Oracle Cloud Infrastructure v1.41.0, Jun 19 24

Oracle Cloud Infrastructure v1.41.0 published on Wednesday, Jun 19, 2024 by Pulumi

oci.AiDocument.getModels

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Oracle Cloud Infrastructure v1.41.0 published on Wednesday, Jun 19, 2024 by Pulumi

Example Usage

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

const testModels = oci.AiDocument.getModels({
    compartmentId: compartmentId,
    displayName: modelDisplayName,
    id: modelId,
    projectId: testProject.id,
    state: modelState,
});

import pulumi
import pulumi_oci as oci

test_models = oci.AiDocument.get_models(compartment_id=compartment_id,
    display_name=model_display_name,
    id=model_id,
    project_id=test_project["id"],
    state=model_state)

package main

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

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := AiDocument.GetModels(ctx, &aidocument.GetModelsArgs{
			CompartmentId: pulumi.StringRef(compartmentId),
			DisplayName:   pulumi.StringRef(modelDisplayName),
			Id:            pulumi.StringRef(modelId),
			ProjectId:     pulumi.StringRef(testProject.Id),
			State:         pulumi.StringRef(modelState),
		}, nil)
		if err != nil {
			return err
		}
		return nil
	})
}

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

return await Deployment.RunAsync(() => 
{
    var testModels = Oci.AiDocument.GetModels.Invoke(new()
    {
        CompartmentId = compartmentId,
        DisplayName = modelDisplayName,
        Id = modelId,
        ProjectId = testProject.Id,
        State = modelState,
    });

});

package generated_program;

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

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

    public static void stack(Context ctx) {
        final var testModels = AiDocumentFunctions.getModels(GetModelsArgs.builder()
            .compartmentId(compartmentId)
            .displayName(modelDisplayName)
            .id(modelId)
            .projectId(testProject.id())
            .state(modelState)
            .build());

    }
}

variables:
  testModels:
    fn::invoke:
      Function: oci:AiDocument:getModels
      Arguments:
        compartmentId: ${compartmentId}
        displayName: ${modelDisplayName}
        id: ${modelId}
        projectId: ${testProject.id}
        state: ${modelState}

Using getModels

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

function getModels(args: GetModelsArgs, opts?: InvokeOptions): Promise<GetModelsResult>
function getModelsOutput(args: GetModelsOutputArgs, opts?: InvokeOptions): Output<GetModelsResult>

def get_models(compartment_id: Optional[str] = None,
               display_name: Optional[str] = None,
               filters: Optional[Sequence[_aidocument.GetModelsFilter]] = None,
               id: Optional[str] = None,
               project_id: Optional[str] = None,
               state: Optional[str] = None,
               opts: Optional[InvokeOptions] = None) -> GetModelsResult
def get_models_output(compartment_id: Optional[pulumi.Input[str]] = None,
               display_name: Optional[pulumi.Input[str]] = None,
               filters: Optional[pulumi.Input[Sequence[pulumi.Input[_aidocument.GetModelsFilterArgs]]]] = None,
               id: Optional[pulumi.Input[str]] = None,
               project_id: Optional[pulumi.Input[str]] = None,
               state: Optional[pulumi.Input[str]] = None,
               opts: Optional[InvokeOptions] = None) -> Output[GetModelsResult]

func GetModels(ctx *Context, args *GetModelsArgs, opts ...InvokeOption) (*GetModelsResult, error)
func GetModelsOutput(ctx *Context, args *GetModelsOutputArgs, opts ...InvokeOption) GetModelsResultOutput

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

public static class GetModels 
{
    public static Task<GetModelsResult> InvokeAsync(GetModelsArgs args, InvokeOptions? opts = null)
    public static Output<GetModelsResult> Invoke(GetModelsInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetModelsResult> getModels(GetModelsArgs args, InvokeOptions options)
// Output-based functions aren't available in Java yet

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

The following arguments are supported:

CompartmentId string: The ID of the compartment in which to list resources.
DisplayName string: A filter to return only resources that match the entire display name given.
Filters List<GetModelsFilter>
Id string: The filter to find the model with the given identifier.
ProjectId string: The ID of the project for which to list the objects.
State string: The filter to match models with the given lifecycleState.

