Instructions to use varungupta0994/NBPDCL_Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use varungupta0994/NBPDCL_Models with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://varungupta0994/NBPDCL_Models") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 644f8fdc99eef4ba1935441f8612a29e6b6cbfda21923f3e300b70f9bce060e8
- Size of remote file:
- 22.8 MB
- SHA256:
- aceef254f5a3ee2ebbb326531caca1658b9ee6809c257442ff832a9174a5aad2
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