README.md

---
license: openrail++
language:
- en
---
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. 
Code for retraining the ensemble is accessible in this [repo](https://github.com/danielschroter/human_value_detector)

## 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
import torch

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}")
```

## Citation

```
@inproceedings{schroter-etal-2023-adam,
    title = "{A}dam-Smith at {S}em{E}val-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models",
    author = "Schroter, Daniel  and
      Dementieva, Daryna  and
      Groh, Georg",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.74",
    doi = "10.18653/v1/2023.semeval-1.74",
    pages = "532--541",
    abstract = "This paper presents the best-performing approach alias {``}Adam Smith{''} for the SemEval-2023 Task 4: {``}Identification of Human Values behind Arguments{''}. The goal of the task was to create systems that automatically identify the values within textual arguments. We train transformer-based models until they reach their loss minimum or f1-score maximum. Ensembling the models by selecting one global decision threshold that maximizes the f1-score leads to the best-performing system in the competition. Ensembling based on stacking with logistic regressions shows the best performance on an additional dataset provided to evaluate the robustness ({``}Nahj al-Balagha{''}). Apart from outlining the submitted system, we demonstrate that the use of the large ensemble model is not necessary and that the system size can be significantly reduced.",
}
```