|
"""A gradio app that renders a static leaderboard. This is used for Hugging Face Space.""" |
|
import ast |
|
import argparse |
|
import glob |
|
import pickle |
|
|
|
import gradio as gr |
|
import numpy as np |
|
import pandas as pd |
|
|
|
|
|
|
|
notebook_url = "https://colab.research.google.com/drive/1KdwokPjirkTmpO_P1WByFNFiqxWQquwH#scrollTo=o_CpbkGEbhrK" |
|
|
|
|
|
basic_component_values = [None] * 6 |
|
leader_component_values = [None] * 5 |
|
|
|
|
|
def make_default_md(arena_df, elo_results): |
|
total_votes = sum(arena_df["num_battles"]) // 2 |
|
total_models = len(arena_df) |
|
|
|
leaderboard_md = f""" |
|
# 🏆 LMSYS Chatbot Arena Leaderboard |
|
| [Vote](https://chat.lmsys.org) | [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) | |
|
|
|
LMSYS [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) is a crowdsourced open platform for LLM evals. |
|
We've collected over **500,000** human preference votes to rank LLMs with the Elo ranking system. |
|
""" |
|
return leaderboard_md |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def make_arena_leaderboard_md(arena_df, arena_chinese_df, arena_long_df, arena_english_df): |
|
|
|
total_votes = sum(arena_df["num_battles"]) // 2 |
|
total_chinese_votes = sum(arena_chinese_df["num_battles"]) // 2 |
|
total_long_votes = sum(arena_long_df["num_battles"]) // 2 |
|
total_english_votes = sum(arena_english_df["num_battles"]) // 2 |
|
|
|
|
|
leaderboard_md = f""" |
|
Last updated: March 29, 2024. |
|
| | **Total** | English | Chinese | Long Context | |
|
| :-------------- | :-----------------------: | :-----------------------: | :-----------------------: | :-----------------------: | |
|
| # Votes | **{"{:,}".format(total_votes)}** | {"{:,}".format(total_english_votes)} | {"{:,}".format(total_chinese_votes)} | {"{:,}".format(total_long_votes)} | |
|
| # Models | **{len(arena_df)}** | {len(arena_english_df)}| {len(arena_chinese_df)} | {len(arena_long_df)} | |
|
|
|
Contribute your vote 🗳️ at [chat.lmsys.org](https://chat.lmsys.org)! Find more analysis in the [notebook]({notebook_url}). |
|
""" |
|
|
|
return leaderboard_md |
|
|
|
|
|
|
|
def make_full_leaderboard_md(elo_results): |
|
leaderboard_md = f""" |
|
Three benchmarks are displayed: **Arena Elo**, **MT-Bench** and **MMLU**. |
|
- [Chatbot Arena](https://chat.lmsys.org/?arena) - a crowdsourced, randomized battle platform. We use 500K+ 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 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. |
|
""" |
|
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) |
|
|
|
|
|
if elo_results_file is None: |
|
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 = 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 get_full_table(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"] |
|
|
|
row.append(model_name) |
|
if model_key in arena_df.index: |
|
idx = arena_df.index.get_loc(model_key) |
|
row.append(round(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]["MMLU"]) |
|
|
|
row.append(model_table_df.iloc[i]["Organization"]) |
|
|
|
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 create_ranking_str(ranking, ranking_difference): |
|
if ranking_difference > 0: |
|
return f"{int(ranking)} (\u2191 {int(ranking_difference)})" |
|
elif ranking_difference < 0: |
|
return f"{int(ranking)} (\u2193 {int(-ranking_difference)})" |
