Instructions to use kykim/bert-kor-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kykim/bert-kor-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kykim/bert-kor-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kykim/bert-kor-base") model = AutoModelForMaskedLM.from_pretrained("kykim/bert-kor-base") - Inference
- Notebooks
- Google Colab
- Kaggle
| language: ko | |
| # Bert 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](https://github.com/kiyoungkim1/LM-kor) | |
| ```python | |
| from transformers import BertTokenizerFast, BertModel | |
| tokenizer_bert = BertTokenizerFast.from_pretrained("kykim/bert-kor-base") | |
| model_bert = BertModel.from_pretrained("kykim/bert-kor-base") | |
| ``` |