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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

This data source provides the list of Models in Oracle Cloud Infrastructure Ai Language service.

Returns a list of models.

Example Usage

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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}
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Using getModels

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

function getModels(args: GetModelsArgs, opts?: InvokeOptions): Promise<GetModelsResult>
function getModelsOutput(args: GetModelsOutputArgs, opts?: InvokeOptions): Output<GetModelsResult>
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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]
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func GetModels(ctx *Context, args *GetModelsArgs, opts ...InvokeOption) (*GetModelsResult, error)
func GetModelsOutput(ctx *Context, args *GetModelsOutputArgs, opts ...InvokeOption) GetModelsResultOutput
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> 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)
}
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public static CompletableFuture<GetModelsResult> getModels(GetModelsArgs args, InvokeOptions options)
public static Output<GetModelsResult> getModels(GetModelsArgs args, InvokeOptions options)
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fn::invoke:
  function: oci:AiLanguage/getModels:getModels
  arguments:
    # arguments dictionary
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The following arguments are supported:

CompartmentId This property is required. 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 Changes to this property will trigger replacement. 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 This property is required. 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 Changes to this property will trigger replacement. []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 This property is required. 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 Changes to this property will trigger replacement. 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 This property is required. 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 Changes to this property will trigger replacement. 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 This property is required. 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 Changes to this property will trigger replacement. 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 This property is required. 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 Changes to this property will trigger replacement. 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 This property is required. string
Values This property is required. List<string>
Regex bool
Name This property is required. string
Values This property is required. []string
Regex bool
name This property is required. String
values This property is required. List<String>
regex Boolean
name This property is required. string
values This property is required. string[]
regex boolean
name This property is required. str
values This property is required. Sequence[str]
regex bool
name This property is required. String
values This property is required. List<String>
regex Boolean

GetModelsModelCollection

Items This property is required. List<GetModelsModelCollectionItem>
Items This property is required. []GetModelsModelCollectionItem
items This property is required. List<GetModelsModelCollectionItem>
items This property is required. GetModelsModelCollectionItem[]
items This property is required. List<Property Map>

GetModelsModelCollectionItem

CompartmentId This property is required. string
The ID of the compartment in which to list resources.
DefinedTags This property is required. Dictionary<string, string>
Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
Description This property is required. string
A short description of the Model.
DisplayName This property is required. string
A filter to return only resources that match the entire display name given.
EvaluationResults This property is required. List<GetModelsModelCollectionItemEvaluationResult>
model training results of different models
FreeformTags This property is required. 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 This property is required. string
Unique identifier model OCID of a model that is immutable on creation
LifecycleDetails This property is required. 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 This property is required. List<GetModelsModelCollectionItemModelDetail>
Possible model types
ProjectId This property is required. string
The ID of the project for which to list the objects.
State This property is required. string
Filter results by the specified lifecycle state. Must be a valid state for the resource type.
SystemTags This property is required. Dictionary<string, string>
Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
TestStrategies This property is required. List<GetModelsModelCollectionItemTestStrategy>
Possible strategy as testing and validation(optional) dataset.
TimeCreated This property is required. string
The time the the model was created. An RFC3339 formatted datetime string.
TimeUpdated This property is required. string
The time the model was updated. An RFC3339 formatted datetime string.
TrainingDatasets This property is required. List<GetModelsModelCollectionItemTrainingDataset>
Possible data set type
Version This property is required. 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 This property is required. string
The ID of the compartment in which to list resources.
DefinedTags This property is required. map[string]string
Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
Description This property is required. string
A short description of the Model.
DisplayName This property is required. string
A filter to return only resources that match the entire display name given.
EvaluationResults This property is required. []GetModelsModelCollectionItemEvaluationResult
model training results of different models
FreeformTags This property is required. 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 This property is required. string
Unique identifier model OCID of a model that is immutable on creation
LifecycleDetails This property is required. 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 This property is required. []GetModelsModelCollectionItemModelDetail
Possible model types
ProjectId This property is required. string
The ID of the project for which to list the objects.
State This property is required. string
Filter results by the specified lifecycle state. Must be a valid state for the resource type.
SystemTags This property is required. map[string]string
Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
TestStrategies This property is required. []GetModelsModelCollectionItemTestStrategy
Possible strategy as testing and validation(optional) dataset.
TimeCreated This property is required. string
The time the the model was created. An RFC3339 formatted datetime string.
TimeUpdated This property is required. string
The time the model was updated. An RFC3339 formatted datetime string.
TrainingDatasets This property is required. []GetModelsModelCollectionItemTrainingDataset
Possible data set type
Version This property is required. 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 This property is required. String
The ID of the compartment in which to list resources.
definedTags This property is required. Map<String,String>
Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description This property is required. String
A short description of the Model.
displayName This property is required. String
A filter to return only resources that match the entire display name given.
evaluationResults This property is required. List<GetModelsModelCollectionItemEvaluationResult>
model training results of different models
freeformTags This property is required. 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 This property is required. String
Unique identifier model OCID of a model that is immutable on creation
lifecycleDetails This property is required. 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 This property is required. List<GetModelsModelCollectionItemModelDetail>
Possible model types
projectId This property is required. String
The ID of the project for which to list the objects.
state This property is required. String
Filter results by the specified lifecycle state. Must be a valid state for the resource type.
systemTags This property is required. Map<String,String>
Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
testStrategies This property is required. List<GetModelsModelCollectionItemTestStrategy>
Possible strategy as testing and validation(optional) dataset.
timeCreated This property is required. String
The time the the model was created. An RFC3339 formatted datetime string.
timeUpdated This property is required. String
The time the model was updated. An RFC3339 formatted datetime string.
trainingDatasets This property is required. List<GetModelsModelCollectionItemTrainingDataset>
Possible data set type
version This property is required. 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 This property is required. string
The ID of the compartment in which to list resources.
definedTags This property is required. {[key: string]: string}
Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description This property is required. string
A short description of the Model.
displayName This property is required. string
A filter to return only resources that match the entire display name given.
evaluationResults This property is required. GetModelsModelCollectionItemEvaluationResult[]
model training results of different models
freeformTags This property is required. {[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 This property is required. string
Unique identifier model OCID of a model that is immutable on creation
lifecycleDetails This property is required. 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 This property is required. GetModelsModelCollectionItemModelDetail[]
Possible model types
projectId This property is required. string
The ID of the project for which to list the objects.
state This property is required. string
Filter results by the specified lifecycle state. Must be a valid state for the resource type.
systemTags This property is required. {[key: string]: string}
Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
testStrategies This property is required. GetModelsModelCollectionItemTestStrategy[]
Possible strategy as testing and validation(optional) dataset.
timeCreated This property is required. string
The time the the model was created. An RFC3339 formatted datetime string.
timeUpdated This property is required. string
The time the model was updated. An RFC3339 formatted datetime string.
