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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a9bed5bae2984a34d2e5d4ecbb21f60db925e8e8405249c2cabca499f31522c
- Size of remote file:
- 14.6 kB
- SHA256:
- 1dee54a6405c581a2d898515cb9ac59dd6ccd12951e29b04d0197d6efe4760ee
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