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
Download model/final.pt from radgraphIT/Radgraph-IT: direct link, hf CLI and curl.
- Browser
- Download file 445 MB
-
https://huggingface.co/radgraphIT/Radgraph-IT/resolve/main/model/final.pt
- Command line
-
hf download hf://radgraphIT/Radgraph-IT/model/final.pt
-
curl -L -o final.pt https://huggingface.co/radgraphIT/Radgraph-IT/resolve/main/model/final.pt
445 MB
- Xet hash:
- 01e45d81bde43dcef62b5172147718b43d8859f8dc8cfd4fee3f322a4f9ee2eb
- Size of remote file:
- 445 MB
- SHA256:
- 7f46768893cabc537ae22d2a51c0113cfad9c8228de2de2cc9939e18f7d90f14
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