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)# 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
- Xet hash:
- c447a39668ac8df60c90485242b01ea0277b73c587bf0a7cd2ad8d4f5c745a25
- Size of remote file:
- 445 MB
- SHA256:
- 987fc75edf3ac78d1a0f05b8260f832c16ccd0cb6d6612432b2c0dbdf00a757a
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