readme

README.md

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+# 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
+
+```python
+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 = ""
+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")
+```
+
+> Make sure to replace `"your-username/..."` with the correct model path.
+
+## 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](LICENSE)
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@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}
+}
+```