CompartmentId string: The ID of the compartment in which to list resources.
DisplayName string: A filter to return only resources that match the entire display name given.
Filters []GetModelsFilter
Id string: The filter to find the model with the given identifier.
ProjectId string: The ID of the project for which to list the objects.
State string: The filter to match models with the given lifecycleState.

compartmentId String: The ID of the compartment in which to list resources.
displayName String: A filter to return only resources that match the entire display name given.
filters List<GetModelsFilter>
id String: The filter to find the model with the given identifier.
projectId String: The ID of the project for which to list the objects.
state String: The filter to match models with the given lifecycleState.

compartmentId string: The ID of the compartment in which to list resources.
displayName string: A filter to return only resources that match the entire display name given.
filters GetModelsFilter[]
id string: The filter to find the model with the given identifier.
projectId string: The ID of the project for which to list the objects.
state string: The filter to match models with the given lifecycleState.

compartment_id str: The ID of the compartment in which to list resources.
display_name str: A filter to return only resources that match the entire display name given.
filters Sequence[aidocument.GetModelsFilter]
id str: The filter to find the model with the given identifier.
project_id str: The ID of the project for which to list the objects.
state str: The filter to match models with the given lifecycleState.

compartmentId String: The ID of the compartment in which to list resources.
displayName String: A filter to return only resources that match the entire display name given.
filters List<Property Map>
id String: The filter to find the model with the given identifier.
projectId String: The ID of the project for which to list the objects.
state String: The filter to match models with the given lifecycleState.

getModels Result

The following output properties are available:

ModelCollections List<GetModelsModelCollection>: The list of model_collection.
CompartmentId string: The compartment identifier.
DisplayName string: A human-friendly name for the model, which can be changed.
Filters List<GetModelsFilter>
Id string: A unique identifier that is immutable after creation.
ProjectId string: The OCID of the project that contains the model.
State string: The current state of the model.

ModelCollections []GetModelsModelCollection: The list of model_collection.
CompartmentId string: The compartment identifier.
DisplayName string: A human-friendly name for the model, which can be changed.
Filters []GetModelsFilter
Id string: A unique identifier that is immutable after creation.
ProjectId string: The OCID of the project that contains the model.
State string: The current state of the model.

modelCollections List<GetModelsModelCollection>: The list of model_collection.
compartmentId String: The compartment identifier.
displayName String: A human-friendly name for the model, which can be changed.
filters List<GetModelsFilter>
id String: A unique identifier that is immutable after creation.
projectId String: The OCID of the project that contains the model.
state String: The current state of the model.

modelCollections GetModelsModelCollection[]: The list of model_collection.
compartmentId string: The compartment identifier.
displayName string: A human-friendly name for the model, which can be changed.
filters GetModelsFilter[]
id string: A unique identifier that is immutable after creation.
projectId string: The OCID of the project that contains the model.
state string: The current state of the model.

model_collections Sequence[aidocument.GetModelsModelCollection]: The list of model_collection.
compartment_id str: The compartment identifier.
display_name str: A human-friendly name for the model, which can be changed.
filters Sequence[aidocument.GetModelsFilter]
id str: A unique identifier that is immutable after creation.
project_id str: The OCID of the project that contains the model.
state str: The current state of the model.

modelCollections List<Property Map>: The list of model_collection.
compartmentId String: The compartment identifier.
displayName String: A human-friendly name for the model, which can be changed.
filters List<Property Map>
id String: A unique identifier that is immutable after creation.
projectId String: The OCID of the project that contains the model.
state String: The current state of the model.

Supporting Types

GetModelsFilter

Name string
Values List<string>
Regex bool

Name string
Values []string
Regex bool

name String
values List<String>
regex Boolean

name string
values string[]
regex boolean

name str
values Sequence[str]
regex bool

name String
values List<String>
regex Boolean

GetModelsModelCollection

Items List<GetModelsModelCollectionItem>

Items []GetModelsModelCollectionItem

items List<GetModelsModelCollectionItem>

items GetModelsModelCollectionItem[]

items Sequence[aidocument.GetModelsModelCollectionItem]

