config.py

import torch

class Config(object):
    def __init__(self, dataset):
        self.train_path = dataset + 'train.txt'     #训练集
        self.dev_path = dataset + 'dev.txt'         #验证集
        self.test_path = dataset + 'test.txt'       #测试集
        self.class_path = dataset + 'class.txt'       #测试集
        self.predict_path = dataset + '/saved_data/' + 'predict.csv'    #预测结果
        self.value_path = dataset + '/saved_data/' + 'value.csv'        #评价效果
        self.save_path = dataset + '/saved_data/' + 'model.ckpl'

        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

        self.k_fold = 10
        self.epochs = 1
        self.batch_size = 64
        self.max_seq = 50
        self.lr = 1e-3
        self.require_improvement = 2

        self.class_list = [x.strip() for x in open(self.class_path, encoding='utf-8').readlines()]
        self.num_classes = len(self.class_list)                     #类别数量
        self.id2class = dict(enumerate(self.class_list))            #标号转类别
        self.class2id = {j: i for i, j in self.id2class.items()}    #类别转标号

        self.kernal_sizes = (2, 3, 4)
        self.kernel_nums =(50, 100, 150)
        self.num_filters = 128
        self.embed_dim = 200