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Oracle Cloud Infrastructure v2.11.0 published on Thursday, Sep 19, 2024 by Pulumi
pulumi/pulumi-oci

oci.AiLanguage.Model

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Oracle Cloud Infrastructure v2.11.0 published on Thursday, Sep 19, 2024 by Pulumi
pulumi/pulumi-oci

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

    Creates a new model for training and train the model with date provided.

    Example Usage

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

const testModel = new oci.ailanguage.Model("test_model", {
    compartmentId: compartmentId,
    modelDetails: {
        modelType: modelModelDetailsModelType,
        classificationMode: {
            classificationMode: modelModelDetailsClassificationModeClassificationMode,
            version: modelModelDetailsClassificationModeVersion,
        },
        languageCode: modelModelDetailsLanguageCode,
        version: modelModelDetailsVersion,
    },
    projectId: testProject.id,
    definedTags: {
        "foo-namespace.bar-key": "value",
    },
    description: modelDescription,
    displayName: modelDisplayName,
    freeformTags: {
        "bar-key": "value",
    },
    testStrategy: {
        strategyType: modelTestStrategyStrategyType,
        testingDataset: {
            datasetType: modelTestStrategyTestingDatasetDatasetType,
            datasetId: testDataset.id,
            locationDetails: {
                bucket: modelTestStrategyTestingDatasetLocationDetailsBucket,
                locationType: modelTestStrategyTestingDatasetLocationDetailsLocationType,
                namespace: modelTestStrategyTestingDatasetLocationDetailsNamespace,
                objectNames: modelTestStrategyTestingDatasetLocationDetailsObjectNames,
            },
        },
        validationDataset: {
            datasetType: modelTestStrategyValidationDatasetDatasetType,
            datasetId: testDataset.id,
            locationDetails: {
                bucket: modelTestStrategyValidationDatasetLocationDetailsBucket,
                locationType: modelTestStrategyValidationDatasetLocationDetailsLocationType,
                namespace: modelTestStrategyValidationDatasetLocationDetailsNamespace,
                objectNames: modelTestStrategyValidationDatasetLocationDetailsObjectNames,
            },
        },
    },
    trainingDataset: {
        datasetType: modelTrainingDatasetDatasetType,
        datasetId: testDataset.id,
        locationDetails: {
            bucket: modelTrainingDatasetLocationDetailsBucket,
            locationType: modelTrainingDatasetLocationDetailsLocationType,
            namespace: modelTrainingDatasetLocationDetailsNamespace,
            objectNames: modelTrainingDatasetLocationDetailsObjectNames,
        },
    },
});

    import pulumi
import pulumi_oci as oci

test_model = oci.ai_language.Model("test_model",
    compartment_id=compartment_id,
    model_details={
        "model_type": model_model_details_model_type,
        "classification_mode": {
            "classification_mode": model_model_details_classification_mode_classification_mode,
            "version": model_model_details_classification_mode_version,
        },
        "language_code": model_model_details_language_code,
        "version": model_model_details_version,
    },
    project_id=test_project["id"],
    defined_tags={
        "foo-namespace.bar-key": "value",
    },
    description=model_description,
    display_name=model_display_name,
    freeform_tags={
        "bar-key": "value",
    },
    test_strategy={
        "strategy_type": model_test_strategy_strategy_type,
        "testing_dataset": {
            "dataset_type": model_test_strategy_testing_dataset_dataset_type,
            "dataset_id": test_dataset["id"],
            "location_details": {
                "bucket": model_test_strategy_testing_dataset_location_details_bucket,
                "location_type": model_test_strategy_testing_dataset_location_details_location_type,
                "namespace": model_test_strategy_testing_dataset_location_details_namespace,
                "object_names": model_test_strategy_testing_dataset_location_details_object_names,
            },
        },
        "validation_dataset": {
            "dataset_type": model_test_strategy_validation_dataset_dataset_type,
            "dataset_id": test_dataset["id"],
            "location_details": {
                "bucket": model_test_strategy_validation_dataset_location_details_bucket,
                "location_type": model_test_strategy_validation_dataset_location_details_location_type,
                "namespace": model_test_strategy_validation_dataset_location_details_namespace,
                "object_names": model_test_strategy_validation_dataset_location_details_object_names,
            },
        },
    },
    training_dataset={
        "dataset_type": model_training_dataset_dataset_type,
        "dataset_id": test_dataset["id"],
        "location_details": {
            "bucket": model_training_dataset_location_details_bucket,
            "location_type": model_training_dataset_location_details_location_type,
            "namespace": model_training_dataset_location_details_namespace,
            "object_names": model_training_dataset_location_details_object_names,
        },
    })

    package main

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

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := AiLanguage.NewModel(ctx, "test_model", &AiLanguage.ModelArgs{
			CompartmentId: pulumi.Any(compartmentId),
			ModelDetails: &ailanguage.ModelModelDetailsArgs{
				ModelType: pulumi.Any(modelModelDetailsModelType),
				ClassificationMode: &ailanguage.ModelModelDetailsClassificationModeArgs{
					ClassificationMode: pulumi.Any(modelModelDetailsClassificationModeClassificationMode),
					Version:            pulumi.Any(modelModelDetailsClassificationModeVersion),
				},
				LanguageCode: pulumi.Any(modelModelDetailsLanguageCode),
				Version:      pulumi.Any(modelModelDetailsVersion),
			},
			ProjectId: pulumi.Any(testProject.Id),
			DefinedTags: pulumi.StringMap{
				"foo-namespace.bar-key": pulumi.String("value"),
			},
			Description: pulumi.Any(modelDescription),
			DisplayName: pulumi.Any(modelDisplayName),
			FreeformTags: pulumi.StringMap{
				"bar-key": pulumi.String("value"),
			},
			TestStrategy: &ailanguage.ModelTestStrategyArgs{
				StrategyType: pulumi.Any(modelTestStrategyStrategyType),
				TestingDataset: &ailanguage.ModelTestStrategyTestingDatasetArgs{
					DatasetType: pulumi.Any(modelTestStrategyTestingDatasetDatasetType),
					DatasetId:   pulumi.Any(testDataset.Id),
					LocationDetails: &ailanguage.ModelTestStrategyTestingDatasetLocationDetailsArgs{
						Bucket:       pulumi.Any(modelTestStrategyTestingDatasetLocationDetailsBucket),
						LocationType: pulumi.Any(modelTestStrategyTestingDatasetLocationDetailsLocationType),
						Namespace:    pulumi.Any(modelTestStrategyTestingDatasetLocationDetailsNamespace),
						ObjectNames:  pulumi.Any(modelTestStrategyTestingDatasetLocationDetailsObjectNames),
					},
				},
				ValidationDataset: &ailanguage.ModelTestStrategyValidationDatasetArgs{
					DatasetType: pulumi.Any(modelTestStrategyValidationDatasetDatasetType),
					DatasetId:   pulumi.Any(testDataset.Id),
					LocationDetails: &ailanguage.ModelTestStrategyValidationDatasetLocationDetailsArgs{
						Bucket:       pulumi.Any(modelTestStrategyValidationDatasetLocationDetailsBucket),
						LocationType: pulumi.Any(modelTestStrategyValidationDatasetLocationDetailsLocationType),
						Namespace:    pulumi.Any(modelTestStrategyValidationDatasetLocationDetailsNamespace),
						ObjectNames:  pulumi.Any(modelTestStrategyValidationDatasetLocationDetailsObjectNames),
					},
				},
			},
			TrainingDataset: &ailanguage.ModelTrainingDatasetArgs{
				DatasetType: pulumi.Any(modelTrainingDatasetDatasetType),
				DatasetId:   pulumi.Any(testDataset.Id),
				LocationDetails: &ailanguage.ModelTrainingDatasetLocationDetailsArgs{
					Bucket:       pulumi.Any(modelTrainingDatasetLocationDetailsBucket),
					LocationType: pulumi.Any(modelTrainingDatasetLocationDetailsLocationType),
					Namespace:    pulumi.Any(modelTrainingDatasetLocationDetailsNamespace),
					ObjectNames:  pulumi.Any(modelTrainingDatasetLocationDetailsObjectNames),
				},
			},
		})
		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 = new Oci.AiLanguage.Model("test_model", new()
    {
        CompartmentId = compartmentId,
        ModelDetails = new Oci.AiLanguage.Inputs.ModelModelDetailsArgs
        {
            ModelType = modelModelDetailsModelType,
            ClassificationMode = new Oci.AiLanguage.Inputs.ModelModelDetailsClassificationModeArgs
            {
                ClassificationMode = modelModelDetailsClassificationModeClassificationMode,
                Version = modelModelDetailsClassificationModeVersion,
            },
            LanguageCode = modelModelDetailsLanguageCode,
            Version = modelModelDetailsVersion,
        },
        ProjectId = testProject.Id,
        DefinedTags = 
        {
            { "foo-namespace.bar-key", "value" },
        },
        Description = modelDescription,
        DisplayName = modelDisplayName,
        FreeformTags = 
        {
            { "bar-key", "value" },
        },
        TestStrategy = new Oci.AiLanguage.Inputs.ModelTestStrategyArgs
        {
            StrategyType = modelTestStrategyStrategyType,
            TestingDataset = new Oci.AiLanguage.Inputs.ModelTestStrategyTestingDatasetArgs
            {
                DatasetType = modelTestStrategyTestingDatasetDatasetType,
                DatasetId = testDataset.Id,
                LocationDetails = new Oci.AiLanguage.Inputs.ModelTestStrategyTestingDatasetLocationDetailsArgs
                {
                    Bucket = modelTestStrategyTestingDatasetLocationDetailsBucket,
                    LocationType = modelTestStrategyTestingDatasetLocationDetailsLocationType,
                    Namespace = modelTestStrategyTestingDatasetLocationDetailsNamespace,
                    ObjectNames = modelTestStrategyTestingDatasetLocationDetailsObjectNames,
                },
            },
            ValidationDataset = new Oci.AiLanguage.Inputs.ModelTestStrategyValidationDatasetArgs
            {
                DatasetType = modelTestStrategyValidationDatasetDatasetType,
                DatasetId = testDataset.Id,
                LocationDetails = new Oci.AiLanguage.Inputs.ModelTestStrategyValidationDatasetLocationDetailsArgs
                {
                    Bucket = modelTestStrategyValidationDatasetLocationDetailsBucket,
                    LocationType = modelTestStrategyValidationDatasetLocationDetailsLocationType,
                    Namespace = modelTestStrategyValidationDatasetLocationDetailsNamespace,
                    ObjectNames = modelTestStrategyValidationDatasetLocationDetailsObjectNames,
                },
            },
        },
        TrainingDataset = new Oci.AiLanguage.Inputs.ModelTrainingDatasetArgs
        {
            DatasetType = modelTrainingDatasetDatasetType,
            DatasetId = testDataset.Id,
            LocationDetails = new Oci.AiLanguage.Inputs.ModelTrainingDatasetLocationDetailsArgs
            {
                Bucket = modelTrainingDatasetLocationDetailsBucket,
                LocationType = modelTrainingDatasetLocationDetailsLocationType,
                Namespace = modelTrainingDatasetLocationDetailsNamespace,
                ObjectNames = modelTrainingDatasetLocationDetailsObjectNames,
            },
        },
    });

});

    package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.oci.AiLanguage.Model;
import com.pulumi.oci.AiLanguage.ModelArgs;
import com.pulumi.oci.AiLanguage.inputs.ModelModelDetailsArgs;
import com.pulumi.oci.AiLanguage.inputs.ModelModelDetailsClassificationModeArgs;
import com.pulumi.oci.AiLanguage.inputs.ModelTestStrategyArgs;
import com.pulumi.oci.AiLanguage.inputs.ModelTestStrategyTestingDatasetArgs;
import com.pulumi.oci.AiLanguage.inputs.ModelTestStrategyTestingDatasetLocationDetailsArgs;
import com.pulumi.oci.AiLanguage.inputs.ModelTestStrategyValidationDatasetArgs;
import com.pulumi.oci.AiLanguage.inputs.ModelTestStrategyValidationDatasetLocationDetailsArgs;
import com.pulumi.oci.AiLanguage.inputs.ModelTrainingDatasetArgs;
import com.pulumi.oci.AiLanguage.inputs.ModelTrainingDatasetLocationDetailsArgs;
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) {
        var testModel = new Model("testModel", ModelArgs.builder()
            .compartmentId(compartmentId)
            .modelDetails(ModelModelDetailsArgs.builder()
                .modelType(modelModelDetailsModelType)
                .classificationMode(ModelModelDetailsClassificationModeArgs.builder()
                    .classificationMode(modelModelDetailsClassificationModeClassificationMode)
                    .version(modelModelDetailsClassificationModeVersion)
                    .build())
                .languageCode(modelModelDetailsLanguageCode)
                .version(modelModelDetailsVersion)
                .build())
            .projectId(testProject.id())
            .definedTags(Map.of("foo-namespace.bar-key", "value"))
            .description(modelDescription)
            .displayName(modelDisplayName)
            .freeformTags(Map.of("bar-key", "value"))
            .testStrategy(ModelTestStrategyArgs.builder()
                .strategyType(modelTestStrategyStrategyType)
                .testingDataset(ModelTestStrategyTestingDatasetArgs.builder()
                    .datasetType(modelTestStrategyTestingDatasetDatasetType)
                    .datasetId(testDataset.id())
                    .locationDetails(ModelTestStrategyTestingDatasetLocationDetailsArgs.builder()
                        .bucket(modelTestStrategyTestingDatasetLocationDetailsBucket)
                        .locationType(modelTestStrategyTestingDatasetLocationDetailsLocationType)
                        .namespace(modelTestStrategyTestingDatasetLocationDetailsNamespace)
                        .objectNames(modelTestStrategyTestingDatasetLocationDetailsObjectNames)
                        .build())
                    .build())
                .validationDataset(ModelTestStrategyValidationDatasetArgs.builder()
                    .datasetType(modelTestStrategyValidationDatasetDatasetType)
                    .datasetId(testDataset.id())
                    .locationDetails(ModelTestStrategyValidationDatasetLocationDetailsArgs.builder()
                        .bucket(modelTestStrategyValidationDatasetLocationDetailsBucket)
                        .locationType(modelTestStrategyValidationDatasetLocationDetailsLocationType)
                        .namespace(modelTestStrategyValidationDatasetLocationDetailsNamespace)
                        .objectNames(modelTestStrategyValidationDatasetLocationDetailsObjectNames)
                        .build())
                    .build())
                .build())
            .trainingDataset(ModelTrainingDatasetArgs.builder()
                .datasetType(modelTrainingDatasetDatasetType)
                .datasetId(testDataset.id())
                .locationDetails(ModelTrainingDatasetLocationDetailsArgs.builder()
                    .bucket(modelTrainingDatasetLocationDetailsBucket)
                    .locationType(modelTrainingDatasetLocationDetailsLocationType)
                    .namespace(modelTrainingDatasetLocationDetailsNamespace)
                    .objectNames(modelTrainingDatasetLocationDetailsObjectNames)
                    .build())
                .build())
            .build());