items List<Property Map>

GetModelsModelCollectionItem

CompartmentId string: The ID of the compartment in which to list resources.
ComponentModels List<GetModelsModelCollectionItemComponentModel>: The OCID collection of active custom Key Value models that need to be composed.
DefinedTags Dictionary<string, object>: Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
Description string: An optional description of the model.
DisplayName string: A filter to return only resources that match the entire display name given.
FreeformTags Dictionary<string, object>: A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
Id string: The filter to find the model with the given identifier.
IsComposedModel bool: Set to true when the model is created by using multiple key value extraction models.
IsQuickMode bool: Set to true when experimenting with a new model type or dataset, so model training is quick, with a predefined low number of passes through the training data.
Labels List<string>: The collection of labels used to train the custom model.
LifecycleDetails string: A message describing the current state in more detail, that can provide actionable information if training failed.
MaxTrainingTimeInHours double: The maximum model training time in hours, expressed as a decimal fraction.
Metrics List<GetModelsModelCollectionItemMetric>: Trained Model Metrics.
ModelId string: The OCID of active custom Key Value model that need to be composed.
ModelType string: The type of the Document model.
ModelVersion string: The version of the model.
ProjectId string: The ID of the project for which to list the objects.
State string: The filter to match models with the given lifecycleState.
SystemTags Dictionary<string, object>: Usage of system tag keys. These predefined keys are scoped to namespaces. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
TenancyId string: The tenancy id of the model.
TestingDatasets List<GetModelsModelCollectionItemTestingDataset>: The base entity which is the input for creating and training a model.
TimeCreated string: When the model was created, as an RFC3339 datetime string.
TimeUpdated string: When the model was updated, as an RFC3339 datetime string.
TrainedTimeInHours double: The total hours actually used for model training.
TrainingDatasets List<GetModelsModelCollectionItemTrainingDataset>: The base entity which is the input for creating and training a model.
ValidationDatasets List<GetModelsModelCollectionItemValidationDataset>: The base entity which is the input for creating and training a model.

CompartmentId string: The ID of the compartment in which to list resources.
ComponentModels []GetModelsModelCollectionItemComponentModel: The OCID collection of active custom Key Value models that need to be composed.
DefinedTags map[string]interface{}: Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
Description string: An optional description of the model.
DisplayName string: A filter to return only resources that match the entire display name given.
FreeformTags map[string]interface{}: A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
Id string: The filter to find the model with the given identifier.
IsComposedModel bool: Set to true when the model is created by using multiple key value extraction models.
IsQuickMode bool: Set to true when experimenting with a new model type or dataset, so model training is quick, with a predefined low number of passes through the training data.
Labels []string: The collection of labels used to train the custom model.
LifecycleDetails string: A message describing the current state in more detail, that can provide actionable information if training failed.
MaxTrainingTimeInHours float64: The maximum model training time in hours, expressed as a decimal fraction.
Metrics []GetModelsModelCollectionItemMetric: Trained Model Metrics.
ModelId string: The OCID of active custom Key Value model that need to be composed.
ModelType string: The type of the Document model.
ModelVersion string: The version of the model.
ProjectId string: The ID of the project for which to list the objects.
State string: The filter to match models with the given lifecycleState.
SystemTags map[string]interface{}: Usage of system tag keys. These predefined keys are scoped to namespaces. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
TenancyId string: The tenancy id of the model.
TestingDatasets []GetModelsModelCollectionItemTestingDataset: The base entity which is the input for creating and training a model.
TimeCreated string: When the model was created, as an RFC3339 datetime string.
TimeUpdated string: When the model was updated, as an RFC3339 datetime string.
TrainedTimeInHours float64: The total hours actually used for model training.
TrainingDatasets []GetModelsModelCollectionItemTrainingDataset: The base entity which is the input for creating and training a model.
ValidationDatasets []GetModelsModelCollectionItemValidationDataset: The base entity which is the input for creating and training a model.

