GPT-2

2000년 도입 · 논문 768편에서 사용

GPT-2 is a Transformer architecture that was notable for its size (1.5 billion parameters) on its release. The model is pretrained on a WebText dataset - text from 45 million website links. It largely follows the previous GPT architecture with some modifications: - Layer normalization is moved to the input of each sub-block, similar to a pre-activation residual network and an additional layer normalization was added after the final self-attention block. - A modified initialization which accounts for the accumulation on the residual path with model depth is used. Weights of residual layers are scaled at initialization by a factor of $1/\sqrt{N}$ where $N$ is the number of residual layers. - The vocabulary is expanded to 50,257. The context size is expanded from 512 to 1024 tokens and a larger batch size of 512 is used.

출처: Language Models are Unsupervised Multitask Learners

소개 논문: Language Models are Unsupervised Multitask Learners