diff --git a/model_zoo/official/nlp/bert/export.py b/model_zoo/official/nlp/bert/export.py
index 6d74c97c691..84a71344ea2 100644
--- a/model_zoo/official/nlp/bert/export.py
+++ b/model_zoo/official/nlp/bert/export.py
@@ -21,23 +21,26 @@ import mindspore.common.dtype as mstype
 from mindspore.train.serialization import load_checkpoint, export
 
 from src.finetune_eval_model import BertCLSModel, BertSquadModel, BertNERModel
-from src.finetune_eval_config import optimizer_cfg, bert_net_cfg
+from src.finetune_eval_config import bert_net_cfg
 from src.bert_for_finetune import BertNER
 from src.utils import convert_labels_to_index
 
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 parser = argparse.ArgumentParser(description='Bert export')
+parser.add_argument("--device_id", type=int, default=0, help="Device id")
 parser.add_argument('--use_crf', type=str, default="false", help='Use cfg, default is false.')
 parser.add_argument('--downstream_task', type=str, choices=["NER", "CLS", "SQUAD"], default="NER",
                     help='at present，support NER only')
 parser.add_argument('--num_class', type=int, default=41, help='The number of class, default is 41.')
+parser.add_argument("--batch_size", type=int, default=16, help="batch size")
 parser.add_argument('--label_file_path', type=str, default="", help='label file path, used in clue benchmark.')
 parser.add_argument('--ckpt_file', type=str, required=True, help='Bert ckpt file.')
 parser.add_argument('--output_file', type=str, default='Bert', help='bert output air name.')
 parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
 args = parser.parse_args()
 
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args.device_id)
+
 label_list = []
 with open(args.label_file_path) as f:
     for label in f:
@@ -57,7 +60,7 @@ else:
 if __name__ == '__main__':
     if args.downstream_task == "NER":
         if args.use_crf.lower() == "true":
-            net = BertNER(bert_net_cfg, optimizer_cfg.batch_size, False, num_labels=number_labels,
+            net = BertNER(bert_net_cfg, args.batch_size, False, num_labels=number_labels,
                           use_crf=True, tag_to_index=tag_to_index)
         else:
             net = BertNERModel(bert_net_cfg, False, number_labels, use_crf=(args.use_crf.lower() == "true"))
@@ -71,10 +74,10 @@ if __name__ == '__main__':
     load_checkpoint(args.ckpt_file, net=net)
     net.set_train(False)
 
-    input_ids = Tensor(np.zeros([optimizer_cfg.batch_size, bert_net_cfg.seq_length]), mstype.int32)
-    input_mask = Tensor(np.zeros([optimizer_cfg.batch_size, bert_net_cfg.seq_length]), mstype.int32)
-    token_type_id = Tensor(np.zeros([optimizer_cfg.batch_size, bert_net_cfg.seq_length]), mstype.int32)
-    label_ids = Tensor(np.zeros([optimizer_cfg.batch_size, bert_net_cfg.seq_length]), mstype.int32)
+    input_ids = Tensor(np.zeros([args.batch_size, bert_net_cfg.seq_length]), mstype.int32)
+    input_mask = Tensor(np.zeros([args.batch_size, bert_net_cfg.seq_length]), mstype.int32)
+    token_type_id = Tensor(np.zeros([args.batch_size, bert_net_cfg.seq_length]), mstype.int32)
+    label_ids = Tensor(np.zeros([args.batch_size, bert_net_cfg.seq_length]), mstype.int32)
 
     if args.downstream_task == "NER" and args.use_crf.lower() == "true":
         input_data = [input_ids, input_mask, token_type_id, label_ids]