Text Classification
Scikit-learn
Joblib
Keras
English
cybersecurity
http-attack-detection
intrusion-detection
web-security
tfidf
xgboost
lightgbm
Instructions to use cycloevan/http-attack-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use cycloevan/http-attack-classification with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("cycloevan/http-attack-classification", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Keras
How to use cycloevan/http-attack-classification with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://cycloevan/http-attack-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec93659cd1b04d18fca19ebae0777e19a60be281ab1844cdb1bdd1c8ef1d767c
- Size of remote file:
- 17.1 MB
- SHA256:
- 6aaebbf1eaf85d1f7c2bf98cda725c5f28acb91d159fc14c8a730d7ca10c1317
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