Add Sentence Transformers usage

README.md

@@ -7,6 +7,8 @@ metrics:
 - recall
 tags:
 - colbert
+- multi-vector
+- sentence-transformers
 - passage-retrieval
 library_name: colbert-ai
 base_model: facebook/xmod-base
@@ -458,6 +460,40 @@ which allows it to learn from monolingual fine-tuning in a high-resource languag
 
 ## Usage
 
+### 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("antoinelouis/colbert-xm")
+
+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) (21, 128)
+
+# MaxSim late-interaction scoring (higher is more relevant)
+scores = model.similarity(query_embeddings, document_embeddings)
+print(scores)
+# tensor([[11.1658, 18.7137, 12.6985, 8.1900]])
+```
+
+### Using Stanford ColBERT
+
 Start by installing the [colbert-ai](https://github.com/stanford-futuredata/ColBERT) and some extra requirements:
 
 ```bash