Text Classification
Transformers
Safetensors
English
chest2vec_labeler
feature-extraction
radiology
chest-ct
report-labeling
multi-label
ct-rate
chexbert-style-f1
custom_code
Instructions to use chest2vec/chest2vec_labeler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chest2vec/chest2vec_labeler with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chest2vec/chest2vec_labeler", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("chest2vec/chest2vec_labeler", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle