oci.AiLanguage.getModel

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func GetModel(ctx *Context, args *GetModelArgs, opts ...InvokeOption) (*GetModelResult, error)
func GetModelOutput(ctx *Context, args *GetModelOutputArgs, opts ...InvokeOption) GetModelResultOutput

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

Id string

Unique identifier model OCID of a model that is immutable on creation

Id string

Unique identifier model OCID of a model that is immutable on creation

id String

Unique identifier model OCID of a model that is immutable on creation

id string

Unique identifier model OCID of a model that is immutable on creation

id str

Unique identifier model OCID of a model that is immutable on creation

id String

Unique identifier model OCID of a model that is immutable on creation

CompartmentId string

The OCID for the model's compartment.

DefinedTags Dictionary<string, object>

Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}

Description string

A short description of the Model.

DisplayName string

A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.

EvaluationResults List<GetModelEvaluationResult>

model training results of different models

FreeformTags Dictionary<string, object>

Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}

Id string

Unique identifier model OCID of a model that is immutable on creation

LifecycleDetails string

A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.

ModelDetails List<GetModelModelDetail>

Possible model types

ProjectId string

The OCID of the project to associate with the model.

State string

The state of the model.

SystemTags Dictionary<string, object>

Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}

TestStrategies List<GetModelTestStrategy>

Possible strategy as testing and validation(optional) dataset.

TimeCreated string

The time the the model was created. An RFC3339 formatted datetime string.

TimeUpdated string

The time the model was updated. An RFC3339 formatted datetime string.

TrainingDatasets List<GetModelTrainingDataset>

Possible data set type

Version string

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

CompartmentId string

The OCID for the model's compartment.

DefinedTags map[string]interface{}

Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}

Description string

A short description of the Model.

DisplayName string

A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.

EvaluationResults []GetModelEvaluationResult

model training results of different models

FreeformTags map[string]interface{}

Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}

Id string

Unique identifier model OCID of a model that is immutable on creation

LifecycleDetails string

A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.

ModelDetails []GetModelModelDetail

Possible model types

ProjectId string

The OCID of the project to associate with the model.

State string

The state of the model.

SystemTags map[string]interface{}

Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}

TestStrategies []GetModelTestStrategy

Possible strategy as testing and validation(optional) dataset.

TimeCreated string

The time the the model was created. An RFC3339 formatted datetime string.

TimeUpdated string

The time the model was updated. An RFC3339 formatted datetime string.

TrainingDatasets []GetModelTrainingDataset

Possible data set type

Version string

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

compartmentId String

The OCID for the model's compartment.

definedTags Map<String,Object>

Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}

description String

A short description of the Model.

displayName String

A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.

evaluationResults List<GetModelEvaluationResult>

model training results of different models

freeformTags Map<String,Object>

Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}

id String

Unique identifier model OCID of a model that is immutable on creation

lifecycleDetails String

A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.

modelDetails List<GetModelModelDetail>

Possible model types

projectId String

The OCID of the project to associate with the model.

state String

The state of the model.

systemTags Map<String,Object>

Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}

testStrategies List<GetModelTestStrategy>

Possible strategy as testing and validation(optional) dataset.

timeCreated String

The time the the model was created. An RFC3339 formatted datetime string.

timeUpdated String

The time the model was updated. An RFC3339 formatted datetime string.

trainingDatasets List<GetModelTrainingDataset>

Possible data set type

version String

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

compartmentId string

The OCID for the model's compartment.

definedTags {[key: string]: any}

Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}

description string

A short description of the Model.

displayName string

A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.

evaluationResults GetModelEvaluationResult[]

model training results of different models

freeformTags {[key: string]: any}

Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}

id string

Unique identifier model OCID of a model that is immutable on creation

lifecycleDetails string

A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.

modelDetails GetModelModelDetail[]

Possible model types

projectId string

The OCID of the project to associate with the model.

state string

The state of the model.

systemTags {[key: string]: any}

Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}

testStrategies GetModelTestStrategy[]

Possible strategy as testing and validation(optional) dataset.

timeCreated string

The time the the model was created. An RFC3339 formatted datetime string.

timeUpdated string

The time the model was updated. An RFC3339 formatted datetime string.

trainingDatasets GetModelTrainingDataset[]

Possible data set type

version string

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

compartment_id str

The OCID for the model's compartment.

defined_tags Mapping[str, Any]

Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}

description str

A short description of the Model.

display_name str

A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.

evaluation_results Sequence[ailanguage.GetModelEvaluationResult]

model training results of different models

freeform_tags Mapping[str, Any]

Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}

id str

Unique identifier model OCID of a model that is immutable on creation

lifecycle_details str

A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.

model_details Sequence[ailanguage.GetModelModelDetail]

Possible model types

project_id str

The OCID of the project to associate with the model.

state str

The state of the model.

system_tags Mapping[str, Any]

Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}

test_strategies Sequence[ailanguage.GetModelTestStrategy]

Possible strategy as testing and validation(optional) dataset.

time_created str

The time the the model was created. An RFC3339 formatted datetime string.

time_updated str

The time the model was updated. An RFC3339 formatted datetime string.

training_datasets Sequence[ailanguage.GetModelTrainingDataset]

Possible data set type

version str

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

compartmentId String

The OCID for the model's compartment.

definedTags Map<Any>

Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}

description String

A short description of the Model.

displayName String

A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.

evaluationResults List<Property Map>

model training results of different models

freeformTags Map<Any>

Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}

id String

Unique identifier model OCID of a model that is immutable on creation

lifecycleDetails String

A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.

modelDetails List<Property Map>

Possible model types

projectId String

The OCID of the project to associate with the model.

state String

The state of the model.

systemTags Map<Any>

Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}

testStrategies List<Property Map>

Possible strategy as testing and validation(optional) dataset.

timeCreated String

The time the the model was created. An RFC3339 formatted datetime string.

timeUpdated String

The time the model was updated. An RFC3339 formatted datetime string.

trainingDatasets List<Property Map>

Possible data set type

version String

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

ClassMetrics List<GetModelEvaluationResultClassMetric>

List of text classification metrics

ConfusionMatrix string

class level confusion matrix

EntityMetrics List<GetModelEvaluationResultEntityMetric>

List of entity metrics

Labels List<string>

labels

Metrics List<GetModelEvaluationResultMetric>

Model level named entity recognition metrics

ModelType string

Model type

ClassMetrics []GetModelEvaluationResultClassMetric

List of text classification metrics

ConfusionMatrix string

class level confusion matrix

EntityMetrics []GetModelEvaluationResultEntityMetric

List of entity metrics

Labels []string

labels

Metrics []GetModelEvaluationResultMetric

Model level named entity recognition metrics

ModelType string

Model type

classMetrics List<GetModelEvaluationResultClassMetric>

List of text classification metrics

confusionMatrix String

class level confusion matrix

entityMetrics List<GetModelEvaluationResultEntityMetric>

List of entity metrics

labels List<String>

labels

metrics List<GetModelEvaluationResultMetric>

Model level named entity recognition metrics

modelType String

Model type

classMetrics GetModelEvaluationResultClassMetric[]

List of text classification metrics

confusionMatrix string

class level confusion matrix

entityMetrics GetModelEvaluationResultEntityMetric[]

List of entity metrics

labels string[]

labels

metrics GetModelEvaluationResultMetric[]

Model level named entity recognition metrics

modelType string

Model type

class_metrics Sequence[ailanguage.GetModelEvaluationResultClassMetric]

List of text classification metrics

confusion_matrix str

class level confusion matrix

entity_metrics Sequence[ailanguage.GetModelEvaluationResultEntityMetric]

List of entity metrics

labels Sequence[str]

labels

metrics Sequence[ailanguage.GetModelEvaluationResultMetric]

