---
license: mit
---
This is a fine-tuned Deberta model to detect human values in arguments. 
The model is part of the ensemble that was the best-performing system in the SemEval2023 task: [Detecting Human Values in arguments](https://touche.webis.de/semeval23/touche23-web/index.html)
It was trained and tested on a dataset of 9324 annotated [arguments](https://zenodo.org/record/7550385#.ZEPzcfzP330). 
The whole ensemble system achieved a F1-Score of 0.56 in the competiton. This model achieves a F1-Score of 0.55. 

## Model Usage

This model is built on custom code. So the inference api cannot be used directly.
To use the model please follow the steps below... 


```python

from transformers import AutoModelForSequenceClassification, AutoTokenizer

tokenizer =  AutoTokenizer.from_pretrained("tum-nlp/Deberta_Human_Value_Detector")
trained_model = AutoModelForSequenceClassification.from_pretrained("tum-nlp/Deberta_Human_Value_Detector", trust_remote_code=True)

example_text ='We should ban whaling because whales are a species at the risk of distinction'

encoding = tokenizer.encode_plus(
        example_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["output"].flatten().numpy()

```

## Prediction
To make a prediction and map the the outputs to the correct labels.
During the competiton a threshold of 0.25 was used to binarize the output. 
```python
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}")
```