Update fbeta_score.py

fbeta_score.py

@@ -46,7 +46,7 @@ _KWARGS_DESCRIPTION = """
 Args:
     predictions (`list` of `int`): Predicted labels.
     references (`list` of `int`): Ground truth labels.
-    beta (`float`): Determines the weight of recall in the combined score.
+    beta (`float`): Determines the weight of recall in the combined score. Defaults to 0.5
     labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
     pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
     average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
@@ -65,7 +65,7 @@ Examples:
     
     Example 1-A simple binary example    
         >>> f_beta = evaluate.load("leslyarun/f_beta")
-        >>> results = f_beta.compute(references=[0, 1], predictions=[0, 1])
+        >>> results = f_beta.compute(references=[0, 1], predictions=[0, 1], beta=0.5)
         >>> print(results)
         {'f_beta_score': 1.0}    
 """
@@ -93,7 +93,7 @@ class F_Beta(evaluate.Metric):
         )
 
 
-    def _compute(self, predictions, references, beta, labels=None, pos_label=1, average="binary", sample_weight=None):
+    def _compute(self, predictions, references, beta=0.5, labels=None, pos_label=1, average="binary", sample_weight=None):
         score = fbeta_score(
             references, predictions, beta, labels=labels, pos_label=pos_label, average=average, sample_weight=sample_weight
         )