pytorch electra pretraining es dataset:Spanish-legal-corpora transformers Spanish Electra Legal
---
language: es
tags:
 - Spanish
 - Electra
 - Legal

datasets:
 - Spanish-legal-corpora

---

## LEGALECTRA ⚖️

**LEGALECTRA** (small) is an Electra like model (discriminator in this case) trained on [A collection of corpora of Spanish legal domain](https://zenodo.org/record/5495529#.YZItp3vMLJw).

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).


## Training details

The model was trained using the Electra base code for 3 days on 1 Tesla V100 16GB.

## Model details ⚙

|Param| # Value|
|-----|--------|
|Layers| 12 |
|Hidden | 256 |
|Params| 14M |

## Evaluation metrics (for discriminator) 🧾

|Metric | # Score |
|-------|---------|
|Accuracy| 0.955|
|Precision| 0.790|
|AUC | 0.971|


## Benchmarks 🔨

WIP 🚧

## How to use the discriminator in `transformers`

TBA

## Acknowledgments

TBA

## Citation
If you want to cite this model you can use this:

```bibtex
@misc{mromero2022legalectra,
  title={Spanish Legal Electra (small)},
  author={Romero, Manuel},
  publisher={Hugging Face},
  journal={Hugging Face Hub},
  howpublished={\url{https://huggingface.co/mrm8488/legalectra-small-spanish},
  year={2022}
}
```


> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)

> Made with <span style="color: #e25555;">&hearts;</span> in Spain
