Instructions to use beomi/KcRoBERTa-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beomi/KcRoBERTa-dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="beomi/KcRoBERTa-dev")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("beomi/KcRoBERTa-dev") model = AutoModelForMaskedLM.from_pretrained("beomi/KcRoBERTa-dev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b6ec2fdba680ec4e509b32d106916d5d906ee2b536cf923af3c2c235b6d810a
- Size of remote file:
- 436 MB
- SHA256:
- 665318449bf8ae09c1e5c544c217888114ff6d203a13ab8e732e050d6b24adaa
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