Oracle Cloud Infrastructure v2.30.0 published on Monday, Apr 14, 2025 by Pulumi
oci.AiLanguage.getModels
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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}
Using getModels
Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.
function getModels(args: GetModelsArgs, opts?: InvokeOptions): Promise<GetModelsResult>
function getModelsOutput(args: GetModelsOutputArgs, opts?: InvokeOptions): Output<GetModelsResult>
def get_models(compartment_id: Optional[str] = None,
display_name: Optional[str] = None,
filters: Optional[Sequence[_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:
- Compartment
Id This property is required. string - The ID of the compartment in which to list resources.
- Display
Name string - A filter to return only resources that match the entire display name given.
- Filters
Changes to this property will trigger replacement.
Models Filter> - Id string
- Unique identifier model OCID of a model that is immutable on creation
- Project
Id 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. string - The ID of the compartment in which to list resources.
- Display
Name string - A filter to return only resources that match the entire display name given.
- Filters
Changes to this property will trigger replacement.
Models Filter - Id string
- Unique identifier model OCID of a model that is immutable on creation
- Project
Id 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. String - The ID of the compartment in which to list resources.
- display
Name String - A filter to return only resources that match the entire display name given.
- filters
Changes to this property will trigger replacement.
Models Filter> - id String
- Unique identifier model OCID of a model that is immutable on creation
- project
Id 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. string - The ID of the compartment in which to list resources.
- display
Name string - A filter to return only resources that match the entire display name given.
- filters
Changes to this property will trigger replacement.
Models Filter[] - id string
- Unique identifier model OCID of a model that is immutable on creation
- project
Id 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.
Get Models Filter] - 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.
- compartment
Id This property is required. String - The ID of the compartment in which to list resources.
- display
Name String - A filter to return only resources that match the entire display name given.
- filters
Changes to this property will trigger replacement.
- id String
- Unique identifier model OCID of a model that is immutable on creation
- project
Id 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:
- Compartment
Id string - The OCID for the model's compartment.
- Model
Collections List<GetModels Model Collection> - The list of model_collection.
- Display
Name 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<Get
Models Filter> - Id string
- Unique identifier model OCID of a model that is immutable on creation
- Project
Id string - The OCID of the project to associate with the model.
- State string
- The state of the model.
- Compartment
Id string - The OCID for the model's compartment.
- Model
Collections []GetModels Model Collection - The list of model_collection.
- Display
Name 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
[]Get
Models Filter - Id string
- Unique identifier model OCID of a model that is immutable on creation
- Project
Id string - The OCID of the project to associate with the model.
- State string
- The state of the model.
- compartment
Id String - The OCID for the model's compartment.
- model
Collections List<GetModels Model Collection> - The list of model_collection.
- display
Name 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<Get
Models Filter> - id String
- Unique identifier model OCID of a model that is immutable on creation
- project
Id String - The OCID of the project to associate with the model.
- state String
- The state of the model.
- compartment
Id string - The OCID for the model's compartment.
- model
Collections GetModels Model Collection[] - The list of model_collection.
- display
Name 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
Get
Models Filter[] - id string
- Unique identifier model OCID of a model that is immutable on creation
- project
Id 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.Get Models Model Collection] - 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.
Get Models Filter] - 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.
- compartment
Id String - The OCID for the model's compartment.
- model
Collections List<Property Map> - The list of model_collection.
- display
Name 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
- project
Id String - The OCID of the project to associate with the model.
- state String
- The state of the model.
Supporting Types
GetModelsFilter
GetModelsModelCollection
- Items
This property is required. List<GetModels Model Collection Item>
- Items
This property is required. []GetModels Model Collection Item
- items
This property is required. List<GetModels Model Collection Item>
- items
This property is required. GetModels Model Collection Item[]
- items
This property is required. Sequence[ailanguage.Get Models Model Collection Item]
- items
This property is required. List<Property Map>
GetModelsModelCollectionItem
- Compartment
Id This property is required. string - The ID of the compartment in which to list resources.
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.
- Display
Name This property is required. string - A filter to return only resources that match the entire display name given.
- Evaluation
Results This property is required. List<GetModels Model Collection Item Evaluation Result> - model training results of different models
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
- Lifecycle
Details 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.
- Model
Details This property is required. List<GetModels Model Collection Item Model Detail> - Possible model types
- Project
Id 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.
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"}
- Test
Strategies This property is required. List<GetModels Model Collection Item Test Strategy> - Possible strategy as testing and validation(optional) dataset.
