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:
- 81c4d9740a37974b87066c0883287d2e9e5293968198de4c54dfbf96252278d0
- Size of remote file:
- 627 Bytes
- SHA256:
- 7ce0d0f0f57fcbf2e7eb34ce1cf7fb652f2a765e585241beb37be82cd9eff654
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