Update README.md

README.md

@@ -54,15 +54,15 @@ The easiest way to use this model is via the Hugging Face `pipeline`.
 ```python
 from transformers import pipeline
 
-# Initialize the pipeline
 classifier = pipeline("text-classification", model="atahanuz/bert-offensive-classifier")
 
-# Predict
 text = "Bu harika bir filmdi, çok beğendim."
-result = classifier(text)
+result = classifier(text)[0]
 
-print(result)
-# Output: [{'label': 'NOT', 'score': 0.99...}]
+# Convert LABEL_1 -> Offensive, LABEL_0 -> Not Offensive
+label = "Offensive" if result['label'] == "LABEL_1" else "Not Offensive"
+
+print(f"Prediction: {label} (Score: {result['score']:.4f})")
 ```
 
 ### Method 2: Manual PyTorch Implementation
@@ -106,6 +106,15 @@ The model outputs the following labels:
 | `0` | **NOT** | **Not Offensive** - Normal, non-hateful speech. |
 | `1` | **OFF** | **Offensive** - Contains insults, threats, or inappropriate language. |
 
+## 📝 Example Predictions
+
+| Text | Label | Prediction |
+| :--- | :--- | :--- |
+| "Bu filmi çok beğendim, oyunculuklar harikaydı." | **NOT** | Non-Offensive |
+| "Beynini kullanmayı denesen belki anlarsın." | **OFF** | Offensive (Insult) |
+| "Maalesef bu konuda sana katılamıyorum." | **NOT** | Non-Offensive |
+| "Senin gibi aptal insanlar yüzünden bu haldeyiz." | **OFF** | Offensive (Toxic) |
+
 ## 📈 Performance
 
 The model was evaluated on the test split of the OffensEval-2020-TR dataset (approx. 3,500 samples).