Feature Extraction
Transformers
Safetensors
Italian
radgraph_it
radiology
information-extraction
named-entity-recognition
relation-extraction
medical
radgraph
custom_code
Instructions to use radgraphIT/Radgraph-IT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use radgraphIT/Radgraph-IT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="radgraphIT/Radgraph-IT", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("radgraphIT/Radgraph-IT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Daniel Rabottini commited on
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Parent(s): 11f691c
docs: link full RadGraph-IT paper
Browse files
README.md
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For complete methodological details, per-class results, and comparisons with RadGraph 1.0 and
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RadGraph-XL, please refer to the full RadGraph-IT paper.
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```
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For complete methodological details, per-class results, and comparisons with RadGraph 1.0 and
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RadGraph-XL, please refer to the [full RadGraph-IT paper](https://drive.google.com/file/d/1mzxNUCsnrCl-JD0XRjprgbnM6LsF724v/view?usp=share_link).
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