def main():
args = get_train_args()
model = init_train_env(args, tbert_type='siamese2')
train_dir = os.path.join(args.data_dir, "train")
valid_dir = os.path.join(args.data_dir, "valid")
train_examples = load_examples(train_dir, model=model, num_limit=args.train_num)
valid_examples = load_examples(valid_dir, model=model, num_limit=args.valid_num)
train(args, train_examples, valid_examples, model, train_with_neg_sampling)
logger.info("Training finished")
def main():
args = get_train_args()
model = init_train_env(args, tbert_type='siamese2')
valid_examples = load_examples(args.data_dir, data_type="valid", model=model, num_limit=args.valid_num,
overwrite=args.overwrite)
train_examples = load_examples(args.data_dir, data_type="train", model=model, num_limit=args.train_num,
overwrite=args.overwrite)
train(args, train_examples, valid_examples, model, train_iter_method=train_with_neg_sampling)
logger.info("Training finished")
def main():
args = get_train_args()
model = init_train_env(args, tbert_type='single')
valid_examples = load_examples(args.data_dir,
data_type="valid",
model=model,
num_limit=args.valid_num,
overwrite=args.overwrite)
train_examples = load_examples(args.data_dir,
data_type="train",
model=model,
num_limit=args.train_num,
overwrite=args.overwrite)
train(args, train_examples, valid_examples, model, train_single_iteration)
logger.info("Training finished")