for key, value in self.idx2label.items()
}
if __name__ == '__main__':
dataset = '../dataset' # 数据集
config = Config(dataset)
seed = config.seed
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True # 保证每次结果一样
config.test_path = dataset + '/unlabeled_data.csv'
start_time = time.time()
data_df = load_data(config.test_path, config, with_label=False)
print('Reading testing data...')
test_data = Mydataset(config=config, data=data_df, with_labels=False)
test_iter = DataLoader(dataset=test_data,
batch_size=config.batch_size,
shuffle=False)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
model = bert_RNN(config).to(config.device)
predict_all = test(config, model, test_iter)
# ---------------------生成文件--------------------------
df_test = pd.read_csv(config.submit_example_path, encoding='utf-8')
id2label, label2id = json.load(open(config.id2label_path))
self.dropout = 0.1
self.num_layers = 2
self.label2idx = {key: int(value) for key, value in self.label2idx.items()}
self.idx2label = {int(key): value for key, value in self.idx2label.items()}
if __name__ == '__main__':
dataset = '../dataset' # 数据集
config = Config(dataset)
seed = config.seed
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True # 保证每次结果一样
config.train_path = dataset + '/all_label_data_3class.csv'
data_df = load_data(config.train_path, config, with_label=True)
train_df, valid_df = train_test_split(data_df, test_size=0.2, shuffle_flag=True, random_state=seed)
print('Reading training data...')
train_dataset = Mydataset(config=config, data=train_df, with_labels=True)
train_iter = DataLoader(dataset=train_dataset, batch_size=config.batch_size, shuffle=True)
print('Reading validation data...')
valid_dataset = Mydataset(config=config, data=valid_df, with_labels=True)
dev_iter = DataLoader(dataset=valid_dataset, batch_size=config.batch_size, shuffle=True)
model = bert_RNN(config).to(config.device)
train(config, model, train_iter, dev_iter)