trainingDatasets This property is required. GetModelsModelCollectionItemTrainingDataset[]
Possible data set type
version This property is required. 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 This property is required. str
The ID of the compartment in which to list resources.
defined_tags This property is required. Mapping[str, str]
Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description This property is required. str
A short description of the Model.
display_name This property is required. str
A filter to return only resources that match the entire display name given.
evaluation_results This property is required. Sequence[ailanguage.GetModelsModelCollectionItemEvaluationResult]
model training results of different models
freeform_tags This property is required. 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 This property is required. str
Unique identifier model OCID of a model that is immutable on creation
lifecycle_details This property is required. 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 This property is required. Sequence[ailanguage.GetModelsModelCollectionItemModelDetail]
Possible model types
project_id This property is required. str
The ID of the project for which to list the objects.
state This property is required. str
Filter results by the specified lifecycle state. Must be a valid state for the resource type.
system_tags This property is required. Mapping[str, str]
Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
test_strategies This property is required. Sequence[ailanguage.GetModelsModelCollectionItemTestStrategy]
Possible strategy as testing and validation(optional) dataset.
time_created This property is required. str
The time the the model was created. An RFC3339 formatted datetime string.
time_updated This property is required. str
The time the model was updated. An RFC3339 formatted datetime string.
training_datasets This property is required. Sequence[ailanguage.GetModelsModelCollectionItemTrainingDataset]
Possible data set type
version This property is required. 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 This property is required. String
The ID of the compartment in which to list resources.
definedTags This property is required. Map<String>
Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description This property is required. String
A short description of the Model.
displayName This property is required. String
A filter to return only resources that match the entire display name given.
evaluationResults This property is required. List<Property Map>
model training results of different models
freeformTags This property is required. 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 This property is required. String
Unique identifier model OCID of a model that is immutable on creation
lifecycleDetails This property is required. 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 This property is required. List<Property Map>
Possible model types
projectId This property is required. String
The ID of the project for which to list the objects.
state This property is required. String
Filter results by the specified lifecycle state. Must be a valid state for the resource type.
systemTags This property is required. Map<String>
Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
testStrategies This property is required. List<Property Map>
Possible strategy as testing and validation(optional) dataset.
timeCreated This property is required. String
The time the the model was created. An RFC3339 formatted datetime string.
timeUpdated This property is required. String
The time the model was updated. An RFC3339 formatted datetime string.
trainingDatasets This property is required. List<Property Map>
Possible data set type
version This property is required. 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 This property is required. List<GetModelsModelCollectionItemEvaluationResultClassMetric>
List of text classification metrics
ConfusionMatrix This property is required. string
class level confusion matrix
EntityMetrics This property is required. List<GetModelsModelCollectionItemEvaluationResultEntityMetric>
List of entity metrics
Labels This property is required. List<string>
labels
Metrics This property is required. List<GetModelsModelCollectionItemEvaluationResultMetric>
Model level named entity recognition metrics
ModelType This property is required. string
Model type
ClassMetrics This property is required. []GetModelsModelCollectionItemEvaluationResultClassMetric
List of text classification metrics
ConfusionMatrix This property is required. string
class level confusion matrix
EntityMetrics This property is required. []GetModelsModelCollectionItemEvaluationResultEntityMetric
List of entity metrics
Labels This property is required. []string
labels
Metrics This property is required. []GetModelsModelCollectionItemEvaluationResultMetric
Model level named entity recognition metrics
ModelType This property is required. string
Model type
classMetrics This property is required. List<GetModelsModelCollectionItemEvaluationResultClassMetric>
List of text classification metrics
confusionMatrix This property is required. String
class level confusion matrix
entityMetrics This property is required. List<GetModelsModelCollectionItemEvaluationResultEntityMetric>
List of entity metrics
labels This property is required. List<String>
labels
metrics This property is required. List<GetModelsModelCollectionItemEvaluationResultMetric>
Model level named entity recognition metrics
modelType This property is required. String
Model type
classMetrics This property is required. GetModelsModelCollectionItemEvaluationResultClassMetric[]
List of text classification metrics
confusionMatrix This property is required. string
class level confusion matrix
entityMetrics This property is required. GetModelsModelCollectionItemEvaluationResultEntityMetric[]
List of entity metrics
labels This property is required. string[]
labels
metrics This property is required. GetModelsModelCollectionItemEvaluationResultMetric[]
Model level named entity recognition metrics
modelType This property is required. string
Model type
class_metrics This property is required. Sequence[ailanguage.GetModelsModelCollectionItemEvaluationResultClassMetric]
List of text classification metrics
confusion_matrix This property is required. str
class level confusion matrix
entity_metrics This property is required. Sequence[ailanguage.GetModelsModelCollectionItemEvaluationResultEntityMetric]
List of entity metrics
labels This property is required. Sequence[str]
labels
metrics This property is required. Sequence[ailanguage.GetModelsModelCollectionItemEvaluationResultMetric]
Model level named entity recognition metrics
model_type This property is required. str
Model type
classMetrics This property is required. List<Property Map>
List of text classification metrics
confusionMatrix This property is required. String
class level confusion matrix
entityMetrics This property is required. List<Property Map>
List of entity metrics
labels This property is required. List<String>
labels
metrics This property is required. List<Property Map>
Model level named entity recognition metrics
modelType This property is required. String
Model type

