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:
# !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://cycloevan/http-attack-classification") - Notebooks
- Google Colab
- Kaggle
Download catboost.joblib from cycloevan/http-attack-classification: direct link, hf CLI and curl.
- Browser
- Download file 3.95 MB
-
https://huggingface.co/cycloevan/http-attack-classification/resolve/main/catboost.joblib
- Command line
-
hf download hf://cycloevan/http-attack-classification/catboost.joblib
-
curl -L -o catboost.joblib https://huggingface.co/cycloevan/http-attack-classification/resolve/main/catboost.joblib
3.95 MB
- Xet hash:
- 116f84febf3cf30071259f1dba95c465cdd90c65c69a188efaeb5bd13329eb77
- Size of remote file:
- 3.95 MB
- SHA256:
- f0f127d164756f6c9621f3ce165eaca26006ca945d782d340a7c1fb6f4077065
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.