chore: update readme

README.md

@@ -46,7 +46,7 @@ This dual capability makes it an excellent tool for multimodal retrieval-augment
 ## Usage
 
 1. The easiest way to starting using jina-clip-v1-en is to use Jina AI's [Embeddings API](https://jina.ai/embeddings/).
-2. Alternatively, you can use Jina CLIP directly via transformers package.
+2. Alternatively, you can use Jina CLIP directly via transformers/sentence-transformers package.
 
 ```python
 !pip install transformers einops timm pillow
@@ -76,6 +76,28 @@ print(text_embeddings[1] @ image_embeddings[0].T) # text-image cross-modal simil
 print(text_embeddings[1] @ image_embeddings[1].T)# text-image cross-modal similarity
 ```
 
+or sentence-transformers:
+
+```python
+# !pip install -U sentence-transformers 
+from sentence_transformers import SentenceTransformer
+
+# Initialize the model
+model = SentenceTransformer('jinaai/jina-clip-v1', trust_remote_code=True)
+
+# New meaningful sentences
+sentences = ['A blue cat', 'A red cat']
+
+# Public image URLs
+image_urls = [
+    'https://i.pinimg.com/600x315/21/48/7e/21487e8e0970dd366dafaed6ab25d8d8.jpg',
+    'https://i.pinimg.com/736x/c9/f2/3e/c9f23e212529f13f19bad5602d84b78b.jpg'
+]
+
+text_embeddings = model.encode(sentences)
+image_embeddings = model.encode(image_urls)
+```
+
 3. JavaScript developers can use Jina CLIP via the [Transformers.js](https://huggingface.co/docs/transformers.js) library. Note that to use this model, you need to install Transformers.js [v3](https://github.com/xenova/transformers.js/tree/v3) from source using `npm install xenova/transformers.js#v3`.
 
 ```js