| 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 |