aveluth/author_regulatory_focus_classifier

Autor-Regulatory Focus Classifier (German)

This model is a fine-tuned transformer-based classifier that detects the regulatory focus in German-language text, classifying whether the language expresses a promotion (aspirational, growth-oriented) or prevention (safety, obligation-oriented) focus.

It is fine-tuned on top of a German-language base model for the task of binary text classification.

Model Details

• Base model: deepset/gbert-large
• Fine-tuned for: Binary classification (Regulatory Focus)
• Language: German
• Framework: Hugging Face Transformers
• Model format: safetensors

Use Cases

• Social psychology and communication research
• Marketing and consumer behavior analysis
• Literary or political discourse analysis
• Behavioral modeling and goal orientation profiling

Example Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained("aveluth/author_regulatory_focus_classifier")
tokenizer = AutoTokenizer.from_pretrained("aveluth/author_regulatory_focus_classifier")

text = "Wir müssen sicherstellen, dass keine Fehler passieren. Sicherheit hat höchste Priorität."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
predicted_class = torch.argmax(outputs.logits).item()

print("Predicted class:", "prevention" if predicted_class == 0 else "promotion")

Labels

Class	Description
0	Prevention-focused language
1	Promotion-focused language

Training Details

• Training data: Custom labeled corpus based on psychological framing
• Loss function: Cross-entropy
• Optimizer: AdamW
• Epochs: 4
• Learning rate: 3e-5

Limitations

• Trained on German-language data only
• Performance may vary on out-of-domain text (e.g., technical manuals, poetry)
• May not generalize across all cultural framings of regulatory focus

License

MIT

Citation

If you use this model in your research, please cite:

@article{velutharambath2023prevention,
  title={Prevention or Promotion? Predicting Author's Regulatory Focus},
  author={Velutharambath, Aswathy and Sassenberg, Kai and Klinger, Roman},
  journal={Northern European Journal of Language Technology},
  volume={9},
  number={1},
  year={2023}
}