File size: 2,316 Bytes
0684ceb 8feab03 80a2654 8feab03 7d8a6c4 80a2654 dc635b2 80a2654 1366939 80a2654 1eb35c8 80a2654 5069e3f 1366939 80a2654 7d8a6c4 80a2654 5328c2d 7d8a6c4 5328c2d 80a2654 1366939 80a2654 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
---
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
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}")
```
|