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# coding: utf-8 # Copyright (c) 2016, 2024, 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: 20221001 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 ModelDetails(object): """ Possible model types """ #: A constant which can be used with the model_type property of a ModelDetails. #: This constant has a value of "NAMED_ENTITY_RECOGNITION" MODEL_TYPE_NAMED_ENTITY_RECOGNITION = "NAMED_ENTITY_RECOGNITION" #: A constant which can be used with the model_type property of a ModelDetails. #: This constant has a value of "TEXT_CLASSIFICATION" MODEL_TYPE_TEXT_CLASSIFICATION = "TEXT_CLASSIFICATION" #: A constant which can be used with the model_type property of a ModelDetails. #: This constant has a value of "PRE_TRAINED_NAMED_ENTITY_RECOGNITION" MODEL_TYPE_PRE_TRAINED_NAMED_ENTITY_RECOGNITION = "PRE_TRAINED_NAMED_ENTITY_RECOGNITION" #: A constant which can be used with the model_type property of a ModelDetails. #: This constant has a value of "PRE_TRAINED_TEXT_CLASSIFICATION" MODEL_TYPE_PRE_TRAINED_TEXT_CLASSIFICATION = "PRE_TRAINED_TEXT_CLASSIFICATION" #: A constant which can be used with the model_type property of a ModelDetails. #: This constant has a value of "PRE_TRAINED_SENTIMENT_ANALYSIS" MODEL_TYPE_PRE_TRAINED_SENTIMENT_ANALYSIS = "PRE_TRAINED_SENTIMENT_ANALYSIS" #: A constant which can be used with the model_type property of a ModelDetails. #: This constant has a value of "PRE_TRAINED_KEYPHRASE_EXTRACTION" MODEL_TYPE_PRE_TRAINED_KEYPHRASE_EXTRACTION = "PRE_TRAINED_KEYPHRASE_EXTRACTION" #: A constant which can be used with the model_type property of a ModelDetails. #: This constant has a value of "PRE_TRAINED_LANGUAGE_DETECTION" MODEL_TYPE_PRE_TRAINED_LANGUAGE_DETECTION = "PRE_TRAINED_LANGUAGE_DETECTION" #: A constant which can be used with the model_type property of a ModelDetails. #: This constant has a value of "PRE_TRAINED_PII" MODEL_TYPE_PRE_TRAINED_PII = "PRE_TRAINED_PII" #: A constant which can be used with the model_type property of a ModelDetails. #: This constant has a value of "PRE_TRAINED_TRANSLATION" MODEL_TYPE_PRE_TRAINED_TRANSLATION = "PRE_TRAINED_TRANSLATION" #: A constant which can be used with the model_type property of a ModelDetails. #: This constant has a value of "PRE_TRAINED_HEALTH_NLU" MODEL_TYPE_PRE_TRAINED_HEALTH_NLU = "PRE_TRAINED_HEALTH_NLU" #: A constant which can be used with the model_type property of a ModelDetails. #: This constant has a value of "PRE_TRAINED_SUMMARIZATION" MODEL_TYPE_PRE_TRAINED_SUMMARIZATION = "PRE_TRAINED_SUMMARIZATION" #: A constant which can be used with the model_type property of a ModelDetails. #: This constant has a value of "PRE_TRAINED_UNIVERSAL" MODEL_TYPE_PRE_TRAINED_UNIVERSAL = "PRE_TRAINED_UNIVERSAL" #: A constant which can be used with the model_type property of a ModelDetails. #: This constant has a value of "PII" MODEL_TYPE_PII = "PII" def __init__(self, **kwargs): """ Initializes a new ModelDetails 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_language.models.PreTrainedKeyPhraseExtractionModelDetails` * :class:`~oci.ai_language.models.PreTrainedHealthNluModelDetails` * :class:`~oci.ai_language.models.PreTrainedUniversalModel` * :class:`~oci.ai_language.models.NamedEntityRecognitionModelDetails` * :class:`~oci.ai_language.models.PiiModelDetails` * :class:`~oci.ai_language.models.PreTrainedLanguageDetectionModelDetails` * :class:`~oci.ai_language.models.PreTrainedNamedEntityRecognitionModelDetails` * :class:`~oci.ai_language.models.PreTrainedSentimentAnalysisModelDetails` * :class:`~oci.ai_language.models.PreTrainedTextClassificationModelDetails` * :class:`~oci.ai_language.models.TextClassificationModelDetails` * :class:`~oci.ai_language.models.PreTrainedSummarization` * :class:`~oci.ai_language.models.PreTrainedPiiModelDetails` The following keyword arguments are supported (corresponding to the getters/setters of this class): :param language_code: The value to assign to the language_code property of this ModelDetails. :type language_code: str :param model_type: The value to assign to the model_type property of this ModelDetails. Allowed values for this property are: "NAMED_ENTITY_RECOGNITION", "TEXT_CLASSIFICATION", "PRE_TRAINED_NAMED_ENTITY_RECOGNITION", "PRE_TRAINED_TEXT_CLASSIFICATION", "PRE_TRAINED_SENTIMENT_ANALYSIS", "PRE_TRAINED_KEYPHRASE_EXTRACTION", "PRE_TRAINED_LANGUAGE_DETECTION", "PRE_TRAINED_PII", "PRE_TRAINED_TRANSLATION", "PRE_TRAINED_HEALTH_NLU", "PRE_TRAINED_SUMMARIZATION", "PRE_TRAINED_UNIVERSAL", "PII", 'UNKNOWN_ENUM_VALUE'. Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'. :type model_type: str """ self.swagger_types = { 'language_code': 'str', 'model_type': 'str' } self.attribute_map = { 'language_code': 'languageCode', 'model_type': 'modelType' } self._language_code = None self._model_type = 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_KEYPHRASE_EXTRACTION': return 'PreTrainedKeyPhraseExtractionModelDetails' if type == 'PRE_TRAINED_HEALTH_NLU': return 'PreTrainedHealthNluModelDetails' if type == 'PRE_TRAINED_UNIVERSAL': return 'PreTrainedUniversalModel' if type == 'NAMED_ENTITY_RECOGNITION': return 'NamedEntityRecognitionModelDetails' if type == 'PII': return 'PiiModelDetails' if type == 'PRE_TRAINED_LANGUAGE_DETECTION': return 'PreTrainedLanguageDetectionModelDetails' if type == 'PRE_TRAINED_NAMED_ENTITY_RECOGNITION': return 'PreTrainedNamedEntityRecognitionModelDetails' if type == 'PRE_TRAINED_SENTIMENT_ANALYSIS': return 'PreTrainedSentimentAnalysisModelDetails' if type == 'PRE_TRAINED_TEXT_CLASSIFICATION': return 'PreTrainedTextClassificationModelDetails' if type == 'TEXT_CLASSIFICATION': return 'TextClassificationModelDetails' if type == 'PRE_TRAINED_SUMMARIZATION': return 'PreTrainedSummarization' if type == 'PRE_TRAINED_PII': return 'PreTrainedPiiModelDetails' else: return 'ModelDetails' @property def language_code(self): """ Gets the language_code of this ModelDetails. supported language default value is en :return: The language_code of this ModelDetails. :rtype: str """ return self._language_code @language_code.setter def language_code(self, language_code): """ Sets the language_code of this ModelDetails. supported language default value is en :param language_code: The language_code of this ModelDetails. :type: str """ self._language_code = language_code @property def model_type(self): """ **[Required]** Gets the model_type of this ModelDetails. Model type Allowed values for this property are: "NAMED_ENTITY_RECOGNITION", "TEXT_CLASSIFICATION", "PRE_TRAINED_NAMED_ENTITY_RECOGNITION", "PRE_TRAINED_TEXT_CLASSIFICATION", "PRE_TRAINED_SENTIMENT_ANALYSIS", "PRE_TRAINED_KEYPHRASE_EXTRACTION", "PRE_TRAINED_LANGUAGE_DETECTION", "PRE_TRAINED_PII", "PRE_TRAINED_TRANSLATION", "PRE_TRAINED_HEALTH_NLU", "PRE_TRAINED_SUMMARIZATION", "PRE_TRAINED_UNIVERSAL", "PII", 'UNKNOWN_ENUM_VALUE'. Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'. :return: The model_type of this ModelDetails. :rtype: str """ return self._model_type @model_type.setter def model_type(self, model_type): """ Sets the model_type of this ModelDetails. Model type :param model_type: The model_type of this ModelDetails. :type: str """ allowed_values = ["NAMED_ENTITY_RECOGNITION", "TEXT_CLASSIFICATION", "PRE_TRAINED_NAMED_ENTITY_RECOGNITION", "PRE_TRAINED_TEXT_CLASSIFICATION", "PRE_TRAINED_SENTIMENT_ANALYSIS", "PRE_TRAINED_KEYPHRASE_EXTRACTION", "PRE_TRAINED_LANGUAGE_DETECTION", "PRE_TRAINED_PII", "PRE_TRAINED_TRANSLATION", "PRE_TRAINED_HEALTH_NLU", "PRE_TRAINED_SUMMARIZATION", "PRE_TRAINED_UNIVERSAL", "PII"] if not value_allowed_none_or_none_sentinel(model_type, allowed_values): model_type = 'UNKNOWN_ENUM_VALUE' self._model_type = model_type 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