Spaces:
Running
Running
File size: 8,525 Bytes
a19f11e 076f69b 65566f3 a19f11e 3b5ccf8 a19f11e 50a5912 076f69b d8b3fa4 076f69b 65566f3 3b5ccf8 65566f3 740b104 a19f11e 3b5ccf8 076f69b 3b5ccf8 a19f11e 076f69b 65566f3 740b104 65566f3 740b104 65566f3 a19f11e 65566f3 a19f11e 65566f3 3b5ccf8 a19f11e 519572d a19f11e 8f146f1 a19f11e 65566f3 076f69b 65566f3 740b104 65566f3 076f69b 3b5ccf8 65566f3 076f69b a19f11e 076f69b 740b104 3b5ccf8 a19f11e 65566f3 b272a27 076f69b a19f11e 076f69b 740b104 65566f3 740b104 65566f3 740b104 65566f3 3b5ccf8 65566f3 3b5ccf8 65566f3 3b5ccf8 65566f3 740b104 a19f11e 65566f3 b272a27 076f69b |
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 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 |
"""
It provides a leaderboard component.
"""
from collections import defaultdict
import enum
import math
import firebase_admin
from firebase_admin import credentials
from firebase_admin import firestore
from google.cloud.firestore_v1 import base_query
import gradio as gr
import lingua
import pandas as pd
from credentials import get_credentials_json
if gr.NO_RELOAD:
firebase_admin.initialize_app(credentials.Certificate(get_credentials_json()))
db = firestore.client()
SUPPORTED_TRANSLATION_LANGUAGES = [
language.name.capitalize() for language in lingua.Language.all()
]
ANY_LANGUAGE = "Any"
class LeaderboardTab(enum.Enum):
SUMMARIZATION = "Summarization"
TRANSLATION = "Translation"
# Ref: https://colab.research.google.com/drive/1RAWb22-PFNI-X1gPVzc927SGUdfr6nsR?usp=sharing#scrollTo=QLGc6DwxyvQc pylint: disable=line-too-long
def compute_elo(battles, k=4, scale=400, base=10, initial_rating=1000):
rating = defaultdict(lambda: initial_rating)
for model_a, model_b, winner in battles[["model_a", "model_b",
"winner"]].itertuples(index=False):
rating_a = rating[model_a]
rating_b = rating[model_b]
expected_score_a = 1 / (1 + base**((rating_b - rating_a) / scale))
expected_score_b = 1 / (1 + base**((rating_a - rating_b) / scale))
scored_point_a = 0.5 if winner == "tie" else int(winner == "model_a")
rating[model_a] += k * (scored_point_a - expected_score_a)
rating[model_b] += k * (1 - scored_point_a - expected_score_b)
return rating
def get_docs(tab: str,
summary_lang: str = None,
source_lang: str = None,
target_lang: str = None):
if tab == LeaderboardTab.SUMMARIZATION:
collection = db.collection("arena-summarizations").order_by("timestamp")
if summary_lang:
collection = collection.where(filter=base_query.FieldFilter(
"model_a_response_language", "==", summary_lang.lower())).where(
filter=base_query.FieldFilter("model_b_response_language", "==",
summary_lang.lower()))
return collection.stream()
if tab == LeaderboardTab.TRANSLATION:
collection = db.collection("arena-translations").order_by("timestamp")
if source_lang and (not source_lang == ANY_LANGUAGE):
collection = collection.where(filter=base_query.FieldFilter(
"source_language", "==", source_lang.lower()))
if target_lang and (not target_lang == ANY_LANGUAGE):
collection = collection.where(filter=base_query.FieldFilter(
"target_language", "==", target_lang.lower()))
return collection.stream()
def load_elo_ratings(tab,
summary_lang: str = None,
source_lang: str = None,
target_lang: str = None):
docs = get_docs(tab, summary_lang, source_lang, target_lang)
battles = []
for doc in docs:
data = doc.to_dict()
battles.append({
"model_a": data["model_a"],
"model_b": data["model_b"],
"winner": data["winner"]
})
if not battles:
return
battles = pd.DataFrame(battles)
ratings = compute_elo(battles)
sorted_ratings = sorted(ratings.items(), key=lambda x: x[1], reverse=True)
rank = 0
last_rating = None
rating_rows = []
for index, (model, rating) in enumerate(sorted_ratings):
int_rating = math.floor(rating + 0.5)
if int_rating != last_rating:
rank = index + 1
rating_rows.append([rank, model, int_rating])
last_rating = int_rating
return rating_rows
LEADERBOARD_UPDATE_INTERVAL = 600 # 10 minutes
LEADERBOARD_INFO = "The leaderboard is updated every 10 minutes."
DEFAULT_FILTER_OPTIONS = {
"summary_language": lingua.Language.ENGLISH.name.capitalize(),
"source_language": ANY_LANGUAGE,
"target_language": lingua.Language.ENGLISH.name.capitalize()
}
def update_filtered_leaderboard(tab, summary_lang: str, source_lang: str,
target_lang: str):
new_value = load_elo_ratings(tab, summary_lang, source_lang, target_lang)
return gr.update(value=new_value)
def build_leaderboard():
with gr.Tabs():
with gr.Tab(LeaderboardTab.SUMMARIZATION.value):
with gr.Accordion("Filter", open=False) as summarization_filter:
with gr.Row():
languages = [
language.name.capitalize() for language in lingua.Language.all()
]
summary_language = gr.Dropdown(
choices=languages,
value=DEFAULT_FILTER_OPTIONS["summary_language"],
label="Summary language",
interactive=True)
with gr.Row():
filtered_summarization = gr.DataFrame(
headers=["Rank", "Model", "Elo rating"],
datatype=["number", "str", "number"],
value=lambda: load_elo_ratings(
LeaderboardTab.SUMMARIZATION, DEFAULT_FILTER_OPTIONS[
"summary_language"]),
elem_classes="leaderboard")
summary_language.change(fn=update_filtered_leaderboard,
inputs=[
gr.State(LeaderboardTab.SUMMARIZATION),
summary_language,
gr.State(),
gr.State()
],
outputs=filtered_summarization)
gr.Dataframe(headers=["Rank", "Model", "Elo rating"],
datatype=["number", "str", "number"],
value=lambda: load_elo_ratings(LeaderboardTab.SUMMARIZATION),
every=LEADERBOARD_UPDATE_INTERVAL,
elem_classes="leaderboard")
gr.Markdown(LEADERBOARD_INFO)
with gr.Tab(LeaderboardTab.TRANSLATION.value):
with gr.Accordion("Filter", open=False) as translation_filter:
with gr.Row():
source_language = gr.Dropdown(
choices=SUPPORTED_TRANSLATION_LANGUAGES + [ANY_LANGUAGE],
label="Source language",
value=DEFAULT_FILTER_OPTIONS["source_language"],
interactive=True)
target_language = gr.Dropdown(
choices=SUPPORTED_TRANSLATION_LANGUAGES + [ANY_LANGUAGE],
label="Target language",
value=DEFAULT_FILTER_OPTIONS["target_language"],
interactive=True)
with gr.Row():
filtered_translation = gr.DataFrame(
headers=["Rank", "Model", "Elo rating"],
datatype=["number", "str", "number"],
value=lambda: load_elo_ratings(
LeaderboardTab.TRANSLATION, DEFAULT_FILTER_OPTIONS[
"source_language"], DEFAULT_FILTER_OPTIONS[
"target_language"]),
elem_classes="leaderboard")
source_language.change(fn=update_filtered_leaderboard,
inputs=[
gr.State(LeaderboardTab.TRANSLATION),
gr.State(), source_language,
target_language
],
outputs=filtered_translation)
target_language.change(fn=update_filtered_leaderboard,
inputs=[
gr.State(LeaderboardTab.TRANSLATION),
gr.State(), source_language,
target_language
],
outputs=filtered_translation)
# When filter options are changed, the accordion keeps closed.
# To avoid this, we open the accordion when the filter options are changed.
summary_language.change(fn=lambda: gr.Accordion(open=True),
outputs=summarization_filter)
source_language.change(fn=lambda: gr.Accordion(open=True),
outputs=translation_filter)
target_language.change(fn=lambda: gr.Accordion(open=True),
outputs=translation_filter)
gr.Dataframe(headers=["Rank", "Model", "Elo rating"],
datatype=["number", "str", "number"],
value=lambda: load_elo_ratings(LeaderboardTab.TRANSLATION),
every=LEADERBOARD_UPDATE_INTERVAL,
elem_classes="leaderboard")
gr.Markdown(LEADERBOARD_INFO)
|