Text Classification
Transformers
PyTorch
Safetensors
German
roberta
radiology
medical-imaging
chest-ct
multi-label-classification
radbert
german
ctrate
custom_code
text-embeddings-inference
Instructions to use suitch/radbert-german-ctrate-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suitch/radbert-german-ctrate-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="suitch/radbert-german-ctrate-classifier", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("suitch/radbert-german-ctrate-classifier", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("suitch/radbert-german-ctrate-classifier", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6271e2e68ccc320b97dc25a69c8b31b1f3c90fceb11b73bead8d627e695d088d
- Size of remote file:
- 499 MB
- SHA256:
- ed6c293735a097bb023a10c7a5bcb1c2cc395bdac5fbedba902f341143f3359b
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