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README.md

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+## Quality Benchmarks
+**PIXIE-Rune-M-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.  
+- **Avg. NDCG**: Average of NDCG@1, @3, @5, and @10 across all benchmark datasets.  
+- **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.
+
+| Model Name | # params | Avg. NDCG | NDCG@1 | NDCG@3 | NDCG@5 | NDCG@10 |
+|------|:---:|:---:|:---:|:---:|:---:|:---:|
+| **telepix/PIXIE-Rune-M-v1.0** | 385M | **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 |
+| BAAI/bge-m3 | 568M | 0.6573 | 0.6099 | 0.6533 | 0.6732 | 0.6930 |
+| Qwen/Qwen3-Embedding-0.6B | 595M | 0.6321 | 0.5894 | 0.6274 | 0.6455 | 0.6662 |
+| Alibaba-NLP/gte-Qwen-7B-instruct | 7711M | 0.6202 | 0.5698 | 0.6200 | 0.6349 | 0.6564 |
+| openai/text-embedding-3-large | N/A | 0.6015 | 0.5466 | 0.5999 | 0.6187 | 0.6409 |
+| Salesforce/SFR-Embedding-2_R | 7111M | 0.5979 | 0.5451 | 0.5959 | 0.6158 | 0.6348 |
+
+Descriptions of the benchmark datasets used for evaluation are as follows:
+- **Ko-StrategyQA**  
+  A Korean multi-hop open-domain question answering dataset designed for complex reasoning over multiple documents.
+- **AutoRAGRetrieval**  
+  A domain-diverse retrieval dataset covering finance, government, healthcare, legal, and e-commerce sectors.
+- **MIRACLRetrieval**  
+  A document retrieval benchmark built on Korean Wikipedia articles.
+- **PublicHealthQA**  
+  A retrieval dataset focused on medical and public health topics.
+- **BelebeleRetrieval**  
+  A dataset for retrieving relevant content from web and news articles in Korean.
+- **MultiLongDocRetrieval**  
+  A long-document retrieval benchmark based on Korean Wikipedia and mC4 corpus.
+- **XPQARetrieval**  
+  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.
+ 
+| Model Name | # params | Avg. NDCG | NDCG@1 | NDCG@3 | NDCG@5 | NDCG@10 |
+|------|:---:|:---:|:---:|:---:|:---:|:---:|
+| **telepix/PIXIE-Rune-M-v1.0** | 385M | **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 |
+| dragonekue/BGE-m3-ko | 568M | 0.5307 | 0.5125 | 0.5174 | 0.5362 | 0.5566 |
+| nlpai-lab/KURE-v1 | 568M | 0.5272 | 0.5017 | 0.5171 | 0.5353 | 0.5548 |
+
+Descriptions of the benchmark datasets used for evaluation are as follows:
+- **ArguAna**  
+  A dataset for argument retrieval based on claim-counterclaim pairs from online debate forums.
+- **FEVER**  
+  A fact verification dataset using Wikipedia for evidence-based claim validation.
+- **FiQA-2018**  
+  A retrieval benchmark tailored to the finance domain with real-world questions and answers.
+- **HotpotQA**  
+  A multi-hop open-domain QA dataset requiring reasoning across multiple documents.
+- **MSMARCO**  
+  A large-scale benchmark using real Bing search queries and corresponding web documents.
+- **NQ**  
+  A Google QA dataset where user questions are answered using Wikipedia articles.
+- **SCIDOCS**  
+  A citation-based document retrieval dataset focused on scientific papers.
+  
 ## Usage
 
 ### Direct Usage (Sentence Transformers)