Spaces:
Sleeping
Sleeping
File size: 5,173 Bytes
723d6ec 620cefc e70d647 620cefc f095c1c 620cefc f095c1c 620cefc 7dc3119 f095c1c e70d647 620cefc |
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 |
import os
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
@st.cache_resource(show_spinner=False)
def load_models():
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)
return laser_model, rolaser_model, c_rolaser_model
@st.cache_data(show_spinner=False)
def load_sample_data():
STD_SENTENCES = ['See you tomorrow.'] * 10
UGC_SENTENCES = [
'See you t03orro3.',
'C. U. tomorrow.',
'sea you tomorrow.',
'See yo utomorrow.',
'See you tmrw.',
'See you tkmoerow.',
'Cu 2moro.',
'See yow tomorrow.',
'C. Yew tomorrow.',
'c ya 2morrow.'
]
return STD_SENTENCES, UGC_SENTENCES
def main():
sample_std, sample_ugc = load_sample_data()
laser_model, rolaser_model, c_rolaser_model = load_models()
st.title('Pairwise Cosine Distance Calculator')
info = '''
:bookmark: **Paper:** [Making Sentence Embeddings Robust to User-Generated Content (Nishimwe et al., 2024)](https://arxiv.org/abs/2403.17220)
:link: **Github:** [https://github.com/lydianish/RoLASER](https://github.com/lydianish/RoLASER)
This demo app computes text embeddings of sentence pairs using the LASER encoder and its robust students RoLASER and c-RoLASER.
The pairwise cosine distances between the sentences are then computed and displayed.
'''
st.markdown(info)
st.header('Standard and Non-standard Text Input Pairs:')
num_pairs = st.sidebar.number_input('Number of Text Input Pairs (1-10)', min_value=1, max_value=10, value=5)
with st.form('text_input_form'):
col1, col2 = st.columns(2)
with col1:
st.write('Enter standard text here:')
with col2:
st.write('Enter non-standard text here:')
std_text_inputs = []
ugc_text_inputs = []
for i in range(num_pairs):
col1, col2 = st.columns(2)
with col1:
text_input1 = st.text_input('Enter standard text here:', key=f'std{i}', value=sample_std[i], label_visibility='collapsed')
std_text_inputs.append(text_input1)
with col2:
text_input2 = st.text_input('Enter non-standard text here:', key=f'ugc{i}', value=sample_ugc[i], label_visibility='collapsed')
ugc_text_inputs.append(text_input2)
st.caption('*The models are case-insensitive: all text will be lowercased.*')
st.form_submit_button('Compute')
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.header('Cosine Distance Scores:')
st.caption('*This bar plot is interactive: Hover on the bars to display values. Click on the legend items to filter models.*')
fig = px.bar(outputs, x='pair', y='cos', color='model', barmode='group', hover_data=['ugc', 'std'])
fig.update_xaxes(title_text='Text Input Pair')
fig.update_yaxes(title_text='Cosine Distance')
st.plotly_chart(fig, use_container_width=True)
st.header('Average Cosine Distance Scores:')
st.caption('*This data table is interactive: Click on a column header to sort values.*')
st.write(outputs.groupby('model')['cos'].describe())
if __name__ == "__main__":
main()
|