Update bias_auc.py

updated _KWARGS_DESCRIPTION

bias_auc.py

@@ -13,7 +13,7 @@ classifier’s score distribution can vary across designated groups.
 The following are computed:
 
 - BNSP (Background Negative, Subgroup Positive); and 
-- BPSN (Background Positive, Subgroup Negative) AUC metrics
+- BPSN (Background Positive, Subgroup Negative) AUC 
 
 """
 
@@ -28,9 +28,42 @@ _CITATION = """\
 """
 
 _KWARGS_DESCRIPTION = """\
-target list[list[str]]:  list containing list of group targeted for each item
-label list[int]: list containing label index for each item
-output list[list[float]]: list of model output values for each 
+
+Args:
+    target list[list[str]]:  list containing list of group targeted for each item
+    label list[int]: list containing label index for each item
+    output list[list[float]]: list of model output values for each
+
+Returns (for each subgroup in target):
+    'Subgroup' : Subgroup AUC score,
+    'BPSN' : BPSN (Background Positive, Subgroup Negative) AUC,
+    'BNSP' : BNSP (Background Negative, Subgroup Positive) AUC score,
+
+
+Example:
+    >>> from evaluate import load
+    
+    >>> target = [['Islam'],
+    ... ['Sexuality'],
+    ... ['Sexuality'],
+    ... ['Islam']]
+    
+    >>> label = [0, 0, 1, 1]
+    
+    >>> output = [[0.44452348351478577, 0.5554765462875366],
+    ... [0.4341845214366913, 0.5658154487609863],
+    ... [0.400595098733902, 0.5994048714637756],
+    ... [0.3840397894382477, 0.6159601807594299]]
+
+    >>> metric = load('Intel/bias_auc')
+    >>> metric.add_batch(target=target,
+                     label=label,
+                     output=output)
+    >>> metric.compute(target=a,
+                     label=b,
+                     output=c,
+                  subgroups = None)
+    
 """
 
 class BiasAUC(evaluate.Metric):