Instructions to use kakaobank/kf-deberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kakaobank/kf-deberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kakaobank/kf-deberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kakaobank/kf-deberta-base") model = AutoModelForMaskedLM.from_pretrained("kakaobank/kf-deberta-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a44ffd217da3dacd8c31c5a951d59f3802aa40abab7e8a651491a5f0f76fab33
- Size of remote file:
- 746 MB
- SHA256:
- 3cd6cd7811b3c9190e97cae7eb41571c2bc0076431baae7d41d449a8c1c18c6c
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