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Create calc_cosine_similarity.py

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  1. calc_cosine_similarity.py +21 -0
calc_cosine_similarity.py ADDED
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+ from sentence_transformers import SentenceTransformer, util
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+ import torch
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+
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+ def find_cosine_similarity(text1, text2):
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+ # Load the pre-trained model
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+ model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
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+
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+ # Define your texts
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+ text1 = "This is a sample text."
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+ text2 = "This text is an example."
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+
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+ # Encode the texts to get their embeddings
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+ embedding1 = model.encode(text1, convert_to_tensor=True)
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+ embedding2 = model.encode(text2, convert_to_tensor=True)
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+
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+ # Compute cosine similarity
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+ cosine_sim = util.pytorch_cos_sim(embedding1, embedding2)
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+
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+ # Print the cosine similarity score
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+ #print(f"Cosine Similarity: {cosine_sim.item()}")
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+ return cosine_sim.item()