german-irony-detector

German Irony Detection Model 🇩🇪

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 Lüdke 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)

Hold-out Test Set Performance

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:

1. Schmidt & Harbusch (2023) dataset:
   • Training set: 200 texts
   • Test set: 100 texts
2. 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]