| language: en | |
| thumbnail: https://huggingface.co/front/thumbnails/google.png | |
| license: apache-2.0 | |
| 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 | |
| from transformers import ElectraForPreTraining, ElectraTokenizerFast | |
| import torch | |
| discriminator = ElectraForPreTraining.from_pretrained("google/electra-base-discriminator") | |
| tokenizer = ElectraTokenizerFast.from_pretrained("google/electra-base-discriminator") | |
| sentence = "The quick brown fox jumps over the lazy dog" | |
| fake_sentence = "The quick brown fox fake over the lazy dog" | |
| fake_tokens = tokenizer.tokenize(fake_sentence) | |
| fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt") | |
| discriminator_outputs = discriminator(fake_inputs) | |
| predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2) | |
| [print("%7s" % token, end="") for token in fake_tokens] | |
| [print("%7s" % int(prediction), end="") for prediction in predictions.tolist()] | |