Update Space (evaluate main: e179b5b8)

README.md

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-title: Spearman Correlation Coefficient Metric
-emoji: 🤗
+title: Spearman Correlation Coefficient Metric 
+emoji: 🤗 
 colorFrom: blue
 colorTo: red
 sdk: gradio
@@ -8,24 +8,10 @@ sdk_version: 3.0.2
 app_file: app.py
 pinned: false
 tags:
-  - 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.
+- evaluate
+- metric
 ---
+
 # Metric Card for Spearman Correlation Coefficient Metric (spearmanr)