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:
- 51034e70c264c088d9964c1e49f275b981a9d86a60533d2e0223124ad3e787b8
- Size of remote file:
- 98.3 MB
- SHA256:
- dffcf31269b20335aa7f78f1c60d24226110100c6838ac291a6c9f4204b99f9c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.