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raphael-r/
bright-gliner-molecular

Name: bright-gliner-molecular
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BRIGHT NER: GLiNER2 fine-tuned for molecular

Description

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

mol_idh1: Statut mutation IDH1
mol_idh2: Statut mutation IDH2
mol_mgmt: Méthylation promoteur MGMT
mol_h3f3a: Mutation H3F3A
mol_hist1h3b: Mutation HIST1H3B
mol_tert: Mutation promoteur TERT
mol_CDKN2A: Délétion homozygote CDKN2A
mol_atrx: Mutation ATRX
mol_cic: Mutation CIC
mol_fubp1: Mutation FUBP1
mol_fgfr1: Mutation FGFR1
mol_egfr_mut: Mutation EGFR
mol_prkca: Mutation PRKCA
mol_pten: Mutation PTEN
mol_p53: Mutation p53
mol_braf: Mutation BRAF

Performance on Validation Set

Aggregates:

Macro F1: 0.3667 (Precision: 0.2956, Recall: 0.7353)
Micro F1: 0.5620 (Precision: 0.4165, Recall: 0.8634)

Per-Label Breakdowns:

Label	Precision	Recall	F1
mol_idh1	0.9356	0.7927	0.8583
mol_idh2	0.6583	0.9357	0.7729
mol_mgmt	0.6730	0.9465	0.7867
mol_h3f3a	0.1333	0.7692	0.2273
mol_hist1h3b	0.0909	0.6667	0.1600
mol_tert	0.5600	0.8750	0.6829
mol_CDKN2A	0.4706	0.9333	0.6257
mol_atrx	0.3365	0.7778	0.4698
mol_cic	0.1488	0.8333	0.2525
mol_fubp1	0.1565	0.7200	0.2571
mol_fgfr1	0.0000	0.0000	0.0000
mol_egfr_mut	0.0079	1.0000	0.0156
mol_prkca	0.0000	0.0000	0.0000
mol_pten	0.0543	0.7143	0.1010
mol_p53	0.0333	1.0000	0.0645
mol_braf	0.4706	0.8000	0.5926

Usage

python

# Inference Code
from gliner2 import GLiNER2

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

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