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rntc/mc-bio-gliner-lymphome
mc-bio-gliner-lymphome is a token classification model from rntc. Use it when you need labels on individual words, such as names. It is set up for gliner2. The card lists the license as mit.
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…
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From the Hugging Face model README
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).
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).
from gliner2 import GLiNER2
model = GLiNER2.from_pretrained("rntc/mc-bio-gliner-lymphome")
rntc/mc-bio-gliner-lymphome — simple supervision (value-F1 0.640 / span-F1 0.503)rntc/mc-bio-gliner-lymphome-joint — joint supervision (value-F1 0.657)