@kaize0409
Thanks your fantastic work !
Hello !
I can use the generate_LDA.py now!
But I still have something confused!
parser.add_argument('--topn', type=int, default=10, help='top n keywords') parser.add_argument('--topics', type=int, help='number of topics')
I know --topn is like your paper mention that top K words right?
But How to determine the number of --topics ?
The number of labels? Can if I use --topics 20 on R8 dataset, it will be error like scipy.sparse.coo_matrix .
So like R8 has --topics :8 R52 has --topics :52
I'm nor really sure:(
Also I am trying to modify the framework of HyperGAT to fit on my Chinese multi label text classification data.
Which have approximately 2500 text with 9 labels.
Have you ever try your HyperGAT on multi label classification task?
such like Kaggle's toxic comment classification datasets ?
@kaize0409
Thanks your fantastic work !
Hello !
I can use the generate_LDA.py now!
But I still have something confused!
parser.add_argument('--topn', type=int, default=10, help='top n keywords') parser.add_argument('--topics', type=int, help='number of topics')I know
--topnis like your paper mention that top K words right?But How to determine the number of
--topics?The number of labels? Can if I use
--topics 20on R8 dataset, it will be error likescipy.sparse.coo_matrix.So like R8 has
--topics :8R52 has --topics :52I'm nor really sure:(
Also I am trying to modify the framework of HyperGAT to fit on my Chinese multi label text classification data.
Which have approximately 2500 text with 9 labels.
Have you ever try your HyperGAT on multi label classification task?
such like Kaggle's toxic comment classification datasets ?