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 CICIDS2017/X_train.npy from JabaleNurAdnan/CS_Checkpoints_Dataset: direct link, hf CLI and curl.
- Browser
- Download file 272 MB
-
https://huggingface.co/JabaleNurAdnan/CS_Checkpoints_Dataset/resolve/main/CICIDS2017/X_train.npy
- Command line
-
hf download hf://JabaleNurAdnan/CS_Checkpoints_Dataset/CICIDS2017/X_train.npy
-
curl -L -o X_train.npy https://huggingface.co/JabaleNurAdnan/CS_Checkpoints_Dataset/resolve/main/CICIDS2017/X_train.npy
272 MB
- Xet hash:
- f66b004dd341146892e06e450bfe4f0179369c7777422512820173b837d423d6
- Size of remote file:
- 272 MB
- SHA256:
- 6b0211f9b749c5a256251cdfd4cfb39a6c8f3e0cb7c3ed1dad941a365c908aef
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