oracle.oci.oci_ai_language_model – Manage a Model resource in Oracle Cloud Infrastructure¶
Note
This plugin is part of the oracle.oci collection (version 5.2.0).
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
.
New in version 2.9.0: of oracle.oci
Synopsis¶
This module allows the user to create, update and delete a Model resource in Oracle Cloud Infrastructure
For state=present, creates a new model for training and train the model with date provided.
This resource has the following action operations in the oracle.oci.oci_ai_language_model_actions module: change_compartment.
Requirements¶
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
Parameters¶
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 OCID for the models compartment.
Required for create using state=present.
Required for update when environment variable
OCI_USE_NAME_AS_IDENTIFIER is set.Required for delete when environment variable
OCI_USE_NAME_AS_IDENTIFIER is set. |
||||
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 . |
||||
defined_tags
dictionary
|
Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: `{"foo-namespace": {"bar-key": "value"}}`
This parameter is updatable.
|
||||
description
string
|
A short description of the a model.
This parameter is updatable.
|
||||
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.
Required for create, update, delete when environment variable
OCI_USE_NAME_AS_IDENTIFIER is set.This parameter is updatable when
OCI_USE_NAME_AS_IDENTIFIER is not set.aliases: name |
||||
force_create
boolean
|
|
Whether to attempt non-idempotent creation of a resource. By default, create resource is an idempotent operation, and doesn't create the resource if it already exists. Setting this option to true, forcefully creates a copy of the resource, even if it already exists.This option is mutually exclusive with key_by.
|
|||
freeform_tags
dictionary
|
Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: `{"bar-key": "value"}`
This parameter is updatable.
|
||||
key_by
list
/ elements=string
|
The list of attributes of this resource which should be used to uniquely identify an instance of the resource. By default, all the attributes of a resource are used to uniquely identify a resource.
|
||||
model_details
dictionary
|
Required for create using state=present.
|
||||
classification_mode
dictionary
|
Applicable when model_type is 'TEXT_CLASSIFICATION'
|
||||
classification_mode
string
/ required
|
|
classification Modes
|
|||
version
string
|
Optional if nothing specified latest base model will be used for training. Supported versions can be found at /modelTypes/{modelType}
|
||||
language_code
string
|
supported language default value is en
|
||||
model_type
string
/ required
|
|
Model type
|
|||
version
string
|
Optional pre trained model version. if nothing specified latest pre trained model will be used. Supported versions can be found at /modelTypes/{modelType}
Applicable when model_type is one of ['NAMED_ENTITY_RECOGNITION', 'PRE_TRAINED_PII', 'PRE_TRAINED_PHI', 'PRE_TRAINED_TEXT_CLASSIFICATION', 'PRE_TRAINED_NAMED_ENTITY_RECOGNITION', 'PRE_TRAINED_HEALTH_NLU', 'PRE_TRAINED_LANGUAGE_DETECTION', 'PRE_TRAINED_KEYPHRASE_EXTRACTION', 'PRE_TRAINED_SENTIMENT_ANALYSIS', 'PRE_TRAINED_SUMMARIZATION', 'PRE_TRAINED_UNIVERSAL']
|
||||
model_id
string
|
unique model OCID.
Required for update using state=present when environment variable
OCI_USE_NAME_AS_IDENTIFIER is not set.Required for delete using state=absent when environment variable
OCI_USE_NAME_AS_IDENTIFIER is not set.aliases: id |
||||
project_id
string
|
The OCID of the project to associate with the model.
Required for create using state=present.
|
||||
realm_specific_endpoint_template_enabled
boolean
|
|
Enable/Disable realm specific endpoint template for service client. By Default, realm specific endpoint template is disabled. If not set, then the value of the OCI_REALM_SPECIFIC_SERVICE_ENDPOINT_TEMPLATE_ENABLED variable, if any, is used.
|
|||
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. |
||||
state
string
|
|
The state of the Model.
Use state=present to create or update a Model.
Use state=absent to delete a Model.
|
|||
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 |
||||
test_strategy
dictionary
|
|||||
strategy_type
string
/ required
|
|
This information will define the test strategy different datasets for test and validation(optional) dataset.
