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French biomedical structured extractor (GLiNER2 architecture, ~150M parameters)
built on the
MedEmbed-v9
sentence-embedding backbone. Fine-tuned on a
synthetic
lymphoma electronic case report form (eCRF) task with 89 fields.
This checkpoint is the one used to produce the reported scores in the thesis
chapter
Evaluating Open-Vocabulary Extraction
(capstone eCRF).
Results (410-document synthetic test split, value-F1 with field competition)
value-F1: 0.657
(best generator-free variant; within 0.018 of the best LLM generator, 27x smaller)
Important note on data
The lymphoma eCRF is
entirely synthetic
(
rntc/lymphome-synth-v4
), not real
hospital data. It imitates a longitudinal clinical study form. See the thesis for
the evaluation protocol (validation-selected threshold, test never used for
selection).
Usage
from gliner2 import GLiNER2
model = GLiNER2.from_pretrained("rntc/mc-bio-gliner-lymphome-joint")
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