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:
- 6cd4ec375b0390847677026e21034cf696ff51fe3e6cb28a0fa73e9bf099e1a6
- Size of remote file:
- 3.39 kB
- SHA256:
- 487d4aa5bcb167e3a835a8a92268ec0c5908d74e0197248f14e53429e50712c8
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