Instructions to use SAVSNET/DogBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SAVSNET/DogBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SAVSNET/DogBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SAVSNET/DogBERT") model = AutoModelForMaskedLM.from_pretrained("SAVSNET/DogBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7ab49b08d0dccdc9d5a96f9cf63e90c6db4e616a21684e9201adf16869c8ee4
- Size of remote file:
- 438 MB
- SHA256:
- 26229613d87276e3ada9c7bab2e72cb253378a6e5e55e775a76920fd8771733f
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