From a8b798e6c437980e933c9e06665f2d55cab64edc Mon Sep 17 00:00:00 2001 From: Manuel Romero Date: Fri, 8 May 2020 15:30:15 +0200 Subject: [PATCH] Model card for spanish electra small (#4196) --- .../README.md | 67 +++++++++++++++++++ 1 file changed, 67 insertions(+) create mode 100644 model_cards/mrm8488/electricidad-small-discriminator/README.md diff --git a/model_cards/mrm8488/electricidad-small-discriminator/README.md b/model_cards/mrm8488/electricidad-small-discriminator/README.md new file mode 100644 index 0000000000..2cb828d7ee --- /dev/null +++ b/model_cards/mrm8488/electricidad-small-discriminator/README.md @@ -0,0 +1,67 @@ +--- +language: spanish +thumbnail: https://i.imgur.com/uxAvBfh.png + + +--- + +## ELECTRICIDAD: The Spanish Electra [Imgur](https://imgur.com/uxAvBfh) + +**ELECTRICIDAD** is a small 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 ⚙ + +|Param| # Value| +|-----|--------| +|Layers| 12 | +|Hidden |256 | +|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 rápido zorro marrón salta sobre el perro perezoso" +fake_sentence = "El rápido zorro marrón falsea sobre el perro perezoso" + +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" % prediction, end="") for prediction in predictions.tolist()] +``` + +## 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. + + + +> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) + +> Made with ♥ in Spain