Add Sentence Transformers usage (#2)

- Add Sentence Transformers usage (43d400b4b46863730c45bebfd95ca42fff0670ec)


Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>

README.md

@@ -1,5 +1,6 @@
 ---
 tags:
+- multi-vector
 - ColBERT
 - PyLate
 - sentence-transformers
@@ -53,7 +54,7 @@ This exceptional efficiency makes it the ideal choice for production environment
 **Efficiency Ratio:** Up to **54× smaller** than comparable performing models
 
 ### Model Description
-- **Model Type:** PyLate model with innovative Late Interaction architecture
+- **Model Type:** Multi-vector embedding model with innovative Late Interaction architecture
 - **Document Length:** 8192 tokens (32× longer than traditional BERT models)
 - **Query Length:** 256 tokens (optimized for complex, multi-part queries)
 - **Output Dimensionality:** 128 tokens (efficient vector representation)
@@ -197,12 +198,47 @@ This breakthrough demonstrates that with the right techniques, compact models ca
 
 ---
 
-# PyLate
+# Model
 
-This is a [PyLate](https://github.com/lightonai/pylate) model trained. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
+This is a multi-vector (ColBERT-style late interaction) embedding model. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
 
 
 ## 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("VAGOsolutions/SauerkrautLM-Multi-Reason-ModernColBERT")
+
+query = "Welcher Planet ist als der Rote Planet bekannt?"
+documents = [
+    "Venus wird wegen ihrer ähnlichen Größe und Nähe oft als Erdzwilling bezeichnet.",
+    "Mars, bekannt für sein rötliches Aussehen, wird oft als der Rote Planet bezeichnet.",
+    "Jupiter, der größte Planet in unserem Sonnensystem, hat einen markanten roten Fleck.",
+    "Saturn, berühmt für seine Ringe, wird manchmal für den Roten Planeten gehalten.",
+]
+
+query_embeddings = model.encode_query(query)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings[0].shape)
+# (256, 128) (29, 128)
+
+# MaxSim late-interaction scoring (higher is more relevant)
+scores = model.similarity(query_embeddings, document_embeddings)
+print(scores)
+# tensor([[211.5445, 217.9270, 215.3372, 216.1647]])
+```
+
+### PyLate
+
 First install the PyLate library:
 
 ```bash