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import streamlit as st | |
from transformers import pipeline | |
from peft import AutoPeftModelForSequenceClassification | |
from transformers import AutoTokenizer | |
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") | |
loraModel = AutoPeftModelForSequenceClassification.from_pretrained("Intradiction/text_classification_WithLORA") | |
# Initialize the two piplelines | |
pipe = pipeline(model="Intradiction/text_classification_NoLORA") | |
LORApipe = pipeline("sentiment-analysis", model=loraModel, tokenizer=tokenizer) | |
text = st.text_area('Input a movie review:') | |
if text: | |
out = pipe(text) | |
LORAout = LORApipe(text) | |
st.json(out) | |
st.json(LORAout) |