This data source provides details about a specific Model resource in Oracle Cloud Infrastructure Ai Language service.
Gets a model by identifier
data "oci_ai_language_model" "test_model" {
#Required
model_id = oci_ai_language_model.test_model.id
}
The following arguments are supported:
model_id
- (Required) unique model OCID.The following attributes are exported:
compartment_id
- The OCID for the model's compartment.defined_tags
- Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description
- A short description of the Model.display_name
- 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
- model training results of different models
class_metrics
- List of text classification metrics
f1
- F1-score, is a measure of a model’s accuracy on a datasetlabel
- Text classification labelprecision
- 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
- 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 of samples in the test setconfusion_matrix
- class level confusion matrix
matrix
- confusion matrix dataentity_metrics
- List of entity metrics
f1
- F1-score, is a measure of a model’s accuracy on a datasetlabel
- Entity labelprecision
- 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
- 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.labels
- labelsmetrics
- Model level named entity recognition metrics
accuracy
- The fraction of the labels that were correctly recognised .macro_f1
- F1-score, is a measure of a model’s accuracy on a datasetmacro_precision
- 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
- 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
- F1-score, is a measure of a model’s accuracy on a datasetmicro_precision
- 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
- 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
- F1-score, is a measure of a model’s accuracy on a datasetweighted_precision
- 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
- 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.model_type
- Model typefreeform_tags
- Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id
- Unique identifier model OCID of a model that is immutable on creationlifecycle_details
- 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
- Possible model types
classification_mode
- possible text classification modes
classification_mode
- classification Modesversion
- Optional if nothing specified latest base model will be used for training. Supported versions can be found at /modelTypes/{modelType}language_code
- supported language default value is enmodel_type
- Model typeversion
- Optional pre trained model version. if nothing specified latest pre trained model will be used. Supported versions can be found at /modelTypes/{modelType} project_id
- The OCID of the project to associate with the model.state
- The state of the model.system_tags
- Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
test_strategy
- Possible strategy as testing and validation(optional) dataset.
strategy_type
- This information will define the test strategy different datasets for test and validation(optional) dataset. testing_dataset
- Possible data set type
dataset_id
- Data Science Labelling Service OCIDdataset_type
- Possible data setslocation_details
- Possible object storage location types
bucket
- Object storage bucket namelocation_type
- Possible object storage location typesnamespace
- Object storage namespaceobject_names
- Array of files which need to be processed in the bucketvalidation_dataset
- Possible data set type
dataset_id
- Data Science Labelling Service OCIDdataset_type
- Possible data setslocation_details
- Possible object storage location types
bucket
- Object storage bucket namelocation_type
- Possible object storage location typesnamespace
- Object storage namespaceobject_names
- Array of files which need to be processed in the buckettime_created
- The time the the model was created. An RFC3339 formatted datetime string.time_updated
- The time the model was updated. An RFC3339 formatted datetime string.training_dataset
- Possible data set type
dataset_id
- Data Science Labelling Service OCIDdataset_type
- Possible data setslocation_details
- Possible object storage location types
bucket
- Object storage bucket namelocation_type
- Possible object storage location typesnamespace
- Object storage namespaceobject_names
- Array of files which need to be processed in the bucketversion
- 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. <