LEGALECTRA βš–οΈ

LEGALECTRA (small) is an Electra like model (discriminator in this case) trained on A collection of corpora of Spanish legal domain.

As mentioned in the original paper: 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. 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 dataset.

For a detailed description and experimental results, please refer the paper ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators.

Training details

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

Model details βš™

Param # Value
Layers 12
Hidden 256
Params 14M

Evaluation metrics (for discriminator) 🧾

Metric # Score
Accuracy 0.955
Precision 0.790
AUC 0.971

Benchmarks πŸ”¨

WIP 🚧

How to use the discriminator in transformers

TBA

Acknowledgments

TBA

Citation

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

@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

Made with β™₯ in Spain

Downloads last month
149
Inference Providers NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API: The model has no pipeline_tag.

Collections including mrm8488/legalectra-small-spanish