diff --git a/fun_text_processing/inverse_text_normalization/run_evaluate.py b/fun_text_processing/inverse_text_normalization/run_evaluate.py
index 76e6e3ce2..bea92fa71 100644
--- a/fun_text_processing/inverse_text_normalization/run_evaluate.py
+++ b/fun_text_processing/inverse_text_normalization/run_evaluate.py
@@ -9,16 +9,14 @@ from fun_text_processing.text_normalization.data_loader_utils import (
     training_data_to_tokens,
 )
 
-
 """
 Runs Evaluation on data in the format of : <semiotic class>\t<unnormalized text>\t<`self` if trivial class or normalized text>
 like the Google text normalization data https://www.kaggle.com/richardwilliamsproat/text-normalization-for-english-russian-and-polish
 """
 
-
 def parse_args():
     parser = ArgumentParser()
-    parser.add_argument("--input", help="input file path", type=str)
+    parser.add_argument("--input", help="input file path", type=str, required=True)
     parser.add_argument(
         "--lang",
         help="language",
@@ -39,15 +37,13 @@ def parse_args():
     )
     return parser.parse_args()
 
-
 if __name__ == "__main__":
     # Example usage:
     # python run_evaluate.py --input=<INPUT> --cat=<CATEGORY> --filter
     args = parse_args()
     if args.lang == "en":
-        from fun_text_processing.inverse_text_normalization.en.clean_eval_data import (
-            filter_loaded_data,
-        )
+        from fun_text_processing.inverse_text_normalization.en.clean_eval_data import filter_loaded_data
+
     file_path = args.input
     inverse_normalizer = InverseNormalizer()
 
@@ -57,6 +53,7 @@ if __name__ == "__main__":
     if args.filter:
         training_data = filter_loaded_data(training_data)
 
+    # Evaluate at sentence level if no specific category is provided
     if args.category is None:
         print("Sentence level evaluation...")
         sentences_un_normalized, sentences_normalized, _ = training_data_to_sentences(training_data)
@@ -68,12 +65,12 @@ if __name__ == "__main__":
         )
         print("- Accuracy: " + str(sentences_accuracy))
 
+    # Evaluate at token level
     print("Token level evaluation...")
     tokens_per_type = training_data_to_tokens(training_data, category=args.category)
     token_accuracy = {}
-    for token_type in tokens_per_type:
+    for token_type, (tokens_un_normalized, tokens_normalized) in tokens_per_type.items():
         print("- Token type: " + token_type)
-        tokens_un_normalized, tokens_normalized = tokens_per_type[token_type]
         print("  - Data: " + str(len(tokens_normalized)) + " tokens")
         tokens_prediction = inverse_normalizer.inverse_normalize_list(tokens_normalized)
         print("  - Denormalized. Evaluating...")
@@ -81,9 +78,9 @@ if __name__ == "__main__":
             tokens_prediction, tokens_un_normalized, input=tokens_normalized
         )
         print("  - Accuracy: " + str(token_accuracy[token_type]))
-    token_count_per_type = {
-        token_type: len(tokens_per_type[token_type][0]) for token_type in tokens_per_type
-    }
+
+    # Calculate weighted token accuracy
+    token_count_per_type = {token_type: len(tokens) for token_type, (tokens, _) in tokens_per_type.items()}
     token_weighted_accuracy = [
         token_count_per_type[token_type] * accuracy
         for token_type, accuracy in token_accuracy.items()
@@ -96,19 +93,17 @@ if __name__ == "__main__":
         if token_type not in known_types:
             raise ValueError("Unexpected token type: " + token_type)
 
+    # Output table summarizing evaluation results if no specific category is provided
     if args.category is None:
         c1 = ["Class", "sent level"] + known_types
         c2 = ["Num Tokens", len(sentences_normalized)] + [
-            token_count_per_type[known_type] if known_type in tokens_per_type else "0"
-            for known_type in known_types
+            str(token_count_per_type.get(known_type, 0)) for known_type in known_types
         ]
-        c3 = ["Denormalization", sentences_accuracy] + [
-            token_accuracy[known_type] if known_type in token_accuracy else "0"
-            for known_type in known_types
+        c3 = ["Denormalization", str(sentences_accuracy)] + [
+            str(token_accuracy.get(known_type, "0")) for known_type in known_types
         ]
-
         for i in range(len(c1)):
-            print(f"{str(c1[i]):10s} | {str(c2[i]):10s} | {str(c3[i]):5s}")
+            print(f"{c1[i]:10s} | {c2[i]:10s} | {c3[i]:5s}")
     else:
         print(f"numbers\t{token_count_per_type[args.category]}")
         print(f"Denormalization\t{token_accuracy[args.category]}")