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sentence-similarity.py
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| from sentence_transformers import SentenceTransformer, util | |
| model = SentenceTransformer('all-MiniLM-L6-v2') | |
| # Two lists of sentences | |
| sentences1 = ['The cat sits outside', | |
| 'A man is playing guitar', | |
| 'The new movie is awesome', | |
| 'Jim can run very fast', | |
| 'My goldfish is hungry'] | |
| sentences2 = ['The dog plays in the garden', | |
| 'A woman watches TV', | |
| 'The new movie is so great', | |
| 'James is the fastest runner', | |
| 'Pluto is a planet!'] | |
| #Compute embedding for both lists | |
| embeddings1 = model.encode(sentences1, convert_to_tensor=True) | |
| embeddings2 = model.encode(sentences2, convert_to_tensor=True) | |
| #Compute cosine-similarities | |
| cosine_scores = util.cos_sim(embeddings1, embeddings2) | |
| #Output the pairs with their score | |
| for i in range(len(sentences1)): | |
| print("{:<28} {:<28} Score: {:.4f}".format(sentences1[i], sentences2[i], cosine_scores[i][i])) |
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