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:
- 470f623de464ed65146de82d5087c8b0d00f47087cccd6f34e63640d29efab00
- Size of remote file:
- 433 MB
- SHA256:
- b1d4dfd81e37fa6aeca818a95b42412fde479507812059f4e1291b33630f0c9e
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