| # coding: UTF-8 | |
| import os | |
| import torch | |
| class Config(object): | |
| def __init__(self, data_dir): | |
| assert os.path.exists(data_dir) | |
| self.train_file = os.path.join(data_dir, "train.txt") | |
| self.dev_file = os.path.join(data_dir, "dev.txt") | |
| self.label_file = os.path.join(data_dir, "label.txt") | |
| assert os.path.isfile(self.train_file) | |
| assert os.path.isfile(self.dev_file) | |
| assert os.path.isfile(self.label_file) | |
| self.saved_model_dir = os.path.join(data_dir, "model") | |
| self.saved_model = os.path.join(self.saved_model_dir, "bert_model.pth") | |
| if not os.path.exists(self.saved_model_dir): | |
| os.mkdir(self.saved_model_dir) | |
| self.label_list = [label.strip() for label in open(self.label_file, "r", encoding="UTF-8").readlines()] | |
| self.num_labels = len(self.label_list) | |
| self.num_epochs = 3 | |
| self.log_batch = 100 | |
| self.batch_size = 128 | |
| self.max_seq_len = 32 | |
| self.require_improvement = 1000 | |
| self.warmup_steps = 0 | |
| self.weight_decay = 0.01 | |
| self.max_grad_norm = 1.0 | |
| self.learning_rate = 5e-5 | |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |