Instructions to use beomi/kcbert-large-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beomi/kcbert-large-dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="beomi/kcbert-large-dev")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("beomi/kcbert-large-dev") model = AutoModelForMaskedLM.from_pretrained("beomi/kcbert-large-dev") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6ed7aa3deb3190f0f00e06ce54919bd91bec80522247e57f98bdd986ac6ca5b
- Size of remote file:
- 1.34 GB
- SHA256:
- be037e638f83d2fa13ec2ed1afef7b71b83252823c907ce1d342c755d84a25f5
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