Model level named entity recognition metrics

model_type str

Model type

classMetrics List<Property Map>

List of text classification metrics

confusionMatrix String

class level confusion matrix

entityMetrics List<Property Map>

List of entity metrics

labels List<String>

labels

metrics List<Property Map>

Model level named entity recognition metrics

modelType String

Model type

F1 double

F1-score, is a measure of a model’s accuracy on a dataset

Label string

Entity label

Precision double

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

Recall double

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

Support double

number of samples in the test set

F1 float64

F1-score, is a measure of a model’s accuracy on a dataset

Label string

Entity label

Precision float64

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

Recall float64

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

Support float64

number of samples in the test set

f1 Double

F1-score, is a measure of a model’s accuracy on a dataset

label String

Entity label

precision Double

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

recall Double

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

support Double

number of samples in the test set

f1 number

F1-score, is a measure of a model’s accuracy on a dataset

label string

Entity label

precision number

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

recall number

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

support number

number of samples in the test set

f1 float

F1-score, is a measure of a model’s accuracy on a dataset

label str

Entity label

precision float

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

recall float

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

support float

number of samples in the test set

f1 Number

F1-score, is a measure of a model’s accuracy on a dataset

label String

Entity label

precision Number

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

recall Number

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

support Number

number of samples in the test set

F1 double

F1-score, is a measure of a model’s accuracy on a dataset

Label string

Entity label

Precision double

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

Recall double

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

F1 float64

F1-score, is a measure of a model’s accuracy on a dataset

Label string

Entity label

Precision float64

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

Recall float64

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

f1 Double

F1-score, is a measure of a model’s accuracy on a dataset

label String

Entity label

precision Double

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

recall Double

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

f1 number

F1-score, is a measure of a model’s accuracy on a dataset

label string

Entity label

precision number

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

recall number

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

f1 float

F1-score, is a measure of a model’s accuracy on a dataset

label str

Entity label

precision float

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

recall float

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

f1 Number

F1-score, is a measure of a model’s accuracy on a dataset

label String

Entity label

precision Number

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

recall Number

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

Accuracy double

The fraction of the labels that were correctly recognised .

MacroF1 double

F1-score, is a measure of a model’s accuracy on a dataset

MacroPrecision double

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

MacroRecall double

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

MicroF1 double

F1-score, is a measure of a model’s accuracy on a dataset

MicroPrecision double

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

MicroRecall double

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

WeightedF1 double

F1-score, is a measure of a model’s accuracy on a dataset

WeightedPrecision double

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

WeightedRecall double

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

Accuracy float64

The fraction of the labels that were correctly recognised .

MacroF1 float64

F1-score, is a measure of a model’s accuracy on a dataset

MacroPrecision float64

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

MacroRecall float64

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

MicroF1 float64

F1-score, is a measure of a model’s accuracy on a dataset

MicroPrecision float64

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

MicroRecall float64

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

WeightedF1 float64

F1-score, is a measure of a model’s accuracy on a dataset

WeightedPrecision float64

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

WeightedRecall float64

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

accuracy Double

The fraction of the labels that were correctly recognised .

macroF1 Double

F1-score, is a measure of a model’s accuracy on a dataset

macroPrecision Double

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

macroRecall Double

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

microF1 Double

F1-score, is a measure of a model’s accuracy on a dataset

microPrecision Double

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

microRecall Double

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

weightedF1 Double

F1-score, is a measure of a model’s accuracy on a dataset

weightedPrecision Double

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

weightedRecall Double

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

accuracy number

The fraction of the labels that were correctly recognised .

macroF1 number

F1-score, is a measure of a model’s accuracy on a dataset

macroPrecision number

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

macroRecall number

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

microF1 number

F1-score, is a measure of a model’s accuracy on a dataset

microPrecision number

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

microRecall number

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

weightedF1 number

F1-score, is a measure of a model’s accuracy on a dataset

weightedPrecision number

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

weightedRecall number

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

accuracy float

The fraction of the labels that were correctly recognised .

macro_f1 float

F1-score, is a measure of a model’s accuracy on a dataset

macro_precision float

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

macro_recall float

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

micro_f1 float

F1-score, is a measure of a model’s accuracy on a dataset

micro_precision float

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

micro_recall float

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

weighted_f1 float

F1-score, is a measure of a model’s accuracy on a dataset

weighted_precision float

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

weighted_recall float

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

accuracy Number

The fraction of the labels that were correctly recognised .

macroF1 Number

F1-score, is a measure of a model’s accuracy on a dataset

macroPrecision Number

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

macroRecall Number

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

microF1 Number

F1-score, is a measure of a model’s accuracy on a dataset

microPrecision Number

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

microRecall Number

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

weightedF1 Number

F1-score, is a measure of a model’s accuracy on a dataset

weightedPrecision Number

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

weightedRecall Number

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

ClassificationModes List<GetModelModelDetailClassificationMode>

classification Modes

LanguageCode string

supported language default value is en

ModelType string

Model type

Version string

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

ClassificationModes []GetModelModelDetailClassificationMode

classification Modes

LanguageCode string

supported language default value is en

ModelType string

Model type

Version string

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

classificationModes List<GetModelModelDetailClassificationMode>

classification Modes

languageCode String

supported language default value is en

modelType String

Model type

version String

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

classificationModes GetModelModelDetailClassificationMode[]

