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license: mit |
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This is a fine-tuned Deberta model to detect human values in arguments. |
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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) |
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It was trained and tested on a dataset of 9324 annotated [arguments](https://zenodo.org/record/7550385#.ZEPzcfzP330). |
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The whole ensemble system achieved a F1-Score of 0.56 in the competiton. This model achieves a F1-Score of 0.55. |
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## Model Usage |
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This model is built on custom code. So the inference api cannot be used directly. |
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To use the model please follow the steps below... |
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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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trained_model = AutoModelForSequenceClassification.from_pretrained("tum-nlp/Deberta_Human_Value_Detector", trust_remote_code=True) |
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example_text ='We should ban whaling because whales are a species at the risk of distinction' |
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encoding = tokenizer.encode_plus( |
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example_text, |
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add_special_tokens=True, |
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max_length=512, |
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return_token_type_ids=False, |
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padding="max_length", |
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return_attention_mask=True, |
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return_tensors='pt', |
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) |
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with torch.no_grad(): |
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test_prediction = trained_model(encoding["input_ids"], encoding["attention_mask"]) |
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test_prediction = test_prediction["output"].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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```python |
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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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continue |
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print(f"{label}: {prediction}") |
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``` |
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