Spaces:
Sleeping
Sleeping
File size: 10,231 Bytes
8bedda3 72650c2 c3df5b3 8bedda3 c3df5b3 8bedda3 72650c2 8bedda3 72650c2 8bedda3 b128151 72650c2 1b50cb2 72650c2 8004241 72650c2 8bedda3 72650c2 8bedda3 72650c2 9b416dc 72650c2 8bedda3 72650c2 f72ce70 72650c2 8bedda3 72650c2 8bedda3 72650c2 43ff4f1 8bedda3 72650c2 8bedda3 72650c2 8bedda3 72650c2 8bedda3 9b416dc 8bedda3 72650c2 8bedda3 72650c2 9b416dc 8bedda3 72650c2 8bedda3 c3df5b3 b353c7d c3df5b3 |
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 |
"""A gradio app that renders a static leaderboard. This is used for Hugging Face Space."""
import ast
import argparse
import pickle
import gradio as gr
import numpy as np
notebook_url = "https://colab.research.google.com/drive/1RAWb22-PFNI-X1gPVzc927SGUdfr6nsR?usp=sharing"
basic_component_values = [None] * 6
leader_component_values = [None] * 5
def make_leaderboard_md(elo_results):
leaderboard_md = f"""
# Leaderboard
| [Vote](https://chat.lmsys.org/?arena) | [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://huggingface.co/datasets/lmsys/chatbot_arena_conversations) | [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 50K+ 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 computed by [InstructEval](https://github.com/declare-lab/instruct-eval) and [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub). Higher values are better for all benchmarks. Empty cells mean not available.
"""
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 update_elo_components(max_num_files, elo_results_file):
log_files = get_log_files(max_num_files)
# Leaderboard
if elo_results_file is None: # Do live update
battles = clean_battle_data(log_files)
elo_results = report_elo_analysis_results(battles)
leader_component_values[0] = make_leaderboard_md_live(elo_results)
leader_component_values[1] = elo_results["win_fraction_heatmap"]
leader_component_values[2] = elo_results["battle_count_heatmap"]
leader_component_values[3] = elo_results["bootstrap_elo_rating"]
leader_component_values[4] = elo_results["average_win_rate_bar"]
# Basic stats
basic_stats = report_basic_stats(log_files)
md0 = f"Last updated: {basic_stats['last_updated_datetime']}"
md1 = "### Action Histogram\n"
md1 += basic_stats["action_hist_md"] + "\n"
md2 = "### Anony. Vote Histogram\n"
md2 += basic_stats["anony_vote_hist_md"] + "\n"
md3 = "### Model Call Histogram\n"
md3 += basic_stats["model_hist_md"] + "\n"
md4 = "### Model Call (Last 24 Hours)\n"
md4 += basic_stats["num_chats_last_24_hours"] + "\n"
basic_component_values[0] = md0
basic_component_values[1] = basic_stats["chat_dates_bar"]
basic_component_values[2] = md1
basic_component_values[3] = md2
basic_component_values[4] = md3
basic_component_values[5] = md4
def update_worker(max_num_files, interval, elo_results_file):
while True:
tic = time.time()
update_elo_components(max_num_files, elo_results_file)
durtaion = time.time() - tic
print(f"update duration: {durtaion:.2f} s")
time.sleep(max(interval - durtaion, 0))
def load_demo(url_params, request: gr.Request):
logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}")
return basic_component_values + leader_component_values
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):
lines = open(filename).readlines()
heads = [v.strip() for v in lines[0].split(",")]
rows = []
for i in range(1, len(lines)):
row = [v.strip() for v in lines[i].split(",")]
for j in range(len(heads)):
item = {}
for h, v in zip(heads, row):
if h == "Arena Elo rating":
if v != "-":
v = int(ast.literal_eval(v))
else:
v = np.nan
elif h == "MMLU":
if v != "-":
v = round(ast.literal_eval(v) * 100, 1)
else:
v = np.nan
elif h == "MT-bench (win rate %)":
if v != "-":
v = round(ast.literal_eval(v[:-1]), 1)
else:
v = np.nan
elif h == "MT-bench (score)":
if v != "-":
v = round(ast.literal_eval(v), 2)
else:
v = np.nan
item[h] = v
if add_hyperlink:
item["Model"] = model_hyperlink(item["Model"], item["Link"])
rows.append(item)
return rows
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 build_leaderboard_tab(elo_results_file, leaderboard_table_file):
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)
md = make_leaderboard_md(elo_results)
p1 = elo_results["win_fraction_heatmap"]
p2 = elo_results["battle_count_heatmap"]
p3 = elo_results["bootstrap_elo_rating"]
p4 = elo_results["average_win_rate_bar"]
md_1 = gr.Markdown(md, elem_id="leaderboard_markdown")
if leaderboard_table_file:
data = load_leaderboard_table_csv(leaderboard_table_file)
headers = [
"Model",
"Arena Elo rating",
"MT-bench (score)",
"MMLU",
"License",
]
values = []
for item in data:
row = []
for key in headers:
value = item[key]
row.append(value)
values.append(row)
values.sort(key=lambda x: -x[1] if not np.isnan(x[1]) else 1e9)
headers[1] = "⭐ " + headers[1]
headers[2] = "📈 " + headers[2]
gr.Dataframe(
headers=headers,
datatype=["markdown", "number", "number", "number", "str"],
value=values,
elem_id="leaderboard_dataframe",
)
gr.Markdown(
"If you want to see more models, please help us [add them](https://github.com/lm-sys/FastChat/blob/main/docs/arena.md#how-to-add-a-new-model)."
)
else:
pass
gr.Markdown(
f"""## More Statistics for Chatbot Arena\n
We added some additional figures to show more statistics. The code for generating them is also included in this [notebook]({notebook_url}).
Please note that you may see different orders from different ranking methods. This is expected for models that perform similarly, as demonstrated by the confidence interval in the bootstrap figure. Going forward, we prefer the classical Elo calculation because of its scalability and interpretability. You can find more discussions in this blog [post](https://lmsys.org/blog/2023-05-03-arena/).
"""
)
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)
return [md_1, plot_1, plot_2, plot_3, plot_4]
block_css = """
#notice_markdown {
font-size: 104%
}
#notice_markdown th {
display: none;
}
#notice_markdown td {
padding-top: 6px;
padding-bottom: 6px;
}
#leaderboard_markdown {
font-size: 104%
}
#leaderboard_markdown td {
padding-top: 6px;
padding-bottom: 6px;
}
#leaderboard_dataframe td {
line-height: 0.1em;
}
"""
def build_demo(elo_results_file, leaderboard_table_file):
text_size = gr.themes.sizes.text_lg
with gr.Blocks(
title="Chatbot Arena Leaderboard",
theme=gr.themes.Base(text_size=text_size),
css=block_css,
) as demo:
leader_components = build_leaderboard_tab(
elo_results_file, leaderboard_table_file
)
return demo
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--share", action="store_true")
args = parser.parse_args()
demo = build_demo("elo_results_20230802.pkl", "leaderboard_table_20230802.csv")
demo.launch(share=args.share)
|