Update README.md

README.md

@@ -1,52 +1,80 @@
 ---
 license: apache-2.0
 datasets:
-- hadyelsahar/ar_res_reviews
+  - hadyelsahar/ar_res_reviews
 language:
-- ar
+  - ar
 metrics:
-- accuracy
-- precision
-- recall
-- f1
-base_model:
-- aubmindlab/bert-base-arabertv02
+  - accuracy
+  - precision
+  - recall
+  - f1
+base_model: aubmindlab/bert-base-arabertv02
 pipeline_tag: text-classification
+tags:
+  - text-classification
+  - sentiment-analysis
+  - arabic
+  - restaurant-reviews
+model-index:
+  - name: ArabReview-Sentiment
+    results:
+      - task:
+          type: text-classification
+        dataset:
+          name: hadyelsahar/ar_res_reviews
+          type: sentiment-analysis
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 86.41
+          - name: Precision
+            type: precision
+            value: 87.01
+          - name: Recall
+            type: recall
+            value: 86.49
+          - name: F1 Score
+            type: f1
+            value: 86.75
 ---
 
 # 🍽️ Arabic Restaurant Review Sentiment Analysis 🚀
 ## 📌 Overview  
-This project fine-tunes a **transformer-based model** to analyze sentiment in **Arabic restaurant reviews**.  
-We utilized **Hugging Face’s model training pipeline** and deployed the final model as an **interactive Gradio web app**.
+This project fine-tunes **AraBERT** to analyze sentiment in **Arabic restaurant reviews**.  
+We leveraged **Hugging Face’s `transformers` library** for training and deployed the model as an **interactive pipeline**.
 
-## 📥 Data Collection  
-The dataset used for fine-tuning was sourced from **Hugging Face Datasets**, specifically:  
+## 📥 Dataset  
+The dataset used for fine-tuning is from:  
 [📂 Arabic Restaurant Reviews Dataset](https://huggingface.co/datasets/hadyelsahar/ar_res_reviews)  
-It contains **restaurant reviews in Arabic** labeled with sentiment polarity.
+It contains restaurant reviews labeled as **Positive** or **Negative**.
 
-## 🔄 Data Preparation  
+## 🔄 Preprocessing  
 - **Cleaning & Normalization**:
-  - Removed non-Arabic text, special characters, and extra spaces.
-  - Normalized Arabic characters (e.g., `إ, أ, آ → ا`, `ة → ه`).
-  - Downsampled positive reviews to balance the dataset.
+  - Removed **non-Arabic** text, special characters, and extra spaces.
+  - **Normalized Arabic characters** (e.g., `إ, أ, آ → ا`, `ة → ه`).
 - **Tokenization**:
-  - Used **AraBERT tokenizer** for efficient text processing.
+  - Used **AraBERT tokenizer** for efficient processing.
+- **Data Balancing**:
+  - 2,418 **Positive** | 2,418 **Negative** (Balanced Dataset).
 - **Train-Test Split**:
   - **80% Training** | **20% Testing**.
 
-## 🏋️ Fine-Tuning & Results  
-The model was fine-tuned using **Hugging Face Transformers** on a dataset of restaurant reviews.
+## 🏋️ Fine-Tuning Details  
+We fine-tuned **`aubmindlab/bert-base-arabertv2`** using full fine-tuning (not LoRA).
 
-### **📊 Evaluation Metrics**
+### **📊 Model Performance**
 | Metric       | Score  |
 |-------------|--------|
-| **Train Loss**| `0.470`|
+| **Train Loss**| `0.470` |
 | **Eval Loss** | `0.373` |
 | **Accuracy**  | `86.41%` |
 | **Precision** | `87.01%` |
 | **Recall**    | `86.49%` |
 | **F1-score**  | `86.75%` |
 
+---
+
 ## ⚙️ Training Parameters  
 ```python
 model_name = "aubmindlab/bert-base-arabertv2"