else:
print("Training finished. Best accuracy is {:0.4f}.".format(best_score))
seed_num = 123
random.seed(seed_num)
torch.manual_seed(seed_num)
np.random.seed(seed_num)
if __name__ == '__main__':
data = Data()
train_config_path = './train.config'
data.readConfig(train_config_path)
data.buildDictionary()
data.getPretrainedEmbedding()
# print parameter summary
printParameterSummary(data)
# build dataloaders
training_instances = getDataLoader(data.training_path, data)
validation_instances = getDataLoader(data.validation_path, data)
evaluation_instances = getDataLoader(data.evaluation_path, data)
data.saveData()
device = torch.device("cuda:"+data.GPU if torch.cuda.is_available() else "cpu")
train(training_instances, validation_instances, evaluation_instances, data)