    }
}

    resources:
  testModel:
    type: oci:AiLanguage:Model
    name: test_model
    properties:
      compartmentId: ${compartmentId}
      modelDetails:
        modelType: ${modelModelDetailsModelType}
        classificationMode:
          classificationMode: ${modelModelDetailsClassificationModeClassificationMode}
          version: ${modelModelDetailsClassificationModeVersion}
        languageCode: ${modelModelDetailsLanguageCode}
        version: ${modelModelDetailsVersion}
      projectId: ${testProject.id}
      definedTags:
        foo-namespace.bar-key: value
      description: ${modelDescription}
      displayName: ${modelDisplayName}
      freeformTags:
        bar-key: value
      testStrategy:
        strategyType: ${modelTestStrategyStrategyType}
        testingDataset:
          datasetType: ${modelTestStrategyTestingDatasetDatasetType}
          datasetId: ${testDataset.id}
          locationDetails:
            bucket: ${modelTestStrategyTestingDatasetLocationDetailsBucket}
            locationType: ${modelTestStrategyTestingDatasetLocationDetailsLocationType}
            namespace: ${modelTestStrategyTestingDatasetLocationDetailsNamespace}
            objectNames: ${modelTestStrategyTestingDatasetLocationDetailsObjectNames}
        validationDataset:
          datasetType: ${modelTestStrategyValidationDatasetDatasetType}
          datasetId: ${testDataset.id}
          locationDetails:
            bucket: ${modelTestStrategyValidationDatasetLocationDetailsBucket}
            locationType: ${modelTestStrategyValidationDatasetLocationDetailsLocationType}
            namespace: ${modelTestStrategyValidationDatasetLocationDetailsNamespace}
            objectNames: ${modelTestStrategyValidationDatasetLocationDetailsObjectNames}
      trainingDataset:
        datasetType: ${modelTrainingDatasetDatasetType}
        datasetId: ${testDataset.id}
        locationDetails:
          bucket: ${modelTrainingDatasetLocationDetailsBucket}
          locationType: ${modelTrainingDatasetLocationDetailsLocationType}
          namespace: ${modelTrainingDatasetLocationDetailsNamespace}
          objectNames: ${modelTrainingDatasetLocationDetailsObjectNames}

    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,
          model_details: Optional[_ailanguage.ModelModelDetailsArgs] = None,
          project_id: Optional[str] = None,
          training_dataset: Optional[_ailanguage.ModelTrainingDatasetArgs] = None,
          defined_tags: Optional[Mapping[str, str]] = None,
          description: Optional[str] = None,
          display_name: Optional[str] = None,
          freeform_tags: Optional[Mapping[str, str]] = None,
          test_strategy: Optional[_ailanguage.ModelTestStrategyArgs] = 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:AiLanguage: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 examplemodelResourceResourceFromAiLanguagemodel = new Oci.AiLanguage.Model("examplemodelResourceResourceFromAiLanguagemodel", new()
{
    CompartmentId = "string",
    ModelDetails = new Oci.AiLanguage.Inputs.ModelModelDetailsArgs
    {
        ModelType = "string",
        ClassificationMode = new Oci.AiLanguage.Inputs.ModelModelDetailsClassificationModeArgs
        {
            ClassificationMode = "string",
            Version = "string",
        },
        LanguageCode = "string",
        Version = "string",
    },
    ProjectId = "string",
    TrainingDataset = new Oci.AiLanguage.Inputs.ModelTrainingDatasetArgs
    {
        DatasetType = "string",
        DatasetId = "string",
        LocationDetails = new Oci.AiLanguage.Inputs.ModelTrainingDatasetLocationDetailsArgs
        {
            Bucket = "string",
            LocationType = "string",
            Namespace = "string",
            ObjectNames = new[]
            {
                "string",
            },
        },
    },
    DefinedTags = 
    {
        { "string", "string" },
    },
    Description = "string",
    DisplayName = "string",
    FreeformTags = 
    {
        { "string", "string" },
    },
    TestStrategy = new Oci.AiLanguage.Inputs.ModelTestStrategyArgs
    {
        StrategyType = "string",
        TestingDataset = new Oci.AiLanguage.Inputs.ModelTestStrategyTestingDatasetArgs
        {
            DatasetType = "string",
            DatasetId = "string",
            LocationDetails = new Oci.AiLanguage.Inputs.ModelTestStrategyTestingDatasetLocationDetailsArgs
            {
                Bucket = "string",
                LocationType = "string",
                Namespace = "string",
                ObjectNames = new[]
                {
                    "string",
                },
            },
        },
        ValidationDataset = new Oci.AiLanguage.Inputs.ModelTestStrategyValidationDatasetArgs
        {
            DatasetType = "string",
            DatasetId = "string",
            LocationDetails = new Oci.AiLanguage.Inputs.ModelTestStrategyValidationDatasetLocationDetailsArgs
            {
                Bucket = "string",
                LocationType = "string",
                Namespace = "string",
                ObjectNames = new[]
                {
                    "string",
                },
            },
        },
    },
});

    example, err := AiLanguage.NewModel(ctx, "examplemodelResourceResourceFromAiLanguagemodel", &AiLanguage.ModelArgs{
	CompartmentId: pulumi.String("string"),
	ModelDetails: &ailanguage.ModelModelDetailsArgs{
		ModelType: pulumi.String("string"),
		ClassificationMode: &ailanguage.ModelModelDetailsClassificationModeArgs{
			ClassificationMode: pulumi.String("string"),
			Version:            pulumi.String("string"),
		},
		LanguageCode: pulumi.String("string"),
		Version:      pulumi.String("string"),
	},
	ProjectId: pulumi.String("string"),
	TrainingDataset: &ailanguage.ModelTrainingDatasetArgs{
		DatasetType: pulumi.String("string"),
		DatasetId:   pulumi.String("string"),
		LocationDetails: &ailanguage.ModelTrainingDatasetLocationDetailsArgs{
			Bucket:       pulumi.String("string"),
			LocationType: pulumi.String("string"),
			Namespace:    pulumi.String("string"),
			ObjectNames: pulumi.StringArray{
				pulumi.String("string"),
			},
		},
	},
	DefinedTags: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	Description: pulumi.String("string"),
	DisplayName: pulumi.String("string"),
	FreeformTags: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	TestStrategy: &ailanguage.ModelTestStrategyArgs{
		StrategyType: pulumi.String("string"),
		TestingDataset: &ailanguage.ModelTestStrategyTestingDatasetArgs{
			DatasetType: pulumi.String("string"),
			DatasetId:   pulumi.String("string"),
			LocationDetails: &ailanguage.ModelTestStrategyTestingDatasetLocationDetailsArgs{
				Bucket:       pulumi.String("string"),
				LocationType: pulumi.String("string"),
				Namespace:    pulumi.String("string"),
				ObjectNames: pulumi.StringArray{
					pulumi.String("string"),
				},
			},
		},
		ValidationDataset: &ailanguage.ModelTestStrategyValidationDatasetArgs{
			DatasetType: pulumi.String("string"),
			DatasetId:   pulumi.String("string"),
			LocationDetails: &ailanguage.ModelTestStrategyValidationDatasetLocationDetailsArgs{
				Bucket:       pulumi.String("string"),
				LocationType: pulumi.String("string"),
				Namespace:    pulumi.String("string"),
				ObjectNames: pulumi.StringArray{
					pulumi.String("string"),
				},
			},
		},
	},
})

