16 GB dataset for finetuning fail on reduce_memory 
Hi, I am using 16GB dataset to finetune bert Model. When I do not use reduce_memory, which is loading dataset into memory first, it will use all of my 120GB memory and then crush because of out of memory. Now I am using reduce_memory model, with the increase of loading lines, the memory use is still increasing. But it is much slower in reduce_memory setting. So, I am thinking whether it would crush at the end. Could anyone have an answer for that ?

Sorry for the bothering, but it is too slow to run with reduce_memory setting, I have no idea weather it would crush or go well. I am afraid that it would keep loading for days and crushed finally. Thanks in advance for any suggestions
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