German Irony Detection Model π©πͺ
Overview
This model detects irony in German text using a fine-tuned BERT architecture. It builds upon the German sentiment analysis work by Guhr et al. (2020) and incorporates tuning procedures from LΓΌdke et al. (2021).
Model Description
- Task: Binary classification (ironic vs. non-ironic)
- Language: German
- Base Model: BERT
- Training Data: Combined dataset from Schmidt & Harbusch (2023) and Claude by Anthropic (2025)
- Training Infrastructure: CPU-based training
Performance Metrics
Cross-Validation Results (5-fold)
Metric | Mean | Std Dev |
---|---|---|
Accuracy | 0.9651 | 0.0154 |
Precision | 0.9731 | 0.0117 |
Recall | 0.9632 | 0.0198 |
F1-Score | 0.9681 | 0.0141 |
Hold-out Test Set Performance
Class | Precision | Recall | F1-Score |
---|---|---|---|
Not Ironic | 0.8000 | 0.9600 | 0.8727 |
Ironic | 0.9500 | 0.7600 | 0.8444 |
Usage
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("username/german-irony-detector")
model = AutoModelForSequenceClassification.from_pretrained("username/german-irony-detector")
def predict(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
outputs = model(**inputs)
probabilities = outputs.logits.softmax(dim=-1)
return probabilities
Limitations
- Optimized for German social media text
- May not capture very subtle forms of irony
- Performance may vary with regional German dialects
- Limited to text-based irony detection
Training Data
The model was trained on:
- Schmidt & Harbusch (2023) dataset:
- Training set: 200 texts
- Test set: 100 texts
- Synthetic data generated by Claude (Anthropic, 2025)
Future Development
- Train on larger datasets
- Add positive/negative irony classification
More Information
For detailed documentation, additional resources, and the full research context, please visit my GitHub repository: https://github.com/JacobSKN/german-irony-detector
Citations
@misc{guhr2020german,
title={German Sentiment Analysis},
author={Guhr, Oliver and et al.},
year={2020},
howpublished={\url{https://github.com/oliverguhr/german-sentiment-lib}}
}
@misc{schmidt2023german,
title={German Irony Dataset},
author={Schmidt and Harbusch},
year={2023}
}
Author
Jacob Schildknecht ([email protected])
Contact
For questions or feedback about this model, please contact: [email protected]
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