Instructions to use fondress/PDeepPP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fondress/PDeepPP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="fondress/PDeepPP")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fondress/PDeepPP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dab6c42372f2da177e459b8f1d5eff92b5a44a41ae2e7b4bd44b1123d529437c
- Size of remote file:
- 98.3 MB
- SHA256:
- c8eb0c69a98cc18540aca50dd4884952fbb3af14ba8a7d8a6841b0532a9e4a67
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