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README.md
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```python
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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LABEL_COLUMNS = ['Self-direction: thought',
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'Self-direction: action',
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'Stimulation',
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'Hedonism',
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'Achievement',
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'Power: dominance',
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'Power: resources',
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'Face',
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'Security: personal',
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'Security: societal',
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'Tradition',
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'Conformity: rules',
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'Conformity: interpersonal',
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'Humility',
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'Benevolence: caring',
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'Benevolence: dependability',
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'Universalism: concern',
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'Universalism: nature',
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'Universalism: tolerance',
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'Universalism: objectivity']
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tokenizer = AutoTokenizer.from_pretrained("tum-nlp/Deberta_Human_Value_Detector")
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model = AutoModelForSequenceClassification.from_pretrained("tum-nlp/Deberta_Human_Value_Detector", trust_remote_code=True)
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test_prediction = trained_model(encoding["input_ids"], encoding["attention_mask"])
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test_prediction = test_prediction["logits"].flatten().numpy()
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print(f"Predictions:")
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for label, prediction in zip(LABEL_COLUMNS, test_prediction):
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if prediction < THRESHOLD:
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```python
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("tum-nlp/Deberta_Human_Value_Detector")
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model = AutoModelForSequenceClassification.from_pretrained("tum-nlp/Deberta_Human_Value_Detector", trust_remote_code=True)
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test_prediction = trained_model(encoding["input_ids"], encoding["attention_mask"])
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test_prediction = test_prediction["logits"].flatten().numpy()
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```
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## Prediction
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To make a prediction and map the the outputs to the correct labels.
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During the competiton a threshold of 0.25 was used to binarize the output.
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```
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THRESHOLD = 0.25
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LABEL_COLUMNS = ['Self-direction: thought','Self-direction: action','Stimulation','Hedonism','Achievement','Power: dominance','Power: resources','Face','Security: personal',
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'Security: societal','Tradition','Conformity: rules','Conformity: interpersonal','Humility','Benevolence: caring','Benevolence: dependability','Universalism: concern','Universalism: nature','Universalism: tolerance','Universalism: objectivity']
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print(f"Predictions:")
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for label, prediction in zip(LABEL_COLUMNS, test_prediction):
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if prediction < THRESHOLD:
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