Update README.md

README.md

@@ -6,48 +6,41 @@ tags:
 - sentence-similarity
 ---
 
-# {MODEL_NAME}
+# clip-ViT-B-32
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a None dimensional dense vector space and can be used for tasks like clustering or semantic search.
+This is the Image & Text model [CLIP](https://arxiv.org/abs/2103.00020), which maps text and images to a shared vector space. For applications of the models, have a look in our documentation [SBERT.net - Image Search](https://www.sbert.net/examples/applications/image-search/README.html)
 
-<!--- Describe your model here -->
+## Usage
 
-## Usage (Sentence-Transformers)
-
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
-
-```
-pip install -U sentence-transformers
-```
-
-Then you can use the model like this:
+After installing [sentence-transformers](https://sbert.net) (`pip install sentence-transformers`), the usage of this model is easy:
 
+  
 ```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
-
+from sentence_transformers import SentenceTransformer, util
+from PIL import Image
 
-## Evaluation Results
+#Load CLIP model
+model = SentenceTransformer('clip-ViT-B-32')
 
-<!--- Describe how your model was evaluated -->
+#Encode an image:
+img_emb = model.encode(Image.open('two_dogs_in_snow.jpg'))
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+#Encode text descriptions
+text_emb = model.encode(['Two dogs in the snow', 'A cat on a table', 'A picture of London at night'])
 
+#Compute cosine similarities 
+cos_scores = util.cos_sim(img_emb, text_emb)
+print(cos_scores)
+```
 
+See our [SBERT.net - Image Search](https://www.sbert.net/examples/applications/image-search/README.html) documentation for more examples how the model can be used for image search, zero-shot image classification, image clustering and image deduplication.
 
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): CLIPModel()
-)
-```
+## Performance
 
-## Citing & Authors
+In the following table we find the zero-shot ImageNet validation set accuracy:
 
-<!--- Describe where people can find more information -->
+| Model | Top 1 Performance |
+| --- | :---: |
+| clip-ViT-B-32 | 63.3 |
+| clip-ViT-B-16 | 68.1 |
+| clip-ViT-L-14 | 75.4 |