raphael-r/bright-gliner-symptoms_evolution

BRIGHT NER: GLiNER2 fine-tuned for symptoms_evolution

Description

This is a GLiNER2 architecture fine-tuned to extract clinical neuro-oncology entities related to the symptoms_evolution 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).

This model repository was specifically designed to fit within the bright_db overarching namespace.

Fields

It extracts the following fields (described in French):

• epilepsie_1er_symptome: Crises comme premier symptôme
• ceph_hic_1er_symptome: Céphalées/HIC comme premier symptôme
• ceph_hic: Mention céphalées/HIC
• deficit_1er_symptome: Déficit neurologique initial
• deficit: Mention déficit neurologique
• cognitif_1er_symptome: Troubles cognitifs comme premier symptôme
• cognitif: Mention troubles cognitifs
• autre_trouble_1er_symptome: Autres premiers symptômes
• contraste_1er_symptome: Prise de contraste sur première imagerie
• prise_de_contraste: Mention générale prise de contraste
• oedeme_1er_symptome: Présence œdème sur première imagerie
• calcif_1er_symptome: Présence calcification sur première imagerie
• epilepsie: Mention épilepsie/crises
• autre_trouble: Autres symptômes actuels
• evol_clinique: Évolution globale (stable, progression)
• progress_clinique: Aggravation symptômes
• progress_radiologique: Croissance tumorale imagerie
• reponse_radiologique: Réponse tumorale imagerie

Performance on Validation Set

Aggregates:

• Macro F1: 0.1530 (Precision: 0.1166, Recall: 0.4584)
• Micro F1: 0.1969 (Precision: 0.1104, Recall: 0.9105)

Per-Label Breakdowns:

Label	Precision	Recall	F1
epilepsie_1er_symptome	0.0099	0.5000	0.0194
ceph_hic_1er_symptome	0.0100	1.0000	0.0198
ceph_hic	0.0000	0.0000	0.0000
deficit_1er_symptome	0.0000	0.0000	0.0000
deficit	0.0000	0.0000	0.0000
cognitif_1er_symptome	0.0000	0.0000	0.0000
cognitif	0.0000	0.0000	0.0000
autre_trouble_1er_symptome	0.5429	0.8261	0.6552
contraste_1er_symptome	0.0500	1.0000	0.0952
prise_de_contraste	0.2100	1.0000	0.3471
oedeme_1er_symptome	0.2115	0.9565	0.3465
calcif_1er_symptome	0.0000	0.0000	0.0000
epilepsie	0.0800	1.0000	0.1481
autre_trouble	0.1154	1.0000	0.2069
evol_clinique	0.8692	0.9688	0.9163
progress_clinique	0.0000	0.0000	0.0000
progress_radiologique	0.0000	0.0000	0.0000
reponse_radiologique	0.0000	0.0000	0.0000

Usage

# Inference Code
from gliner2 import GLiNER2

model = GLiNER2.from_pretrained("raphael-r/bright-gliner-symptoms_evolution")
text = "Patient presenting with epileptic seizures..."
entities = model.extract_entities(text)

for entity in entities:
    print(entity["text"], "=>", entity["label"])