|
|||
testing_dataset
dictionary
/ required
|
|||||
dataset_id
string
|
Data Science Labelling Service OCID
Required when dataset_type is 'DATA_SCIENCE_LABELING'
|
||||
dataset_type
string
/ required
|
|
Possible data sets
|
|||
location_details
dictionary
|
Required when dataset_type is 'OBJECT_STORAGE'
|
||||
bucket_name
string
/ required
|
Object storage bucket name
|
||||
location_type
string
/ required
|
|
Possible object storage location types
|
|||
namespace_name
string
/ required
|
Object storage namespace
|
||||
object_names
list
/ elements=string / required
|
Array of files which need to be processed in the bucket
|
||||
validation_dataset
dictionary
|
|||||
dataset_id
string
|
Data Science Labelling Service OCID
Required when dataset_type is 'DATA_SCIENCE_LABELING'
|
||||
dataset_type
string
/ required
|
|
Possible data sets
|
|||
location_details
dictionary
|
Required when dataset_type is 'OBJECT_STORAGE'
|
||||
bucket_name
string
/ required
|
Object storage bucket name
|
||||
location_type
string
/ required
|
|
Possible object storage location types
|
|||
namespace_name
string
/ required
|
Object storage namespace
|
||||
object_names
list
/ elements=string / required
|
Array of files which need to be processed in the bucket
|
||||
training_dataset
dictionary
|
|||||
dataset_id
string
|
Data Science Labelling Service OCID
Required when dataset_type is 'DATA_SCIENCE_LABELING'
|
||||
dataset_type
string
/ required
|
|
Possible data sets
|
|||
location_details
dictionary
|
Required when dataset_type is 'OBJECT_STORAGE'
|
||||
bucket_name
string
/ required
|
Object storage bucket name
|
||||
location_type
string
/ required
|
|
Possible object storage location types
|
|||
namespace_name
string
/ required
|
Object storage namespace
|
||||
object_names
list
/ elements=string / required
|
Array of files which need to be processed in the bucket
|
||||
wait
boolean
|
|
Whether to wait for create or delete operation to complete.
|
|||
wait_timeout
integer
|
Time, in seconds, to wait when wait=yes. Defaults to 1200 for most of the services but some services might have a longer wait timeout.
|
Notes¶
Note
For OCI python sdk configuration, please refer to https://oracle-cloud-infrastructure-python-sdk.readthedocs.io/en/latest/configuration.html
Examples¶
- name: Create model
oci_ai_language_model:
# required
compartment_id: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx"
project_id: "ocid1.project.oc1..xxxxxxEXAMPLExxxxxx"
model_details:
# required
model_type: PRE_TRAINED_KEYPHRASE_EXTRACTION
# optional
language_code: language_code_example
version: version_example
# optional
training_dataset:
# required
dataset_id: "ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx"
dataset_type: DATA_SCIENCE_LABELING
test_strategy:
# required
strategy_type: TEST_AND_VALIDATION_DATASET
testing_dataset:
# required
dataset_id: "ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx"
dataset_type: DATA_SCIENCE_LABELING
# optional
validation_dataset:
# required
dataset_id: "ocid1.dataset.oc1..xxxxxxEXAMPLExxxxxx"
dataset_type: DATA_SCIENCE_LABELING
display_name: display_name_example
description: description_example
freeform_tags: {'Department': 'Finance'}
defined_tags: {'Operations': {'CostCenter': 'US'}}
- name: Update model
oci_ai_language_model:
# required
model_id: "ocid1.model.oc1..xxxxxxEXAMPLExxxxxx"
# optional
display_name: display_name_example
description: description_example
freeform_tags: {'Department': 'Finance'}
defined_tags: {'Operations': {'CostCenter': 'US'}}
- name: Update model using name (when environment variable OCI_USE_NAME_AS_IDENTIFIER is set)
oci_ai_language_model:
# required
compartment_id: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx"
display_name: display_name_example
# optional
description: description_example
freeform_tags: {'Department': 'Finance'}
defined_tags: {'Operations': {'CostCenter': 'US'}}
- name: Delete model
oci_ai_language_model:
# required
model_id: "ocid1.model.oc1..xxxxxxEXAMPLExxxxxx"
state: absent
- name: Delete model using name (when environment variable OCI_USE_NAME_AS_IDENTIFIER is set)
oci_ai_language_model:
# required
compartment_id: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx"
display_name: display_name_example
state: absent
Return Values¶
Common return values are documented here, the following are the fields unique to this module:
Key | Returned | Description | ||||
---|---|---|---|---|---|---|
model
complex
|
on success |
Details of the Model resource acted upon by the current operation
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, 'support': 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', 'version': 'version_example'}, 'language_code': 'language_code_example', 'model_type': 'NAMED_ENTITY_RECOGNITION', 'version': 'version_example'}, '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 |
|
||||
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
|
||||
support
float
|
on success |
number of samples in the test set
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
|
||||
version
string
|
on success |
Optional if nothing specified latest base model will be used for training. Supported versions can be found at /modelTypes/{modelType}
Sample:
version_example
|
||||
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
|
||||
version
string
|
on success |
Optional if nothing specified latest base model will be used for training. Supported versions can be found at /modelTypes/{modelType}
Sample:
version_example
|
||||
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 |
|
||||
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.
Sample:
2013-10-20T19:20:30+01:00
|
||||
training_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
|
||||
version
string
|
on success |
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. <<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
|
Authors¶
Oracle (@oracle)