- Time
Created This property is required. string - The time the the model was created. An RFC3339 formatted datetime string.
- Time
Updated This property is required. string - The time the model was updated. An RFC3339 formatted datetime string.
- Training
Datasets This property is required. List<GetModels Model Collection Item Training Dataset> - 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. string - The ID of the compartment in which to list resources.
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.
- Display
Name This property is required. string - A filter to return only resources that match the entire display name given.
- Evaluation
Results This property is required. []GetModels Model Collection Item Evaluation Result - model training results of different models
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
- Lifecycle
Details 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.
- Model
Details This property is required. []GetModels Model Collection Item Model Detail - Possible model types
- Project
Id 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.
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"}
- Test
Strategies This property is required. []GetModels Model Collection Item Test Strategy - Possible strategy as testing and validation(optional) dataset.
- Time
Created This property is required. string - The time the the model was created. An RFC3339 formatted datetime string.
- Time
Updated This property is required. string - The time the model was updated. An RFC3339 formatted datetime string.
- Training
Datasets This property is required. []GetModels Model Collection Item Training Dataset - 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. String - The ID of the compartment in which to list resources.
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.
- display
Name This property is required. String - A filter to return only resources that match the entire display name given.
- evaluation
Results This property is required. List<GetModels Model Collection Item Evaluation Result> - model training results of different models
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
- lifecycle
Details 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.
- model
Details This property is required. List<GetModels Model Collection Item Model Detail> - Possible model types
- project
Id 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.
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"}
- test
Strategies This property is required. List<GetModels Model Collection Item Test Strategy> - Possible strategy as testing and validation(optional) dataset.
- time
Created This property is required. String - The time the the model was created. An RFC3339 formatted datetime string.
- time
Updated This property is required. String - The time the model was updated. An RFC3339 formatted datetime string.
- training
Datasets This property is required. List<GetModels Model Collection Item Training Dataset> - 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. string - The ID of the compartment in which to list resources.
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.
- display
Name This property is required. string - A filter to return only resources that match the entire display name given.
- evaluation
Results This property is required. GetModels Model Collection Item Evaluation Result[] - model training results of different models
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
- lifecycle
Details 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.
- model
Details This property is required. GetModels Model Collection Item Model Detail[] - Possible model types
- project
Id 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.
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"}
- test
Strategies This property is required. GetModels Model Collection Item Test Strategy[] - Possible strategy as testing and validation(optional) dataset.
- time
Created This property is required. string - The time the the model was created. An RFC3339 formatted datetime string.
- time
Updated This property is required. string - The time the model was updated. An RFC3339 formatted datetime string.
- training
Datasets This property is required. GetModels Model Collection Item Training Dataset[] - 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.
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.Get Models Model Collection Item Evaluation Result] - model training results of different models
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.Get Models Model Collection Item Model Detail] - 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.
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.Get Models Model Collection Item Test Strategy] - 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.Get Models Model Collection Item Training Dataset] - 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
- compartment
Id This property is required. String - The ID of the compartment in which to list resources.
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.
- display
Name This property is required. String - A filter to return only resources that match the entire display name given.
- evaluation
Results This property is required. List<Property Map> - model training results of different models
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
- lifecycle
Details 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.
- model
Details This property is required. List<Property Map> - Possible model types
- project
Id 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.
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"}
- test
Strategies This property is required. List<Property Map> - Possible strategy as testing and validation(optional) dataset.
- time
Created This property is required. String - The time the the model was created. An RFC3339 formatted datetime string.
- time
Updated This property is required. String - The time the model was updated. An RFC3339 formatted datetime string.
