I modelled a LSTM based text generator using a data set I have. The purpose of the model is to predict the end of sentences. My training is showing a validation accuracy of around 81%. When reading through a couple of articles, I found that unlike a classification problem I should be worried more about loss rather than accuracy. Is this the case, and if so what would be an ideal loss value? Right now my loss is around 1.5+.
There is no minimum limit for accuracy in any of the machine learning or Deep Learning problem.It's as many say garbage IN, garbage OUT
Quality of data and with a decent model will give you good accuracy.
Generally, these accuracy benchmark is set for the standard dataset available on an open internet like SQUAD, RACE, SWAG, GLUE and many more.
Usually, the state of the art models will check their performance on these datasets and set a accuarcy benchmark specific to these dataset.
Coming to your problem, you can tell the model is performing goog based on accuracy, and the evaluation metric you are using, generally in NLP to calculate loss is bit tricky. Considering your case where you are trying to predict the end of a sentence where there is no fixed dimension the reason being that the same information can be expressed in multiple ways with varying number of words.
By looking at the validation and test accuracy of your model it looks decent, but before pushing the accuracy you should worry about the overfitting problem also, the model should not be biased on your data.
You can try with different metrics to evaluate the model and you can compare the results on your own.
I hope this answers your question, Happy Learning!