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README.md
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---
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title:
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datasets:
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tags:
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- evaluate
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- metric
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description: "
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sdk: gradio
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sdk_version: 3.0.2
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app_file: app.py
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pinned: false
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---
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# Metric Card for
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***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.*
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## Metric Description
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*
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## How to Use
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*List all input arguments in the format below*
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- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
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### Output Values
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*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}*
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*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."*
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#### Values from Popular Papers
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*Give examples, preferrably with links to leaderboards or publications, to papers that have reported this metric, along with the values they have reported.*
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### Examples
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*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.*
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## Limitations and Bias
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*Note any known limitations or biases that the metric has, with links and references if possible.*
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## Citation
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## Further References
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---
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title: FBeta_Score
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datasets:
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tags:
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- evaluate
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- metric
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description: "Calculate FBeta_Score"
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sdk: gradio
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sdk_version: 3.0.2
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app_file: app.py
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pinned: false
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---
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# Metric Card for FBeta_Score
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***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.*
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## Metric Description
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*Compute the F-beta score.
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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.
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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).*
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## How to Use
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``` python
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f_beta = evaluate.load("leslyarun/f_beta")
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results = f_beta.compute(references=[0, 1], predictions=[0, 1], beta=0.5)
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print(results)
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{'f_beta_score': 1.0}
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```
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## Citation
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@article{scikit-learn,
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title={Scikit-learn: Machine Learning in {P}ython},
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author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
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and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
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and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
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Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
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journal={Journal of Machine Learning Research},
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volume={12},
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pages={2825--2830},
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year={2011}
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## Further References
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https://scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.html#sklearn.metrics.fbeta_score
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