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✏️ Mengedit: named_entity_recognition_model_metrics.cpython-39.pyc
a ���f]0 � @ s8 d dl mZmZmZ d dlmZ eG dd� de��ZdS )� )�formatted_flat_dict� NONE_SENTINEL�#value_allowed_none_or_none_sentinel)�init_model_state_from_kwargsc @ s e Zd ZdZdd� Zedd� �Zejdd� �Zedd� �Zejd d� �Zed d� �Z e jdd� �Z ed d� �Z e jdd� �Z edd� �Zejdd� �Zedd� �Zejdd� �Zedd� �Z e jdd� �Z edd� �Zejdd� �Zedd� �Zejdd� �Zdd � Zd!d"� Zd#d$� Zd%S )&�"NamedEntityRecognitionModelMetricsz6 Model level named entity recognition metrics c K sn dddddddddd� | _ ddddddd d dd� | _d| _d| _d| _d| _d| _d| _d| _d| _ d| _ dS ) a� Initializes a new NamedEntityRecognitionModelMetrics object with values from keyword arguments. The following keyword arguments are supported (corresponding to the getters/setters of this class): :param micro_f1: The value to assign to the micro_f1 property of this NamedEntityRecognitionModelMetrics. :type micro_f1: float :param micro_precision: The value to assign to the micro_precision property of this NamedEntityRecognitionModelMetrics. :type micro_precision: float :param micro_recall: The value to assign to the micro_recall property of this NamedEntityRecognitionModelMetrics. :type micro_recall: float :param macro_f1: The value to assign to the macro_f1 property of this NamedEntityRecognitionModelMetrics. :type macro_f1: float :param macro_precision: The value to assign to the macro_precision property of this NamedEntityRecognitionModelMetrics. :type macro_precision: float :param macro_recall: The value to assign to the macro_recall property of this NamedEntityRecognitionModelMetrics. :type macro_recall: float :param weighted_f1: The value to assign to the weighted_f1 property of this NamedEntityRecognitionModelMetrics. :type weighted_f1: float :param weighted_precision: The value to assign to the weighted_precision property of this NamedEntityRecognitionModelMetrics. :type weighted_precision: float :param weighted_recall: The value to assign to the weighted_recall property of this NamedEntityRecognitionModelMetrics. :type weighted_recall: float �float) �micro_f1�micro_precision�micro_recall�macro_f1�macro_precision�macro_recall�weighted_f1�weighted_precision�weighted_recallZmicroF1ZmicroPrecisionZmicroRecallZmacroF1ZmacroPrecisionZmacroRecallZ weightedF1ZweightedPrecisionZweightedRecallN)Z swagger_typesZ attribute_map� _micro_f1�_micro_precision� _micro_recall� _macro_f1�_macro_precision� _macro_recall�_weighted_f1�_weighted_precision�_weighted_recall)�self�kwargs� r �a/usr/lib/python3.9/site-packages/oci/ai_language/models/named_entity_recognition_model_metrics.py�__init__ s: +� �z+NamedEntityRecognitionModelMetrics.__init__c C s | j S )u **[Required]** Gets the micro_f1 of this NamedEntityRecognitionModelMetrics. F1-score, is a measure of a model’s accuracy on a dataset :return: The micro_f1 of this NamedEntityRecognitionModelMetrics. :rtype: float �r �r r r r r ^ s z+NamedEntityRecognitionModelMetrics.micro_f1c C s || _ dS )u� Sets the micro_f1 of this NamedEntityRecognitionModelMetrics. F1-score, is a measure of a model’s accuracy on a dataset :param micro_f1: The micro_f1 of this NamedEntityRecognitionModelMetrics. :type: float Nr )r r r r r r j s c C s | j S )a� **[Required]** Gets the micro_precision of this NamedEntityRecognitionModelMetrics. 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) :return: The micro_precision of this NamedEntityRecognitionModelMetrics. :rtype: float �r r r r r r v s z2NamedEntityRecognitionModelMetrics.micro_precisionc C s || _ dS )a� Sets the micro_precision of this NamedEntityRecognitionModelMetrics. 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) :param micro_precision: The micro_precision of this NamedEntityRecognitionModelMetrics. :type: float Nr! )r r r r r r � s c C s | j S )a� **[Required]** Gets the micro_recall of this NamedEntityRecognitionModelMetrics. 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. :return: The micro_recall of this NamedEntityRecognitionModelMetrics. :rtype: float �r r r r r r � s z/NamedEntityRecognitionModelMetrics.micro_recallc C s || _ dS )a� Sets the micro_recall of this NamedEntityRecognitionModelMetrics. 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. :param micro_recall: The micro_recall of this NamedEntityRecognitionModelMetrics. :type: float Nr"