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:
- bcc807af6975dbd41de8711167a7224139181757a6db699b3a62da0730b22f34
- Size of remote file:
- 961 MB
- SHA256:
- 36576955057d15a6d8acefb207b1ab2b376df056c706db5760419fa500b1c017
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