llm-leaderboard / streamlit_app.py
Ludwig Stumpp
Notes on definition of "open" model
2322286
raw
history blame
10.2 kB
import io
import re
from collections.abc import Iterable
import pandas as pd
import streamlit as st
from pandas.api.types import is_bool_dtype, is_datetime64_any_dtype, is_numeric_dtype
GITHUB_URL = "https://github.com/LudwigStumpp/llm-leaderboard"
NON_BENCHMARK_COLS = ["Open?", "Publisher"]
def extract_table_and_format_from_markdown_text(markdown_table: str) -> pd.DataFrame:
"""Extracts a table from a markdown text and formats it as a pandas DataFrame.
Args:
text (str): Markdown text containing a table.
Returns:
pd.DataFrame: Table as pandas DataFrame.
"""
df = (
pd.read_table(io.StringIO(markdown_table), sep="|", header=0, index_col=1)
.dropna(axis=1, how="all") # drop empty columns
.iloc[1:] # drop first row which is the "----" separator of the original markdown table
.sort_index(ascending=True)
.apply(lambda x: x.str.strip() if x.dtype == "object" else x)
.replace("", float("NaN"))
.astype(float, errors="ignore")
)
# remove whitespace from column names and index
df.columns = df.columns.str.strip()
df.index = df.index.str.strip()
df.index.name = df.index.name.strip()
return df
def extract_markdown_table_from_multiline(multiline: str, table_headline: str, next_headline_start: str = "#") -> str:
"""Extracts the markdown table from a multiline string.
Args:
multiline (str): content of README.md file.
table_headline (str): Headline of the table in the README.md file.
next_headline_start (str, optional): Start of the next headline. Defaults to "#".
Returns:
str: Markdown table.
Raises:
ValueError: If the table could not be found.
"""
# extract everything between the table headline and the next headline
table = []
start = False
for line in multiline.split("\n"):
if line.startswith(table_headline):
start = True
elif line.startswith(next_headline_start):
start = False
elif start:
table.append(line + "\n")
if len(table) == 0:
raise ValueError(f"Could not find table with headline '{table_headline}'")
return "".join(table)
def remove_markdown_links(text: str) -> str:
"""Modifies a markdown text to remove all markdown links.
Example: [DISPLAY](LINK) to DISPLAY
First find all markdown links with regex.
Then replace them with: $1
Args:
text (str): Markdown text containing markdown links
Returns:
str: Markdown text without markdown links.
"""
# find all markdown links
markdown_links = re.findall(r"\[([^\]]+)\]\(([^)]+)\)", text)
# remove link keep display text
for display, link in markdown_links:
text = text.replace(f"[{display}]({link})", display)
return text
def filter_dataframe_by_row_and_columns(df: pd.DataFrame, ignore_columns: list[str] | None = None) -> pd.DataFrame:
"""
Filter dataframe by the rows and columns to display.
This does not select based on the values in the dataframe, but rather on the index and columns.
Modified from https://blog.streamlit.io/auto-generate-a-dataframe-filtering-ui-in-streamlit-with-filter_dataframe/
Args:
df (pd.DataFrame): Original dataframe
ignore_columns (list[str], optional): Columns to ignore. Defaults to None.
Returns:
pd.DataFrame: Filtered dataframe
"""
df = df.copy()
if ignore_columns is None:
ignore_columns = []
modification_container = st.container()
with modification_container:
to_filter_index = st.multiselect("Filter by model:", sorted(df.index))
if to_filter_index:
df = pd.DataFrame(df.loc[to_filter_index])
to_filter_columns = st.multiselect(
"Filter by benchmark:", sorted([c for c in df.columns if c not in ignore_columns])
)
if to_filter_columns:
df = pd.DataFrame(df[ignore_columns + to_filter_columns])
return df
def filter_dataframe_by_column_values(df: pd.DataFrame) -> pd.DataFrame:
"""
Filter dataframe by the values in the dataframe.
