i have small dataset 4840 samples (60% negative ,28% positive,12% negative) i use data augmentation on training set (70%train 30% test) and i have about 2000 samples for each class while test is unbalanced (800 neutral ,400 positive ,200 negative). I use synonym replace from wordnet and insert words with contextual embedding takeed from nlpaug library. i use 10-folds cross validation the permonace doesn't increase very much . without data aug i have 86% accuracy 84 f1 score , with data aug 87% accuracy 85 f1 .

what is the problem ?

i have small dataset 4840 samples (60% negative ,28% positive,12% negative) i use data augmentation on training set (70%train 30% test) and i have about 2000 samples for each class while test is unbalanced (800 neutral ,400 positive ,200 negative). I use synonym replace from wordnet and insert words with contextual embedding takeed from nlpaug library. i use 10-folds cross validation the permonace doesn't increase very much . without data aug i have 86% accuracy 84 f1 score , with data aug 87% accuracy 85 f1 .

p.s. I try different data augmentation the result are quite similar i have this value of train and val loss , I was expecting overfitting.

The train and val loss value after 10 epochs val_loss = 0.22798433965072035 train_loss = 1.6610426288098097

what is the problem ?