[D] Techniques for handling input documents with a large number of tokens in BERT/GPT2 style models?

Hi,

I'm wondering if anyone has a survey of the easiest way to handle classification tasks where the input token space is >> 512 (or whatever the single GPU models are limited to).

I'm working in a complex space. I'm looking at a ranking (actually may even be simply binary, with a class imbalance) type problem, so not generative, but where the text's contents is important, not just some pooled version of the embeddings, and so I'd like to make …

bert classification documents gpu machinelearning ranking space style survey tasks token tokens