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import gradio as gr
import requests
import pandas as pd
from huggingface_hub.hf_api import SpaceInfo
from huggingface_hub import HfApi, hf_hub_download
from huggingface_hub.repocard import metadata_load
path = f"https://huggingface.co/api/spaces"
def get_blocks_party_spaces():
r = requests.get(path)
d = r.json()
spaces = [SpaceInfo(**x) for x in d]
blocks_spaces = {}
for i in range(0,len(spaces)):
if spaces[i].id.split('/')[0] == 'Gradio-Blocks' and hasattr(spaces[i], 'likes') and spaces[i].id != 'Gradio-Blocks/Leaderboard' and spaces[i].id != 'Gradio-Blocks/README':
blocks_spaces[spaces[i].id]=spaces[i].likes
df = pd.DataFrame(
[{"Spaces_Name": Spaces, "likes": likes} for Spaces,likes in blocks_spaces.items()])
df = df.sort_values(by=['likes'],ascending=False)
return df
def make_clickable_model(model_name):
# remove user from model name
model_name_show = ' '.join(model_name.split('/')[1:])
link = "https://huggingface.co/" + model_name
return f'<a target="_blank" style="text-decoration: underline" href="{link}">{model_name_show}</a>'
def get_mteb_data(task="Clustering", metric="v_measure", lang=None):
api = HfApi()
models = api.list_models(filter="mteb")
df_list = []
for model in models:
readme_path = hf_hub_download(model.modelId, filename="README.md")
meta = metadata_load(readme_path)
# Use "get" instead of dict indexing to ignore incompat metadata instead of erroring out
if lang is None:
out = list(
map(
lambda x: {x["dataset"]["name"].replace("MTEB ", ""): round(list(filter(lambda x: x["type"] == metric, x["metrics"]))[0]["value"], 2)},
filter(lambda x: x.get("task", {}).get("type", "") == task, meta["model-index"][0]["results"])
)
)
else:
# Multilingual
out = list(
map(
lambda x: {x["dataset"]["name"].replace("MTEB ", ""): round(list(filter(lambda x: x["type"] == metric, x["metrics"]))[0]["value"], 2)},
filter(lambda x: (x.get("task", {}).get("type", "") == task) and (x.get("dataset", {}).get("config", "") in ("default", *lang)), meta["model-index"][0]["results"])
)
)
out = {k: v for d in out for k, v in d.items()}
out["Model"] = make_clickable_model(model.modelId)
df_list.append(out)
df = pd.DataFrame(df_list)
# Put 'Model' column first
cols = sorted(list(df.columns))
cols.insert(0, cols.pop(cols.index("Model")))
df = df[cols]
df.fillna('', inplace=True)
return df.astype(str) # Cast to str as Gradio does not accept floats
block = gr.Blocks()
with block:
gr.Markdown("""Leaderboard for XX most popular Blocks Event Spaces. To learn more and join, see <a href="https://huggingface.co/Gradio-Blocks" target="_blank" style="text-decoration: underline">Blocks Party Event</a>""")
with gr.Tabs():
with gr.TabItem("Classification"):
with gr.TabItem("English"):
with gr.Row():
gr.Markdown("""Leaderboard for Classification""")
with gr.Row():
data_classification_en = gr.components.Dataframe(
datatype=["markdown"] * 500,
type="pandas",
col_count=(13, "fixed"),
)
with gr.Row():
data_run = gr.Button("Refresh")
task_classification_en = gr.Variable(value="Classification")
metric_classification_en = gr.Variable(value="accuracy")
lang_classification_en = gr.Variable(value=["en"])
data_run.click(get_mteb_data, inputs=[task_classification_en, metric_classification_en, lang_classification_en], outputs=data_classification_en)
with gr.TabItem("Multilingual"):
with gr.Row():
gr.Markdown("""Multilingual Classification""")
with gr.Row():
data_classification = gr.components.Dataframe(
datatype=["markdown"] * 500,
type="pandas",
)
with gr.Row():
data_run = gr.Button("Refresh")
task_classification = gr.Variable(value="Classification")
metric_classification = gr.Variable(value="accuracy")
data_run.click(get_mteb_data, inputs=[task_classification, metric_classification], outputs=data_classification)
with gr.TabItem("Clustering"):
