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LEGALECTRA (base) 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


Model details ⚙

Name # Value
Layers 12
Hidden 768
Params 110M

Evaluation metrics (for discriminator) 🧾

Metric # Score
Accuracy 0.941
AUC 0.794

Benchmarks 🔨


How to use the discriminator in transformers




Created by Manuel Romero/@mrm8488 Made with in Spain

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