Update README.md

README.md

@@ -15,7 +15,6 @@ pipeline_tag: text-classification
 ---
 
 # 🍽️ Arabic Restaurant Review Sentiment Analysis 🚀
-**Model Is Under Development**
 ## 📌 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**.
@@ -41,22 +40,35 @@ The model was fine-tuned using **Hugging Face Transformers** on a dataset of res
 ### **📊 Evaluation Metrics**
 | Metric       | Score  |
 |-------------|--------|
-| **Eval Loss** | `****` |
-| **Accuracy**  | `88.71%` |
-| **Precision** | `91.07%` |
-| **Recall**    | `93.31%` |
-| **F1-score**  | `92.17%` |
+| **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"
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2, classifier_dropout=0.5).to(device)
+
 training_args = TrainingArguments(
     output_dir="./results",
-    evaluation_strategy="epoch",
-    per_device_train_batch_size=4,
-    per_device_eval_batch_size=4,
-    num_train_epochs=5,
-    weight_decay=0.01,
-    learning_rate=3e-5,
+    evaluation_strategy="epoch",       
+    save_strategy="epoch",             
+    per_device_train_batch_size=8,  
+    per_device_eval_batch_size=8,   
+    num_train_epochs=4,  
+    weight_decay=1,  
+    learning_rate=1e-5,  
+    lr_scheduler_type="cosine",  
+    warmup_ratio=0.1,  
     fp16=True,
-    report_to="none"
+    report_to="none",
+    save_total_limit=2,
+    gradient_accumulation_steps=2,
+    load_best_model_at_end=True,
+    max_grad_norm=1.0,
+    metric_for_best_model="eval_loss",
+    greater_is_better=False,
 )