pytorch tf jax electra masked-lm en transformers license:apache-2.0 fill-mask
.gitattributes README.md config.json flax_model.msgpack pytorch_model.bin tf_model.h5 tokenizer.json tokenizer_config.json vocab.txt
---
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 generator in `transformers`

```python
from transformers import pipeline

fill_mask = pipeline(
	"fill-mask",
	model="google/electra-small-generator",
	tokenizer="google/electra-small-generator"
)

print(
	fill_mask(f"HuggingFace is creating a {nlp.tokenizer.mask_token} that the community uses to solve NLP tasks.")
)

```
