update the right path to the database, update model metrics.

README.md

@@ -29,7 +29,7 @@ task_categories:
 
 ## 1. Project Overview
 
-The **ExomeBench** dataset is derived from [ClinVar](https://www.ncbi.nlm.nih.gov/clinvar) [(March 2024 release)](https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/), a publicly accessible database maintained by the National Center for Biotechnology Information (NCBI). ClinVar provides comprehensive information on the clinical significance of genetic variants and their associations with human diseases. This dataset focuses on variants located in exome-specific regions and includes input sequences generated from the [Human Reference Genome (HRG, GRCh38)](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001405.40/).
+The **ExomeBench** dataset is derived from [ClinVar](https://www.ncbi.nlm.nih.gov/clinvar) [(Nov 2024 release)](https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/archive/variant_summary_2024-11.txt.gz), a publicly accessible database maintained by the National Center for Biotechnology Information (NCBI). ClinVar provides comprehensive information on the clinical significance of genetic variants and their associations with human diseases. This dataset focuses on variants located in exome-specific regions and includes input sequences generated from the [Human Reference Genome (HRG, GRCh38)](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001405.40/).
 
 This dataset provides a valuable resource for researchers and practitioners working on genetic variant analysis and its clinical implications. Exome-specific regions are critically important because they encompass all protein-coding regions of the genome, where disease-associated variants are most likely to occur. By focusing on exome-specific regions and using sequences from the Human Reference Genome, this dataset enables robust evaluation of models on clinically significant tasks.
 
@@ -134,31 +134,49 @@ ExomeBench includes **five supervised tasks**, each framed as a classification p
 <table border="1" cellspacing="0" cellpadding="8">
   <thead>
     <tr>
-      <th rowspan="2">Model</th>
-      <th colspan="5">Task<br>Acc/Precision/Recall/F1/ROCAUC</th>
+      <th align="center" rowspan="2">Model</th>
+      <th align="center" colspan="5">Task (MCC)</th>
     </tr>
     <tr>
-      <th>PV</th>
-      <th>CPS</th>
-      <th>CP</th>
-      <th>BRCA</th>
-      <th>TFG</th>
+      <th align="center">PV</th>
+      <th align="center">CPS</th>
+      <th align="center">CP</th>
+      <th align="center">BRCA</th>
+      <th align="center">TFG</th>
     </tr>
   </thead>
   <tbody>
+    <tr>
+      <td>DNABERT-2</td>
+      <td align="center">0.1615</td>
+      <td align="center">0.8763</td>
+      <td align="center">0.5488</td>
+      <td align="center">0.5518</td>
+      <td align="center">0.9959</td>
+    </tr>
+    <tr>
+      <td>HyenaDNA</td>
+      <td align="center">0.1345</td>
+      <td align="center">0.8160</td>
+      <td align="center">0.4450</td>
+      <td align="center">0.7000</td>
+      <td align="center">0.9940</td>
+    </tr>
     <tr>
       <td>NT-Multispecies-2.5B</td>
-      <td align="center">0, 0, 0, 0, 0</td>
-      <td align="center">0, 0, 0, 0, 0</td>
-      <td align="center">0, 0, 0, 0, 0</td>
-      <td align="center">0, 0, 0, 0, 0</td>
-      <td align="center">0, 0, 0, 0, 0</td>
+      <td align="center">0.3064</td>
+      <td align="center">0.6244</td>
+      <td align="center">0.2934</td>
+      <td align="center">0.4224</td>
+      <td align="center">0.9906</td>
     </tr>
   </tbody>
 </table>
 
 </div>
 
+> **Note**: For some models and tasks, the seed settings in the [STRAND paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5183178) were slightly different from those used in this repository, which may lead to minor variations in the reported results.
+
 
 ## 5. Usage
 ```python