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Migrate model card from transformers-repo

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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-generator/README.md

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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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+
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+ license: apache-2.0
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+ ---
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+
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+ ## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
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+
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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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+
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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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+
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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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+
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+ ## How to use the generator in `transformers`
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+
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+ ```python
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+ from transformers import pipeline
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+
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+ fill_mask = pipeline(
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+ "fill-mask",
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+ model="google/electra-large-generator",
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+ tokenizer="google/electra-large-generator"
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+ )
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+
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+ print(
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+ fill_mask(f"HuggingFace is creating a {nlp.tokenizer.mask_token} that the community uses to solve NLP tasks.")
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+ )
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+
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+ ```