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fill-mask mask_token: [MASK]
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krevas/finance-koelectra-small-generator krevas/finance-koelectra-small-generator
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Contributed by

krevas Wonchul Kim
6 models

How to use this model directly from the πŸ€—/transformers library:

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from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("krevas/finance-koelectra-small-generator") model = AutoModelWithLMHead.from_pretrained("krevas/finance-koelectra-small-generator")

πŸ“ˆ 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 ν•΄λ‹Ή μ’…λͺ©μ΄ λŒ€ν­ {fill_mask.tokenizer.mask_token}ν•  것이닀."))

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