Update README.md

README.md

@@ -11,8 +11,8 @@ library_name: sentence-transformers
     <img src="https://cdn-uploads.huggingface.co/production/uploads/61d6f4a4d49065ee28a1ee7e/V8n2En7BlMNHoi1YXVv8Q.png" width="400"/>
 <p>
 
-# PIXIE-Rune-M-v1.0
-**PIXIE-Rune-M-v1.0** is an encoder-based embedding model trained on Korean and English triplets, developed by [TelePIX Co., Ltd](https://telepix.net/). 
+# PIXIE-Rune-v1.0
+**PIXIE-Rune-v1.0** is an encoder-based embedding model trained on Korean and English triplets, developed by [TelePIX Co., Ltd](https://telepix.net/). 
 **PIXIE** stands for Tele**PIX** **I**ntelligent **E**mbedding, representing TelePIX’s high-performance embedding technology. 
 The model is multilingual, specifically optimized for both Korean and English. 
 It demonstrates strong performance on retrieval tasks in both languages, achieving robust results across a wide range of Korean- and English-language benchmarks. 
@@ -39,7 +39,7 @@ SentenceTransformer(
 ```
 
 ## Quality Benchmarks
-**PIXIE-Rune-M-v1.0** is a multilingual embedding model specialized for Korean and English retrieval tasks. 
+**PIXIE-Rune-v1.0** is a multilingual embedding model specialized for Korean and English retrieval tasks. 
 It delivers consistently strong performance across a diverse set of domain-specific and open-domain benchmarks in both languages, demonstrating its effectiveness in real-world semantic search applications.
 The table below presents the retrieval performance of several embedding models evaluated on a variety of Korean and English benchmarks.
 We report **Normalized Discounted Cumulative Gain (NDCG)** scores, which measure how well a ranked list of documents aligns with ground truth relevance. Higher values indicate better retrieval quality.  
@@ -47,11 +47,11 @@ We report **Normalized Discounted Cumulative Gain (NDCG)** scores, which measure
 - **NDCG@k**: Relevance quality of the top-*k* retrieved results.
  
 #### Korean Retrieval Benchmarks
-Our model, **telepix/PIXIE-Rune-M-v1.0**, achieves state-of-the-art performance across most metrics and benchmarks, demonstrating strong generalization across domains such as multi-hop QA, long-document retrieval, public health, and e-commerce.
+Our model, **telepix/PIXIE-Rune-v1.0**, achieves state-of-the-art performance across most metrics and benchmarks, demonstrating strong generalization across domains such as multi-hop QA, long-document retrieval, public health, and e-commerce.
 
 | Model Name | # params | Avg. NDCG | NDCG@1 | NDCG@3 | NDCG@5 | NDCG@10 |
 |------|:---:|:---:|:---:|:---:|:---:|:---:|
-| **telepix/PIXIE-Rune-M-v1.0** | 568M | **0.6905** | **0.6461** | **0.6859** | **0.7063** | **0.7238** |
+| **telepix/PIXIE-Rune-v1.0** | 568M | **0.6905** | **0.6461** | **0.6859** | **0.7063** | **0.7238** |
 | nlpai-lab/KURE-v1 | 568M | 0.6751 | 0.6277 | 0.6725 | 0.6907 | 0.7095 |
 | dragonekue/BGE-m3-ko | 568M | 0.6658 | 0.6225 | 0.6627 | 0.6795 | 0.6985 |
 | Snowflake/snowflake-arctic-embed-l-v2.0 | 568M | 0.6592 | 0.6118 | 0.6542 | 0.6759 | 0.6949 |
@@ -78,11 +78,11 @@ Descriptions of the benchmark datasets used for evaluation are as follows:
   A real-world dataset constructed from user queries and relevant product documents in a Korean e-commerce platform.
 
 #### English Retrieval Benchmarks
-Our model, **telepix/PIXIE-Rune-M-v1.0**, achieves strong performance on a wide range of tasks, including fact verification, multi-hop question answering, financial QA, and scientific document retrieval, demonstrating competitive generalization across diverse domains.
+Our model, **telepix/PIXIE-Rune-v1.0**, achieves strong performance on a wide range of tasks, including fact verification, multi-hop question answering, financial QA, and scientific document retrieval, demonstrating competitive generalization across diverse domains.
  
 | Model Name | # params | Avg. NDCG | NDCG@1 | NDCG@3 | NDCG@5 | NDCG@10 |
 |------|:---:|:---:|:---:|:---:|:---:|:---:|
-| **telepix/PIXIE-Rune-M-v1.0** | 568M | **123** | **123** | **123** | **123** | **123** |
+| **telepix/PIXIE-Rune-v1.0** | 568M | **123** | **123** | **123** | **123** | **123** |
 | Snowflake/snowflake-arctic-embed-l-v2.0 | 568M | 0.5812 | 0.5725 | 0.5705 | 0.5811 | 0.6006 |
 | Qwen/Qwen3-Embedding-0.6B | 595M | 0.5558 | 0.5321 | 0.5451 | 0.5620 | 0.5839 |
 | BAAI/bge-m3 | 568M | 0.5318 | 0.5078 | 0.5231 | 0.5389 | 0.5573 |
@@ -120,7 +120,7 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
   
 # Load the model
-model_name = 'PIXIE-Rune-M-v1.0'
+model_name = 'PIXIE-Rune-v1.0'
 model = SentenceTransformer(model_name)
 
 # Define the queries and documents