Instructions to use fav-kky/FERNET-C5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fav-kky/FERNET-C5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="fav-kky/FERNET-C5")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("fav-kky/FERNET-C5") model = AutoModelForMaskedLM.from_pretrained("fav-kky/FERNET-C5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c11070121d9fd8b51d2282a889c2f20e1e1ed96e6b7b56e08f89733f3a5d58ea
- Size of remote file:
- 654 MB
- SHA256:
- a8ed80e2cdcc4af0ae1f27c87fa4796a243794bf22e8a56f9d2de8a96d85c8c2
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