version
    testingDataset ModelTestingDataset
    The base entity which is the input for creating and training a model.
    trainingDataset ModelTrainingDataset
    The base entity which is the input for creating and training a model.
    validationDataset ModelValidationDataset
    The base entity which is the input for creating and training a model.
    compartment_id str
    (Updatable) The compartment identifier.
    model_type str
    The type of the Document model.
    project_id str
    The OCID of the project that contains the model.
    component_models Sequence[aidocument.ModelComponentModelArgs]
    The OCID list of active custom Key Value models that need to be composed.
    defined_tags Mapping[str, str]
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description str
    (Updatable) An optional description of the model.
    display_name str
    (Updatable) A human-friendly name for the model, which can be changed.
    freeform_tags Mapping[str, str]
    (Updatable) 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"}
    is_quick_mode bool
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    max_training_time_in_hours float
    The maximum model training time in hours, expressed as a decimal fraction.
    model_id str
    model_version str
    The model version
    testing_dataset aidocument.ModelTestingDatasetArgs
    The base entity which is the input for creating and training a model.
    training_dataset aidocument.ModelTrainingDatasetArgs
    The base entity which is the input for creating and training a model.
    validation_dataset aidocument.ModelValidationDatasetArgs
    The base entity which is the input for creating and training a model.
    compartmentId String
    (Updatable) The compartment identifier.
    modelType String
    The type of the Document model.
    projectId String
    The OCID of the project that contains the model.
    componentModels List<Property Map>
    The OCID list of active custom Key Value models that need to be composed.
    definedTags Map<String>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description String
    (Updatable) An optional description of the model.
    displayName String
    (Updatable) A human-friendly name for the model, which can be changed.
    freeformTags Map<String>
    (Updatable) 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"}
    isQuickMode Boolean
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    maxTrainingTimeInHours Number
    The maximum model training time in hours, expressed as a decimal fraction.
    modelId String
    modelVersion String
    The model version
    testingDataset Property Map
    The base entity which is the input for creating and training a model.
    trainingDataset Property Map
    The base entity which is the input for creating and training a model.
    validationDataset Property Map
    The base entity which is the input for creating and training a model.

    Outputs

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

    Id string
    The provider-assigned unique ID for this managed resource.
    IsComposedModel bool
    Set to true when the model is created by using multiple key value extraction models.
    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.
    Metrics List<ModelMetric>
    Trained Model Metrics.
    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.
    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.
    Id string
    The provider-assigned unique ID for this managed resource.
    IsComposedModel bool
    Set to true when the model is created by using multiple key value extraction models.
    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.
    Metrics []ModelMetric
    Trained Model Metrics.
    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.
    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.
    id String
    The provider-assigned unique ID for this managed resource.
    isComposedModel Boolean
    Set to true when the model is created by using multiple key value extraction models.
    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.
    metrics List<ModelMetric>
    Trained Model Metrics.
    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.
    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.
    id string
    The provider-assigned unique ID for this managed resource.
    isComposedModel boolean
    Set to true when the model is created by using multiple key value extraction models.
    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.
    metrics ModelMetric[]
    Trained Model Metrics.
    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.
    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.
    id str
    The provider-assigned unique ID for this managed resource.
    is_composed_model bool
    Set to true when the model is created by using multiple key value extraction models.
    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.
    metrics Sequence[aidocument.ModelMetric]
    Trained Model Metrics.
    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.
    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.
    id String
    The provider-assigned unique ID for this managed resource.
    isComposedModel Boolean
    Set to true when the model is created by using multiple key value extraction models.
    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.
    metrics List<Property Map>
    Trained Model Metrics.
    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.
    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.

    Look up Existing Model Resource

    Get an existing Model resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.

