Text Classification
Scikit-learn
Joblib
Indonesian
English
phishing
tf-idf
logistic-regression
multilingual
security
Instructions to use Febriyansyah/phishing-email-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use Febriyansyah/phishing-email-classifier with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Febriyansyah/phishing-email-classifier", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
File size: 680 Bytes
31dea96 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"blueprint": "TF-IDF(char_wb,(2,4)) + LogisticRegression(L2)",
"origin": "/run/media/bugs/Data/Febriyansyah-Other/Project/github-repo/huggingface-workspace/data/phishing_email/phishing_email_multilingual.csv",
"records": 600,
"flagged_true": 300,
"flag_free": 300,
"keepaside": {
"rate": 0.336,
"salt": 424242
},
"partitions": {
"learning": 398,
"auditing": 202
},
"benchmarks": {
"precision_recall_area": null,
"correct_rate": 1.0,
"harmonic_f1": 1.0
},
"mistake_grid": [
[
93,
0
],
[
0,
109
]
],
"go_noGo_at": 0.488,
"guardrail": "edu-defense-only",
"credit": "Febriyansyah"
} |