Oracle Cloud Infrastructure v2.30.0, Apr 14 25

Oracle Cloud Infrastructure v2.30.0 published on Monday, Apr 14, 2025 by Pulumi

oci.AiLanguage.getModels

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Oracle Cloud Infrastructure v2.30.0 published on Monday, Apr 14, 2025 by Pulumi

Example Usage

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Python
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Java
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variables:
  testModels:
    fn::invoke:
      function: oci:AiLanguage:getModels
      arguments:
        compartmentId: ${compartmentId}
        displayName: ${modelDisplayName}
        modelId: ${testModel.id}
        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.

TypeScript
Python
Go
C#
Java
YAML

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[_ailanguage.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[_ailanguage.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)
public static Output<GetModelsResult> getModels(GetModelsArgs args, InvokeOptions options)

fn::invoke:
  function: oci:AiLanguage/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: Unique identifier model OCID of a model that is immutable on creation
ProjectId string: The ID of the project for which to list the objects.
State string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

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: Unique identifier model OCID of a model that is immutable on creation
ProjectId string: The ID of the project for which to list the objects.
State string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

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: Unique identifier model OCID of a model that is immutable on creation
projectId String: The ID of the project for which to list the objects.
state String: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

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: Unique identifier model OCID of a model that is immutable on creation
projectId string: The ID of the project for which to list the objects.
state string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

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[ailanguage.GetModelsFilter]
id str: Unique identifier model OCID of a model that is immutable on creation
project_id str: The ID of the project for which to list the objects.
state str: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

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: Unique identifier model OCID of a model that is immutable on creation
projectId String: The ID of the project for which to list the objects.
state String: Filter results by the specified lifecycle state. Must be a valid state for the resource type.

getModels Result

The following output properties are available:

CompartmentId string: The OCID for the model's compartment.
ModelCollections List<GetModelsModelCollection>: The list of model_collection.
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.
Filters List<GetModelsFilter>
Id string: Unique identifier model OCID of a model that is immutable on creation
ProjectId string: The OCID of the project to associate with the model.
State string: The state of the model.

CompartmentId string: The OCID for the model's compartment.
ModelCollections []GetModelsModelCollection: The list of model_collection.
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.
Filters []GetModelsFilter
Id string: Unique identifier model OCID of a model that is immutable on creation
ProjectId string: The OCID of the project to associate with the model.
State string: The state of the model.

compartmentId String: The OCID for the model's compartment.
modelCollections List<GetModelsModelCollection>: The list of model_collection.
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.
filters List<GetModelsFilter>
id String: Unique identifier model OCID of a model that is immutable on creation
projectId String: The OCID of the project to associate with the model.
state String: The state of the model.

compartmentId string: The OCID for the model's compartment.
modelCollections GetModelsModelCollection[]: The list of model_collection.
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.
filters GetModelsFilter[]
id string: Unique identifier model OCID of a model that is immutable on creation
projectId string: The OCID of the project to associate with the model.
state string: The state of the model.

compartment_id str: The OCID for the model's compartment.
model_collections Sequence[ailanguage.GetModelsModelCollection]: The list of model_collection.
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.
filters Sequence[ailanguage.GetModelsFilter]
id str: Unique identifier model OCID of a model that is immutable on creation
project_id str: The OCID of the project to associate with the model.
state str: The state of the model.

compartmentId String: The OCID for the model's compartment.
modelCollections List<Property Map>: The list of model_collection.
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.
filters List<Property Map>
id String: Unique identifier model OCID of a model that is immutable on creation
projectId String: The OCID of the project to associate with the model.
state String: The 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[ailanguage.GetModelsModelCollectionItem]

items List<Property Map>

GetModelsModelCollectionItem

CompartmentId string: The ID of the compartment in which to list resources.
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 filter to return only resources that match the entire display name given.
EvaluationResults List<GetModelsModelCollectionItemEvaluationResult>: 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<GetModelsModelCollectionItemModelDetail>: Possible model types
ProjectId string: The ID of the project for which to list the objects.
State string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
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<GetModelsModelCollectionItemTestStrategy>: 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<GetModelsModelCollectionItemTrainingDataset>: 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 ID of the compartment in which to list resources.
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 filter to return only resources that match the entire display name given.
EvaluationResults []GetModelsModelCollectionItemEvaluationResult: 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 []GetModelsModelCollectionItemModelDetail: Possible model types
ProjectId string: The ID of the project for which to list the objects.
State string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
SystemTags map[string]string: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
TestStrategies []GetModelsModelCollectionItemTestStrategy: 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 []GetModelsModelCollectionItemTrainingDataset: 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 ID of the compartment in which to list resources.
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 filter to return only resources that match the entire display name given.
evaluationResults List<GetModelsModelCollectionItemEvaluationResult>: 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<GetModelsModelCollectionItemModelDetail>: Possible model types
projectId String: The ID of the project for which to list the objects.
state String: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
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<GetModelsModelCollectionItemTestStrategy>: 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<GetModelsModelCollectionItemTrainingDataset>: 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 ID of the compartment in which to list resources.
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 filter to return only resources that match the entire display name given.
evaluationResults GetModelsModelCollectionItemEvaluationResult[]: 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 GetModelsModelCollectionItemModelDetail[]: Possible model types
projectId string: The ID of the project for which to list the objects.
state string: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
systemTags {[key: string]: string}: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
testStrategies GetModelsModelCollectionItemTestStrategy[]: 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 GetModelsModelCollectionItemTrainingDataset[]: 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 ID of the compartment in which to list resources.
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 filter to return only resources that match the entire display name given.
evaluation_results Sequence[ailanguage.GetModelsModelCollectionItemEvaluationResult]: 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[ailanguage.GetModelsModelCollectionItemModelDetail]: Possible model types
project_id str: The ID of the project for which to list the objects.
state str: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
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[ailanguage.GetModelsModelCollectionItemTestStrategy]: 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[ailanguage.GetModelsModelCollectionItemTrainingDataset]: 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 ID of the compartment in which to list resources.
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 filter to return only resources that match the entire display name given.
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 ID of the project for which to list the objects.
state String: Filter results by the specified lifecycle state. Must be a valid state for the resource type.
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

