diff --git a/README.md b/README.md
index 0811f1e..f5a32d3 100644
--- a/README.md
+++ b/README.md
@@ -35,8 +35,8 @@ towhee.dc(["A teddybear on a skateboard in Times Square."]) \
       .image_text_embedding.lightningdot(modality='text') \
       .show()
 ```
-<img src="https://towhee.io/towhee/lightningdot/raw/branch/main/vec1.png" alt="result1" style="height:20px;"/>
-<img src="https://towhee.io/towhee/lightningdot/raw/branch/main/vec2.png" alt="result2" style="height:20px;"/>
+<img src="https://towhee.io/image-text-embedding/lightningdot/raw/branch/main/vec1.png" alt="result1" style="height:20px;"/>
+<img src="https://towhee.io/image-text-embedding/lightningdot/raw/branch/main/vec2.png" alt="result2" style="height:20px;"/>
 
 *Write a same pipeline with explicit inputs/outputs name specifications:*
 
@@ -66,10 +66,18 @@ towhee.dc['text'](["A teddybear on a skateboard in Times Square."]) \
 
 Create the operator via the following factory method
 
-***lightningdot(modality)***
+***lightningdot(model_name, modality)***
 
 **Parameters:**
 
+
+​   ***model_name:*** *str*
+
+​   The model name of LightningDOT. Supported model names: 
+- lightningdot_base
+- lightningdot_coco_ft
+- lightningdot_flickr_ft     
+
 ​   ***modality:*** *str*
 
 ​   Which modality(*image* or *text*) is used to generate the embedding. 
diff --git a/lightningdot.py b/lightningdot.py
index fdc611e..c54b598 100644
--- a/lightningdot.py
+++ b/lightningdot.py
@@ -79,7 +79,7 @@ class LightningDOT(NNOperator):
                     state_dict[k[5:]] = state_dict.pop(k)
                 else:
                     state_dict.pop(k)
-            bi_encoder.load_state_dict(state_dict, strict=True)
+            self.bi_encoder.load_state_dict(state_dict, strict=True)
 
         img_model, txt_model = self.bi_encoder.img_model, self.bi_encoder.txt_model
         img_model.eval()
@@ -138,7 +138,7 @@ class LightningDOT(NNOperator):
             vec = self._inference_from_text(data)
         else:
             raise ValueError("modality[{}] not implemented.".format(self._modality))
-        return vec.detach().cpu().numpy()
+        return vec.detach().cpu().numpy().flatten()
     
     def _inference_from_text(self, data):
         ids = self.tokenizer.encode(data)