Instructions to use SBB/eynollah-page-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use SBB/eynollah-page-extraction with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy), and from_pretrained_keras was removed in huggingface_hub 1.0. # See https://github.com/keras-team/tf-keras for more details. # !pip install "huggingface_hub<1.0" tf_keras from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("SBB/eynollah-page-extraction") - Notebooks
- Google Colab
- Kaggle
Download saved_model.pb from SBB/eynollah-page-extraction: direct link, hf CLI and curl.
- Browser
- Download file 4.42 MB
-
https://huggingface.co/SBB/eynollah-page-extraction/resolve/main/saved_model.pb
- Command line
-
hf download hf://SBB/eynollah-page-extraction/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/SBB/eynollah-page-extraction/resolve/main/saved_model.pb
4.42 MB
- Xet hash:
- 2947904036a708ca4c810c7b5bb40adb5274e68899613f07fdc425585e802769
- Size of remote file:
- 4.42 MB
- SHA256:
- 6a9639d6f77afec409d0fdb18f41ab3978ff1686eae10a0ce262ebfbd9f689a0
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