Add description to card metadata

README.md

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-title: Spearman Correlation Coefficient Metric 
-emoji: 🤗 
+title: Spearman Correlation Coefficient Metric
+emoji: 🤗
 colorFrom: blue
 colorTo: red
 sdk: gradio
@@ -8,10 +8,24 @@ sdk_version: 3.0.2
 app_file: app.py
 pinned: false
 tags:
-- evaluate
-- metric
+  - evaluate
+  - metric
+description: |-
+  The Spearman rank-order correlation coefficient is a measure of the
+  relationship between two datasets. Like other correlation coefficients,
+  this one varies between -1 and +1 with 0 implying no correlation.
+  Positive correlations imply that as data in dataset x increases, so
+  does data in dataset y. Negative correlations imply that as x increases,
+  y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
+
+  Unlike the Pearson correlation, the Spearman correlation does not
+  assume that both datasets are normally distributed.
+
+  The p-value roughly indicates the probability of an uncorrelated system
+  producing datasets that have a Spearman correlation at least as extreme
+  as the one computed from these datasets. The p-values are not entirely
+  reliable but are probably reasonable for datasets larger than 500 or so.
 ---
-
 # Metric Card for Spearman Correlation Coefficient Metric (spearmanr)