adding methodology
Browse files
app.py
CHANGED
@@ -46,7 +46,7 @@ demo = gr.Blocks()
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with demo:
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gr.Markdown(
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"""# Energy Star Leaderboard - v.
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### Welcome to the leaderboard for the [AI Energy Star Project!](https://huggingface.co/EnergyStarAI)
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Click through the tasks below to see how different models measure up in terms of energy efficiency"""
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)
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@@ -122,5 +122,11 @@ with demo:
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plot = gr.Plot(get_plots('question_answering.csv'))
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with gr.Column():
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table = gr.Dataframe(get_model_names('question_answering.csv'), datatype="markdown")
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demo.launch()
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with demo:
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gr.Markdown(
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"""# Energy Star Leaderboard - v.0 (2024) π π» π
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### Welcome to the leaderboard for the [AI Energy Star Project!](https://huggingface.co/EnergyStarAI)
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Click through the tasks below to see how different models measure up in terms of energy efficiency"""
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)
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plot = gr.Plot(get_plots('question_answering.csv'))
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with gr.Column():
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table = gr.Dataframe(get_model_names('question_answering.csv'), datatype="markdown")
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with gr.Accordion("Methodology"):
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gr.Markdown(
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"""For each of the ten tasks above, we created a custom dataset with 1,000 entries (see all of the datasets on our [org Hub page](https://huggingface.co/EnergyStarAI)).
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We then tested each of the models from the leaderboard on the appropriate task, measuring the energy consumed using [Code Carbon](https://mlco2.github.io/codecarbon/), an open-source Python package for tracking the environmental impacts of code.
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We developed and used a [Docker container](https://github.com/huggingface/EnergyStarAI/) to maximize the reproducibility of results, and to enable members of the community to benchmark internal models.
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Reach out to us if you want to collaborate!
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""")
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demo.launch()
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