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Add my new, shiny module.
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README.md
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title: llm_harness_mistral_arc
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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: "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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***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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Testing LLM harness in HF Evaluate
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