I am trying to train a text classification model. For all sentence examples, I limit them up to 32 words, and if there are not exist 32 words, I am creating zero pad arrays. To convert each word to vector, I used a pre-trained word2vec model.
In the final setting, the shape of my data is :
x_train: 15000 samples and each sample has 32 vectors in which each vector size is 100.
(15000, 32, 100).
y_train: 15000 binary targets
So my question is that, should I apply 1D CNN on my x_train or 2D CNN? I think that I can do it in both ways, but is there the main correct approach to this kind of problem? I read some stuff about 1D CNN on text classification but also there are some examples with 2D CNN. What are the cons and pros?