Update README.md

README.md

@@ -19,8 +19,10 @@ tokenizer = AutoTokenizer.from_pretrained('SajjadAyoubi/clip-fa-text')
 text = 'something'
 image = PIL.Image.open('my_favorite_image.jpg')
 # compute embeddings
-text_embedding = text_encoder(**tokenizer(text, return_tensors='pt')).pooler_output
-image_embedding = vision_encoder(**preprocessor(image, return_tensors='pt')).pooler_output
+text_embedding = text_encoder(**tokenizer(text,
+                                          return_tensors='pt')).pooler_output
+image_embedding = vision_encoder(**preprocessor(image, 
+                                                return_tensors='pt')).pooler_output
 text_embedding.shape == image_embedding.shape
 ```
 
@@ -30,7 +32,7 @@ The followings are just some use cases of CLIPfa on 25K [`Unsplash images`](http
 ```python
 from clipfa import CLIPDemo
 demo = CLIPDemo(vision_encoder, text_encoder, tokenizer)
-demo.compute_text_embeddings(['سیب','موز' ,'آلبالو'])
+demo.compute_text_embeddings(['گاو' ,'اسب' ,'ماهی'])
 demo.compute_image_embeddings(test_df.image_path.to_list())
 ```
 ### Image Search: