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import streamlit as st
from app.draw_diagram import *
def dashboard():
with st.container():
st.title("SeaEval")
st.markdown("""
[gh]: https://github.com/SeaEval/SeaEval
[][gh]
[][gh]
""")
seaeval_url = "https://seaeval.github.io/"
st.divider()
st.markdown("#### What is [SeaEval](%s)" % seaeval_url)
with st.container():
left_co, cent_co,last_co = st.columns(3)
with cent_co:
st.image("./style/seaeval_overall.png",
# caption="SeaEval data range",
width=500)
st.markdown('''
''')
st.markdown("##### A new benchmark for multilingual foundation models consisting of 28 dataset.")
st.markdown(''':star: How models understand and reason with natural language?
:balloon: Languages: English, Chinese, Malay, Spainish, Indonedian, Vietnamese, Filipino.
''')
st.markdown(''':star: How models comprehend cultural practices, nuances and values?
:balloon: 4 new datasets on Cultural Understanding.
''')
st.markdown(''':star: How models perform across languages in terms of consistency?
:balloon: 2 new datasets with curated metrics for Cross-Linugal Consistency.
''')
with st.container():
left_co, cent_co,last_co = st.columns(3)
with cent_co:
st.image("./style/consistency.png",
# caption="SeaEval data range",
width=500)
st.markdown("##### Evaluation with enhanced cross-lingual capabilities.")
st.markdown(''':star: How models perform according to different (paraphrased) instructions?
:balloon: Each dataset is equipped with 5 different prompts to avoid randomness introduced by instructions,
which is non-negligible..
''')
st.markdown(''':star: Multilingual accuracy and performance consistency across languages.
:balloon: If you can answer the question in your native language, can you answer the same question
correctly in your second/third language?
''')
st.divider()
with st.container():
st.markdown("##### Citations")
st.markdown('''
:round_pushpin: SeaEval Paper \n
@article{SeaEval,
title={SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning},
author={Wang, Bin and Liu, Zhengyuan and Huang, Xin and Jiao, Fangkai and Ding, Yang and Aw, Ai Ti and Chen, Nancy F.},
journal={NAACL},
year={2024}
}
''')
def cross_lingual_consistency():
st.title("Cross-Lingual Consistency")
filters_levelone = ['Zero Shot', 'Few Shot']
filters_leveltwo = ['Cross-MMLU', 'Cross-XQUAD', 'Cross-LogiQA']
category_one_dict = {'Zero Shot': 'zero_shot',
'Few Shot': 'few_shot'}
category_two_dict = {'Cross-MMLU': 'cross_mmlu',
'Cross-XQUAD': 'cross_xquad',
'Cross-LogiQA': 'cross_logiqa'}
left, center, _, middle,right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
with left:
category_one = st.selectbox('Select Zero / Few shot', filters_levelone)
with center:
category_two = st.selectbox('Select the sub-category', filters_leveltwo)
with middle:
sort = st.selectbox('Sort', ['Accuracy','Cross-Lingual Consistency', 'AC3',
'English', 'Chinese', 'Spanish', 'Vietnamese'])
with right:
sorted = st.selectbox('by', ['Ascending', 'Descending'])
if category_one or category_two or sort or sorted:
category_one = category_one_dict[category_one]
category_two = category_two_dict[category_two]
draw_cross_lingual(category_one, category_two, sort, sorted)
else:
draw_cross_lingual('zero_shot', 'cross_mmlu', 'Accuracy', 'Descending')
def cultural_reasoning():
st.title("Cultural Reasoning")
filters_levelone = ['Zero Shot', 'Few Shot']
filters_leveltwo = ['SG EVAL',
'SG EVAL V1 Cleaned',
'SG EVAL V2 MCQ',
'SG EVAL V2 Open Ended',
'CN EVAL', 'PH EVAL', 'US EVAL']
category_one_dict = {'Zero Shot': 'zero_shot',
'Few Shot': 'few_shot'}
left, center, _, right = st.columns([0.2, 0.2, 0.4, 0.2])
with left:
category_one = st.selectbox('Select Zero / Few shot', filters_levelone)
with center:
category_two = st.selectbox('Select the sub-category', filters_leveltwo)
with right:
sorted = st.selectbox('sorted by', ['Ascending', 'Descending'])
if category_one or category_two or sorted:
category_one = category_one_dict[category_one]
draw_only_acc('cultural_reasoning', category_one, category_two, sorted)
else:
draw_only_acc('cultural_reasoning', 'zero_shot', 'sg_eval', 'Descending')
