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from gensim.models import Word2Vec, FastText
import joblib
def create_tfidf():
vectorizer = joblib.load("tfidf_vectorizer.pkl")
def _inner(docs):
return vectorizer.transform(docs).toarray()
return _inner, vectorizer # возвращаем и функцию, и векторaйзер
def create_w2v():
model = Word2Vec.load("./word2vec.model")
def _inner(word):
if word in model.wv:
return model.wv[word]
else:
return None
return _inner, model # возвращаем и функцию, и модель
def create_fasttext():
model = FastText.load("./fasttext.model")
def _inner(word):
if word in model.wv:
return model.wv[word]
else:
return None
return _inner, model # возвращаем и функцию, и модель |