  Recently, pre-trained Transformer based language models such as BERT and GPT,
have shown great improvement in many Natural Language Processing (NLP) tasks.
However, these models contain a large amount of parameters. The emergence of
even larger and more accurate models such as GPT2 and Megatron, suggest a trend
of large pre-trained Transformer models. However, using these large models in
production environments is a complex task requiring a large amount of compute,
memory and power resources. In this work we show how to perform
quantization-aware training during the fine-tuning phase of BERT in order to
compress BERT by $4\times$ with minimal accuracy loss. Furthermore, the
produced quantized model can accelerate inference speed if it is optimized for
8bit Integer supporting hardware.

Computation and Language;Machine Learning