README.md

---
license: mit
language:
- de
metrics:
- name: f1
  value: 0.79
- name: auc
  value: 0.91
- name: accuracy
  value: 0.84
base_model:
- google-bert/bert-base-german-cased
pipeline_tag: text-classification
---
# Horbee/bert-german-offensive-comment-classifier aka SauerBERT

SauerBERT is a fine-tuned German BERT-based transformer model for offensive comment detection. 
It was trained on a balanced dataset of 8,000 examples from the GermEval 2018 and 2019 shared tasks, fine-tuned for 2 epochs. The model achieves strong performance metrics on German online comments, including:

- Accuracy: 84.3%
- F1 Score: 0.796
- Precision: 0.784
- Recall: 0.808
- AUC: 0.91

SauerBERT is designed to help detect offensive language, and rude comments in German text, making it suitable for moderation systems, research, or content analysis pipelines.

## Intended Use:

Detection of offensive, or inappropriate German-language comments

Social media moderation tools

## Example Use:

```python
from transformers import pipeline

classifier = pipeline("text-classification",
                      model="Horbee/bert-german-offensive-comment-classifier")

sequence_to_classify = "Ich kann es nicht ausstehen, mit so einem Idioten im selben Raum zu sein."

result = classifier(sequence_to_classify)

print(result) # [{'label': 'Offensive', 'score': 0.9911119341850281}]

```

## Limitations:

Trained only on GermEval 2018/2019 data — performance on out-of-domain or highly informal texts may vary.

May not capture all forms of subtle toxicity or sarcasm.

Designed for German-language content; not suitable for other languages.

## Author comments

Thank you for using my model, let me know if it helped you out. I would appreciate any constructive feedback.