classification Modes

languageCode string

supported language default value is en

modelType string

Model type

version string

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

classification_modes Sequence[ailanguage.GetModelModelDetailClassificationMode]

classification Modes

language_code str

supported language default value is en

model_type str

Model type

version str

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

classificationModes List<Property Map>

classification Modes

languageCode String

supported language default value is en

modelType String

Model type

version String

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

ClassificationMode string

classification Modes

Version string

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

ClassificationMode string

classification Modes

Version string

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

classificationMode String

classification Modes

version String

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

classificationMode string

classification Modes

version string

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

classification_mode str

classification Modes

version str

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

classificationMode String

classification Modes

version String

For pre trained models this will identify model type version used for model creation For custom identifying the model by model id is difficult. This param provides ease of use for end customer. <>::<>_<>::<> ex: ai-lang::NER_V1::CUSTOM-V0

StrategyType string

This information will define the test strategy different datasets for test and validation(optional) dataset.

TestingDatasets List<GetModelTestStrategyTestingDataset>

Possible data set type

ValidationDatasets List<GetModelTestStrategyValidationDataset>

Possible data set type

StrategyType string

This information will define the test strategy different datasets for test and validation(optional) dataset.

TestingDatasets []GetModelTestStrategyTestingDataset

Possible data set type

ValidationDatasets []GetModelTestStrategyValidationDataset

Possible data set type

strategyType String

This information will define the test strategy different datasets for test and validation(optional) dataset.

testingDatasets List<GetModelTestStrategyTestingDataset>

Possible data set type

validationDatasets List<GetModelTestStrategyValidationDataset>

Possible data set type

strategyType string

This information will define the test strategy different datasets for test and validation(optional) dataset.

testingDatasets GetModelTestStrategyTestingDataset[]

Possible data set type

validationDatasets GetModelTestStrategyValidationDataset[]

Possible data set type

strategy_type str

This information will define the test strategy different datasets for test and validation(optional) dataset.

testing_datasets Sequence[ailanguage.GetModelTestStrategyTestingDataset]

Possible data set type

validation_datasets Sequence[ailanguage.GetModelTestStrategyValidationDataset]

Possible data set type

strategyType String

This information will define the test strategy different datasets for test and validation(optional) dataset.

testingDatasets List<Property Map>

Possible data set type

validationDatasets List<Property Map>

Possible data set type

DatasetId string

Data Science Labelling Service OCID

DatasetType string

Possible data sets

LocationDetails List<GetModelTestStrategyTestingDatasetLocationDetail>

Possible object storage location types

DatasetId string

Data Science Labelling Service OCID

DatasetType string

Possible data sets

LocationDetails []GetModelTestStrategyTestingDatasetLocationDetail

Possible object storage location types

datasetId String

Data Science Labelling Service OCID

datasetType String

Possible data sets

locationDetails List<GetModelTestStrategyTestingDatasetLocationDetail>

Possible object storage location types

datasetId string

Data Science Labelling Service OCID

datasetType string

Possible data sets

locationDetails GetModelTestStrategyTestingDatasetLocationDetail[]

Possible object storage location types

dataset_id str

Data Science Labelling Service OCID

dataset_type str

Possible data sets

location_details Sequence[ailanguage.GetModelTestStrategyTestingDatasetLocationDetail]

Possible object storage location types

datasetId String

Data Science Labelling Service OCID

datasetType String

Possible data sets

locationDetails List<Property Map>

Possible object storage location types

Bucket string

Object storage bucket name

LocationType string

Possible object storage location types

Namespace string

Object storage namespace

ObjectNames List<string>

Array of files which need to be processed in the bucket

Bucket string

Object storage bucket name

LocationType string

Possible object storage location types

Namespace string

Object storage namespace

ObjectNames []string

Array of files which need to be processed in the bucket

bucket String

Object storage bucket name

locationType String

Possible object storage location types

namespace String

Object storage namespace

objectNames List<String>

Array of files which need to be processed in the bucket

bucket string

Object storage bucket name

locationType string

Possible object storage location types

namespace string

Object storage namespace

objectNames string[]

Array of files which need to be processed in the bucket

bucket str

Object storage bucket name

location_type str

Possible object storage location types

namespace str

Object storage namespace

object_names Sequence[str]

