README.md

---

license: mit
language: en
datasets:
- amananandrai/clickbait-dataset
metrics:
- accuracy
tags:
- sklearn
- text-classification
- clickbait
widget:
- text: "You Won't Believe What Happens Next!"
  example_title: "Clickbait Example"
- text: "Scientists Discover New Planet in Solar System"
  example_title: "Non-Clickbait Example"
---


# Clickbait Detection Model (Logistic Regression)

هذا نموذج تعلم آلة (Scikit-learn Pipeline) تم تدريبه لتصنيف عناوين الأخبار (Headlines) إلى "Clickbait" (عنوان مثير) أو "Not Clickbait" (عنوان عادي).

## 🚀 كيف تستخدم النموذج

تم حفظ النموذج كـ `Pipeline` كامل من `sklearn`، وهو يتضمن `TfidfVectorizer` و `LogisticRegression`. هذا يعني أنه يتعامل مع النص مباشرة.

```python

import joblib



# قم بتحميل النموذج من Hugging Face Hub

# (تأكد من تثبيت huggingface_hub: pip install huggingface_hub)

from huggingface_hub import hf_hub_download



model_path = hf_hub_download(repo_id="[Ma120]/[clickbait-detector]", filename="clickbait_model.pkl")

model = joblib.load(model_path)



# اختبر النموذج

headlines = [

    "You Won't Believe What Happens Next!",

    "Local Library Announces Summer Reading Program",

    "10 Signs You're a Genius (Number 7 Will Shock You)",

    "Government Passes New Budget Bill"

]



predictions = model.predict(headlines)



# 1 = Clickbait, 0 = Not Clickbait

for headline, pred in zip(headlines, predictions):

    label = "Clickbait" if pred == 1 else "Not Clickbait"

    print(f"[{label}] {headline}")



# يمكنك أيضاً الحصول على الاحتمالات

# probabilities = model.predict_proba(headlines)

# print(probabilities)