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raphael-r/bright-eds-treatment
bright-eds-treatment 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 treatment 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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From the Hugging Face model README
This is a EDS-NLP (CamemBERT + CRF) architecture fine-tuned to extract clinical neuro-oncology entities related to the treatment 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.
It extracts the following fields (described in French):
Aggregates:
Per-Label Breakdowns:
| Label | Precision | Recall | F1 |
|---|---|---|---|
| chimios | 0.8750 | 0.7925 | 0.8317 |
| chimio_protocole | 0.9179 | 0.8311 | 0.8723 |
| chm_cycles | 0.7222 | 0.7324 | 0.7273 |
| chm_date_debut | 1.0000 | 1.0000 | 1.0000 |
| chm_date_fin | 0.0000 | 0.0000 | 0.0000 |
| type_chirurgie | 0.9651 | 0.9468 | 0.9559 |
| qualite_exerese | 0.6667 | 0.0400 | 0.0755 |
| chir_date | 0.0000 | 0.0000 | 0.0000 |
| rx_dose | 0.9586 | 0.9456 | 0.9521 |
| rx_fractionnement | 0.9745 | 0.9444 | 0.9592 |
| rx_date_debut | 0.0000 | 0.0000 | 0.0000 |
| rx_date_fin | 0.0000 | 0.0000 | 0.0000 |
| localisation_radiotherapie | 0.5946 | 0.3860 | 0.4681 |
| corticoides | 0.8736 | 0.9383 | 0.9048 |
| anti_epileptiques | 0.8910 | 0.9392 | 0.9145 |
| optune | 0.0000 | 0.0000 | 0.0000 |
| essai_therapeutique | 0.6667 | 0.7692 | 0.7143 |
# Inference Code
import edsnlp
nlp = edsnlp.load("raphael-r/bright-eds-treatment")
doc = nlp("Patient presenting with epileptic seizures...")
for ent in doc.ents:
print(ent.text, "=>", ent.label_)