File size: 2,203 Bytes
73a9525 1edab07 4c24185 ca872a1 38d9f8d 1edab07 4c24185 1edab07 ca872a1 73a9525 4c24185 ca872a1 ed56ab7 1edab07 ed56ab7 4c24185 1edab07 ed56ab7 4c24185 1edab07 4c24185 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
import streamlit as st
import pandas as pd
# CSS样式
st.markdown("""
<style>
body {
color: #fff;
background-color: #111;
}
/* 调整全局容器宽度为屏幕的 90%，自适应屏幕 */
.css-1d391kg {
padding: 1rem 1rem; /* 调整内边距 */
}
/* 设置主体内容最大宽度为100%，自适应屏幕 */
.css-1lcbmhc {
max-width: 100%;
}
.stDataFrame {
font-family: Helvetica;
font-size: 16px;
width: 100%;
min-width: 100%;
}
h1 {
color: #ffdf92;
}
</style>
""", unsafe_allow_html=True)
# 标题
st.title('AEOLLM leaderboard')
# 描述
st.markdown("""
This leaderboard is used to show the performance of the **automation evaluation methods of LLMs** submitted by the **AEOLLM team** on four tasks:
- Summary Generation (SG)
- Non-Factoid QA (NFQA)
- Dialogue Generation (DG)
- Text Expansion (TE).
""", unsafe_allow_html=True)
# 创建示例数据
SG = {
"methods": ["Model A", "Model B", "Model C"],
"team": ["U1", "U2", "U3"],
"acc": [0.75, 0.64, 0.83],
"tau": [0.05, 0.28, 0.16],
"s": [0.12, 0.27, 0.18]
}
df1 = pd.DataFrame(SG)
NFQA = {
"methods": ["Model A", "Model B", "Model C"],
"team": ["U1", "U2", "U3"],
"acc": [0.75, 0.64, 0.83],
"tau": [0.05, 0.28, 0.16],
"s": [0.12, 0.27, 0.18]
}
df2 = pd.DataFrame(NFQA)
DG = {
"methods": ["Model A", "Model B", "Model C"],
"team": ["U1", "U2", "U3"],
"acc": [0.75, 0.64, 0.83],
"tau": [0.05, 0.28, 0.16],
"s": [0.12, 0.27, 0.18]
}
df3 = pd.DataFrame(DG)
TE = {
"methods": ["Model A", "Model B", "Model C"],
"team": ["U1", "U2", "U3"],
"acc": [0.75, 0.64, 0.83],
"tau": [0.05, 0.28, 0.16],
"s": [0.12, 0.27, 0.18]
}
df4 = pd.DataFrame(TE)
# 创建标签页
tab1, tab2, tab3, tab4 = st.tabs(["SG", "NFQA", "DG", "TE"])
# 在标签页 1 中添加内容
with tab1:
st.header("Summary Generation")
st.dataframe(df1)
# 在标签页 2 中添加内容
with tab2:
st.header("Non-Factoid QA")
st.dataframe(df2)
# 在标签页 3 中添加内容
with tab3:
st.header("Dialogue Generation")
st.dataframe(df3)
# 在标签页 4 中添加内容
with tab4:
st.header("Text Expansion")
st.dataframe(df4)
|