Oracle Cloud Infrastructure v2.33.0, May 1 25

Oracle Cloud Infrastructure v2.33.0 published on Thursday, May 1, 2025 by Pulumi

oci.AiDocument.getModel

Explore with Pulumi AI

Oracle Cloud Infrastructure v2.33.0 published on Thursday, May 1, 2025 by Pulumi

Example Usage

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

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

import pulumi
import pulumi_oci as oci

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

package main

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

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

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

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

});

package generated_program;

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

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

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

    }
}

variables:
  testModel:
    fn::invoke:
      function: oci:AiDocument:getModel
      arguments:
        modelId: ${testModelOciAiDocumentModel.id}

Using getModel

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

function getModel(args: GetModelArgs, opts?: InvokeOptions): Promise<GetModelResult>
function getModelOutput(args: GetModelOutputArgs, opts?: InvokeOptions): Output<GetModelResult>

def get_model(model_id: Optional[str] = None,
              opts: Optional[InvokeOptions] = None) -> GetModelResult
def get_model_output(model_id: Optional[pulumi.Input[str]] = None,
              opts: Optional[InvokeOptions] = None) -> Output[GetModelResult]

func LookupModel(ctx *Context, args *LookupModelArgs, opts ...InvokeOption) (*LookupModelResult, error)
func LookupModelOutput(ctx *Context, args *LookupModelOutputArgs, opts ...InvokeOption) LookupModelResultOutput

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

public static class GetModel 
{
    public static Task<GetModelResult> InvokeAsync(GetModelArgs args, InvokeOptions? opts = null)
    public static Output<GetModelResult> Invoke(GetModelInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetModelResult> getModel(GetModelArgs args, InvokeOptions options)
public static Output<GetModelResult> getModel(GetModelArgs args, InvokeOptions options)

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

The following arguments are supported:

ModelId string: A unique model identifier.

ModelId string: A unique model identifier.

modelId String: A unique model identifier.

modelId string: A unique model identifier.

model_id str: A unique model identifier.

modelId String: A unique model identifier.

getModel Result

The following output properties are available:

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

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

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

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

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

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

Supporting Types

GetModelComponentModel

ModelId string: A unique model identifier.

ModelId string: A unique model identifier.

modelId String: A unique model identifier.

modelId string: A unique model identifier.

model_id str: A unique model identifier.

modelId String: A unique model identifier.

GetModelMetric

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

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

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

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

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

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

GetModelMetricDatasetSummary

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

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

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

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

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

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

GetModelMetricLabelMetricsReport

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

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

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

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

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

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

GetModelMetricLabelMetricsReportConfidenceEntry

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

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

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

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

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

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

GetModelMetricOverallMetricsReport

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

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

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

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

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

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

GetModelMetricOverallMetricsReportConfidenceEntry

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

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

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

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

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

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

GetModelTestingDataset

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

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

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

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

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

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

GetModelTrainingDataset

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

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

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

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

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

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

GetModelValidationDataset

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

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

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

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

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

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

Package Details

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

Oracle Cloud Infrastructure v2.33.0 published on Thursday, May 1, 2025 by Pulumi

pulumi/pulumi-oci

oci.AiDocument.getModel

On this page

On this page

Example Usage

Using getModel

getModel Result

Supporting Types

GetModelComponentModel

GetModelMetric

GetModelMetricDatasetSummary

GetModelMetricLabelMetricsReport

GetModelMetricLabelMetricsReportConfidenceEntry

GetModelMetricOverallMetricsReport

GetModelMetricOverallMetricsReportConfidenceEntry

GetModelTestingDataset

GetModelTrainingDataset

GetModelValidationDataset

Package Details

On this page

On this page