diff --git a/applications/Chat/coati/dataset/sft_dataset.py b/applications/Chat/coati/dataset/sft_dataset.py
index 3702d00cc..3038fbe07 100644
--- a/applications/Chat/coati/dataset/sft_dataset.py
+++ b/applications/Chat/coati/dataset/sft_dataset.py
@@ -74,15 +74,10 @@ class SFTDataset(Dataset):
         return dict(input_ids=self.input_ids[idx], labels=self.labels[idx])
 
 
-def _tokenize_fn(strings: Sequence[str],
-                 tokenizer: transformers.PreTrainedTokenizer,
-                 max_length: int
-                 ) -> Dict[str, torch.Tensor]:
+def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer,
+                 max_length: int) -> Dict[str, torch.Tensor]:
     """Tokenize a list of strings."""
-    tokenized_list = tokenizer(
-        strings, return_tensors="pt", padding="longest",
-        max_length=max_length, truncation=True
-    )
+    tokenized_list = tokenizer(strings, return_tensors="pt", padding="longest", max_length=max_length, truncation=True)
     input_ids = labels = tokenized_list["input_ids"]
     input_ids_lens = labels_lens = \
         tokenized_list["input_ids"].ne(tokenizer.pad_token_id).sum(dim=-1)
@@ -103,8 +98,7 @@ def preprocess(
     """Preprocess the data by tokenizing."""
     examples = [s + t for s, t in zip(sources, targets)]
     examples_tokenized, sources_tokenized = [
-        _tokenize_fn(strings, tokenizer, max_length)
-        for strings in (examples, sources)
+        _tokenize_fn(strings, tokenizer, max_length) for strings in (examples, sources)
     ]
     input_ids = examples_tokenized["input_ids"]
     labels = copy.deepcopy(input_ids)
@@ -116,7 +110,11 @@ def preprocess(
 class SupervisedDataset(Dataset):
     """Dataset for supervised fine-tuning."""
 
-    def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer, max_datasets_size: int = None, max_length: int = 512):
+    def __init__(self,
+                 data_path: str,
+                 tokenizer: transformers.PreTrainedTokenizer,
+                 max_datasets_size: int = None,
+                 max_length: int = 512):
         super(SupervisedDataset, self).__init__()
         logger.info("Loading data...")
         list_data_dict = jload(data_path)