diff --git a/examples/semantic_indexing/evaluate.py b/examples/semantic_indexing/evaluate.py
index 7107c08961aa..bfa086e521c9 100644
--- a/examples/semantic_indexing/evaluate.py
+++ b/examples/semantic_indexing/evaluate.py
@@ -17,10 +17,23 @@
 import numpy as np
 
 parser = argparse.ArgumentParser()
-parser.add_argument("--similar_text_pair", type=str, default="", help="The full path of similat pair file")
-parser.add_argument("--recall_result_file", type=str, default="", help="The full path of recall result file")
 parser.add_argument(
-    "--recall_num", type=int, default=10, help="Most similair number of doc recalled from corpus per query"
+    "--similar_text_pair",
+    type=str,
+    default="",
+    help="The full path of similat pair file",
+)
+parser.add_argument(
+    "--recall_result_file",
+    type=str,
+    default="",
+    help="The full path of recall result file",
+)
+parser.add_argument(
+    "--recall_num",
+    type=int,
+    default=10,
+    help="Most similair number of doc recalled from corpus per query",
 )
 args = parser.parse_args()
 
@@ -57,11 +70,6 @@ def recall(rs, N=10):
     with open(args.recall_result_file, "r", encoding="utf-8") as f:
         relevance_labels = []
         for index, line in enumerate(f):
-
-            if index % args.recall_num == 0 and index != 0:
-                rs.append(relevance_labels)
-                relevance_labels = []
-
             text, recalled_text, cosine_sim = line.rstrip().split("\t")
             if text == recalled_text:
                 continue
@@ -70,6 +78,10 @@ def recall(rs, N=10):
             else:
                 relevance_labels.append(0)
 
+            if (index + 1) % args.recall_num == 0:
+                rs.append(relevance_labels)
+                relevance_labels = []
+
     recall_N = []
     for topN in (10, 50):
         R = round(100 * recall(rs, N=topN), 3)