GetModelsModelCollectionItemEvaluationResultClassMetric

F1 This property is required. double
F1-score, is a measure of a model’s accuracy on a dataset
Label This property is required. string
Entity label
Precision This property is required. 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 This property is required. 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 This property is required. double
number of samples in the test set
F1 This property is required. float64
F1-score, is a measure of a model’s accuracy on a dataset
Label This property is required. string
Entity label
Precision This property is required. 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 This property is required. 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 This property is required. float64
number of samples in the test set
f1 This property is required. Double
F1-score, is a measure of a model’s accuracy on a dataset
label This property is required. String
Entity label
precision This property is required. 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 This property is required. 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 This property is required. Double
number of samples in the test set
f1 This property is required. number
F1-score, is a measure of a model’s accuracy on a dataset
label This property is required. string
Entity label
precision This property is required. 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 This property is required. 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 This property is required. number
number of samples in the test set
f1 This property is required. float
F1-score, is a measure of a model’s accuracy on a dataset
label This property is required. str
Entity label
precision This property is required. 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 This property is required. 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 This property is required. float
number of samples in the test set
f1 This property is required. Number
F1-score, is a measure of a model’s accuracy on a dataset
label This property is required. String
Entity label
precision This property is required. 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 This property is required. 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 This property is required. Number
number of samples in the test set

