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| import streamlit as st | |
| from sentence_transformers import SentenceTransformer, util | |
| # Load pre-trained BERT model | |
| def load_bert_model(): | |
| model = SentenceTransformer('paraphrase-MiniLM-L6-v2') | |
| return model | |
| model = load_bert_model() | |
| # Calculate similarity score using BERT | |
| def calculate_similarity(text1, text2): | |
| embeddings1 = model.encode(text1, convert_to_tensor=True) | |
| embeddings2 = model.encode(text2, convert_to_tensor=True) | |
| similarity_score = util.pytorch_cos_sim(embeddings1, embeddings2) | |
| return similarity_score.item() | |
| # Streamlit app | |
| def main(): | |
| st.title("Semantic Textual Similarity") | |
| text1 = st.text_input("Enter Text 1:", "") | |
| text2 = st.text_input("Enter Text 2:", "") | |
| if st.button("Calculate Similarity"): | |
| st.write("Running similarity calculation...") | |
| similarity_score = calculate_similarity(text1, text2) | |
| st.write("Similarity Score:", similarity_score) | |
| if __name__ == "__main__": | |
| main() | |