NewsBERT-germ-210m

NewsBERT-germ-210m

German news article classifier based on EuroBERT-210m, fine-tuned on hand-annotated news articles about the 2025 German federal election (Bundestagswahl).

Model Description

• Base Model: EuroBERT/EuroBERT-210m (210M parameters)
• Task: Multi-class text classification (13 categories)
• Language: German
• Training Data: Hand-annotated German news articles (1580 train, 395 validation, 385 test)
• Dataset: Zorryy/news_articles_2025_elections_germany

Performance (Test Set)

Per-Class Performance

Hyperparameters

Optimized via Optuna TPE sampler with 3-fold stratified cross-validation (Best Trial: 1, CV F1 Macro: 0.8369).

Training Data

The model was trained on hand-annotated German news articles related to the 2025 German federal election. Articles were manually labeled into 13 topic categories by human annotators. The dataset is available at Zorryy/news_articles_2025_elections_germany.

Important: All labels are hand-annotated (not machine-generated), ensuring high label quality.

Usage

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="Zorryy/NewsBERT-germ-210m",
    trust_remote_code=True,
)

text = "Die Bundesregierung plant neue Massnahmen zur Reduzierung der CO2-Emissionen."
result = classifier(text)
print(result)
# [{"label": "Klima / Energie", "score": 0.95}]

Categories

The model classifies articles into 13 categories:

1. Klima / Energie
2. Zuwanderung
3. Renten
4. Soziales Gefälle
5. AfD/Rechte
6. Arbeitslosigkeit
7. Wirtschaftslage
8. Politikverdruss
9. Gesundheitswesen, Pflege
10. Kosten/Löhne/Preise
11. Ukraine/Krieg/Russland
12. Bundeswehr/Verteidigung
13. Andere

Limitations

• Trained specifically on German news articles about the 2025 Bundestagswahl
• May not generalize well to other domains, time periods, or languages
• Performance varies by category (see per-class metrics above)

Citation

If you use this model, please cite the underlying dataset and base model.