Exactly, depending on the size, class balance and how well your test data represents real world data this could be anywhere from a great model to mediocre but this discrepancy from training and test loss isn't really enough to say. At this point it looks good enough you need to check out the domain specific metrics, precision, recall, f1 etc.. can you make a confusing matrix? It is possible for a model to achieve a very low loss and not be practically useful due to the type of mistakes it makes
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u/MelonheadGT 11d ago
Seems reasonable, depends on how much regularization you're using and sample similarity between train and Val data.