# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("kykim/albert-kor-base")
model = AutoModelForMaskedLM.from_pretrained("kykim/albert-kor-base")Quick Links
Albert base model for Korean
- 70GB Korean text dataset and 42000 lower-cased subwords are used
- Check the model performance and other language models for Korean in github
from transformers import BertTokenizerFast, AlbertModel
tokenizer_albert = BertTokenizerFast.from_pretrained("kykim/albert-kor-base")
model_albert = AlbertModel.from_pretrained("kykim/albert-kor-base")
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kykim/albert-kor-base")