|
else: |
|
return f"{int(ranking)}" |
|
|
|
def get_arena_table(arena_df, model_table_df, arena_subset_df=None): |
|
arena_df = arena_df.sort_values(by=["final_ranking"], ascending=True) |
|
|
|
if arena_subset_df is not None: |
|
arena_subset_df = arena_subset_df.sort_values(by=["final_ranking"], ascending=True) |
|
|
|
arena_df = arena_subset_df.join(arena_df["final_ranking"], rsuffix="_global", how="inner") |
|
arena_df['ranking_difference'] = arena_df['final_ranking_global'] - arena_df['final_ranking'] |
|
arena_df["final_ranking"] = arena_df.apply(lambda x: create_ranking_str(x["final_ranking"], x["ranking_difference"]), axis=1) |
|
|
|
values = [] |
|
for i in range(len(arena_df)): |
|
row = [] |
|
model_key = arena_df.index[i] |
|
try: |
|
model_name = model_table_df[model_table_df["key"] == model_key]["Model"].values[ |
|
0 |
|
] |
|
|
|
|
|
ranking = arena_df.iloc[i].get("final_ranking") or i+1 |
|
row.append(ranking) |
|
|
|
row.append(model_name) |
|
|
|
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}") |
|
|
|
row.append(round(arena_df.iloc[i]["num_battles"])) |
|
|
|
row.append( |
|
model_table_df[model_table_df["key"] == model_key]["Organization"].values[0] |
|
) |
|
|
|
row.append( |
|
model_table_df[model_table_df["key"] == model_key]["License"].values[0] |
|
) |
|
|
|
cutoff_date = model_table_df[model_table_df["key"] == model_key]["Knowledge cutoff date"].values[0] |
|
if cutoff_date == "-": |
|
row.append("Unknown") |
|
else: |
|
row.append(cutoff_date) |
|
values.append(row) |
|
except Exception as e: |
|
print(f"{model_key} - {e}") |
|
return values |
|
|
|
def get_plots(elo_subset_results): |
|
p1 = elo_subset_results["win_fraction_heatmap"] |
|
p2 = elo_subset_results["battle_count_heatmap"] |
|
p3 = elo_subset_results["bootstrap_elo_rating"] |
|
p4 = elo_subset_results["average_win_rate_bar"] |
|
return p1, p2, p3, p4 |
|
|
|
|
|
def build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=False): |
|
if elo_results_file is None: |
|
default_md = "Loading ..." |
|
p1 = p2 = p3 = p4 = None |
|
else: |
|
with open(elo_results_file, "rb") as fin: |
|
elo_results = pickle.load(fin) |
|
if "full" in elo_results: |
|
elo_chinese_results = elo_results["chinese"] |
|
elo_long_results = elo_results["long"] |
|
elo_english_results = elo_results["english"] |
|
elo_results = elo_results["full"] |
|
|
|
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"] |
|
arena_df = elo_results["leaderboard_table_df"] |
|
arena_chinese_df = elo_chinese_results["leaderboard_table_df"] |
|
arena_long_df = elo_long_results["leaderboard_table_df"] |
|
arena_english_df = elo_english_results["leaderboard_table_df"] |
|
default_md = make_default_md(arena_df, elo_results) |
|
|
|
md_1 = gr.Markdown(default_md, elem_id="leaderboard_markdown") |
|
if leaderboard_table_file: |
|
data = load_leaderboard_table_csv(leaderboard_table_file) |
|
model_table_df = pd.DataFrame(data) |
|
|
|
with gr.Tabs() as tabs: |
|
|
|
arena_table_vals = get_arena_table(arena_df, model_table_df) |
|
with gr.Tab("Arena Elo", id=0): |
|
md = make_arena_leaderboard_md(arena_df, arena_chinese_df, arena_long_df, arena_english_df) |
|
gr.Markdown(md, elem_id="leaderboard_markdown") |
|
with gr.Row(): |
|
overall_rating = gr.Button("Overall") |
|
update_overall_rating_df = lambda _: get_arena_table(arena_df, model_table_df) |