    var examplemodelResourceResourceFromAiLanguagemodel = new Model("examplemodelResourceResourceFromAiLanguagemodel", ModelArgs.builder()
    .compartmentId("string")
    .modelDetails(ModelModelDetailsArgs.builder()
        .modelType("string")
        .classificationMode(ModelModelDetailsClassificationModeArgs.builder()
            .classificationMode("string")
            .version("string")
            .build())
        .languageCode("string")
        .version("string")
        .build())
    .projectId("string")
    .trainingDataset(ModelTrainingDatasetArgs.builder()
        .datasetType("string")
        .datasetId("string")
        .locationDetails(ModelTrainingDatasetLocationDetailsArgs.builder()
            .bucket("string")
            .locationType("string")
            .namespace("string")
            .objectNames("string")
            .build())
        .build())
    .definedTags(Map.of("string", "string"))
    .description("string")
    .displayName("string")
    .freeformTags(Map.of("string", "string"))
    .testStrategy(ModelTestStrategyArgs.builder()
        .strategyType("string")
        .testingDataset(ModelTestStrategyTestingDatasetArgs.builder()
            .datasetType("string")
            .datasetId("string")
            .locationDetails(ModelTestStrategyTestingDatasetLocationDetailsArgs.builder()
                .bucket("string")
                .locationType("string")
                .namespace("string")
                .objectNames("string")
                .build())
            .build())
        .validationDataset(ModelTestStrategyValidationDatasetArgs.builder()
            .datasetType("string")
            .datasetId("string")
            .locationDetails(ModelTestStrategyValidationDatasetLocationDetailsArgs.builder()
                .bucket("string")
                .locationType("string")
                .namespace("string")
                .objectNames("string")
                .build())
            .build())
        .build())
    .build());

    examplemodel_resource_resource_from_ai_languagemodel = oci.ai_language.Model("examplemodelResourceResourceFromAiLanguagemodel",
    compartment_id="string",
    model_details=oci.ai_language.ModelModelDetailsArgs(
        model_type="string",
        classification_mode=oci.ai_language.ModelModelDetailsClassificationModeArgs(
            classification_mode="string",
            version="string",
        ),
        language_code="string",
        version="string",
    ),
    project_id="string",
    training_dataset=oci.ai_language.ModelTrainingDatasetArgs(
        dataset_type="string",
        dataset_id="string",
        location_details=oci.ai_language.ModelTrainingDatasetLocationDetailsArgs(
            bucket="string",
            location_type="string",
            namespace="string",
            object_names=["string"],
        ),
    ),
    defined_tags={
        "string": "string",
    },
    description="string",
    display_name="string",
    freeform_tags={
        "string": "string",
    },
    test_strategy=oci.ai_language.ModelTestStrategyArgs(
        strategy_type="string",
        testing_dataset=oci.ai_language.ModelTestStrategyTestingDatasetArgs(
            dataset_type="string",
            dataset_id="string",
            location_details=oci.ai_language.ModelTestStrategyTestingDatasetLocationDetailsArgs(
                bucket="string",
                location_type="string",
                namespace="string",
                object_names=["string"],
            ),
        ),
        validation_dataset=oci.ai_language.ModelTestStrategyValidationDatasetArgs(
            dataset_type="string",
            dataset_id="string",
            location_details=oci.ai_language.ModelTestStrategyValidationDatasetLocationDetailsArgs(
                bucket="string",
                location_type="string",
                namespace="string",
                object_names=["string"],
            ),
        ),
    ))

    const examplemodelResourceResourceFromAiLanguagemodel = new oci.ailanguage.Model("examplemodelResourceResourceFromAiLanguagemodel", {
    compartmentId: "string",
    modelDetails: {
        modelType: "string",
        classificationMode: {
            classificationMode: "string",
            version: "string",
        },
        languageCode: "string",
        version: "string",
    },
    projectId: "string",
    trainingDataset: {
        datasetType: "string",
        datasetId: "string",
        locationDetails: {
            bucket: "string",
            locationType: "string",
            namespace: "string",
            objectNames: ["string"],
        },
    },
    definedTags: {
        string: "string",
    },
    description: "string",
    displayName: "string",
    freeformTags: {
        string: "string",
    },
    testStrategy: {
        strategyType: "string",
        testingDataset: {
            datasetType: "string",
            datasetId: "string",
            locationDetails: {
                bucket: "string",
                locationType: "string",
                namespace: "string",
                objectNames: ["string"],
            },
        },
        validationDataset: {
            datasetType: "string",
            datasetId: "string",
            locationDetails: {
                bucket: "string",
                locationType: "string",
                namespace: "string",
                objectNames: ["string"],
            },
        },
    },
});

    type: oci:AiLanguage:Model
properties:
    compartmentId: string
    definedTags:
        string: string
    description: string
    displayName: string
    freeformTags:
        string: string
    modelDetails:
        classificationMode:
            classificationMode: string
            version: string
        languageCode: string
        modelType: string
        version: string
    projectId: string
    testStrategy:
        strategyType: string
        testingDataset:
            datasetId: string
            datasetType: string
            locationDetails:
                bucket: string
                locationType: string
                namespace: string
                objectNames:
                    - string
        validationDataset:
            datasetId: string
            datasetType: string
            locationDetails:
                bucket: string
                locationType: string
                namespace: string
                objectNames:
                    - string
    trainingDataset:
        datasetId: string
        datasetType: string
        locationDetails:
            bucket: string
            locationType: string
            namespace: string
            objectNames:
                - 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

    The Model resource accepts the following input properties:

