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:
- 51866501c362eabffd2d0faf5fa582a48516e1b8ef4204e8f741397a69e6e5cc
- Size of remote file:
- 433 MB
- SHA256:
- 6dbecf2356292a164e93d4ced55407cb97bccd76ab62b2079835361e5efb7c96
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