Write a fasttext customised transformer

I have a trained customised fasttext model. I want to rewrite it into a customised transformer so I can fit it into sklearn pipeline as it only accepts transformer.

At the moment, I’m able to get the word vectors.

def name2vector(name=None):
    vec = [np.array(model.get_word_vector(w)) for w in name.lower().split(' ')]
    name_vec = np.sum(vec, axis=0) # If "name" is multiple words, sum the vectors
    return (name_vec)

The function returns vectors of the word I feed in. For example:

array([-0.01087821,  0.01030535, -0.01402427,  0.0310982 ,  0.08786983,
        -0.00404521, -0.03286128, -0.00842709,  0.03934859, -0.02717219,
         0.01151722, -0.03253938, -0.02435859,  0.03330994, -0.03696496], dtype=float32))

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