This PR is to fix #29 and it introduces three new classes: ModelInfo, ModelDetails, and ModelMetrics. These classes are designed to encapsulate all relevant information about an Inference model within a inference pipeline.
ModelMetrics holds the performance metrics of a inference learning model, such as accuracy, precision, recall, F1 score, and loss value. Each of these metrics can be accessed using their respective property methods.
ModelDetails encapsulates various details about the model, including the model's name, version, the framework used for training, the target, and the data type of the model.
ModelInfo ties all this information together, containing an instance of ModelDetails, the model's upload date, and an instance of ModelMetrics. Additionally, it includes a unique identifier for each model using Python's built-in uuid library.
leyao-daily
commented
1 year ago
This PR is to fix #29 and it introduces three new classes: ModelInfo, ModelDetails, and ModelMetrics. These classes are designed to encapsulate all relevant information about an Inference model within a inference pipeline.
ModelMetrics holds the performance metrics of a inference learning model, such as accuracy, precision, recall, F1 score, and loss value. Each of these metrics can be accessed using their respective property methods.
ModelDetails encapsulates various details about the model, including the model's name, version, the framework used for training, the target, and the data type of the model.
ModelInfo ties all this information together, containing an instance of ModelDetails, the model's upload date, and an instance of ModelMetrics. Additionally, it includes a unique identifier for each model using Python's built-in uuid library.