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
| { | |
| "tag": "UNSW_NB15__BiLSTM__FedAvg__iid", | |
| "dataset": "UNSW_NB15", | |
| "model": "BiLSTM", | |
| "fl_algo": "FedAvg", | |
| "partition": "iid", | |
| "n_clients": 30, | |
| "n_rounds": 30, | |
| "max_cap": 2000, | |
| "total_time_s": 914.4, | |
| "model_params": 79233, | |
| "model_mb": 0.3169, | |
| "final": { | |
| "accuracy": 0.900067915009217, | |
| "precision": 0.9294037647204277, | |
| "recall": 0.9129801123424928, | |
| "f1_binary": 0.9211187354490871, | |
| "f1_macro": 0.8924052202469086, | |
| "f1_weighted": 0.9003921852303807, | |
| "fpr": 0.12279569892473119, | |
| "auc_roc": 0.9677508843584055, | |
| "threshold": 0.41568511724472046, | |
| "round": 30, | |
| "comm_mb": 0, | |
| "round_time_s": 0 | |
| } | |
| } |