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
| { | |
| "base_model": "/vol/ideadata/ac54awik/Medical_report_evaluation/radbert_local", | |
| "checkpoint": "classifier_training/classifier_de/RadBertClassifier_best.pth", | |
| "labels": [ | |
| "Medical material", | |
| "Arterial wall calcification", | |
| "Cardiomegaly", | |
| "Pericardial effusion", | |
| "Coronary artery wall calcification", | |
| "Hiatal hernia", | |
| "Lymphadenopathy", | |
| "Emphysema", | |
| "Atelectasis", | |
| "Lung nodule", | |
| "Lung opacity", | |
| "Pulmonary fibrotic sequela", | |
| "Pleural effusion", | |
| "Mosaic attenuation pattern", | |
| "Peribronchial thickening", | |
| "Consolidation", | |
| "Bronchiectasis", | |
| "Interlobular septal thickening" | |
| ], | |
| "tokenizer_source": "/vol/ideadata/ac54awik/Medical_report_evaluation/radbert_local" | |
| } |