def generate_model(dictionary, bow_corpus, corpus_path):
try:
tfidf = TfidfModel.load(corpus_path + 'wiki-tfidf.model')
print('tfidf model generated')
except:
tfidf = TfidfModel()
tfidf = TfidfModel(bow_corpus, dictionary)
tfidf._smart_save(corpus_path + 'wiki-tfidf.model')
pass
return tfidf
config.read('config.ini')
#TODO: Generalize this step puting corpus_path as your actual corpus
#Config file must allow wikipedia, Gutenberg, ...
corpus_path = config['WIKI']['en'][1:-1]
dictionary = Dictionary.load_from_text(
os.path.relpath(corpus_path + '_wordids.txt.bz2'))
bow_corpus = MmCorpus(os.path.relpath(corpus_path + '_bow.mm'))
try:
tfidf = TfidfModel.load(corpus_path + 'wiki-tfidf.model')
except:
tfidf = TfidfModel()
tfidf = TfidfModel(bow_corpus, dictionary)
tfidf._smart_save(corpus_path + 'wiki-tfidf.model')
pass
#testing sentences
sentence1 = 'pilar pescado en la tarde es fatal'
sentence2 = 'machacar pescado al atardecer es terrible'
#Transforming sentences
sent1 = sentence1.split()
sent2 = sentence2.split()
bow_sent1 = dictionary.doc2bow(sent1)
bow_sent2 = dictionary.doc2bow(sent2)
bow_sent1_tfidf = tfidf[bow_sent1]
bow_sent2_tfidf = tfidf[bow_sent2]
