readme: ---
  language: es
  thumbnail: https://i.imgur.com/uxAvBfh.png
  tags:
   - Spanish
   - Electra
  
  datasets:
   -large_spanish_corpus
  
  ---
  
  ## ELECTRICIDAD: The Spanish Electra [Imgur](https://imgur.com/uxAvBfh)
  
  **Electricidad-base-discriminator** (uncased) is a ```base``` Electra like model (discriminator in this case) trained on a [Large Spanish Corpus](https://github.com/josecannete/spanish-corpora) (aka BETO's corpus)
  
  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).
  
  
  ## Model details ⚙
  
  |Name| # Value|
  |-----|--------|
  |Layers| 12 |
  |Hidden | 768 |
  |Params| 110M |
  
  ## Evaluation metrics (for discriminator) 🧾
  
  |Metric | # Score |
  |-------|---------|
  |Accuracy| 0.985|
  |Precision| 0.726|
  |AUC | 0.922|
  
  
  
  ## Fast example of usage 🚀
  
  ```python
  from transformers import ElectraForPreTraining, ElectraTokenizerFast
  import torch
  
  discriminator = ElectraForPreTraining.from_pretrained("mrm8488/electricidad-base-discriminator")
  tokenizer = ElectraTokenizerFast.from_pretrained("mrm8488/electricidad-base-discriminator")
  
  sentence = "El rápido zorro marrón salta sobre el perro perezoso"
  fake_sentence = "El rápido zorro marrón amar sobre el perro perezoso"
  
  fake_tokens = tokenizer.tokenize(fake_sentence)
  fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
  discriminator_outputs = discriminator(fake_inputs)
  predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
  
  [print("%7s" % token, end="") for token in fake_tokens]
  
  [print("%7s" % prediction, end="") for prediction in predictions.tolist()]
  
  # Output:
  '''
  el rapido  zorro  marro    ##n   amar  sobre     el  perro   pere ##zoso    0.0    0.0    0.0    0.0    0.0    0.0    1.0    1.0    0.0    0.0    0.0    0.0    0.0[None, None, None, None, None, None, None, None, None, None, None, None, None
  '''
  ```
  As you can see there are **1s** in the places where the model detected a fake token. So, it works! 🎉
  
  
  ### Some models fine-tuned on a downstream task 🛠️
  
  [Question Answering](https://huggingface.co/mrm8488/electricidad-base-finetuned-squadv1-es)
  
  [POS](https://huggingface.co/mrm8488/electricidad-base-finetuned-pos)
  
  [NER](https://huggingface.co/mrm8488/electricidad-base-finetuned-ner)
  
  
  ### Spanish LM model comparison 📊
  | Dataset     | Metric   | RoBERTa-b | RoBERTa-l | BETO   | mBERT  | BERTIN | Electricidad-b |
  |-------------|----------|-----------|-----------|--------|--------|--------|---------|
  | UD-POS      | F1       | 0.9907    | 0.9901    | 0.9900 | 0.9886 | 0.9904 | 0.9818  |
  | Conll-NER   | F1       | 0.8851    | 0.8772    | 0.8759 | 0.8691 | 0.8627 | 0.7954  |
  | Capitel-POS | F1       | 0.9846    | 0.9851    | 0.9836 | 0.9839 | 0.9826 | 0.9816  |
  | Capitel-NER | F1       | 0.8959    | 0.8998    | 0.8771 | 0.8810 | 0.8741 | 0.8035  |
  | STS         | Combined | 0.8423    | 0.8420    | 0.8216 | 0.8249 | 0.7822 | 0.8065  |
  | MLDoc       | Accuracy | 0.9595    | 0.9600    | 0.9650 | 0.9560 | 0.9673 | 0.9490  |
  | PAWS-X      | F1       | 0.9035    | 0.9000    | 0.8915 | 0.9020 | 0.8820 | **0.9045**  |
  | XNLI        | Accuracy | 0.8016    | 0.7958    | 0.8130 | 0.7876 | 0.7864 | 0.7878  |
  
  
  
  ## Acknowledgments
  
  I thank [🤗/transformers team](https://github.com/huggingface/transformers) for allowing me to train the model (specially to [Julien Chaumond](https://twitter.com/julien_c)).
  
  
  ## Citation
  If you want to cite this model you can use this:
  
  ```bibtex
  @misc{mromero2020electricidad-base-discriminator,
    title={Spanish Electra by Manuel Romero},
    author={Romero, Manuel},
    publisher={Hugging Face},
    journal={Hugging Face Hub},
    howpublished={\url{https://huggingface.co/mrm8488/electricidad-base-discriminator/}},
    year={2020}
  }
  ```
  
  
  
  > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
  
  > Made with <span style="color: #e25555;">&hearts;</span> in Spain
  