Update Space (evaluate main: e179b5b8)

README.md

@@ -1,6 +1,6 @@
 ---
 title: ROC AUC
-emoji: 🤗
+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: >-
-  This metric computes the area under the curve (AUC) for the Receiver Operating
-  Characteristic Curve (ROC). The return values represent how well the model
-  used is predicting the correct classes, based on the input data. A score of
-  `0.5` means that the model is predicting exactly at chance, i.e. the model's
-  predictions are correct at the same rate as if the predictions were being
-  decided by the flip of a fair coin or the roll of a fair die. A score above
-  `0.5` indicates that the model is doing better than chance, while a score
-  below `0.5` indicates that the model is doing worse than chance.
-
-
-  This metric has three separate use cases:
-      - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.
-      - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.
-      - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.
+- evaluate
+- metric
 ---
+
 # Metric Card for ROC AUC