    CompartmentId string
    (Updatable) The OCID for the models compartment.
    ModelDetails ModelModelDetails
    Possible model types
    ProjectId string
    The OCID of the project to associate with the model.
    TrainingDataset ModelTrainingDataset
    Possible data set type
    DefinedTags Dictionary<string, string>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    Description string
    (Updatable) A short description of the a model.
    DisplayName string
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    FreeformTags Dictionary<string, string>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    TestStrategy ModelTestStrategy
    Possible strategy as testing and validation(optional) dataset.
    CompartmentId string
    (Updatable) The OCID for the models compartment.
    ModelDetails ModelModelDetailsArgs
    Possible model types
    ProjectId string
    The OCID of the project to associate with the model.
    TrainingDataset ModelTrainingDatasetArgs
    Possible data set type
    DefinedTags map[string]string
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    Description string
    (Updatable) A short description of the a model.
    DisplayName string
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    FreeformTags map[string]string
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    TestStrategy ModelTestStrategyArgs
    Possible strategy as testing and validation(optional) dataset.
    compartmentId String
    (Updatable) The OCID for the models compartment.
    modelDetails ModelModelDetails
    Possible model types
    projectId String
    The OCID of the project to associate with the model.
    trainingDataset ModelTrainingDataset
    Possible data set type
    definedTags Map<String,String>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description String
    (Updatable) A short description of the a model.
    displayName String
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    freeformTags Map<String,String>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    testStrategy ModelTestStrategy
    Possible strategy as testing and validation(optional) dataset.
    compartmentId string
    (Updatable) The OCID for the models compartment.
    modelDetails ModelModelDetails
    Possible model types
    projectId string
    The OCID of the project to associate with the model.
    trainingDataset ModelTrainingDataset
    Possible data set type
    definedTags {[key: string]: string}
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description string
    (Updatable) A short description of the a model.
    displayName string
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    freeformTags {[key: string]: string}
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    testStrategy ModelTestStrategy
    Possible strategy as testing and validation(optional) dataset.
    compartment_id str
    (Updatable) The OCID for the models compartment.
    model_details ailanguage.ModelModelDetailsArgs
    Possible model types
    project_id str
    The OCID of the project to associate with the model.
    training_dataset ailanguage.ModelTrainingDatasetArgs
    Possible data set type
    defined_tags Mapping[str, str]
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description str
    (Updatable) A short description of the a model.
    display_name str
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    freeform_tags Mapping[str, str]
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    test_strategy ailanguage.ModelTestStrategyArgs
    Possible strategy as testing and validation(optional) dataset.
    compartmentId String
    (Updatable) The OCID for the models compartment.
    modelDetails Property Map
    Possible model types
    projectId String
    The OCID of the project to associate with the model.
    trainingDataset Property Map
    Possible data set type
    definedTags Map<String>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description String
    (Updatable) A short description of the a model.
    displayName String
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    freeformTags Map<String>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    testStrategy Property Map
    Possible strategy as testing and validation(optional) dataset.

    Outputs

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

    EvaluationResults List<ModelEvaluationResult>
    model training results of different models
    Id string
    The provider-assigned unique ID for this managed resource.
    LifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.
    State string
    The state of the model.
    SystemTags Dictionary<string, string>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    TimeCreated string
    The time the the model was created. An RFC3339 formatted datetime string.
    TimeUpdated string
    The time the model was updated. An RFC3339 formatted datetime string.
    Version string
    For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0
    EvaluationResults []ModelEvaluationResult
    model training results of different models
    Id string
    The provider-assigned unique ID for this managed resource.
    LifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.
    State string
    The state of the model.
    SystemTags map[string]string
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    TimeCreated string
    The time the the model was created. An RFC3339 formatted datetime string.
    TimeUpdated string
    The time the model was updated. An RFC3339 formatted datetime string.
    Version string
    For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0
    evaluationResults List<ModelEvaluationResult>
    model training results of different models
    id String
    The provider-assigned unique ID for this managed resource.
    lifecycleDetails String
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.
    state String
    The state of the model.
    systemTags Map<String,String>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated String
    The time the the model was created. An RFC3339 formatted datetime string.
    timeUpdated String
    The time the model was updated. An RFC3339 formatted datetime string.
    version String
    For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0
    evaluationResults ModelEvaluationResult[]
    model training results of different models
    id string
    The provider-assigned unique ID for this managed resource.
    lifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.
    state string
    The state of the model.
    systemTags {[key: string]: string}
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated string
    The time the the model was created. An RFC3339 formatted datetime string.
    timeUpdated string
    The time the model was updated. An RFC3339 formatted datetime string.
    version string
    For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0
    evaluation_results Sequence[ailanguage.ModelEvaluationResult]
    model training results of different models
    id str
    The provider-assigned unique ID for this managed resource.
    lifecycle_details str
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.
    state str
    The state of the model.
    system_tags Mapping[str, str]
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    time_created str
    The time the the model was created. An RFC3339 formatted datetime string.
    time_updated str
    The time the model was updated. An RFC3339 formatted datetime string.
    version str
    For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0
    evaluationResults List<Property Map>
    model training results of different models
    id String
    The provider-assigned unique ID for this managed resource.
    lifecycleDetails String
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.
    state String
    The state of the model.
    systemTags Map<String>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated String
    The time the the model was created. An RFC3339 formatted datetime string.
    timeUpdated String
    The time the model was updated. An RFC3339 formatted datetime string.
    version String
    For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

    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,
        defined_tags: Optional[Mapping[str, str]] = None,
        description: Optional[str] = None,
        display_name: Optional[str] = None,
        evaluation_results: Optional[Sequence[_ailanguage.ModelEvaluationResultArgs]] = None,
        freeform_tags: Optional[Mapping[str, str]] = None,
        lifecycle_details: Optional[str] = None,
        model_details: Optional[_ailanguage.ModelModelDetailsArgs] = None,
        project_id: Optional[str] = None,
        state: Optional[str] = None,
        system_tags: Optional[Mapping[str, str]] = None,
        test_strategy: Optional[_ailanguage.ModelTestStrategyArgs] = None,
        time_created: Optional[str] = None,
        time_updated: Optional[str] = None,
        training_dataset: Optional[_ailanguage.ModelTrainingDatasetArgs] = None,
        version: Optional[str] = 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 OCID for the models compartment.
    DefinedTags Dictionary<string, string>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    Description string
    (Updatable) A short description of the a model.
    DisplayName string
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    EvaluationResults List<ModelEvaluationResult>
    model training results of different models
    FreeformTags Dictionary<string, string>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    LifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.
    ModelDetails ModelModelDetails
    Possible model types
    ProjectId string
    The OCID of the project to associate with the model.
    State string
    The state of the model.
    SystemTags Dictionary<string, string>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    TestStrategy ModelTestStrategy
    Possible strategy as testing and validation(optional) dataset.
    TimeCreated string
    The time the the model was created. An RFC3339 formatted datetime string.
    TimeUpdated string
    The time the model was updated. An RFC3339 formatted datetime string.
    TrainingDataset ModelTrainingDataset
    Possible data set type
    Version string
    For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0
    CompartmentId string
    (Updatable) The OCID for the models compartment.
    DefinedTags map[string]string
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    Description string
    (Updatable) A short description of the a model.
    DisplayName string
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    EvaluationResults []ModelEvaluationResultArgs
    model training results of different models
    FreeformTags map[string]string
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    LifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.
    ModelDetails ModelModelDetailsArgs
    Possible model types
    ProjectId string
    The OCID of the project to associate with the model.
    State string
    The state of the model.
    SystemTags map[string]string
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    TestStrategy ModelTestStrategyArgs
    Possible strategy as testing and validation(optional) dataset.
    TimeCreated string
    The time the the model was created. An RFC3339 formatted datetime string.
    TimeUpdated string
    The time the model was updated. An RFC3339 formatted datetime string.
    TrainingDataset ModelTrainingDatasetArgs
    Possible data set type
    Version string
    For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0
    compartmentId String
    (Updatable) The OCID for the models compartment.
    definedTags Map<String,String>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description String
    (Updatable) A short description of the a model.
    displayName String
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    evaluationResults List<ModelEvaluationResult>
    model training results of different models
    freeformTags Map<String,String>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    lifecycleDetails String
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.
    modelDetails ModelModelDetails
    Possible model types
    projectId String
    The OCID of the project to associate with the model.
    state String
    The state of the model.
    systemTags Map<String,String>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    testStrategy ModelTestStrategy
    Possible strategy as testing and validation(optional) dataset.
    timeCreated String
    The time the the model was created. An RFC3339 formatted datetime string.
    timeUpdated String
    The time the model was updated. An RFC3339 formatted datetime string.
    trainingDataset ModelTrainingDataset
    Possible data set type
    version String
    For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0
    compartmentId string
    (Updatable) The OCID for the models compartment.
    definedTags {[key: string]: string}
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description string
    (Updatable) A short description of the a model.
    displayName string
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    evaluationResults ModelEvaluationResult[]
    model training results of different models
    freeformTags {[key: string]: string}
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    lifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.
    modelDetails ModelModelDetails
    Possible model types
    projectId string
    The OCID of the project to associate with the model.
    state string
    The state of the model.
    systemTags {[key: string]: string}
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    testStrategy ModelTestStrategy
    Possible strategy as testing and validation(optional) dataset.
    timeCreated string
    The time the the model was created. An RFC3339 formatted datetime string.
    timeUpdated string
    The time the model was updated. An RFC3339 formatted datetime string.
    trainingDataset ModelTrainingDataset
    Possible data set type
    version string
    For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0
    compartment_id str
    (Updatable) The OCID for the models compartment.
    defined_tags Mapping[str, str]
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description str
    (Updatable) A short description of the a model.
    display_name str
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    evaluation_results Sequence[ailanguage.ModelEvaluationResultArgs]
    model training results of different models
    freeform_tags Mapping[str, str]
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    lifecycle_details str
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.
    model_details ailanguage.ModelModelDetailsArgs
    Possible model types
    project_id str
    The OCID of the project to associate with the model.
    state str
    The state of the model.
    system_tags Mapping[str, str]
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    test_strategy ailanguage.ModelTestStrategyArgs
    Possible strategy as testing and validation(optional) dataset.
    time_created str
    The time the the model was created. An RFC3339 formatted datetime string.
    time_updated str
    The time the model was updated. An RFC3339 formatted datetime string.
    training_dataset ailanguage.ModelTrainingDatasetArgs
    Possible data set type
    version str
    For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0
    compartmentId String
    (Updatable) The OCID for the models compartment.
    definedTags Map<String>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description String
    (Updatable) A short description of the a model.
    displayName String
    (Updatable) A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
    evaluationResults List<Property Map>
    model training results of different models
    freeformTags Map<String>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    lifecycleDetails String
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.
    modelDetails Property Map
    Possible model types
    projectId String
    The OCID of the project to associate with the model.
    state String
    The state of the model.
    systemTags Map<String>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    testStrategy Property Map
    Possible strategy as testing and validation(optional) dataset.
    timeCreated String
    The time the the model was created. An RFC3339 formatted datetime string.
    timeUpdated String
    The time the model was updated. An RFC3339 formatted datetime string.
    trainingDataset Property Map
    Possible data set type
    version String
    For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

