@sgugger Already explained why it is being removed.
“A core requirement for NLU applications” is a stretch. SOTA applications typically finetune full models on their downstream task. Sentence embeddings as such (by averaging or otherwise pooling without any tuning) are a approach but hardly the best one out there. Averaging token embeddings is as simple as torch.mean if that is what you want, and getting a CLS token’s output (if it even exists for that LM) isn’t hard either (cf. SequenceSummary).
I think this topic can conclude here. I would advise you to either implement this yourself, and perhaps start from SequenceSummary and build from that if you are capable, and otherwise use the sentence-transformers library or similar which provides finetuned models to get better sentence representations.