example.py

from gensim.models.keyedvectors import KeyedVectors
from DocSim import DocSim

# Using the pre-trained word2vec model trained using Google news corpus of 3 billion running words.
# The model can be downloaded here: https://bit.ly/w2vgdrive (~1.4GB)
# Feel free to use to your own model.
googlenews_model_path = './data/GoogleNews-vectors-negative300.bin'
stopwords_path = "./data/stopwords_en.txt"

model = KeyedVectors.load_word2vec_format(googlenews_model_path, binary=True)
with open(stopwords_path, 'r') as fh:
    stopwords = fh.read().split(",")
ds = DocSim(model,stopwords=stopwords)

source_doc = "how to delete an invoice"
target_docs = ['delete a invoice', 'how do i remove an invoice', "purge an invoice"]

sim_scores = ds.calculate_similarity(source_doc, target_docs)

print(sim_scores)

# Prints:
##   [ {'score': 0.99999994, 'doc': 'delete a invoice'}, 
##   {'score': 0.79869318, 'doc': 'how do i remove an invoice'}, 
##   {'score': 0.71488398, 'doc': 'purge an invoice'} ]