compartmentId String: The ID of the compartment in which to list resources.
componentModels List<GetModelsModelCollectionItemComponentModel>: The OCID collection of active custom Key Value models that need to be composed.
definedTags Map<String,Object>: Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
description String: An optional description of the model.
displayName String: A filter to return only resources that match the entire display name given.
freeformTags Map<String,Object>: A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
id String: The filter to find the model with the given identifier.
isComposedModel Boolean: Set to true when the model is created by using multiple key value extraction models.
isQuickMode Boolean: Set to true when experimenting with a new model type or dataset, so model training is quick, with a predefined low number of passes through the training data.
labels List<String>: The collection of labels used to train the custom model.
lifecycleDetails String: A message describing the current state in more detail, that can provide actionable information if training failed.
maxTrainingTimeInHours Double: The maximum model training time in hours, expressed as a decimal fraction.
metrics List<GetModelsModelCollectionItemMetric>: Trained Model Metrics.
modelId String: The OCID of active custom Key Value model that need to be composed.
modelType String: The type of the Document model.
modelVersion String: The version of the model.
projectId String: The ID of the project for which to list the objects.
state String: The filter to match models with the given lifecycleState.
systemTags Map<String,Object>: Usage of system tag keys. These predefined keys are scoped to namespaces. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
tenancyId String: The tenancy id of the model.
testingDatasets List<GetModelsModelCollectionItemTestingDataset>: The base entity which is the input for creating and training a model.
timeCreated String: When the model was created, as an RFC3339 datetime string.
timeUpdated String: When the model was updated, as an RFC3339 datetime string.
trainedTimeInHours Double: The total hours actually used for model training.
trainingDatasets List<GetModelsModelCollectionItemTrainingDataset>: The base entity which is the input for creating and training a model.
validationDatasets List<GetModelsModelCollectionItemValidationDataset>: The base entity which is the input for creating and training a model.

compartmentId string: The ID of the compartment in which to list resources.
componentModels GetModelsModelCollectionItemComponentModel[]: The OCID collection of active custom Key Value models that need to be composed.
definedTags {[key: string]: any}: Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
description string: An optional description of the model.
displayName string: A filter to return only resources that match the entire display name given.
freeformTags {[key: string]: any}: A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
id string: The filter to find the model with the given identifier.
isComposedModel boolean: Set to true when the model is created by using multiple key value extraction models.
isQuickMode boolean: Set to true when experimenting with a new model type or dataset, so model training is quick, with a predefined low number of passes through the training data.
labels string[]: The collection of labels used to train the custom model.
lifecycleDetails string: A message describing the current state in more detail, that can provide actionable information if training failed.
maxTrainingTimeInHours number: The maximum model training time in hours, expressed as a decimal fraction.
metrics GetModelsModelCollectionItemMetric[]: Trained Model Metrics.
modelId string: The OCID of active custom Key Value model that need to be composed.
modelType string: The type of the Document model.
modelVersion string: The version of the model.
projectId string: The ID of the project for which to list the objects.
state string: The filter to match models with the given lifecycleState.
systemTags {[key: string]: any}: Usage of system tag keys. These predefined keys are scoped to namespaces. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
tenancyId string: The tenancy id of the model.
testingDatasets GetModelsModelCollectionItemTestingDataset[]: The base entity which is the input for creating and training a model.
timeCreated string: When the model was created, as an RFC3339 datetime string.
timeUpdated string: When the model was updated, as an RFC3339 datetime string.
trainedTimeInHours number: The total hours actually used for model training.
trainingDatasets GetModelsModelCollectionItemTrainingDataset[]: The base entity which is the input for creating and training a model.
validationDatasets GetModelsModelCollectionItemValidationDataset[]: The base entity which is the input for creating and training a model.

compartment_id str: The ID of the compartment in which to list resources.
component_models Sequence[aidocument.GetModelsModelCollectionItemComponentModel]: The OCID collection of active custom Key Value models that need to be composed.
defined_tags Mapping[str, Any]: Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
description str: An optional description of the model.
display_name str: A filter to return only resources that match the entire display name given.
freeform_tags Mapping[str, Any]: A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
id str: The filter to find the model with the given identifier.
is_composed_model bool: Set to true when the model is created by using multiple key value extraction models.
is_quick_mode bool: Set to true when experimenting with a new model type or dataset, so model training is quick, with a predefined low number of passes through the training data.
labels Sequence[str]: The collection of labels used to train the custom model.
lifecycle_details str: A message describing the current state in more detail, that can provide actionable information if training failed.
max_training_time_in_hours float: The maximum model training time in hours, expressed as a decimal fraction.
metrics Sequence[aidocument.GetModelsModelCollectionItemMetric]: Trained Model Metrics.
model_id str: The OCID of active custom Key Value model that need to be composed.
model_type str: The type of the Document model.
model_version str: The version of the model.
project_id str: The ID of the project for which to list the objects.
state str: The filter to match models with the given lifecycleState.
system_tags Mapping[str, Any]: Usage of system tag keys. These predefined keys are scoped to namespaces. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
tenancy_id str: The tenancy id of the model.
testing_datasets Sequence[aidocument.GetModelsModelCollectionItemTestingDataset]: The base entity which is the input for creating and training a model.
time_created str: When the model was created, as an RFC3339 datetime string.
time_updated str: When the model was updated, as an RFC3339 datetime string.
trained_time_in_hours float: The total hours actually used for model training.
training_datasets Sequence[aidocument.GetModelsModelCollectionItemTrainingDataset]: The base entity which is the input for creating and training a model.
validation_datasets Sequence[aidocument.GetModelsModelCollectionItemValidationDataset]: The base entity which is the input for creating and training a model.

