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import streamlit as st
from utils import get_res
st.sidebar.title('Tokenizers demo')
#x = st.slider('Select a value')
#st.write(x, 'squared is', x * x)
model_option = ['deepseek-ai/deepseek-coder-1.3b-instruct',
'MediaTek-Research/Breeze-7B-Instruct-64k-v0_1',
'microsoft/phi-2',
'mistralai/Mistral-7B-Instruct-v0.2',
'codellama/CodeLlama-7b-hf',
'hf-internal-testing/llama-tokenizer',
'gpt2'
'enter by myself']
input_option = ['123.5', 'hello world!!!', '大雨＋寒流來襲！全台極凍72小時「探5度以下」',
'大雨＋寒流来袭！全台极冻72小时「探5度以下」',
'enter by myself']
st.sidebar.subheader('Choose the tokenizer', divider='grey')
st.sidebar.write('You can choose `enter by myself` to paste the model you want.')
model_name_A = st.sidebar.selectbox(
'Model Name A',
model_option)
if model_name_A == 'enter by myself':
model_name_A = st.sidebar.text_input('Please enter Model Name A', 'deepseek-ai/deepseek-coder-1.3b-instruct')
model_name_B = st.sidebar.selectbox(
'Model Name B',
model_option)
if model_name_B == 'enter by myself':
model_name_B = st.sidebar.text_input('Please enter Model Name B', 'deepseek-ai/deepseek-coder-1.3b-instruct')
#with st.sidebar.expander("Models that you might want"):
# for m in model_option:
# st.write(m)
#'Your choice:', model_name
st.sidebar.subheader('Choose the input sentence', divider='grey')
st.sidebar.write('You can choose `enter by myself` to enter the text you want.')
input_data = st.sidebar.selectbox(
'Input Sentence',
input_option)
if input_data == 'enter by myself':
input_data = st.sidebar.text_area('Write the Input Sentence', 'Hello sunshine!!!')
#with st.sidebar.expander("Input that you might want to test"):
# for m in input_option:
# st.write(m)
col1, col2 = st.columns(2)
with col1:
st.subheader(model_name_A, divider='grey')
res, token_num = get_res(model_name=model_name_A, input_sentence=input_data, single_print=False)
st.subheader('Tokenized result')
st.markdown(res, unsafe_allow_html=True)
st.subheader('Number of tokens')
st.markdown(f'<span style="font-size:1.875em">{str(token_num)}</span>',
unsafe_allow_html=True)
with col2:
st.subheader(model_name_B, divider='grey')
res, token_num = get_res(model_name=model_name_B, input_sentence=input_data, single_print=False)
st.subheader('Tokenized result')
st.markdown(res, unsafe_allow_html=True)
st.subheader('Number of tokens')
st.markdown(f'<span style="font-size:1.875em">{str(token_num)}</span>',
unsafe_allow_html=True)
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