📈 Financial Korean ELECTRA model

Pretrained ELECTRA Language Model for Korean (finance-koelectra-small-generator)

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 or in the official ELECTRA repository on GitHub.


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.


from transformers import pipeline

fill_mask = pipeline(

print(fill_mask(f"내일 해당 종목이 대폭 {fill_mask.tokenizer.mask_token}할 것이다."))

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Fill Mask

Mask token: [MASK]