Update README.md

README.md

@@ -20,10 +20,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 889743413.000
+    num_bytes: 889743413
     num_examples: 1008
   - name: eval
-    num_bytes: 235780103.000
+    num_bytes: 235780103
     num_examples: 253
 - config_name: Agent
   features:
@@ -39,10 +39,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 3749359214.000
+    num_bytes: 3749359214
     num_examples: 4552
   - name: eval
-    num_bytes: 930931836.000
+    num_bytes: 930931836
     num_examples: 1138
 configs:
 - config_name: User
@@ -59,6 +59,9 @@ configs:
     path: Agent/eval/*
 tags:
 - medical
+- healthcare
+size_categories:
+- 1K<n<10K
 ---
 
 ## MMedFD: A Real-World Healthcare Benchmark for Multi-Turn Full-Duplex Automatic Speech Recognition
@@ -91,7 +94,7 @@ If this code or our benchmark is useful for your research, please consider citin
 ```bibtex
 @misc{chen2025mmedfdrealworldhealthcarebenchmark,
       title={MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition}, 
-      author={Hongzhao Chen and XiaoYang Wang and Jing Lan and Hexiao Ding and Yufeng Jiang MingHui Yang and DanHui Xu and Jun Luo and Nga-Chun Ng and Gerald W. Y. Cheng and Yunlin Mao and Jung Sun Yoo},
+      author={Hongzhao Chen and XiaoYang Wang and Jing Lan and Hexiao Ding and Yufeng Jiang and MingHui Yang and DanHui Xu and Jun Luo and Nga-Chun Ng and Gerald W. Y. Cheng and Yunlin Mao and Jung Sun Yoo},
       year={2025},
       eprint={2509.19817},
       archivePrefix={arXiv},