Update README.md

README.md

@@ -1,35 +1,47 @@
-# Spam Detection — Arabic (Naive Bayes)
+---
+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
+---
 
-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.
+# 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
+* **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`).
+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
+```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
-\```
-
-## Limitations
-- Arabic NLP tooling is less mature than English; preprocessing uses 
-  stemming/normalization rather than full lemmatization
-- Trained on a specific dataset distribution; performance may vary on 
-  dialectal Arabic vs. Modern Standard Arabic
-- Naive Bayes assumes word independence — does not capture context or word order
+model = joblib.load("spam_ar_nb.joblib") 
+prediction = model.predict(["مبروك! لقد ربحت جائزة مجانية، اضغط هنا الآن"]) 
+print(prediction) # 1 = spam, 0 = ham