| 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 |
|
|
| 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 |
|
|
| 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 |