def general_reasoning():
st.title("General Reasoning")
filters_levelone = ['Zero Shot', 'Few Shot']
filters_leveltwo = ['MMLU', 'C Eval', 'CMMLU', 'ZBench', 'IndoMMLU']
category_one_dict = {'Zero Shot': 'zero_shot',
'Few Shot': 'few_shot'}
left, center, _, right = st.columns([0.2, 0.2, 0.4, 0.2])
with left:
category_one = st.selectbox('Select Zero / Few shot', filters_levelone)
with center:
category_two = st.selectbox('Select the sub-category', filters_leveltwo)
with right:
sorted = st.selectbox('sorted by', ['Ascending', 'Descending'])
if category_one or category_two or sorted:
category_one = category_one_dict[category_one]
draw_only_acc('general_reasoning', category_one, category_two, sorted)
else:
draw_only_acc('general_reasoning', 'zero_shot', 'MMLU Full', 'Descending')
def flores():
st.title("FLORES-Translation")
filters_levelone = ['Zero Shot', 'Few Shot']
filters_leveltwo = ['Indonesian to English',
'Vitenamese to English',
'Chinese to English',
'Malay to English']
category_one_dict = {'Zero Shot': 'zero_shot',
'Few Shot': 'few_shot'}
left, center, _, right = st.columns([0.2, 0.2, 0.4, 0.2])
with left:
category_one = st.selectbox('Select Zero / Few shot', filters_levelone)
with center:
category_two = st.selectbox('Select the sub-category', filters_leveltwo)
with right:
sorted = st.selectbox('sorted by', ['Ascending', 'Descending'])
if category_one or category_two or sorted:
category_one = category_one_dict[category_one]
draw_flores_translation(category_one, category_two, sorted)
else:
draw_flores_translation('zero_shot', 'Indonesian to English', 'Descending')
def emotion():
st.title("Emotion")
filters_levelone = ['Zero Shot', 'Few Shot']
filters_leveltwo = ['Indonesian Emotion Classification', 'SST2']
category_one_dict = {'Zero Shot': 'zero_shot',
'Few Shot': 'few_shot'}
left, center, _, right = st.columns([0.2, 0.2, 0.4, 0.2])
with left:
category_one = st.selectbox('Select Zero / Few shot', filters_levelone)
with center:
category_two = st.selectbox('Select the sub-category', filters_leveltwo)
with right:
sorted = st.selectbox('sorted by', ['Ascending', 'Descending'])
if category_one or category_two or sorted:
category_one = category_one_dict[category_one]
draw_only_acc('emotion', category_one, category_two, sorted)
else:
draw_only_acc('emotion', 'zero_shot', 'Indonesian Emotion Classification', 'Descending')
def dialogue():
st.title("Dialogue")
filters_levelone = ['Zero Shot', 'Few Shot']
filters_leveltwo = ['DREAM', 'SAMSum', 'DialogSum']
category_one_dict = {'Zero Shot': 'zero_shot',
'Few Shot': 'few_shot'}
left, center, _, middle,right = st.columns([0.2, 0.2, 0.2, 0.2 ,0.2])
with left:
category_one = st.selectbox('Select Zero / Few shot', filters_levelone)
with center:
category_two = st.selectbox('Select the sub-category', filters_leveltwo)
with middle:
if category_two == 'DREAM':
sort = st.selectbox('Sort', ['Accuracy'])
else:
sort = st.selectbox('Sort', ['Average', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L'])
with right:
sorted = st.selectbox('by', ['Ascending', 'Descending'])
if category_one or category_two or sort or sorted:
category_one = category_one_dict[category_one]
draw_dialogue(category_one, category_two, sort, sorted)
else:
draw_dialogue('zero_shot', 'DREAM', sort[0],'Descending')
def fundamental_nlp_tasks():
st.title("Fundamental NLP Tasks")
filters_levelone = ['Zero Shot', 'Few Shot']
filters_leveltwo = ['OCNLI', 'C3', 'COLA', 'QQP', 'MNLI', 'QNLI', 'WNLI', 'RTE', 'MRPC']
category_one_dict = {'Zero Shot': 'zero_shot',
'Few Shot': 'few_shot'}
left, center, _, right = st.columns([0.2, 0.2, 0.4, 0.2])
with left:
category_one = st.selectbox('Select Zero / Few shot', filters_levelone)
with center:
category_two = st.selectbox('Select the sub-category', filters_leveltwo)
with right:
sorted = st.selectbox('sorted by', ['Ascending', 'Descending'])
if category_one or category_two or sorted:
category_one = category_one_dict[category_one]
draw_only_acc('fundamental_nlp_tasks', category_one, category_two, sorted)
else:
draw_only_acc('fundamental_nlp_tasks', 'zero_shot', 'OCNLI', 'Descending') |