diff --git a/transformer_embedder/tokenizer.py b/transformer_embedder/tokenizer.py
index e9c8457..e19781a 100644
--- a/transformer_embedder/tokenizer.py
+++ b/transformer_embedder/tokenizer.py
@@ -35,7 +35,9 @@ def __init__(
             self.config = tr.AutoConfig.from_pretrained(model)
         else:
             self.huggingface_tokenizer = model
-            self.config = tr.AutoConfig.from_pretrained(self.huggingface_tokenizer.name_or_path)
+            self.config = tr.AutoConfig.from_pretrained(
+                self.huggingface_tokenizer.name_or_path
+            )
         # spacy tokenizer, lazy load. None at first
         self.spacy_tokenizer = None
         # default multilingual model
@@ -129,10 +131,16 @@ def __call__(
         )
 
         # if text is str or a list of str and they are not split, then text needs to be tokenized
-        if isinstance(text, str) or (not is_split_into_words and isinstance(text[0], str)):
+        if isinstance(text, str) or (
+            not is_split_into_words and isinstance(text[0], str)
+        ):
             if not is_batched:
                 text = self.pretokenize(text, use_spacy=use_spacy)
-                text_pair = self.pretokenize(text_pair, use_spacy=use_spacy) if text_pair else None
+                text_pair = (
+                    self.pretokenize(text_pair, use_spacy=use_spacy)
+                    if text_pair
+                    else None
+                )
             else:
                 text = [self.pretokenize(t, use_spacy=use_spacy) for t in text]
                 text_pair = (
@@ -216,13 +224,17 @@ def build_tokens(
         Returns:
             a dictionary with A and B encoded
         """
-        words, input_ids, token_type_ids, offsets = self._build_tokens(text, max_len=max_len)
+        words, input_ids, token_type_ids, offsets = self._build_tokens(
+            text, max_len=max_len
+        )
         if text_pair:
             words_b, input_ids_b, token_type_ids_b, offsets_b = self._build_tokens(
                 text_pair, True, max_len
             )
             # align offsets of sentence b
-            offsets_b = [(o[0] + len(input_ids), o[1] + len(input_ids)) for o in offsets_b]
+            offsets_b = [
+                (o[0] + len(input_ids), o[1] + len(input_ids)) for o in offsets_b
+            ]
             offsets = offsets + offsets_b
             input_ids += input_ids_b
             token_type_ids += token_type_ids_b
@@ -290,7 +302,9 @@ def _build_tokens(
         token_type_ids += [token_type_id]
         return words, input_ids, token_type_ids, offsets
 
-    def pad_batch(self, batch: Dict[str, list], max_length: int = None) -> Dict[str, list]:
+    def pad_batch(
+        self, batch: Dict[str, list], max_length: int = None
+    ) -> Dict[str, list]:
         """
         Pad the batch to its maximum length.
 
@@ -376,7 +390,9 @@ def pretokenize(self, text: str, use_spacy: bool = False) -> List[str]:
             return [t.text for t in text]
         return text.split(" ")
 
-    def add_special_tokens(self, special_tokens_dict: Dict[str, Union[str, tr.AddedToken]]) -> int:
+    def add_special_tokens(
+        self, special_tokens_dict: Dict[str, Union[str, tr.AddedToken]]
+    ) -> int:
         """
         Add a dictionary of special tokens (eos, pad, cls, etc.) to the encoder.
         If special tokens are NOT in the vocabulary, they are added to it (indexed starting from the last
@@ -442,7 +458,8 @@ def to_tensor(self, batch: Union[List[dict], dict]) -> Dict[str, "torch.Tensor"]
         """
         # convert to tensor
         batch = {
-            k: torch.as_tensor(v) if k in self.to_tensor_inputs else v for k, v in batch.items()
+            k: torch.as_tensor(v) if k in self.to_tensor_inputs else v
+            for k, v in batch.items()
         }
         return batch
 
@@ -457,7 +474,9 @@ def _load_spacy(self) -> "spacy.tokenizer.Tokenizer":
         try:
             spacy_tagger = spacy.load(self.language, exclude=["ner", "parser"])
         except OSError:
-            logger.info(f"Spacy model '{self.language}' not found. Downloading and installing.")
+            logger.info(
+                f"Spacy model '{self.language}' not found. Downloading and installing."
+            )
             spacy_download(self.language)
             spacy_tagger = spacy.load(self.language, exclude=["ner", "parser"])
         self.spacy_tokenizer = spacy_tagger.tokenizer
@@ -564,9 +583,9 @@ def num_special_tokens(self) -> int:
             int: the number of special tokens
 
         """
-        if isinstance(self.huggingface_tokenizer, MODELS_WITH_DOUBLE_SEP) and isinstance(
-            self.huggingface_tokenizer, MODELS_WITH_STARTING_TOKEN
-        ):
+        if isinstance(
+            self.huggingface_tokenizer, MODELS_WITH_DOUBLE_SEP
+        ) and isinstance(self.huggingface_tokenizer, MODELS_WITH_STARTING_TOKEN):
             return 4
         if isinstance(
             self.huggingface_tokenizer,