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0f8411f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | import spacy
import numpy as np
nlp = spacy.load("en_core_web_md")
def sim_score(ref,gen):
a = nlp(ref).similarity(nlp(gen))
return a
def calculate_ratio(table):
ratios = []
maximum = []
for column in table.T: # Iterate over the columns of the table
max_value = np.max(column)
average_value = np.mean(column)
if max_value + average_value != 0:
ratio = max_value / (max_value + average_value)
else:
ratio=0
ratios.append(ratio)
return ratios
def similarity_score(ref_term, gen_term):
# Calculate the similarity scores between each pair of terms
similarity_scores = [[sim_score(ref, gen) for gen in gen_term] for ref in ref_term]
ratio = calculate_ratio(np.array(similarity_scores))
# Determine the length of the longest term in each list
# avg_score = sum(sum(similarity_scores, [])) / (len(ref_term) * len(gen_term))
return ratio |