| 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)) | |