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--- |
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language: es |
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tags: |
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- Spanish |
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- Electra |
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- Legal |
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datasets: |
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- Spanish-legal-corpora |
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--- |
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## LEGALECTRA ⚖️ |
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**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). |
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As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB): |
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**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. |
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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). |
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## Training details |
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The model was trained using the Electra base code for 3 days on 1 Tesla V100 16GB. |
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## Model details ⚙ |
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|Param| # Value| |
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|-----|--------| |
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|Layers| 12 | |
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|Hidden | 256 | |
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|Params| 14M | |
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## Evaluation metrics (for discriminator) 🧾 |
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|Metric | # Score | |
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|-------|---------| |
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|Accuracy| 0.955| |
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|Precision| 0.790| |
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|AUC | 0.971| |
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## Benchmarks 🔨 |
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WIP 🚧 |
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## How to use the discriminator in `transformers` |
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TBA |
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## Acknowledgments |
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TBA |
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## Citation |
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If you want to cite this model you can use this: |
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```bibtex |
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@misc{mromero2022legalectra, |
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title={Spanish Legal Electra (small)}, |
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author={Romero, Manuel}, |
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publisher={Hugging Face}, |
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journal={Hugging Face Hub}, |
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howpublished={\url{https://huggingface.co/mrm8488/legalectra-small-spanish}, |
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year={2022} |
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} |
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``` |
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> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) |
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> Made with <span style="color: #e25555;">♥</span> in Spain |
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