Migrate model card from transformers-repo

Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/google/electra-large-discriminator/README.md

README.md

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+---
+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-large-discriminator")
+tokenizer = ElectraTokenizerFast.from_pretrained("google/electra-large-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()]
+```