Array of files which need to be processed in the bucket

bucket String

Object storage bucket name

locationType String

Possible object storage location types

namespace String

Object storage namespace

objectNames List<String>

Array of files which need to be processed in the bucket

DatasetId string

Data Science Labelling Service OCID

DatasetType string

Possible data sets

LocationDetails List<GetModelTestStrategyValidationDatasetLocationDetail>

Possible object storage location types

DatasetId string

Data Science Labelling Service OCID

DatasetType string

Possible data sets

LocationDetails []GetModelTestStrategyValidationDatasetLocationDetail

Possible object storage location types

datasetId String

Data Science Labelling Service OCID

datasetType String

Possible data sets

locationDetails List<GetModelTestStrategyValidationDatasetLocationDetail>

Possible object storage location types

datasetId string

Data Science Labelling Service OCID

datasetType string

Possible data sets

locationDetails GetModelTestStrategyValidationDatasetLocationDetail[]

Possible object storage location types

dataset_id str

Data Science Labelling Service OCID

dataset_type str

Possible data sets

location_details Sequence[ailanguage.GetModelTestStrategyValidationDatasetLocationDetail]

Possible object storage location types

datasetId String

Data Science Labelling Service OCID

datasetType String

Possible data sets

locationDetails List<Property Map>

Possible object storage location types

Bucket string

Object storage bucket name

LocationType string

Possible object storage location types

Namespace string

Object storage namespace

ObjectNames List<string>

Array of files which need to be processed in the bucket

Bucket string

Object storage bucket name

LocationType string

Possible object storage location types

Namespace string

Object storage namespace

ObjectNames []string

Array of files which need to be processed in the bucket

bucket String

Object storage bucket name

locationType String

Possible object storage location types

namespace String

Object storage namespace

objectNames List<String>

Array of files which need to be processed in the bucket

bucket string

Object storage bucket name

locationType string

Possible object storage location types

namespace string

Object storage namespace

objectNames string[]

Array of files which need to be processed in the bucket

bucket str

Object storage bucket name

location_type str

Possible object storage location types

namespace str

Object storage namespace

object_names Sequence[str]

Array of files which need to be processed in the bucket

bucket String

Object storage bucket name

locationType String

Possible object storage location types

namespace String

Object storage namespace

objectNames List<String>

Array of files which need to be processed in the bucket

DatasetId string

Data Science Labelling Service OCID

DatasetType string

Possible data sets

LocationDetails List<GetModelTrainingDatasetLocationDetail>

Possible object storage location types

DatasetId string

Data Science Labelling Service OCID

DatasetType string

Possible data sets

LocationDetails []GetModelTrainingDatasetLocationDetail

Possible object storage location types

datasetId String

Data Science Labelling Service OCID

datasetType String

Possible data sets

locationDetails List<GetModelTrainingDatasetLocationDetail>

Possible object storage location types

datasetId string

Data Science Labelling Service OCID

datasetType string

Possible data sets

locationDetails GetModelTrainingDatasetLocationDetail[]

Possible object storage location types

dataset_id str

Data Science Labelling Service OCID

dataset_type str

Possible data sets

location_details Sequence[ailanguage.GetModelTrainingDatasetLocationDetail]

Possible object storage location types

datasetId String

Data Science Labelling Service OCID

datasetType String

Possible data sets

locationDetails List<Property Map>

Possible object storage location types

Bucket string

Object storage bucket name

LocationType string

Possible object storage location types

Namespace string

Object storage namespace

ObjectNames List<string>

Array of files which need to be processed in the bucket

Bucket string

Object storage bucket name

LocationType string

Possible object storage location types

Namespace string

Object storage namespace

ObjectNames []string

Array of files which need to be processed in the bucket

bucket String

Object storage bucket name

locationType String

Possible object storage location types

namespace String

Object storage namespace

objectNames List<String>

Array of files which need to be processed in the bucket

bucket string

Object storage bucket name

locationType string

Possible object storage location types

namespace string

Object storage namespace

objectNames string[]

Array of files which need to be processed in the bucket

bucket str

Object storage bucket name

location_type str

Possible object storage location types

namespace str

Object storage namespace

object_names Sequence[str]

Array of files which need to be processed in the bucket

bucket String

Object storage bucket name

locationType String

Possible object storage location types

namespace String

Object storage namespace

objectNames List<String>

Array of files which need to be processed in the bucket