Instructions to use fav-kky/FERNET-CC_sk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fav-kky/FERNET-CC_sk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="fav-kky/FERNET-CC_sk")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("fav-kky/FERNET-CC_sk") model = AutoModelForMaskedLM.from_pretrained("fav-kky/FERNET-CC_sk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 769e88844c8afe02dc2c1e6afa5a634c8b3adf916f4ce27fee22683c4fb0c893
- Size of remote file:
- 654 MB
- SHA256:
- b0c293f3d9f77399ea33431cebf1b34699daf4c841aa81c6c4b811bb9b07eb19
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