- orphan
oracle.oci.oci_ai_language_model_facts -- Fetches details about one or multiple Model resources in Oracle Cloud Infrastructure
Note
This plugin is part of the oracle.oci collection (version 4.13.0).
You might already have this collection installed if you are using the ansible
package. It is not included in ansible-core
. To check whether it is installed, run ansible-galaxy collection list
.
To install it, use: ansible-galaxy collection install oracle.oci
.
To use it in a playbook, specify: oracle.oci.oci_ai_language_model_facts
.
2.9.0 of oracle.oci
- Fetches details about one or multiple Model resources in Oracle Cloud Infrastructure
- Returns a list of models.
- If model_id is specified, the details of a single Model will be returned.
The below requirements are needed on the host that executes this module.
- python >= 3.6
- Python SDK for Oracle Cloud Infrastructure https://oracle-cloud-infrastructure-python-sdk.readthedocs.io
Parameter | Choices/Defaults | Comments |
---|---|---|
api_user
string
|
The OCID of the user, on whose behalf, OCI APIs are invoked. If not set, then the value of the OCI_USER_ID environment variable, if any, is used. This option is required if the user is not specified through a configuration file (See
config_file_location ). To get the user's OCID, please refer https://docs.us-phoenix-1.oraclecloud.com/Content/API/Concepts/apisigningkey.htm. |
|
api_user_fingerprint
string
|
Fingerprint for the key pair being used. If not set, then the value of the OCI_USER_FINGERPRINT environment variable, if any, is used. This option is required if the key fingerprint is not specified through a configuration file (See
config_file_location ). To get the key pair's fingerprint value please refer https://docs.us-phoenix-1.oraclecloud.com/Content/API/Concepts/apisigningkey.htm. |
|
api_user_key_file
string
|
Full path and filename of the private key (in PEM format). If not set, then the value of the OCI_USER_KEY_FILE variable, if any, is used. This option is required if the private key is not specified through a configuration file (See
config_file_location ). If the key is encrypted with a pass-phrase, the api_user_key_pass_phrase option must also be provided. |
|
api_user_key_pass_phrase
string
|
Passphrase used by the key referenced in
api_user_key_file , if it is encrypted. If not set, then the value of the OCI_USER_KEY_PASS_PHRASE variable, if any, is used. This option is required if the key passphrase is not specified through a configuration file (See config_file_location ). |
|
auth_purpose
string
|
|
The auth purpose which can be used in conjunction with 'auth_type=instance_principal'. The default auth_purpose for instance_principal is None.
|
auth_type
string
|
|
The type of authentication to use for making API requests. By default
auth_type="api_key" based authentication is performed and the API key (see api_user_key_file) in your config file will be used. If this 'auth_type' module option is not specified, the value of the OCI_ANSIBLE_AUTH_TYPE, if any, is used. Use auth_type="instance_principal" to use instance principal based authentication when running ansible playbooks within an OCI compute instance. |
cert_bundle
string
|
The full path to a CA certificate bundle to be used for SSL verification. This will override the default CA certificate bundle. If not set, then the value of the OCI_ANSIBLE_CERT_BUNDLE variable, if any, is used.
|
|
compartment_id
string
|
The ID of the compartment in which to list resources.
Required to list multiple models.
|
|
config_file_location
string
|
Path to configuration file. If not set then the value of the OCI_CONFIG_FILE environment variable, if any, is used. Otherwise, defaults to ~/.oci/config.
|
|
config_profile_name
string
|
The profile to load from the config file referenced by
config_file_location . If not set, then the value of the OCI_CONFIG_PROFILE environment variable, if any, is used. Otherwise, defaults to the "DEFAULT" profile in config_file_location . |
|
display_name
string
|
A filter to return only resources that match the entire display name given.
aliases: name |
|
lifecycle_state
string
|
|
<b>Filter</b> results by the specified lifecycle state. Must be a valid state for the resource type.
|
model_id
string
|
unique model OCID.
