Hello,
I have a similar problem where I have manually passed batches(size=128) of  sentences to my model for training and testing and now am not sure how to use my model to evaluate a single sentence now. Am converting each sentence to a image like representation and convolving over each.
My convolution layer was taking batches in the following sequence:
1 - Batch size x Length of sentence - 128 x 9 (number of words)
2 -  Batch size x Length of sentence x Embedding size (each word is embedded to dim = 50)
3 -  Unsqueeze at dim = 1 to accommodate channels
4 - Finally the cnn layer 1 sees data as 128 x 1 x 9 x 50 and am making it extract an output of dimensions 128 x 9 x 9 —> 9x9 outputs for a batch of 128
5- Did the same for testing where the model made predictions over unseen data of the same batch size.
But, for the sake of user-interfacing i want it to give me a similar output for a single sentence instead of a batch size of 128. The problem, my model is expecting inputs in batches of 128 and outputs accordingly.
What could be the smartest possible/minimal change that my code needs to go through?
Apologies upfront, am a beginner and I’ve just learnt creating models but, I’ve never tested one before.