techniques to lower memory consumption and increase the training speed of BERT. Comprehensive empirical evidence shows
that our proposed methods lead to models that scale much better compared to the original BERT. We also use a
self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks
with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and
SQuAD benchmarks while having fewer parameters compared to BERT-large.
Tips:
ALBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather
  than the left.
ALBERT uses repeating layers which results in a small memory footprint, however the computational cost remains
  similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same
