readme: ---
  language: ko
  ---
  
  # 📈 Financial Korean ELECTRA model
  
  Pretrained ELECTRA Language Model for Korean (`finance-koelectra-base-discriminator`)
  
  > 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.
  
  More details about ELECTRA can be found in the [ICLR paper](https://openreview.net/forum?id=r1xMH1BtvB)
  or in the [official ELECTRA repository](https://github.com/google-research/electra) on GitHub.
  
  ## Stats
  
  The current version of the model is trained on a financial news data of Naver news.
  
  The final training corpus has a size of 25GB and 2.3B tokens.
  
  This model was trained a cased model on a TITAN RTX for 500k steps.
  
  ## Usage
  
  ```python
  from transformers import ElectraForPreTraining, ElectraTokenizer
  import torch
  discriminator = ElectraForPreTraining.from_pretrained("krevas/finance-koelectra-base-discriminator")
  tokenizer = ElectraTokenizer.from_pretrained("krevas/finance-koelectra-base-discriminator")
  sentence = "내일 해당 종목이 대폭 상승할 것이다"
  fake_sentence = "내일 해당 종목이 맛있게 상승할 것이다"
  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" % int(prediction), end="") for prediction in predictions.tolist()[1:-1]]
  print("fake token : %s" % fake_tokens[predictions.tolist()[1:-1].index(1)])
  ```
  
  # Huggingface model hub
  
  All models are available on the [Huggingface model hub](https://huggingface.co/krevas).
  