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:
- af698a5cffc71797d079af10b9a4949ea55b9eca9d367899fc7065fd888284e9
- Size of remote file:
- 482 kB
- SHA256:
- f89106945a40a0fda223d16ea8df4aec663aedf1eaf874155ef745ea5e6e76f0
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