Update README.md

README.md

@@ -13,9 +13,9 @@ language:
 - ko
 ---
 
-# colbert-ko-v1.0
+# colbert-ko-v1
 
-**colbert-ko-v1.0** is a Korean colbert model finetuned with [PyLate](https://github.com/lightonai/pylate). This model is trained exclusively on Korean dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
+**colbert-ko-v1** is a Korean colbert model finetuned with [PyLate](https://github.com/lightonai/pylate). This model is trained exclusively on Korean 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
 
@@ -57,7 +57,7 @@ We omit MIRACLRetrieval and MrTidyRetrieval in evalution due to our device condi
 
 | Model                                         | Parameters | Average Recall@10 | Average Precision@10 | Average NDCG@10 | Average F1@10 |
 |-----------------------------------------------|------------|----------------|-------------------|--------------|------------|
-| **colbert-ko-v1.0**                   | **0.1B**     | **0.7999**    | **0.0930**       | **0.7172**  | **0.1655**|
+| **colbert-ko-v1**                   | **0.1B**     | **0.7999**    | **0.0930**       | **0.7172**  | **0.1655**|
 | [jina-colbert-v2](https://huggingface.co/jinaai/jina-colbert-v2)                     | 0.5B         | 0.7518        | 0.0888           | 0.6671      | 0.1577    |
 
 ## Usage
@@ -84,7 +84,7 @@ documents_ids = [
 ]
 
 model = models.ColBERT(
-    model_name_or_path="yjoonjang/colbert-ko-v1.0",
+    model_name_or_path="yjoonjang/colbert-ko-v1",
 )
 
 queries_embeddings = model.encode(
@@ -141,7 +141,7 @@ from pylate import indexes, models, retrieve
 
 # Step 1: Load the ColBERT model
 model = models.ColBERT(
-    model_name_or_path="yjoonjang/colbert-ko-v1.0",
+    model_name_or_path="yjoonjang/colbert-ko-v1",
 )
 
 # Step 2: Initialize the PLAID index