Update README.md

README.md

@@ -1,50 +1,47 @@
 ---
-title: F_Beta
+title: FBeta_Score
 datasets:
 -  
 tags:
 - evaluate
 - metric
-description: "TODO: add a description here"
+description: "Calculate FBeta_Score"
 sdk: gradio
 sdk_version: 3.0.2
 app_file: app.py
 pinned: false
 ---
 
-# Metric Card for F_Beta
+# 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
-*Give a brief overview of this metric, including what task(s) it is usually used for, if any.*
+*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.
+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).*
 
 ## How to Use
-*Give general statement of how to use the metric*
+``` python
 
-*Provide simplest possible example for using the metric*
+f_beta = evaluate.load("leslyarun/f_beta")
+results = f_beta.compute(references=[0, 1], predictions=[0, 1], beta=0.5)
+print(results)
+{'f_beta_score': 1.0}   
 
-### Inputs
-*List all input arguments in the format below*
-- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
-
-### Output Values
-
-*Explain what this metric outputs and provide an example of what the metric output looks like. Modules should return a dictionary with one or multiple key-value pairs, e.g. {"bleu" : 6.02}*
-
-*State the range of possible values that the metric's output can take, as well as what in that range is considered good. For example: "This metric can take on any value between 0 and 100, inclusive. Higher scores are better."*
-
-#### Values from Popular Papers
-*Give examples, preferrably with links to leaderboards or publications, to papers that have reported this metric, along with the values they have reported.*
-
-### Examples
-*Give code examples of the metric being used. Try to include examples that clear up any potential ambiguity left from the metric description above. If possible, provide a range of examples that show both typical and atypical results, as well as examples where a variety of input parameters are passed.*
-
-## Limitations and Bias
-*Note any known limitations or biases that the metric has, with links and references if possible.*
+```
 
 ## Citation
-*Cite the source where this metric was introduced.*
+@article{scikit-learn,
+    title={Scikit-learn: Machine Learning in {P}ython},
+    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
+           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
+           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
+           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
+    journal={Journal of Machine Learning Research},
+    volume={12},
+    pages={2825--2830},
+    year={2011}
 
 ## Further References
-*Add any useful further references.*
+https://scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.html#sklearn.metrics.fbeta_score