compartmentId String: The ID of the compartment in which to list resources.
componentModels List<Property Map>: The OCID collection of active custom Key Value models that need to be composed.
definedTags Map<Any>: Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
description String: An optional description of the model.
displayName String: A filter to return only resources that match the entire display name given.
freeformTags Map<Any>: A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
id String: The filter to find the model with the given identifier.
isComposedModel Boolean: Set to true when the model is created by using multiple key value extraction models.
isQuickMode Boolean: Set to true when experimenting with a new model type or dataset, so model training is quick, with a predefined low number of passes through the training data.
labels List<String>: The collection of labels used to train the custom model.
lifecycleDetails String: A message describing the current state in more detail, that can provide actionable information if training failed.
maxTrainingTimeInHours Number: The maximum model training time in hours, expressed as a decimal fraction.
metrics List<Property Map>: Trained Model Metrics.
modelId String: The OCID of active custom Key Value model that need to be composed.
modelType String: The type of the Document model.
modelVersion String: The version of the model.
projectId String: The ID of the project for which to list the objects.
state String: The filter to match models with the given lifecycleState.
systemTags Map<Any>: Usage of system tag keys. These predefined keys are scoped to namespaces. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
tenancyId String: The tenancy id of the model.
testingDatasets List<Property Map>: The base entity which is the input for creating and training a model.
timeCreated String: When the model was created, as an RFC3339 datetime string.
timeUpdated String: When the model was updated, as an RFC3339 datetime string.
trainedTimeInHours Number: The total hours actually used for model training.
trainingDatasets List<Property Map>: The base entity which is the input for creating and training a model.
validationDatasets List<Property Map>: The base entity which is the input for creating and training a model.

GetModelsModelCollectionItemComponentModel

ModelId string: The OCID of active custom Key Value model that need to be composed.

ModelId string: The OCID of active custom Key Value model that need to be composed.

modelId String: The OCID of active custom Key Value model that need to be composed.

modelId string: The OCID of active custom Key Value model that need to be composed.

model_id str: The OCID of active custom Key Value model that need to be composed.

modelId String: The OCID of active custom Key Value model that need to be composed.

GetModelsModelCollectionItemMetric

DatasetSummaries List<GetModelsModelCollectionItemMetricDatasetSummary>: Summary of count of samples used during model training.
LabelMetricsReports List<GetModelsModelCollectionItemMetricLabelMetricsReport>: List of metrics entries per label.
ModelType string: The type of the Document model.
OverallMetricsReports List<GetModelsModelCollectionItemMetricOverallMetricsReport>: Overall Metrics report for Document Classification Model.

DatasetSummaries []GetModelsModelCollectionItemMetricDatasetSummary: Summary of count of samples used during model training.
LabelMetricsReports []GetModelsModelCollectionItemMetricLabelMetricsReport: List of metrics entries per label.
ModelType string: The type of the Document model.
OverallMetricsReports []GetModelsModelCollectionItemMetricOverallMetricsReport: Overall Metrics report for Document Classification Model.

datasetSummaries List<GetModelsModelCollectionItemMetricDatasetSummary>: Summary of count of samples used during model training.
labelMetricsReports List<GetModelsModelCollectionItemMetricLabelMetricsReport>: List of metrics entries per label.
modelType String: The type of the Document model.
overallMetricsReports List<GetModelsModelCollectionItemMetricOverallMetricsReport>: Overall Metrics report for Document Classification Model.

datasetSummaries GetModelsModelCollectionItemMetricDatasetSummary[]: Summary of count of samples used during model training.
labelMetricsReports GetModelsModelCollectionItemMetricLabelMetricsReport[]: List of metrics entries per label.
modelType string: The type of the Document model.
overallMetricsReports GetModelsModelCollectionItemMetricOverallMetricsReport[]: Overall Metrics report for Document Classification Model.

dataset_summaries Sequence[aidocument.GetModelsModelCollectionItemMetricDatasetSummary]: Summary of count of samples used during model training.
label_metrics_reports Sequence[aidocument.GetModelsModelCollectionItemMetricLabelMetricsReport]: List of metrics entries per label.
model_type str: The type of the Document model.
overall_metrics_reports Sequence[aidocument.GetModelsModelCollectionItemMetricOverallMetricsReport]: Overall Metrics report for Document Classification Model.

datasetSummaries List<Property Map>: Summary of count of samples used during model training.
labelMetricsReports List<Property Map>: List of metrics entries per label.
modelType String: The type of the Document model.
overallMetricsReports List<Property Map>: Overall Metrics report for Document Classification Model.

