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** is a small 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 ⚙
  
  |Param| # Value|
  |-----|--------|
  |Layers|\t12   |
  |Hidden |256 \t|
  |Params| 14M|
  
  ## Evaluation metrics (for discriminator) 🧾
  
  |Metric | # Score |
  |-------|---------|
  |Accuracy| 0.94|
  |Precision| 0.76|
  |AUC | 0.92|
  
  ## Benchmarks 🔨
  
  WIP 🚧
  
  ## How to use the discriminator in `transformers`
  
  ```python
  from transformers import ElectraForPreTraining, ElectraTokenizerFast
  import torch
  
  discriminator = ElectraForPreTraining.from_pretrained("mrm8488/electricidad-small-discriminator")
  tokenizer = ElectraTokenizerFast.from_pretrained("mrm8488/electricidad-small-discriminator")
  
  sentence = "el zorro rojo es muy rápido"
  fake_sentence = "el zorro rojo es muy ser"
  
  fake_tokens = tokenizer.tokenize(sentence)
  fake_inputs = tokenizer.encode(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" % int(prediction), end="") for prediction in predictions.tolist()[1:-1]]
  
  # Output:
  '''
  el  zorro   rojo     es    muy    ser      0      0      0      0      0      1[None, None, None, None, None, None]
  '''
  ```
  
  As you can see there is a **1** in the place where the model detected the fake token (**ser**). So, it works! 🎉
  
  
  [Electricidad-small fine-tuned models](https://huggingface.co/models?search=electricidad-small)
  
  
  ## Acknowledgments
  
  I thank [🤗/transformers team](https://github.com/huggingface/transformers) for answering my doubts and Google for helping me with the [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc) program.
  
  ## Citation
  If you want to cite this model you can use this:
  
  ```bibtex
  @misc{mromero2020electricidad-small-discriminator,
    title={Spanish Electra (small) by Manuel Romero},
    author={Romero, Manuel},
    publisher={Hugging Face},
    journal={Hugging Face Hub},
    howpublished={\url{https://huggingface.co/mrm8488/electricidad-small-discriminator}},
    year={2020}
  }
  ```
  
  
  
  > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
  
  > Made with <span style="color: #e25555;">&hearts;</span> in Spain
  