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:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://JabaleNurAdnan/CS_Checkpoints_Dataset") - Notebooks
- Google Colab
- Kaggle
Download UNSW_NB15/X_train.npy from JabaleNurAdnan/CS_Checkpoints_Dataset: direct link, hf CLI and curl.
- Browser
- Download file 24.7 MB
-
https://huggingface.co/JabaleNurAdnan/CS_Checkpoints_Dataset/resolve/main/UNSW_NB15/X_train.npy
- Command line
-
hf download hf://JabaleNurAdnan/CS_Checkpoints_Dataset/UNSW_NB15/X_train.npy
-
curl -L -o X_train.npy https://huggingface.co/JabaleNurAdnan/CS_Checkpoints_Dataset/resolve/main/UNSW_NB15/X_train.npy
24.7 MB
- Xet hash:
- 8f739cb4e713a1c917acbf94f1101491d0cd63dbb5560290527bd2104489aec3
- Size of remote file:
- 24.7 MB
- SHA256:
- 4bd5d8781b98a69622cacc84791c2c646a172238e78f20d6d9f0cadd1ba4b1f3
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