    public static get(name: string, id: Input<ID>, state?: ModelState, opts?: CustomResourceOptions): Model
    @staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        compartment_id: Optional[str] = None,
        component_models: Optional[Sequence[_aidocument.ModelComponentModelArgs]] = None,
        defined_tags: Optional[Mapping[str, str]] = None,
        description: Optional[str] = None,
        display_name: Optional[str] = None,
        freeform_tags: Optional[Mapping[str, str]] = None,
        is_composed_model: Optional[bool] = None,
        is_quick_mode: Optional[bool] = None,
        labels: Optional[Sequence[str]] = None,
        lifecycle_details: Optional[str] = None,
        max_training_time_in_hours: Optional[float] = None,
        metrics: Optional[Sequence[_aidocument.ModelMetricArgs]] = None,
        model_id: Optional[str] = None,
        model_type: Optional[str] = None,
        model_version: Optional[str] = None,
        project_id: Optional[str] = None,
        state: Optional[str] = None,
        system_tags: Optional[Mapping[str, str]] = None,
        tenancy_id: Optional[str] = None,
        testing_dataset: Optional[_aidocument.ModelTestingDatasetArgs] = None,
        time_created: Optional[str] = None,
        time_updated: Optional[str] = None,
        trained_time_in_hours: Optional[float] = None,
        training_dataset: Optional[_aidocument.ModelTrainingDatasetArgs] = None,
        validation_dataset: Optional[_aidocument.ModelValidationDatasetArgs] = None) -> Model
    func GetModel(ctx *Context, name string, id IDInput, state *ModelState, opts ...ResourceOption) (*Model, error)
    public static Model Get(string name, Input<string> id, ModelState? state, CustomResourceOptions? opts = null)
    public static Model get(String name, Output<String> id, ModelState state, CustomResourceOptions options)
    Resource lookup is not supported in YAML
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    resource_name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    The following state arguments are supported:
    CompartmentId string
    (Updatable) The compartment identifier.
    ComponentModels List<ModelComponentModel>
    The OCID list of active custom Key Value models that need to be composed.
    DefinedTags Dictionary<string, string>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    Description string
    (Updatable) An optional description of the model.
    DisplayName string
    (Updatable) A human-friendly name for the model, which can be changed.
    FreeformTags Dictionary<string, string>
    (Updatable) 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"}
    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 the 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<ModelMetric>
    Trained Model Metrics.
    ModelId string
    ModelType string
    The type of the Document model.
    ModelVersion string
    The model version
    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.
    TestingDataset ModelTestingDataset
    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.
    TrainingDataset ModelTrainingDataset
    The base entity which is the input for creating and training a model.
    ValidationDataset ModelValidationDataset
    The base entity which is the input for creating and training a model.
    CompartmentId string
    (Updatable) The compartment identifier.
    ComponentModels []ModelComponentModelArgs
    The OCID list of active custom Key Value models that need to be composed.
    DefinedTags map[string]string
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    Description string
    (Updatable) An optional description of the model.
    DisplayName string
    (Updatable) A human-friendly name for the model, which can be changed.
    FreeformTags map[string]string
    (Updatable) 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"}
    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 the 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 []ModelMetricArgs
    Trained Model Metrics.
    ModelId string
    ModelType string
    The type of the Document model.
    ModelVersion string
    The model version
    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.
    TestingDataset ModelTestingDatasetArgs
    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.
    TrainingDataset ModelTrainingDatasetArgs
    The base entity which is the input for creating and training a model.
    ValidationDataset ModelValidationDatasetArgs
    The base entity which is the input for creating and training a model.
    compartmentId String
    (Updatable) The compartment identifier.
    componentModels List<ModelComponentModel>
    The OCID list of active custom Key Value models that need to be composed.
    definedTags Map<String,String>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description String
    (Updatable) An optional description of the model.
    displayName String
    (Updatable) A human-friendly name for the model, which can be changed.
    freeformTags Map<String,String>
    (Updatable) 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"}
    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 the 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<ModelMetric>
    Trained Model Metrics.
    modelId String
    modelType String
    The type of the Document model.
    modelVersion String
    The model version
    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.
    testingDataset ModelTestingDataset
    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.
    trainingDataset ModelTrainingDataset
    The base entity which is the input for creating and training a model.
    validationDataset ModelValidationDataset
    The base entity which is the input for creating and training a model.
    compartmentId string
    (Updatable) The compartment identifier.
    componentModels ModelComponentModel[]
    The OCID list of active custom Key Value models that need to be composed.
    definedTags {[key: string]: string}
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description string
    (Updatable) An optional description of the model.
    displayName string
    (Updatable) A human-friendly name for the model, which can be changed.
    freeformTags {[key: string]: string}
    (Updatable) 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"}
    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 the 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 ModelMetric[]
    Trained Model Metrics.
    modelId string
    modelType string
    The type of the Document model.
    modelVersion string
    The model version
    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.
    testingDataset ModelTestingDataset
    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.
    trainingDataset ModelTrainingDataset
    The base entity which is the input for creating and training a model.
    validationDataset ModelValidationDataset
    The base entity which is the input for creating and training a model.
    compartment_id str
    (Updatable) The compartment identifier.
    component_models Sequence[aidocument.ModelComponentModelArgs]
    The OCID list of active custom Key Value models that need to be composed.
    defined_tags Mapping[str, str]
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description str
    (Updatable) An optional description of the model.
    display_name str
    (Updatable) A human-friendly name for the model, which can be changed.
    freeform_tags Mapping[str, str]
    (Updatable) 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"}
    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 the 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.ModelMetricArgs]
    Trained Model Metrics.
    model_id str
    model_type str
    The type of the Document model.
    model_version str
    The model version
    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_dataset aidocument.ModelTestingDatasetArgs
    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_dataset aidocument.ModelTrainingDatasetArgs
    The base entity which is the input for creating and training a model.
    validation_dataset aidocument.ModelValidationDatasetArgs
    The base entity which is the input for creating and training a model.
    compartmentId String
    (Updatable) The compartment identifier.
    componentModels List<Property Map>
    The OCID list of active custom Key Value models that need to be composed.
    definedTags Map<String>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description String
    (Updatable) An optional description of the model.
    displayName String
    (Updatable) A human-friendly name for the model, which can be changed.
    freeformTags Map<String>
    (Updatable) 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"}
    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 the 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
    modelType String
    The type of the Document model.
    modelVersion String
    The model version
    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.
    testingDataset 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.
    trainingDataset Property Map
    The base entity which is the input for creating and training a model.
    validationDataset Property Map
    The base entity which is the input for creating and training a model.

