Update README.md

README.md

@@ -22,8 +22,10 @@ language:
   - id
 pipeline_tag: text-classification
 widget:
-  - text: Orang bijak taat pajak
   - text: sudah beli makan buat sahur?
+  - example_title: "contoh tidak relevan"
+  - text: Mengawal APBN, Indonesia Maju
+  - example_title: "contoh kalimat"
 ---
 
 # Kemenkeu-Sentiment-Classifier
@@ -37,12 +39,22 @@ Leaderboard score:
 - Public score: 0.63733
 - Private score: 0.65733
 
-
 ## Model description & limitations
 
 - This model can be used to classify text with four possible outputs [netral, tdk-relevan, negatif, and positif]
 - only for specific cases related to the Ministry Of Finance Indonesia
 
+## How to use
+You can use this model directly with a pipeline
+
+```python
+pretrained_name = "hanifnoerr/Kemenkeu-Sentiment-Classifier"
+class_model = pipeline(tokenizer=pretrained_name, model=pretrained_name)
+
+test_data = "Mengawal APBN, Indonesia Maju"
+class_model(test_data)
+```
+
 ## Training and evaluation data
 
 The following hyperparameters were used during training: