Add Sentence Transformers usage

README.md

@@ -3,6 +3,7 @@ language:
 - en
 tags:
 - ColBERT
+- multi-vector
 - PyLate
 - sentence-transformers
 - sentence-similarity
@@ -153,14 +154,14 @@ If you rather want to use a simpler version without the prefix (just reasoning +
 
 `get_document` is a net improvement in most cases, but the gains depend on **both** the retrieval model and the agent. The very strong numbers above are not a given for every combination. We tried adding more `get_document` data points, but were not able to reproduce the original AgentIR results exactly (small deltas remain after fixing environments). We therefore omit those points from the comparison: although they look favorable to Agent-ModernColBERT, we can't yet rule out GPT-OSS-side errors.
 
-# PyLate model based on lightonai/GTE-ModernColBERT-v1
+# Multi-vector embedding model based on lightonai/GTE-ModernColBERT-v1
 
-This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [lightonai/GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) on the [agent_ir-data](https://huggingface.co/datasets/Tevatron/AgentIR-data) dataset. 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 finetuned from [lightonai/GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) on the [agent_ir-data](https://huggingface.co/datasets/Tevatron/AgentIR-data) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
 
 ## Model Details
 
 ### Model Description
-- **Model Type:** PyLate model
+- **Model Type:** Multi-vector embedding model
 - **Base model:** [lightonai/GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) 
 - **Document Length:** 4096 tokens
 - **Query Length:** 8192 tokens
@@ -187,6 +188,41 @@ ColBERT(
 ```
 
 ## 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("lightonai/Agent-ModernColBERT")
+
+query = "Which planet is known as the Red Planet?"
+documents = [
+    "Venus is often called Earth's twin because of its similar size and proximity.",
+    "Mars, known for its reddish appearance, is often referred to as the Red Planet.",
+    "Jupiter, the largest planet in our solar system, has a prominent red spot.",
+    "Saturn, famous for its rings, is sometimes mistaken for the Red Planet.",
+]
+
+query_embeddings = model.encode_query(query)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings[0].shape)
+# (12, 128) (18, 128)
+
+# MaxSim late-interaction scoring (higher is more relevant)
+scores = model.similarity(query_embeddings, document_embeddings)
+print(scores)
+# tensor([[10.4239, 11.1039, 10.6760, 10.9058]])
+```
+
+### PyLate
+
 First install the PyLate library:
 
 ```bash