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import streamlit as st
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from transformers import AutoTokenizer, AutoModel
import torch
# Load Romanian BERT model and tokenizer
model_name = 'dumitrescustefan/bert-base-romanian-cased-v1'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
# Load pre-saved embeddings and sentences
saved_embeddings = np.load("sentence_embeddings.npy")
sentences = np.load("sentences.npy")
# Function to get sentence embedding
def get_sentence_embedding(sentence, model, tokenizer):
inputs = tokenizer(sentence, return_tensors='pt', padding=True, truncation=True, max_length=128)
with torch.no_grad():
outputs = model(**inputs)
cls_embedding = outputs.last_hidden_state[:, 0, :] # CLS token embedding
return cls_embedding.numpy()
# Streamlit UI
st.title("Sentence Similarity with Pre-trained BERT")
st.write("Enter a sentence in Romanian to find similar sentences.")
# User input
user_input = st.text_input("Your sentence")
# Check if user input exists
if user_input:
# Embed the user input
user_embedding = get_sentence_embedding(user_input, model, tokenizer)
# Compute similarity with saved embeddings
similarities = cosine_similarity(user_embedding, saved_embeddings.reshape(saved_embeddings.shape[0], -1))
# Get the top 5 most similar sentences
top_n = 5
top_indices = np.argsort(similarities[0])[::-1][:top_n]
st.write("Top similar sentences:")
# Display the most similar sentences with similarity scores
for idx in top_indices:
st.write(f"Sentence: {sentences[idx]}")
st.write(f"Similarity score: {similarities[0][idx]:.4f}")
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