GetModelsModelCollectionItemEvaluationResultEntityMetric

F1 This property is required. double
F1-score, is a measure of a model’s accuracy on a dataset
Label This property is required. string
Entity label
Precision This property is required. 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 This property is required. 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 This property is required. float64
F1-score, is a measure of a model’s accuracy on a dataset
Label This property is required. string
Entity label
Precision This property is required. 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 This property is required. 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 This property is required. Double
F1-score, is a measure of a model’s accuracy on a dataset
label This property is required. String
Entity label
precision This property is required. 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 This property is required. 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 This property is required. number
F1-score, is a measure of a model’s accuracy on a dataset
label This property is required. string
Entity label
precision This property is required. 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 This property is required. 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 This property is required. float
F1-score, is a measure of a model’s accuracy on a dataset
label This property is required. str
Entity label
precision This property is required. 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 This property is required. 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 This property is required. Number
F1-score, is a measure of a model’s accuracy on a dataset
label This property is required. String
Entity label
precision This property is required. 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 This property is required. 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 This property is required. double
The fraction of the labels that were correctly recognised .
MacroF1 This property is required. double
F1-score, is a measure of a model’s accuracy on a dataset
MacroPrecision This property is required. 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 This property is required. 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 This property is required. double
F1-score, is a measure of a model’s accuracy on a dataset
MicroPrecision This property is required. 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 This property is required. 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 This property is required. double
F1-score, is a measure of a model’s accuracy on a dataset
WeightedPrecision This property is required. 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 This property is required. 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 This property is required. float64
The fraction of the labels that were correctly recognised .
MacroF1 This property is required. float64
F1-score, is a measure of a model’s accuracy on a dataset
MacroPrecision This property is required. 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 This property is required. 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 This property is required. float64
F1-score, is a measure of a model’s accuracy on a dataset
MicroPrecision This property is required. 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 This property is required. 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 This property is required. float64
F1-score, is a measure of a model’s accuracy on a dataset
WeightedPrecision This property is required. 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 This property is required. 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 This property is required. Double
The fraction of the labels that were correctly recognised .
macroF1 This property is required. Double
F1-score, is a measure of a model’s accuracy on a dataset
macroPrecision This property is required. 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 This property is required. 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 This property is required. Double
F1-score, is a measure of a model’s accuracy on a dataset
microPrecision This property is required. 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 This property is required. 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 This property is required. Double
F1-score, is a measure of a model’s accuracy on a dataset
weightedPrecision This property is required. 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 This property is required. 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 This property is required. number
The fraction of the labels that were correctly recognised .
macroF1 This property is required. number
F1-score, is a measure of a model’s accuracy on a dataset
macroPrecision This property is required. 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 This property is required. 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 This property is required. number
F1-score, is a measure of a model’s accuracy on a dataset
microPrecision This property is required. 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 This property is required. 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 This property is required. number
F1-score, is a measure of a model’s accuracy on a dataset
weightedPrecision This property is required. 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 This property is required. 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 This property is required. float
The fraction of the labels that were correctly recognised .
macro_f1 This property is required. float
F1-score, is a measure of a model’s accuracy on a dataset
macro_precision This property is required. 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 This property is required. 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 This property is required. float
F1-score, is a measure of a model’s accuracy on a dataset
micro_precision This property is required. 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 This property is required. 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 This property is required. float
F1-score, is a measure of a model’s accuracy on a dataset
weighted_precision This property is required. 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 This property is required. 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 This property is required. Number
The fraction of the labels that were correctly recognised .
macroF1 This property is required. Number
F1-score, is a measure of a model’s accuracy on a dataset
macroPrecision This property is required. 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 This property is required. 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 This property is required. Number
F1-score, is a measure of a model’s accuracy on a dataset
microPrecision This property is required. 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 This property is required. 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 This property is required. Number
F1-score, is a measure of a model’s accuracy on a dataset
weightedPrecision This property is required. 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 This property is required. 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 This property is required. List<GetModelsModelCollectionItemModelDetailClassificationMode>
classification Modes
LanguageCode This property is required. string
supported language default value is en
ModelType This property is required. string
Model type
Version This property is required. 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 This property is required. []GetModelsModelCollectionItemModelDetailClassificationMode
classification Modes
LanguageCode This property is required. string
supported language default value is en
ModelType This property is required. string
Model type
Version This property is required. 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 This property is required. List<GetModelsModelCollectionItemModelDetailClassificationMode>
classification Modes
languageCode This property is required. String
supported language default value is en
modelType This property is required. String
Model type
version This property is required. 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 This property is required. GetModelsModelCollectionItemModelDetailClassificationMode[]
classification Modes
languageCode This property is required. string
supported language default value is en
modelType This property is required. string
Model type
version This property is required. 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 This property is required. Sequence[ailanguage.GetModelsModelCollectionItemModelDetailClassificationMode]
classification Modes
language_code This property is required. str
supported language default value is en
model_type This property is required. str
Model type
version This property is required. 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 This property is required. List<Property Map>
classification Modes
languageCode This property is required. String
supported language default value is en
modelType This property is required. String
Model type
version This property is required. 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 This property is required. string
classification Modes
Version This property is required. 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 This property is required. string
classification Modes
Version This property is required. 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 This property is required. String
classification Modes
version This property is required. 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 This property is required. string
classification Modes
version This property is required. 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 This property is required. str
classification Modes
version This property is required. 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 This property is required. String
classification Modes
version This property is required. 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 This property is required. string
This information will define the test strategy different datasets for test and validation(optional) dataset.
TestingDatasets This property is required. List<GetModelsModelCollectionItemTestStrategyTestingDataset>
Possible data set type
ValidationDatasets This property is required. List<GetModelsModelCollectionItemTestStrategyValidationDataset>
Possible data set type
StrategyType This property is required. string
This information will define the test strategy different datasets for test and validation(optional) dataset.
TestingDatasets This property is required. []GetModelsModelCollectionItemTestStrategyTestingDataset
Possible data set type
ValidationDatasets This property is required. []GetModelsModelCollectionItemTestStrategyValidationDataset
Possible data set type
strategyType This property is required. String
This information will define the test strategy different datasets for test and validation(optional) dataset.
testingDatasets This property is required. List<GetModelsModelCollectionItemTestStrategyTestingDataset>
Possible data set type
validationDatasets This property is required. List<GetModelsModelCollectionItemTestStrategyValidationDataset>
Possible data set type
strategyType This property is required. string
This information will define the test strategy different datasets for test and validation(optional) dataset.
testingDatasets This property is required. GetModelsModelCollectionItemTestStrategyTestingDataset[]
Possible data set type
validationDatasets This property is required. GetModelsModelCollectionItemTestStrategyValidationDataset[]
Possible data set type
strategy_type This property is required. str
This information will define the test strategy different datasets for test and validation(optional) dataset.
testing_datasets This property is required. Sequence[ailanguage.GetModelsModelCollectionItemTestStrategyTestingDataset]
Possible data set type
validation_datasets This property is required. Sequence[ailanguage.GetModelsModelCollectionItemTestStrategyValidationDataset]
Possible data set type
strategyType This property is required. String
This information will define the test strategy different datasets for test and validation(optional) dataset.
testingDatasets This property is required. List<Property Map>
Possible data set type
validationDatasets This property is required. List<Property Map>
Possible data set type

