demessifyme/doc2vec.py
def embed_document(text):
    """
    Return a 300-dim embedding of text.

    :param text: String, text to embed
    :return: numpy array with shape=(300,), dtype=np.float
    """
    tokens = tokenize(text)
    embedding = np.zeros((300,))
    num_tokens = 0
    for token in tokens:
        if token in eng_stopwords:
            continue
        num_tokens += 1
        if token not in model:
            # token doesn't exist in the model, for now skip
            # future approach: generate random vector and save it
            # print(token)
            if token not in custom_model:
                custom_model[token] = np.random.random(size=(300,)) - 0.5
            embedding += custom_model[token]
            continue
        embedding += model[token]
    return embedding / num_tokens

    Return a 300-dim embedding of text.

    :param text: String, text to embed
    :return: numpy array with shape=(300,), dtype=np.float
    
Return a 300-dim embedding of text.
Return a 300 - dim embedding of text .
def embed_document(text):
    tokens = tokenize(text)
    embedding = np.zeros((300,))
    num_tokens = 0
    for token in tokens:
        if token in eng_stopwords:
            continue
        num_tokens += 1
        if token not in model:
            if token not in custom_model:
                custom_model[token] = np.random.random(size=(300,)) - 0.5
            embedding += custom_model[token]
            continue
        embedding += model[token]
    return embedding / num_tokens
def embed_document ( text ) : tokens = tokenize ( text ) embedding = np . zeros ( ( 300 , ) ) num_tokens = 0 for token in tokens : if token in eng_stopwords : continue num_tokens += 1 if token not in model : if token not in custom_model : custom_model [ token ] = np . random . random ( size = ( 300 , ) ) - 0.5 embedding += custom_model [ token ] continue embedding += model [ token ] return embedding / num_tokens
Return a 300-dim embedding of text.
Return a 300 - dim embedding of text .
"""
    Return a 300-dim embedding of text.

    :param text: String, text to embed
    :return: numpy array with shape=(300,), dtype=np.float
    """ # token doesn't exist in the model, for now skip # future approach: generate random vector and save it # print(token)
numpy array with shape=(300,), dtype=np.float
numpy array with shape = ( 300 ) dtype = np . float
text
String, text to embed
String text to embed