From efc7460553871cad22921bea39c9e942fa4d773f Mon Sep 17 00:00:00 2001 From: Manuel Romero Date: Fri, 21 Aug 2020 11:04:29 +0200 Subject: [PATCH] model card for Spanish electra base (#6633) --- .../electricidad-base-discriminator/README.md | 73 +++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 model_cards/mrm8488/electricidad-base-discriminator/README.md diff --git a/model_cards/mrm8488/electricidad-base-discriminator/README.md b/model_cards/mrm8488/electricidad-base-discriminator/README.md new file mode 100644 index 0000000000..ffaf65b631 --- /dev/null +++ b/model_cards/mrm8488/electricidad-base-discriminator/README.md @@ -0,0 +1,73 @@ +--- +language: es +thumbnail: https://i.imgur.com/uxAvBfh.png + + +--- + +## 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 + 20 GB of the [OSCAR](https://oscar-corpus.com/) Spanish 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("/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! 🎉 + +## 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)). + + + +> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) + +> Made with ♥ in Spain