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
File size: 743 Bytes
0c0131f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"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"
} |