[model_cards] ELECTRA (w/ examples of usage)

Co-Authored-By: Kevin Clark <clarkkev@users.noreply.github.com>
Co-Authored-By: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

model_cards/google/electra-small-generator/README.md

@@ -0,0 +1,29 @@
+---
+language: english
+thumbnail: https://huggingface.co/front/thumbnails/google.png
+---
+
+## 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.")
+)
+
+```