- training
Datasets 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
- Class
Metrics This property is required. List<GetModels Model Collection Item Evaluation Result Class Metric> - List of text classification metrics
- Confusion
Matrix This property is required. string - class level confusion matrix
- Entity
Metrics This property is required. List<GetModels Model Collection Item Evaluation Result Entity Metric> - List of entity metrics
- Labels
This property is required. List<string> - labels
- Metrics
This property is required. List<GetModels Model Collection Item Evaluation Result Metric> - Model level named entity recognition metrics
- Model
Type This property is required. string - Model type
- Class
Metrics This property is required. []GetModels Model Collection Item Evaluation Result Class Metric - List of text classification metrics
- Confusion
Matrix This property is required. string - class level confusion matrix
- Entity
Metrics This property is required. []GetModels Model Collection Item Evaluation Result Entity Metric - List of entity metrics
- Labels
This property is required. []string - labels
- Metrics
This property is required. []GetModels Model Collection Item Evaluation Result Metric - Model level named entity recognition metrics
- Model
Type This property is required. string - Model type
- class
Metrics This property is required. List<GetModels Model Collection Item Evaluation Result Class Metric> - List of text classification metrics
- confusion
Matrix This property is required. String - class level confusion matrix
- entity
Metrics This property is required. List<GetModels Model Collection Item Evaluation Result Entity Metric> - List of entity metrics
- labels
This property is required. List<String> - labels
- metrics
This property is required. List<GetModels Model Collection Item Evaluation Result Metric> - Model level named entity recognition metrics
- model
Type This property is required. String - Model type
- class
Metrics This property is required. GetModels Model Collection Item Evaluation Result Class Metric[] - List of text classification metrics
- confusion
Matrix This property is required. string - class level confusion matrix
- entity
Metrics This property is required. GetModels Model Collection Item Evaluation Result Entity Metric[] - List of entity metrics
- labels
This property is required. string[] - labels
- metrics
This property is required. GetModels Model Collection Item Evaluation Result Metric[] - Model level named entity recognition metrics
- model
Type This property is required. string - Model type
- class_
metrics This property is required. Sequence[ailanguage.Get Models Model Collection Item Evaluation Result Class Metric] - List of text classification metrics
- confusion_
matrix This property is required. str - class level confusion matrix
- entity_
metrics This property is required. Sequence[ailanguage.Get Models Model Collection Item Evaluation Result Entity Metric] - List of entity metrics
- labels
This property is required. Sequence[str] - labels
- metrics
This property is required. Sequence[ailanguage.Get Models Model Collection Item Evaluation Result Metric] - Model level named entity recognition metrics
- model_
type This property is required. str - Model type
- class
Metrics This property is required. List<Property Map> - List of text classification metrics
- confusion
Matrix This property is required. String - class level confusion matrix
- entity
Metrics 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
- model
Type 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 .
- Macro
F1 This property is required. double - F1-score, is a measure of a model’s accuracy on a dataset
- Macro
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)
- Macro
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.
- Micro
F1 This property is required. double - F1-score, is a measure of a model’s accuracy on a dataset
- Micro
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)
- Micro
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.
- Weighted
F1 This property is required. double - F1-score, is a measure of a model’s accuracy on a dataset
- Weighted
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)
- Weighted
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.
- Accuracy
This property is required. float64 - The fraction of the labels that were correctly recognised .
- Macro
F1 This property is required. float64 - F1-score, is a measure of a model’s accuracy on a dataset
- Macro
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)
- Macro
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.
- Micro
F1 This property is required. float64 - F1-score, is a measure of a model’s accuracy on a dataset
- Micro
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)
- Micro
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.
- Weighted
F1 This property is required. float64 - F1-score, is a measure of a model’s accuracy on a dataset
- Weighted
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)
- Weighted
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.
- accuracy
This property is required. Double - The fraction of the labels that were correctly recognised .
- macro
F1 This property is required. Double - F1-score, is a measure of a model’s accuracy on a dataset
- macro
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)
- macro
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.
- micro
F1 This property is required. Double - F1-score, is a measure of a model’s accuracy on a dataset
- micro
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)
- micro
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.
- weighted
F1 This property is required. Double - F1-score, is a measure of a model’s accuracy on a dataset
- weighted
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)
- weighted
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.
- accuracy
This property is required. number - The fraction of the labels that were correctly recognised .
- macro
F1 This property is required. number - F1-score, is a measure of a model’s accuracy on a dataset
- macro
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)
- macro
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.
- micro
F1 This property is required. number - F1-score, is a measure of a model’s accuracy on a dataset
- micro
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)
- micro
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.
- weighted
F1 This property is required. number - F1-score, is a measure of a model’s accuracy on a dataset
- weighted
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)
- weighted
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.
- 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 .
- macro
F1 This property is required. Number - F1-score, is a measure of a model’s accuracy on a dataset
- macro
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)
- macro
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.
- micro
F1 This property is required. Number - F1-score, is a measure of a model’s accuracy on a dataset
- micro
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)
- micro
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.
- weighted
F1 This property is required. Number - F1-score, is a measure of a model’s accuracy on a dataset
- weighted
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)
- weighted
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.
