SqueezeBERT

2000년 도입 · 논문 1편에서 사용

SqueezeBERT is an efficient architectural variant of BERT for natural language processing that uses grouped convolutions. It is much like BERT-base, but with positional feedforward connection layers implemented as convolutions, and grouped convolution for many of the layers.

출처: SqueezeBERT: What can computer vision teach NLP about efficient neural networks?

소개 논문: SqueezeBERT: What can computer vision teach NLP about efficient neural networks?