Oracle Cloud Infrastructure v2.33.0, May 1 25

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

oci.AiLanguage.getModel

Explore with Pulumi AI

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

Example Usage

Coming soon!

Coming soon!

Coming soon!

Coming soon!

Coming soon!

variables:
  testModel:
    fn::invoke:
      function: oci:AiLanguage:getModel
      arguments:
        modelId: ${testModelOciAiLanguageModel.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(id: Optional[str] = None,
              opts: Optional[InvokeOptions] = None) -> GetModelResult
def get_model_output(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:AiLanguage/getModel:getModel
  arguments:
    # arguments dictionary

The following arguments are supported:

Id string: Unique identifier model OCID of a model that is immutable on creation

Id string: Unique identifier model OCID of a model that is immutable on creation

id String: Unique identifier model OCID of a model that is immutable on creation

id string: Unique identifier model OCID of a model that is immutable on creation

id str: Unique identifier model OCID of a model that is immutable on creation

id String: Unique identifier model OCID of a model that is immutable on creation

getModel Result

The following output properties are available:

CompartmentId string: The OCID for the model's compartment.
DefinedTags Dictionary<string, string>: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
Description string: A short description of the Model.
DisplayName string: 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<GetModelEvaluationResult>: model training results of different models
FreeformTags Dictionary<string, string>: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
Id string: Unique identifier model OCID of a model that is immutable on creation
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 List<GetModelModelDetail>: 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"}
TestStrategies List<GetModelTestStrategy>: 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.
TrainingDatasets List<GetModelTrainingDataset>: 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: The OCID for the model's compartment.
DefinedTags map[string]string: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
Description string: A short description of the Model.
DisplayName string: A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
EvaluationResults []GetModelEvaluationResult: model training results of different models
FreeformTags map[string]string: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
Id string: Unique identifier model OCID of a model that is immutable on creation
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 []GetModelModelDetail: 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"}
TestStrategies []GetModelTestStrategy: 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.
TrainingDatasets []GetModelTrainingDataset: 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: The OCID for the model's compartment.
definedTags Map<String,String>: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description String: A short description of the Model.
displayName String: 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<GetModelEvaluationResult>: model training results of different models
freeformTags Map<String,String>: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id String: Unique identifier model OCID of a model that is immutable on creation
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 List<GetModelModelDetail>: 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"}
testStrategies List<GetModelTestStrategy>: 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.
trainingDatasets List<GetModelTrainingDataset>: 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: The OCID for the model's compartment.
definedTags {[key: string]: string}: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description string: A short description of the Model.
displayName string: A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
evaluationResults GetModelEvaluationResult[]: model training results of different models
freeformTags {[key: string]: string}: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id string: Unique identifier model OCID of a model that is immutable on creation
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 GetModelModelDetail[]: 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"}
testStrategies GetModelTestStrategy[]: 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.
trainingDatasets GetModelTrainingDataset[]: 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: The OCID for the model's compartment.
defined_tags Mapping[str, str]: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description str: A short description of the Model.
display_name str: 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[GetModelEvaluationResult]: model training results of different models
freeform_tags Mapping[str, str]: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id str: Unique identifier model OCID of a model that is immutable on creation
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 Sequence[GetModelModelDetail]: 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_strategies Sequence[GetModelTestStrategy]: 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_datasets Sequence[GetModelTrainingDataset]: 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: The OCID for the model's compartment.
definedTags Map<String>: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description String: A short description of the Model.
displayName String: 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>: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id String: Unique identifier model OCID of a model that is immutable on creation
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 List<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"}
testStrategies List<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.
trainingDatasets List<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

GetModelEvaluationResult

ClassMetrics List<GetModelEvaluationResultClassMetric>: List of text classification metrics
ConfusionMatrix string: class level confusion matrix
EntityMetrics List<GetModelEvaluationResultEntityMetric>: List of entity metrics
Labels List<string>: labels
Metrics List<GetModelEvaluationResultMetric>: Model level named entity recognition metrics
ModelType string: Model type

