| import streamlit as st | |
| from random import choice | |
| from annotated_text import annotated_text | |
| from resources import * | |
| from helpers import * | |
| base_model = "xlnet-base-cased" | |
| session = load_variables() | |
| sentences = load_sentences() | |
| baseline_classifier = load_model(f"Dagobert42/{base_model}-biored-finetuned") | |
| augmented_classifier = load_model(f"Dagobert42/{base_model}-biored-augmented") | |
| st.title("Semantic Frame Augmentation") | |
| st.subheader("Analysing difficult low-resource domains with only a handful of examples") | |
| st.write("This space uses a xlnet-base-cased 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(txt, tokens)) | |