sasha HF staff commited on
Commit
68c7721
β€’
1 Parent(s): 2c55bb2

adding methodology

Browse files
Files changed (1) hide show
  1. app.py +8 -2
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.1 (2024) 🌎 πŸ’» 🌟
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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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  )
@@ -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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-
 
 
 
 
 
 
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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) 🌎 πŸ’» 🌟
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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()