Instructions to use beomi/kcbert-base-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beomi/kcbert-base-dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="beomi/kcbert-base-dev")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("beomi/kcbert-base-dev") model = AutoModelForMaskedLM.from_pretrained("beomi/kcbert-base-dev") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b4a1b93e9d6236c337db3844dc49859a1c65b19a61fc1ca0227f4dfddfa391e
- Size of remote file:
- 436 MB
- SHA256:
- 8c417a8a73432166561f641fd74f0e78a3f772ee56acddb36fd84554736af85d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.