Instructions to use SAVSNET/PetBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SAVSNET/PetBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SAVSNET/PetBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SAVSNET/PetBERT") model = AutoModelForMaskedLM.from_pretrained("SAVSNET/PetBERT") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30376eddeb3a3ee53f0144cdf61fab9fb1b9d9d251c508041752d76ecc04077e
- Size of remote file:
- 867 MB
- SHA256:
- 89b6f007b46e7165325ed415fe616a29150b674034378bf802acbcfe96d7c6ad
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