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import streamlit as st
import random
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModel
from aligner import Aligner
from utils import align_matrix_heatmap, plot_align_matrix_heatmap
device = "cuda" if torch.cuda.is_available() else "cpu"
torch.manual_seed(42)
np.random.seed(42)
random.seed(42)
@st.cache_resource
def init_model(model: str):
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModel.from_pretrained(model, output_hidden_states=True).to(device).eval()
return tokenizer, model
@st.cache_resource(max_entries=100)
def init_aligner(ot_type: str, sinkhorn: bool, distortion: float, threshhold: float, tau: float):
return Aligner(
ot_type=ot_type,
sinkhorn=sinkhorn,
chimera=False,
dist_type="cos",
weight_type="uniform",
distortion=distortion,
thresh=threshhold,
tau=tau,
div_type="--"
)
def encode_sentence(sent, pair, tokenizer, model, layer: int):
if pair == None:
inputs = tokenizer(sent, padding=False, truncation=False, is_split_into_words=True, return_offsets_mapping=True,
return_tensors="pt")
with torch.no_grad():
outputs = model(inputs['input_ids'].to(device), inputs['attention_mask'].to(device),
inputs['token_type_ids'].to(device))
else:
inputs = tokenizer(text=sent, text_pair=pair, padding=False, truncation=True,
is_split_into_words=True,
return_offsets_mapping=True, return_tensors="pt")
with torch.no_grad():
outputs = model(inputs['input_ids'].to(device), inputs['attention_mask'].to(device),
inputs['token_type_ids'].to(device))
return outputs.hidden_states[layer][0], inputs['input_ids'][0], inputs['offset_mapping'][0]
def centering(hidden_outputs):
"""
hidden_outputs : [tokens, hidden_size]
"""
# 全てのトークンの埋め込みについて足し上げ、その平均ベクトルを求める
mean_vec = torch.sum(hidden_outputs, dim=0) / hidden_outputs.shape[0]
hidden_outputs = hidden_outputs - mean_vec
print(hidden_outputs.shape)
return hidden_outputs
def convert_to_word_embeddings(offset_mapping, token_ids, hidden_tensors, tokenizer, pair):
word_idx = -1
subword_to_word_conv = np.full((hidden_tensors.shape[0]), -1)
# Bug in hugging face tokenizer? Sometimes Metaspace is inserted
metaspace = getattr(tokenizer.decoder, "replacement", None)
metaspace = tokenizer.decoder.prefix if metaspace is None else metaspace
tokenizer_bug_idxes = [i for i, x in enumerate(tokenizer.convert_ids_to_tokens(token_ids)) if
x == metaspace]
for subw_idx, offset in enumerate(offset_mapping):
if subw_idx in tokenizer_bug_idxes:
continue
elif offset[0] == offset[1]: # Special token
continue
elif offset[0] == 0:
word_idx += 1
subword_to_word_conv[subw_idx] = word_idx
else:
subword_to_word_conv[subw_idx] = word_idx
word_embeddings = torch.vstack(
([torch.mean(hidden_tensors[subword_to_word_conv == word_idx], dim=0) for word_idx in range(word_idx + 1)]))
print(word_embeddings.shape)
if pair:
sep_tok_indices = [i for i, x in enumerate(token_ids) if x == tokenizer.sep_token_id]
s2_start_idx = subword_to_word_conv[
sep_tok_indices[0] + np.argmax(subword_to_word_conv[sep_tok_indices[0]:] > -1)]
s1_word_embeddigs = word_embeddings[0:s2_start_idx, :]
s2_word_embeddigs = word_embeddings[s2_start_idx:, :]
return s1_word_embeddigs, s2_word_embeddigs
else:
return word_embeddings
def main():
st.set_page_config(layout="wide")
# Sidebar
st.sidebar.markdown("## Settings & Parameters")
model = st.sidebar.selectbox('model', ['microsoft/deberta-v3-base', 'bert-base-uncased'])
layer = st.sidebar.slider(
'layer number for embeddings', 0, 11, value=9
)
is_centering = st.sidebar.checkbox('centering embeddings', value=True)
ot_type = st.sidebar.selectbox('ot_type', ['OT', 'POT', 'UOT'])
ot_type = ot_type.lower()
sinkhorn = st.sidebar.checkbox('sinkhorn', value=True)
distortion = st.sidebar.slider(
'distortion: $\kappa$', 0.0, 1.0, value=0.20
)
tau = st.sidebar.slider(
'tau: $\\tau$', 0.0, 1.0, value=0.98
) # with 0.02 interva
threshhold = st.sidebar.slider(
'threshhold: $\lambda$', 0.0, 1.0
) # with 0.01 interval
# Content
st.markdown('## Playground: Unbalanced Optimal Transport for Unbalanced Word Alignment')
col1, col2 = st.columns(2)
with col1:
sent1 = st.text_area(
'sentence 1',
'By one estimate , fewer than 20,000 lions exist in the wild , a drop of about 40 percent in the past two decades .'
)
with col2:
sent2 = st.text_area(
'sentence 2',
'Today there are only around 20,000 wild lions left in the world .'
)
tokenizer, model = init_model(model)
aligner = init_aligner(ot_type, sinkhorn, distortion, threshhold, tau)
with st.container():
st.write("word alignment matrix")
if sent1 != '' and sent2 != '':
sent1 = sent1.lower().split()
sent2 = sent2.lower().split()
hidden_output, input_id, offset_map = encode_sentence(sent1, sent2, tokenizer, model, layer=layer)
if is_centering:
hidden_output = centering(hidden_output)
s1_vec, s2_vec = convert_to_word_embeddings(offset_map, input_id, hidden_output, tokenizer, pair=True)
aligner.compute_alignment_matrixes([s1_vec], [s2_vec])
align_matrix = aligner.align_matrixes[0]
print(align_matrix.shape)
#fig = align_matrix_heatmap(align_matrix.T, sent1, sent2, threshhold)
#st.plotly_chart(fig, use_container_width=True)
fig = plot_align_matrix_heatmap(align_matrix.T, sent1, sent2, threshhold)
st.pyplot(fig, dpi=300)
st.divider()
st.markdown("Note that the centering in this demo is applied only to the input sentences, so the variance may be large.")
st.subheader('Refs')
st.write("Yuki Arase, Han Bao, Sho Yokoi, [Unbalanced Optimal Transport for Unbalanced Word Alignment](https://arxiv.org/abs/2306.04116), ACL2023 [[github](https://github.com/yukiar/OTAlign/tree/main)]")
if __name__ == '__main__':
main() |