    Supporting Types

    ModelEvaluationResult, ModelEvaluationResultArgs

    ClassMetrics List<ModelEvaluationResultClassMetric>
    List of text classification metrics
    ConfusionMatrix string
    class level confusion matrix
    EntityMetrics List<ModelEvaluationResultEntityMetric>
    List of entity metrics
    Labels List<string>
    labels
    Metrics List<ModelEvaluationResultMetric>
    Model level named entity recognition metrics
    ModelType string
    Model type
    ClassMetrics []ModelEvaluationResultClassMetric
    List of text classification metrics
    ConfusionMatrix string
    class level confusion matrix
    EntityMetrics []ModelEvaluationResultEntityMetric
    List of entity metrics
    Labels []string
    labels
    Metrics []ModelEvaluationResultMetric
    Model level named entity recognition metrics
    ModelType string
    Model type
    classMetrics List<ModelEvaluationResultClassMetric>
    List of text classification metrics
    confusionMatrix String
    class level confusion matrix
    entityMetrics List<ModelEvaluationResultEntityMetric>
    List of entity metrics
    labels List<String>
    labels
    metrics List<ModelEvaluationResultMetric>
    Model level named entity recognition metrics
    modelType String
    Model type
    classMetrics ModelEvaluationResultClassMetric[]
    List of text classification metrics
    confusionMatrix string
    class level confusion matrix
    entityMetrics ModelEvaluationResultEntityMetric[]
    List of entity metrics
    labels string[]
    labels
    metrics ModelEvaluationResultMetric[]
    Model level named entity recognition metrics
    modelType string
    Model type
    class_metrics Sequence[ailanguage.ModelEvaluationResultClassMetric]
    List of text classification metrics
    confusion_matrix str
    class level confusion matrix
    entity_metrics Sequence[ailanguage.ModelEvaluationResultEntityMetric]
    List of entity metrics
    labels Sequence[str]
    labels
    metrics Sequence[ailanguage.ModelEvaluationResultMetric]
    Model level named entity recognition metrics
    model_type str
    Model type
    classMetrics List<Property Map>
    List of text classification metrics
    confusionMatrix String
    class level confusion matrix
    entityMetrics List<Property Map>
    List of entity metrics
    labels List<String>
    labels
    metrics List<Property Map>
    Model level named entity recognition metrics
    modelType String
    Model type

    ModelEvaluationResultClassMetric, ModelEvaluationResultClassMetricArgs

    F1 double
    F1-score, is a measure of a model’s accuracy on a dataset
    Label string
    Entity label
    Precision double
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    Recall double
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    Support double
    number of samples in the test set
    F1 float64
    F1-score, is a measure of a model’s accuracy on a dataset
    Label string
    Entity label
    Precision float64
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    Recall float64
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    Support float64
    number of samples in the test set
    f1 Double
    F1-score, is a measure of a model’s accuracy on a dataset
    label String
    Entity label
    precision Double
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    recall Double
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    support Double
    number of samples in the test set
    f1 number
    F1-score, is a measure of a model’s accuracy on a dataset
    label string
    Entity label
    precision number
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    recall number
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    support number
    number of samples in the test set
    f1 float
    F1-score, is a measure of a model’s accuracy on a dataset
    label str
    Entity label
    precision float
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    recall float
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    support float
    number of samples in the test set
    f1 Number
    F1-score, is a measure of a model’s accuracy on a dataset
    label String
    Entity label
    precision Number
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    recall Number
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    support Number
    number of samples in the test set

    ModelEvaluationResultEntityMetric, ModelEvaluationResultEntityMetricArgs

    F1 double
    F1-score, is a measure of a model’s accuracy on a dataset
    Label string
    Entity label
    Precision double
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    Recall double
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    F1 float64
    F1-score, is a measure of a model’s accuracy on a dataset
    Label string
    Entity label
    Precision float64
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    Recall float64
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    f1 Double
    F1-score, is a measure of a model’s accuracy on a dataset
    label String
    Entity label
    precision Double
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    recall Double
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    f1 number
    F1-score, is a measure of a model’s accuracy on a dataset
    label string
    Entity label
    precision number
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    recall number
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    f1 float
    F1-score, is a measure of a model’s accuracy on a dataset
    label str
    Entity label
    precision float
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    recall float
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    f1 Number
    F1-score, is a measure of a model’s accuracy on a dataset
    label String
    Entity label
    precision Number
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    recall Number
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

