Generator and Discriminator of the ELECTRA Model
Understand how the ELECTRA model uses two components: the generator predicts masked tokens by learning from a small percentage of masked input, while the discriminator classifies whether tokens are original or replaced. This lesson reveals how ELECTRA improves training efficiency compared to traditional BERT by using all tokens for learning, enhancing token classification accuracy in NLP applications.
We'll cover the following...
The generator model
First, let's have a look at the generator. The generator performs the MLM task. We randomly mask a few tokens with a 15% mask rate and train the generator to predict the masked token. Let's represent our input tokens as