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--- |
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language: en |
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thumbnail: https://huggingface.co/front/thumbnails/google.png |
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license: apache-2.0 |
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--- |
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## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators |
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**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. |
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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). |
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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/)). |
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## How to use the discriminator in `transformers` |
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```python |
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from transformers import ElectraForPreTraining, ElectraTokenizerFast |
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import torch |
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discriminator = ElectraForPreTraining.from_pretrained("google/electra-large-discriminator") |
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tokenizer = ElectraTokenizerFast.from_pretrained("google/electra-large-discriminator") |
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sentence = "The quick brown fox jumps over the lazy dog" |
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fake_sentence = "The quick brown fox fake over the lazy dog" |
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fake_tokens = tokenizer.tokenize(fake_sentence) |
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fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt") |
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discriminator_outputs = discriminator(fake_inputs) |
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predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2) |
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[print("%7s" % token, end="") for token in fake_tokens] |
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[print("%7s" % int(prediction), end="") for prediction in predictions.tolist()] |
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``` |
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