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
feat: initial professional model — TF-IDF char_wb + LogReg (ID/EN), synthetic edu-only
31dea96 verified Download metadata.json from Febriyansyah/phishing-email-classifier: direct link, hf CLI and curl.
- Browser
- Download file 680 Bytes
-
https://huggingface.co/Febriyansyah/phishing-email-classifier/resolve/main/metadata.json
- Command line
-
hf download hf://Febriyansyah/phishing-email-classifier/metadata.json
-
curl -L -o metadata.json https://huggingface.co/Febriyansyah/phishing-email-classifier/resolve/main/metadata.json
680 Bytes
| { | |
| "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" | |
| } |