Spaces:
Running
on
Zero
Running
on
Zero
File size: 13,482 Bytes
e368cec 4636973 e368cec e70b763 e368cec 4636973 e368cec 62f5658 e368cec 62f5658 e368cec |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 |
"""
Live monitor of the website statistics and leaderboard.
Dependency:
sudo apt install pkg-config libicu-dev
pip install pytz gradio gdown plotly polyglot pyicu pycld2 tabulate
"""
import argparse
import ast
import pickle
import os
import threading
import time
import gradio as gr
import numpy as np
import pandas as pd
basic_component_values = [None] * 6
leader_component_values = [None] * 5
# def make_leaderboard_md(elo_results):
# leaderboard_md = f"""
# # 🏆 Chatbot Arena Leaderboard
# | [Blog](https://lmsys.org/blog/2023-05-03-arena/) | [GitHub](https://github.com/lm-sys/FastChat) | [Paper](https://arxiv.org/abs/2306.05685) | [Dataset](https://github.com/lm-sys/FastChat/blob/main/docs/dataset_release.md) | [Twitter](https://twitter.com/lmsysorg) | [Discord](https://discord.gg/HSWAKCrnFx) |
# This leaderboard is based on the following three benchmarks.
# - [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) - a crowdsourced, randomized battle platform. We use 100K+ user votes to compute Elo ratings.
# - [MT-Bench](https://arxiv.org/abs/2306.05685) - a set of challenging multi-turn questions. We use GPT-4 to grade the model responses.
# - [MMLU](https://arxiv.org/abs/2009.03300) (5-shot) - a test to measure a model's multitask accuracy on 57 tasks.
# 💻 Code: The Arena Elo ratings are computed by this [notebook]({notebook_url}). The MT-bench scores (single-answer grading on a scale of 10) are computed by [fastchat.llm_judge](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge). The MMLU scores are mostly computed by [InstructEval](https://github.com/declare-lab/instruct-eval). Higher values are better for all benchmarks. Empty cells mean not available. Last updated: November, 2023.
# """
# return leaderboard_md
def make_leaderboard_md(elo_results):
leaderboard_md = f"""
# 🏆 GenAI-Arena Leaderboard
| [Code](https://huggingface.co/spaces/TIGER-Lab/GenAI-Arena/tree/main) | [Dataset](https://huggingface.co/datasets/TIGER-Lab/GenAI-Bench) | [Twitter](https://twitter.com/TianleLI123/status/1757245259149422752) |
"""
return leaderboard_md
def make_leaderboard_md_live(elo_results):
leaderboard_md = f"""
# Leaderboard
Last updated: {elo_results["last_updated_datetime"]}
{elo_results["leaderboard_table"]}
"""
return leaderboard_md
def model_hyperlink(model_name, link):
return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
def load_leaderboard_table_csv(filename, add_hyperlink=True):
df = pd.read_csv(filename)
for col in df.columns:
if "Arena Elo rating" in col:
df[col] = df[col].apply(lambda x: int(x) if x != "-" else np.nan)
elif col == "MMLU":
df[col] = df[col].apply(lambda x: round(x * 100, 1) if x != "-" else np.nan)
elif col == "MT-bench (win rate %)":
df[col] = df[col].apply(lambda x: round(x, 1) if x != "-" else np.nan)
elif col == "MT-bench (score)":
df[col] = df[col].apply(lambda x: round(x, 2) if x != "-" else np.nan)
if add_hyperlink and col == "Model":
df[col] = df.apply(lambda row: model_hyperlink(row[col], row["Link"]), axis=1)
return df
def build_basic_stats_tab():
empty = "Loading ..."
