Understanding the values of loss and accuracy

hello, can someone help me explan the meaning of the values below and scale used


Loss tells us how wrong a model’s predictions are it is always above 0, a model with lesser loss performs better than a model with higher loss. Loss in the image above is shown for training and validation set respectively. training loss is what the optimizer tries to minimize during training.

Accuracy tells us how well the model is performing on a given dataset, it is always between 0 and 1. The two values for accuracy denote accuracy on the training set and the validation set respectively. A higher value of accuracy means that the model performs well on a given dataset.