diff --git a/auto_transformers.py b/auto_transformers.py
index f7dc4d8..050cfb1 100644
--- a/auto_transformers.py
+++ b/auto_transformers.py
@@ -38,11 +38,14 @@ class AutoTransformers(NNOperator):
             Which model to use for the embeddings.
     """
 
-    def __init__(self, model_name: str = "bert-base-uncased") -> None:
+    def __init__(self, model_name: str = "bert-base-uncased", device=None) -> None:
         super().__init__()
+        if device is None:
+            device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.device = device
         self.model_name = model_name
         try:
-            self.model = AutoModel.from_pretrained(model_name)
+            self.model = AutoModel.from_pretrained(model_name).to(self.device)
             self.model.eval()
         except Exception as e:
             model_list = self.supported_model_names()
@@ -59,7 +62,7 @@ class AutoTransformers(NNOperator):
 
     def __call__(self, txt: str) -> numpy.ndarray:
         try:
-            inputs = self.tokenizer(txt, return_tensors="pt")
+            inputs = self.tokenizer(txt, return_tensors="pt").to(self.device)
         except Exception as e:
             log.error(f'Invalid input for the tokenizer: {self.model_name}')
             raise e
@@ -73,7 +76,7 @@ class AutoTransformers(NNOperator):
         except Exception as e:
             log.error(f'Fail to extract features by model: {self.model_name}')
             raise e
-        vec = features.detach().numpy()
+        vec = features.cpu().detach().numpy()
         return vec
 
     def save_model(self, format: str = 'pytorch', path: str = 'default'):