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## 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-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()] | |
``` |