abullard1/germeval2025-vio-moderngbert-cw

🏆 GermEval 2025: Violence Detection (Class-Weighted)

abullardUR@GermEval Shared Task 2025 Submission

---

🎯 Model Summary

This model is a fine-tuned version of LSX-UniWue/ModernGBERT_134M, specifically designed for Violence Detection (VIO) in German social media content. It was developed as part of the GermEval 2025 Shared Task on Harmful Content Detection.

🏅 Competition Performance

• Final Ranking: 2nd out of 8 teams 🥈
• Primary Metric (Macro-F1): 0.82 (+19% over official baseline (0.69 → 0.82))
• Approach: Class-weighted cross-entropy loss for severe class imbalance (12.8:1 ratio)

📊 Task Details

• Task Type: Binary classification
• Classes: False (no violence), True (violence-related content detected)
• Domain: German social media (Twitter, 2014-2016)
• Data Source: Right-wing extremist network posts

⚠️ Limitations and Bias

Known Limitations

• Domain Specificity: Trained on 2014-2016 German Twitter data from right-wing extremist networks
• Temporal Bias: Language patterns may not reflect contemporary usage
• Class Imbalance: 92.8% non-violent vs 7.2% violent content (12.8:1 ratio)
• Cultural Context: May not generalize to other German-speaking regions or contexts
• Implicit Violence: May struggle with subtle threats or coded violent language

Ethical Considerations

• Model trained on potentially harmful content for research purposes only
• Should not be used to amplify or generate violent content
• Requires careful handling due to sensitive training data
• May exhibit bias toward certain types of violence or contexts

🚀 How to Use

Quick Start

from transformers import AutoProcessor, AutoModelForSequenceClassification

# Load model and processor
model_id = "abullard1/germeval2025-vio-moderngbert-cw"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained(model_id, trust_remote_code=True).eval()

# Run inference
text = "Diese Situation macht mich wütend!"
inputs = processor(text, return_tensors="pt", truncation=True)
probs = model(**inputs).logits.softmax(-1).detach().cpu().numpy()
print(f"Predictions: {probs}")

Class Labels

• Label 0: No violence detected
• Label 1: Violence-related content detected

📈 Training Details

Training Data

• Source: GermEval 2025 Shared Task VIO dataset
• Size: 7,783 samples
• Split: 80% training (6,226), 20% validation (1,557)
• Class Distribution: 92.8% non-violent, 7.2% violent

Training Procedure

• Base Model: ModernGBERT-134M (8192 token context)
• Architecture: Mean-pooling classification head
• Loss Function: Class-weighted cross-entropy (inverse frequency weighting)
• Optimizer: AdamW with linear scheduling
• Early Stopping: Patience of 5 epochs on validation Macro-F1

Hyperparameters

• Learning Rate: 5e-5
• Weight Decay: 0.0301
• Batch Size: 16/32 (train/eval)
• Epochs: 3
• Warmup Steps: 300

📚 Citation

@inproceedings{bullard2025germeval,
  title   = {abullardUR@GermEval Shared Task 2025: Fine-tuning ModernGBERT on Highly Imbalanced German Social Media for Harmful Content Detection},
  author  = {Bullard, Samuel},
  year    = {2025},
  booktitle = {Proceedings of KONVENS 2025 Workshops}
}

🙏 Acknowledgments

• GermEval 2025 Organizers: University of Stuttgart and University of Mannheim
• Prof. Dr. Udo Kruschwitz (University of Regensburg) for supervision
• ModernGBERT Team: LSX-UniWue for the ModernGBERT-134M German language base-model

📄 License

This model inherits the Research-only RAIL-M license from ModernGBERT. See license details.