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
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README.md
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pmid = {37865683},
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keywords = {Data mining, Machine learning}
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}
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```
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pmid = {37865683},
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keywords = {Data mining, Machine learning}
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}
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```
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## Acknowledgements
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This work would not have been possible without the contribution of practicing vets across the UK contributing to the SAVSNET project and without the help and support of the SAVSNET core team comprising Bethaney Brant, Steve Smyth and Gina Pinchbeck.
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