 Example using ElectraForPreTraining  ```python import torch from transformers import ElectraForPreTraining, ElectraTokenizer  discriminator = ElectraForPreTraining.from_pretrained("monologg/koelectra-base-v3-discriminator") tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-v3-discriminator")  sentence = "나는 방금 밥을 먹었다." fake_sentence = "나는 내일 밥을 먹었다."  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(list(zip(fake_tokens, predictions.tolist()[1:-1]))) ``` 