Instructions to use beomi/kobert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beomi/kobert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="beomi/kobert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("beomi/kobert") model = AutoModelForMaskedLM.from_pretrained("beomi/kobert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (4a24176fd7c5282ebea07b4291566308bdffab6a) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>a9108e9
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use BERT wordpiece Tokenizer
372ec67 -
initial commit
5399b8e