GetModelsModelCollectionItemMetricDatasetSummary

TestSampleCount int: Number of samples used for testing the model.
TrainingSampleCount int: Number of samples used for training the model.
ValidationSampleCount int: Number of samples used for validating the model.

TestSampleCount int: Number of samples used for testing the model.
TrainingSampleCount int: Number of samples used for training the model.
ValidationSampleCount int: Number of samples used for validating the model.

testSampleCount Integer: Number of samples used for testing the model.
trainingSampleCount Integer: Number of samples used for training the model.
validationSampleCount Integer: Number of samples used for validating the model.

testSampleCount number: Number of samples used for testing the model.
trainingSampleCount number: Number of samples used for training the model.
validationSampleCount number: Number of samples used for validating the model.

test_sample_count int: Number of samples used for testing the model.
training_sample_count int: Number of samples used for training the model.
validation_sample_count int: Number of samples used for validating the model.

testSampleCount Number: Number of samples used for testing the model.
trainingSampleCount Number: Number of samples used for training the model.
validationSampleCount Number: Number of samples used for validating the model.

GetModelsModelCollectionItemMetricLabelMetricsReport

ConfidenceEntries List<GetModelsModelCollectionItemMetricLabelMetricsReportConfidenceEntry>: List of document classification confidence report.
DocumentCount int: Total test documents in the label.
Label string: Label name
MeanAveragePrecision double: Mean average precision under different thresholds

ConfidenceEntries []GetModelsModelCollectionItemMetricLabelMetricsReportConfidenceEntry: List of document classification confidence report.
DocumentCount int: Total test documents in the label.
Label string: Label name
MeanAveragePrecision float64: Mean average precision under different thresholds

confidenceEntries List<GetModelsModelCollectionItemMetricLabelMetricsReportConfidenceEntry>: List of document classification confidence report.
documentCount Integer: Total test documents in the label.
label String: Label name
meanAveragePrecision Double: Mean average precision under different thresholds

confidenceEntries GetModelsModelCollectionItemMetricLabelMetricsReportConfidenceEntry[]: List of document classification confidence report.
documentCount number: Total test documents in the label.
label string: Label name
meanAveragePrecision number: Mean average precision under different thresholds

confidence_entries Sequence[aidocument.GetModelsModelCollectionItemMetricLabelMetricsReportConfidenceEntry]: List of document classification confidence report.
document_count int: Total test documents in the label.
label str: Label name
mean_average_precision float: Mean average precision under different thresholds

confidenceEntries List<Property Map>: List of document classification confidence report.
documentCount Number: Total test documents in the label.
label String: Label name
meanAveragePrecision Number: Mean average precision under different thresholds

GetModelsModelCollectionItemMetricLabelMetricsReportConfidenceEntry

Accuracy double: accuracy under the threshold
F1score double: f1Score under the threshold
Precision double: Precision under the threshold
Recall double: Recall under the threshold
Threshold double: Threshold used to calculate precision and recall.

Accuracy float64: accuracy under the threshold
F1score float64: f1Score under the threshold
Precision float64: Precision under the threshold
Recall float64: Recall under the threshold
Threshold float64: Threshold used to calculate precision and recall.

accuracy Double: accuracy under the threshold
f1score Double: f1Score under the threshold
precision Double: Precision under the threshold
recall Double: Recall under the threshold
threshold Double: Threshold used to calculate precision and recall.

accuracy number: accuracy under the threshold
f1score number: f1Score under the threshold
precision number: Precision under the threshold
recall number: Recall under the threshold
threshold number: Threshold used to calculate precision and recall.

accuracy float: accuracy under the threshold
f1score float: f1Score under the threshold
precision float: Precision under the threshold
recall float: Recall under the threshold
threshold float: Threshold used to calculate precision and recall.

accuracy Number: accuracy under the threshold
f1score Number: f1Score under the threshold
precision Number: Precision under the threshold
recall Number: Recall under the threshold
threshold Number: Threshold used to calculate precision and recall.

