---
language:
- ar
license: mit
tags:
- text-classification
- naive-bayes
- tf-idf
- arabic-nlp
- spam-detection
pipeline_tag: text-classification
metrics:
- accuracy
model-index:
- name: Spam Detection — Arabic (Naive Bayes)
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Arabic Spam Dataset
      type: custom
    metrics:
    - type: accuracy
      value: 0.976
      name: Accuracy
---

# Spam Detection — Arabic (Naive Bayes)
A spam/ham text classifier for Arabic messages, built with a custom Arabic-aware preprocessing pipeline (tatweel/tashkeel stripping, tokenization, stopword removal) and TF-IDF features feeding into a Multinomial Naive Bayes classifier.

## Model Details
* **Architecture:** TF-IDF + Multinomial Naive Bayes (scikit-learn Pipeline)
* **Preprocessing:** Custom transformer — hashtag/punctuation removal, tatweel (تطويل) and tashkeel (تشكيل) stripping via pyarabic, tokenization, Arabic stopword removal
* **Hyperparameters:** Tuned via GridSearchCV (alpha smoothing)
* **Accuracy:** 97.6% on held-out test set

## Intended Use
Binary spam classification for Arabic text messages/emails. Part of a multilingual spam detection system that automatically routes text to a language-specific model (English or Arabic) based on detected language (via `langdetect`).

## How to Use
```python
import joblib

model = joblib.load("spam_ar_nb.joblib") 
prediction = model.predict(["مبروك! لقد ربحت جائزة مجانية، اضغط هنا الآن"]) 
print(prediction) # 1 = spam, 0 = ham