metadata
license: mit
tags:
- spam
- text-classification
- scikit-learn
- tfidf
- spaCy
- logistic-regression
language: en
datasets: custom
model-index:
- name: Spam Classifier (Scikit-learn + spaCy)
results: []
📧 Spam Classifier (Scikit-learn + spaCy)
This model classifies messages as spam or ham using traditional NLP techniques.
🧠 Model Details
- Preprocessing: Tokenization + Lemmatization using spaCy
- Vectorization: TF-IDF (1-2 grams)
- Feature Selection: Chi2 with top 1000 features
- Model: Logistic Regression (
class_weight="balanced",max_iter=1000) - Performance: ~87% accuracy on balanced test set (800 spam, 800 ham)
📦 Files
spam_classifier_bundle.joblib: Includes trained model, TF-IDF vectorizer, label encoder, and feature selector
📥 Load Model (Example)
from huggingface_hub import hf_hub_download
import joblib
bundle = joblib.load(hf_hub_download("mageshcruz/spam-classifier-scikit", "spam_classifier.joblib"))
model = bundle["model"]
vector = bundle["vectorizer"]
selector = bundle["selector"]
le = bundle["label_encoder"]