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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("chest2vec/chest2vec_labeler", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Evaluation: replace threshold-based F1 tables with AUROC / 3-class F1 / binary F1 (uncertain→neg) on CT-RATE + SNUH, with 95% CIs; add ≥1-positive subset
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Upload config.json with huggingface_hub
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chest2vec CT report labeler (0.6B): self-contained AutoModel, 137-leaf ternary, CheXbert-style report F1
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initial commit
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