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# coding: utf-8 # Copyright (c) 2016, 2026, Oracle and/or its affiliates. All rights reserved. # This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license. # NOTE: This class is auto generated by OracleSDKGenerator. DO NOT EDIT. API Version: 20221109 from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401 from oci.decorators import init_model_state_from_kwargs @init_model_state_from_kwargs class ModelMetrics(object): """ Trained Model Metrics. """ #: A constant which can be used with the model_type property of a ModelMetrics. #: This constant has a value of "KEY_VALUE_EXTRACTION" MODEL_TYPE_KEY_VALUE_EXTRACTION = "KEY_VALUE_EXTRACTION" #: A constant which can be used with the model_type property of a ModelMetrics. #: This constant has a value of "DOCUMENT_CLASSIFICATION" MODEL_TYPE_DOCUMENT_CLASSIFICATION = "DOCUMENT_CLASSIFICATION" #: A constant which can be used with the model_type property of a ModelMetrics. #: This constant has a value of "PRE_TRAINED_TEXT_EXTRACTION" MODEL_TYPE_PRE_TRAINED_TEXT_EXTRACTION = "PRE_TRAINED_TEXT_EXTRACTION" #: A constant which can be used with the model_type property of a ModelMetrics. #: This constant has a value of "PRE_TRAINED_TABLE_EXTRACTION" MODEL_TYPE_PRE_TRAINED_TABLE_EXTRACTION = "PRE_TRAINED_TABLE_EXTRACTION" #: A constant which can be used with the model_type property of a ModelMetrics. #: This constant has a value of "PRE_TRAINED_KEY_VALUE_EXTRACTION" MODEL_TYPE_PRE_TRAINED_KEY_VALUE_EXTRACTION = "PRE_TRAINED_KEY_VALUE_EXTRACTION" #: A constant which can be used with the model_type property of a ModelMetrics. #: This constant has a value of "PRE_TRAINED_DOCUMENT_CLASSIFICATION" MODEL_TYPE_PRE_TRAINED_DOCUMENT_CLASSIFICATION = "PRE_TRAINED_DOCUMENT_CLASSIFICATION" #: A constant which can be used with the model_type property of a ModelMetrics. #: This constant has a value of "PRE_TRAINED_DOCUMENT_ELEMENTS_EXTRACTION" MODEL_TYPE_PRE_TRAINED_DOCUMENT_ELEMENTS_EXTRACTION = "PRE_TRAINED_DOCUMENT_ELEMENTS_EXTRACTION" def __init__(self, *kwargs): """ Initializes a new ModelMetrics object with values from keyword arguments. This class has the following subclasses and if you are using this class as input to a service operations then you should favor using a subclass over the base class: :class:`~oci.ai_document.models.PreTrainedDocumentElementsExtractionModelDetails` * :class:`~oci.ai_document.models.DocumentClassificationModelMetrics` * :class:`~oci.ai_document.models.PretrainedDocumentClassificationModelDetails` * :class:`~oci.ai_document.models.PretrainedTableExtractionModelDetails` * :class:`~oci.ai_document.models.KeyValueDetectionModelMetrics` * :class:`~oci.ai_document.models.PretrainedKeyValueExtractionModelDetails` * :class:`~oci.ai_document.models.PretrainedTextExtractionModelDetails` The following keyword arguments are supported (corresponding to the getters/setters of this class): :param model_type: The value to assign to the model_type property of this ModelMetrics. Allowed values for this property are: "KEY_VALUE_EXTRACTION", "DOCUMENT_CLASSIFICATION", "PRE_TRAINED_TEXT_EXTRACTION", "PRE_TRAINED_TABLE_EXTRACTION", "PRE_TRAINED_KEY_VALUE_EXTRACTION", "PRE_TRAINED_DOCUMENT_CLASSIFICATION", "PRE_TRAINED_DOCUMENT_ELEMENTS_EXTRACTION", 'UNKNOWN_ENUM_VALUE'. Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'. :type model_type: str :param dataset_summary: The value to assign to the dataset_summary property of this ModelMetrics. :type dataset_summary: oci.ai_document.models.DatasetSummary """ self.swagger_types = { 'model_type': 'str', 'dataset_summary': 'DatasetSummary' } self.attribute_map = { 'model_type': 'modelType', 'dataset_summary': 'datasetSummary' } self._model_type = None self._dataset_summary = None @staticmethod def get_subtype(object_dictionary): """ Given the hash representation of a subtype of this class, use the info in the hash to return the class of the subtype. """ type = object_dictionary['modelType'] if type == 'PRE_TRAINED_DOCUMENT_ELEMENTS_EXTRACTION': return 'PreTrainedDocumentElementsExtractionModelDetails' if type == 'DOCUMENT_CLASSIFICATION': return 'DocumentClassificationModelMetrics' if type == 'PRE_TRAINED_DOCUMENT_CLASSIFICATION': return 'PretrainedDocumentClassificationModelDetails' if type == 'PRE_TRAINED_TABLE_EXTRACTION': return 'PretrainedTableExtractionModelDetails' if type == 'KEY_VALUE_EXTRACTION': return 'KeyValueDetectionModelMetrics' if type == 'PRE_TRAINED_KEY_VALUE_EXTRACTION': return 'PretrainedKeyValueExtractionModelDetails' if type == 'PRE_TRAINED_TEXT_EXTRACTION': return 'PretrainedTextExtractionModelDetails' else: return 'ModelMetrics' @property def model_type(self): """ [Required] Gets the model_type of this ModelMetrics. The type of custom model trained. Allowed values for this property are: "KEY_VALUE_EXTRACTION", "DOCUMENT_CLASSIFICATION", "PRE_TRAINED_TEXT_EXTRACTION", "PRE_TRAINED_TABLE_EXTRACTION", "PRE_TRAINED_KEY_VALUE_EXTRACTION", "PRE_TRAINED_DOCUMENT_CLASSIFICATION", "PRE_TRAINED_DOCUMENT_ELEMENTS_EXTRACTION", 'UNKNOWN_ENUM_VALUE'. Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'. :return: The model_type of this ModelMetrics. :rtype: str """ return self._model_type @model_type.setter def model_type(self, model_type): """ Sets the model_type of this ModelMetrics. The type of custom model trained. :param model_type: The model_type of this ModelMetrics. :type: str """ allowed_values = ["KEY_VALUE_EXTRACTION", "DOCUMENT_CLASSIFICATION", "PRE_TRAINED_TEXT_EXTRACTION", "PRE_TRAINED_TABLE_EXTRACTION", "PRE_TRAINED_KEY_VALUE_EXTRACTION", "PRE_TRAINED_DOCUMENT_CLASSIFICATION", "PRE_TRAINED_DOCUMENT_ELEMENTS_EXTRACTION"] if not value_allowed_none_or_none_sentinel(model_type, allowed_values): model_type = 'UNKNOWN_ENUM_VALUE' self._model_type = model_type @property def dataset_summary(self): """ Gets the dataset_summary of this ModelMetrics. :return: The dataset_summary of this ModelMetrics. :rtype: oci.ai_document.models.DatasetSummary """ return self._dataset_summary @dataset_summary.setter def dataset_summary(self, dataset_summary): """ Sets the dataset_summary of this ModelMetrics. :param dataset_summary: The dataset_summary of this ModelMetrics. :type: oci.ai_document.models.DatasetSummary """ self._dataset_summary = dataset_summary def __repr__(self): return formatted_flat_dict(self) def __eq__(self, other): if other is None: return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

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