diff --git a/mteb/leaderboard/app.py b/mteb/leaderboard/app.py
index 82bbbb3a9d..c0bbce60d7 100644
--- a/mteb/leaderboard/app.py
+++ b/mteb/leaderboard/app.py
@@ -116,7 +116,7 @@ def update_description(
     n_tasks = len(benchmark.tasks)
     n_domains = len(domains)
     description += f" - **Number of languages**: {n_languages}\n"
-    description += f" - **Number of datasets**: {n_tasks}\n"
+    description += f" - **Number of tasks**: {n_tasks}\n"
     description += f" - **Number of task types**: {n_task_types}\n"
     description += f" - **Number of domains**: {n_domains}\n"
     if str(benchmark.reference) != "None":
@@ -428,6 +428,18 @@ def filter_models(
 Based on community feedback and research findings, This definition could change in the future.
             """
             )
+        with gr.Accordion(
+            "What does the other columns mean?",
+            open=False,
+        ):
+            gr.Markdown(
+                """
+- **Number of Parameters**: This is the total number of parameters in the model including embedding parameters. A higher value means the model requires more CPU/GPU memory to run; thus, less is generally desirable.
+- **Embedding Dimension**: This is the vector dimension of the embeddings that the model produces. When saving embeddings to disk, a higher dimension will require more space, thus less is usually desirable.
+- **Max tokens**: This refers to how many tokens (=word pieces) the model can process. Generally, a larger value is desirable.
+- **Zero-shot**: This indicates if the model is zero-shot on the benchmark. For more information on zero-shot see the info-box below.
+            """
+            )
         with gr.Accordion(
             "Why is a model missing or not showing up?",
             open=False,