GetModelsModelCollectionItemEvaluationResult

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

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

classMetrics List<GetModelsModelCollectionItemEvaluationResultClassMetric>: List of text classification metrics
confusionMatrix String: class level confusion matrix
entityMetrics List<GetModelsModelCollectionItemEvaluationResultEntityMetric>: List of entity metrics
labels List<String>: labels
metrics List<GetModelsModelCollectionItemEvaluationResultMetric>: Model level named entity recognition metrics
modelType String: Model type

classMetrics GetModelsModelCollectionItemEvaluationResultClassMetric[]: List of text classification metrics
confusionMatrix string: class level confusion matrix
entityMetrics GetModelsModelCollectionItemEvaluationResultEntityMetric[]: List of entity metrics
labels string[]: labels
metrics GetModelsModelCollectionItemEvaluationResultMetric[]: Model level named entity recognition metrics
modelType string: Model type

class_metrics Sequence[ailanguage.GetModelsModelCollectionItemEvaluationResultClassMetric]: List of text classification metrics
confusion_matrix str: class level confusion matrix
entity_metrics Sequence[ailanguage.GetModelsModelCollectionItemEvaluationResultEntityMetric]: List of entity metrics
labels Sequence[str]: labels
metrics Sequence[ailanguage.GetModelsModelCollectionItemEvaluationResultMetric]: 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

GetModelsModelCollectionItemEvaluationResultClassMetric

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

GetModelsModelCollectionItemEvaluationResultEntityMetric

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.

GetModelsModelCollectionItemEvaluationResultMetric

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.

GetModelsModelCollectionItemModelDetail

ClassificationModes List<GetModelsModelCollectionItemModelDetailClassificationMode>: 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 []GetModelsModelCollectionItemModelDetailClassificationMode: 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<GetModelsModelCollectionItemModelDetailClassificationMode>: 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 GetModelsModelCollectionItemModelDetailClassificationMode[]: 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[ailanguage.GetModelsModelCollectionItemModelDetailClassificationMode]: 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

GetModelsModelCollectionItemModelDetailClassificationMode

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

GetModelsModelCollectionItemTestStrategy

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

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

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

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

GetModelsModelCollectionItemTestStrategyTestingDataset

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

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

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

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

dataset_id str: Data Science Labelling Service OCID
dataset_type str: Possible data sets
location_details Sequence[ailanguage.GetModelsModelCollectionItemTestStrategyTestingDatasetLocationDetail]: 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

GetModelsModelCollectionItemTestStrategyTestingDatasetLocationDetail

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

GetModelsModelCollectionItemTestStrategyValidationDataset

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

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

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

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

dataset_id str: Data Science Labelling Service OCID
dataset_type str: Possible data sets
location_details Sequence[ailanguage.GetModelsModelCollectionItemTestStrategyValidationDatasetLocationDetail]: 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

GetModelsModelCollectionItemTestStrategyValidationDatasetLocationDetail

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

GetModelsModelCollectionItemTrainingDataset

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

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

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

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

dataset_id str: Data Science Labelling Service OCID
dataset_type str: Possible data sets
location_details Sequence[ailanguage.GetModelsModelCollectionItemTrainingDatasetLocationDetail]: 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

GetModelsModelCollectionItemTrainingDatasetLocationDetail

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 v2.30.0 published on Monday, Apr 14, 2025 by Pulumi

pulumi/pulumi-oci

oci.AiLanguage.getModels

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Example Usage

Using getModels

getModels Result

Supporting Types

GetModelsFilter

GetModelsModelCollection

GetModelsModelCollectionItem

GetModelsModelCollectionItemEvaluationResult

GetModelsModelCollectionItemEvaluationResultClassMetric

GetModelsModelCollectionItemEvaluationResultEntityMetric

GetModelsModelCollectionItemEvaluationResultMetric

GetModelsModelCollectionItemModelDetail

GetModelsModelCollectionItemModelDetailClassificationMode

GetModelsModelCollectionItemTestStrategy

GetModelsModelCollectionItemTestStrategyTestingDataset

GetModelsModelCollectionItemTestStrategyTestingDatasetLocationDetail

GetModelsModelCollectionItemTestStrategyValidationDataset

GetModelsModelCollectionItemTestStrategyValidationDatasetLocationDetail

GetModelsModelCollectionItemTrainingDataset

GetModelsModelCollectionItemTrainingDatasetLocationDetail

Package Details

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