Update README.md

README.md

@@ -65,38 +65,6 @@ These weights are directly usable in OpenCLIP (image + text).
 | GeoDE                  | 0.9253  |
 | **Average**                | **0.68039** |
 
-## Model Usage
-### With OpenCLIP
-```
-import torch
-import torch.nn.functional as F
-from urllib.request import urlopen
-from PIL import Image
-from open_clip import create_model_from_pretrained, get_tokenizer 
-
-model, preprocess = create_model_from_pretrained('hf-hub:apple/DFN2B-CLIP-ViT-L-14')
-tokenizer = get_tokenizer('ViT-L-14')
-
-image = Image.open(urlopen(
-    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
-))
-image = preprocess(image).unsqueeze(0)
-
-labels_list = ["a dog", "a cat", "a donut", "a beignet"]
-text = tokenizer(labels_list, context_length=model.context_length)
-
-with torch.no_grad(), torch.cuda.amp.autocast():
-    image_features = model.encode_image(image)
-    text_features = model.encode_text(text)
-    image_features = F.normalize(image_features, dim=-1)
-    text_features = F.normalize(text_features, dim=-1)
-
-    text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)
-
-zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]]))
-print("Label probabilities: ", zipped_list)
-```
-
 ## Citation
 ```bibtex
 @article{fang2023data,