ELECTRA: Pre training Text Encoders as Discriminators Rather Than Generators ELECTRA is a new method for self supervised language representation learning. It can be used to pre train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a GAN. At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state of the art results on the SQuAD 2.0 dataset. For a detailed description and experimental results, please refer to our paper ELECTRA: Pre training Text Encoders as Discriminators Rather Than Generators. This repository contains code to pre train ELECTRA, including small ELECTRA models on a single GPU. It also supports fine tuning ELECTRA on downstream tasks including classification tasks (e.g,. GLUE), QA tasks (e.g., SQuAD), and sequence tagging tasks (e.g., text chunking). How to use the discriminator in transformers
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy