Jaccard similarity

Jaccard similarity calculates the similarity between two sets by the ratio of common words (intersection) to totally unique words (union) in both sets. It takes a list of unique words in each sentence or document. It is useful where the repetition of words does not matter. Jaccard similarity ranges from 0-100%; the higher the percentage, the more similar the two populations:

Let's look at a Jaccard similarity example:

def jaccard_similarity(sent1, sent2):
    """Find text similarity using jaccard similarity"""
    # Tokenize sentences
    token1 = set(sent1.split())
    token2 = set(sent2.split())
     
    # intersection between tokens of two sentences    
    intersection_tokens = token1.intersection(token2)
    
    # Union between tokens of two sentences
    union_tokens=token1.union(token2)
    
    # Cosine Similarity
    sim_= float(len(intersection_tokens) / len(union_tokens))
    return sim_

jaccard_similarity('I love pets.',...