Update README.md

README.md

@@ -14,8 +14,6 @@ pinned: false
 
 # Metric Card for FBeta_Score
 
-***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.*
-
 ## Metric Description
 *Compute the F-beta score.
 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.
@@ -24,8 +22,8 @@ The beta parameter determines the weight of recall in the combined score. beta <
 ## How to Use
 ``` python
 
-f_beta = evaluate.load("leslyarun/f_beta")
-results = f_beta.compute(references=[0, 1], predictions=[0, 1], beta=0.5)
+fbeta_score = evaluate.load("leslyarun/fbeta_score")
+results = fbeta_score.compute(references=[0, 1], predictions=[0, 1], beta=0.5)
 print(results)
 {'f_beta_score': 1.0}