Hi, I’ve just started NLP and using pytorch.
I’m struggling building a model for predicting rate of the movie with review as input.
I’ve preprocessed all my data and it’s shape is like below
   v1     v2     v3     v4 ....  vp

w1  1.1    0.2    0.1   2.1       x.xx
w2  …     …
w3
w4
.
.
wl
each Wi represent tokenized word of the review and
V1~p represent the vector for the word.
So, know I’ve got every word represented 300 dimension vector.
For my model, I limited length of the sentence (the number of words in sentence) 140, so each review is represented as (140, 300) which is 140 words for each sentence and 300 size vector for one word.
Now here’s the problem

I’d like to design my model using 2d CNN as above, which will result in looking at 2 words each convolution.
How do I implement this?
I’m really confused with torch.nn.Conv2d’s parameters and struggling understanding it!
Please someone help!