Update README.md

README.md

@@ -4,3 +4,57 @@ pipeline_tag: text-classification
 widget:
 - 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"
 ---
+
+
+## Model Usage
+
+```python
+
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+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']
+
+tokenizer =  AutoTokenizer.from_pretrained("tum-nlp/Deberta_Human_Value_Detector")
+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,
+        add_special_tokens=True,
+        max_length=512,
+        return_token_type_ids=False,
+        padding="max_length",
+        return_attention_mask=True,
+        return_tensors='pt',
+    )
+
+with torch.no_grad():
+        test_prediction = trained_model(encoding["input_ids"], encoding["attention_mask"])
+        test_prediction = test_prediction["logits"].flatten().numpy()
+
+print(f"Predictions:")
+        for label, prediction in zip(LABEL_COLUMNS, test_prediction):
+            if prediction < THRESHOLD:
+                continue
+            print(f"{label}: {prediction}")
+            res[label] = prediction
+```