Update src/about.py

src/about.py

@@ -91,8 +91,8 @@ LLM_BENCHMARKS_TEXT = f"""
 EVALUATION_QUEUE_TEXT = """
 # **Benchmarking your own representation model**
 ## To run the benchmarks, the following representation vectors need to be generated:
-    - For benchmarks 1, 2, and 3 (similarity, function, and family), you will need to generate representation vectors for all human proteins. The amino acid sequences for canonical isoforms of human proteins can be found [here](https://drive.google.com/file/d/1wXF2lmj4ZTahMrl66QpYM2TvHmbcIL6b/view?usp=sharing).
-    - For benchmark 4 (affinity), representation vectors will need to be generated for the samples in the SKEMPI dataset, which can be accessed [here](https://drive.google.com/file/d/1m5jssC0RMsiFT_w-Ykh629Pw_An3PInI/view?usp=sharing).
+- For benchmarks 1, 2, and 3 (similarity, function, and family), you will need to generate representation vectors for all human proteins. The amino acid sequences for canonical isoforms of human proteins can be found [here](https://drive.google.com/file/d/1wXF2lmj4ZTahMrl66QpYM2TvHmbcIL6b/view?usp=sharing).
+- For benchmark 4 (affinity), representation vectors will need to be generated for the samples in the SKEMPI dataset, which can be accessed [here](https://drive.google.com/file/d/1m5jssC0RMsiFT_w-Ykh629Pw_An3PInI/view?usp=sharing).
 
 ## Format of the both protein representation files:
     1. Each row corresponds to the representation vector of a particular protein.
@@ -133,11 +133,9 @@ family_prediction_dataset_options = ["nc", "uc50", "uc30", "mm15"]
 
 benchmark_specific_metrics = {
     'similarity': ['sparse_MF_correlation', 'sparse_BP_correlation', 'sparse_CC_correlation', 'sparse_Ave_correlation',
-                  'sparse_MF_pvalue', 'sparse_BP_pvalue', 'sparse_CC_pvalue', 'sparse_Ave_pvalue',
                   '200_MF_correlation', '200_BP_correlation', '200_CC_correlation', '200_Ave_correlation',
-                  '200_MF_pvalue', '200_BP_pvalue', '200_CC_pvalue', '200_Ave_pvalue',
                   '500_MF_correlation', '500_BP_correlation', '500_CC_correlation', '500_Ave_correlation',
-                  '500_MF_pvalue', '500_BP_pvalue', '500_CC_pvalue', '500_Ave_pvalue',],
+],
     'function': {
         'aspect_types': ['MF', 'BP', 'CC'],
         'dataset_types': ['accuracy', 'F1', 'precision', 'recall']