language: es
thumbnail: https://i.imgur.com/uxAvBfh.png
ELECTRICIDAD: The Spanish Electra Imgur
Electricidad-base-discriminator (uncased) is a base Electra like model (discriminator in this case) trained on a + 20 GB of the OSCAR Spanish corpus.
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.
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 🚀
from transformers import ElectraForPreTraining, ElectraTokenizerFast
import torch
discriminator = ElectraForPreTraining.from_pretrained("/content/electricidad-base-discriminator")
tokenizer = ElectraTokenizerFast.from_pretrained("/content/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 🛠️
Acknowledgments
I thank 🤗/transformers team for allowing me to train the model (specially to Julien Chaumond).
Created by Manuel Romero/@mrm8488
Made with ♥ in Spain