diff --git a/chapters/zh-TW/chapter3/2.mdx b/chapters/zh-TW/chapter3/2.mdx
index 4cc8f883d..49039a4ea 100644
--- a/chapters/zh-TW/chapter3/2.mdx
+++ b/chapters/zh-TW/chapter3/2.mdx
@@ -164,8 +164,8 @@ from transformers import AutoTokenizer
 
 checkpoint = "bert-base-uncased"
 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-tokenized_sentences_1 = tokenizer(raw_datasets["train"]["sentence1"])
-tokenized_sentences_2 = tokenizer(raw_datasets["train"]["sentence2"])
+tokenized_sentences_1 = tokenizer(list(raw_datasets["train"]["sentence1"]))
+tokenized_sentences_2 = tokenizer(list(raw_datasets["train"]["sentence2"]))
 ```
 
 然而，在兩句話傳遞給模型，預測這兩句話是否是同義之前。我們需要這兩句話依次進行適當的預處理。幸運的是，標記器不僅僅可以輸入單個句子還可以輸入一組句子，並按照我們的BERT模型所期望的輸入進行處理：
@@ -221,8 +221,8 @@ tokenizer.convert_ids_to_tokens(inputs["input_ids"])
 
 ```py
 tokenized_dataset = tokenizer(
-    raw_datasets["train"]["sentence1"],
-    raw_datasets["train"]["sentence2"],
+    raw_datasets["train"]["sentence1"].to_pylist(),
+    raw_datasets["train"]["sentence2"].to_pylist(),
     padding=True,
     truncation=True,
 )