Downloads · 30 days
0
bright-eds-diagnosis
raphael-r/bright-eds-diagnosis
bright-eds-diagnosis 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.
This is a EDS-NLP (CamemBERT + CRF) architecture fine-tuned to extract clinical neuro-oncology entities related to the diagnosis semantic group. It was trained on a synthetic dataset generated for the properly de-iden…
Access
Public
Updated Apr 7, 2026
Repo size
483 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors483 MB · 99%
At a glance
- Access
- Public
- Created
- Apr 7, 2026
- Updated
- Apr 7, 2026
- SHA
- cb24bc0d
Model card
From the Hugging Face model README
BRIGHT NER: EDS-NLP (CamemBERT + CRF) fine-tuned for diagnosis
Description
This is a EDS-NLP (CamemBERT + CRF) architecture fine-tuned to extract clinical neuro-oncology entities related to the diagnosis 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):
- diag_histologique: Diagnostic anatomopathologique
- diag_integre: Diagnostic intégré OMS 2021
- classification_oms: Classification OMS utilisée (2007, 2016 ou 2021)
- grade: Grade OMS (1, 2, 3 ou 4)
- num_labo: Numéro échantillon laboratoire anatomopathologie
Performance on Validation Set
Aggregates:
- Macro F1: 0.8299 (Precision: 0.8327, Recall: 0.8400)
- Micro F1: 0.8554 (Precision: 0.8532, Recall: 0.8576)
Per-Label Breakdowns:
| Label | Precision | Recall | F1 |
|---|---|---|---|
| diag_histologique | 0.9869 | 0.9467 | 0.9664 |
| diag_integre | 0.7157 | 0.9145 | 0.8030 |
| classification_oms | 0.8700 | 1.0000 | 0.9305 |
| grade | 0.9615 | 0.8814 | 0.9197 |
| num_labo | 0.6296 | 0.4574 | 0.5299 |
Usage
# Inference Code
import edsnlp
nlp = edsnlp.load("raphael-r/bright-eds-diagnosis")
doc = nlp("Patient presenting with epileptic seizures...")
for ent in doc.ents:
print(ent.text, "=>", ent.label_)
- Languages
- fr
- Created
- Apr 7, 2026
- Updated
- Apr 7, 2026