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
| { | |
| "name": "Edge_IIoTset", | |
| "n_train": 400000, | |
| "n_test": 100000, | |
| "n_features": 21, | |
| "features": [ | |
| "tcp.dstport", | |
| "tcp.srcport", | |
| "tcp.ack", | |
| "tcp.seq", | |
| "tcp.len", | |
| "tcp.ack_raw", | |
| "mqtt.msgtype", | |
| "mqtt.len", | |
| "tcp.connection.syn", | |
| "tcp.connection.rst", | |
| "mqtt.conflag.cleansess", | |
| "tcp.connection.fin", | |
| "mqtt.topic_len", | |
| "mqtt.ver", | |
| "mqtt.proto_len", | |
| "tcp.connection.synack", | |
| "tcp.flags.ack", | |
| "arp.opcode", | |
| "http.content_length", | |
| "http.response", | |
| "arp.hw.size" | |
| ], | |
| "n_clients": 30, | |
| "alpha": 0.5 | |
| } |