diff --git a/chinese-cosmopedia.py b/chinese-cosmopedia.py
new file mode 100644
index 0000000..6f55162
--- /dev/null
+++ b/chinese-cosmopedia.py
@@ -0,0 +1,14 @@
+from utils import process_dataset
+
+if __name__ == "__main__":
+    max_chunk_num = 10
+    chunk_size = 1000000
+    item_size = max_chunk_num * chunk_size
+    
+    process_dataset(
+        dataset_name="chinese-cosmopedia",
+        output_subdir="chinese-wiki",
+        split=f"train[:{item_size}]",
+        max_chunk_num=max_chunk_num,
+        chunk_size=chunk_size,
+    )
\ No newline at end of file
diff --git a/chinese-wiki.py b/chinese-wiki.py
deleted file mode 100644
index 2e08cc1..0000000
--- a/chinese-wiki.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from utils import process_dataset
-
-if __name__ == "__main__":
-    process_dataset(
-        dataset_name="Blaze7451/Wiki-zh-20250601",
-        output_subdir="chinese-wiki",
-        max_chunk_size=5,
-    )
\ No newline at end of file
diff --git a/english-fineweb.py b/english-fineweb.py
index 3333b4f..4d53dab 100644
--- a/english-fineweb.py
+++ b/english-fineweb.py
@@ -1,6 +1,5 @@
 from utils import process_dataset
 
-
 if __name__ == "__main__":
     process_dataset(
         dataset_name="HuggingFaceFW/fineweb",
diff --git a/tokenizer.py b/tokenizer.py
index 689e3fc..ba52a31 100644
--- a/tokenizer.py
+++ b/tokenizer.py
@@ -93,8 +93,8 @@ class BpeTokenizer:
             else:
                 return [encoding.tokens for encoding in encodings]
 
-    def decode(self, tokens: List[int]) -> str:
-        return self._tokenizer.decode(tokens)
+    def decode(self, tokens: List[int], skip_special_tokens=True) -> str:
+        return self._tokenizer.decode(tokens, skip_special_tokens=skip_special_tokens)
     
     def __len__(self) -> int:
         return self._tokenizer.get_vocab_size()
diff --git a/utils.py b/utils.py
index 131b110..a2441b4 100644
--- a/utils.py
+++ b/utils.py
@@ -61,19 +61,24 @@ def process_dataset(
     dataset_name: str,
     output_subdir: str,
     dataset_config: str = None,
-    max_chunk_size: int = None,
+    max_chunk_num: int = None,
+    split: str = None,
     split_name: str = "train",
     column_name: str = "text",
     chunk_size: int = 1000000,
     normalization_func=comprehensive_normalization
 ):
 
-    dataset_dict = load_dataset(dataset_name, dataset_config)
+    dataset_dict = load_dataset(
+        data_dir=dataset_name, 
+        data_files=dataset_config,
+        split=split
+    )
+    
     train_dataset = dataset_dict[split_name]
-
     total_samples = len(train_dataset)
     num_chunks = (total_samples // chunk_size) + 1
-    lim_chunks = min(max_chunk_size, num_chunks) if max_chunk_size else num_chunks
+    lim_chunks = min(max_chunk_num, num_chunks) if max_chunk_num else num_chunks
 
     script_dir = os.path.dirname(os.path.abspath(__file__))
     output_dir = os.path.join(script_dir, "dataset", output_subdir)