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bright-eds-histology
raphael-r/bright-eds-histology
bright-eds-histology 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 histology semantic group. It was trained on a synthetic dataset generated for the properly de-iden…
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Updated Apr 7, 2026
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At a glance
- Access
- Public
- Created
- Apr 7, 2026
- Updated
- Apr 7, 2026
- SHA
- 7a055240
Model card
From the Hugging Face model README
BRIGHT NER: EDS-NLP (CamemBERT + CRF) fine-tuned for histology
Description
This is a EDS-NLP (CamemBERT + CRF) architecture fine-tuned to extract clinical neuro-oncology entities related to the histology 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):
- histo_necrose: Présence nécrose
- histo_pec: Prolifération endothéliale/microvasculaire
- histo_mitoses: Nombre mitoses ou index mitotique
- aspect_cellulaire: Aspect cellulaire (astrocytaire, oligodendroglial)
Performance on Validation Set
Aggregates:
- Macro F1: 0.6069 (Precision: 0.5036, Recall: 0.9521)
- Micro F1: 0.6445 (Precision: 0.4913, Recall: 0.9365)
Per-Label Breakdowns:
| Label | Precision | Recall | F1 |
|---|---|---|---|
| histo_necrose | 0.2732 | 1.0000 | 0.4292 |
| histo_pec | 0.2011 | 1.0000 | 0.3348 |
| histo_mitoses | 0.8424 | 1.0000 | 0.9145 |
| aspect_cellulaire | 0.6978 | 0.8083 | 0.7490 |
Usage
# Inference Code
import edsnlp
nlp = edsnlp.load("raphael-r/bright-eds-histology")
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