diff --git a/model_zoo/official/nlp/bert/run_ner.py b/model_zoo/official/nlp/bert/run_ner.py
index a2ebdc5b3e..19746a69d9 100644
--- a/model_zoo/official/nlp/bert/run_ner.py
+++ b/model_zoo/official/nlp/bert/run_ner.py
@@ -230,7 +230,7 @@ def run_ner():
                                                            ds.get_dataset_size(), epoch_num, "ner")
 
     if args_opt.do_eval.lower() == "true":
-        ds = create_ner_dataset(batch_size=bert_net_cfg.batch_size, repeat_count=1,
+        ds = create_ner_dataset(batch_size=optimizer_cfg.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"))
diff --git a/model_zoo/official/nlp/bert/scripts/run_distributed_pretrain_ascend.sh b/model_zoo/official/nlp/bert/scripts/run_distributed_pretrain_ascend.sh
index 0843b01c9d..40b214ab06 100644
--- a/model_zoo/official/nlp/bert/scripts/run_distributed_pretrain_ascend.sh
+++ b/model_zoo/official/nlp/bert/scripts/run_distributed_pretrain_ascend.sh
@@ -16,8 +16,8 @@
 
 echo "=============================================================================================================="
 echo "Please run the scipt as: "
-echo "bash run_distributed_pretrain.sh DATA_DIR RANK_TABLE_FILE"
-echo "for example: bash run_distributed_pretrain.sh /path/dataset /path/hccl.json"
+echo "bash run_distributed_pretrain_ascend.sh DATA_DIR RANK_TABLE_FILE"
+echo "for example: bash run_distributed_pretrain_ascend.sh /path/dataset /path/hccl.json"
 echo "It is better to use absolute path."
 echo "For hyper parameter, please note that you should customize the scripts:
           '{CUR_DIR}/scripts/ascend_distributed_launcher/hyper_parameter_config.ini' "
diff --git a/model_zoo/official/nlp/bert/scripts/run_standalone_pretrain_ascend.sh b/model_zoo/official/nlp/bert/scripts/run_standalone_pretrain_ascend.sh
index ae07a1bda9..1cbfababfd 100644
--- a/model_zoo/official/nlp/bert/scripts/run_standalone_pretrain_ascend.sh
+++ b/model_zoo/official/nlp/bert/scripts/run_standalone_pretrain_ascend.sh
@@ -16,8 +16,8 @@
 
 echo "=============================================================================================================="
 echo "Please run the scipt as: "
-echo "bash run_standalone_pretrain.sh DEVICE_ID EPOCH_SIZE DATA_DIR SCHEMA_DIR"
-echo "for example: bash run_standalone_pretrain.sh 0 40 /path/zh-wiki/ /path/Schema.json"
+echo "bash run_standalone_pretrain_ascend.sh DEVICE_ID EPOCH_SIZE DATA_DIR SCHEMA_DIR"
+echo "for example: bash run_standalone_pretrain_ascend.sh 0 40 /path/zh-wiki/ /path/Schema.json"
 echo "=============================================================================================================="
 
 DEVICE_ID=$1
diff --git a/model_zoo/official/nlp/bert/src/bert_model.py b/model_zoo/official/nlp/bert/src/bert_model.py
index ea973030a7..9115045620 100644
--- a/model_zoo/official/nlp/bert/src/bert_model.py
+++ b/model_zoo/official/nlp/bert/src/bert_model.py
@@ -542,7 +542,7 @@ class BertAttention(nn.Cell):
             attention_probs_r = self.reshape(
                 attention_probs_t,
                 (self.from_seq_length,
-                 self.batch_num,
+                 -1,
                  self.to_seq_length))
             # value_position_scores is [F, B * N, H]
             value_position_scores = self.matmul(attention_probs_r,
@@ -550,7 +550,7 @@ class BertAttention(nn.Cell):
             # value_position_scores_r is [F, B, N, H]
             value_position_scores_r = self.reshape(value_position_scores,
                                                    (self.from_seq_length,
-                                                    self.batch_size,
+                                                    -1,
                                                     self.num_attention_heads,
                                                     self.size_per_head))
             # value_position_scores_r_t is [B, N, F, H]