Instructions to use kugler/distilbert-base-german-cased-defakts-fake-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kugler/distilbert-base-german-cased-defakts-fake-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kugler/distilbert-base-german-cased-defakts-fake-binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kugler/distilbert-base-german-cased-defakts-fake-binary") model = AutoModelForSequenceClassification.from_pretrained("kugler/distilbert-base-german-cased-defakts-fake-binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# destilbert-base-german-cased
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This
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It achieves the following results on the evaluation set:
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- Loss: 0.3515
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- Accuracy: 0.8526
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# destilbert-base-german-cased
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This Model is finetuned for sequence classification (binary fake-news classification task) on the german DeFaktS-Dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3515
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- Accuracy: 0.8526
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