basic_component_values[:] = [empty, None, empty, empty, empty, empty]
md0 = gr.Markdown(empty)
gr.Markdown("#### Figure 1: Number of model calls and votes")
plot_1 = gr.Plot(show_label=False)
with gr.Row():
with gr.Column():
md1 = gr.Markdown(empty)
with gr.Column():
md2 = gr.Markdown(empty)
with gr.Row():
with gr.Column():
md3 = gr.Markdown(empty)
with gr.Column():
md4 = gr.Markdown(empty)
return [md0, plot_1, md1, md2, md3, md4]
def get_full_table(anony_arena_df, full_arena_df, model_table_df):
values = []
for i in range(len(model_table_df)):
row = []
model_key = model_table_df.iloc[i]["key"]
model_name = model_table_df.iloc[i]["Model"]
# model display name
row.append(model_name)
if model_key in anony_arena_df.index:
idx = anony_arena_df.index.get_loc(model_key)
row.append(round(anony_arena_df.iloc[idx]["rating"]))
else:
row.append(np.nan)
if model_key in full_arena_df.index:
idx = full_arena_df.index.get_loc(model_key)
row.append(round(full_arena_df.iloc[idx]["rating"]))
else:
row.append(np.nan)
# row.append(model_table_df.iloc[i]["MT-bench (score)"])
# row.append(model_table_df.iloc[i]["Num Battles"])
# row.append(model_table_df.iloc[i]["MMLU"])
# Organization
row.append(model_table_df.iloc[i]["Organization"])
# license
row.append(model_table_df.iloc[i]["License"])
values.append(row)
values.sort(key=lambda x: -x[1] if not np.isnan(x[1]) else 1e9)
return values
def get_arena_table(arena_df, model_table_df):
# sort by rating
arena_df = arena_df.sort_values(by=["rating"], ascending=False)
values = []
for i in range(len(arena_df)):
row = []
model_key = arena_df.index[i]
model_name = model_table_df[model_table_df["key"] == model_key]["Model"].values[
0
]
# rank
row.append(i + 1)
# model display name
row.append(model_name)
# elo rating
row.append(round(arena_df.iloc[i]["rating"]))
upper_diff = round(arena_df.iloc[i]["rating_q975"] - arena_df.iloc[i]["rating"])
lower_diff = round(arena_df.iloc[i]["rating"] - arena_df.iloc[i]["rating_q025"])
row.append(f"+{upper_diff}/-{lower_diff}")
# num battles
print(arena_df.iloc[i])
row.append(round(arena_df.iloc[i]["num_battles"]))
# Organization
row.append(
model_table_df[model_table_df["key"] == model_key]["Organization"].values[0]
)
# license
row.append(
model_table_df[model_table_df["key"] == model_key]["License"].values[0]
)
values.append(row)
return values
def make_arena_leaderboard_md(elo_results):
arena_df = elo_results["leaderboard_table_df"]
last_updated = elo_results["last_updated_datetime"]
total_votes = sum(arena_df["num_battles"]) // 2
total_models = len(arena_df)
leaderboard_md = f"""
Total #models: **{total_models}**(anonymous). Total #votes: **{total_votes}**. Last updated: {last_updated}.
(Note: Only anonymous votes are considered here. Check the full leaderboard for all votes.)
Contribute the votes 🗳️ at [GenAI-Arena](https://huggingface.co/spaces/TIGER-Lab/GenAI-Arena)!
If you want to see more models, please help us [add them](https://huggingface.co/spaces/TIGER-Lab/GenAI-Arena/tree/main?tab=readme-ov-file#-contributing-).
"""
return leaderboard_md
def make_full_leaderboard_md(elo_results):
arena_df = elo_results["leaderboard_table_df"]
last_updated = elo_results["last_updated_datetime"]
total_votes = sum(arena_df["num_battles"]) // 2
total_models = len(arena_df)
leaderboard_md = f"""
Total #models: **{total_models}**(full:anonymous+open). Total #votes: **{total_votes}**. Last updated: {last_updated}.
Contribute your vote 🗳️ at [vision-arena](https://huggingface.co/spaces/WildVision/vision-arena)!
"""
return leaderboard_md
def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=True):
if elo_results_file is None: # Do live update
md = "Loading ..."
