Update README.md

README.md

@@ -58,47 +58,24 @@ To encode data:
 
 ```python
 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_embedding = model.encode_image(image_data)
-text_embedding = model.encode_text(text_data)
-score, joint_embedding = model.encode_multimodal(
-    image_features=image_features,
-    text_features=text_features,
-    attention_mask=text_data['attention_mask'],
-    return_scores=True
-)
-```
 
-To get features:
+image_data = processor.preprocess_image(image)
+text_data = processor.preprocess_text(text)
 
-```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(
+score, joint_embedding = model.encode_multimodal(
     image_features=image_features,
     text_features=text_features,
-    attention_mask=text_data['attention_mask']
+    attention_mask=text_data['attention_mask'],
+    return_scores=True
 )
 ```
 
-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
-
-```python
-import torch.nn.functional as F
-similarity = F.cosine_similarity(image_embedding, text_embedding)
-```
-
-The `similarity` will belong to the `[-1, 1]` range, `1` meaning the absolute match.
+There are two options to calculate semantic compatibility between an image and a text: cosine similarity and [Matching Score](#matching-score).
 
 __Pros__: