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import streamlit as st | |
import torch | |
import plotly.express as px | |
from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
txt = st.text_area('Text to analyze', ''' | |
It was the best of times, it was the worst of times, it was | |
the age of wisdom, it was the age of foolishness, it was | |
the epoch of belief, it was the epoch of incredulity, it | |
was the season of Light, it was the season of Darkness, it | |
was the spring of hope, it was the winter of despair, (...) | |
''') | |
# load tokenizer and model weights | |
tokenizer = AutoTokenizer.from_pretrained("s-nlp/roberta_toxicity_classifier") | |
model = AutoModelForSequenceClassification.from_pretrained("s-nlp/roberta_toxicity_classifier") | |
# prepare the input | |
batch = tokenizer.encode('txt', return_tensors='pt') | |
# inference | |
result = model(batch) | |
fig = px.bar(result, x="", y="", orientation='h') | |
fig.show() | |