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import gradio as gr |
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from gradio_leaderboard import Leaderboard, SelectColumns, ColumnFilter |
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from pathlib import Path |
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from utils import LLM_BENCHMARKS_ABOUT_TEXT, LLM_BENCHMARKS_SUBMIT_TEXT, custom_css, jsonl_to_dataframe, add_average_column_to_df, apply_markdown_format_for_columns, submit, PART_LOGO, sort_dataframe_by_column |
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abs_path = Path(__file__).parent |
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leaderboard_df = jsonl_to_dataframe(str(abs_path / "leaderboard_data.jsonl")) |
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average_column_name = "Average Accuracy" |
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all_columns = ["Model", average_column_name, "Precision", "#Params (B)", "MMLU", "GSM8K", "TruthfulQA", "Winogrande", "ARC Easy", "Hellaswag", "Belebele"] |
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columns_to_average = ["MMLU", "GSM8K", "TruthfulQA", "Winogrande", "ARC Easy", "Hellaswag", "Belebele"] |
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leaderboard_df = add_average_column_to_df(leaderboard_df, columns_to_average, index=3, average_column_name=average_column_name) |
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leaderboard_df = apply_markdown_format_for_columns(df=leaderboard_df, model_column_name="Model") |
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leaderboard_df = sort_dataframe_by_column(leaderboard_df, column_name=average_column_name) |
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columns_data_type = ["markdown" for i in range(len(leaderboard_df.columns))] |
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NUM_MODELS=len(leaderboard_df) |
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with gr.Blocks(css=custom_css) as demo: |
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gr.Markdown(""" |
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# Open Lithuanian LLM Leaderboard |
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""") |
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gr.Markdown(f""" |
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- **Total Models**: {NUM_MODELS} |
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""") |
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with gr.Tab("ποΈ Lithuanian Leaderboard"): |
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Leaderboard( |
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value=leaderboard_df, |
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datatype=columns_data_type, |
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select_columns=SelectColumns( |
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default_selection=all_columns, |
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cant_deselect=["Model"], |
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label="Select Columns to Show", |
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), |
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search_columns=["model_name_for_query"], |
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hide_columns=["model_name_for_query",], |
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filter_columns=["Precision", "#Params (B)"], |
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) |
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with gr.TabItem("π About"): |
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gr.Markdown(LLM_BENCHMARKS_ABOUT_TEXT) |
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with gr.Tab("βοΈ Submit"): |
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gr.Markdown(LLM_BENCHMARKS_SUBMIT_TEXT) |
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model_name = gr.Textbox(label="Model name") |
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model_id = gr.Textbox(label="username/space e.g neurotechnology/Lt-Llama-2-7b-hf") |
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contact_email = gr.Textbox(label="Contact E-Mail") |
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submit_btn = gr.Button("Submit") |
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submit_btn.click(submit, inputs=[model_name, model_id, contact_email], outputs=[]) |
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gr.Markdown(""" |
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Please find more information about Neurotechnology on [www.neurotechnology.com](https://www.neurotechnology.com/natural-language-processing.html)""") |
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if __name__ == "__main__": |
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demo.launch() |
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