Update README.md

README.md

@@ -35,43 +35,6 @@ While speech interaction is becoming a primary interface for IR systems, perform
 * **Robustness Evaluation:** Explicitly models four acoustic conditions: **Clean, Low Noise (20dB), Medium Noise (10dB), and High Noise (0dB)**.
 * **MTEB Compatibility:** Follows standard JSONL/BEIR formatting for seamless integration into modern retrieval pipelines.
 
----
-license: cc-by-sa-4.0
-task_categories:
-- text-retrieval
-- automatic-speech-recognition
-language:
-- en
-- zh
-tags:
-- spoken-query-retrieval
-- information-retrieval
-- audio-text-retrieval
-- mteb
-- c-mteb
-- robustness
-pretty_name: SQuTR
-size_categories:
-- 10K<n<100K
----
-
-# SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval
-
-[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue)](https://github.com/ttoyekk1a/SQuTR-Spoken-Query-to-Text-Retrieval)
-
-**SQuTR** (Spoken Query-to-Text Retrieval) is a large-scale bilingual benchmark designed to evaluate the robustness of information retrieval systems under realistic acoustic perturbations. 
-
-While speech interaction is becoming a primary interface for IR systems, performance often degrades significantly in noisy environments. SQuTR provides a standardized framework featuring **37,317** complex queries across **6 domains**, synthesized with **200 real speakers**, and evaluated under **4 graded noise levels**.
-
----
-
-## 🌟 Key Features
-
-* **Bilingual & Multi-Domain:** Includes 6 subsets from MTEB and C-MTEB covering Wikipedia, Finance, Medical, and Encyclopedia domains.
-* **High-Fidelity Synthesis:** Generated using **CosyVoice-3** with diverse speaker profiles, totaling **190.4 hours** of audio.
-* **Robustness Evaluation:** Explicitly models four acoustic conditions: **Clean, Low Noise (20dB), Medium Noise (10dB), and High Noise (0dB)**.
-* **MTEB Compatibility:** Follows standard JSONL/BEIR formatting for seamless integration into modern retrieval pipelines.
-
 ---
 
 ## 📂 Dataset Structure