ClassMetrics []GetModelEvaluationResultClassMetric: List of text classification metrics
ConfusionMatrix string: class level confusion matrix
EntityMetrics []GetModelEvaluationResultEntityMetric: List of entity metrics
Labels []string: labels
Metrics []GetModelEvaluationResultMetric: Model level named entity recognition metrics
ModelType string: Model type

classMetrics List<GetModelEvaluationResultClassMetric>: List of text classification metrics
confusionMatrix String: class level confusion matrix
entityMetrics List<GetModelEvaluationResultEntityMetric>: List of entity metrics
labels List<String>: labels
metrics List<GetModelEvaluationResultMetric>: Model level named entity recognition metrics
modelType String: Model type

classMetrics GetModelEvaluationResultClassMetric[]: List of text classification metrics
confusionMatrix string: class level confusion matrix
entityMetrics GetModelEvaluationResultEntityMetric[]: List of entity metrics
labels string[]: labels
metrics GetModelEvaluationResultMetric[]: Model level named entity recognition metrics
modelType string: Model type

class_metrics Sequence[GetModelEvaluationResultClassMetric]: List of text classification metrics
confusion_matrix str: class level confusion matrix
entity_metrics Sequence[GetModelEvaluationResultEntityMetric]: List of entity metrics
labels Sequence[str]: labels
metrics Sequence[GetModelEvaluationResultMetric]: 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

GetModelEvaluationResultClassMetric

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

GetModelEvaluationResultEntityMetric

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.

GetModelEvaluationResultMetric

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.

GetModelModelDetail

ClassificationModes List<GetModelModelDetailClassificationMode>: classification Modes
LanguageCode string: supported language default value is en
ModelType string: Model 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

ClassificationModes []GetModelModelDetailClassificationMode: classification Modes
LanguageCode string: supported language default value is en
ModelType string: Model 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

classificationModes List<GetModelModelDetailClassificationMode>: classification Modes
languageCode String: supported language default value is en
modelType String: Model 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

classificationModes GetModelModelDetailClassificationMode[]: classification Modes
languageCode string: supported language default value is en
modelType string: Model 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

classification_modes Sequence[GetModelModelDetailClassificationMode]: classification Modes
language_code str: supported language default value is en
model_type str: Model 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

classificationModes List<Property Map>: classification Modes
languageCode String: supported language default value is en
modelType String: Model 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

GetModelModelDetailClassificationMode

ClassificationMode string: classification Modes
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

ClassificationMode string: classification Modes
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

classificationMode String: classification Modes
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

classificationMode string: classification Modes
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

classification_mode str: classification Modes
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

classificationMode String: classification Modes
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

GetModelTestStrategy

StrategyType string: This information will define the test strategy different datasets for test and validation(optional) dataset.
TestingDatasets List<GetModelTestStrategyTestingDataset>: Possible data set type
ValidationDatasets List<GetModelTestStrategyValidationDataset>: Possible data set type

StrategyType string: This information will define the test strategy different datasets for test and validation(optional) dataset.
TestingDatasets []GetModelTestStrategyTestingDataset: Possible data set type
ValidationDatasets []GetModelTestStrategyValidationDataset: Possible data set type

strategyType String: This information will define the test strategy different datasets for test and validation(optional) dataset.
testingDatasets List<GetModelTestStrategyTestingDataset>: Possible data set type
validationDatasets List<GetModelTestStrategyValidationDataset>: Possible data set type

strategyType string: This information will define the test strategy different datasets for test and validation(optional) dataset.
testingDatasets GetModelTestStrategyTestingDataset[]: Possible data set type
validationDatasets GetModelTestStrategyValidationDataset[]: Possible data set type

strategy_type str: This information will define the test strategy different datasets for test and validation(optional) dataset.
testing_datasets Sequence[GetModelTestStrategyTestingDataset]: Possible data set type
validation_datasets Sequence[GetModelTestStrategyValidationDataset]: Possible data set type

strategyType String: This information will define the test strategy different datasets for test and validation(optional) dataset.
testingDatasets List<Property Map>: Possible data set type
validationDatasets List<Property Map>: Possible data set type

