Instructions to use JabaleNurAdnan/CS_Checkpoints_Dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use JabaleNurAdnan/CS_Checkpoints_Dataset with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://JabaleNurAdnan/CS_Checkpoints_Dataset") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c2edc2bd7e47a3c9ee1af3002a482e98d6b4e4c18538bba1ce4ba8871ed0e84
- Size of remote file:
- 6.18 MB
- SHA256:
- 5fe20a341065d0da1eca99223323449a8ba37c035e8c85f2ed7469af35150bc6
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