your text size most be greater or equal to your maximum filter size
for example:
filter_sizes = [2, 3, 4]

maximum filter size is 4 so your text length should be 4 words or more
example of predicting batch of one text
def pred(text):
    
    # preprocessing text
    tokenized= text_cleaner(text).split() 
    
    # pad to maximum filter size:
    if len(tokenized) < 4:
       text += ['<pad>'] * (5 - len(tokenized)) # i used '<pad>' because my vocab dict map '<pad>' to 1 
    
    # convert words to index
    vectors = [TEXT.vocab.stoi.get(w, 0) for w in tokenized] # stoi.get(w, 0) it map w to its index if the word in the dict else it map to '<unk>' which is mapped to 0
    
    # convert the list of vectors to torch.tensor
    vectors = torch.tensor(vectors, dtype=torch.long, device=device).unsqueeze(dim=0)
        
    # predict text
    with torch.no_grad():
        output = model(vectors)

    return output

even if your batch size is greater then one (example: 32) thier length it mustn’t be less then maximum filter size
I hope it’s clear now