| language: en | |
| thumbnail: https://huggingface.co/front/thumbnails/google.png | |
| license: apache-2.0 | |
| **WARNING**: This is the official generator checkpoint as in the [ELECTRA original codebase](https://github.com/google-research/electra). However, this model is not scaled properly for pre-training with [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator). The paper recommends a hyperparameter multiplier of 1/4 between the discriminator and generator for this given model to avoid training instabilities. This would not be the case when using `google/electra-small-generator` and `google/electra-small-discriminator`, which are similar in size. | |
| ## 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.") | |
| ) | |
| ``` | |