def run_ner():
"""run ner task"""
args_opt = parse_args()
epoch_num = args_opt.epoch_num
assessment_method = args_opt.assessment_method.lower()
load_pretrain_checkpoint_path = args_opt.load_pretrain_checkpoint_path
save_finetune_checkpoint_path = args_opt.save_finetune_checkpoint_path
load_finetune_checkpoint_path = args_opt.load_finetune_checkpoint_path
target = args_opt.device_target
if target == "Ascend":
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
elif target == "GPU":
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
if bert_net_cfg.compute_type != mstype.float32:
logger.warning('GPU only support fp32 temporarily, run with fp32.')
bert_net_cfg.compute_type = mstype.float32
else:
raise Exception("Target error, GPU or Ascend is supported.")
label_list = []
with open(args_opt.label_file_path) as f:
for label in f:
label_list.append(label.strip())
tag_to_index = convert_labels_to_index(label_list)
if args_opt.use_crf.lower() == "true":
max_val = max(tag_to_index.values())
tag_to_index["<START>"] = max_val + 1
tag_to_index["<STOP>"] = max_val + 2
number_labels = len(tag_to_index)
else:
number_labels = args_opt.num_class
if args_opt.do_train.lower() == "true":
netwithloss = BertNER(bert_net_cfg, args_opt.train_batch_size, True, num_labels=number_labels,
use_crf=(args_opt.use_crf.lower() == "true"),
tag_to_index=tag_to_index, dropout_prob=0.1)
ds = create_ner_dataset(batch_size=args_opt.train_batch_size, repeat_count=1,
assessment_method=assessment_method, data_file_path=args_opt.train_data_file_path,
schema_file_path=args_opt.schema_file_path,
do_shuffle=(args_opt.train_data_shuffle.lower() == "true"))
do_train(ds, netwithloss, load_pretrain_checkpoint_path, save_finetune_checkpoint_path, epoch_num)
if args_opt.do_eval.lower() == "true":
if save_finetune_checkpoint_path == "":
load_finetune_checkpoint_dir = _cur_dir
else:
load_finetune_checkpoint_dir = make_directory(save_finetune_checkpoint_path)
load_finetune_checkpoint_path = LoadNewestCkpt(load_finetune_checkpoint_dir,
ds.get_dataset_size(), epoch_num, "ner")
if args_opt.do_eval.lower() == "true":
ds = create_ner_dataset(batch_size=args_opt.eval_batch_size, repeat_count=1,
assessment_method=assessment_method, data_file_path=args_opt.eval_data_file_path,
schema_file_path=args_opt.schema_file_path,
do_shuffle=(args_opt.eval_data_shuffle.lower() == "true"))
do_eval(ds, BertNER, args_opt.use_crf, number_labels, assessment_method,
args_opt.eval_data_file_path, load_finetune_checkpoint_path, args_opt.vocab_file_path,
args_opt.label_file_path, tag_to_index, args_opt.eval_batch_size)
type=str,
default="Ascend",
choices=["Ascend", "GPU", "CPU"],
help="device target (default: Ascend)")
args = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
if args.device_target == "Ascend":
context.set_context(device_id=args.device_id)
label_list = []
with open(args.label_file_path) as f:
for label in f:
label_list.append(label.strip())
tag_to_index = convert_labels_to_index(label_list)
if args.use_crf.lower() == "true":
max_val = max(tag_to_index.values())
tag_to_index["<START>"] = max_val + 1
tag_to_index["<STOP>"] = max_val + 2
number_labels = len(tag_to_index)
else:
number_labels = args.num_class
if __name__ == "__main__":
if args.downstream_task == "NER":
if args.use_crf.lower() == "true":
net = BertNER(bert_net_cfg,
args.batch_size,
False,
