readme: ---
  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](https://arxiv.org/pdf/1406.2661.pdf). 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](https://rajpurkar.github.io/SQuAD-explorer/) dataset.
  
  For a detailed description and experimental results, please refer to our paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB).
  
  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](https://gluebenchmark.com/)), QA tasks (e.g., [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/)), and sequence tagging tasks (e.g., [text chunking](https://www.clips.uantwerpen.be/conll2000/chunking/)).
  
  ## How to use the discriminator in `transformers`
  
  ```python
  from transformers import ElectraForPreTraining, ElectraTokenizerFast
  import torch
  
  discriminator = ElectraForPreTraining.from_pretrained("google/electra-small-discriminator")
  tokenizer = ElectraTokenizerFast.from_pretrained("google/electra-small-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.squeeze().tolist()]
  ```
  