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
chest2vec CT report labeler (0.6B): self-contained AutoModel, 137-leaf ternary, CheXbert-style report F1
0008ed1 verified - Xet hash:
- e1c5ae75d50677cf0cce3ad0441fbf25396122838224b9473c03004e92077538
- Size of remote file:
- 11.4 MB
- SHA256:
- def76fb086971c7867b829c23a26261e38d9d74e02139253b38aeb9df8b4b50a
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