GetModelTestStrategyTestingDataset

DatasetId string: Data Science Labelling Service OCID
DatasetType string: Possible data sets
LocationDetails List<GetModelTestStrategyTestingDatasetLocationDetail>: Possible object storage location types

DatasetId string: Data Science Labelling Service OCID
DatasetType string: Possible data sets
LocationDetails []GetModelTestStrategyTestingDatasetLocationDetail: Possible object storage location types

datasetId String: Data Science Labelling Service OCID
datasetType String: Possible data sets
locationDetails List<GetModelTestStrategyTestingDatasetLocationDetail>: Possible object storage location types

datasetId string: Data Science Labelling Service OCID
datasetType string: Possible data sets
locationDetails GetModelTestStrategyTestingDatasetLocationDetail[]: Possible object storage location types

dataset_id str: Data Science Labelling Service OCID
dataset_type str: Possible data sets
location_details Sequence[GetModelTestStrategyTestingDatasetLocationDetail]: Possible object storage location types

datasetId String: Data Science Labelling Service OCID
datasetType String: Possible data sets
locationDetails List<Property Map>: Possible object storage location types

GetModelTestStrategyTestingDatasetLocationDetail

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

GetModelTestStrategyValidationDataset

DatasetId string: Data Science Labelling Service OCID
DatasetType string: Possible data sets
LocationDetails List<GetModelTestStrategyValidationDatasetLocationDetail>: Possible object storage location types

DatasetId string: Data Science Labelling Service OCID
DatasetType string: Possible data sets
LocationDetails []GetModelTestStrategyValidationDatasetLocationDetail: Possible object storage location types

datasetId String: Data Science Labelling Service OCID
datasetType String: Possible data sets
locationDetails List<GetModelTestStrategyValidationDatasetLocationDetail>: Possible object storage location types

datasetId string: Data Science Labelling Service OCID
datasetType string: Possible data sets
locationDetails GetModelTestStrategyValidationDatasetLocationDetail[]: Possible object storage location types

dataset_id str: Data Science Labelling Service OCID
dataset_type str: Possible data sets
location_details Sequence[GetModelTestStrategyValidationDatasetLocationDetail]: Possible object storage location types

datasetId String: Data Science Labelling Service OCID
datasetType String: Possible data sets
locationDetails List<Property Map>: Possible object storage location types

GetModelTestStrategyValidationDatasetLocationDetail

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

GetModelTrainingDataset

DatasetId string: Data Science Labelling Service OCID
DatasetType string: Possible data sets
LocationDetails List<GetModelTrainingDatasetLocationDetail>: Possible object storage location types

DatasetId string: Data Science Labelling Service OCID
DatasetType string: Possible data sets
LocationDetails []GetModelTrainingDatasetLocationDetail: Possible object storage location types

datasetId String: Data Science Labelling Service OCID
datasetType String: Possible data sets
locationDetails List<GetModelTrainingDatasetLocationDetail>: Possible object storage location types

datasetId string: Data Science Labelling Service OCID
datasetType string: Possible data sets
locationDetails GetModelTrainingDatasetLocationDetail[]: Possible object storage location types

dataset_id str: Data Science Labelling Service OCID
dataset_type str: Possible data sets
location_details Sequence[GetModelTrainingDatasetLocationDetail]: Possible object storage location types

datasetId String: Data Science Labelling Service OCID
datasetType String: Possible data sets
locationDetails List<Property Map>: Possible object storage location types

GetModelTrainingDatasetLocationDetail

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

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.AiLanguage.getModel

On this page

On this page

Example Usage

Using getModel

getModel Result

Supporting Types

GetModelEvaluationResult

GetModelEvaluationResultClassMetric

GetModelEvaluationResultEntityMetric

GetModelEvaluationResultMetric

GetModelModelDetail

GetModelModelDetailClassificationMode

GetModelTestStrategy

GetModelTestStrategyTestingDataset

GetModelTestStrategyTestingDatasetLocationDetail

GetModelTestStrategyValidationDataset

GetModelTestStrategyValidationDatasetLocationDetail

GetModelTrainingDataset

GetModelTrainingDatasetLocationDetail

Package Details

On this page

On this page