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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, multicultral foundation model evaluation consisting of 28 dataset as the core and keep expanding over time.") | |
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: | |
sortby = st.selectbox('by', ['Ascending', 'Descending']) | |
if category_one or category_two or sort or sortby: | |
category_one = category_one_dict[category_one] | |
category_two = category_two_dict[category_two] | |
draw('cross_lingual', category_one, category_two, sort, sortby) | |
# else: | |
# draw('zero_shot', 'cross_mmlu', 'Accuracy', 'Descending') | |
def cultural_reasoning(): | |
st.title("Cultural Reasoning") | |
filters_levelone = ['Zero Shot', 'Few Shot'] | |
filters_leveltwo = [ | |
'SG EVAL V2 MCQ', | |
'SG EVAL V2 Open Ended', | |
'SG EVAL', | |
'SG EVAL V1 Cleaned', | |
'CN EVAL', | |
'PH EVAL', | |
'US EVAL' | |
] | |
category_one_dict = {'Zero Shot': 'zero_shot', | |
'Few Shot': 'few_shot'} | |
category_two_dict = {'SG EVAL': 'sg_eval', | |
'SG EVAL V1 Cleaned': 'sg_eval_v1_cleaned', | |
'SG EVAL V2 MCQ': 'sg_eval_v2_mcq', | |
'SG EVAL V2 Open Ended': 'sg_eval_v2_open', | |
'US EVAL': 'us_eval', | |
'CN EVAL': 'cn_eval', | |
'PH EVAL': 'ph_eval'} | |
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: | |
sortby = st.selectbox('sorted by', ['Ascending', 'Descending']) | |
if category_one or category_two or sortby: | |
category_one = category_one_dict[category_one] | |
category_two = category_two_dict[category_two] | |
draw('cultural_reasoning', category_one, category_two, 'Accuracy',sortby) | |
# 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', | |
'CMMLU', | |
'IndoMMLU', | |
'C Eval', | |
'ZBench', | |
] | |
category_one_dict = {'Zero Shot': 'zero_shot', | |
'Few Shot': 'few_shot'} | |
category_two_dict = {'MMLU': 'mmlu', | |
'C Eval': 'c_eval', | |
'CMMLU': 'cmmlu', | |
'ZBench': 'zbench', | |
'IndoMMLU': 'indommlu'} | |
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: | |
sortby = st.selectbox('sorted by', ['Ascending', 'Descending']) | |
if category_one or category_two or sortby: | |
category_one = category_one_dict[category_one] | |
category_two = category_two_dict[category_two] | |
draw('general_reasoning', category_one, category_two, 'Accuracy',sortby) | |
# 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'} | |
category_two_dict = {'Indonesian to English': 'ind2eng', | |
'Vitenamese to English': 'vie2eng', | |
'Chinese to English': 'zho2eng', | |
'Malay to English': 'zsm2eng'} | |
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: | |
sortby = st.selectbox('sorted by', ['Ascending', 'Descending']) | |
if category_one or category_two or sortby: | |
category_one = category_one_dict[category_one] | |
category_two = category_two_dict[category_two] | |
draw('flores_translation', category_one, category_two, 'BLEU',sortby) | |
# 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'} | |
category_two_dict = {'Indonesian Emotion Classification': 'ind_emotion', | |
'SST2': 'sst2'} | |
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: | |
sortby = st.selectbox('sorted by', ['Ascending', 'Descending']) | |
if category_one or category_two or sortby: | |
category_one = category_one_dict[category_one] | |
category_two = category_two_dict[category_two] | |
draw('emotion', category_one, category_two, 'Accuracy', sortby) | |
# 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'} | |
category_two_dict = {'DREAM': 'dream', | |
'SAMSum': 'samsum', | |
'DialogSum': 'dialogsum'} | |
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: | |
sortby = st.selectbox('by', ['Ascending', 'Descending']) | |
if category_one or category_two or sort or sortby: | |
category_one = category_one_dict[category_one] | |
category_two = category_two_dict[category_two] | |
draw('dialogue', category_one, category_two, sort, sortby) | |
# 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'} | |
category_two_dict = {'OCNLI': 'ocnli', | |
'C3': 'c3', | |
'COLA': 'cola', | |
'QQP': 'qqp', | |
'MNLI': 'mnli', | |
'QNLI': 'qnli', | |
'WNLI': 'wnli', | |
'RTE': 'rte', | |
'MRPC': 'mrpc'} | |
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: | |
sortby = st.selectbox('sorted by', ['Ascending', 'Descending']) | |
if category_one or category_two or sortby: | |
category_one = category_one_dict[category_one] | |
category_two = category_two_dict[category_two] | |
draw('fundamental_nlp_tasks', category_one, category_two, 'Accuracy', sortby) | |
# else: | |
# draw_only_acc('fundamental_nlp_tasks', 'zero_shot', 'OCNLI', 'Descending') |