Required to get a specific model.
aliases: id |
|
project_id
string
|
The ID of the project for which to list the objects.
|
|
region
string
|
The Oracle Cloud Infrastructure region to use for all OCI API requests. If not set, then the value of the OCI_REGION variable, if any, is used. This option is required if the region is not specified through a configuration file (See
config_file_location ). Please refer to https://docs.us-phoenix-1.oraclecloud.com/Content/General/Concepts/regions.htm for more information on OCI regions. |
|
sort_by
string
|
|
Specifies the field to sort by. Accepts only one field. By default, when you sort by `timeCreated`, the results are shown in descending order. When you sort by `displayName`, the results are shown in ascending order. Sort order for the `displayName` field is case sensitive.
|
sort_order
string
|
|
The sort order to use, either 'asc' or 'desc'.
|
tenancy
string
|
OCID of your tenancy. If not set, then the value of the OCI_TENANCY variable, if any, is used. This option is required if the tenancy OCID is not specified through a configuration file (See
config_file_location ). To get the tenancy OCID, please refer https://docs.us-phoenix-1.oraclecloud.com/Content/API/Concepts/apisigningkey.htm |
Note
- For OCI python sdk configuration, please refer to https://oracle-cloud-infrastructure-python-sdk.readthedocs.io/en/latest/configuration.html
- name: Get a specific model
oci_ai_language_model_facts:
# required
model_id: "ocid1.model.oc1..xxxxxxEXAMPLExxxxxx"
- name: List models
oci_ai_language_model_facts:
# required
compartment_id: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx"
# optional
model_id: "ocid1.model.oc1..xxxxxxEXAMPLExxxxxx"
project_id: "ocid1.project.oc1..xxxxxxEXAMPLExxxxxx"
lifecycle_state: DELETING
display_name: display_name_example
sort_order: ASC
sort_by: timeCreated
Common return values are documented here <common_return_values>
, the following are the fields unique to this module:
Key | Returned | Description | ||||
---|---|---|---|---|---|---|
models
complex
|
on success |
List of Model resources
Sample:
[{'compartment_id': 'ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx', 'defined_tags': {'Operations': {'CostCenter': 'US'}}, 'description': 'description_example', 'display_name': 'display_name_example', 'evaluation_results': {'class_metrics': [{'f1': 3.4, 'label': 'label_example', 'precision': 3.4, 'recall': 3.4}], 'confusion_matrix': {'matrix': {}}, 'entity_metrics': [{'f1': 3.4, 'label': 'label_example', 'precision': 3.4, 'recall': 3.4}], 'labels': [], 'metrics': {'accuracy': 3.4, 'macro_f1': 3.4, 'macro_precision': 3.4, 'macro_recall': 3.4, 'micro_f1': 3.4, 'micro_precision': 3.4, 'micro_recall': 3.4, 'weighted_f1': 3.4, 'weighted_precision': 3.4, 'weighted_recall': 3.4}, 'model_type': 'NAMED_ENTITY_RECOGNITION'}, 'freeform_tags': {'Department': 'Finance'}, 'id': 'ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx', 'lifecycle_details': 'lifecycle_details_example', 'lifecycle_state': 'DELETING', 'model_details': {'classification_mode': {'classification_mode': 'MULTI_CLASS'}, 'language_code': 'language_code_example', 'model_type': 'NAMED_ENTITY_RECOGNITION'}, 'project_id': 'ocid1.project.oc1..xxxxxxEXAMPLExxxxxx', 'system_tags': {}, 'test_strategy': {'strategy_type': 'TEST_AND_VALIDATION_DATASET', 'testing_dataset': {'dataset_id': 'ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx', 'dataset_type': 'OBJECT_STORAGE', 'location_details': {'bucket_name': 'bucket_name_example', 'location_type': 'OBJECT_LIST', 'namespace_name': 'namespace_name_example', 'object_names': []}}, 'validation_dataset': {'dataset_id': 'ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx', 'dataset_type': 'OBJECT_STORAGE', 'location_details': {'bucket_name': 'bucket_name_example', 'location_type': 'OBJECT_LIST', 'namespace_name': 'namespace_name_example', 'object_names': []}}}, 'time_created': '2013-10-20T19:20:30+01:00', 'time_updated': '2013-10-20T19:20:30+01:00', 'training_dataset': {'dataset_id': 'ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx', 'dataset_type': 'OBJECT_STORAGE', 'location_details': {'bucket_name': 'bucket_name_example', 'location_type': 'OBJECT_LIST', 'namespace_name': 'namespace_name_example', 'object_names': []}}, 'version': 'version_example'}]
|
||||
compartment_id
string
|
on success |
The OCID for the model's compartment.