|
english_rating = gr.Button("English") |
|
update_english_rating_df = lambda _: get_arena_table(arena_df, model_table_df, arena_english_df) |
|
chinese_rating = gr.Button("Chinese") |
|
update_chinese_rating_df = lambda _: get_arena_table(arena_df, model_table_df, arena_chinese_df) |
|
long_context_rating = gr.Button("Long Context") |
|
update_long_context_rating_df = lambda _: get_arena_table(arena_df, model_table_df, arena_long_df) |
|
elo_display_df = gr.Dataframe( |
|
headers=[ |
|
"Rank", |
|
"🤖 Model", |
|
"⭐ Arena Elo", |
|
"📊 95% CI", |
|
"🗳️ Votes", |
|
"Organization", |
|
"License", |
|
"Knowledge Cutoff", |
|
], |
|
datatype=[ |
|
"str", |
|
"markdown", |
|
"number", |
|
"str", |
|
"number", |
|
"str", |
|
"str", |
|
"str", |
|
], |
|
value=arena_table_vals, |
|
elem_id="arena_leaderboard_dataframe", |
|
height=700, |
|
column_widths=[70, 190, 120, 100, 90, 140, 150, 140], |
|
wrap=True, |
|
) |
|
|
|
overall_rating.click(fn=update_overall_rating_df, inputs=overall_rating, outputs=elo_display_df) |
|
english_rating.click(fn=update_english_rating_df, inputs=english_rating, outputs=elo_display_df) |
|
chinese_rating.click(fn=update_chinese_rating_df, inputs=chinese_rating ,outputs=elo_display_df) |
|
long_context_rating.click(fn=update_long_context_rating_df, inputs=long_context_rating, outputs=elo_display_df) |
|
|
|
with gr.Tab("Full Leaderboard", id=1): |
|
md = make_full_leaderboard_md(elo_results) |
|
gr.Markdown(md, elem_id="leaderboard_markdown") |
|
full_table_vals = get_full_table(arena_df, model_table_df) |
|
gr.Dataframe( |
|
headers=[ |
|
"🤖 Model", |
|
"⭐ Arena Elo", |
|
"📈 MT-bench", |
|
"📚 MMLU", |
|
"Organization", |
|
"License", |
|
], |
|
datatype=["markdown", "number", "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, |
|
) |
|
if not show_plot: |
|
gr.Markdown( |
|
""" ## Visit our [HF space](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard) for more analysis! |
|
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). |
|
""", |
|
elem_id="leaderboard_markdown", |
|
) |
|
else: |
|
pass |
|
|
|
gr.Markdown( |
|
f"""Note: we take the 95% confidence interval into account when determining a model's ranking. |
|
A model is ranked higher only if its lower bound of model score is higher than the upper bound of the other model's score. |
|
See Figure 3 below for visualization of the confidence intervals. |
|
""", |
|
elem_id="leaderboard_markdown" |
|
) |
|
|
|
leader_component_values[:] = [default_md, p1, p2, p3, p4] |
|
|
|
if show_plot: |
|
gr.Markdown( |
|
f"""## More Statistics for Chatbot Arena\n |
|
Below are figures for more statistics. The code for generating them is also included in this [notebook]({notebook_url}). |
|
You can find more discussions in this blog [post](https://lmsys.org/blog/2023-12-07-leaderboard/). |
|
""", |
|
elem_id="leaderboard_markdown" |
|
) |
|
with gr.Row(): |
|
overall_plots = gr.Button("Overall") |
|
update_overall_plots = lambda _: get_plots(elo_results) |
|
english_plots = gr.Button("English") |
|
update_english_plot = lambda _: get_plots(elo_english_results) |
|
chinese_plots = gr.Button("Chinese") |
|
update_chinese_plot = lambda _: get_plots(elo_chinese_results) |
|
long_context_plots = gr.Button("Long Context") |
|
update_long_context_plot = lambda _: get_plots(elo_long_results) |
|
