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:
- 7ed409e2ca045e5f61b73fa19c2e2efbedf1251f6a454db88435538c8b93ca55
- Size of remote file:
- 98.3 MB
- SHA256:
- c08029359d9ffa9d234dbc7b86e082ac442750472748be1e3074792062249443
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.