GetModelsModelCollectionItemMetricOverallMetricsReport

ConfidenceEntries List<GetModelsModelCollectionItemMetricOverallMetricsReportConfidenceEntry>: List of document classification confidence report.
DocumentCount int: Total test documents in the label.
MeanAveragePrecision double: Mean average precision under different thresholds

ConfidenceEntries []GetModelsModelCollectionItemMetricOverallMetricsReportConfidenceEntry: List of document classification confidence report.
DocumentCount int: Total test documents in the label.
MeanAveragePrecision float64: Mean average precision under different thresholds

confidenceEntries List<GetModelsModelCollectionItemMetricOverallMetricsReportConfidenceEntry>: List of document classification confidence report.
documentCount Integer: Total test documents in the label.
meanAveragePrecision Double: Mean average precision under different thresholds

confidenceEntries GetModelsModelCollectionItemMetricOverallMetricsReportConfidenceEntry[]: List of document classification confidence report.
documentCount number: Total test documents in the label.
meanAveragePrecision number: Mean average precision under different thresholds

confidence_entries Sequence[aidocument.GetModelsModelCollectionItemMetricOverallMetricsReportConfidenceEntry]: List of document classification confidence report.
document_count int: Total test documents in the label.
mean_average_precision float: Mean average precision under different thresholds

confidenceEntries List<Property Map>: List of document classification confidence report.
documentCount Number: Total test documents in the label.
meanAveragePrecision Number: Mean average precision under different thresholds

GetModelsModelCollectionItemMetricOverallMetricsReportConfidenceEntry

Accuracy double: accuracy under the threshold
F1score double: f1Score under the threshold
Precision double: Precision under the threshold
Recall double: Recall under the threshold
Threshold double: Threshold used to calculate precision and recall.

Accuracy float64: accuracy under the threshold
F1score float64: f1Score under the threshold
Precision float64: Precision under the threshold
Recall float64: Recall under the threshold
Threshold float64: Threshold used to calculate precision and recall.

accuracy Double: accuracy under the threshold
f1score Double: f1Score under the threshold
precision Double: Precision under the threshold
recall Double: Recall under the threshold
threshold Double: Threshold used to calculate precision and recall.

accuracy number: accuracy under the threshold
f1score number: f1Score under the threshold
precision number: Precision under the threshold
recall number: Recall under the threshold
threshold number: Threshold used to calculate precision and recall.

accuracy float: accuracy under the threshold
f1score float: f1Score under the threshold
precision float: Precision under the threshold
recall float: Recall under the threshold
threshold float: Threshold used to calculate precision and recall.

accuracy Number: accuracy under the threshold
f1score Number: f1Score under the threshold
precision Number: Precision under the threshold
recall Number: Recall under the threshold
threshold Number: Threshold used to calculate precision and recall.

GetModelsModelCollectionItemTestingDataset

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

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

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

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

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

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

GetModelsModelCollectionItemTrainingDataset

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

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

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

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

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

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

GetModelsModelCollectionItemValidationDataset

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

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

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

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

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

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

Package Details

Repository: oci pulumi/pulumi-oci
License: Apache-2.0
Notes: This Pulumi package is based on the oci Terraform Provider.

Oracle Cloud Infrastructure v1.41.0 published on Wednesday, Jun 19, 2024 by Pulumi

pulumi/pulumi-oci

oci.AiDocument.getModels

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Example Usage

Using getModels

getModels Result

Supporting Types

GetModelsFilter

GetModelsModelCollection

GetModelsModelCollectionItem

GetModelsModelCollectionItemComponentModel

GetModelsModelCollectionItemMetric

GetModelsModelCollectionItemMetricDatasetSummary

GetModelsModelCollectionItemMetricLabelMetricsReport

GetModelsModelCollectionItemMetricLabelMetricsReportConfidenceEntry

GetModelsModelCollectionItemMetricOverallMetricsReport

GetModelsModelCollectionItemMetricOverallMetricsReportConfidenceEntry

GetModelsModelCollectionItemTestingDataset

GetModelsModelCollectionItemTrainingDataset

GetModelsModelCollectionItemValidationDataset

Package Details

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