Update README.md

README.md

@@ -85,6 +85,46 @@ passage_embeddings = model.encode(
 ```
 </details>
 
+<details>
+  <summary>via <a href="https://sbert.net/">sentence-transformers</a></summary>
+
+```python
+# !pip install sentence_transformers>=5.0.0 torch>=2.7.1
+
+import torch
+from sentence_transformers import SentenceTransformer
+
+# Load the model
+model = SentenceTransformer(
+    "jinaai/jina-embeddings-c1-0.5B",
+    model_kwargs={
+        "torch_dtype": torch.bfloat16,
+        "attn_implementation": "flash_attention_2",
+        "device_map": "auto"
+    }
+)
+
+# The queries and documents to embed
+queries = [
+    "print hello world in python",
+    "initialize array of 5 zeros in c++"
+]
+documents = [
+    "print('Hello World!')",
+    "int arr[5] = {0, 0, 0, 0, 0};"
+]
+
+query_embeddings = model.encode(queries, prompt_name="nl2code_query")
+document_embeddings = model.encode(documents, prompt_name="nl2code_document")
+
+# Compute the (cosine) similarity between the query and document embeddings
+similarity = model.similarity(query_embeddings, document_embeddings)
+print(similarity)
+# tensor([[0.8157, 0.1222],
+#         [0.1201, 0.5500]])
+```
+</details>
+
 ## Training & Evaluation
 
 Please refer to our technical report of jina-embeddings-c1 for training details and benchmarks.