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