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:
- 68aa3ca0b393771ee89d5c7153a5b4a339988f900ccf6f6b50f8f0c39e1b809e
- Size of remote file:
- 98.3 MB
- SHA256:
- cb9f785cd9084a0233cb8ef027de07dee652c68a925535b68e343c14c6a31aa9
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