readme: ---
  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
  