p1 = p2 = p3 = p4 = None
else:
with open(elo_results_file, "rb") as fin:
elo_results = pickle.load(fin)
anony_elo_results = elo_results["anony"]
full_elo_results = elo_results["full"]
anony_arena_df = anony_elo_results["leaderboard_table_df"]
full_arena_df = full_elo_results["leaderboard_table_df"]
p1 = anony_elo_results["win_fraction_heatmap"]
p2 = anony_elo_results["battle_count_heatmap"]
p3 = anony_elo_results["bootstrap_elo_rating"]
p4 = anony_elo_results["average_win_rate_bar"]
md = make_leaderboard_md(anony_elo_results)
md_1 = gr.Markdown(md, elem_id="leaderboard_markdown")
if leaderboard_table_file:
model_table_df = load_leaderboard_table_csv(leaderboard_table_file)
with gr.Tabs() as tabs:
# arena table
arena_table_vals = get_arena_table(anony_arena_df, model_table_df)
with gr.Tab("Arena Elo", id=0):
md = make_arena_leaderboard_md(anony_elo_results)
gr.Markdown(md, elem_id="leaderboard_markdown")
gr.Dataframe(
headers=[
"Rank",
"🤖 Model",
"⭐ Arena Elo",
"📊 95% CI",
"🗳️ Votes",
"Organization",
"License",
],
datatype=[
"str",
"markdown",
"number",
"str",
"number",
"str",
"str",
],
value=arena_table_vals,
elem_id="arena_leaderboard_dataframe",
height=700,
column_widths=[50, 200, 100, 100, 100, 150, 150],
wrap=True,
)
with gr.Tab("Full Leaderboard", id=1):
md = make_full_leaderboard_md(full_elo_results)
gr.Markdown(md, elem_id="leaderboard_markdown")
full_table_vals = get_full_table(anony_arena_df, full_arena_df, model_table_df)
gr.Dataframe(
headers=[
"🤖 Model",
"⭐ Arena Elo (anony)",
"⭐ Arena Elo (full)",
"Organization",
"License",
],
datatype=["markdown", "number", "number", "str", "str"],
value=full_table_vals,
elem_id="full_leaderboard_dataframe",
column_widths=[200, 100, 100, 100, 150, 150],
height=700,
wrap=True,
)
gr.Markdown(
""" ## We are still collecting more votes on more models. The ranking will be updated very fruquently. Please stay tuned!
""",
elem_id="leaderboard_markdown",
)
if show_plot:
win_fraction_heatmap = anony_elo_results["win_fraction_heatmap"]
battle_count_heatmap = anony_elo_results["battle_count_heatmap"]
bootstrap_elo_rating = anony_elo_results["bootstrap_elo_rating"]
average_win_rate_bar = anony_elo_results["average_win_rate_bar"]
with gr.Row():
with gr.Column():
gr.Markdown(
"#### Figure 1: Fraction of Model A Wins for All Non-tied A vs. B Battles"
)
plot_1 = gr.Plot(win_fraction_heatmap, show_label=False)
with gr.Column():
gr.Markdown(
"#### Figure 2: Battle Count for Each Combination of Models (without Ties)"
)
plot_2 = gr.Plot(battle_count_heatmap, show_label=False)
with gr.Row():
with gr.Column():
gr.Markdown(
"#### Figure 3: Bootstrap of Elo Estimates (1000 Rounds of Random Sampling)"
)
plot_3 = gr.Plot(bootstrap_elo_rating, show_label=False)
with gr.Column():
gr.Markdown(
"#### Figure 4: Average Win Rate Against All Other Models (Assuming Uniform Sampling and No Ties)"
)
plot_4 = gr.Plot(average_win_rate_bar, show_label=False)
else:
pass
leader_component_values[:] = [md, p1, p2, p3, p4]
"""
with gr.Row():
with gr.Column():
gr.Markdown(
"#### Figure 1: Fraction of Model A Wins for All Non-tied A vs. B Battles"
)
plot_1 = gr.Plot(p1, show_label=False)
with gr.Column():
gr.Markdown(
"#### Figure 2: Battle Count for Each Combination of Models (without Ties)"
)
plot_2 = gr.Plot(p2, show_label=False)
with gr.Row():
with gr.Column():
gr.Markdown(
"#### Figure 3: Bootstrap of Elo Estimates (1000 Rounds of Random Sampling)"
)
plot_3 = gr.Plot(p3, show_label=False)
with gr.Column():
gr.Markdown(
"#### Figure 4: Average Win Rate Against All Other Models (Assuming Uniform Sampling and No Ties)"
)
plot_4 = gr.Plot(p4, show_label=False)
"""
from .utils import acknowledgment_md
gr.Markdown(acknowledgment_md)
# return [md_1, plot_1, plot_2, plot_3, plot_4]
return [md_1] |