Update with instructions on using with SentenceTransformers

README.md

@@ -12,10 +12,33 @@ The model is intended to be used as a sentence encoder, similar to [Google's Uni
 
 Please see [our repo](https://github.com/JohnGiorgi/DeCLUTR) for full details. A simple example is shown below.
 
+##### With SentenceTransformers
+
 ```python
-import torch
 from scipy.spatial.distance import cosine
+from sentence_transformers import SentenceTransformer
+
+# Load the model
+model = SentenceTransformer("johngiorgi/declutr-sci-base")
+
+# Prepare some text to embed
+text = [
+    "Oncogenic KRAS mutations are common in cancer.",
+    "Notably, c-Raf has recently been found essential for development of K-Ras-driven NSCLCs.",
+]
 
+# Embed the text
+embeddings = model.encode(texts)
+
+# Compute a semantic similarity via the cosine distance
+semantic_sim = 1 - cosine(embeddings[0], embeddings[1])
+```
+
+##### With 🤗 Transformers
+
+```python
+import torch
+from scipy.spatial.distance import cosine
 from transformers import AutoModel, AutoTokenizer
 
 # Load the model