Modified from https://blog.streamlit.io/auto-generate-a-dataframe-filtering-ui-in-streamlit-with-filter_dataframe/
Args:
df (pd.DataFrame): Original dataframe
Returns:
pd.DataFrame: Filtered dataframe
"""
df = df.copy()
modification_container = st.container()
with modification_container:
to_filter_columns = st.multiselect("Filter results on:", df.columns)
left, right = st.columns((1, 20))
for column in to_filter_columns:
if is_bool_dtype(df[column]):
user_bool_input = right.checkbox(f"{column}", value=True)
df = df[df[column] == user_bool_input]
elif is_numeric_dtype(df[column]):
_min = float(df[column].min())
_max = float(df[column].max())
if (_min != _max) and pd.notna(_min) and pd.notna(_max):
step = 0.01
user_num_input = right.slider(
f"Values for {column}:",
min_value=round(_min - step, 2),
max_value=round(_max + step, 2),
value=(_min, _max),
step=step,
)
df = df[df[column].between(*user_num_input)]
elif is_datetime64_any_dtype(df[column]):
user_date_input = right.date_input(
f"Values for {column}:",
value=(
df[column].min(),
df[column].max(),
),
)
if isinstance(user_date_input, Iterable) and len(user_date_input) == 2:
user_date_input_datetime = tuple(map(pd.to_datetime, user_date_input))
start_date, end_date = user_date_input_datetime
df = df.loc[df[column].between(start_date, end_date)]
else:
selected_values = right.multiselect(
f"Values for {column}:",
sorted(df[column].unique()),
)
if selected_values:
df = df[df[column].isin(selected_values)]
return df
def setup_basic():
title = "πŸ† LLM-Leaderboard"
st.set_page_config(
page_title=title,
page_icon="πŸ†",
layout="wide",
)
st.title(title)
st.markdown(
"A joint community effort to create one central leaderboard for LLMs."
f" Visit [llm-leaderboard]({GITHUB_URL}) to contribute. \n"
'We refer to a model being "open" if it can be locally deployed and used for commercial purposes.'
)
def setup_leaderboard(readme: str):
leaderboard_table = extract_markdown_table_from_multiline(readme, table_headline="## Leaderboard")
leaderboard_table = remove_markdown_links(leaderboard_table)
df_leaderboard = extract_table_and_format_from_markdown_text(leaderboard_table)
df_leaderboard["Open?"] = df_leaderboard["Open?"].map({"yes": 1, "no": 0}).astype(bool)
st.markdown("## Leaderboard")
modify = st.checkbox("Add filters")
if modify:
df_leaderboard = filter_dataframe_by_row_and_columns(df_leaderboard, ignore_columns=NON_BENCHMARK_COLS)
df_leaderboard = filter_dataframe_by_column_values(df_leaderboard)
df_leaderboard = df_leaderboard.dropna(axis=1, how="all")
benchmark_columns = [c for c in df_leaderboard.columns if df_leaderboard[c].dtype == float]
rows_wo_any_benchmark = df_leaderboard[benchmark_columns].isna().all(axis=1)
df_leaderboard = df_leaderboard[~rows_wo_any_benchmark]
st.dataframe(df_leaderboard)
st.download_button(
"Download current selection as .html",
df_leaderboard.to_html().encode("utf-8"),
"leaderboard.html",
"text/html",
key="download-html",
)
st.download_button(
"Download current selection as .csv",
df_leaderboard.to_csv().encode("utf-8"),
"leaderboard.csv",
"text/csv",
key="download-csv",
)
def setup_benchmarks(readme: str):
benchmarks_table = extract_markdown_table_from_multiline(readme, table_headline="## Benchmarks")
df_benchmarks = extract_table_and_format_from_markdown_text(benchmarks_table)
st.markdown("## Covered Benchmarks")
selected_benchmark = st.selectbox("Select a benchmark to learn more:", df_benchmarks.index.unique())
df_selected = df_benchmarks.loc[selected_benchmark]
text = [
f"Name: {selected_benchmark}",
]
for key in df_selected.keys():
text.append(f"{key}: {df_selected[key]} ")
st.markdown(" \n".join(text))
def setup_sources():
st.markdown("## Sources")
st.markdown(
"The results of this leaderboard are collected from the individual papers and published results of the model "
"authors. If you are interested in the sources of each individual reported model value, please visit the "
f"[llm-leaderboard]({GITHUB_URL}) repository."
)
st.markdown(
"""
Special thanks to the following pages:
- [MosaicML - Model benchmarks](https://www.mosaicml.com/blog/mpt-7b)
- [lmsys.org - Chatbot Arena benchmarks](https://lmsys.org/blog/2023-05-03-arena/)
- [Papers With Code](https://paperswithcode.com/)
- [Stanford HELM](https://crfm.stanford.edu/helm/latest/)
"""
)
def setup_disclaimer():
st.markdown("## Disclaimer")
st.markdown(
"Above information may be wrong. If you want to use a published model for commercial use, please contact a "
"lawyer."
)
def setup_footer():
st.markdown(
"""
---
Made with ❀️ by the awesome open-source community from all over 🌍.
"""
)
def main():
setup_basic()
with open("README.md", "r") as f:
readme = f.read()
setup_leaderboard(readme)
setup_benchmarks(readme)
setup_sources()
setup_disclaimer()
setup_footer()
if __name__ == "__main__":
main()