Update README.md

README.md

@@ -46,8 +46,9 @@ SparseEncoder(
 
 ## Quality Benchmarks
 **PIXIE-Splade-Preview** delivers consistently strong performance across a diverse set of domain-specific and open-domain benchmarks in Korean, demonstrating its effectiveness in real-world search applications. 
-The table below presents the retrieval performance of several embedding models evaluated on a variety of Korean MTEM benchmarks. 
+The table below presents the retrieval performance of several embedding models evaluated on a variety of Korean MTEB 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.
+All evaluations were conducted using the open-source **[Korean-MTEB-Retrieval-Evaluators](https://github.com/BM-K/Korean-MTEB-Retrieval-Evaluators)** codebase to ensure consistent dataset handling, indexing, retrieval, and NDCG@k computation across models.
 
 ### 7 Datasets of MTEB (Korean)
 Our model, **telepix/PIXIE-Splade-Preview**, achieves strong performance across most metrics and benchmarks, 
@@ -257,8 +258,6 @@ if __name__ == "__main__":
         top_k_tokens=5,  # Top 10 contributing tokens for each document
         min_weight=0.0,
     )
-
-
 ```
 
 ## License