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/metadata.json from JabaleNurAdnan/CS_Checkpoints_Dataset: direct link, hf CLI and curl.
- Browser
- Download file 585 Bytes
-
https://huggingface.co/JabaleNurAdnan/CS_Checkpoints_Dataset/resolve/main/UNSW_NB15/metadata.json
- Command line
-
hf download hf://JabaleNurAdnan/CS_Checkpoints_Dataset/UNSW_NB15/metadata.json
-
curl -L -o metadata.json https://huggingface.co/JabaleNurAdnan/CS_Checkpoints_Dataset/resolve/main/UNSW_NB15/metadata.json
585 Bytes
| { | |
| "name": "UNSW_NB15", | |
| "n_train": 206138, | |
| "n_test": 51535, | |
| "n_features": 30, | |
| "features": [ | |
| "sbytes", | |
| "id", | |
| "smean", | |
| "sload", | |
| "dbytes", | |
| "ct_state_ttl", | |
| "sttl", | |
| "dttl", | |
| "rate", | |
| "dur", | |
| "dmean", | |
| "dinpkt", | |
| "dload", | |
| "dpkts", | |
| "sinpkt", | |
| "tcprtt", | |
| "synack", | |
| "ackdat", | |
| "sjit", | |
| "spkts", | |
| "ct_dst_sport_ltm", | |
| "djit", | |
| "dloss", | |
| "sloss", | |
| "ct_srv_dst", | |
| "ct_src_dport_ltm", | |
| "ct_dst_ltm", | |
| "ct_srv_src", | |
| "ct_dst_src_ltm", | |
| "ct_src_ltm" | |
| ], | |
| "n_clients": 30, | |
| "alpha": 0.5 | |
| } |