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leaderboard.py
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leaderboard.py
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import pickle
import gradio as gr
import numpy as np
import pandas as pd
leader_component_values = [None] * 5
TASK_TYPE_TO_EMOJI = {
"Retrieval": "🔎",
"Clustering": "✨",
"STS": "☘️",
}
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}**. Total #votes: **{total_votes}**. Only anonymous votes count. Last updated: {last_updated}.
Contribute your votes 🗳️ at [MTEB Arena](https://huggingface.co/spaces/mteb/arena)! **Rank** is only based on the MTEB Arena Elo. The MTEB Avg scores are just displayed for information and taken from the [MTEB LB](https://hf.co/spaces/mteb/leaderboard).
"""
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 in ("MTEB Overall Avg", "MTEB Retrieval Avg", "MTEB Clustering Avg", "MTEB STS Avg"):
df[col] = df[col].apply(lambda x: x 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 get_arena_table(arena_df, model_table_df, task_type="Retrieval"):
# 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
row.append(round(arena_df.iloc[i]["num_battles"]))
row.append(model_table_df.iloc[i]["MTEB Overall Avg"])
row.append(model_table_df.iloc[i][f"MTEB {task_type} Avg"])
# 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 build_leaderboard_tab(elo_results_file, leaderboard_table_file, show_plot=False, task_type="Retrieval"):
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"]
anony_arena_df = anony_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 = f"""
# 🏆 MTEB Arena Leaderboard: {task_type} {TASK_TYPE_TO_EMOJI[task_type]}
"""
# | [GitHub](https://github.com/embeddings-benchmark) |
md_1 = gr.Markdown(md, elem_id="leaderboard_markdown")
if leaderboard_table_file:
model_table_df = load_leaderboard_table_csv(leaderboard_table_file)
arena_table_vals = get_arena_table(anony_arena_df, model_table_df, task_type=task_type)
md = make_arena_leaderboard_md(anony_elo_results)
gr.Markdown(md, elem_id="leaderboard_markdown")
gr.Dataframe(
headers=[
"Rank",
"🤖 Model",
"⭐ MTEB Arena Elo",
"📊 95% CI",
"🗳️ Votes",
"🥇 MTEB Overall Avg",
f"🥇 MTEB {task_type} Avg",
"Organization",
"License",
],
datatype=[
"str",
"markdown",
"number",
"str",
"number",
"number",
"number",
"str",
"str",
],
value=arena_table_vals,
elem_id="arena_leaderboard_dataframe",
height=700,
column_widths=[50, 150, 100, 100, 100, 100, 100, 150, 150],
wrap=True,
)
if not show_plot:
gr.Markdown(
"""## We are still collecting more votes on more models. The ranking will be updated very frequently. Please stay tuned!""",
elem_id="leaderboard_markdown",
)
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)
"""
# return [md_1, plot_1, plot_2, plot_3, plot_4]
return [md_1]