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import torch
import streamlit as st
from random import choice
from annotated_text import annotated_text
from helpers import *
with open("sentences.pt", 'rb') as f:
sentences = torch.load(f)
sentence = choice(sentences)
st.title("Semantic Frame Augmentation")
st.subheader("Analysing difficult low-resource domains with only a handful of examples")
st.write("This space uses a google/mobilebert-uncased model for NER")
augment = st.toggle('Use augmented model for NER', value=False)
txt = st.text_area(
"Text to analyze",
sentence,
max_chars=500
)
if augment:
st.write("with augmentation:")
tokens = augmented_classifier(txt)
else:
st.write("without augmentation:")
tokens = baseline_classifier(txt)
st.subheader("Entity analysis:")
annotated_text(annotate_sentence(sentence, tokens))
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