---
library_name: transformers
datasets:
- SinclairSchneider/trainset_political_text_yes_no_german
language:
- de
base_model:
- EuroBERT/EuroBERT-210m
---

This classifier tells whether a German text is a political text or not. 
It is based on the model EuroBERT/EuroBERT-210m and trained on the dataset SinclairSchneider/trainset_political_text_yes_no_german.
The train script can be found under this [link](https://huggingface.co/SinclairSchneider/german_politic_DeBERTa-v2-base/blob/main/train.py)
The model achieved an F1 score of 0.99 on the testset.
Contrary to SinclairSchneider/german_politic_DeBERTa-v2-base this model is capable to cover a 8k context length.

```python
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer

model_name = "SinclairSchneider/german_politic_EuroBERT-210m"
model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, do_lower_case=False, TOKENIZERS_PARALLELISM=True, trust_remote_code=True)

political_classifier = pipeline("text-classification", model=model, tokenizer=tokenizer, trust_remote_code=True)

political_classifier("Trump und Putin einigen sich auf begrenzte Waffenruhe")
[{'label': 'politic', 'score': 0.9991077780723572}]

political_classifier("Franck Ribéry und Arjen Robben feiern beim \"Beckenbauer Cup\" ihr Comeback beim FC Bayern. Sie präsentieren sich wie zu besten Zeiten und wecken eine große Sehnsucht beim Rekordmeister.")
[{'label': 'other', 'score': 0.9865761995315552}]
```

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