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
| { | |
| "radgraph-it__ner_labels": { | |
| "": 0, | |
| "Anatomy::definitely present": 1, | |
| "Observation::definitely present": 2, | |
| "Observation::definitely absent": 3, | |
| "Observation::uncertain": 4, | |
| "Anatomy::definitely absent": 5, | |
| "Anatomy::uncertain": 6 | |
| }, | |
| "radgraph-it__relation_labels": { | |
| "": 0, | |
| "modify": 1, | |
| "located_at": 2, | |
| "suggestive_of": 3 | |
| } | |
| } |