Model card for spanish electra small (#4196)

model_cards/mrm8488/electricidad-small-discriminator/README.md

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+---
+language: spanish
+thumbnail: https://i.imgur.com/uxAvBfh.png
+
+
+---
+
+## ELECTRICIDAD: The Spanish Electra [Imgur](https://imgur.com/uxAvBfh)
+
+**ELECTRICIDAD** is a small Electra like model (discriminator in this case) trained on a + 20 GB of  the [OSCAR](https://oscar-corpus.com/) Spanish corpus.
+
+As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB):
+**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 the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB).
+
+## Model details ⚙
+
+|Param| # Value|
+|-----|--------|
+|Layers|	12   |
+|Hidden |256 	|
+|Params| 14M|
+
+## Evaluation metrics (for discriminator) 🧾
+
+|Metric | # Score |
+|-------|---------|
+|Accuracy| 0.94|
+|Precision| 0.76|
+|AUC | 0.92|
+
+## Benchmarks 🔨
+
+WIP 🚧
+
+## How to use the discriminator in `transformers`
+
+```python
+from transformers import ElectraForPreTraining, ElectraTokenizerFast
+import torch
+
+discriminator = ElectraForPreTraining.from_pretrained("mrm8488/electricidad-small-discriminator")
+tokenizer = ElectraTokenizerFast.from_pretrained("mrm8488/electricidad-small-discriminator")
+
+sentence = "El rápido zorro marrón salta sobre el perro perezoso"
+fake_sentence = "El rápido zorro marrón falsea sobre el perro perezoso"
+
+fake_tokens = tokenizer.tokenize(sentence)
+fake_inputs = tokenizer.encode(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" % prediction, end="") for prediction in predictions.tolist()]
+```
+
+## Acknowledgments
+
+I thank [🤗/transformers team](https://github.com/huggingface/transformers) for answering my doubts and Google for helping me with the [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc) program.
+
+
+
+> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
+
+> Made with <span style="color: #e25555;">&hearts;</span> in Spain