Instructions to use keras-io/MPNN-for-molecular-property-prediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use keras-io/MPNN-for-molecular-property-prediction 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("keras-io/MPNN-for-molecular-property-prediction") - Notebooks
- Google Colab
- Kaggle
Download variables/variables.data-00000-of-00001 from keras-io/MPNN-for-molecular-property-prediction: direct link, hf CLI and curl.
- Browser
- Download file 1.18 MB
-
https://huggingface.co/keras-io/MPNN-for-molecular-property-prediction/resolve/main/variables/variables.data-00000-of-00001
- Command line
-
hf download hf://keras-io/MPNN-for-molecular-property-prediction/variables/variables.data-00000-of-00001
-
curl -L -o variables.data-00000-of-00001 https://huggingface.co/keras-io/MPNN-for-molecular-property-prediction/resolve/main/variables/variables.data-00000-of-00001
1.18 MB
- Xet hash:
- 5e6c5176f5fa5cdd9763c81803d8dee5b3429588a4506ce09b86d0fcdafef5d1
- Size of remote file:
- 1.18 MB
- SHA256:
- c64c65a5f81a9165995289576561a42c9ef17343cef65a218462f1b3025b23d1
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