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import gradio as gr
import pandas as pd
from pathlib import Path
abs_path = Path(__file__).parent.absolute()
df = pd.read_json(str(abs_path / "assets/leaderboard_data.json"))
invisible_df = df.copy()
COLS = [
"T",
"Model",
"Average ⬆️",
"ARC",
"HellaSwag",
"MMLU",
"TruthfulQA",
"Winogrande",
"GSM8K",
"Type",
"Architecture",
"Precision",
"Merged",
"Hub License",
"#Params (B)",
"Hub ❤️",
"Model sha",
"model_name_for_query",
]
ON_LOAD_COLS = [
"T",
"Model",
"Average ⬆️",
"ARC",
"HellaSwag",
"MMLU",
"TruthfulQA",
"Winogrande",
"GSM8K",
"model_name_for_query",
]
TYPES = [
"str",
"markdown",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"str",
"str",
"str",
"str",
"bool",
"str",
"number",
"number",
"bool",
"str",
"bool",
"bool",
"str",
]
NUMERIC_INTERVALS = {
"?": pd.Interval(-1, 0, closed="right"),
"~1.5": pd.Interval(0, 2, closed="right"),
"~3": pd.Interval(2, 4, closed="right"),
"~7": pd.Interval(4, 9, closed="right"),
"~13": pd.Interval(9, 20, closed="right"),
"~35": pd.Interval(20, 45, closed="right"),
"~60": pd.Interval(45, 70, closed="right"),
"70+": pd.Interval(70, 10000, closed="right"),
}
MODEL_TYPE = [str(s) for s in df["T"].unique()]
Precision = [str(s) for s in df["Precision"].unique()]
# Searching and filtering
def update_table(
hidden_df: pd.DataFrame,
columns: list,
type_query: list,
precision_query: str,
size_query: list,
query: str,
):
filtered_df = filter_models(hidden_df, type_query, size_query, precision_query)
filtered_df = filter_queries(query, filtered_df)
df = select_columns(filtered_df, columns)
return df
def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
return df[(df["model_name_for_query"].str.contains(query, case=False))]
def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
# We use COLS to maintain sorting
filtered_df = df[[c for c in COLS if c in df.columns and c in columns]]
return filtered_df
def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
final_df = []
if query != "":
queries = [q.strip() for q in query.split(";")]
for _q in queries:
_q = _q.strip()
if _q != "":
temp_filtered_df = search_table(filtered_df, _q)
if len(temp_filtered_df) > 0:
final_df.append(temp_filtered_df)
if len(final_df) > 0:
filtered_df = pd.concat(final_df)
filtered_df = filtered_df.drop_duplicates(
subset=["Model", "Precision", "Model sha"]
)
return filtered_df
def filter_models(
df: pd.DataFrame,
type_query: list,
size_query: list,
precision_query: list,
) -> pd.DataFrame:
# Show all models
filtered_df = df
type_emoji = [t[0] for t in type_query]
filtered_df = filtered_df.loc[df["T"].isin(type_emoji)]
filtered_df = filtered_df.loc[df["Precision"].isin(precision_query + ["None"])]
numeric_interval = pd.IntervalIndex(
sorted([NUMERIC_INTERVALS[s] for s in size_query])
)
params_column = pd.to_numeric(df["#Params (B)"], errors="coerce")
mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
filtered_df = filtered_df.loc[mask]
return filtered_df
demo = gr.Blocks(css=str(abs_path / "assets/leaderboard_data.json"))
with demo:
gr.Markdown("""Test Space of the LLM Leaderboard""", elem_classes="markdown-text")
with gr.Tabs(elem_classes="tab-buttons") as tabs:
with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
with gr.Row():
with gr.Column():
with gr.Row():
search_bar = gr.Textbox(
placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
show_label=False,
elem_id="search-bar",
)
with gr.Row():
shown_columns = gr.CheckboxGroup(
choices=COLS,
value=ON_LOAD_COLS,
label="Select columns to show",
elem_id="column-select",
interactive=True,
)
with gr.Column(min_width=320):
filter_columns_type = gr.CheckboxGroup(
label="Model types",
choices=MODEL_TYPE,
value=MODEL_TYPE,
interactive=True,
elem_id="filter-columns-type",
)
filter_columns_precision = gr.CheckboxGroup(
label="Precision",
choices=Precision,
value=Precision,
interactive=True,
elem_id="filter-columns-precision",
)
filter_columns_size = gr.CheckboxGroup(
label="Model sizes (in billions of parameters)",
choices=list(NUMERIC_INTERVALS.keys()),
value=list(NUMERIC_INTERVALS.keys()),
interactive=True,
elem_id="filter-columns-size",
)
leaderboard_table = gr.components.Dataframe(
value=df[ON_LOAD_COLS],
headers=ON_LOAD_COLS,
datatype=TYPES,
elem_id="leaderboard-table",
interactive=False,
visible=True,
column_widths=["2%", "33%"],
)
# Dummy leaderboard for handling the case when the user uses backspace key
hidden_leaderboard_table_for_search = gr.components.Dataframe(
value=invisible_df[COLS],
headers=COLS,
datatype=TYPES,
visible=False,
)
search_bar.submit(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
search_bar,
],
leaderboard_table,
)
for selector in [
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
]:
selector.change(
update_table,
[
hidden_leaderboard_table_for_search,
shown_columns,
filter_columns_type,
filter_columns_precision,
filter_columns_size,
search_bar,
],
leaderboard_table,
queue=True,
)
if __name__ == "__main__":
demo.queue(default_concurrency_limit=40).launch()
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