NUM_EPOCHS = 8        #Number of epochs to train for

BATCH_SIZE = 12       #Change this depending on GPU memory

NUM_WORKERS = 4       #A value of 0 means the main process loads the data

LEARNING_RATE = 2e-5

LOG_EVERY = 200       #iterations after which to log status during training

VALID_NITER = 2000    #iterations after which to evaluate model and possibly save (if dev performance is a new max)

PRETRAIN_PATH = None  #path to pretrained model, such as BlueBERT or BioBERT

PAD_IDX = 0           #padding index as required by the tokenizer 

#CONDITIONS is a list of all 14 medical observations 

CONDITIONS = ['Enlarged Cardiomediastinum', 'Cardiomegaly', 'Lung Opacity',

              'Lung Lesion', 'Edema', 'Consolidation', 'Pneumonia', 'Atelectasis',

              'Pneumothorax', 'Pleural Effusion', 'Pleural Other', 'Fracture',

              'Support Devices', 'No Finding']

CLASS_MAPPING = {0: "Blank", 1: "Positive", 2: "Negative", 3: "Uncertain"}