diff --git a/model2vec/distill/distillation.py b/model2vec/distill/distillation.py
index 454173a1..be196b82 100644
--- a/model2vec/distill/distillation.py
+++ b/model2vec/distill/distillation.py
@@ -304,24 +304,35 @@ def _clean_vocabulary(tokenizer: Tokenizer, vocabulary: list[str], added_tokens:
     n_duplicates = 0
     n_multiword = 0
     for token in vocabulary:
-        if tokenizer.normalizer is not None:
-            token = tokenizer.normalizer.normalize_str(token)
+        normalizer = tokenizer.normalizer
+        if normalizer is not None:
+            token = normalizer.normalize_str(token)
 
         if not token:
             n_empty += 1
             continue
-        if token in seen_tokens or token in added_tokens_set:
-            n_duplicates += 1
-            continue
 
         pre_tokenizer = tokenizer.pre_tokenizer
+        # We need to check whether the pretokenized token is a single word or not.
         if pre_tokenizer is not None:
             pretokenized_tokens = pre_tokenizer.pre_tokenize_str(token)
             if len(pretokenized_tokens) != 1:
                 n_multiword += 1
                 continue
+            new_token = pretokenized_tokens[-1][0]
+        else:
+            new_token = token
+
+        # We need to check whether the pretokenized token is in the vocabulary.
+        # But we need to return the original token, because that will be tokenized
+        # again by the tokenizer during featurization.
+        if new_token in seen_tokens or new_token in added_tokens_set:
+            n_duplicates += 1
+            continue
 
-        seen_tokens.add(token)
+        # Add the possibly pretokenized token to _seen_
+        seen_tokens.add(new_token)
+        # Add the original string to the vocabulary.
         cleaned_vocabulary.append(token)
 
     if n_duplicates: