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
File size: 381 Bytes
cf2c0b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"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
}
} |