GetModelsModelCollectionItemTestStrategyTestingDataset

DatasetId This property is required. string
Data Science Labelling Service OCID
DatasetType This property is required. string
Possible data sets
LocationDetails This property is required. List<GetModelsModelCollectionItemTestStrategyTestingDatasetLocationDetail>
Possible object storage location types
DatasetId This property is required. string
Data Science Labelling Service OCID
DatasetType This property is required. string
Possible data sets
LocationDetails This property is required. []GetModelsModelCollectionItemTestStrategyTestingDatasetLocationDetail
Possible object storage location types
datasetId This property is required. String
Data Science Labelling Service OCID
datasetType This property is required. String
Possible data sets
locationDetails This property is required. List<GetModelsModelCollectionItemTestStrategyTestingDatasetLocationDetail>
Possible object storage location types
datasetId This property is required. string
Data Science Labelling Service OCID
datasetType This property is required. string
Possible data sets
locationDetails This property is required. GetModelsModelCollectionItemTestStrategyTestingDatasetLocationDetail[]
Possible object storage location types
dataset_id This property is required. str
Data Science Labelling Service OCID
dataset_type This property is required. str
Possible data sets
location_details This property is required. Sequence[ailanguage.GetModelsModelCollectionItemTestStrategyTestingDatasetLocationDetail]
Possible object storage location types
datasetId This property is required. String
Data Science Labelling Service OCID
datasetType This property is required. String
Possible data sets
locationDetails This property is required. List<Property Map>
Possible object storage location types

GetModelsModelCollectionItemTestStrategyTestingDatasetLocationDetail

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

GetModelsModelCollectionItemTestStrategyValidationDataset

DatasetId This property is required. string
Data Science Labelling Service OCID
DatasetType This property is required. string
Possible data sets
LocationDetails This property is required. List<GetModelsModelCollectionItemTestStrategyValidationDatasetLocationDetail>
Possible object storage location types
DatasetId This property is required. string
Data Science Labelling Service OCID
DatasetType This property is required. string
Possible data sets
LocationDetails This property is required. []GetModelsModelCollectionItemTestStrategyValidationDatasetLocationDetail
Possible object storage location types
datasetId This property is required. String
Data Science Labelling Service OCID
datasetType This property is required. String
Possible data sets
locationDetails This property is required. List<GetModelsModelCollectionItemTestStrategyValidationDatasetLocationDetail>
Possible object storage location types
datasetId This property is required. string
Data Science Labelling Service OCID
datasetType This property is required. string
Possible data sets
locationDetails This property is required. GetModelsModelCollectionItemTestStrategyValidationDatasetLocationDetail[]
Possible object storage location types
dataset_id This property is required. str
Data Science Labelling Service OCID
dataset_type This property is required. str
Possible data sets
location_details This property is required. Sequence[ailanguage.GetModelsModelCollectionItemTestStrategyValidationDatasetLocationDetail]
Possible object storage location types
datasetId This property is required. String
Data Science Labelling Service OCID
datasetType This property is required. String
Possible data sets
locationDetails This property is required. List<Property Map>
Possible object storage location types

GetModelsModelCollectionItemTestStrategyValidationDatasetLocationDetail

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

GetModelsModelCollectionItemTrainingDataset

DatasetId This property is required. string
Data Science Labelling Service OCID
DatasetType This property is required. string
Possible data sets
LocationDetails This property is required. List<GetModelsModelCollectionItemTrainingDatasetLocationDetail>
Possible object storage location types
DatasetId This property is required. string
Data Science Labelling Service OCID
DatasetType This property is required. string
Possible data sets
LocationDetails This property is required. []GetModelsModelCollectionItemTrainingDatasetLocationDetail
Possible object storage location types
datasetId This property is required. String
Data Science Labelling Service OCID
datasetType This property is required. String
Possible data sets
locationDetails This property is required. List<GetModelsModelCollectionItemTrainingDatasetLocationDetail>
Possible object storage location types
datasetId This property is required. string
Data Science Labelling Service OCID
datasetType This property is required. string
Possible data sets
locationDetails This property is required. GetModelsModelCollectionItemTrainingDatasetLocationDetail[]
Possible object storage location types
dataset_id This property is required. str
Data Science Labelling Service OCID
dataset_type This property is required. str
Possible data sets
location_details This property is required. Sequence[ailanguage.GetModelsModelCollectionItemTrainingDatasetLocationDetail]
Possible object storage location types
datasetId This property is required. String
Data Science Labelling Service OCID
datasetType This property is required. String
Possible data sets
locationDetails This property is required. List<Property Map>
Possible object storage location types

GetModelsModelCollectionItemTrainingDatasetLocationDetail

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