def main():
parser = ArgumentParser()
# add task level args
parser = add_common_specific_args(parser)
parser = add_tune_specific_args(parser)
parser = add_task_specific_args(parser)
# add model specific args
parser = Model.add_model_specific_args(parser)
parser = optimization.add_optimizer_specific_args(parser)
parser = Trainer.add_argparse_args(parser)
parser.set_defaults(min_epochs=1, max_epochs=10)
parser.set_defaults(gradient_clip_val=1.0,
lr_layers_getter='get_layer_lrs_with_crf')
args = parser.parse_args()
if args.ltp_model is not None and args.resume_from_checkpoint is not None:
deploy_model(args, args.ltp_version)
elif args.tune:
tune_train(args,
model_class=Model,
task_info=task_info,
build_method=build_method)
else:
common_train(args,
model_class=Model,
task_info=task_info,
build_method=build_method)
def main():
# 如果要输出 LTP master 分支可以使用的模型，传入 ltp_adapter 参数为输出文件夹路径，如 ltp_model
parser = ArgumentParser()
parser = add_task_specific_args(parser)
parser = Model.add_model_specific_args(parser)
parser = optimization.add_optimizer_specific_args(parser)
parser = Trainer.add_argparse_args(parser)
parser.set_defaults(min_epochs=1, max_epochs=10)
parser.set_defaults(gradient_clip_val=1.0,
lr_layers_getter='get_layer_lrs_with_crf')
args = parser.parse_args()
if args.ltp_model is not None and args.resume_from_checkpoint is not None:
deploy_model(args, args.ltp_version)
elif args.build_ner_dataset:
build_ner_distill_dataset(args)
elif args.tune:
tune_train(args,
model_class=Model,
task_info=task_info,
build_method=build_method)
else:
common_train(args,
model_class=Model,
task_info=task_info,
build_method=build_method)
def main():
# 如果要输出 LTP master 分支可以使用的模型，传入 ltp_adapter 参数为输出文件夹路径，如 ltp_model
parser = ArgumentParser()
# add task level args
parser = add_common_specific_args(parser)
parser = add_tune_specific_args(parser)
parser = add_task_specific_args(parser)
# add model specific args
parser = Model.add_model_specific_args(parser)
parser = optimization.add_optimizer_specific_args(parser)
parser = Trainer.add_argparse_args(parser)
parser.set_defaults(min_epochs=1, max_epochs=10)
parser.set_defaults(gradient_clip_val=1.0, lr_layers_getter='get_layer_lrs_with_crf')
args = parser.parse_args()
if args.ltp_model is not None and args.resume_from_checkpoint is not None:
deploy_model(args, args.ltp_version)
elif args.build_ner_dataset:
build_ner_distill_dataset(args)
elif args.tune:
from ltp.utils.common_train import tune
tune_config = {
# 3e-4 for Small, 1e-4 for Base, 5e-5 for Large
"lr": tune.loguniform(args.tune_min_lr, args.tune_max_lr),
# dataset split
"tau": tune.choice([0.8, 0.9, 1.0]),
# 梯度衰减
"weight_decay": tune.choice([0.0, 0.01]),
# 梯度裁剪
"gradient_clip_val": tune.choice([1.0, 2.0, 3.0, 4.0, 5.0]),
# lr scheduler
"lr_scheduler": tune.choice([
'linear_schedule_with_warmup',
'polynomial_decay_schedule_with_warmup',
]),
}
tune_train(args, model_class=Model, task_info=task_info, build_method=build_method, tune_config=tune_config)
else:
common_train(args, model_class=Model, task_info=task_info, build_method=build_method)
def main():
parser = ArgumentParser()
parser = add_task_specific_args(parser)
parser = Model.add_model_specific_args(parser)
parser = optimization.add_optimizer_specific_args(parser)
parser = Trainer.add_argparse_args(parser)
parser.set_defaults(gradient_clip_val=1.0)
args = parser.parse_args()
if args.ltp_model is not None and args.resume_from_checkpoint is not None:
deploy_model(args, args.ltp_version)
else:
common_train(args,
metric=f'val_{task_info.metric_name}',
model_class=Model,
build_method=build_method,
task=task_info.task_name)