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Modify example of usage (#4413)

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I followed the google example of usage for its electra small model but i have seen it is not meaningful, so i created a better example
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Manuel Romero 2020-05-19 00:17:33 +02:00 committed by GitHub
parent 9ddd3a6548
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1 changed files with 10 additions and 3 deletions
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model_cards/mrm8488/electricidad-small-discriminator/README.md
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@ -43,8 +43,8 @@ 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"
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")
@ -53,9 +53,16 @@ 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()]
[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! 🎉
## 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.