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--- |
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title: action_generation |
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datasets: |
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- |
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tags: |
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- evaluate |
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- metric |
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description: "TODO: add a description here" |
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sdk: gradio |
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sdk_version: 3.19.1 |
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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 action_generation |
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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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*Give a brief overview of this metric, including what task(s) it is usually used for, if any.* |
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## How to Use |
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*Give general statement of how to use the metric* |
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*Provide simplest possible example for using the metric* |
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### Inputs |
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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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*Cite the source where this metric was introduced.* |
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## Further References |
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*Add any useful further references.* |
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