You can work with 2 splits, which is a common practice. For a small dataset you can use 5 or 10 fold crossvalidation with shuffling on 75-80% of the dataset (train) for hyperparameter tunning / model selection, fit the best model on the entirety of that set, and then evaluate/test on the remaining 25%-20% that you held out. You can repeat the process multiple times with different seeds to get a better estimation of the expected performance, assuming that the input data when you do inference comes from the same distribution as your dataset.