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from HebEMO import HebEMO | |
from transformers import pipeline | |
import streamlit as st | |
import matplotlib.pyplot as plt | |
import pandas as pd | |
from spider_plot import spider_plot | |
st.title("Emotion Recognition in Hebrew Texts") | |
st.write("HebEMO is a tool to detect polarity and extract emotions from Hebrew user-generated content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of *surprise*, which the model failed to capture (F1 = 0.41). More information can be found in our git: https://github.com/avichaychriqui/HeBERT") | |
st.write("Write Hebrew sentences in the text box below to analyze (each sentence in a different rew). It takes a while, be patient :). An additional demo can be found in the Colab notebook: https://colab.research.google.com/drive/1Jw3gOWjwVMcZslu-ttXoNeD17lms1-ff ") | |
def HebEMO_model(): | |
return HebEMO() | |
HebEMO_model = HebEMO_model() | |
sent = st.text_area("Text", "ืืืืื ืืคืื ืืืืืฉืจืื", height = 20) | |
# interact(HebEMO_model.hebemo, text='ืืืืื ืืคืื ืืืืืฉืจื', plot=fixed(True), input_path=fixed(False), save_results=fixed(False),) | |
hebEMO_df = HebEMO_model.hebemo(sent, read_lines=True, plot=False) | |
hebEMO = pd.DataFrame() | |
for emo in hebEMO_df.columns[1::2]: | |
hebEMO[emo] = abs(hebEMO_df[emo]-(1-hebEMO_df['confidence_'+emo])) | |
st.write (hebEMO) | |