This is a GLiNER2 architecture fine-tuned to extract clinical neuro-oncology entities related to the demographics semantic group. It was trained on a synthetic dataset generated for the properly de-identified BRIGHT project dataset (see the generated_data folder in the primary repository).
1# Inference Code
2from gliner2 import GLiNER2
3
4model = GLiNER2.from_pretrained("raphael-r/bright-gliner-demographics")
5text = "Patient presenting with epileptic seizures..."
6entities = model.extract_entities(text)
7
8for entity in entities:
9 print(entity["text"], "=>", entity["label"])
