Fix architectures field, add Sentence Transformers usage

README.md

@@ -11,6 +11,8 @@ language:
 pipeline_tag: sentence-similarity
 tags:
 - ColBERT
+- multi-vector
+- sentence-transformers
 base_model:
 - cl-tohoku/bert-base-japanese-v3
 - bclavie/JaColBERT
@@ -25,6 +27,39 @@ This model largely outperforms all previous approaches, including JaColBERTV2 mu
 This page will be updated with the full details and the model report in the next few days.
 
 
+## Sentence Transformers
+
+As of [Sentence Transformers](https://www.sbert.net/) v6.0.0, this model can also be loaded directly as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`:
+
+```bash
+pip install "sentence-transformers>=6.0.0" fugashi unidic-lite
+```
+
+```python
+from sentence_transformers import MultiVectorEncoder
+
+model = MultiVectorEncoder("answerdotai/JaColBERTv2.5")
+
+query = "日本で一番高い山は何ですか？"
+documents = [
+    "富士山は日本で最も高い山で、標高は3776メートルです。",
+    "東京は日本の首都で、世界最大の都市圏の一つです。",
+    "北岳は南アルプスにある山で、日本で二番目に高い山です。",
+    "富士山は静岡県と山梨県にまたがる活火山です。",
+]
+
+query_embeddings = model.encode_query(query)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings[0].shape)
+# torch.Size([32, 128]) torch.Size([21, 128])
+
+# MaxSim late-interaction scoring (higher is more relevant)
+scores = model.similarity(query_embeddings, document_embeddings)
+print(scores)
+# tensor([[30.6709, 28.9073, 29.6979, 29.0785]], device='cuda:0')
+```
+
+
 ```
 @misc{clavié2024jacolbertv25optimisingmultivectorretrievers,
       title={JaColBERTv2.5: Optimising Multi-Vector Retrievers to Create State-of-the-Art Japanese Retrievers with Constrained Resources}, 

config.json

@@ -1,7 +1,7 @@
 {
   "_name_or_path": "./step_48003",
   "architectures": [
-    "BertModel"
+    "HF_ColBERT"
   ],
   "attention_probs_dropout_prob": 0.1,
   "classifier_dropout": null,