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import streamlit as st |
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import numpy as np |
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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:', 'Convert this into a sea shanty') |
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sentence2 = st.text_input('Enter the second sentence:', 'Improve this text by making it a shanty.') |
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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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def sharpened_cosine_similarity(s, k, q=1e-6, p=3): |
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cosine_sim = np.dot(s, k) / (np.linalg.norm(s) * np.linalg.norm(k)) |
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return np.sign(cosine_sim) * np.power(np.abs(cosine_sim) / (np.linalg.norm(s) + q), p) |
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similarity = sharpened_cosine_similarity(embeddings[0], embeddings[1]) |
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st.write(f'Sharpened Cosine Similarity: {similarity:.4f}') |
