---
language: es
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 zorro rojo es muy rápido"
fake_sentence = "el zorro rojo es muy ser"

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" % int(prediction), end="") for prediction in predictions.tolist()[1:-1]]

# Output:
'''
el  zorro   rojo     es    muy    ser      0      0      0      0      0      1[None, None, None, None, None, None]
'''
```

As you can see there is a **1** in the place where the model detected the fake token (**ser**). So, it works! 🎉

## 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
