Hello all, I am pretty new to PyTorch, so I hope this is not too dumb a question, but I am running into a problem with prediction on a dataset using my trained PyTorch model. I have a trained model for sentiment analysis, that all went well. But I need to apply it to a dataset of about 1.1 million unlabeled texts. I have tried doing so a few times, but I tend to end up with too high RAM usage, causing either my Colab session or my own computer to crash.
Someone suggested doing the prediction in batches, but I don’t understand how I would do so. Could someone please point me in the right direction for how I am supposed to predict new labels in this use case? Any help would be greatly appreciated!