Instructions to use monologg/kobert-lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use monologg/kobert-lm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="monologg/kobert-lm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("monologg/kobert-lm") model = AutoModelForMaskedLM.from_pretrained("monologg/kobert-lm") - Notebooks
- Google Colab
- Kaggle
KoBERT-LM
- Further pretrained model for re-training LM Mask Head
How to use
If you want to import KoBERT tokenizer with
AutoTokenizer, you should givetrust_remote_code=True.
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("monologg/kobert-lm")
tokenizer = AutoTokenizer.from_pretrained("monologg/kobert-lm", trust_remote_code=True)
Reference
- Downloads last month
- 26