Add 1.5 instead of 0.5 in Readme

README.md

@@ -19,14 +19,14 @@ pinned: false
 The F-beta score is the weighted harmonic mean of precision and recall, reaching its optimal value at 1 and its worst value at 0.
 The beta parameter determines the weight of recall in the combined score. beta < 1 lends more weight to precision, while beta > 1 favors recall (beta -> 0 considers only precision, beta -> +inf only recall).*
 
-Note: The default value of Beta is set as 0.5 to calculate the frequently used FBeta 0.5. Please set a different Beta value according to your needs.
+Note: The default value of Beta is set as 1.5 to calculate the frequently used FBeta 1.5. Please set a different Beta value according to your needs.
 
 ## How to Use
 ``` python
 import evaluate
 
 fbeta_score = evaluate.load("leslyarun/fbeta_score")
-results = fbeta_score.compute(references=[0, 1], predictions=[0, 1], beta=0.5)
+results = fbeta_score.compute(references=[0, 1], predictions=[0, 1], beta=1.5)
 print(results)
 {'f_beta_score': 1.0}   
 ```
@@ -44,4 +44,4 @@ print(results)
     year={2011}
 
 ## Further References
-https://scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.html#sklearn.metrics.fbeta_score
+https://scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.html#sklearn.metrics.fbeta_score