Update app.py

app.py

@@ -100,8 +100,8 @@ table.index = table.index + 1
 st.markdown("# XTREME-S: Evaluating Cross-lingual Speech Representations")
 
 st.markdown(
-    f"""
-    This is the leaderboard of the XTREME-S benchmark.
+    """
+    This is the leaderboard for the XTREME-S benchmark.
     Submitted systems are ranked by the **average score**, which is a weighted average of
     the mandatory test sets:
     \n
@@ -133,7 +133,7 @@ st.markdown(
     """
     ## Submitting to XTREME-S
     \n
-    To submit to XTREME-S, download the audio data for the mandatory XTREME-S test sets from [xtreme-s/datasets](https://huggingface.co/datasets/xtreme-s/datasets). The test sets contain audio data only. Evaluate your system on the nine test sets by generating predictions for the unlabelled audio samples. For each test set, save the predictions to a .txt file in the order that the audio samples are provided, with one prediction per line. Name the .txt file according to the XTREME-S test set names shown in the table (e.g. the predictions for Fleurs should be named fleurs.txt).
+    To submit to XTREME-S, download the audio data for the mandatory XTREME-S test sets from [xtreme-s/datasets](https://huggingface.co/datasets/xtreme-s/datasets). The test sets contain audio data only. Evaluate your system on the six test sets by generating predictions for the unlabelled audio samples. For each test set, save the predictions to a .txt file in the order that the audio samples are provided, with one prediction per line. Name the .txt file according to the XTREME-S test set names shown in the table (e.g. the predictions for FLEURS should be named fleurs.txt).
     \n
     Once you have evaluated your system on all of the six mandatory test sets, move the predictions into one folder and zip it. The name you assign to the zipped folder will be the name that is shown on the table (e.g. mSLAM.zip will be displayed as mSLAM). Upload your zipped submissions for scoring and placement on the leaderboard. 
     \n