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raphael-r/bright-gliner-symptoms_evolution

sourceHugging Faceupdated 6mo agoView on Hugging Face

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Model Card

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 | |---|---|---|---| | epilepsie1ersymptome | 0.0099 | 0.5000 | 0.0194 | | cephhic1ersymptome | 0.0100 | 1.0000 | 0.0198 | | cephhic | 0.0000 | 0.0000 | 0.0000 | | deficit1ersymptome | 0.0000 | 0.0000 | 0.0000 | | deficit | 0.0000 | 0.0000 | 0.0000 | | cognitif1ersymptome | 0.0000 | 0.0000 | 0.0000 | | cognitif | 0.0000 | 0.0000 | 0.0000 | | autretrouble1ersymptome | 0.5429 | 0.8261 | 0.6552 | | contraste1ersymptome | 0.0500 | 1.0000 | 0.0952 | | prisedecontraste | 0.2100 | 1.0000 | 0.3471 | | oedeme1ersymptome | 0.2115 | 0.9565 | 0.3465 | | calcif1ersymptome | 0.0000 | 0.0000 | 0.0000 | | epilepsie | 0.0800 | 1.0000 | 0.1481 | | autretrouble | 0.1154 | 1.0000 | 0.2069 | | evolclinique | 0.8692 | 0.9688 | 0.9163 | | progressclinique | 0.0000 | 0.0000 | 0.0000 | | progressradiologique | 0.0000 | 0.0000 | 0.0000 | | reponseradiologique | 0.0000 | 0.0000 | 0.0000 |

Usage

python

# 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"])