descriptions = ["he liked dogs", "she liked cats", "she hated cars"]
tfidf = TfidfVectorizer()
trained_model = tfidf.fit_transform(descriptions)

d = [tfidf_score, length_document, num_words]

(Pdb) trained_model
<5801x8954 sparse matrix of type '<type 'numpy.float64'>'
    with 48369 stored elements in Compressed Sparse Row format>
(Pdb) trained_model[0]
<1x8954 sparse matrix of type '<type 'numpy.float64'>'
    with 4 stored elements in Compressed Sparse Row format>
(Pdb) trained_model[1]
<1x8954 sparse matrix of type '<type 'numpy.float64'>'
    with 11 stored elements in Compressed Sparse Row format>