Back to all models
fill-mask mask_token: [MASK]
Query this model
πŸ”₯ This model is currently loaded and running on the Inference API. ⚠️ This model could not be loaded by the inference API. ⚠️ This model can be loaded on the Inference API on-demand.
JSON Output
API endpoint  

⚑️ Upgrade your account to access the Inference API

Share Copied link to clipboard

Monthly model downloads

krevas/finance-koelectra-base-generator krevas/finance-koelectra-base-generator
29 downloads
last 30 days

pytorch

tf

Contributed by

krevas Wonchul Kim
6 models

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

			
Copy to clipboard
from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("krevas/finance-koelectra-base-generator") model = AutoModelForMaskedLM.from_pretrained("krevas/finance-koelectra-base-generator")

πŸ“ˆ 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 or in the official ELECTRA repository 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

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

Huggingface model hub

All models are available on the Huggingface model hub.