with gr.Row():
gr.Markdown("""Leaderboard for Clustering""")
with gr.Row():
data_clustering = gr.components.Dataframe(
datatype=["markdown"] * 500,
type="pandas",
)
with gr.Row():
data_run = gr.Button("Refresh")
task_clustering = gr.Variable(value="Clustering")
metric_clustering = gr.Variable(value="v_measure")
data_run.click(get_mteb_data, inputs=[task_clustering, metric_clustering], outputs=data_clustering)
with gr.TabItem("Retrieval"):
with gr.Row():
gr.Markdown("""Leaderboard for Retrieval""")
with gr.Row():
data_retrieval = gr.components.Dataframe(
datatype=["markdown"] * 500,
type="pandas",
)
with gr.Row():
data_run = gr.Button("Refresh")
task_retrieval = gr.Variable(value="Retrieval")
metric_retrieval = gr.Variable(value="ndcg_at_10")
data_run.click(get_mteb_data, inputs=[task_retrieval, metric_retrieval], outputs=data_retrieval)
with gr.TabItem("Reranking"):
with gr.Row():
gr.Markdown("""Leaderboard for Reranking""")
with gr.Row():
data_reranking = gr.components.Dataframe(
datatype=["markdown"] * 500,
type="pandas",
#col_count=(12, "fixed"),
)
with gr.Row():
data_run = gr.Button("Refresh")
task_reranking = gr.Variable(value="Reranking")
metric_reranking = gr.Variable(value="map")
data_run.click(get_mteb_data, inputs=[task_reranking, metric_reranking], outputs=data_reranking)
with gr.TabItem("STS"):
with gr.TabItem("English"):
with gr.Row():
gr.Markdown("""Leaderboard for STS""")
with gr.Row():
data_sts_en = gr.components.Dataframe(
datatype=["markdown"] * 500,
type="pandas",
)
with gr.Row():
data_run_en = gr.Button("Refresh")
task_sts_en = gr.Variable(value="STS")
metric_sts_en = gr.Variable(value="cos_sim_spearman")
lang_sts_en = gr.Variable(value=["en", "en-en"])
data_run.click(get_mteb_data, inputs=[task_sts_en, metric_sts_en, lang_sts_en], outputs=data_sts_en)
with gr.TabItem("Multilingual"):
with gr.Row():
gr.Markdown("""Leaderboard for STS""")
with gr.Row():
data_sts = gr.components.Dataframe(
datatype=["markdown"] * 500,
type="pandas",
)
with gr.Row():
data_run = gr.Button("Refresh")
task_sts = gr.Variable(value="STS")
metric_sts = gr.Variable(value="cos_sim_spearman")
data_run.click(get_mteb_data, inputs=[task_sts, metric_sts], outputs=data_sts)
with gr.TabItem("Summarization"):
with gr.Row():
gr.Markdown("""Leaderboard for Summarization""")
with gr.Row():
data_summarization = gr.components.Dataframe(
datatype=["markdown"] * 500,
type="pandas",
)
with gr.Row():
data_run = gr.Button("Refresh")
task_summarization = gr.Variable(value="Summarization")
metric_summarization = gr.Variable(value="cos_sim_spearman")
data_run.click(get_mteb_data, inputs=[task_summarization, metric_summarization], outputs=data_summarization)
with gr.TabItem("Blocks Party Leaderboard2"):
with gr.Row():
data = gr.components.Dataframe(type="pandas")
with gr.Row():
data_run = gr.Button("Refresh")
data_run.click(get_blocks_party_spaces, inputs=None, outputs=data)
# running the function on page load in addition to when the button is clicked
block.load(get_mteb_data, inputs=[task_classification_en, metric_classification_en], outputs=data_classification_en)
block.load(get_mteb_data, inputs=[task_classification, metric_classification], outputs=data_classification)
block.load(get_mteb_data, inputs=[task_clustering, metric_clustering], outputs=data_clustering)
block.load(get_mteb_data, inputs=[task_retrieval, metric_retrieval], outputs=data_retrieval)
block.load(get_mteb_data, inputs=[task_reranking, metric_reranking], outputs=data_reranking)
block.load(get_mteb_data, inputs=[task_sts, metric_sts], outputs=data_sts)
block.load(get_mteb_data, inputs=[task_summarization, metric_summarization], outputs=data_summarization)
block.load(get_blocks_party_spaces, inputs=None, outputs=data)
block.launch()
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