File size: 1,214 Bytes
75a1cfc 0a745c3 75a1cfc 0a745c3 75a1cfc 6beea90 75a1cfc 6beea90 75a1cfc 9141fcd 75a1cfc 6beea90 75a1cfc 6beea90 75a1cfc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 |
import streamlit as st
import torch
from peft import PeftModel
from transformers import AutoModel, AutoTokenizer
model_name = 'intfloat/multilingual-e5-large'
adapters_name = './checkpoint-21170'
model = AutoModel.from_pretrained(model_name)
model = PeftModel.from_pretrained(model, adapters_name)
model = model.merge_and_unload()
tokenizer = AutoTokenizer.from_pretrained(model_name)
st.header("Irrelevant Review Detections :sunglasses:")
description = st.text_input("Product description")
review = st.text_input("Review")
if st.button("Detect") and description and review:
input_texts = [
f'query: {review}',
f'passage: {description}'
]
batch_dict = tokenizer(input_texts, max_length=512,
padding=True, truncation=True, return_tensors='pt')
query_embedding, doc_embedding = model(**batch_dict, return_dict=True).pooler_output
query_embedding = query_embedding.unsqueeze(0)
doc_embedding = doc_embedding.unsqueeze(0)
similarity = torch.nn.functional.cosine_similarity(
query_embedding, doc_embedding)
threshold = 0.83
if similarity > threshold:
st.write('Relevant')
else:
st.write('Irrelevant')
|