diff --git a/README.md b/README.md
index a8345a9c4..bab729dd7 100644
--- a/README.md
+++ b/README.md
@@ -234,7 +234,7 @@ $ pip install fuse-med-ml[all,examples]
     * [**Mortality prediction for ICU patients**](./fuse_examples/multimodality/ehr_transformer) - Example of EHR transformer applied to the data of Intensive Care Units patients for in-hospital mortality prediction. The dataset is from [PhysioNet Computing in Cardiology Challenge (2012)](https://physionet.org/content/challenge-2012/1.0.0/)
 * Pre-training
     * [**Medical Imaging Pre-training and Downstream Task Validation**](./fuse_examples/imaging/oai_example) - pre-training a model on 3D MRI medical imaging and then using it for classification and segmentation downstream tasks.
-   
+
 
 ## Walkthrough template
 * [**Walkthrough Template**](./fuse/dl/templates/walkthrough_template.py) - includes several TODO notes, marking the minimal scope of code required to get your pipeline up and running. The template also includes useful explanations and tips.
diff --git a/fuse/data/tokenizers/modular_tokenizer/op.py b/fuse/data/tokenizers/modular_tokenizer/op.py
index 0f2b85697..9ccf6650a 100644
--- a/fuse/data/tokenizers/modular_tokenizer/op.py
+++ b/fuse/data/tokenizers/modular_tokenizer/op.py
@@ -116,7 +116,7 @@ def get_max_token_id(self) -> Tuple[str, int]:
     def get_min_max_sentinels(
         self,
         sentinel_prefix: Optional[str] = "<SENTINEL_ID_",
-        integer_find_regex: Optional[str] = "\d{1,}",
+        integer_find_regex: Optional[str] = r"\d{1,}",
     ) -> Tuple[int, int]:
         """
         returns a Tuple [min encountered sentinel name, max encountered sentinel name]
@@ -186,6 +186,22 @@ def get_max_len(
         """
         return self._tokenizer.get_expected_max_len(override_max_len=override_max_len)
 
+    def add_new_special_tokens(self, new_special_tokens: list[str]) -> int:
+        """add new special tokens if they are not in the tokenizer.
+        Skipps allready existing special tokens.
+
+        Args:
+            new_special_tokens (list[str]): the tokens to add
+        Returns:
+            `int`: The number of tokens that were created in the vocabulary
+
+        Will raise an exception if any of the tokens are allready in the tokenizer as _regular_ tokens.
+        """
+
+        tokenizer = self._tokenizer
+        num_new_tokens = tokenizer.add_special_tokens(new_special_tokens)
+        return num_new_tokens
+
     def __call__(
         self,
         sample_dict: NDict,