GetModelsModelCollectionItemModelDetail
- Classification
Modes This property is required. List<GetModels Model Collection Item Model Detail Classification Mode> - classification Modes
- Language
Code This property is required. string - supported language default value is en
- Model
Type 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. []GetModels Model Collection Item Model Detail Classification Mode - classification Modes
- Language
Code This property is required. string - supported language default value is en
- Model
Type 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. List<GetModels Model Collection Item Model Detail Classification Mode> - classification Modes
- language
Code This property is required. String - supported language default value is en
- model
Type 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. GetModels Model Collection Item Model Detail Classification Mode[] - classification Modes
- language
Code This property is required. string - supported language default value is en
- model
Type 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.Get Models Model Collection Item Model Detail Classification Mode] - 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
- classification
Modes This property is required. List<Property Map> - classification Modes
- language
Code This property is required. String - supported language default value is en
- model
Type 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
- Classification
Mode 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. 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. 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. 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
- classification
Mode 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
- Strategy
Type This property is required. string - This information will define the test strategy different datasets for test and validation(optional) dataset.
- Testing
Datasets This property is required. List<GetModels Model Collection Item Test Strategy Testing Dataset> - Possible data set type
- Validation
Datasets This property is required. List<GetModels Model Collection Item Test Strategy Validation Dataset> - Possible data set type
- Strategy
Type This property is required. string - This information will define the test strategy different datasets for test and validation(optional) dataset.
- Testing
Datasets This property is required. []GetModels Model Collection Item Test Strategy Testing Dataset - Possible data set type
- Validation
Datasets This property is required. []GetModels Model Collection Item Test Strategy Validation Dataset - Possible data set type
- strategy
Type This property is required. String - This information will define the test strategy different datasets for test and validation(optional) dataset.
- testing
Datasets This property is required. List<GetModels Model Collection Item Test Strategy Testing Dataset> - Possible data set type
- validation
Datasets This property is required. List<GetModels Model Collection Item Test Strategy Validation Dataset> - Possible data set type
- strategy
Type This property is required. string - This information will define the test strategy different datasets for test and validation(optional) dataset.
- testing
Datasets This property is required. GetModels Model Collection Item Test Strategy Testing Dataset[] - Possible data set type
- validation
Datasets This property is required. GetModels Model Collection Item Test Strategy Validation Dataset[] - 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.Get Models Model Collection Item Test Strategy Testing Dataset] - Possible data set type
- validation_
datasets This property is required. Sequence[ailanguage.Get Models Model Collection Item Test Strategy Validation Dataset] - Possible data set type
- strategy
Type This property is required. String - This information will define the test strategy different datasets for test and validation(optional) dataset.
- testing
Datasets This property is required. List<Property Map> - Possible data set type
- validation
Datasets This property is required. List<Property Map> - Possible data set type
GetModelsModelCollectionItemTestStrategyTestingDataset
- Dataset
Id This property is required. string - Data Science Labelling Service OCID
- Dataset
Type This property is required. string - Possible data sets
- Location
Details This property is required. List<GetModels Model Collection Item Test Strategy Testing Dataset Location Detail> - Possible object storage location types
- Dataset
Id This property is required. string - Data Science Labelling Service OCID
- Dataset
Type This property is required. string - Possible data sets
- Location
Details This property is required. []GetModels Model Collection Item Test Strategy Testing Dataset Location Detail - Possible object storage location types
- dataset
Id This property is required. String - Data Science Labelling Service OCID
- dataset
Type This property is required. String - Possible data sets
- location
Details This property is required. List<GetModels Model Collection Item Test Strategy Testing Dataset Location Detail> - Possible object storage location types
- dataset
Id This property is required. string - Data Science Labelling Service OCID
- dataset
Type This property is required. string - Possible data sets
- location
Details This property is required. GetModels Model Collection Item Test Strategy Testing Dataset Location Detail[] - 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.Get Models Model Collection Item Test Strategy Testing Dataset Location Detail] - Possible object storage location types
- dataset
Id This property is required. String - Data Science Labelling Service OCID
- dataset
Type This property is required. String - Possible data sets
- location
Details This property is required. List<Property Map> - Possible object storage location types
GetModelsModelCollectionItemTestStrategyTestingDatasetLocationDetail
- Bucket
This property is required. string - Object storage bucket name
- Location
Type This property is required. string - Possible object storage location types
- Namespace
This property is required. string - Object storage namespace
- Object
Names 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
- Location
Type This property is required. string - Possible object storage location types
- Namespace
This property is required. string - Object storage namespace
- Object
Names 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
- location
Type This property is required. String - Possible object storage location types
- namespace
This property is required. String - Object storage namespace
- object
Names 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
- location
Type This property is required. string - Possible object storage location types
- namespace
This property is required. string - Object storage namespace
- object
Names 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
- location
Type This property is required. String - Possible object storage location types
- namespace
This property is required. String - Object storage namespace
- object
Names This property is required. List<String> - Array of files which need to be processed in the bucket
GetModelsModelCollectionItemTestStrategyValidationDataset
- Dataset
Id This property is required. string - Data Science Labelling Service OCID
- Dataset
Type This property is required. string - Possible data sets
- Location
Details This property is required. List<GetModels Model Collection Item Test Strategy Validation Dataset Location Detail> - Possible object storage location types
- Dataset
Id This property is required. string - Data Science Labelling Service OCID
- Dataset
Type This property is required. string - Possible data sets
- Location
Details This property is required. []GetModels Model Collection Item Test Strategy Validation Dataset Location Detail - Possible object storage location types
- dataset
Id This property is required. String - Data Science Labelling Service OCID
- dataset
Type This property is required. String - Possible data sets
- location
Details This property is required. List<GetModels Model Collection Item Test Strategy Validation Dataset Location Detail> - Possible object storage location types
- dataset
Id This property is required. string - Data Science Labelling Service OCID
- dataset
Type This property is required. string - Possible data sets
- location
Details This property is required. GetModels Model Collection Item Test Strategy Validation Dataset Location Detail[] - 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.Get Models Model Collection Item Test Strategy Validation Dataset Location Detail] - Possible object storage location types
- dataset
Id This property is required. String - Data Science Labelling Service OCID
- dataset
Type This property is required. String - Possible data sets
- location
Details This property is required. List<Property Map> - Possible object storage location types
GetModelsModelCollectionItemTestStrategyValidationDatasetLocationDetail
- Bucket
This property is required. string - Object storage bucket name
- Location
Type This property is required. string - Possible object storage location types
- Namespace
This property is required. string - Object storage namespace
- Object
Names 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
- Location
Type This property is required. string - Possible object storage location types
- Namespace
This property is required. string - Object storage namespace
- Object
Names 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
- location
Type This property is required. String - Possible object storage location types
- namespace
This property is required. String - Object storage namespace
- object
Names 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
- location
Type This property is required. string - Possible object storage location types
- namespace
This property is required. string - Object storage namespace
- object
Names 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
- location
Type This property is required. String - Possible object storage location types
- namespace
This property is required. String - Object storage namespace
- object
Names This property is required. List<String> - Array of files which need to be processed in the bucket
GetModelsModelCollectionItemTrainingDataset
- Dataset
Id This property is required. string - Data Science Labelling Service OCID
- Dataset
Type This property is required. string - Possible data sets
- Location
Details This property is required. List<GetModels Model Collection Item Training Dataset Location Detail> - Possible object storage location types
- Dataset
Id This property is required. string - Data Science Labelling Service OCID
- Dataset
Type This property is required. string - Possible data sets
- Location
Details This property is required. []GetModels Model Collection Item Training Dataset Location Detail - Possible object storage location types
- dataset
Id This property is required. String - Data Science Labelling Service OCID
- dataset
Type This property is required. String - Possible data sets
- location
Details This property is required. List<GetModels Model Collection Item Training Dataset Location Detail> - Possible object storage location types
- dataset
Id This property is required. string - Data Science Labelling Service OCID
- dataset
Type This property is required. string - Possible data sets
- location
Details This property is required. GetModels Model Collection Item Training Dataset Location Detail[] - 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.Get Models Model Collection Item Training Dataset Location Detail] - Possible object storage location types
- dataset
Id This property is required. String - Data Science Labelling Service OCID
- dataset
Type This property is required. String - Possible data sets
- location
Details This property is required. List<Property Map> - Possible object storage location types
GetModelsModelCollectionItemTrainingDatasetLocationDetail
- Bucket
This property is required. string - Object storage bucket name
- Location
Type This property is required. string - Possible object storage location types
- Namespace
This property is required. string - Object storage namespace
- Object
Names 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
- Location
Type This property is required. string - Possible object storage location types
- Namespace
This property is required. string - Object storage namespace
- Object
Names 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
- location
Type This property is required. String - Possible object storage location types
- namespace
This property is required. String - Object storage namespace
- object
Names 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
- location
Type This property is required. string - Possible object storage location types
- namespace
This property is required. string - Object storage namespace
- object
Names 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
- location
Type This property is required. String - Possible object storage location types
- namespace
This property is required. String - Object storage namespace
- object
Names 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.