Add Sentence Transformers usage

README.md

@@ -13,6 +13,7 @@ tags:
 - lfm2
 - edge
 - ColBERT
+- multi-vector
 - PyLate
 - sentence-transformers
 - sentence-similarity
@@ -842,6 +843,38 @@ ColBERT(
 
 <a href="https://colab.research.google.com/drive/1tXSAXGpjuTvliuTrSSHDEcmIe48uolrD?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width=120 alt="Colab link"></a>
 
+### Sentence Transformers
+
+This model can be used with [Sentence Transformers](https://www.sbert.net/) as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`:
+
+```bash
+pip install "sentence-transformers>=6.0.0"
+```
+
+```python
+from sentence_transformers import MultiVectorEncoder
+
+model = MultiVectorEncoder("LiquidAI/LFM2-ColBERT-350M")
+
+query = "Which planet is known as the Red Planet?"
+documents = [
+    "Venus wird oft als Zwilling der Erde bezeichnet, wegen ihrer ähnlichen Größe.",
+    "Mars, connue pour son apparence rougeâtre, est souvent appelée la planète rouge.",
+    "Júpiter es el planeta más grande del sistema solar.",
+    "Saturno è famoso per i suoi bellissimi anelli.",
+]
+
+query_embeddings = model.encode_query(query)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings[0].shape)
+# (32, 128) (17, 128)
+
+# MaxSim late-interaction scoring (higher is more relevant)
+scores = model.similarity(query_embeddings, document_embeddings)
+print(scores)
+# tensor([[30.3632, 30.4892, 30.4024, 30.3004]])
+```
+
 First, install the PyLate and transformers library:
 
 ```bash