    ModelEvaluationResultMetric, ModelEvaluationResultMetricArgs

    Accuracy double
    The fraction of the labels that were correctly recognised .
    MacroF1 double
    F1-score, is a measure of a model’s accuracy on a dataset
    MacroPrecision double
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    MacroRecall double
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    MicroF1 double
    F1-score, is a measure of a model’s accuracy on a dataset
    MicroPrecision double
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    MicroRecall double
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    WeightedF1 double
    F1-score, is a measure of a model’s accuracy on a dataset
    WeightedPrecision double
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    WeightedRecall double
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    Accuracy float64
    The fraction of the labels that were correctly recognised .
    MacroF1 float64
    F1-score, is a measure of a model’s accuracy on a dataset
    MacroPrecision float64
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    MacroRecall float64
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    MicroF1 float64
    F1-score, is a measure of a model’s accuracy on a dataset
    MicroPrecision float64
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    MicroRecall float64
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    WeightedF1 float64
    F1-score, is a measure of a model’s accuracy on a dataset
    WeightedPrecision float64
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    WeightedRecall float64
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    accuracy Double
    The fraction of the labels that were correctly recognised .
    macroF1 Double
    F1-score, is a measure of a model’s accuracy on a dataset
    macroPrecision Double
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    macroRecall Double
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    microF1 Double
    F1-score, is a measure of a model’s accuracy on a dataset
    microPrecision Double
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    microRecall Double
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    weightedF1 Double
    F1-score, is a measure of a model’s accuracy on a dataset
    weightedPrecision Double
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    weightedRecall Double
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    accuracy number
    The fraction of the labels that were correctly recognised .
    macroF1 number
    F1-score, is a measure of a model’s accuracy on a dataset
    macroPrecision number
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    macroRecall number
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    microF1 number
    F1-score, is a measure of a model’s accuracy on a dataset
    microPrecision number
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    microRecall number
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    weightedF1 number
    F1-score, is a measure of a model’s accuracy on a dataset
    weightedPrecision number
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    weightedRecall number
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    accuracy float
    The fraction of the labels that were correctly recognised .
    macro_f1 float
    F1-score, is a measure of a model’s accuracy on a dataset
    macro_precision float
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    macro_recall float
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    micro_f1 float
    F1-score, is a measure of a model’s accuracy on a dataset
    micro_precision float
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    micro_recall float
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    weighted_f1 float
    F1-score, is a measure of a model’s accuracy on a dataset
    weighted_precision float
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    weighted_recall float
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    accuracy Number
    The fraction of the labels that were correctly recognised .
    macroF1 Number
    F1-score, is a measure of a model’s accuracy on a dataset
    macroPrecision Number
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    macroRecall Number
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    microF1 Number
    F1-score, is a measure of a model’s accuracy on a dataset
    microPrecision Number
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    microRecall Number
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.
    weightedF1 Number
    F1-score, is a measure of a model’s accuracy on a dataset
    weightedPrecision Number
    Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
    weightedRecall Number
    Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

    ModelModelDetails, ModelModelDetailsArgs

    ModelType string
    Model type
    ClassificationMode ModelModelDetailsClassificationMode
    possible text classification modes
    LanguageCode string
    supported language default value is en
    Version string
    Optional pre trained model version. if nothing specified latest pre trained model will be used. Supported versions can be found at /modelTypes/{modelType}
    ModelType string
    Model type
    ClassificationMode ModelModelDetailsClassificationMode
    possible text classification modes
    LanguageCode string
    supported language default value is en
    Version string
    Optional pre trained model version. if nothing specified latest pre trained model will be used. Supported versions can be found at /modelTypes/{modelType}
    modelType String
    Model type
    classificationMode ModelModelDetailsClassificationMode
    possible text classification modes
    languageCode String
    supported language default value is en
    version String
    Optional pre trained model version. if nothing specified latest pre trained model will be used. Supported versions can be found at /modelTypes/{modelType}
    modelType string
    Model type
    classificationMode ModelModelDetailsClassificationMode
    possible text classification modes
    languageCode string
    supported language default value is en
    version string
    Optional pre trained model version. if nothing specified latest pre trained model will be used. Supported versions can be found at /modelTypes/{modelType}
    model_type str
    Model type
    classification_mode ailanguage.ModelModelDetailsClassificationMode
    possible text classification modes
    language_code str
    supported language default value is en
    version str
    Optional pre trained model version. if nothing specified latest pre trained model will be used. Supported versions can be found at /modelTypes/{modelType}
    modelType String
    Model type
    classificationMode Property Map
    possible text classification modes
    languageCode String
    supported language default value is en
    version String
    Optional pre trained model version. if nothing specified latest pre trained model will be used. Supported versions can be found at /modelTypes/{modelType}

    ModelModelDetailsClassificationMode, ModelModelDetailsClassificationModeArgs

    ClassificationMode string
    classification Modes
    Version string
    Optional if nothing specified latest base model will be used for training. Supported versions can be found at /modelTypes/{modelType}
    ClassificationMode string
    classification Modes
    Version string
    Optional if nothing specified latest base model will be used for training. Supported versions can be found at /modelTypes/{modelType}
    classificationMode String
    classification Modes
    version String
    Optional if nothing specified latest base model will be used for training. Supported versions can be found at /modelTypes/{modelType}
    classificationMode string
    classification Modes
    version string
    Optional if nothing specified latest base model will be used for training. Supported versions can be found at /modelTypes/{modelType}
    classification_mode str
    classification Modes
    version str
    Optional if nothing specified latest base model will be used for training. Supported versions can be found at /modelTypes/{modelType}
    classificationMode String
    classification Modes
    version String
    Optional if nothing specified latest base model will be used for training. Supported versions can be found at /modelTypes/{modelType}

    ModelTestStrategy, ModelTestStrategyArgs

    StrategyType string
    This information will define the test strategy different datasets for test and validation(optional) dataset.
    TestingDataset ModelTestStrategyTestingDataset
    Possible data set type
    ValidationDataset ModelTestStrategyValidationDataset
    Possible data set type
    StrategyType string
    This information will define the test strategy different datasets for test and validation(optional) dataset.
    TestingDataset ModelTestStrategyTestingDataset
    Possible data set type
    ValidationDataset ModelTestStrategyValidationDataset
    Possible data set type
    strategyType String
    This information will define the test strategy different datasets for test and validation(optional) dataset.
    testingDataset ModelTestStrategyTestingDataset
    Possible data set type
    validationDataset ModelTestStrategyValidationDataset
    Possible data set type
    strategyType string
    This information will define the test strategy different datasets for test and validation(optional) dataset.
    testingDataset ModelTestStrategyTestingDataset
    Possible data set type
    validationDataset ModelTestStrategyValidationDataset
    Possible data set type
    strategy_type str
    This information will define the test strategy different datasets for test and validation(optional) dataset.
    testing_dataset ailanguage.ModelTestStrategyTestingDataset
    Possible data set type
    validation_dataset ailanguage.ModelTestStrategyValidationDataset
    Possible data set type
    strategyType String
    This information will define the test strategy different datasets for test and validation(optional) dataset.
    testingDataset Property Map
    Possible data set type
    validationDataset Property Map
    Possible data set type

    ModelTestStrategyTestingDataset, ModelTestStrategyTestingDatasetArgs

    DatasetType string
    Possible data sets
    DatasetId string
    Data Science Labelling Service OCID
    LocationDetails ModelTestStrategyTestingDatasetLocationDetails
    Possible object storage location types
    DatasetType string
    Possible data sets
    DatasetId string
    Data Science Labelling Service OCID
    LocationDetails ModelTestStrategyTestingDatasetLocationDetails
    Possible object storage location types
    datasetType String
    Possible data sets
    datasetId String
    Data Science Labelling Service OCID
    locationDetails ModelTestStrategyTestingDatasetLocationDetails
    Possible object storage location types
    datasetType string
    Possible data sets
    datasetId string
    Data Science Labelling Service OCID
    locationDetails ModelTestStrategyTestingDatasetLocationDetails
    Possible object storage location types
    dataset_type str
    Possible data sets
    dataset_id str
    Data Science Labelling Service OCID
    location_details ailanguage.ModelTestStrategyTestingDatasetLocationDetails
    Possible object storage location types
    datasetType String
    Possible data sets
    datasetId String
    Data Science Labelling Service OCID
    locationDetails Property Map
    Possible object storage location types

    ModelTestStrategyTestingDatasetLocationDetails, ModelTestStrategyTestingDatasetLocationDetailsArgs

    Bucket string
    Object storage bucket name
    LocationType string
    Possible object storage location types
    Namespace string
    Object storage namespace
    ObjectNames List<string>
    Array of files which need to be processed in the bucket
    Bucket string
    Object storage bucket name
    LocationType string
    Possible object storage location types
    Namespace string
    Object storage namespace
    ObjectNames []string
    Array of files which need to be processed in the bucket
    bucket String
    Object storage bucket name
    locationType String
    Possible object storage location types
    namespace String
    Object storage namespace
    objectNames List<String>
    Array of files which need to be processed in the bucket
    bucket string
    Object storage bucket name
    locationType string
    Possible object storage location types
    namespace string
    Object storage namespace
    objectNames string[]
    Array of files which need to be processed in the bucket
    bucket str
    Object storage bucket name
    location_type str
    Possible object storage location types
    namespace str
    Object storage namespace
    object_names Sequence[str]
    Array of files which need to be processed in the bucket
    bucket String
    Object storage bucket name
    locationType String
    Possible object storage location types
    namespace String
    Object storage namespace
    objectNames List<String>
    Array of files which need to be processed in the bucket

    ModelTestStrategyValidationDataset, ModelTestStrategyValidationDatasetArgs

    DatasetType string
    Possible data sets
    DatasetId string
    Data Science Labelling Service OCID
    LocationDetails ModelTestStrategyValidationDatasetLocationDetails
    Possible object storage location types
    DatasetType string
    Possible data sets
    DatasetId string
    Data Science Labelling Service OCID
    LocationDetails ModelTestStrategyValidationDatasetLocationDetails
    Possible object storage location types
    datasetType String
    Possible data sets
    datasetId String
    Data Science Labelling Service OCID
    locationDetails ModelTestStrategyValidationDatasetLocationDetails
    Possible object storage location types
    datasetType string
    Possible data sets
    datasetId string
    Data Science Labelling Service OCID
    locationDetails ModelTestStrategyValidationDatasetLocationDetails
    Possible object storage location types
    dataset_type str
    Possible data sets
    dataset_id str
    Data Science Labelling Service OCID
    location_details ailanguage.ModelTestStrategyValidationDatasetLocationDetails
    Possible object storage location types
    datasetType String
    Possible data sets
    datasetId String
    Data Science Labelling Service OCID
    locationDetails Property Map
    Possible object storage location types

    ModelTestStrategyValidationDatasetLocationDetails, ModelTestStrategyValidationDatasetLocationDetailsArgs

    Bucket string
    Object storage bucket name
    LocationType string
    Possible object storage location types
    Namespace string
    Object storage namespace
    ObjectNames List<string>
    Array of files which need to be processed in the bucket
    Bucket string
    Object storage bucket name
    LocationType string
    Possible object storage location types
    Namespace string
    Object storage namespace
    ObjectNames []string
    Array of files which need to be processed in the bucket
    bucket String
    Object storage bucket name
    locationType String
    Possible object storage location types
    namespace String
    Object storage namespace
    objectNames List<String>
    Array of files which need to be processed in the bucket
    bucket string
    Object storage bucket name
    locationType string
    Possible object storage location types
    namespace string
    Object storage namespace
    objectNames string[]
    Array of files which need to be processed in the bucket
    bucket str
    Object storage bucket name
    location_type str
    Possible object storage location types
    namespace str
    Object storage namespace
    object_names Sequence[str]
    Array of files which need to be processed in the bucket
    bucket String
    Object storage bucket name
    locationType String
    Possible object storage location types
    namespace String
    Object storage namespace
    objectNames List<String>
    Array of files which need to be processed in the bucket

    ModelTrainingDataset, ModelTrainingDatasetArgs

    DatasetType string
    Possible data sets
    DatasetId string
    Data Science Labelling Service OCID
    LocationDetails ModelTrainingDatasetLocationDetails
    Possible object storage location types
    DatasetType string
    Possible data sets
    DatasetId string
    Data Science Labelling Service OCID
    LocationDetails ModelTrainingDatasetLocationDetails
    Possible object storage location types
    datasetType String
    Possible data sets
    datasetId String
    Data Science Labelling Service OCID
    locationDetails ModelTrainingDatasetLocationDetails
    Possible object storage location types
    datasetType string
    Possible data sets
    datasetId string
    Data Science Labelling Service OCID
    locationDetails ModelTrainingDatasetLocationDetails
    Possible object storage location types
    dataset_type str
    Possible data sets
    dataset_id str
    Data Science Labelling Service OCID
    location_details ailanguage.ModelTrainingDatasetLocationDetails
    Possible object storage location types
    datasetType String
    Possible data sets
    datasetId String
    Data Science Labelling Service OCID
    locationDetails Property Map
    Possible object storage location types

    ModelTrainingDatasetLocationDetails, ModelTrainingDatasetLocationDetailsArgs

    Bucket string
    Object storage bucket name
    LocationType string
    Possible object storage location types
    Namespace string
    Object storage namespace
    ObjectNames List<string>

    Array of files which need to be processed in the bucket

    ** 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

    Bucket string
    Object storage bucket name
    LocationType string
    Possible object storage location types
    Namespace string
    Object storage namespace
    ObjectNames []string

    Array of files which need to be processed in the bucket

    ** 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

    bucket String
    Object storage bucket name
    locationType String
    Possible object storage location types
    namespace String
    Object storage namespace
    objectNames List<String>

    Array of files which need to be processed in the bucket

    ** 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

    bucket string
    Object storage bucket name
    locationType string
    Possible object storage location types
    namespace string
    Object storage namespace
    objectNames string[]

    Array of files which need to be processed in the bucket

    ** 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

    bucket str
    Object storage bucket name
    location_type str
    Possible object storage location types
    namespace str
    Object storage namespace
    object_names Sequence[str]

    Array of files which need to be processed in the bucket

    ** 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

    bucket String
    Object storage bucket name
    locationType String
    Possible object storage location types
    namespace String
    Object storage namespace
    objectNames List<String>

    Array of files which need to be processed in the bucket

    ** 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:AiLanguage/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 v2.11.0 published on Thursday, Sep 19, 2024 by Pulumi
    pulumi/pulumi-oci