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import streamlit as st |
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from sentence_transformers import SentenceTransformer |
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from sklearn.metrics.pairwise import cosine_similarity |
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st.set_page_config(page_title='Sentence Similarity Demo') |
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st.title('Sentence Similarity Demo') |
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sentence1 = st.text_input('Enter the first sentence:', 'This is an example sentence') |
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sentence2 = st.text_input('Enter the second sentence:', 'Each sentence is converted') |
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@st.cache_resource |
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def load_model(): |
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return SentenceTransformer('sentence-transformers/sentence-t5-base') |
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model = load_model() |
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embeddings = model.encode([sentence1, sentence2]) |
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similarity = cosine_similarity([embeddings[0]], [embeddings[1]])[0][0] |
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st.write(f'Cosine Similarity: {similarity:.4f}') |