Update README.md

README.md

@@ -63,11 +63,11 @@ from PIL import Image
 text = 'a small red panda in a zoo'
 image = Image.open('red_panda.jpg')
 
-image_data = model.preprocess_image(image)
-text_data = model.preprocess_text(text)
+image_data = processor.preprocess_image(image)
+text_data = processor.preprocess_text(text)
 
-image_embedding = model.encode_image(image_data)
-text_embedding = model.encode_text(text_data)
+image_features, image_embedding = model.encode_image(image_data, return_features=True)
+text_features, text_embedding = model.encode_text(text_data, return_features=True)
 score, joint_embedding = model.encode_multimodal(
     image_features=image_features,
     text_features=text_features,
@@ -76,23 +76,6 @@ score, joint_embedding = model.encode_multimodal(
 )
 ```
 
-To get features:
-
-```python
-image_features, image_embedding = model.encode_image(image_data, return_features=True)
-text_features, text_embedding = model.encode_text(text_data, return_features=True)
-```
-
-These features can later be used to produce joint multimodal encodings faster, as the first layers of the transformer can be skipped:
-
-```python
-joint_embedding = model.encode_multimodal(
-    image_features=image_features,
-    text_features=text_features,
-    attention_mask=text_data['attention_mask']
-)
-```
-
 There are two options to calculate semantic compatibility between an image and a text: [Cosine Similarity](#cosine-similarity) and [Matching Score](#matching-score).
 
 ### Cosine Similarity