<a href=https://camembert-bio-model.fr/>

CamemBERT-bio-gliner is a Named Entity Recognition (NER) model capable of identifying any french biomedical entity type using a BERT-like encoder. It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. CamemBERT-bio is used as a backbone. This model is based on the fantastic work of Urchade Zaratiana on the GLiNER architecture.

This is the v0.1 of the CamemBERT-bio-gliner model. There might be a few quirks or unexpected predictions. So, if you notice anything off or have suggestions for improvements, we'd really appreciate hearing from you!

To use this model, you must install the GLiNER Python library:

Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using GLiNER.from_pretrained and predict entities with predict_entities.