Update README.md

README.md

@@ -14,7 +14,7 @@ We release four variants of SciMult here:
 
 **scimult_vanilla.ckpt** and **scimult_moe.ckpt** can be used for various scientific literature understanding tasks. Their difference is that **scimult_vanilla.ckpt** adopts a typical 12-layer Transformer architecture (i.e., the same as [BERT base](https://huggingface.co/bert-base-uncased)), whereas **scimult_moe.ckpt** adopts a Mixture-of-Experts Transformer architecture with task-specific multi-head attention (MHA) sublayers. Experimental results show that **scimult_moe.ckpt** achieves better performance in general.
 
-**scimult_moe_pmcpatients_par.ckpt** and **scimult_moe_pmcpatients_ppr.ckpt** are initialized from **scimult_moe.ckpt** and continuously pre-trained on the training sets of [PMC-Patients](https://github.com/pmc-patients/pmc-patients) patient-to-article retrieval and patient-to-patient retrieval tasks, respectively. As of October 2023, these two models rank 1st in their corresponding tasks on the [PMC-Patients Leaderboard](https://pmc-patients.github.io/).
+**scimult_moe_pmcpatients_par.ckpt** and **scimult_moe_pmcpatients_ppr.ckpt** are initialized from **scimult_moe.ckpt** and continuously pre-trained on the training sets of [PMC-Patients](https://github.com/pmc-patients/pmc-patients) patient-to-article retrieval and patient-to-patient retrieval tasks, respectively. As of December 2023, these two models rank 1st and 2nd in their corresponding tasks, respectively, on the [PMC-Patients Leaderboard](https://pmc-patients.github.io/).
 
 
 ## Pre-training Data