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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6c61936c42d41ec373a91f3332559806b7134005d3096ad8e5a1fe2aa974c5b
- Size of remote file:
- 433 MB
- SHA256:
- a9ccc2ee0f907852571306d44b8de90d98716d9cb15a125e966bc835890dd3fa
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