Check the training metrics.
Are they much better than the test metrics? That means the model learned a bit too well. It memorised the data and doesn’t perform as well on new information. You need more data, or a different model altogether.
Now if the training metrics suck? That means the model sucked. You need to tweak it. Add more epochs or layers, change the loss or activation function. If nothing works, it might be time to switch out the model.
This is called overfitting and underfitting, if you want to be fancy.
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