linter+fmt

nlp/normalize.py

@@ -1,5 +1,8 @@
 import torch
 from transformers import ByT5Tokenizer, T5ForConditionalGeneration
+import logging
+
+logger = logging.getLogger("nlp.normalize")
 
 # Use ByT5 for the ByT5 model
 tokenizer = ByT5Tokenizer.from_pretrained("google/byt5-small")
@@ -48,8 +51,8 @@ def normalize(text):
 
     if is_russian_wording(t):
         # Normalize the text by replacing characters
-        normalized_text = (t
-            .replace("e", "е")
+        normalized_text = (
+            t.replace("e", "е")
             .replace("o", "о")
             .replace("x", "х")
             .replace("a", "а")
@@ -73,7 +76,7 @@ def normalize(text):
         )
 
         return normalized_text
-    logger.debug('normalized: ', t)
+    logger.debug("normalized: ", t)
     return t