    Supporting Types

    ModelComponentModel, ModelComponentModelArgs

    ModelId string
    The OCID of active custom Key Value model that need to be composed.
    ModelId string
    The OCID of active custom Key Value model that need to be composed.
    modelId String
    The OCID of active custom Key Value model that need to be composed.
    modelId string
    The OCID of active custom Key Value model that need to be composed.
    model_id str
    The OCID of active custom Key Value model that need to be composed.
    modelId String
    The OCID of active custom Key Value model that need to be composed.

    ModelMetric, ModelMetricArgs

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

    ModelMetricDatasetSummary, ModelMetricDatasetSummaryArgs

    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.

    ModelMetricLabelMetricsReport, ModelMetricLabelMetricsReportArgs

    ConfidenceEntries List<ModelMetricLabelMetricsReportConfidenceEntry>
    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 []ModelMetricLabelMetricsReportConfidenceEntry
    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<ModelMetricLabelMetricsReportConfidenceEntry>
    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 ModelMetricLabelMetricsReportConfidenceEntry[]
    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.ModelMetricLabelMetricsReportConfidenceEntry]
    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

    ModelMetricLabelMetricsReportConfidenceEntry, ModelMetricLabelMetricsReportConfidenceEntryArgs

    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.

    ModelMetricOverallMetricsReport, ModelMetricOverallMetricsReportArgs

    ConfidenceEntries List<ModelMetricOverallMetricsReportConfidenceEntry>
    List of document classification confidence report.
    DocumentCount int
    Total test documents in the label.
    MeanAveragePrecision double
    Mean average precision under different thresholds
    ConfidenceEntries []ModelMetricOverallMetricsReportConfidenceEntry
    List of document classification confidence report.
    DocumentCount int
    Total test documents in the label.
    MeanAveragePrecision float64
    Mean average precision under different thresholds
    confidenceEntries List<ModelMetricOverallMetricsReportConfidenceEntry>
    List of document classification confidence report.
    documentCount Integer
    Total test documents in the label.
    meanAveragePrecision Double
    Mean average precision under different thresholds
    confidenceEntries ModelMetricOverallMetricsReportConfidenceEntry[]
    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.ModelMetricOverallMetricsReportConfidenceEntry]
    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

    ModelMetricOverallMetricsReportConfidenceEntry, ModelMetricOverallMetricsReportConfidenceEntryArgs

    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.

    ModelTestingDataset, ModelTestingDatasetArgs

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

    ModelTrainingDataset, ModelTrainingDatasetArgs

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

    ModelValidationDataset, ModelValidationDatasetArgs

    DatasetType string
    The dataset type, based on where it is stored.
    Bucket string
    The name of the Object Storage bucket that contains the input data file.
    DatasetId string
    OCID of the Data Labeling dataset.
    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.

    ** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

    DatasetType string
    The dataset type, based on where it is stored.
    Bucket string
    The name of the Object Storage bucket that contains the input data file.
    DatasetId string
    OCID of the Data Labeling dataset.
    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.

    ** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

    datasetType String
    The dataset type, based on where it is stored.
    bucket String
    The name of the Object Storage bucket that contains the input data file.
    datasetId String
    OCID of the Data Labeling dataset.
    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.

    ** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

    datasetType string
    The dataset type, based on where it is stored.
    bucket string
    The name of the Object Storage bucket that contains the input data file.
    datasetId string
    OCID of the Data Labeling dataset.
    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.

    ** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

    dataset_type str
    The dataset type, based on where it is stored.
    bucket str
    The name of the Object Storage bucket that contains the input data file.
    dataset_id str
    OCID of the Data Labeling dataset.
    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.

    ** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

    datasetType String
    The dataset type, based on where it is stored.
    bucket String
    The name of the Object Storage bucket that contains the input data file.
    datasetId String
    OCID of the Data Labeling dataset.
    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.

    ** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

    Import

    Models can be imported using the id, e.g.

    $ pulumi import oci:AiDocument/model:Model test_model "id"

    To learn more about importing existing cloud resources, see Importing resources.

    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