| language: ko | |
| # ๐ Financial Korean ELECTRA model | |
| Pretrained ELECTRA Language Model for Korean (`finance-koelectra-base-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](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 pipeline | |
| fill_mask = pipeline( | |
| "fill-mask", | |
| model="krevas/finance-koelectra-base-generator", | |
| tokenizer="krevas/finance-koelectra-base-generator" | |
| ) | |
| print(fill_mask(f"๋ด์ผ ํด๋น ์ข ๋ชฉ์ด ๋ํญ {fill_mask.tokenizer.mask_token}ํ ๊ฒ์ด๋ค.")) | |
| ``` | |
| # Huggingface model hub | |
| All models are available on the [Huggingface model hub](https://huggingface.co/krevas). | |