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bright-gliner-symptoms_evolution
raphael-r/bright-gliner-symptoms_evolution
bright-gliner-symptoms_evolution is a machine learning model from raphael-r. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for gliner.
This is a GLiNER2 architecture fine-tuned to extract clinical neuro-oncology entities related to the symptomsevolution semantic group. It was trained on a synthetic dataset generated for the properly de-identified BRI…
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Updated Apr 7, 2026
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At a glance
- Library
- gliner
- Model type
- extractor
- Access
- Public
- Created
- Apr 7, 2026
- Updated
- Apr 7, 2026
- SHA
- 801819ea
Model card
From the Hugging Face model README
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"])
- Library
- gliner
- Type
- extractor
- Languages
- fr
- Created
- Apr 7, 2026
- Updated
- Apr 7, 2026