diff --git a/__init__.py b/__init__.py
index 9d2895f..2f252bf 100644
--- a/__init__.py
+++ b/__init__.py
@@ -14,5 +14,5 @@
 
 from .jclip import Jaclip
 
-def jclip(model_name: str, modality: str):
+def japanese_clip(model_name: str, modality: str):
     return Jaclip(model_name, modality)
diff --git a/jclip.py b/jclip.py
index aa3d4dc..c11e8bf 100644
--- a/jclip.py
+++ b/jclip.py
@@ -34,6 +34,7 @@ class Jaclip(NNOperator):
         import japanese_clip as ja_clip
         sys.path.pop()
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self._modality = modality
         model, preprocess = ja_clip.load("rinna/japanese-clip-vit-b-16", cache_dir="{}/weights/japanese_clip".format(path), device=self.device)
         self.model = model
         self.tfms = preprocess
@@ -51,20 +52,20 @@ class Jaclip(NNOperator):
         return vec.detach().cpu().numpy().flatten()
 
     def _inference_from_text(self, text):
-        encodings = ja_clip.tokenize(
+        encodings = self.ja_clip.tokenize(
             texts=[text],
             max_seq_len=77,
             device=self.device,
             tokenizer=self.tokenizer, # this is optional. if you don't pass, load tokenizer each time
         )
-        text_feature = model.get_text_features(**encodings)
+        text_feature = self.model.get_text_features(**encodings)
         return text_feature
 
     @arg(1, to_image_color('RGB'))
     def _inference_from_image(self, img):
         img = self._preprocess(img)
         caption = ''
-        image_feature = self.model.get_image_features(image)
+        image_feature = self.model.get_image_features(img)
         return image_feature
 
     def _preprocess(self, img):