diff --git a/experimental/eval/main.py b/experimental/eval/main.py
index d085335..dd15f61 100644
--- a/experimental/eval/main.py
+++ b/experimental/eval/main.py
@@ -36,12 +36,13 @@ def run_eval(args):
     except:
         print(f"Tabby Server is not ready, please check if '{api}' is correct.")
         return
-    
-    items = [x for x in processing.items_from_filepattern(args.filepattern) if valid_item(x)];
+
+    items = [
+        x for x in processing.items_from_filepattern(args.filepattern) if valid_item(x)
+    ]
     if len(items) > args.max_records:
-        random.seed(0xbadbeef)
+        random.seed(0xBADBEEF)
         items = random.sample(items, args.max_records)
-    
 
     for item in items:
         if not valid_item(item):
@@ -56,10 +57,10 @@ def run_eval(args):
         prediction = resp.choices[0].text
 
         block_score = scorer(label, prediction)
-        
+
         label_lines = label.splitlines()
         prediction_lines = prediction.splitlines()
-        
+
         if len(label_lines) > 0 and len(prediction_lines) > 0:
             line_score = scorer(label_lines[0], prediction_lines[0])
 
@@ -71,13 +72,19 @@ def run_eval(args):
             line_score=line_score,
         )
 
+
 if __name__ == "__main__":
     logging.basicConfig(stream=sys.stderr, level=logging.INFO)
 
-    parser = argparse.ArgumentParser(description='SxS eval for tabby')
-    parser.add_argument('filepattern', type=str, help='File pattern to dataset.')
-    parser.add_argument('max_records', type=int, help='Max number of records to be evaluated.')
+    parser = argparse.ArgumentParser(
+        description="SxS eval for tabby",
+        epilog="Example usage: python main.py ./tabby/dataset/data.jsonl 5 > output.jsonl",
+    )
+    parser.add_argument("filepattern", type=str, help="File pattern to dataset.")
+    parser.add_argument(
+        "max_records", type=int, help="Max number of records to be evaluated."
+    )
     args = parser.parse_args()
     logging.info("args %s", args)
     df = pd.DataFrame(run_eval(args))
-    print(df.to_json(orient='records', lines=True))
+    print(df.to_json(orient="records", lines=True))