Sample:
ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx
|
||||
defined_tags
dictionary
|
on success |
Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: `{"foo-namespace": {"bar-key": "value"}}`
Sample:
{'Operations': {'CostCenter': 'US'}}
|
||||
description
string
|
on success |
A short description of the Model.
Sample:
description_example
|
||||
display_name
string
|
on success |
A user-friendly display name for the resource. It does not have to be unique and can be modified. Avoid entering confidential information.
Sample:
display_name_example
|
||||
evaluation_results
complex
|
on success |
Returned for get operation
|
||||
class_metrics
complex
|
on success |
List of text classification metrics
|
||||
f1
float
|
on success |
F1-score, is a measure of a model's accuracy on a dataset
Sample:
3.4
|
||||
label
string
|
on success |
Text classification label
Sample:
label_example
|
||||
precision
float
|
on success |
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)
Sample:
3.4
|
||||
recall
float
|
on success |
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.
Sample:
3.4
|
||||
confusion_matrix
complex
|
on success |
class level confusion matrix
|
||||
matrix
dictionary
|
on success |
confusion matrix data
|
||||
entity_metrics
complex
|
on success |
List of entity metrics
|
||||
f1
float
|
on success |
F1-score, is a measure of a model's accuracy on a dataset
Sample:
3.4
|
||||
label
string
|
on success |
Entity label
Sample:
label_example
|
||||
precision
float
|
on success |
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)
Sample:
3.4
|
||||
recall
float
|
on success |
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.
Sample:
3.4
|
||||
labels
list
/ elements=string
|
on success |
labels
|
||||
metrics
complex
|
on success |
|
||||
accuracy
float
|
on success |
The fraction of the labels that were correctly recognised .
Sample:
3.4
|
||||
macro_f1
float
|
on success |
F1-score, is a measure of a model's accuracy on a dataset
Sample:
3.4
|
||||
macro_precision
float
|
on success |
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)
Sample:
3.4
|
||||
macro_recall
float
|
on success |
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.
Sample:
3.4
|
||||
micro_f1
float
|
on success |
F1-score, is a measure of a model's accuracy on a dataset
Sample:
3.4
|
||||
micro_precision
float
|
on success |
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)
Sample:
3.4
|
||||
micro_recall
float
|
on success |
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.
Sample:
3.4
|
||||
weighted_f1
float
|
on success |
F1-score, is a measure of a model's accuracy on a dataset
Sample:
3.4
|
||||
weighted_precision
float
|
on success |
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)
Sample:
3.4
|
||||
weighted_recall
float
|
on success |
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.
Sample:
3.4
|
||||
model_type
string
|
on success |
Model type
Sample:
NAMED_ENTITY_RECOGNITION
|
||||
freeform_tags
dictionary
|
on success |
Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: `{"bar-key": "value"}`
Sample:
{'Department': 'Finance'}
|
||||
id
string
|
on success |
Unique identifier model OCID of a model that is immutable on creation
Sample:
ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx
|
||||
lifecycle_details
string
|
on success |
A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in failed state.
Sample:
lifecycle_details_example
|
||||
lifecycle_state
string
|
on success |
The state of the model.
