Update README.md

README.md

@@ -13,12 +13,12 @@ widget:
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
 tokenizer =  AutoTokenizer.from_pretrained("tum-nlp/Deberta_Human_Value_Detector")
-model = AutoModelForSequenceClassification.from_pretrained("tum-nlp/Deberta_Human_Value_Detector", trust_remote_code=True)
+trained_model = AutoModelForSequenceClassification.from_pretrained("tum-nlp/Deberta_Human_Value_Detector", trust_remote_code=True)
 
 example_text ='whaling is part of the culture of various indigenous population and should be allowed for the purpose of maintaining this tradition and way of life and sustenance, among other uses of a whale. against We should ban whaling'
 
 encoding = tokenizer.encode_plus(
-        text,
+        example_text,
         add_special_tokens=True,
         max_length=512,
         return_token_type_ids=False,
@@ -41,9 +41,8 @@ THRESHOLD = 0.25
 LABEL_COLUMNS = ['Self-direction: thought','Self-direction: action','Stimulation','Hedonism','Achievement','Power: dominance','Power: resources','Face','Security: personal',
                  'Security: societal','Tradition','Conformity: rules','Conformity: interpersonal','Humility','Benevolence: caring','Benevolence: dependability','Universalism: concern','Universalism: nature','Universalism: tolerance','Universalism: objectivity']
 print(f"Predictions:")
-        for label, prediction in zip(LABEL_COLUMNS, test_prediction):
-            if prediction < THRESHOLD:
-                continue
-            print(f"{label}: {prediction}")
-            res[label] = prediction
+for label, prediction in zip(LABEL_COLUMNS, test_prediction):
+    if prediction < THRESHOLD:
+        continue
+    print(f"{label}: {prediction}")
 ```