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import os, sys
import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
from sklearn.metrics.pairwise import paired_cosine_distances
from sklearn.preprocessing import normalize
from rolaser import RoLaserEncoder
laser_checkpoint = f"{os.environ['LASER']}/models/laser2.pt"
laser_vocab = f"{os.environ['LASER']}/models/laser2.cvocab"
laser_tokenizer = 'spm'
laser_model = RoLaserEncoder(model_path=laser_checkpoint, vocab=laser_vocab, tokenizer=laser_tokenizer)
rolaser_checkpoint = f"{os.environ['ROLASER']}/models/rolaser.pt"
rolaser_vocab = f"{os.environ['ROLASER']}/models/rolaser.cvocab"
rolaser_tokenizer = 'roberta'
rolaser_model = RoLaserEncoder(model_path=rolaser_checkpoint, vocab=rolaser_vocab, tokenizer=rolaser_tokenizer)
c_rolaser_checkpoint = f"{os.environ['ROLASER']}/models/c-rolaser.pt"
c_rolaser_vocab = f"{os.environ['ROLASER']}/models/c-rolaser.cvocab"
c_rolaser_tokenizer = 'char'
c_rolaser_model = RoLaserEncoder(model_path=c_rolaser_checkpoint, vocab=c_rolaser_vocab, tokenizer=c_rolaser_tokenizer)
STD_SENTENCES = ['See you tomorrow.'] * 10
UGC_SENTENCES = [
'See you tmrw.',
'See you t03orro3.',
'C. U. tomorrow.',
'sea you tomorrow.',
'See yo utomorrow.',
'See you tkmoerow.',
'Cu 2moro.',
'See yow tomorrow.',
'C. Yew tomorrow.',
'c ya 2morrow.'
]
def add_text_inputs(i):
col1, col2 = st.columns(2)
with col1:
text_input1 = st.text_input('Enter standard text here:', key=f'std{i}', value=STD_SENTENCES[i])
with col2:
text_input2 = st.text_input('Enter non-standard text here:', key=f'ugc{i}', value=UGC_SENTENCES[i])
return text_input1, text_input2
def main():
st.title('Pairwise Cosine Distance Calculator')
num_pairs = st.sidebar.number_input('Number of Text Input Pairs', min_value=1, max_value=10, value=5)
std_text_inputs = []
ugc_text_inputs = []
for i in range(num_pairs):
pair = add_text_inputs(i)
std_text_inputs.append(pair[0])
ugc_text_inputs.append(pair[1])
if st.button('Submit'):
X_std_laser = normalize(laser_model.encode(std_text_inputs))
X_ugc_laser = normalize(laser_model.encode(ugc_text_inputs))
X_cos_laser = paired_cosine_distances(X_std_laser, X_ugc_laser)
X_std_rolaser = normalize(rolaser_model.encode(std_text_inputs))
X_ugc_rolaser = normalize(rolaser_model.encode(ugc_text_inputs))
X_cos_rolaser = paired_cosine_distances(X_std_rolaser, X_ugc_rolaser)
X_std_c_rolaser = normalize(c_rolaser_model.encode(std_text_inputs))
X_ugc_c_rolaser = normalize(c_rolaser_model.encode(ugc_text_inputs))
X_cos_c_rolaser = paired_cosine_distances(X_std_c_rolaser, X_ugc_c_rolaser)
outputs = pd.DataFrame(columns=[ 'model', 'pair', 'ugc', 'std', 'cos'])
outputs['model'] = np.repeat(['LASER', 'RoLASER', 'c-RoLASER'], num_pairs)
outputs['pair'] = np.tile(np.arange(1,num_pairs+1), 3)
outputs['std'] = np.tile(std_text_inputs, 3)
outputs['ugc'] = np.tile(ugc_text_inputs, 3)
outputs['cos'] = np.concatenate([X_cos_laser, X_cos_rolaser, X_cos_c_rolaser])
st.write('## Cosine Distance Scores:')
fig = px.bar(outputs, x='x_column', y='y_column', color='model', barmode='group')
fig.update_layout(title='Cosine Distance Scores')
fig.update_xaxes(title_text='Text Input Pair')
fig.update_yaxes(title_text='Cosine Distance')
st.plotly_chart(fig, use_container_width=True)
st.write('## Average Cosine Distance Scores:')
st.write(outputs.groupby('model')['cos'].describe())
if __name__ == "__main__":
main()
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