SinclairSchneider/trainset_political_text_yes_no_german
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How to use SinclairSchneider/german_politic_DeBERTa-v2-base with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="SinclairSchneider/german_politic_DeBERTa-v2-base") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("SinclairSchneider/german_politic_DeBERTa-v2-base")
model = AutoModelForSequenceClassification.from_pretrained("SinclairSchneider/german_politic_DeBERTa-v2-base")This classifier tells whether a German text is a political text or not. It is based on the model ikim-uk-essen/geberta-base and trained on the dataset SinclairSchneider/trainset_political_text_yes_no_german. The train script can be found under this link The model achieved an F1 score of 0.99 on the testset.
from transformers import pipeline
political_classifier = pipeline("text-classification", model="SinclairSchneider/german_politic_DeBERTa-v2-base")
political_classifier("Trump und Putin einigen sich auf begrenzte Waffenruhe")
[{'label': 'politic', 'score': 0.9999680519104004}]
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': 0.9969837069511414}]
Base model
ikim-uk-essen/geberta-base