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language: es |
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thumbnail: https://i.imgur.com/uxAvBfh.png |
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## ELECTRICIDAD: The Spanish Electra [Imgur](https://imgur.com/uxAvBfh) |
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**Electricidad-base-discriminator** (uncased) is a ```base``` Electra like model (discriminator in this case) trained on a + 20 GB of the [OSCAR](https://oscar-corpus.com/) Spanish corpus. |
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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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## Model details ⚙ |
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|Name| # Value| |
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|-----|--------| |
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|Layers| 12 | |
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|Hidden |768 | |
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|Params| 110M| |
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## Evaluation metrics (for discriminator) 🧾 |
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|Metric | # Score | |
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|-------|---------| |
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|Accuracy| 0.985| |
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|Precision| 0.726| |
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|AUC | 0.922| |
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## Fast example of usage 🚀 |
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```python |
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from transformers import ElectraForPreTraining, ElectraTokenizerFast |
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import torch |
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discriminator = ElectraForPreTraining.from_pretrained("/content/electricidad-base-discriminator") |
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tokenizer = ElectraTokenizerFast.from_pretrained("/content/electricidad-base-discriminator") |
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sentence = "El rápido zorro marrón salta sobre el perro perezoso" |
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fake_sentence = "El rápido zorro marrón amar sobre el perro perezoso" |
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fake_tokens = tokenizer.tokenize(fake_sentence) |
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fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt") |
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discriminator_outputs = discriminator(fake_inputs) |
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predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2) |
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[print("%7s" % token, end="") for token in fake_tokens] |
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[print("%7s" % prediction, end="") for prediction in predictions.tolist()] |
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# Output: |
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''' |
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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 |
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''' |
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``` |
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As you can see there are **1s** in the places where the model detected a fake token. So, it works! 🎉 |
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### Some models fine-tuned on a downstream task 🛠️ |
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[Question Answering](https://huggingface.co/mrm8488/electricidad-base-finetuned-squadv1-es) |
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[POS](https://huggingface.co/mrm8488/electricidad-base-finetuned-pos) |
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[NER](https://huggingface.co/mrm8488/electricidad-base-finetuned-ner) |
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[Paraphrase Identification](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-pawsx-es) |
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## Acknowledgments |
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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)). |
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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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