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class Param():
def __init__(self, args):
self.hyper_param = self.get_hyper_parameters(args)
def get_hyper_parameters(self, args):
"""
Args:
bert_model (directory): The path for the pre-trained bert model.
num_train_epochs (int): The number of training epochs.
num_labels (autofill): The output dimension.
max_seq_length (autofill): The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.
freeze_backbone_parameters (binary): Whether to freeze all parameters but the last layer.
feat_dim (int): The feature dimension.
warmup_proportion (float): The warmup ratio for learning rate.
lr (float): The learning rate of backbone.
activation (str): The activation function of the hidden layer (support 'relu' and 'tanh').
weibull_tail_size (int): The factor of weibull model.
alpharank (int): The factor of alpha rank.
distance_type (str): The distance_type.
threshold (float): The probability threshold for detecting the open samples.
train_batch_size (int): The batch size for training.
eval_batch_size (int): The batch size for evaluation.
test_batch_size (int): The batch size for testing.
wait_patient (int): Patient steps for Early Stop.
"""
hyper_parameters = {
'bert_model': "/home/sharing/disk1/pretrained_embedding/bert/uncased_L-12_H-768_A-12/",
'num_train_epochs': 100,
'num_labels': None,
'max_seq_length': None,
'freeze_backbone_parameters': True,
'feat_dim': 768,
'warmup_proportion': 0.1,
'lr': 2e-5,
'activation': 'tanh',
'weibull_tail_size': 20,
'alpharank': 10,
'distance_type': 'cosine',
'threshold': 0.5,
'train_batch_size': 128,
'eval_batch_size': 64,
'test_batch_size': 64,
'wait_patient': 15
}
return hyper_parameters |