I've a text dataset with ~20000 samples (which is not enough). I used text augmentation to "invent" more samples so essentially I've multiplied each sample by 10 - ending up with ~200000 samples (each of the 10 is a different kind of augmentation method).
I did that for the whole dataset before splitting it to train and test datasets. Should I've done it only for the training dataset?
based on the answer given, I've a follow up question:
What should the order of operations should be? I understood by now that there augmentation should be done only on the train dataset, but what about tokenization and stemming?
Is the below the correct order?
- splitting data set into 2 datasets: train and test
- perform augmentation only on train dataset
- stemming and tokenize both datasets
- text encoding
- create model
- fit data on model
I guess my question is regarding step #3. Is it correctly placed in the above order?