Update README.md

README.md

@@ -11,6 +11,9 @@ metrics:
 base_model:
 - Turkish-NLP/t5-efficient-base-turkish
 pipeline_tag: text2text-generation
+widget:
+  - text: "Pilav çok lezzetliydi ama servis yavaştı."
+    example_title: "Demo"
 ---
 
 # **Sengil/t5-turkish-aspect-term-extractor** 🇹🇷
@@ -19,20 +22,6 @@ A Turkish sequence-to-sequence model based on `Turkish-NLP/t5-efficient-base-tur
 
 Given a Turkish sentence, the model generates a list of **aspect terms** (e.g., *kahve*, *servis*, *fiyatlar*) that reflect the primary discussed entities or features.
 
-
-## Demo
-
-Try it out below 👇
-
-```python
-from transformers import pipeline
-
-pipe = pipeline("text2text-generation", model="Sengil/t5-turkish-aspect-term-extractor")
-text = "Yemekler çok lezzetliydi ama garsonlar çok yavaştı."
-output = pipe(text)
-print(output)
-````
-
 ---
 
 ## ✨ Example