Text Classification
Transformers
PyTorch
Safetensors
German
bert
cefr
proficiency assessment
written text
Eval Results (legacy)
Instructions to use BramVanroy/gbert-base-finetuned-cefr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BramVanroy/gbert-base-finetuned-cefr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BramVanroy/gbert-base-finetuned-cefr")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BramVanroy/gbert-base-finetuned-cefr") model = AutoModelForSequenceClassification.from_pretrained("BramVanroy/gbert-base-finetuned-cefr") - Notebooks
- Google Colab
- Kaggle
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Evaluation results
- accuracyself-reported0.830
- f1self-reported0.832
- precisionself-reported0.838
- qwkself-reported0.950
- recallself-reported0.830