def testone(): ##按默认执行 # get_data() args = parser.parse_args() np.random.seed(args.random_seed) random.seed(args.random_seed) args.do_model = 'mcls' schema_labels, predict_data, predict_sents = process_data(args) shiyan = """ mcls """ write_title('./work/log/' + args.do_model + '.txt', args, shiyan) args.checkpoint_dir = 'models/' + args.do_model + str(id) one(args, schema_labels, predict_data, predict_sents, str(id))
def testone(id=1, do_model="mrc_relation"): ##按默认执行 # get_data() args = parser.parse_args() # get_submit_postprocess(args, id) np.random.seed(args.random_seed) random.seed(args.random_seed) args.do_model = do_model shiyan = """ mrc_relation """ write_title('./work/log/' + args.do_model + '.txt', args, shiyan) if (args.use_cross_validation): id = cross_validation(args, id) else: schema_labels, predict_data, predict_sents = process_data(args, 4) args.checkpoint_dir = 'models/' + args.do_model + str(id) one(args, schema_labels, predict_data, predict_sents, str(id))
def bzsearch(): args = parser.parse_args() # args.do_model = 'role' schema_labels, predict_data, predict_sents = process_data(args) # # 创建一个 LogWriter 对象 log_writer # log_writer = LogWriter("./log", sync_cycle=10) shiyan = """ ###################################################################################################################################### trigger_batch_size gridsearch ###################################################################################################################################### """ write_title('./work/log/' + args.do_model + '.txt', args, shiyan) id = 4 # str(datetime.now().strftime('%m%d%H%M')) print(id) for bz in [32, 16, 8]: args.batch_size = bz args.checkpoint_dir = 'models/' + args.do_model + str(id) one(args, schema_labels, predict_data, predict_sents, str(id)) id += 1
def lrsearch(): args = parser.parse_args() # args.do_model = 'role' schema_labels, predict_data, predict_sents = process_data(args) # # 创建一个 LogWriter 对象 log_writer # log_writer = LogWriter("./log", sync_cycle=10) shiyan = """ ###################################################################################################################################### trigger_lrgridsearch ###################################################################################################################################### """ write_title('./work/log/' + args.do_model + '.txt', args, shiyan) id = 1 # str(datetime.now().strftime('%m%d%H%M')) print(id) for lr in [3e-5, 1e-5, 1e-4]: args.learning_rate = lr args.checkpoint_dir = 'models/' + args.do_model + str(id) one(args, schema_labels, predict_data, predict_sents, str(id)) id += 1
def my(): args = parser.parse_args() args.do_model = 'role' schema_labels = read_label('{}/entity2id.txt'.format(args.data_dir)) # # 创建一个 LogWriter 对象 log_writer # log_writer = LogWriter("./log", sync_cycle=10) shiyan = """ ###################################################################################################################################### 202,不复制,不考虑重叠 ###################################################################################################################################### """ write_title('./work/log/' + args.do_model + '.txt', args, shiyan) id = 5 # str(datetime.now().strftime('%m%d%H%M')) args.checkpoint_dir = 'models/' + args.do_model + str(id) one(args, schema_labels, str(id)) schema_labels, predict_data, predict_sents = process_data(args) predict_by_model_path(args,args.checkpoint_dir, schema_labels, predict_data, predict_sents, id)
def lrepochsearch(): args = parser.parse_args() args.do_model = 'role' schema_labels = read_label('{}/entity2id.txt'.format(args.data_dir)) # # 创建一个 LogWriter 对象 log_writer # log_writer = LogWriter("./log", sync_cycle=10) shiyan = """ ###################################################################################################################################### 202,不复制,不考虑重叠,lrepochsearch ###################################################################################################################################### """ write_title('./work/log/' + args.do_model + '.txt', args, shiyan) id = 6 # str(datetime.now().strftime('%m%d%H%M')) for lr in [5e-5, 7e-5, 1e-4, 3e-4]: #[3e-5,1e-5,5e-6,1e-6] args.learning_rate = lr args.checkpoint_dir = 'models/' + args.do_model + str(id) for epoch in range(1, 4): args.num_epoch = epoch one(args, schema_labels, str(id)) predict_by_model_path(args, args.checkpoint_dir, id) id += 1
c.to_csv('./work/data.csv', header=None, index=False, sep='\t') if __name__ == "__main__": # get_data() args = parser.parse_args() np.random.seed(args.random_seed) random.seed(args.random_seed) args.do_model = 'role' schema_labels, predict_data, predict_sents = process_data(args) shiyan = """ ###################################################################################################################################### trigger_batch_size lr gridsearch ###################################################################################################################################### """ write_title('./work/log/' + args.do_model + '.txt', args, shiyan) id = 1 args.checkpoint_dir = 'models/' + args.do_model + str(id) one(args, schema_labels, predict_data, predict_sents, str(id)) # args = parser.parse_args() # args.do_model = 'role' # schema_labels, predict_data, predict_sents = process_data(args) # # # 创建一个 LogWriter 对象 log_writer # # log_writer = LogWriter("./log", sync_cycle=10) # # id = str(datetime.now().strftime('%m%d%H%M%S')) # print(id) # # args.checkpoint_dir = 'models/trigger' + str(id)