| from sentence_transformers import SentenceTransformer | |
| # sentences = [ | |
| # "I like rainy days because they make me feel relaxed.", | |
| # "I don't like rainy days because they don't make me feel relaxed." | |
| # ] | |
| # model = SentenceTransformer('dmlls/all-mpnet-base-v2-negation') | |
| # embeddings = model.encode(sentences) | |
| # from sklearn.metrics.pairwise import cosine_similarity | |
| # similarity_score = cosine_similarity([embeddings[0]], [embeddings[1]])[0][0] | |
| # similarity = (similarity_score + 1) / 2 | |
| # print("Similarity:", similarity) | |
| def checkSimilarity(text1, text2): | |
| model = SentenceTransformer('dmlls/all-mpnet-base-v2-negation') | |
| embeddings = model.encode([text1, text2]) | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| similarity_score = cosine_similarity([embeddings[0]], [embeddings[1]])[0][0] | |
| similarity = (similarity_score + 1) / 2 | |
| return similarity |