| import streamlit as st |
|
|
| from draw_utils import PAGE_MARKDOWN, PAGE_INFO, LENGTHS |
| from draw_utils import load_results, style_dataframe |
|
|
| st.set_page_config(layout="wide", page_title="Leaderboard App") |
| st.markdown(PAGE_MARKDOWN, unsafe_allow_html=True) |
|
|
|
|
| def draw_leaderboard(): |
| df = load_results() |
|
|
| tasks = ['avg(qa1-5)'] + [f"qa{i}" for i in range(1, 11)] |
| columns = ["model_name", "≤32k", "≤128k"] + LENGTHS |
|
|
| st.title("🔎📚🪡📚❓ BABILong Leaderboard 🏆") |
| st.markdown(PAGE_INFO) |
| st.subheader("Evaluation results:") |
| st.text('Each tab corresponds to a task, avg - averaged scores over qa1-5 tasks.') |
| st.markdown('Predictions of all evaluated models: ' |
| '[BABILong evals](https://huggingface.co/datasets/RMT-team/babilong_evals)') |
| search_term = st.text_input("Search models:", "") |
|
|
| tabs = st.tabs(tasks) |
| for i, tab in enumerate(tabs): |
| with tab: |
| task_df = df[df.task == tasks[i]][columns] |
| if i == 0: |
| task_df = task_df.loc[~task_df[task_df.columns[:5]].isna().any(axis=1)] |
|
|
| if search_term: |
| task_df = task_df[task_df['model_name'].str.contains(search_term, case=False)] |
| task_df.reset_index(drop=True, inplace=True) |
|
|
| row_height = 35 |
| height = (len(task_df) + 1) * row_height |
|
|
| styled_df = style_dataframe(task_df).format(precision=1) |
|
|
| st.dataframe( |
| styled_df, |
| width=1070, |
| height=height, |
| column_config={ |
| "model_name": st.column_config.Column(width=260, pinned=True) |
| } |
| ) |
|
|
|
|
| if __name__ == "__main__": |
| draw_leaderboard() |
|
|
