Update README.md

README.md

@@ -13,3 +13,53 @@ base_model:
 - aubmindlab/bert-base-arabertv02
 pipeline_tag: text-classification
 ---
+
+# 🍽️ 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**.
+
+## 📥 Data Collection  
+The dataset used for fine-tuning was sourced from **Hugging Face Datasets**, specifically:  
+[📂 Arabic Restaurant Reviews Dataset](https://huggingface.co/datasets/hadyelsahar/ar_res_reviews)  
+It contains **restaurant reviews in Arabic** labeled with sentiment polarity.
+
+## 🔄 Data Preparation  
+- **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.
+- **Tokenization**:
+  - Used **AraBERT tokenizer** for efficient text processing.
+- **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.
+
+### **📊 Evaluation Metrics**
+| Metric       | Score  |
+|-------------|--------|
+| **Eval Loss** | `0.5665` |
+| **Accuracy**  | `70.37%` |
+| **Precision** | `70.36%` |
+| **Recall**    | `70.37%` |
+| **F1-score**  | `69.75%` |
+| **Eval Runtime** | `11.5 sec` |
+
+## ⚙️ Training Parameters  
+```python
+training_args = TrainingArguments(
+    output_dir="./results",
+    evaluation_strategy="steps",
+    eval_steps=200,
+    per_device_train_batch_size=2,
+    per_device_eval_batch_size=2,
+    num_train_epochs=5,
+    weight_decay=0.01,
+    learning_rate=3e-5,
+    logging_steps=100,
+    fp16=True,
+    report_to="none"
+)