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: Confidence Intervals on Model Strength (via Bootstrapping)" |
|
) |
|
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) |
|
|
|
overall_plots.click(fn=update_overall_plots, inputs=overall_plots, outputs=[plot_1, plot_2, plot_3, plot_4]) |
|
english_plots.click(fn=update_english_plot, inputs=english_plots, outputs=[plot_1, plot_2, plot_3, plot_4]) |
|
chinese_plots.click(fn=update_chinese_plot, inputs=chinese_plots, outputs=[plot_1, plot_2, plot_3, plot_4]) |
|
long_context_plots.click(fn=update_long_context_plot, inputs=long_context_plots, outputs=[plot_1, plot_2, plot_3, plot_4]) |
|
|
|
gr.Markdown(acknowledgment_md) |
|
|
|
if show_plot: |
|
return [md_1, plot_1, plot_2, plot_3, plot_4] |
|
return [md_1] |
|
|
|
|
|
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; |
|
} |
|
|
|
#arena_leaderboard_dataframe td { |
|
line-height: 0.15em; |
|
font-size: 20px; |
|
} |
|
#arena_leaderboard_dataframe th { |
|
font-size: 20px; |
|
} |
|
|
|
|
|
#full_leaderboard_dataframe td { |
|
line-height: 0.15em; |
|
font-size: 20px; |
|
} |
|
#full_leaderboard_dataframe th { |
|
font-size: 20px; |
|
} |
|
|
|
footer { |
|
display:none !important |
|
} |
|
.sponsor-image-about img { |
|
margin: 0 20px; |
|
margin-top: 20px; |
|
height: 40px; |
|
max-height: 100%; |
|
width: auto; |
|
float: left; |
|
} |
|
""" |
|
|
|
acknowledgment_md = """ |
|
### Acknowledgment |
|
We thank [Kaggle](https://www.kaggle.com/), [MBZUAI](https://mbzuai.ac.ae/), [a16z](https://www.a16z.com/), [Together AI](https://www.together.ai/), [Anyscale](https://www.anyscale.com/), [HuggingFace](https://huggingface.co/) for their generous [sponsorship](https://lmsys.org/donations/). |
|
|
|
<div class="sponsor-image-about"> |
|
<img src="https://storage.googleapis.com/public-arena-asset/kaggle.png" alt="Kaggle"> |
|
<img src="https://storage.googleapis.com/public-arena-asset/mbzuai.jpeg" alt="MBZUAI"> |
|
<img src="https://storage.googleapis.com/public-arena-asset/a16z.jpeg" alt="a16z"> |
|
<img src="https://storage.googleapis.com/public-arena-asset/together.png" alt="Together AI"> |
|
<img src="https://storage.googleapis.com/public-arena-asset/anyscale.png" alt="AnyScale"> |
|
<img src="https://storage.googleapis.com/public-arena-asset/huggingface.png" alt="HuggingFace"> |
|
</div> |
|
""" |
|
|
|
def build_demo(elo_results_file, leaderboard_table_file): |
|
text_size = gr.themes.sizes.text_lg |
|
print("text_size", text_size) |
|
|
|
with gr.Blocks( |
|
title="Chatbot Arena Leaderboard", |
|
|
|
|
|
theme = gr.themes.Base.load("theme.json"), |
|
css=block_css, |
|
) as demo: |
|
leader_components = build_leaderboard_tab( |
|
elo_results_file, leaderboard_table_file, show_plot=True |
|
) |
|
return demo |
|
|
|
|
|
if __name__ == "__main__": |
|
parser = argparse.ArgumentParser() |
|
parser.add_argument("--share", action="store_true") |
|
args = parser.parse_args() |
|
|
|
elo_result_files = glob.glob("elo_results_*.pkl") |
|
elo_result_files.sort(key=lambda x: int(x[12:-4])) |
|
elo_result_file = elo_result_files[-1] |
|
|
|
leaderboard_table_files = glob.glob("leaderboard_table_*.csv") |
|
leaderboard_table_files.sort(key=lambda x: int(x[18:-4])) |
|
leaderboard_table_file = leaderboard_table_files[-1] |
|
|
|
demo = build_demo(elo_result_file, leaderboard_table_file) |
|
demo.launch(share=args.share) |
|
|
