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import streamlit as st
import pandas as pd
from utils.style import style_long_context
@st.cache_data
def load_data():
df = pd.read_csv("data/long_context.csv")
df.dropna(inplace=True) # Drop rows with any missing values
return df
def show():
st.title("Long Context Leaderboard")
# Load and style data
df = load_data()
styled_df = style_long_context(df)
st.markdown(styled_df, unsafe_allow_html=True) # No need to call to_html() again
# st.dataframe(styled_df, use_container_width=True)
# st.html(styled_df)
# Optionally, keep some explanatory text
st.markdown("""
**Context Lengths**:
- 8K: 8,000 tokens
- 16K: 16,000 tokens
- 32K: 32,000 tokens
**Colors**:
- Yellow: reasoning model
- Green: linear attention hybrid model
- Blue: SSM hybrid model
**Benchmark Details**:
- Evaluated on Symbolic, Medium, and Hard subtasks.
- Area Under Curve(AUC) Metrics is Used to Compare between LLM Performance.
- AUC is calculated using np.trapz function.
- AUC scores aggregated across context lengths.
- Larger context evaluations limited by compute constraints and model performance.
""") |