Sample:
DELETING
|
||||
model_details
complex
|
on success |
|
||||
classification_mode
complex
|
on success |
|
||||
classification_mode
string
|
on success |
classification Modes
Sample:
MULTI_CLASS
|
||||
language_code
string
|
on success |
supported language default value is en
Sample:
language_code_example
|
||||
model_type
string
|
on success |
Model type
Sample:
NAMED_ENTITY_RECOGNITION
|
||||
project_id
string
|
on success |
The OCID of the project to associate with the model.
Sample:
ocid1.project.oc1..xxxxxxEXAMPLExxxxxx
|
||||
system_tags
dictionary
|
on success |
Usage of system tag keys. These predefined keys are scoped to namespaces. Example: `{ "orcl-cloud": { "free-tier-retained": "true" } }`
|
||||
test_strategy
complex
|
on success |
Returned for get operation
|
||||
strategy_type
string
|
on success |
This information will define the test strategy different datasets for test and validation(optional) dataset.
Sample:
TEST_AND_VALIDATION_DATASET
|
||||
testing_dataset
complex
|
on success |
|
||||
dataset_id
string
|
on success |
Data Science Labelling Service OCID
Sample:
ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx
|
||||
dataset_type
string
|
on success |
Possible data sets
Sample:
OBJECT_STORAGE
|
||||
location_details
complex
|
on success |
|
||||
bucket_name
string
|
on success |
Object storage bucket name
Sample:
bucket_name_example
|
||||
location_type
string
|
on success |
Possible object storage location types
Sample:
OBJECT_LIST
|
||||
namespace_name
string
|
on success |
Object storage namespace
Sample:
namespace_name_example
|
||||
object_names
list
/ elements=string
|
on success |
Array of files which need to be processed in the bucket
|
||||
validation_dataset
complex
|
on success |
|
||||
dataset_id
string
|
on success |
Data Science Labelling Service OCID
Sample:
ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx
|
||||
dataset_type
string
|
on success |
Possible data sets
Sample:
OBJECT_STORAGE
|
||||
location_details
complex
|
on success |
|
||||
bucket_name
string
|
on success |
Object storage bucket name
Sample:
bucket_name_example
|
||||
location_type
string
|
on success |
Possible object storage location types
Sample:
OBJECT_LIST
|
||||
namespace_name
string
|
on success |
Object storage namespace
Sample:
namespace_name_example
|
||||
object_names
list
/ elements=string
|
on success |
Array of files which need to be processed in the bucket
|
||||
time_created
string
|
on success |
The time the the model was created. An RFC3339 formatted datetime string.
Sample:
2013-10-20T19:20:30+01:00
|
||||
time_updated
string
|
on success |
The time the model was updated. An RFC3339 formatted datetime string.
Returned for get operation
Sample:
2013-10-20T19:20:30+01:00
|
||||
training_dataset
complex
|
on success |
Returned for get operation
|
||||
dataset_id
string
|
on success |
Data Science Labelling Service OCID
Sample:
ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx
|
||||
dataset_type
string
|
on success |
Possible data sets
Sample:
OBJECT_STORAGE
|
||||
location_details
complex
|
on success |
|
||||
bucket_name
string
|
on success |
Object storage bucket name
Sample:
bucket_name_example
|
||||
location_type
string
|
on success |
Possible object storage location types
Sample:
OBJECT_LIST
|
||||
namespace_name
string
|
on success |
Object storage namespace
Sample:
namespace_name_example
|
||||
object_names
list
/ elements=string
|
on success |
Array of files which need to be processed in the bucket
|
||||
version
string
|
on success |
Identifying the model by model id is difficult. This param provides ease of use for end customer. <<service>>::<<service-name>>_<<model-type-version>>::<<custom model on which this training has to be done>> ex: ai-lang::NER_V1::CUSTOM-V0
Sample:
version_example
|
- Oracle (@oracle)