File size: 1,384 Bytes
545c4d5 | 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 | import gradio as gr
import json
from huggingface_hub import HfApi
import pandas as pd
def compute_df():
api = HfApi()
# download all files in https://huggingface.co/illuin-cde/baselines
files = [
f
for f in api.list_repo_files("illuin-cde/baselines")
if f.startswith("metrics")
]
print(files)
metrics = []
for file in files:
result_path = api.hf_hub_download("illuin-cde/baselines", filename=file)
with open(result_path, "r") as f:
dic = json.load(f)
dic.update(dic["metrics"])
del dic["metrics"]
metrics.append(dic)
df = pd.DataFrame(metrics)
df = df[
[
"model",
"dataset",
"split",
"is_contextual",
"ndcg_at_1",
"ndcg_at_5",
"ndcg_at_10",
"ndcg_at_100",
]
]
df["model"] = df["model"].apply(lambda x: x.split("/")[-1])
df["dataset"] = df["dataset"].apply(lambda x: x.split("/")[-1])
# round all numeric columns
df = df.round(3)
# sort by ndcg_at_5
df = df.sort_values("ndcg_at_5", ascending=False)
# gradio display
gradio_df = gr.Dataframe(df)
return gradio_df
# refresh button and precompute
gr.Interface(
fn=compute_df, title="Results Leaderboard", inputs=None, outputs="dataframe"
).launch()
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