ALBERT Hydraulic pump ABT1-55-4R02-Z11 ALBERT ABT1-25-1R00-B10E ABT1-55-2R02-C1 vane pump servo hydraulic pump.

Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations and

longer training times. To address these problems, we present two parameterreduction techniques to lower memory consumption and increase the training

speed of BERT (Devlin et al., 2019). 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. The code and the pretrained models are available