Update README.md

README.md

@@ -86,14 +86,22 @@ Positive: 0.79%
 ```pyhton
 from transformers import pipeline
 
-url = "https://huggingface.co/spaces/delarosajav95/tw-roberta-base-sentiment-FT-v2"
+url = "delarosajav95/tw-roberta-base-sentiment-FT-v2"
 
 classifier = pipeline("sentiment-analysis", model=url)
 
 text = "text to classify"
 
-result = classifier(text)
-print(result)
+result = classifier(text, return_all_scores=True)
+
+label_mapping = {"LABEL_0": "Negative", "LABEL_1": "Neutral", "LABEL_2": "Positive"}
+for i, predictions in enumerate(result):
+  print("==================================")
+  print(f"Text {i + 1}: {text}")
+  for pred in predictions:
+    label = label_mapping.get(pred['label'], pred['label'])
+    score = pred['score']
+    print(f"{label}: {score:.2%}")
 ```
 
 ## Metrics and results: